Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer

Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer Sinja Taavitsainen, Nikolai Engedal, Shaolong Cao, Florian Handle, and 29 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-384422/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Sep, 2021 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Prostate cancer is heterogeneous and patients would benefit from methods that stratify those who are likely to respond to systemic therapy. We employed single-cell assays for transposase-accessible chromatin (ATAC) and RNA sequencing in models of early treatment response and resistance to enzalutamide. In doing so, we identified pre-existing and treatment-persistent cell subpopulations that possess transcriptional stem-like features and regenerative potential when subjected to treatment. We found distinct chromatin landscapes associated with enzalutamide treatment and resistance that were linked to alternative transcriptional programs. Transcriptional profiles characteristic of persistent stem-like cells were able to stratify the treatment response of patients. Ultimately, we show that defining changes in chromatin and gene expression in single-cell populations from pre-clinical models can reveal hitherto unrecognized molecular predictors of treatment response. This suggests that the high analytical resolution of pre-clinical models enabled by single-cell methods may powerfully inform clinical decision-making. Cancer Biology Oncology Urology & Nephrology single-cell ATAC sequencing single-cell RNA sequencing cancer genomics gene signature score prostate cancer enzalutamide cancer treatment resistance treatment response prediction chromatin reprogramming Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Significance We used models of resistance to approved androgen receptor-targeted therapies for prostate cancer to identify subpopulations of treatment-persistent and pre-existing cells. We established that chromatin structure reconfigurations led to alterations in gene expression and drove alternative tumor adaptations and treatment escape. Motivated by the need for pre-treatment biomarkers in prostate cancer, we identified molecular predictors of therapy response based on the presence of treatment-persistent and pre-existing cells. Summary Here we show that subpopulations of treatment-persistent cells with stem-like and regenerative properties foster alternative trajectories of enzalutamide resistance in prostate cancer. Alternative transcriptional patterns of resistance are induced by divergent chromatin reprogramming. Transcriptional enrichment of signals from these treatment-persistent cells stratifies patient outcomes in both early stage treatment-naive and treatment-exposed tumors. Highlights Identification of prostate cancer cells with gene expression patterns of regenerative potential that persist and exist prior to enzalutamide treatment. Profiling of chromatin and transcriptional features from subpopulations of treatment-challenged prostate cancer cells. Identification of gene signatures associated with stem-like and regenerative potential. Stratification of prostate cancer patients from “bulk” RNA sequencing data based on identified stemness- and regeneration-related gene signatures. Introduction Prostate cancer (PC) relies on androgen receptor (AR) signaling for development and progression. Progression on androgen deprivation therapy (ADT) or AR signaling inhibitors (ARSIs) such as the second-generation AR antagonist enzalutamide (ENZ) leads to castration resistant (CRPC) or treatment-induced neuroendocrine prostate cancer (NEPC) (Beltran et al., 2016 ). The most frequently characterized mechanisms of PC or CRPC resistance to ARSIs, ADT, or both, revolve around the re-establishment of AR signaling via AR overexpression or AR mutations (Abida et al., 2019 ; Alumkal et al., 2020 ; Devlies et al., 2020 ; He et al., 2018 ). PC is profoundly heterogeneous (Haffner et al., 2020 ; Løvf et al., 2019 ; Tomlins et al., 2015 ; Woodcock et al., 2020 ) and patients would benefit from methods that differentiate between clinically mild disease and more aggressive forms. Although evidence of clonal expansion has been shown (Haffner et al., 2020 ), most studies to date have characterized genetic mutations (Gerhauser et al., 2018 ; Grasso et al., 2012 ; Taylor et al., 2010 ) that do not allow for understanding tissue complexity or biological bases of the emergence of treatment resistance. In contrast, although more frequent, non-genetic changes in transcriptomics, chromatin structure, and DNA accessibility of transcription factor (TF) binding motifs are less understood in PC drug resistance (Abida et al., 2019 ; Alumkal et al., 2020 ; Devlies et al., 2020 ; He et al., 2018 ). DNA accessibility is the first layer of gene regulation and transcriptomic changes are now being used to identify molecular predictors of cancer treatment response (Doultsinos and Mills, 2021 ). However, most RNA sequencing data are obtained from the bulk of the tumors and therefore cannot account for PC heterogeneity. This is because the transcriptome is the result of several biological processes contributing to differential gene regulation and such processes are not necessarily synchronized in all cells within the tumor bulk (Su et al., 2020 ; Zhang et al., 2020 ). The development of single-cell sequencing technologies has enabled the more detailed examination of genomic features in treatment resistant cancers, but the limited analytical methods are just beginning to reveal their potential. To explore how heterogeneous PCs respond to ARSIs, we analyzed the emergence of resistance in the epithelial-derived component of PC cells in models of ENZ exposed and resistant PC cell lines at a single-cell level. Through enrichment analysis of transcriptional signals from molecular gene classifiers derived in this study, we show evidence of treatment-persistent and pre-existing PC cells that can predict treatment response in both primary and advanced patients. Results Chromatin reprogramming underpins enzalutamide resistance To study molecular consequences of AR signaling suppression and drug resistance dynamics in PC, we used LNCaP parental and LNCaP-derived ENZ-resistant cell lines RES-A and RES-B generated via long-term exposure to AR-targeting agents (Handle et al., 2019 ) (see Methods ), as well as other independently generated LNCaP- and VCaP-derived models (Fig. 1 A). We hypothesized that chromatin structure would be reshaped in ENZ-resistant cells and lead to modification of the transcriptome (Sönmezer et al., 2020 ; Strickfaden et al., 2020 ). To extrapolate the contribution of chromatin structure to ENZ resistance, we performed single-cell (sc) assays for transposase-accessible chromatin and sequencing (scATAC-seq) on four samples: (1) LNCaP parental cells (LNCaP), (2) LNCaP exposed to short-term (48 hours) ENZ (10 µM) treatment (LNCaP-ENZ48), (3) RES-A and (4) RES-B (Fig. 1 A). We first analyzed the scATAC-seq data as if it would have been sequenced in bulk cells (see Methods ). The ATAC-seq signal at transcription start sites (TSS) decreased in ENZ-resistant cells compared to parental, particularly in RES-B cells (average enrichment score 4.8 in RES-B vs 6.2 in LNCaP, p < 0.001, t -test) (Fig. 1 B). RES-A and RES-B cells shared a large proportion (14% in RES-A and 17% in RES-B) of “ENZ-resistant-specific” open chromatin regions not found in parental LNCaP. Additionally, RES-A cells had a higher proportion of unique open sites compared to RES-B (19% vs 5%, P < 0.001, chi-square test) and LNCaP (19% vs 7%, P < 0.001, chi-square test) (Fig. 1 C). These findings are consistent with TSS non-targeted opening (Jiang and Zhang, 2021 ) of the chromatin upon ENZ resistance. We corroborated the extent of chromatin opening and reprogramming in ENZ-resistant cells by performing formaldehyde-assisted isolation of regulatory elements (FAIRE) sequencing (Giresi et al., 2007 ) on the parental LNCaP and RES-A cells subjected to androgen starvation, or exposed to androgens, ENZ, or both agents ( Figure S1A-D ) (see Methods ). Even in this bulk assay, ENZ and androgen starvation appeared to be more significant drivers of reprogramming in RES-A than in parental LNCaP. While there was no difference in the total number of open chromatin sites, ENZ-resistant samples had a higher proportion of unique open sites compared to parental in the presence of androgens ( P < 0.001, chi-square test) ( Figure S1E ) and in androgen-deprived (castrate) conditions ( P < 0.001, chi-square test) ( Figure S1C ). Read distribution analysis (see Methods) demonstrated that the chromatin of ENZ-resistant cells is more open in castrate conditions ( P = 0.018, t -test) ( Figure S1D ) but not in presence of androgens ( P = 0.239, t -test) ( Figure S1F ), and that ENZ has an additive effect on castration in inducing chromatin compaction in parental ENZ-sensitive cells that is counteracted by androgens ( Figure S1D, Figure S1F ). Next, we used all samples with scATAC-seq to generate cluster visualizations of cell subpopulations with different chromatin accessibility profiles (Fig. 1 D) (see Methods ). We identified clusters that we termed “unique” or “shared” across the samples (Fig. 1 E). Unique clusters were specific to RES-A, RES-B, or both (named “ENZ-induced clusters”), or specific to the untreated and/or short-term ENZ-treated parental line (named “initial clusters”). Shared clusters were present at similar proportions across the samples and were named “persistent clusters” (Fig. 1 E). We compared each cluster to all other clusters to determine its unique chromatin profile based on differentially accessible chromatin regions (DARs; Table S1 ). The most prevalent chromatin-based scATAC-seq clusters (0, 1, and 2) were persistent (Fig. 1 E) and defined by fewer than 20 unique DARs, suggesting that 74% of the cells share an overall similar chromatin accessibility profile during development of ENZ resistance. We then assessed for changes in cluster chromatin DARs between the parental LNCaP, LNCaP-ENZ48, and in RES-A and RES-B ( Table S1 ). DARs were observed around MYC and TP53 in several clusters during the short-term response to enzalutamide, including in cluster 6 that arises during enzalutamide resistance in RES-A. Studies on PC cell lines cultured for an extended time without androgens tend to display neuroendocrine-like phenotypes (Braadland et al., 2019 ; Fraser et al., 2019 ). The largest fold changes in chromatin accessibility based on average signal from all cells showed enrichment for neural system and neurite development processes between the parental (LNCaP-ENZ48 or DMSO) and resistant cells (RES-A or RES-B) ( P < 0.001 in RES-A and P = 0.001 in RES-B). Using Gene Set Variation Analysis (GSVA) for gene expression scoring (see Methods) , we found elevated expression of NEPC-derived signatures among upregulated genes (Braadland et al., 2019 ; Tsai et al., 2017 ) in RES-A and RES-B cells (particularly EZH2, AURKA, STMN1, DNMT1 , and CDC25B ), as well as increased expression of NEPC-downregulated genes in initial clusters ( Figure S1G ). Interestingly, in the same cell lines, enrichment analysis of NEPC signatures showed high NEPC signal in RES-A cells only ( Figure S1H ). Overall, these data show extensive chromatin reprogramming during the emergence of resistance to AR-targeting agents. Enzalutamide resistance reconfigures availability of TF binding DNA motifs in the chromatin Chromatin accessibility determines transcriptional output by exposing a footprint of TF DNA binding motifs. We hypothesized that increased chromatin opening in resistant cells would change the footprint of TF DNA motifs exposed. To this end, we first utilized AR and MYC binding site maps in LNCaP cells (Barfeld et al., 2017 ) and explored their relationship with open chromatin sites in the bulk FAIRE-seq data from RES-A cells. Using read distribution analysis, we observed a significant increase in open chromatin at MYC binding sites in ENZ-resistant cells ( P < 0.001 in castrate conditions and with androgens, t -test) (Fig. 2 A, Figure S2A ), as well as a reduction of open chromatin at AR binding sites ( P < 0.001 in castrate conditions and with androgens, t -test) (Fig. 2 B, Figure S2B ). These findings suggest that chromatin dysregulation in ENZ-resistance is associated with reconfiguration of AR and MYC chromatin binding, consistent with previously reported increased MYC and reduced AR transcriptional activity in these cells (Handle et al., 2019 ). To resolve how chromatin reprogramming affects TF DNA motif exposure at the single-cell level, we performed a TF motif enrichment analysis on the marker DARs characterizing the scATAC-seq cell clusters in each sample (Fig. 2 C, Table S1 ). This analysis confirmed the enrichment of motifs for several PC-associated TFs such as AR and MYC, as well as GATA2, HOXB13, and others in persistent clusters 3 and 5, as well as initial cluster 4 in parental and LNCaP-ENZ48 (Fig. 2 C ) . Clusters 3 and 5 remained enrichment of a subset of the same TFs motifs in RES-A and RES-B, with cluster 5 showing a consistent enrichment profile in all samples (Fig. 2 C). AR, CREB1, E2F1, GATA2, and ZFX were common motifs. Cluster 3 was characterized by FOXA1 and JUND, while cluster 5 was characterized by CTCF, ETS-like, and MYC. Although they possessed distinct sets of DARs, the ENZ-induced clusters 6 and 7 did not display enrichment of TF motifs in RES-A or RES-B. Between pairs of samples, DARs were predominantly closing in cluster 3 compared to all other clusters (8% vs 4% DARs differentially closing, P < 0.001, chi-square test) and predominantly opening in cluster 4 (11% vs 8% DARs differentially opening, P < 0.001, chi-square test) ( Figure S2C ). We performed selective TFs motif enrichment analysis in DARs opened (Fig. 2 D ) and closed ( Figure S2D ) between pairs of samples (see Methods ). While we observed no enrichments after short-term ENZ-treatment (LNCaP-ENZ48 vs parental; Fig. 2 D), comparing open DARs in RES-A or RES-B vs LNCaP parental retrieved distinct sets of TFs, with MYC and ESR1 being the most common across all clusters in RES-A and RES-B, respectively (Fig. 2 D). Similarly, comparing open DARs in RES-A or RES-B vs LNCaP-ENZ48 showed enrichment of most of the PCa-related TF motifs tested in most clusters (Fig. 2 D), and to an even greater extent when considering closing DARs between sample conditions ( Figure S2D ). These analyses demonstrate that ENZ-resistance is associated with reconfiguration of TF DNA motif footprints. Transcriptional patterns of enzalutamide resistance are induced by divergent chromatin reprogramming To study transcriptional patterns in relation to reconfiguration of chromatin structure at the single-cell level, we performed scRNA-seq in the LNCaP parental, RES-A and B models. Integrated clustering of four LNCaP samples (Fig. 3 A) (see Methods ) showed 7 persistent, 3 ENZ-induced, and 3 initial cell clusters (Fig. 3 B) defined by sets of marker differentially expressed genes (DEGs; Table S2 ; between 17 and 283 DEGs in the 13 clusters). To confirm that these cell subpopulations are relevant in other independent models of ENZ-resistance, we used the label transfer approach (Stuart et al., 2019 ) to query for matching cell populations in independent scRNA-seq datasets: a LNCaP parental sample, LNCaP ENZ-treated for 1 week (LNCaP-ENZ168), and an independent ENZ-resistant (RES-C) LNCaP-derived cell line (Fig. 1 A). Transferring scRNA-seq cluster labels confirmed the presence of initial clusters (4, 6, and 10) in LNCaP parental ( Figure S3A) and RES-C ( Figure S3B ). Presence of ENZ-induced clusters was confirmed in RES-C (17% of cells in cluster 3) and LNCaP-ENZ168 (79% in cluster 3), suggesting that one week of ENZ treatment is sufficient to give rise to this cluster prior to the development of resistance ( Figure S3C ). Most importantly, we could retrieve persistent subpopulations of cells in the alternative LNCaP-parental sample (4%), in LNCaP-ENZ168 (13%), and in RES-C (31%), suggesting that these persistent cells are consistently found during emergence of ENZ-resistance. We additionally performed scRNA-seq on a VCaP parental cell line treated with DMSO or ENZ for 48 hours to test for the generalizability of our results beyond a single cell line (Fig. 1 A). A similar analysis with VCaP cells confirmed the prevalence of persistent cells in the VCaP parental (93% of cells), as well as initial and ENZ-induced cells in VCaP-ENZ48 (38% and 55% of cells, respectively) (Fig. 3 C). We then sought to determine whether the observed scRNA-seq clusters (Fig. 3 A) could be the result of enriched TFs binding activity in alternative open DARs. Using annotated databases, we queried the transcriptional targets of the enriched TFs in the open DARs when comparing RES-A or B to the parental LNCaP ( Fig. 2 D ) in the matching scRNA-seq samples (see Methods ). Chromatin remodeling affected TF activity and consequently DEGs in the scRNA-seq for up to a maximum of 11% in cluster 0 in RES-A and 7.1% in cluster 1 in RES-B (Fig. 3 D). While target DEGs for TFs such as MYC, JUND, and E2F where found in most clusters in both RES-A and B, other target DEGs for TFs such as AR, RELA (a NFkB subunit), and GRHL2 appeared more specific to RES-A or B, consistent with proposed stoichiometric models of TFs chromatin binding (Klemm et al., 2019 ). This analysis confirmed that alternative open DARs in ENZ-resistance can activate divergent transcriptional programs. Next, we connected the scRNA-seq clusters to their matching scATAC-seq clusters. We took advantage once more of the label transfer approach to identify matching scRNA- and scATAC-seq cell states in the same sample conditions (see Methods ). In this process, we assigned cell cluster labels within the scRNA-seq to the scATAC-seq clusters, or vice versa ( Figure S3D ). We found that a chromatin state can correspond to multiple transcriptional states (96% in scATAC-RNA vs 48% in scRNA-ATAC of cells assigned on average across all samples, P < 0.001, chi-square test). Querying the integrated scRNA-seq clusters (Fig. 3 A-B) from the scATAC-seq data, we could find matching cell states in the scATAC-seq for scRNA clusters 9, 10, and 11 ( Figure S3E ). Across the sample conditions, 95% of the cells projected to belong to scRNA-seq cluster 10 belonged to scATAC-seq cluster 4, while 72% of cells projected to belong to scRNA-seq cluster 9 or 11 belonged to scATAC-seq cluster 3 (Fig. 3 E). Taken together, these data show that transcriptional configuration of ENZ-resistant cells, especially cells persisting during treatment, emerges from processes driven partially by chromatin structure and TF-mediated transcriptional reprogramming. These processes affect a number of important regulators of cell fate, consistent with lineage commitment recently observed in tissue development (Ma et al., 2020 ). Prostate cancer cell subpopulations with features of stemness precede enzalutamide resistance Cell cycle phase can be a strong determinant of the integrative clustering of scRNA-seq data. Accordingly, we found that persistent clusters 8, 9, and 11 scored highly for S and G2/M phase related genes using cell cycle scoring in Seurat (see Methods ) (Fig. 4 A), suggesting that cells in these clusters are more actively cycling and proliferative. However, we found that cells in clusters 9 and 11 were characterized not only by cell cycle genes, but also by expression of genes involved in chromatin remodeling and organization (CTCF, LAMINB, ATAD2), increased cell cycle turnover and stemness (FOXM1, (Ketola et al., 2017 ), and DNA repair (BRCA2, FANCI, RAD51C, POLQ) (Fig. 4 B). Clusters 5 and 11 showed high expression of a gene set, which we named “Stem-Like”, composed of stemness-related genes mainly from Horning et al (Horning et al., 2018 ) (Fig. 4 C). Karthaus et al recently identified activated luminal prostate cells able to regenerate the epithelium following castration (Karthaus et al., 2020 ). We extracted the gene expression profile associated with these prostate luminal cells (see Methods ) and used it to score each scRNA-seq cluster. We found cluster 10, an initial cluster, to score highly for this gene signature, which we renamed PROSGenesis (Fig. 4 D, Figure S4A ). We visualized the expression of PROSGenesis and Stem-Like in the VCaP scRNA-seq samples, confirming the presence of these subpopulations of cells in other models (Fig. 4 E). We then set out to reconstruct the trajectories of how these clusters of interest were generated during the development of ENZ-resistance. Based on cytoTRACE (Gulati et al., 2020 ), cells in clusters 10 and 11 were the least differentiated across most of the sample conditions (Fig. 4 F), suggesting that the other cells could derive from cells in these clusters. RNA velocity analysis estimated cluster 10 as a precursor of the enzalutamide-induced clusters (Fig. 4 G ) , concordant with a state derived from activated regenerative luminal prostate cells as previously suggested (Karthaus et al., 2020 ). Cluster-specific differential velocity analysis in RES-A and RES-B revealed downregulation of many PC-related genes, such as ATAD2, as well as upregulation of genes such as UBE2T, PIAS2, PFKFB4, and EGFR (Figure S4B-C) . ATAD2 and UBE2T were otherwise upregulated in persistent clusters 8, 9, and 11 ( Figure S4C ), suggesting additional transcriptional reprogramming in ENZ-induced clusters. These analyses point at two distinct subpopulations of PC cells which precede ENZ resistance: one persistent cell cluster (cluster 11) matching “Stem-Like” and one initial cluster (cluster 10) matching PROSGenesis, a signature derived from tissue regeneration (Karthaus et al., 2020 ). Collectively, our data suggest that there exists a small number of PC cells within the bulk with stem-like and regenerative potential. Model-based characterization of gene signatures in prostate cancer bulk RNA sequencing The use of molecular gene classifiers or signature scores is an attractive strategy to select cancer patients for treatment (Doultsinos and Mills, 2021 ; Eggener et al., 2020 ). According to an unbiased enrichment and differential expression analysis of hallmark gene sets (see Methods ), most of the persistent clusters and cluster 10 also showed significant enrichment of E2F targets, G2M checkpoint, and MYC target genes ( Figure S4D ). These data are largely concordant with the bulk RNA-seq data on the same cells in our previous study (Handle et al., 2019 ), reflecting the fact that signals from subpopulations of cells can be retrieved in bulk RNA-seq data. Differential expression within clusters ( Figure S4E-G ) further revealed that oxidative phosphorylation was immediately upregulated in LNCaP-ENZ48, and this process is maintained highly selectively in RES-A but not in RES-B. Moreover, genes regulated by activated mTORC1 signaling were consistently upregulated in most of the clusters as ENZ resistance develops ( Figure S4E-G ), in agreement with previous reports showing its activation during ENZ treatment in patients (Ma et al., 2020 ). We therefore used a collection of signatures derived from the scRNA-seq analysis to describe features of the same cells in bulk RNA-seq datasets. In addition to Stem-Like and PROSGenesis, we included (1) NEPC markers ( Figure S1G ), (2) a BRCAness gene signature (Li et al., 2017 ) as RES-A and RES-B maintain sensitivity to PARP inhibition (Handle et al., 2019 ) and the persistent cluster 11 is characterized by markers of DNA repair (Fig. 4 B), (3) gene sets as proxies of AR signaling activation (He et al., 2018 ), including activation of AR splice variants (AR-Vs), (4) the DEGs defining our scRNA-seq clusters, and (5) gene sets for mTORC1 signaling and MYC targets ( Figure S4D-G ) ( Table S3 ). In the bulk, the ENZ-induced DEGs selectively appeared in the RES-B cells (Fig. 5 A). Similarly, the persistent clusters were associated with the Stem-Like signature only in RES-A and RES-B when induced with DHT (Fig. 5 A). On the other hand, the PROSGenesis signature was elevated only in RES-B (Fig. 5 A). The NEPC features in RES-A were associated with MYC activation (Fig. 5 A). Consistent with both resistant lines remaining responsive to PARP inhibition (Handle et al., 2019 ), we found samples from RES-A and RES-B to score highly for BRCAness (Fig. 5 A), which is known to downregulate DNA repair machinery (Li et al., 2017 ). BRCAness was associated with the AR-V signature as previously shown (Kounatidou et al., 2019 ) (Fig. 5 A). To confirm the properties of different signatures, we used VCaP cells to develop an independent model of resistance to AR signaling-targeted treatments including ADT, bicalutamide, ENZ, and bicalutamide/ENZ multi-resistant sublines, and performed bulk RNA-seq (Fig. 1 A). These VCaP-based sublines did not show NE features (Fig. 5 B). Only ENZ-resistant VCaP cells scored highly for the ENZ-induced DEGs, confirming the specificity of this signature to ENZ treatment and resistance. Parental and ENZ-resistant VCaP cells scored highly for the PROSGenesis signature, while the scores of the persistent, Stem-Like, mTORC1 signaling, and MYC target signatures scored highly selectively in resistant VCaP sublines (Fig. 5 B). This suggests a convergent mechanism of resistance to these agents in independent models. Next, we scored xenografts of AR + /NE − , AR − /NE + , or AR − /NE − CRPC and NEPC tumors resistant to ENZ (Labrecque et al., 2019 ; Lam et al., 2020 ) with the same signature sets ( Figure S5A ). AR + /NE − xenograft samples clustered into two separate clusters. AR − tumors clustered together with a series of AR + /NE − tumors due to low mTORC and MYC signaling, while one cluster of AR + /NE − scored highly for all of the gene sets except for markers upregulated in NEPC (“NEPC upregulated”). Interestingly the PROSGenesis signature, along with initial clusters and ENZ-induced clusters, scored particularly high in AR + tumors while the Stem-Like signature, along with the persistent clusters, scored high in both AR + /NE − and AR − /NE + tumors ( Figure S5A ), suggesting that the two signatures capture different tumor biologies. In a transcriptome dataset based on an independent xenograft model (King et al., 2017 ), we found ENZ resistance to be uniquely associated with higher AR activity, higher expression of MYC target genes, PROSGenesis high score, and high expression of ENZ-induced cluster gene sets ( Figure S5B ). These data suggest that the Stem-Like status is independent of the AR status and that persistent cells might mediate the development of both AR positive CRPCs and negative NEPCs. Collectively, the persistent, initial, PROSGenesis, and Stem-Like derived gene signatures show potential for identifying aggressive, regenerative features of PC from bulk RNA-seq. Transcriptional signal enrichment analysis identifies treatment-persistent cells and prognostic gene signatures in prostate cancer patients We hypothesized that we could use enrichment of gene signature expression to stratify advanced and primary PC patients. To this end, we interrogated clinical data of CRPC patients treated with ENZ reported in Alumkal et al. (Alumkal et al., 2020 ). The patients aggregated into two clusters based on our complete signature set ( Figure S5C ), but patients in neither cluster had significantly shorter progression-free survival (PFS; P > 0.05, log-rank test). Utilizing a stepwise variable selection process we identified five significant signatures (NEPC upregulated, PROSGenesis, MYC targets, AR activity, and ARV) that are able to identify patients with significantly shorter PFS ( Figure S5D ). Moreover, PFS analysis of individual gene signatures revealed association with shorter time to progression for patients scoring high for the Stem-Like signature ( P = 0.025, log-rank test) or for genes upregulated in NEPC ( P < 0.001, log-rank test) (Fig. 5 C-D), while patients with longer PFS scored highly for PROSGenesis ( P = 0.021, log-rank test) and for the initial cluster signature( P = 0.018, log-rank test) ( Figure S5E ). None of the cluster marker gene sets showed a significant difference between Stand Up To Cancer (SU2C) CRPC abiraterone/ENZ-naive and abiraterone/ENZ-exposed patients (Abida et al., 2019 ) according to their latest treatment regime, suggesting that differences between the tumors based on the signatures may be difficult to retrieve using bulk sequencing from heavily pre-treated patients. Despite the challenges of applying single-cell derived signatures to bulk data however, Stem-Like was still significantly associated with poor overall survival in these patients (Fig. 5 E), supporting the potential significant activity of the persistent cells in this group of patients. Similarly, we could not stratify patients that developed resistance to ENZ in the SU2C West Coast DT Quigley et al dataset ( Figure S5F ) (Quigley et al., 2018 ), although in this case, ENZ-sensitive patients had higher expression of PROSGenesis ( P = 0.024, Wilcoxon rank-sum test) ( Figure S5G ). These data show that the Stem-Like signature associated with persistent cells (cluster 11) from our single-cell analysis of ENZ resistance is a consistent classifier with the potential of stratifying patients for response to second line AR-targeted treatments (Fig. 5 F). We then hypothesized that we could systematically use the persistent cluster 11, Stem-Like, initial cluster 10, and PROSGenesis signatures as a proxy for the presence of PC cells with different transcriptional features in clinical settings, to capture signals from such types of pre-existing subclones with metastatic potential in primary untreated tumors. To this end, we took advantage of a recently published scRNA-seq dataset on clinically relevant PCs specimens (Fig. 6 A) (Chen et al., 2021 ). We used GSVA score to highlight our 13 scRNA-seq clusters in 36424 cells from primary untreated PC specimens of 13 patients ( Figure S6A ). The analysis showed that our LNCaP model-derived cell clusters scored higher in luminal and basal/intermediate cells compared to fibroblasts ( P = 0.047, t -test) ( Figure S6A ). Additionally, luminal cells had higher expression of genes associated with our initial scRNA-seq clusters compared to the basal/intermediate cells ( P = 0.02, t -test) and compared to fibroblasts ( P < 0.001, t -test). We then scored the cells for expression of genes from the Stem-Like and PROSGenesis signatures, along with the associated clusters (11 and 10, respectively) and control signatures linked to AR activity (ARV, AR-FL, and AR activation), BRCAness, and NEPC (Fig. 6 B). We defined a high score for a gene signature to be above the 90th percentile. 48% percent of the cells that scored highly for the Stem-Like signature were luminal cells (Fig. 6 C). Cells scoring highly for the PROSGenesis signature were mostly basal/intermediate (78% of high scorers) (Fig. 6 D). Each single patient harbored on average 8% of cells scoring high for the Stem-Like signature (ranging from 2% in patient 173 to 23% in patient 156) and 8% of cells scoring high for the PROSGenesis signature (ranging from 0.9% in patient 153 to 33% in patient 172) (Fig. 6 E). To reconcile the presence of these cells and their relative histopathological position, we assessed gene expression within two sections (Prostate A and B) of primary untreated PC with spatial transcriptomics (see Methods ). We reconstructed the gene expression signal from stromal and epithelial components in an average of 1682 spots per sample using clustering analysis and annotated the tissue architecture in 5 clusters of stromal tissue (ST), benign epithelium (BE), and adenocarcinoma (PC-AC) (Fig. 6 F, Figure S6B ). PROSGenesis and Stem-Like signatures, as well as the companion model-derived cluster 10 signature, showed high expression scores within the sections compared to scores from housekeeping gene signatures (Fig. 6 G, Figure S6C ). We compared the score distributions of our signatures to the housekeeping gene set score distributions and determined the 90th percentile as a score cutoff for high expression by allowing for 5% false positives (see Methods ). Spots with high signal were found interspersed in all 5 clusters in both sections (Fig. 6 H, Figure S6D ). In Prostate A, however, spots scoring highly for the Stem-Like signature were more prevalent in the PC-AC cluster compared to ST ( P = 0.005, chi-square test). Spots scoring highly for PROSGenesis were further enriched in the BE and PC-AC clusters compared to ST ( P < 0.001 in both cases, chi-square test), while spots scoring highly for cluster 10 were enriched in the BE clusters compared to all other tissue regions ( P < 0.001 for each comparison, chi-square test) (Fig. 6 H). Concordant observations were made for PROSGenesis and cluster 10 signatures in Prostate B ( Figure S6D ). To validate these findings, we undertook a similar approach to re-analyze spatial transcriptomics data from prostate Sect. 3.3, 1.2, and 2.4 from Berglund et al (Berglund et al., 2018 ), which were annotated to contain a significant proportion of cancer. Similarly to our initial observation, these sections showed enrichment of spots scoring highly for the PROSGenesis in the PC-AC clusters compared to ST or prostatic intraepithelial neoplasia clusters ( Figure S6E-G ). Spots scoring highly for cluster 10 were more prevalent in both benign and cancerous clusters. Taken together these data suggest the presence of treatment-persistent cells interspersed within the primary untreated prostate tissue of PC patients with high metastatic potential. Finally, we verified whether we could predict recurrence in primary PC patients using the signature genes derived in these cells. We interrogated legacy primary tumor TCGA PRAD ( https://www.cancer.gov/tcga ) (Fig. 7 A ) and early onset PC (EOPC) ICGC (Gerhauser et al., 2018 ) RNA-seq data ( Figure S7A ) for our gene signatures of interest. Using all signatures for clustering the TCGA PRAD cohort separated 54% of Gleason score (GS)-7 and 15% of GS-8 + patients which would not benefit from additional treatment, as they had relatively good prognosis (Fig. 7 B). A similar trend was also observed in the ICGC cohort ( Figure S7B ). ENZ-induced (Fig. 7 C), PROSGenesis (Fig. 7 D), Stem-Like (Fig. 7 E), and persistent (Fig. 7 F) gene signatures were the most significant ( P < 0.05, log-rank test) contributors to cluster separation in the TCGA cohort, while NEPC downregulated genes were the major determinant in the ICGC cohort ( Figure S7C ). In line with previous reports (Alumkal et al., 2020 ), signatures reflecting AR activity (AR activity and full length) in these tumors were consistently associated with longer time to progression in the TCGA cohort (Fig. 7 G-H), suggesting a better response to inhibition of AR signaling in AR driven tumors. In the EOPC cohort, which is enriched in GS-7 tumors compared to the TCGA PRAD, the persistent and PROSGenesis signatures significantly stratified GS-7 patients ( P < 0.05, log-rank test) ( Figure S7D-E ), suggesting the ability of these signatures to further refine GS-based risk stratification in patients and avoid overtreatment. High PROSGenesis score was associated with good prognosis together with the gene set from the initial cluster 10 (Fig. 7 I). Individually, 8 out 13 clusters-derived signatures showed association with PFS in the TCGA cohort (Fig. 7 I), pointing at the utility of these signatures in PC patient risk stratification. Discussion In this study we provide a molecular perspective of the emergence of resistance to AR-targeted treatment at a single-cell level. Karthaus and colleagues recently found that luminal prostate cells that persist after ADT in a mouse model can contribute to tissue regeneration of the normal prostate epithelium by assuming stem-like transcriptional properties (Karthaus et al., 2020 ). Using PC specimen tumor DNA, we recently showed the presence of subclones within the primary tumors that preserve the ability to expand and metastasize years after treatment and are found interlayered within different lesions of multifocal tumors (Woodcock et al., 2020 ). Similarly, a recent work studying lung cancer metastases found that metastatic capacity arises from pre-existing and heritable differences in gene expression (Quinn et al., 2021 ). Here we find that during exposure to AR-targeting agents, a small proportion of persistent cells remain transcriptionally unperturbed by the treatment. We visualize these cells in primary untreated PC specimens and find them to be enriched in cancerous regions of histopathologically relevant tumors using spatial transcriptomics, as well as interspersed in apparent benign tissue. To understand the presence and function of these cells in histopathologically non-cancerous regions will warrant further studies. Our data show evidence of a hierarchical model of emergence of resistance to enzalutamide (Maitland, 2021 ) in which treatment-persistent cells are able to regenerate the bulk of the resistant ones. We describe the properties of the persistent cells using RNA velocity and show different intermediate states in alternative trajectories of treatment resistance. This process is partially driven by chromatin remodeling, which is consistent with chromatin accessibility lineage-priming (Ma et al., 2020 ; Martin et al., 2020 ). In PC, gain of function of bromodomain-containing proteins such as BRD4 (Asangani et al., 2014 ; Urbanucci et al., 2017 ) and ATAD2 (Morozumi et al., 2016 ; Urbanucci et al., 2017 ), as well as loss of function of chromatin remodeler CHD (Zhang et al., 2020 ), have been shown to contribute to PC progression and lineage plasticity in therapy resistance. This process is likely accompanied by chromatin reprogramming (Braadland et al., 2019 ; Urbanucci et al., 2017 ; Uusi-Mäkelä et al.). While many groups have focused on the effect of AR-targeted treatment on chromatin-associated factors such as CREB5 (Hwang et al., 2019 ), or TFs such as GR (Arora et al., 2013 ) and AR (Yuan et al., 2019 ), in this study we found that exposure to AR-targeting agents increases the overall relaxation of the chromatin. Applying the label transfer method across datasets revealed that subpopulations of cells with different chromatin states lead to multiple transcriptional configurations, including those of persistent cells. Using different cell line models mimicking alternative trajectories of treatment resistance, we infer that differential DNA motif exposure determined by chromatin structure may partially contribute to TF activity-mediated transcriptional reprogramming in the different cell subpopulations induced by exposure to enzalutamide. According to this analysis, specific subpopulations of PC cells are more subjected than others to TFs activity reprogramming. This is consistent with recent studies showing simultaneous detection of multiple transcription factors on single DNA molecules and TFs co-occupancy frequently occurring at sites of competition with nucleosomes (Strickfaden et al., 2020 ). We show that treatment-persistent cells have high cell cycle turnover, compatible with high regenerative potential (Poli et al., 2018 ; Wang et al., 2020 ), and assume states of stemness from their transcriptional profiles. As these features have been associated with more aggressive tumors, we developed transcriptional signatures derived from two states in particular: one state derived in ADT-treated prostate cells by Karthaus et al. (Karthaus et al., 2020 ), which we renamed PROSGenesis, tightly associated with initial and enzalutamide-induced clusters in our model of enzalutamide resistance, and one state that we called “Stem-Like”, associated with persistent cells during the emergence of enzalutamide resistance. PROSGenesis, Stem-Like, and associated signatures can capture different tumor types and stratify ARSI-exposed CRPC patients' outcome. Moreover, we show that in primary PC patients undergoing ADT treatment, high signature scores in treatment-naive specimens are associated with shorter time to progression (biochemical recurrence). Interestingly, in primary treatment-naive patients, high score for PROSGenesis is associated with longer response to ADT, possibly due to the stronger contribution of AR activity in these tumors. Overall, we have identified and characterized gene signatures that can be used to profile subpopulations of treatment-persistent cells with stem-like and regenerative properties that foster alternative trajectories of AR-targeted treatment resistant PCs. Methods Cell lines and culture LNCaP and VCaP cell lines were obtained from American Type Culture Collection (ATCC; LGC Standards) and authenticated periodically (HPA cultures or Eurofins). RES-A and RES-B cells were generated by prolonged exposure to the second-generation anti-androgens enzalutamide and RD-162 as described earlier (Handle et al., 2019 ). LNCaP parental (ATCC), RES-A, and RES-B cells were cultured in RPMI 1640 (Sigma R0883) supplemented with 10% FBS (Sigma F7524), 2 mM Alanyl-glutamine (Sigma G8541), 1 mM sodium pyruvate (Merck TMS-005-C), 2.5 g/L glucose (Sigma G8769), and 1x Antibiotic-Antimycotic (Gibco, 15240062) in a humidified 37°C incubator with 5% CO 2 . RES-A and RES-B cells additionally received 10 µM enzalutamide (MedChemExpress HY-70002) with each cell splitting/feeding. VCaP cells were cultured in DMEM (Gibco) supplemented with 10% FBS in a humidified 37°C incubator with 5% CO 2 . For experimental treatments, ~1x10 6 cells were seeded into 5 cm culture plate dishes, and allowed to settle before exposure to 10 µM enzalutamide or DMSO vehicle control (0.1%) for 48 h or 168 h. The additional LNCaP cells (ATCC) and RES-C cells were cultured in a humidified CO2-incubator at 37°C in Gibco™ RPMI 1640 (1X) media (Thermo Fisher Scientific) supplemented with 10% FBS (Gibco standard FBS, Thermo Fisher Scientific), 2 mM L-Glutamine (Gibco®, Thermo Fisher Scientific), and a combination of 100 U/ml Penicillin and 100 µg/ml Streptomycin (Gibco® Pen Strep, Thermo Fisher Scientific). The enzalutamide resistant LNCaP RES-C cell line was generated by passaging of LNCaP cells with continuous treatment with 10 µM enzalutamide for 9 months and maintained in the same medium as LNCaP except for the supplementation with 10 µM enzalutamide. Generation of resistant VCaP subline derivatives and RNA-seq Androgen-sensitive VCaP prostate cancer cell line (passage (p.) 15.) was a gift from Dr. Tapio Visakorpi, Tampere University, Finland. Cells were cultured in RPMI 1640 supplemented with 10 % DCC-FBS, 1 % L-glutamine, 1 % A/A, and 10 nM testosterone (T) for seven months to establish T-dependent subclone VCaP-T. VCaP-T cells were then cultured at low testosterone (0.1 nM) for 10 months to establish VCaP-CT, an androgen-independent cell line able to grow despite low testosterone. VCaP-CT were then cultured at 10 µM enzalutamide until the cells regained ability to grow despite enzalutamide, creating enzalutamide resistant cell line VCaP-CT-ET. Another cell line was created by incubating first VCaP-CT cells with bicalutamide and subsequently with enzalutamide upon reaching bicalutamide insensitivity. Ultimately these cells also gained the ability to grow despite enzalutamide, creating the multiresistant cell line VCaP-CT-Br-ER. RNA sequencing was performed with Illumina HiSeq 3000. We sequenced 3 replicates, obtaining an average of 111 million paired-end reads per sample. Reads were aligned using STAR aligner v2.5.4b (Dobin et al., 2013 ) and Ensembl reference genome GRCh38. Genewise read counts were quantified using featureCounts v1.6.2 (Liao et al., 2014 ) and Gencode annotations release 28. Single-cell samples preparation and sequencing LNCaP parental (treated for 48 hours with enzalutamide or DMSO), RES-A, and, RES-B cells were harvested with 0.05% Trypsin-EDTA (Sigma T3924). After neutralization with complete medium, centrifugation (300 x g for 5 min), and resuspension in PBS/0.5% BSA, the cells were filtered through a 35 µm Cell Strainer (Corning 352235) and a single-cell suspension of living cells was acquired through sorting on a FACS Aria II cell sorter. The cell concentration of the single-cell suspension was assessed with a Countess II FL Automated Cell Counter and ~ 3 x 10 4 cells were pelleted (300 x g for 5 min) for further processing for using the Chromium Single Cell 3’ Library, Gel Bead & Multiplex Kit, and Chip Kit (v3, 10x Genomics). For the additional LNCaP parental and RES-C cells, 1 million cells were thawed in RPMI (Gibco) with 10% FBS (Gibco) and centrifuged at 300g for 5 min. The cells were then suspended in PBS with 0.04% BSA (Ambion) and filtered with Flowmi™ cell strainer (Bel-Art). Before loading, the cells’ viability and concentration was determined using Trypan blue with Cellometer Mini Automated Cell Counter (Nexcelom Bioscience). Chromium Single Cell 5` RNA-seq was performed using the 10X Genomics Chromium technology, according to the Chromium Next GEM Single Cell V(D)J Reagent Kits v1.1 kit User guide CG000208 Rev D with loading concentration of 1000 − 200 cells/µl. The LNCaP-ENZ168, VCaP, and VCaP-ENZ48 single-cell RNA-seq samples were prepared with Drop-seq (Macosko et al., 2015 ) using the Dolomite cell encapsulation system (Dolomite Bio). Cells were trypsinized with TrypLE™ Express Enzyme (ThermoFisher Scientific, #12604021), spun down (5 min at 300xg) and washed with 0.1% BSA-PBS. After pelleting, the cells were resuspended in plain PBS and passed through a 40-micron filter. The number of viable cells was estimated with the use of trypan blue staining and Fuchs-Rosenthal hemocytometer chamber. The concentration of cells was brought down to 3x105 cells/mL in 0.1% BSA-PBS. For single-cell encapsulation, single-cell suspension, beads in lysis buffer and oil were connected with the loops and tubing to the Mitos P pumps and run through the glass microfluidic chip at the following flow rates: 100µL/min (Oil channel), 20µL/min (Bead channel); 350 mbar (Cell channel). Droplets were separated by centrifugation and beads counted with the use of Fuchs-Rosenthal hemocytometer chamber and up to 90000 beads were collected into one tube for Reverse Transcription reaction, exonuclease treatment, and amplification of cDNA library according to the original protocol (Macosko et al., 2015 ). Tagmentation of cDNA was performed with the Nextera XT DNA Library Preparation Kit (Illumina, #FC-131-1024). The PCR product was cleaned-up with AMPure XP beads, eluted in 10µL H2O and sequenced using Illumina HiSeq 2500 Rapid run. For scATAC-seq, cell nuclei were isolated following the 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing (CG000169-Rev C). Briefly, the cell suspension was washed once in PBS/0.04%BSA, and 2 x 10 5 cells were pelleted (300 x g for 5 min), resuspended in 100 µl freshly prepared Lysis Buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl 2 , 0.1% Tween-20, 0.1% NP40 Substitute, 0.01% Digitonin, 1% BSA), and incubated on ice for 4 min (LNCaP parental cells), 6 min (RES-A), or 5 min (RES-B). The lysates were diluted with 1 ml wash buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl 2 , 0.1% Tween-20, 1% BSA), and the nuclei were pelleted (500 x g for 5 min) and resuspended in 30 µl 1x Nuclei Buffer (10x Genomics PN-2000153). Successful preparation of intact, isolated nuclei was confirmed through visual inspection in a phase-contrast microscopy, and nuclei concentration was assessed with a Countess II FL Automated Cell Counter, before proceeding immediately to processing for Single Cell ATAC sequencing using 10x Chromium, 10x Genomics library preparation and the Chromium Single Cell ATAC Reagent Kits (v1) User Guide (CG000168 Rev D). Sequencing was performed on the Illumina NextSeq500 instrument at the genomics core facility at the Oslo University Hospital, while sequencing of the additional LNCaP parental and RES-C was performed with Novogene Company Limited, Cambridge, UK´s sequencing core facility was used with a PE150 NovaSeq sequencer, aiming at 50000 reads per cell. For scRNA-seq, sequencing reads were processed into FASTQ format and single-cell feature counts using Cell Ranger v3.0.2 (Zheng et al., 2017 ). Similarly, Cell Ranger ATAC v1.1.0 (Satpathy et al., 2019 ) was used to process sequencing reads from scATAC-seq into FASTQ format and peak-barcode counts. In both cases, we used the Cell Ranger pre-built GRCh38 reference. The LNCaP-ENZ168, VCaP, and VCaP-ENZ48 Drop-seq samples were pre-processed, aligned, and processed to cell count matrices with the Drop-seq tools v2.3.0 pipeline (as described in https://github.com/broadinstitute/Drop-seq/blob/master/doc/ Drop-seq_Alignment_Cookbook.pdf) using default parameters and with the expectation that each sample contained 1000 cells (Macosko et al., 2015 ). The pipeline uses the STAR aligner v2.7.3a (Dobin et al., 2013 ) and Picard Tools v2.18.22 ( http://broadinstitute.github.io/picard/ ). We utilized the human reference genome version GRCh38, along with Gencode annotations version 33. Formaldehyde-assisted isolation of regulatory elements (FAIRE) sequencing and analysis FAIRE was performed on parental and LNCaP-ResA cells in biological triplicate according to the standard protocol (Simon et al., 2012 ). Prior to FAIRE-seq, cells were cultured for three days in RPMI medium supplemented with 5% DCC FBS and 10 µM enzalutamide was added only to the resistant cell line. Both sublines were then treated with DMSO (control), DHT (10nM; Sigma Aldrich), enzalutamide (10µM, Selleckchem), or a combination of DHT and enzalutamide for 18 hours. The DNA fragments isolated by FAIRE were used for library preparation with the Roche KAPA library prep kit according to the manual and sequenced on the Illumina HiSeq 2500 to produce 50 bp single-end reads at the Genomics core (KU Leuven) and aligned using bwa v0.7.8-r455 (Li and Durbin, 2010 ) against hg19. Duplicates were marked and realigned using Picard 1.118. Peak calling was performed on the aligned files using MACS2 v2.1.0 (Zhang et al., 2008 ). MSPC v4.0.2 (Jalili et al., 2018 ) was used to jointly analyze the peaks called in the three technical replicates from each sample and to derive a common peak set. DiffBind v2.14.0 (Stark et al., 2011 ) was used to explore peak overlaps and differential accessibility between samples. Read distribution analysis around transcription start sites, MYC binding sites, and AR binding sites was performed by counting the average number of reads across replicates for each sample condition in 100bp bins extending 1kb up- and downstream of the sites. The value at the center (position 0) of the resulting distributions was compared between samples using the t -test to assess for differences in chromatin openness at these sites. Software and statistical testing Analyses were performed using R v3.6.3 or Python v3.7.0. Statistical testing was performed using R v3.6.3. Statistical tests used are indicated in the text and in figure legends. The Shapiro-Wilk test was used to test for normality. Single-cell RNA pre-processing and quality control The Cell Ranger output was used as the input to Seurat v3.2.0 (Butler et al., 2018 ; Stuart et al., 2019 ) for further analysis of the scRNA-seq samples. For each sample, poor quality cells were filtered based on the number of detected genes, the total number of molecules detected, and the percentage of reads arising from the mitochondrial genome. Specific thresholds for each filtering criterium were adjusted per sample to preserve a maximal number of cells. To address the effects of cell cycle heterogeneity in the data, each cell was scored for its expression of genes associated with S or G2/M phases (gene sets provided within Seurat) using the Seurat CellCycleScoring function. The difference between the G2/M and S phase scores was regressed out using sctransform (Hafemeister and Satija, 2019 ). Single-cell RNA clustering The mutual nearest neighbor approach fastMNN (Haghverdi et al., 2018 ) was used to integrate the four LNCaP samples using 2000 integration features and account for batch effect. Clustering and UMAP non-linear dimensionality reduction were performed using Seurat v3.2.0, and we refer to the result as our integrated clusters. The marker genes of each cluster were determined by identifying genes differentially expressed in each cluster compared to all other clusters based on the generalized linear model MAST framework v1.12.0 (Finak et al., 2015 ), using the number of RNA reads as a latent variable. A gene was considered to be differentially expressed with Bonferroni corrected p-value < 0.01, at least 10% of the cells in the cluster expressing the gene, and an average log-fold change of at least 0.25. Cluster and sample characterization We utilized hallmark gene sets from the Molecular Signatures Database (MSigDB) v7.2 (Liberzon et al., 2015 ; Subramanian et al., 2005 ) to characterize clusters and samples based on their differentially expressed genes. Gene set variation analysis (GSVA) was performed using the GSVA package v1.36.2 to characterize the average expression profile of each cluster. See the “Bulk RNA-seq and clinical data analysis” section for a more detailed description of the method. To characterize the gene expression changes within each cluster between samples, all genes were ranked based on their average log-fold change. The fgsea package v1.14.0 was then used to perform gene set enrichment analysis for the MSigDB hallmark gene sets using 1000 permutations. Differentiation states of each cell in each sample were predicted using cytoTRACE v0.3.3 (Gulati et al., 2020 ). The RNA velocities of single cells in the scRNA-seq samples were assessed using scVelo v0.2.2 (Bergen et al., 2020 ). Loom input files for scVelo were generated from the FASTQ files of each sample using loompy v3.0.0, and the metadata for running scVelo (filtered cell identifiers, UMAP coordinates, and cluster information) were extracted from the integrated Seurat object and integrated with the Loom files. Single-cell ATAC pre-processing and quality control The output of the Cell Ranger ATAC pipeline was used as the input to Signac package v0.2.5 (Stuart et al., 2020 ) for further analysis of the scATAC-seq samples. For each sample, poor quality cells were filtered based on the following features: strength of nucleosome binding pattern, transcription start site enrichment score as defined by ENCODE, total number of fragments in peaks, fraction of fragments in peaks, and percentage of reads in ENCODE blacklisted genomic regions. Specific thresholds for each adjusted per sample to preserve the maximal number of cells. Data normalization and dimensionality reduction was performed using Signac with latent semantic indexing (LSI), consisting of term frequency-inverse document frequency (TF-IDF) normalization and singular value decomposition (SVD) for dimensionality reduction, using the top 50% of peaks in terms of their variability across the samples. The first LSI component reflected sequencing depth across the samples and was not utilized in downstream analyses. Single-cell ATAC clustering Integrated clustering of the scATAC-seq samples was performed with harmony v1.0 (Korsunsky et al., 2019 ) using LSI embeddings. The resulting harmony-adjusted cell embeddings were used as input for UMAP non-linear dimensionality reduction and clustering using default parameters and the smart local moving (SLM) algorithm for modularity optimization. A “pseudo-bulk” analysis of changes in chromatin accessibility in the scATAC-seq samples was performed by pooling the reads from all good-quality cells in each sample. Visualization of peak overlap between samples was generated using R package ggradar v0.2. Differentially accessible regions in the clusters were identified using logistic regression with the total number of peaks as a latent variable. Regions were considered differentially accessible with Bonferroni corrected p-value < 0.05, at least 10% of the cells showing accessibility in the region, and an average log-fold change of at least 0.25. Differentially accessible regions were annotated with their closest gene using the Signac ClosestFeature function. Transcription factor motif enrichment Transcription factor motif enrichment was performed using Signac in differentially accessible regions between sample conditions and between clusters in each sample with R package TFBSTools v1.26.0 and JASPAR database position frequency matrices retrieved from the R JASPAR2018 data package v1.1.1. The hypergeometric test was used to test for significant motif enrichments, taking into account sequence characteristics of the peaks (e.g. GC-frequency). P-values were adjusted with the Benjamini-Hochberg method and motifs with adjusted p-values less than 0.05 were considered to be enriched. Transcription factors that are known to play a role in PC were filtered based on their expression in the single cell dataset. Chromatin states in scATAC-seq (as defined by the enriched TFs in differentially open chromatin regions) were connected to transcriptional outputs in the scRNA-seq by assessing for overlap between the target genes of enriched transcription factors and differentially expressed genes in the scRNA-seq clusters. Transcription factor target genes were obtained using the GTRD database v18.06 (Kolmykov et al., 2021 ) and selecting those with differentially accessible regions observed between castration-resistant prostate cancer and prostate cancer patients in Uusi-Mäkelä et al (Uusi-Mäkelä et al.). Integration of scRNA-seq datasets and scRNA- and scATAC-seq datasets using label transfer The clusters identified from the integrated clustering of scRNA-seq from LNCaP, LNCaP-ENZ48, RES-A, and RES-B (Fig. 3 A) were queried in additional scRNA-seq samples (alternative LNCaP parental, LNCaP-ENZ168, RES-C, VCaP parental, and VCaP-ENZ48) (Fig. 1 A) using the label transfer approach implemented in Seurat v3.2.0 (Stuart et al., 2019 ). The additional scRNA-seq samples were individually clustered and anchors were identified for each additional scRNA-seq sample (the query) and the LNCaP integrated clusters (the reference). This was done using the FindTransferAnchors function with principal component analysis (PCA). The anchors were used to transfer cluster label identifiers between the two data types using the TransferData function. Each cell in the query was assigned the cluster label with the highest confidence score, and only query cells with confidence scores above 0.5 were considered to have been successfully label transferred. LNCaP, LNCaP-ENZ48, RES-A, and RES-B had scRNA-seq and scATAC-seq data available from each sample (Fig. 1 A). These data types were integrated using the cluster label transfer procedure as implemented in Signac v0.2.5 and Seurat v3.2.0. Each scRNA-seq sample was clustered individually and its cluster labels were projected onto the matching, individually clustered scATAC-seq sample, or vice versa. The clustering resolution of each sample was assessed and decided using clustree v0.4.3 (Zappia and Oshlack, 2018 ). Briefly, RNA-seq expression levels were imputed from the scATAC-seq data by defining for each gene a genomic region including the gene body and 2kb upstream of the transcription start site and taking the sum of scATAC-seq fragments within the region. Anchors were identified for condition-matched scRNA- and scATAC-seq samples using the FindTransferAnchors function and canonical correlation analysis (CCA) was performed on the scRNA expression values and the scATAC imputed gene expression values. The anchors were used to transfer cluster label identifiers between the two data types using the TransferData function. Each cell in the query was assigned the cluster label with the highest confidence score, and only query cells with confidence scores above 0.4 were considered to have been successfully label transferred. Signature gene selection To generate the PROSGenesis signature, we extracted the gene expression profile associated with the regenerative mouse prostate luminal 2 cells reported in Karthaus et al (Karthaus et al., 2020 ) and found 78 genes with homologues in humans that were profiled in our scRNA-seq dataset. The mTORC1 signaling and MYC target gene signatures were obtained from the hallmark gene sets from the Molecular Signatures Database (MSigDB). Other signature gene sets were retrieved from previous publications or from our scRNA-seq data analysis as indicated in the main text. Bulk RNA-seq and clinical data analysis Each gene signature or set was assessed for enrichment and scored in a sample using the GSVA package v1.36.2, which is a non-parametric, unsupervised method for estimating gene set enrichment of each sample from gene expression data. For GSVA analysis, first, scale normalization at the seventy-fifth percentile based on the DSS package (Wu et al., 2013 ) was applied to the raw read counts from samples in datasets where these counts were available. For the TCGA and ICGC cohorts, we then filtered out genes with a zero count in any of the tumor samples. For each gene, GSVA performed a Poisson kernel transformation based on its empirical cumulative density function (CDF) across all samples. For RNA-sequencing datasets where only log-normalized expression values rather than raw counts were available, Gaussian kernels were utilized instead of Poisson kernels in the GSVA calculation. The kernel transformed expression values were then converted to ranks for each sample across all genes and the ranks were normalized to centered at zero. Next, for a given gene signature or set, following a similar procedure as GSEA (Subramanian et al., 2005 ), the Kolmogorov-Smirnov-like random walk statistics were calculated using the normalized ranks based on two statistics: 1) a running sum of the genes which belong to the gene set. It is denoted as \({S}_{1}\) . 2) a running sum for the genes which do not belong to the gene set. It is denoted as \({S}_{2}\) . For sample j , and gene signature k , we define \({ES}_{jk}^{+}\) as the largest positive deviations from zero of \({S}_{1}\) - \({S}_{2}\) , and \({ES}_{jk}^{-}\) as the smallest negative deviations from zero of \({S}_{1}\) - \({S}_{2}\) . The final GSVA enrichment score of sample j and gene signature k is \(\left|{ES}_{jk}^{+}\right|\) - \(\left|{ES}_{jk}^{-}\right|\) . The GSVA enrichment score emphasizes genes in pathways that are concordantly activated in one direction only, either over-expressed or under-expressed relative to the overall population. For pathways containing genes strongly acting in both directions, the deviations of \(\left|{ES}_{jk}^{+}\right|\) and \(\left|{ES}_{jk}^{-}\right|\) will cancel each other out and show little or no enrichment. In cases where the expression of a gene set was assessed at the single-cell level, the GetModuleScore function in Seurat was used to generate an average expression score per cell. Survival analyses were performed using the survival package v3.2-3 and Kaplan-Meier curves were plotted using the survminer package v0.4.8. For single signature survival analyses, median GSVA score was used to stratify patients into low and high expressing groups for the signature. For survival analyses of multiple signatures, samples were clustered using their GSVA enrichment scores for each signature using Euclidean distance and hierarchical clustering. The clustering result was then used to define the two-group split of samples for the survival analysis. We utilized a published scRNA-seq dataset of prostate cancer patient tumor samples from Chen et al (Chen et al., 2021 ) to assess for the presence of our gene signatures in different cell types. The data was processed and visualized according to the code provided as part of the publication ( https://github.com/chensujun/scRNA ) using Seurat v3.2.0. Cell types were identified from the data using the marker genes reported in Fig. 1 B of the publication. Spatial transcriptomics analysis of primary prostate cancer tissue Two sections of cryopreserved prostate cancer tissue from one patient (pT = 2b, T1c, Gleason 6, PSA 3.5 ng/mL) were profiled for spatial transcriptomics using the Visium Spatial library preparation protocol from 10x Genomics with a resolution of 55 µm (1–10 cells) per spot. The tissues were cryosectioned at 10 µm thickness to Visium library preparation slide, fixed in ice-cold 100% methanol for 30 min, H&E stained with KEDEE KD-RS3 automatic slide stainer and the whole-slide was imaged using Hamamatsu NanoZoomer S60 digital slide scanner. Sequencing library preparation was performed according to Visium Spatial Gene Expression user guide (CG000239 Rev D, 10x Genomics), using 24 min tissue permeabilization time. Sequencing was done on the Illumina NovaSeq PE150 sequencer at Novogene Company Limited, Cambridge, UK´s sequencing core facility, aiming at 50,000 read pairs per tissue covered spot. Sequenced data was first processed using Space Ranger v1.2.0 from 10x Genomics to obtain per-spot expression matrices for both sections. Downstream processing and clustering was then performed using Seurat v3.2.0. Normalization of the data was performed with sctransform to account for differences in sequencing depth across spots. Clustering was performed using the FindClusters function using a resolution parameter value of 0.8. The resulting clusters were found to correspond to histological characteristics of the tissue. The GetModuleScore function of Seurat was used to score the spots for our scRNA-seq derived gene signatures, as well as length-matched random housekeeping gene signatures from the Housekeeping and Reference Transcript Atlas v1.0 (Hounkpe et al., 2021 ). The distributions of the gene expression scores for the housekeeping gene sets and our scRNA-seq signatures were compared to determine the 90th percentile as a score cutoff at which we considered a spot to have high expression of the scRNA-seq signature, allowing for 5% false positives (spots scoring above the threshold for housekeeping gene sets). To validate our spatial transcriptomics findings, we utilized prostate Sect. 1.2, 2.4, and 3.3 from the spatial transcriptomics publication by Berglund et al (Berglund et al., 2018 ). H&E images and spot count matrices were provided by the Lundeberg lab. Processing, clustering, and signature scoring of the data was performed identically to sections of Prostate A and B, but requiring that each spot would have a minimum of 500 reads counts. Similar to the analysis for Prostate A and B, the 90th percentile cutoff for high versus low scoring spots for the gene sets enrichment was assessed and confirmed using comparisons to housekeeping gene set scores for each spot. Declarations Acknowledgements We thank the Genomics Core Facility at Institute for Cancer Research, OUH, for the support during preparation of the scATAC- and RNA-seq, the Tampere University histology core facility and Sari Toivola for skillful assistance during Visium experiments, and the UEF Bioinformatics Center, University of Eastern Finland, Finland. The study was financially supported by the Finnish Cultural Foundation (ST), Academy of Finland (#312043 (MN), #310829 (MN), #324009 (KK), #328928 (KK), #333545 (KG, EMV, SH), #3121330724 (TM)), Finnish Cancer Society #3122800563 (TM) Cancer Foundation Finland (MN, KK, KG), Sigrid Jusélius Foundation (MN, KK, KG), Emil Aaltonen Foundation (KG), Finnish Cancer Institute (MN), Norwegian Cancer Society (#198016-2018)(AU, NE), Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital (MN, TLJT, TV, TM), The Norman Jaffe Professorship in Pediatrics Endowment Fund (SC), MD Anderson Colorectal Cancer Moon Shot Program (SC), Oncode Institute (SP), Finnish Cultural Foundation North Savo Regional fund (KK, RK), University of Eastern Finland Doctoral Programme in Molecular Medicine (RK), K. Albin Johansson Foundation (KK), John Black Foundation (IM), Human Cell Atlas Seed Network - Retina (WW), Chan Zuckerberg Institute (WW), NIH R01CA183793 (WW, SC), NIH R01CA239342 (WW), NIH R01CA158113 (WW), P30CA016672 (WW), the Fonds Wetenschappelijk Onderzoek-Vlaanderen #GOA9816N (FC), KU Leuven #C14/19/100 (FC), Kom op tegen Kanker #KOTK (FC, WD), Cancer Research UK # A22744 (GA, DW, KN, PC), Cancer Research UK #C57899/A25812 (AE, ADL). The results published here are in part based upon data generated by The Cancer Genome Atlas project established by the NCI and NHGRI. Declaration of interests GA receives a reward from the Institute of Cancer Research for his role as an inventor of abiraterone. GA has received honoraria, consulting fees, or travel support from Janssen, Astellas, Pfizer, Novartis, Bayer, Amgen, AstraZeneca, Sanofi, and Sapience, grant support from Janssen and Astellas, and is a principal investigator for clinical trials sponsored by Janssen, Pfizer, and Astellas. 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Tables Data and code availability Dataset Source Identifier Single-cell RNA- and ATAC-sequencing of LNCaP This study GEO: GSE168669 FAIRE-seq of LNCaP This study GEO: GSE168669 Spatial transcriptomics data from prostate sections Prostate A and Prostate B This study European Genome-phenome Archive: EGAS00001000526 Single cell RNA-sequencing of LNCaP and VCaP from group of Prof. Gerhardt Attard, UCL This study GEO: GSE168733 RNA-sequencing of VCaP models of CRPC and resistance to AR signaling-targeted treatments from group of Prof. Teemu Murtola, Tampere University This study GEO: GSE168669 AR and c-MYC binding sites map (Barfeld et al., 2017) GEO: GSE73994 Bulk RNA-sequencing of LNCaP samples (Handle et al., 2019) GEO: GSE130534 Xenografts of AR-positive / NE-negative and AR-negative / NE-positive CRPC tumors (Labrecque et al., 2019; Lam et al., 2020) GEO: GSE124704 GEO: GSE126078 LNCaP xenograft model of CRPC (King et al., 2017) Supplementary File 1 in publication Patient RNA-sequencing from enzalutamide responders and non-responders (Alumkal et al., 2020) Received from authors RNA-sequencing from SU2C CRPC patient samples (Abida et al., 2019) Received from authors RNA-sequencing from SU2C West Coast DT patient samples (Quigley et al., 2018) Received from authors Single cell RNA-sequencing of LNCaP from group of Dr Kirsi Ketola, University of Eastern Finland Unpublished Received from Ketola lab scRNA-seq of 12 treatment-naive prostate cancer patient tissue samples (Chen et al., 2021) GEO: GSE141445 Spatial transcriptomics data from prostate tissue sections 1.2, 2.4, and 3.3 (Berglund et al., 2018) Received from authors TCGA-PRAD RNA-seq https://portal.gdc.cancer.gov/ https://portal.gdc.cancer.gov/ ICGC-EOPC RNA-seq (Gerhauser et al., 2018) Received from authors Hallmark gene sets from the Molecular Signatures Database (MSigDB) v7.2 (Liberzon et al., 2015; Subramanian et al., 2005) http://www.gsea-msigdb.org/gsea/msigdb/index.jsp GTRD database v18.06 (Kolmykov et al., 2021) https://gtrd.biouml.org/ Housekeeping and Reference Transcript Atlas v1.0 (Hounkpe et al., 2021) http://www.housekeeping.unicamp.br/ Methods Table 1 . Details of datasets utilized as part of the study. Resource Source Identifier Cell Ranger (version 3.0.2) (Zheng et al., 2017) 10x Genomics Cell Ranger ATAC (version 1.1.0) (Satpathy et al., 2019) 10x Genomics Space Ranger (version 1.2.0) - 10x Genomics Seurat (version 3.2.0) (Butler et al., 2018; Stuart et al., 2019) https://cran.r-project.org/web/packages/Seurat/ sctransform (version 0.3.1) (Hafemeister and Satija, 2019) https://cran.r-project.org/web/packages/sctransform/ fastMNN / batchelor (version 1.2.4) (Haghverdi et al., 2018) https://bioconductor.org/packages/release/bioc/html/batchelor.html MAST (version 1.12.0) (Finak et al., 2015) https://www.bioconductor.org/packages/release/bioc/html/MAST.html GSVA (version 1.36.2) (Hänzelmann et al., 2013) https://bioconductor.org/packages/release/bioc/html/GSVA.html fgsea (version 1.14.0) (Korotkevich et al.) https://bioconductor.org/packages/release/bioc/html/fgsea.html scVelo (version 0.2.2) (Bergen et al., 2020) https://pypi.org/project/scvelo/ cytoTRACE (version 0.3.3) (Gulati et al., 2020) https://cytotrace.stanford.edu/ Signac (version 0.2.5) (Stuart et al., 2020) https://github.com/timoast/signac harmony (version 1.0) (Korsunsky et al., 2019) https://github.com/immunogenomics/harmony ggradar (version 0.2) - https://github.com/ricardo-bion/ggradar TFBSTools (version 1.26.0) (Tan and Lenhard, 2016) http://bioconductor.org/packages/release/bioc/html/TFBSTools.html clustree (version 0.4.3) (Zappia and Oshlack, 2018) https://github.com/lazappi/clustree survival (version 3.2-3) (Therneau and Grambsch, 2000) https://cran.r-project.org/package=survival/ survminer (version 0.4.8) - https://cran.r-project.org/we/packages/survminer/ Drop-seq tools (version 2.3.0) (Macosko et al., 2015) https://github.com/broadinstitute/Drop-seq bwa (version 0.7.8-r455) (Li and Durbin, 2010) http://bio-bwa.sourceforge.net/ Picard (versions 1.118 and 2.18.22) - https://broadinstitute.githu.io/picard/ MACS2 (version 2.1.0) (Zhang et al., 2008) https://github.com/jsh58/MACS MSPC (version 4.0.2) (Jalili et al., 2018) https://genometric.github.io/MSPC/ DiffBind (version 2.14.0) (Stark et al., 2011) https://www.bioconductor.org/packages/release/bioc/html/DiffBind.html featureCounts (version 1.6.2) (Liao et al., 2014) http://subread.sourceforge.net/ STAR (versions 2.5.4b and 2.7.3a) (Dobin et al., 2013) https://github.com/alexdobin/STAR JASPAR2018 (version 1.1.1) - https://bioconductor.org/packages/release/data/annotation/html/JASPAR2018.html loompy (version 3.0.0) - http://loompy.org/ Methods Table 2 . Software and tools used in the study. The version and download location of each tool is indicated. Additional Declarations Yes there is potential Competing Interest. GA receives a reward from the Institute of Cancer Research for his role as an inventor of abiraterone. GA has received honoraria, consulting fees, or travel support from Janssen, Astellas, Pfizer, Novartis, Bayer, Amgen, AstraZeneca, Sanofi, and Sapience, grant support from Janssen and Astellas, and is a principal investigator for clinical trials sponsored by Janssen, Pfizer, and Astellas. TM receives consultant fees from Astellas, Janssen, and Bayer; lecture fees from Novartis, Janssen, and Sanofi. He is a stockholder of Arocell ab. Supplementary Files SupplementaryTable1scATACseq.xlsx Table S1. Single-cell ATAC sequencing. 1) Set of differentially accessible chromatin regions (DARs) in each scATAC-seq cluster (compared to all other clusters), referred to as marker differentially accessible regions. The regions are annotated with their nearest gene. 2-4) Sets of DARs for each pairwise sample comparison (LNCaP-ENZ48 vs LNCaP, RES-A vs LNCaP, and RES-B vs LNCaP). The regions are annotated with their nearest gene. 5-7) Set of DARs in each scATAC-seq cluster in each sample, compared to all other clusters in the sample. In each, the table header indicates the scATAC-seq cluster, the differentially accessible region, the average log-fold change in accessibility for the cells in the cluster, the proportion of cells with accessible chromatin in the region in each sample condition or cluster, the p-value for the region, and the adjusted p-value for the region. SupplementaryTable2scRNAseq.xlsx Table S2. Single-cell RNA sequencing. 1) Set of differentially expressed genes (DEGs) in each scRNA-seq cluster (compared to all other clusters), referred to as marker differentially expressed genes. 2-4) Sets of DEGs for each pairwise sample comparison (LNCaP-ENZ48 vs LNCaP, RES-A vs LNCaP, and RES-B vs LNCaP). In each, the table header indicates the scRNA-seq cluster, the differentially expressed gene, the average log-fold change for the cells in the cluster, the proportion of cells expressing the gene in each sample condition or cluster, the p-value for the gene, and the adjusted p-value for the gene. SupplementaryTable3Genesignatures.xlsx Table S3. Signature gene sets derived and used in the study. Signatures are grouped by 1) individual single-cell cluster marker gene sets, which are genes that define each single-cell RNA sequencing cluster from LNCaP; 2) combined cluster marker gene sets, which are the individual single-cell cluster marker gene sets grouped by cluster type (either initial, ENZ-induced, or persistent); and 3) gene sets representing pathways and processes, including our Stem-Like and PROSGenesis signatures. SupplementaryFigures.pdf SI Figures 1 - 7 Cite Share Download PDF Status: Published Journal Publication published 06 Sep, 2021 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-384422","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":20617547,"identity":"2e93f2ed-2a36-49b6-8d93-38925023c836","order_by":0,"name":"Sinja Taavitsainen","email":"","orcid":"","institution":"Tampere University","correspondingAuthor":false,"prefix":"","firstName":"Sinja","middleName":"","lastName":"Taavitsainen","suffix":""},{"id":20617548,"identity":"c3847de9-defc-4865-b2a0-a4461538f418","order_by":1,"name":"Nikolai Engedal","email":"","orcid":"","institution":"Department of Tumor Biology, Institute for Cancer Research, Oslo 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(A) Overview of the cell lines models, assays, and treatments included in the study. Boxes with sample names are colored according to the data types generated from the sample (single-cell ATAC-, single-cell RNA-, bulk RNA-, and/or FAIRE-sequencing). (B) LNCaP parental, LNCaP-ENZ48, RES-A, and RES-B single-cell (sc) ATAC-seq enrichment score in a 2kb window around the transcription start site (TSS). Enrichment scores at each TSS (position 0 in the plot) were used as the enrichment values and compared between pairs of samples. Each sample comparison is indicated using colored dots within the plot and the t-test p-value is shown with asterisks (*** p-value \u003c 0.001). (C) Venn diagram of shared and unique chromatin regions in LNCaP parental, LNCaP-ENZ48, RES-A, and RES-B according to bulk analysis of scATAC-seq. (D) UMAP scATAC-seq clustering visualization of LNCaP parental, LNCaP-ENZ48, RES-A, and RES-B. (E) Proportions of cells in scATAC-seq clusters. Clusters are colored according to cluster type: initial (present in prevalence in LNCaP parental and LNCaP-ENZ48), ENZ-induced (present in prevalence in RES-A or RES-B), or persistent (present in similar proportions in all samples). See also Figure S1.","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/288db665524ba5c361119a90.jpg"},{"id":7933486,"identity":"5c820a1b-da09-4db5-b015-5868e3a11ae8","added_by":"auto","created_at":"2021-04-12 22:32:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78723,"visible":true,"origin":"","legend":"Contribution of enzalutamide treatment-mediated chromatin reprogramming to transcription factor DNA motif footprint. (A-B) Mean FAIRE-seq read count distribution in androgen-deprived conditions within a 2kb interval around MYC binding sites (A) and AR binding sites (B) in LNCaP cells. (C) Prostate cancer-associated transcription factor (TF) motif enrichment in open differentially accessible regions (DARs) for each scATAC-seq sample. Enrichments with a Benjamini-Hochberg method adjusted hypergeometric test p-value \u003c 0.05 are shown in colors, while non-significant enrichment are shown in white. The barplots above the matrices indicate the number of open DARs found for each cluster in each sample. (D) TF motif enrichments in open DARs observed comparing the indicated conditions. Enrichments with a Benjamini-Hochberg method adjusted hypergeometric test p-value \u003c 0.05 are shown in colors, while non-significant enrichment are shown in white. See also Figure S2.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/bad03fed67091a1bc96f9025.jpg"},{"id":7933485,"identity":"6fb17fcc-7ddc-4851-9b3f-211305f161e8","added_by":"auto","created_at":"2021-04-12 22:32:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99693,"visible":true,"origin":"","legend":"Chromatin states of enzalutamide resistance can result in multiple transcriptional programs. (A) UMAP clustering visualization of single-cell RNA sequencing (scRNA-seq) of LNCaP parental, LNCaP-ENZ48, RES-A, and RES-B. (B) Proportions of cells in clusters identified from scRNA-seq. Clusters are colored according to cluster type: initial (present in prevalence in LNCaP parental and LNCaP-ENZ48), ENZ-induced (present in prevalence in RES-A or RES-B), or persistent (present in similar proportions in all samples). (C) Cluster label transfer from the integrated clustering of the LNCaP scRNA-seq data to VCaP parental (left) and VCaP treated with enzalutamide for 48 hours (right), confirming the presence of these cell states in the alternate model. In the UMAP, each cell is colored according to the LNCaP scRNA-seq cluster that it is predicted to belong to. The barplot shows the proportion of the projected cluster labels for each scRNA-seq cluster. (D) Proportion of differentially expressed genes in each LNCaP scRNA-seq cluster for the indicated sample comparisons that is composed of enriched transcription factor (TF) target genes. The contributions of enriched TFs identified in the scATAC-seq are shown as a stacked barplot. (E) Identification of matching cell clusters between the scRNA and scATAC-seq data from LNCaP visualized as heatmap. The heatmap shows the proportions of scATAC-seq cells across all sample conditions assigned to each scRNA-seq cluster as part of the label transfer process. The proportions were calculated for each scRNA-seq cluster, with the total as the number of cells from the scATAC-seq that could be confidently assigned to a scRNA-seq cluster (confidence score \u003e 0.4). See also Figure S3.","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/d1d4eb5646ccdfa8eb4043e6.jpg"},{"id":7933488,"identity":"c6447b31-b997-4cc0-8164-9b11ad17521c","added_by":"auto","created_at":"2021-04-12 22:32:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110564,"visible":true,"origin":"","legend":"Transcriptional states of stemness in enzalutamide resistance. (A) Average expression of cell-cycle related genes (S and G2/M phases) in cells from the scRNA-seq data. (B-C) Dot plot of average gene expression of the (B) indicated genes and of the (C) genes within the Stem-Like signature in each scRNA-seq cluster. The size of the dot reflects the percentage of cells in the cluster that express each gene. (D) UMAP visualization showing the average expression score of each cell for the genes in the PROSGenesis gene signature derived from Karthaus et al (Karthaus et al., 2020). (E) Cells in VCaP and VCaP-ENZ48 (enzalutamide-treated for 48 hours) scored for their expression of Stem-Like and PROSGenesis gene signatures. (F) Predicted differentiation states of cells in the four LNCaP scRNA-seq samples. Each cell is a dot colored according to its differentiation state. The scRNA-seq clusters are labeled with numbers. (G) RNA velocities based on scRNA-seq depicted as streamlines. Clusters are shown in different colors and are numbered. See also Figure S4.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/82886df1bf72e8b177a1cde3.jpg"},{"id":7933209,"identity":"0ba0ce2f-81a2-493b-a5eb-7b4b1323087a","added_by":"auto","created_at":"2021-04-12 22:29:17","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70998,"visible":true,"origin":"","legend":"Gene signatures derived from single-cell RNA sequencing capture important features of prostate cancer models and stratify advanced PC patients. (A) Heatmap of gene signature GSVA enrichment scores in bulk RNA-sequencing of LNCaP treated with DHT or enzalutamide, and either sensitive or resistant to enzalutamide. (B) Heatmap of gene signature GSVA enrichment scores in bulk RNA-sequencing from VCaP subline derivatives VCaP-T (long term cultured with 10 uM testosterone), VCaP-CT (VCaP-T long term cultured with 0.1 nM testosterone), VCaP-CT-ET (VCaP-CT cultured long term with 10 µM enzalutamide), VCaP-CT-Br (VCaP-CT cultured long term with bicalutamide), and VCaP-CT-Br-ER (VCaP-CT-Br long term treated with enzalutamide upon reaching bicalutamide insensitivity). (C) Kaplan-Meier progression-free survival curves for Alumkal et al (Alumkal et al., 2020) patients stratified into two groups based on median GSVA score for the Stem-Like gene signature. Log-rank p-value is indicated above the curve. (D) Kaplan-Meier progression-free survival curves for Alumkal et al patients stratified into two groups based on median GSVA score for the NEPC upregulated gene signature. Log-rank p-value is indicated above the curve. (E) Kaplan-Meier overall survival curve for abiraterone and enzalutamide-naive patients from the Stand Up 2 Cancer (SU2C) CRPC cohort stratified into two groups based on median GSVA score for the Stem-Like gene signature. Log-rank p-value is shown above the curve. (F) Summary table of gene signature GSVA score associations with progression-free survival (PFS) or overall survival (OS) in the clinical datasets. Only gene signatures significantly associated with PFS or OS in one or more datasets are shown. Good indicates a higher score for the signature (a score higher than the median) is associated with better survival outcome, while poor indicates that a higher signature score (a score higher than the median) is associated with worse survival outcome. Log-rank p-values are shown with asterisks (* p-value \u003c 0.05, ** p-value \u003c 0.01, *** p-value \u003c 0.001). For each dataset, the header indicates the number of samples included, along with other qualifying information of the dataset. We used abiraterone (ABI)/ENZ naive patients from the SU2C CRPC dataset. See also Figure S5.","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/2293eb29c1b2118b5597dc9a.jpg"},{"id":7933489,"identity":"2c5f4089-d5b1-4bf1-8236-f4a3fd523570","added_by":"auto","created_at":"2021-04-12 22:32:17","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":127159,"visible":true,"origin":"","legend":"Transcriptional signal enrichment analysis identifies treatment persistent cells in prostate cancer. (A) tSNE visualization of prostate cell types from 12 treatment-naive prostate cancer (PC) patients from Chen et al (Chen et al., 2021). (B) GSVA enrichment scores for gene signatures in luminal, basal/intermediate, and fibroblast cells from Chen et al. GSVA enrichment scores were generated from the average expression profile of each cell type. (C-D) tSNE plot of PC cells from Chen et al colored according to their average expression of the genes in (D) the Stem-Like signature and in (D) the PROSGenesis signature. The adjacent histograms show the distribution of average expression scores in the cells, with a red dotted line denoting the 90th percentile of scores. (E) Percentage of cells scoring at or above the 90th percentile for the Stem-Like and PROSGenesis signatures belonging to each patient. (F-H) Spatial transcriptomics (ST) from a prostate cancer tissue section, Prostate A. (F) H\u0026E staining of the tissue section (left most panel), UMAP visualization (central panel) of the clusters of the spots on the ST slide (right most panel). Each cluster is also labeled according to its histological tissue type, with clusters 0 and 1 corresponding to stroma, cluster 2 corresponding to benign epithelium (BE), cluster 3 corresponding to the prostate adenocarcinoma, and 4 corresponding to benign epithelium (BE). (G) Sensitivity analysis of Stem-Like and PROSGenesis signatures scores in ST against the score distributions of control housekeeping gene signatures (see Methods). (H) The leftmost panel shows the ST UMAP clusters of spots overlaid on the H\u0026E slide. Each spot was scored according to its expression of genes in the Stem-Like, PROSGenesis, and cluster 10 signatures. For each signature, spots scoring at or above the 90th percentile (“high”) are colored in red, while spots scoring below the 90th percentile (“low”) are colored in yellow. The barplots indicate the percentage of spots in each cluster scoring high or low for each signature. The bars are labeled with their histology and their cluster number in parentheses, with BE referring to benign epithelium. Differences in proportions of high scoring spots were tested between clusters with the chi-square test and p-values are indicated with asterisks (** p-value \u003c 0.01, *** p-value \u003c 0.001). See also Figure S6.","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/729a21e6be1cf6399972ead4.jpg"},{"id":7933212,"identity":"0f45ef13-df2f-45f4-9e83-5d5c0e2f4ac6","added_by":"auto","created_at":"2021-04-12 22:29:17","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":111261,"visible":true,"origin":"","legend":"Transcriptional signal from persistent prostate cancer cells can be used to stratify untreated patients. (A) Heatmap of GSVA enrichment scores for all single-cell-derived gene signatures in the TCGA-PRAD cohort, including the marker gene sets for each scRNA-seq cluster. Hierarchical clustering of the GSVA scores was used to separate the samples into two groups, marked Branch 1 and Branch 2. (B) Kaplan–Meier survival curve for TCGA-PRAD patients stratified into two groups as indicated in Panel A. (C-H) Kaplan-Meier survival curves for TCGA-PRAD patients stratified into two groups based on median GSVA score for signatures of ENZ-induced cluster, PROSGenesis, Stem-Like, persistent cluster, AR activity, and ARFL. In each plot, the log-rank p-value is indicated above the plotted curves. (I) Summary table of gene signature GSVA score associations with progression-free survival (PFS) in the TCGA and ICGC datasets. Only gene signatures significantly associated with PFS in one or both datasets are shown. Good indicates a higher score for the signature (a score higher than the median) is associated with better survival outcome, while poor indicates that a higher signature score (a score higher than the median) is associated with worse survival outcome. Log-rank p-values are shown with asterisks (* p-value \u003c 0.05, ** p-value \u003c 0.01, *** p-value \u003c 0.001). For each dataset, the header indicates the number of samples included.","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/d73b84f89bbec61500a73455.jpg"},{"id":15780008,"identity":"a308cf64-2b67-49bf-8f8a-b9f7cab0d0da","added_by":"auto","created_at":"2021-11-22 15:40:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1288636,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/92c5f7c2-620e-4dba-ad97-519f3a7b4703.pdf"},{"id":7933206,"identity":"5c0fbd17-c5c0-463d-96d6-efdde7d50887","added_by":"auto","created_at":"2021-04-12 22:29:17","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4822346,"visible":true,"origin":"","legend":"Table S1. Single-cell ATAC sequencing. 1) Set of differentially accessible chromatin regions (DARs) in each scATAC-seq cluster (compared to all other clusters), referred to as marker differentially accessible regions. The regions are annotated with their nearest gene. 2-4) Sets of DARs for each pairwise sample comparison (LNCaP-ENZ48 vs LNCaP, RES-A vs LNCaP, and RES-B vs LNCaP). The regions are annotated with their nearest gene. 5-7) Set of DARs in each scATAC-seq cluster in each sample, compared to all other clusters in the sample. In each, the table header indicates the scATAC-seq cluster, the differentially accessible region, the average log-fold change in accessibility for the cells in the cluster, the proportion of cells with accessible chromatin in the region in each sample condition or cluster, the p-value for the region, and the adjusted p-value for the region.","description":"","filename":"SupplementaryTable1scATACseq.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/83cb77594c50cebe858e7571.xlsx"},{"id":7933484,"identity":"e6121cda-d2ce-4e23-aec8-9ca4f06a3c15","added_by":"auto","created_at":"2021-04-12 22:32:17","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":580700,"visible":true,"origin":"","legend":"Table S2. Single-cell RNA sequencing. 1) Set of differentially expressed genes (DEGs) in each scRNA-seq cluster (compared to all other clusters), referred to as marker differentially expressed genes. 2-4) Sets of DEGs for each pairwise sample comparison (LNCaP-ENZ48 vs LNCaP, RES-A vs LNCaP, and RES-B vs LNCaP). In each, the table header indicates the scRNA-seq cluster, the differentially expressed gene, the average log-fold change for the cells in the cluster, the proportion of cells expressing the gene in each sample condition or cluster, the p-value for the gene, and the adjusted p-value for the gene.","description":"","filename":"SupplementaryTable2scRNAseq.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/6cb3697da53bcfdc83fabdc9.xlsx"},{"id":7933483,"identity":"1d6055ee-98e0-4fab-a3f1-405b5040cc01","added_by":"auto","created_at":"2021-04-12 22:32:17","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":35123,"visible":true,"origin":"","legend":"Table S3. Signature gene sets derived and used in the study. Signatures are grouped by 1) individual single-cell cluster marker gene sets, which are genes that define each single-cell RNA sequencing cluster from LNCaP; 2) combined cluster marker gene sets, which are the individual single-cell cluster marker gene sets grouped by cluster type (either initial, ENZ-induced, or persistent); and 3) gene sets representing pathways and processes, including our Stem-Like and PROSGenesis signatures.","description":"","filename":"SupplementaryTable3Genesignatures.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/f7b446b2915725698bf5c9cb.xlsx"},{"id":7933929,"identity":"2fe8e4d3-06e2-4f1d-b818-8d6aeca0a525","added_by":"auto","created_at":"2021-04-12 22:35:17","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2111588,"visible":true,"origin":"","legend":"SI Figures 1 - 7","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-384422/v1/a34930dfab2f46f8cb5ff5e5.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nGA receives a reward from the Institute of Cancer Research for his role as an inventor of abiraterone. GA has received honoraria, consulting fees, or travel support from Janssen, Astellas, Pfizer, Novartis, Bayer, Amgen, AstraZeneca, Sanofi, and Sapience, grant support from Janssen and Astellas, and is a principal investigator for clinical trials sponsored by Janssen, Pfizer, and Astellas. TM receives consultant fees from Astellas, Janssen, and Bayer; lecture fees from Novartis, Janssen, and Sanofi. He is a stockholder of Arocell ab.","formattedTitle":"Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer","fulltext":[{"header":"Significance","content":"\u003cp\u003eWe used models of resistance to approved androgen receptor-targeted therapies for prostate cancer to identify subpopulations of treatment-persistent and pre-existing cells. We established that chromatin structure reconfigurations led to alterations in gene expression and drove alternative tumor adaptations and treatment escape. Motivated by the need for pre-treatment biomarkers in prostate cancer, we identified molecular predictors of therapy response based on the presence of treatment-persistent and pre-existing cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere we show that subpopulations of treatment-persistent cells with stem-like and regenerative properties foster alternative trajectories of enzalutamide resistance in prostate cancer. Alternative transcriptional patterns of resistance are induced by divergent chromatin reprogramming. Transcriptional enrichment of signals from these treatment-persistent cells stratifies patient outcomes in both early stage treatment-naive and treatment-exposed tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHighlights\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIdentification of prostate cancer cells with gene expression patterns of regenerative potential that persist and exist prior to enzalutamide treatment.\u003c/li\u003e\n\u003cli\u003eProfiling of chromatin and transcriptional features from subpopulations of treatment-challenged prostate cancer cells.\u003c/li\u003e\n\u003cli\u003eIdentification of gene signatures associated with stem-like and regenerative potential.\u003c/li\u003e\n\u003cli\u003eStratification of prostate cancer patients from \u0026ldquo;bulk\u0026rdquo; RNA sequencing data based on identified stemness- and regeneration-related gene signatures.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PC) relies on androgen receptor (AR) signaling for development and progression. Progression on androgen deprivation therapy (ADT) or AR signaling inhibitors (ARSIs) such as the second-generation AR antagonist enzalutamide (ENZ) leads to castration resistant (CRPC) or treatment-induced neuroendocrine prostate cancer (NEPC) (Beltran et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The most frequently characterized mechanisms of PC or CRPC resistance to ARSIs, ADT, or both, revolve around the re-establishment of AR signaling via AR overexpression or AR mutations (Abida et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alumkal et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Devlies et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; He et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePC is profoundly heterogeneous (Haffner et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; L\u0026oslash;vf et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tomlins et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Woodcock et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and patients would benefit from methods that differentiate between clinically mild disease and more aggressive forms. Although evidence of clonal expansion has been shown (Haffner et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), most studies to date have characterized genetic mutations (Gerhauser et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Grasso et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Taylor et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) that do not allow for understanding tissue complexity or biological bases of the emergence of treatment resistance. In contrast, although more frequent, non-genetic changes in transcriptomics, chromatin structure, and DNA accessibility of transcription factor (TF) binding motifs are less understood in PC drug resistance (Abida et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Alumkal et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Devlies et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; He et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). DNA accessibility is the first layer of gene regulation and transcriptomic changes are now being used to identify molecular predictors of cancer treatment response (Doultsinos and Mills, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, most RNA sequencing data are obtained from the bulk of the tumors and therefore cannot account for PC heterogeneity. This is because the transcriptome is the result of several biological processes contributing to differential gene regulation and such processes are not necessarily synchronized in all cells within the tumor bulk (Su et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The development of single-cell sequencing technologies has enabled the more detailed examination of genomic features in treatment resistant cancers, but the limited analytical methods are just beginning to reveal their potential.\u003c/p\u003e\n\u003cp\u003eTo explore how heterogeneous PCs respond to ARSIs, we analyzed the emergence of resistance in the epithelial-derived component of PC cells in models of ENZ exposed and resistant PC cell lines at a single-cell level. Through enrichment analysis of transcriptional signals from molecular gene classifiers derived in this study, we show evidence of treatment-persistent and pre-existing PC cells that can predict treatment response in both primary and advanced patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eChromatin reprogramming underpins enzalutamide resistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo study molecular consequences of AR signaling suppression and drug resistance dynamics in PC, we used LNCaP parental and LNCaP-derived ENZ-resistant cell lines RES-A and RES-B generated via long-term exposure to AR-targeting agents (Handle et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) (see \u003cstrong\u003eMethods\u003c/strong\u003e), as well as other independently generated LNCaP- and VCaP-derived models (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). We hypothesized that chromatin structure would be reshaped in ENZ-resistant cells and lead to modification of the transcriptome (S\u0026ouml;nmezer et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Strickfaden et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo extrapolate the contribution of chromatin structure to ENZ resistance, we performed single-cell (sc) assays for transposase-accessible chromatin and sequencing (scATAC-seq) on four samples: (1) LNCaP parental cells (LNCaP), (2) LNCaP exposed to short-term (48 hours) ENZ (10 \u0026micro;M) treatment (LNCaP-ENZ48), (3) RES-A and (4) RES-B (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). We first analyzed the scATAC-seq data as if it would have been sequenced in bulk cells (see \u003cstrong\u003eMethods\u003c/strong\u003e). The ATAC-seq signal at transcription start sites (TSS) decreased in ENZ-resistant cells compared to parental, particularly in RES-B cells (average enrichment score 4.8 in RES-B vs 6.2 in LNCaP, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003et\u003c/em\u003e-test) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). RES-A and RES-B cells shared a large proportion (14% in RES-A and 17% in RES-B) of \u0026ldquo;ENZ-resistant-specific\u0026rdquo; open chromatin regions not found in parental LNCaP. Additionally, RES-A cells had a higher proportion of unique open sites compared to RES-B (19% vs 5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, chi-square test) and LNCaP (19% vs 7%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, chi-square test) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). These findings are consistent with TSS non-targeted opening (Jiang and Zhang, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) of the chromatin upon ENZ resistance.\u003c/p\u003e\n\u003cp\u003eWe corroborated the extent of chromatin opening and reprogramming in ENZ-resistant cells by performing formaldehyde-assisted isolation of regulatory elements (FAIRE) sequencing (Giresi et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) on the parental LNCaP and RES-A cells subjected to androgen starvation, or exposed to androgens, ENZ, or both agents (\u003cstrong\u003eFigure S1A-D\u003c/strong\u003e) (see \u003cstrong\u003eMethods\u003c/strong\u003e). Even in this bulk assay, ENZ and androgen starvation appeared to be more significant drivers of reprogramming in RES-A than in parental LNCaP. While there was no difference in the total number of open chromatin sites, ENZ-resistant samples had a higher proportion of unique open sites compared to parental in the presence of androgens (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, chi-square test) (\u003cstrong\u003eFigure S1E\u003c/strong\u003e) and in androgen-deprived (castrate) conditions (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, chi-square test) (\u003cstrong\u003eFigure S1C\u003c/strong\u003e). Read distribution analysis (see \u003cstrong\u003eMethods)\u003c/strong\u003e demonstrated that the chromatin of ENZ-resistant cells is more open in castrate conditions (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, \u003cem\u003et\u003c/em\u003e-test) (\u003cstrong\u003eFigure S1D\u003c/strong\u003e) but not in presence of androgens (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.239, \u003cem\u003et\u003c/em\u003e-test) (\u003cstrong\u003eFigure S1F\u003c/strong\u003e), and that ENZ has an additive effect on castration in inducing chromatin compaction in parental ENZ-sensitive cells that is counteracted by androgens (\u003cstrong\u003eFigure S1D, Figure S1F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eNext, we used all samples with scATAC-seq to generate cluster visualizations of cell subpopulations with different chromatin accessibility profiles (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD) (see \u003cstrong\u003eMethods\u003c/strong\u003e). We identified clusters that we termed \u0026ldquo;unique\u0026rdquo; or \u0026ldquo;shared\u0026rdquo; across the samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). Unique clusters were specific to RES-A, RES-B, or both (named \u0026ldquo;ENZ-induced clusters\u0026rdquo;), or specific to the untreated and/or short-term ENZ-treated parental line (named \u0026ldquo;initial clusters\u0026rdquo;). Shared clusters were present at similar proportions across the samples and were named \u0026ldquo;persistent clusters\u0026rdquo; (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE). We compared each cluster to all other clusters to determine its unique chromatin profile based on differentially accessible chromatin regions (DARs; \u003cstrong\u003eTable S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe most prevalent chromatin-based scATAC-seq clusters (0, 1, and 2) were persistent (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE) and defined by fewer than 20 unique DARs, suggesting that 74% of the cells share an overall similar chromatin accessibility profile during development of ENZ resistance. We then assessed for changes in cluster chromatin DARs between the parental LNCaP, LNCaP-ENZ48, and in RES-A and RES-B (\u003cstrong\u003eTable S1\u003c/strong\u003e). DARs were observed around MYC and TP53 in several clusters during the short-term response to enzalutamide, including in cluster 6 that arises during enzalutamide resistance in RES-A.\u003c/p\u003e\n\u003cp\u003eStudies on PC cell lines cultured for an extended time without androgens tend to display neuroendocrine-like phenotypes (Braadland et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fraser et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The largest fold changes in chromatin accessibility based on average signal from all cells showed enrichment for neural system and neurite development processes between the parental (LNCaP-ENZ48 or DMSO) and resistant cells (RES-A or RES-B) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in RES-A and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 in RES-B). Using Gene Set Variation Analysis (GSVA) for gene expression scoring (see \u003cstrong\u003eMethods)\u003c/strong\u003e, we found elevated expression of NEPC-derived signatures among upregulated genes (Braadland et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tsai et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) in RES-A and RES-B cells (particularly \u003cem\u003eEZH2, AURKA, STMN1, DNMT1\u003c/em\u003e, and \u003cem\u003eCDC25B\u003c/em\u003e), as well as increased expression of NEPC-downregulated genes in initial clusters (\u003cstrong\u003eFigure S1G\u003c/strong\u003e). Interestingly, in the same cell lines, enrichment analysis of NEPC signatures showed high NEPC signal in RES-A cells only (\u003cstrong\u003eFigure S1H\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eOverall, these data show extensive chromatin reprogramming during the emergence of resistance to AR-targeting agents.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eEnzalutamide resistance reconfigures availability of TF binding DNA motifs in the chromatin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChromatin accessibility determines transcriptional output by exposing a footprint of TF DNA binding motifs. We hypothesized that increased chromatin opening in resistant cells would change the footprint of TF DNA motifs exposed. To this end, we first utilized AR and MYC binding site maps in LNCaP cells (Barfeld et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and explored their relationship with open chromatin sites in the bulk FAIRE-seq data from RES-A cells. Using read distribution analysis, we observed a significant increase in open chromatin at MYC binding sites in ENZ-resistant cells (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in castrate conditions and with androgens, \u003cem\u003et\u003c/em\u003e-test) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cstrong\u003eFigure S2A\u003c/strong\u003e), as well as a reduction of open chromatin at AR binding sites (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in castrate conditions and with androgens, \u003cem\u003et\u003c/em\u003e-test) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, \u003cstrong\u003eFigure S2B\u003c/strong\u003e). These findings suggest that chromatin dysregulation in ENZ-resistance is associated with reconfiguration of AR and MYC chromatin binding, consistent with previously reported increased MYC and reduced AR transcriptional activity in these cells (Handle et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTo resolve how chromatin reprogramming affects TF DNA motif exposure at the single-cell level, we performed a TF motif enrichment analysis on the marker DARs characterizing the scATAC-seq cell clusters in each sample (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cstrong\u003eTable S1\u003c/strong\u003e). This analysis confirmed the enrichment of motifs for several PC-associated TFs such as AR and MYC, as well as GATA2, HOXB13, and others in persistent clusters 3 and 5, as well as initial cluster 4 in parental and LNCaP-ENZ48 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e. Clusters 3 and 5 remained enrichment of a subset of the same TFs motifs in RES-A and RES-B, with cluster 5 showing a consistent enrichment profile in all samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). AR, CREB1, E2F1, GATA2, and ZFX were common motifs. Cluster 3 was characterized by FOXA1 and JUND, while cluster 5 was characterized by CTCF, ETS-like, and MYC. Although they possessed distinct sets of DARs, the ENZ-induced clusters 6 and 7 did not display enrichment of TF motifs in RES-A or RES-B.\u003c/p\u003e\n\u003cp\u003eBetween pairs of samples, DARs were predominantly closing in cluster 3 compared to all other clusters (8% vs 4% DARs differentially closing, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, chi-square test) and predominantly opening in cluster 4 (11% vs 8% DARs differentially opening, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, chi-square test) (\u003cstrong\u003eFigure S2C\u003c/strong\u003e). We performed selective TFs motif enrichment analysis in DARs opened (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cstrong\u003e)\u003c/strong\u003e and closed (\u003cstrong\u003eFigure S2D\u003c/strong\u003e) between pairs of samples (see \u003cstrong\u003eMethods\u003c/strong\u003e). While we observed no enrichments after short-term ENZ-treatment (LNCaP-ENZ48 vs parental; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD), comparing open DARs in RES-A or RES-B vs LNCaP parental retrieved distinct sets of TFs, with MYC and ESR1 being the most common across all clusters in RES-A and RES-B, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). Similarly, comparing open DARs in RES-A or RES-B vs LNCaP-ENZ48 showed enrichment of most of the PCa-related TF motifs tested in most clusters (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD), and to an even greater extent when considering closing DARs between sample conditions (\u003cstrong\u003eFigure S2D\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThese analyses demonstrate that ENZ-resistance is associated with reconfiguration of TF DNA motif footprints.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptional patterns of enzalutamide resistance are induced by divergent chromatin reprogramming\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo study transcriptional patterns in relation to reconfiguration of chromatin structure at the single-cell level, we performed scRNA-seq in the LNCaP parental, RES-A and B models. Integrated clustering of four LNCaP samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA) (see \u003cstrong\u003eMethods\u003c/strong\u003e) showed 7 persistent, 3 ENZ-induced, and 3 initial cell clusters (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB) defined by sets of marker differentially expressed genes (DEGs; \u003cstrong\u003eTable S2\u003c/strong\u003e; between 17 and 283 DEGs in the 13 clusters). To confirm that these cell subpopulations are relevant in other independent models of ENZ-resistance, we used the label transfer approach (Stuart et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) to query for matching cell populations in independent scRNA-seq datasets: a LNCaP parental sample, LNCaP ENZ-treated for 1 week (LNCaP-ENZ168), and an independent ENZ-resistant (RES-C) LNCaP-derived cell line (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Transferring scRNA-seq cluster labels confirmed the presence of initial clusters (4, 6, and 10) in LNCaP parental (\u003cstrong\u003eFigure S3A)\u003c/strong\u003e and RES-C (\u003cstrong\u003eFigure S3B\u003c/strong\u003e). Presence of ENZ-induced clusters was confirmed in RES-C (17% of cells in cluster 3) and LNCaP-ENZ168 (79% in cluster 3), suggesting that one week of ENZ treatment is sufficient to give rise to this cluster prior to the development of resistance (\u003cstrong\u003eFigure S3C\u003c/strong\u003e). Most importantly, we could retrieve persistent subpopulations of cells in the alternative LNCaP-parental sample (4%), in LNCaP-ENZ168 (13%), and in RES-C (31%), suggesting that these persistent cells are consistently found during emergence of ENZ-resistance.\u003c/p\u003e\n\u003cp\u003eWe additionally performed scRNA-seq on a VCaP parental cell line treated with DMSO or ENZ for 48 hours to test for the generalizability of our results beyond a single cell line (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). A similar analysis with VCaP cells confirmed the prevalence of persistent cells in the VCaP parental (93% of cells), as well as initial and ENZ-induced cells in VCaP-ENZ48 (38% and 55% of cells, respectively) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\n\u003cp\u003eWe then sought to determine whether the observed scRNA-seq clusters (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA) could be the result of enriched TFs binding activity in alternative open DARs. Using annotated databases, we queried the transcriptional targets of the enriched TFs in the open DARs when comparing RES-A or B to the parental LNCaP \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cstrong\u003e)\u003c/strong\u003e in the matching scRNA-seq samples (see \u003cstrong\u003eMethods\u003c/strong\u003e). Chromatin remodeling affected TF activity and consequently DEGs in the scRNA-seq for up to a maximum of 11% in cluster 0 in RES-A and 7.1% in cluster 1 in RES-B (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). While target DEGs for TFs such as MYC, JUND, and E2F where found in most clusters in both RES-A and B, other target DEGs for TFs such as AR, RELA (a NFkB subunit), and GRHL2 appeared more specific to RES-A or B, consistent with proposed stoichiometric models of TFs chromatin binding (Klemm et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). This analysis confirmed that alternative open DARs in ENZ-resistance can activate divergent transcriptional programs.\u003c/p\u003e\n\u003cp\u003eNext, we connected the scRNA-seq clusters to their matching scATAC-seq clusters. We took advantage once more of the label transfer approach to identify matching scRNA- and scATAC-seq cell states in the same sample conditions (see \u003cstrong\u003eMethods\u003c/strong\u003e). In this process, we assigned cell cluster labels within the scRNA-seq to the scATAC-seq clusters, or vice versa (\u003cstrong\u003eFigure S3D\u003c/strong\u003e). We found that a chromatin state can correspond to multiple transcriptional states (96% in scATAC-RNA vs 48% in scRNA-ATAC of cells assigned on average across all samples, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, chi-square test). Querying the integrated scRNA-seq clusters (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-B) from the scATAC-seq data, we could find matching cell states in the scATAC-seq for scRNA clusters 9, 10, and 11 (\u003cstrong\u003eFigure S3E\u003c/strong\u003e). Across the sample conditions, 95% of the cells projected to belong to scRNA-seq cluster 10 belonged to scATAC-seq cluster 4, while 72% of cells projected to belong to scRNA-seq cluster 9 or 11 belonged to scATAC-seq cluster 3 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eTaken together, these data show that transcriptional configuration of ENZ-resistant cells, especially cells persisting during treatment, emerges from processes driven partially by chromatin structure and TF-mediated transcriptional reprogramming. These processes affect a number of important regulators of cell fate, consistent with lineage commitment recently observed in tissue development (Ma et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eProstate cancer cell subpopulations with features of stemness precede enzalutamide resistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell cycle phase can be a strong determinant of the integrative clustering of scRNA-seq data. Accordingly, we found that persistent clusters 8, 9, and 11 scored highly for S and G2/M phase related genes using cell cycle scoring in Seurat (see \u003cstrong\u003eMethods\u003c/strong\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA), suggesting that cells in these clusters are more actively cycling and proliferative. However, we found that cells in clusters 9 and 11 were characterized not only by cell cycle genes, but also by expression of genes involved in chromatin remodeling and organization (CTCF, LAMINB, ATAD2), increased cell cycle turnover and stemness (FOXM1, (Ketola et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), and DNA repair (BRCA2, FANCI, RAD51C, POLQ) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Clusters 5 and 11 showed high expression of a gene set, which we named \u0026ldquo;Stem-Like\u0026rdquo;, composed of stemness-related genes mainly from Horning \u003cem\u003eet al\u003c/em\u003e (Horning et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Karthaus \u003cem\u003eet al\u003c/em\u003e recently identified activated luminal prostate cells able to regenerate the epithelium following castration (Karthaus et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). We extracted the gene expression profile associated with these prostate luminal cells (see \u003cstrong\u003eMethods\u003c/strong\u003e) and used it to score each scRNA-seq cluster. We found cluster 10, an initial cluster, to score highly for this gene signature, which we renamed PROSGenesis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD, \u003cstrong\u003eFigure S4A\u003c/strong\u003e). We visualized the expression of PROSGenesis and Stem-Like in the VCaP scRNA-seq samples, confirming the presence of these subpopulations of cells in other models (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eWe then set out to reconstruct the trajectories of how these clusters of interest were generated during the development of ENZ-resistance. Based on cytoTRACE (Gulati et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), cells in clusters 10 and 11 were the least differentiated across most of the sample conditions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF), suggesting that the other cells could derive from cells in these clusters. RNA velocity analysis estimated cluster 10 as a precursor of the enzalutamide-induced clusters (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG\u003cstrong\u003e)\u003c/strong\u003e, concordant with a state derived from activated regenerative luminal prostate cells as previously suggested (Karthaus et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cluster-specific differential velocity analysis in RES-A and RES-B revealed downregulation of many PC-related genes, such as ATAD2, as well as upregulation of genes such as UBE2T, PIAS2, PFKFB4, and EGFR \u003cstrong\u003e(Figure S4B-C)\u003c/strong\u003e. ATAD2 and UBE2T were otherwise upregulated in persistent clusters 8, 9, and 11 (\u003cstrong\u003eFigure S4C\u003c/strong\u003e), suggesting additional transcriptional reprogramming in ENZ-induced clusters.\u003c/p\u003e\n\u003cp\u003eThese analyses point at two distinct subpopulations of PC cells which precede ENZ resistance: one persistent cell cluster (cluster 11) matching \u0026ldquo;Stem-Like\u0026rdquo; and one initial cluster (cluster 10) matching PROSGenesis, a signature derived from tissue regeneration (Karthaus et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Collectively, our data suggest that there exists a small number of PC cells within the bulk with stem-like and regenerative potential.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eModel-based characterization of gene signatures in prostate cancer bulk RNA sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of molecular gene classifiers or signature scores is an attractive strategy to select cancer patients for treatment (Doultsinos and Mills, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eggener et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to an unbiased enrichment and differential expression analysis of hallmark gene sets (see \u003cstrong\u003eMethods\u003c/strong\u003e), most of the persistent clusters and cluster 10 also showed significant enrichment of E2F targets, G2M checkpoint, and MYC target genes (\u003cstrong\u003eFigure S4D\u003c/strong\u003e). These data are largely concordant with the bulk RNA-seq data on the same cells in our previous study (Handle et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), reflecting the fact that signals from subpopulations of cells can be retrieved in bulk RNA-seq data. Differential expression within clusters (\u003cstrong\u003eFigure S4E-G\u003c/strong\u003e) further revealed that oxidative phosphorylation was immediately upregulated in LNCaP-ENZ48, and this process is maintained highly selectively in RES-A but not in RES-B. Moreover, genes regulated by activated mTORC1 signaling were consistently upregulated in most of the clusters as ENZ resistance develops (\u003cstrong\u003eFigure S4E-G\u003c/strong\u003e), in agreement with previous reports showing its activation during ENZ treatment in patients (Ma et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe therefore used a collection of signatures derived from the scRNA-seq analysis to describe features of the same cells in bulk RNA-seq datasets. In addition to Stem-Like and PROSGenesis, we included (1) NEPC markers (\u003cstrong\u003eFigure S1G\u003c/strong\u003e), (2) a BRCAness gene signature (Li et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) as RES-A and RES-B maintain sensitivity to PARP inhibition (Handle et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the persistent cluster 11 is characterized by markers of DNA repair (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB), (3) gene sets as proxies of AR signaling activation (He et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), including activation of AR splice variants (AR-Vs), (4) the DEGs defining our scRNA-seq clusters, and (5) gene sets for mTORC1 signaling and MYC targets (\u003cstrong\u003eFigure S4D-G\u003c/strong\u003e) (\u003cstrong\u003eTable S3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn the bulk, the ENZ-induced DEGs selectively appeared in the RES-B cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). Similarly, the persistent clusters were associated with the Stem-Like signature only in RES-A and RES-B when induced with DHT (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). On the other hand, the PROSGenesis signature was elevated only in RES-B (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). The NEPC features in RES-A were associated with MYC activation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). Consistent with both resistant lines remaining responsive to PARP inhibition (Handle et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), we found samples from RES-A and RES-B to score highly for BRCAness (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA), which is known to downregulate DNA repair machinery (Li et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). BRCAness was associated with the AR-V signature as previously shown (Kounatidou et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eTo confirm the properties of different signatures, we used VCaP cells to develop an independent model of resistance to AR signaling-targeted treatments including ADT, bicalutamide, ENZ, and bicalutamide/ENZ multi-resistant sublines, and performed bulk RNA-seq (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). These VCaP-based sublines did not show NE features (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). Only ENZ-resistant VCaP cells scored highly for the ENZ-induced DEGs, confirming the specificity of this signature to ENZ treatment and resistance. Parental and ENZ-resistant VCaP cells scored highly for the PROSGenesis signature, while the scores of the persistent, Stem-Like, mTORC1 signaling, and MYC target signatures scored highly selectively in resistant VCaP sublines (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). This suggests a convergent mechanism of resistance to these agents in independent models.\u003c/p\u003e\n\u003cp\u003eNext, we scored xenografts of AR\u003csup\u003e+\u003c/sup\u003e/NE\u003csup\u003e\u0026minus;\u003c/sup\u003e, AR\u003csup\u003e\u0026minus;\u003c/sup\u003e/NE\u003csup\u003e+\u003c/sup\u003e, or AR\u003csup\u003e\u0026minus;\u003c/sup\u003e/NE\u003csup\u003e\u0026minus;\u003c/sup\u003e CRPC and NEPC tumors resistant to ENZ (Labrecque et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lam et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) with the same signature sets (\u003cstrong\u003eFigure S5A\u003c/strong\u003e). AR\u003csup\u003e+\u003c/sup\u003e/NE\u003csup\u003e\u0026minus;\u003c/sup\u003e xenograft samples clustered into two separate clusters. AR\u003csup\u003e\u0026minus;\u003c/sup\u003e tumors clustered together with a series of AR\u003csup\u003e+\u003c/sup\u003e/NE\u003csup\u003e\u0026minus;\u003c/sup\u003e tumors due to low mTORC and MYC signaling, while one cluster of AR\u003csup\u003e+\u003c/sup\u003e/NE\u003csup\u003e\u0026minus;\u003c/sup\u003e scored highly for all of the gene sets except for markers upregulated in NEPC (\u0026ldquo;NEPC upregulated\u0026rdquo;). Interestingly the PROSGenesis signature, along with initial clusters and ENZ-induced clusters, scored particularly high in AR\u003csup\u003e+\u003c/sup\u003e tumors while the Stem-Like signature, along with the persistent clusters, scored high in both AR\u003csup\u003e+\u003c/sup\u003e/NE\u003csup\u003e\u0026minus;\u003c/sup\u003e and AR\u003csup\u003e\u0026minus;\u003c/sup\u003e/NE\u003csup\u003e+\u003c/sup\u003e tumors (\u003cstrong\u003eFigure S5A\u003c/strong\u003e), suggesting that the two signatures capture different tumor biologies. In a transcriptome dataset based on an independent xenograft model (King et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), we found ENZ resistance to be uniquely associated with higher AR activity, higher expression of MYC target genes, PROSGenesis high score, and high expression of ENZ-induced cluster gene sets (\u003cstrong\u003eFigure S5B\u003c/strong\u003e). These data suggest that the Stem-Like status is independent of the AR status and that persistent cells might mediate the development of both AR positive CRPCs and negative NEPCs.\u003c/p\u003e\n\u003cp\u003eCollectively, the persistent, initial, PROSGenesis, and Stem-Like derived gene signatures show potential for identifying aggressive, regenerative features of PC from bulk RNA-seq.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptional signal enrichment analysis identifies treatment-persistent cells and prognostic gene signatures in prostate cancer patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe hypothesized that we could use enrichment of gene signature expression to stratify advanced and primary PC patients.\u003c/p\u003e\n\u003cp\u003eTo this end, we interrogated clinical data of CRPC patients treated with ENZ reported in Alumkal et al. (Alumkal et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The patients aggregated into two clusters based on our complete signature set (\u003cstrong\u003eFigure S5C\u003c/strong\u003e), but patients in neither cluster had significantly shorter progression-free survival (PFS; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, log-rank test). Utilizing a stepwise variable selection process we identified five significant signatures (NEPC upregulated, PROSGenesis, MYC targets, AR activity, and ARV) that are able to identify patients with significantly shorter PFS (\u003cstrong\u003eFigure S5D\u003c/strong\u003e). Moreover, PFS analysis of individual gene signatures revealed association with shorter time to progression for patients scoring high for the Stem-Like signature (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025, log-rank test) or for genes upregulated in NEPC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, log-rank test) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC-D), while patients with longer PFS scored highly for PROSGenesis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021, log-rank test) and for the initial cluster signature(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, log-rank test) (\u003cstrong\u003eFigure S5E\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eNone of the cluster marker gene sets showed a significant difference between Stand Up To Cancer (SU2C) CRPC abiraterone/ENZ-naive and abiraterone/ENZ-exposed patients (Abida et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) according to their latest treatment regime, suggesting that differences between the tumors based on the signatures may be difficult to retrieve using bulk sequencing from heavily pre-treated patients. Despite the challenges of applying single-cell derived signatures to bulk data however, Stem-Like was still significantly associated with poor overall survival in these patients (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE), supporting the potential significant activity of the persistent cells in this group of patients. Similarly, we could not stratify patients that developed resistance to ENZ in the SU2C West Coast DT Quigley \u003cem\u003eet al\u003c/em\u003e dataset (\u003cstrong\u003eFigure S5F\u003c/strong\u003e) (Quigley et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), although in this case, ENZ-sensitive patients had higher expression of PROSGenesis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024, Wilcoxon rank-sum test) (\u003cstrong\u003eFigure S5G\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThese data show that the Stem-Like signature associated with persistent cells (cluster 11) from our single-cell analysis of ENZ resistance is a consistent classifier with the potential of stratifying patients for response to second line AR-targeted treatments (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e\n\u003cp\u003eWe then hypothesized that we could systematically use the persistent cluster 11, Stem-Like, initial cluster 10, and PROSGenesis signatures as a proxy for the presence of PC cells with different transcriptional features in clinical settings, to capture signals from such types of pre-existing subclones with metastatic potential in primary untreated tumors. To this end, we took advantage of a recently published scRNA-seq dataset on clinically relevant PCs specimens (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA) (Chen et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). We used GSVA score to highlight our 13 scRNA-seq clusters in 36424 cells from primary untreated PC specimens of 13 patients (\u003cstrong\u003eFigure S6A\u003c/strong\u003e). The analysis showed that our LNCaP model-derived cell clusters scored higher in luminal and basal/intermediate cells compared to fibroblasts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047, \u003cem\u003et\u003c/em\u003e-test) (\u003cstrong\u003eFigure S6A\u003c/strong\u003e). Additionally, luminal cells had higher expression of genes associated with our initial scRNA-seq clusters compared to the basal/intermediate cells (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, \u003cem\u003et\u003c/em\u003e-test) and compared to fibroblasts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003et\u003c/em\u003e-test).\u003c/p\u003e\n\u003cp\u003eWe then scored the cells for expression of genes from the Stem-Like and PROSGenesis signatures, along with the associated clusters (11 and 10, respectively) and control signatures linked to AR activity (ARV, AR-FL, and AR activation), BRCAness, and NEPC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). We defined a high score for a gene signature to be above the 90th percentile. 48% percent of the cells that scored highly for the Stem-Like signature were luminal cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC). Cells scoring highly for the PROSGenesis signature were mostly basal/intermediate (78% of high scorers) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). Each single patient harbored on average 8% of cells scoring high for the Stem-Like signature (ranging from 2% in patient 173 to 23% in patient 156) and 8% of cells scoring high for the PROSGenesis signature (ranging from 0.9% in patient 153 to 33% in patient 172) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e\n\u003cp\u003eTo reconcile the presence of these cells and their relative histopathological position, we assessed gene expression within two sections (Prostate A and B) of primary untreated PC with spatial transcriptomics (see \u003cstrong\u003eMethods\u003c/strong\u003e). We reconstructed the gene expression signal from stromal and epithelial components in an average of 1682 spots per sample using clustering analysis and annotated the tissue architecture in 5 clusters of stromal tissue (ST), benign epithelium (BE), and adenocarcinoma (PC-AC) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eF, \u003cstrong\u003eFigure S6B\u003c/strong\u003e). PROSGenesis and Stem-Like signatures, as well as the companion model-derived cluster 10 signature, showed high expression scores within the sections compared to scores from housekeeping gene signatures (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eG, \u003cstrong\u003eFigure S6C\u003c/strong\u003e). We compared the score distributions of our signatures to the housekeeping gene set score distributions and determined the 90th percentile as a score cutoff for high expression by allowing for 5% false positives (see \u003cstrong\u003eMethods\u003c/strong\u003e). Spots with high signal were found interspersed in all 5 clusters in both sections (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eH, \u003cstrong\u003eFigure S6D\u003c/strong\u003e). In Prostate A, however, spots scoring highly for the Stem-Like signature were more prevalent in the PC-AC cluster compared to ST (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, chi-square test). Spots scoring highly for PROSGenesis were further enriched in the BE and PC-AC clusters compared to ST (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in both cases, chi-square test), while spots scoring highly for cluster 10 were enriched in the BE clusters compared to all other tissue regions (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for each comparison, chi-square test) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eH). Concordant observations were made for PROSGenesis and cluster 10 signatures in Prostate B (\u003cstrong\u003eFigure S6D\u003c/strong\u003e). To validate these findings, we undertook a similar approach to re-analyze spatial transcriptomics data from prostate Sect.\u0026nbsp;3.3, 1.2, and 2.4 from Berglund \u003cem\u003eet al\u003c/em\u003e (Berglund et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), which were annotated to contain a significant proportion of cancer. Similarly to our initial observation, these sections showed enrichment of spots scoring highly for the PROSGenesis in the PC-AC clusters compared to ST or prostatic intraepithelial neoplasia clusters (\u003cstrong\u003eFigure S6E-G\u003c/strong\u003e). Spots scoring highly for cluster 10 were more prevalent in both benign and cancerous clusters. Taken together these data suggest the presence of treatment-persistent cells interspersed within the primary untreated prostate tissue of PC patients with high metastatic potential.\u003c/p\u003e\n\u003cp\u003eFinally, we verified whether we could predict recurrence in primary PC patients using the signature genes derived in these cells. We interrogated legacy primary tumor TCGA PRAD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/tcga\u003c/span\u003e\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e and early onset PC (EOPC) ICGC (Gerhauser et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) RNA-seq data (\u003cstrong\u003eFigure S7A\u003c/strong\u003e) for our gene signatures of interest. Using all signatures for clustering the TCGA PRAD cohort separated 54% of Gleason score (GS)-7 and 15% of GS-8\u0026thinsp;+\u0026thinsp;patients which would not benefit from additional treatment, as they had relatively good prognosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). A similar trend was also observed in the ICGC cohort (\u003cstrong\u003eFigure S7B\u003c/strong\u003e). ENZ-induced (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC), PROSGenesis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD), Stem-Like (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eE), and persistent (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eF) gene signatures were the most significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, log-rank test) contributors to cluster separation in the TCGA cohort, while NEPC downregulated genes were the major determinant in the ICGC cohort (\u003cstrong\u003eFigure S7C\u003c/strong\u003e). In line with previous reports (Alumkal et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), signatures reflecting AR activity (AR activity and full length) in these tumors were consistently associated with longer time to progression in the TCGA cohort (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eG-H), suggesting a better response to inhibition of AR signaling in AR driven tumors. In the EOPC cohort, which is enriched in GS-7 tumors compared to the TCGA PRAD, the persistent and PROSGenesis signatures significantly stratified GS-7 patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, log-rank test) (\u003cstrong\u003eFigure S7D-E\u003c/strong\u003e), suggesting the ability of these signatures to further refine GS-based risk stratification in patients and avoid overtreatment. High PROSGenesis score was associated with good prognosis together with the gene set from the initial cluster 10 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eI). Individually, 8 out 13 clusters-derived signatures showed association with PFS in the TCGA cohort (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eI), pointing at the utility of these signatures in PC patient risk stratification.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we provide a molecular perspective of the emergence of resistance to AR-targeted treatment at a single-cell level. Karthaus and colleagues recently found that luminal prostate cells that persist after ADT in a mouse model can contribute to tissue regeneration of the normal prostate epithelium by assuming stem-like transcriptional properties (Karthaus et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Using PC specimen tumor DNA, we recently showed the presence of subclones within the primary tumors that preserve the ability to expand and metastasize years after treatment and are found interlayered within different lesions of multifocal tumors (Woodcock et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, a recent work studying lung cancer metastases found that metastatic capacity arises from pre-existing and heritable differences in gene expression (Quinn et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Here we find that during exposure to AR-targeting agents, a small proportion of persistent cells remain transcriptionally unperturbed by the treatment.\u003c/p\u003e\n\u003cp\u003eWe visualize these cells in primary untreated PC specimens and find them to be enriched in cancerous regions of histopathologically relevant tumors using spatial transcriptomics, as well as interspersed in apparent benign tissue. To understand the presence and function of these cells in histopathologically non-cancerous regions will warrant further studies. Our data show evidence of a hierarchical model of emergence of resistance to enzalutamide (Maitland, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) in which treatment-persistent cells are able to regenerate the bulk of the resistant ones. We describe the properties of the persistent cells using RNA velocity and show different intermediate states in alternative trajectories of treatment resistance. This process is partially driven by chromatin remodeling, which is consistent with chromatin accessibility lineage-priming (Ma et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Martin et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn PC, gain of function of bromodomain-containing proteins such as BRD4 (Asangani et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Urbanucci et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) and ATAD2 (Morozumi et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Urbanucci et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), as well as loss of function of chromatin remodeler CHD (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), have been shown to contribute to PC progression and lineage plasticity in therapy resistance. This process is likely accompanied by chromatin reprogramming (Braadland et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Urbanucci et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Uusi-M\u0026auml;kel\u0026auml; et al.). While many groups have focused on the effect of AR-targeted treatment on chromatin-associated factors such as CREB5 (Hwang et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), or TFs such as GR (Arora et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) and AR (Yuan et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), in this study we found that exposure to AR-targeting agents increases the overall relaxation of the chromatin. Applying the label transfer method across datasets revealed that subpopulations of cells with different chromatin states lead to multiple transcriptional configurations, including those of persistent cells. Using different cell line models mimicking alternative trajectories of treatment resistance, we infer that differential DNA motif exposure determined by chromatin structure may partially contribute to TF activity-mediated transcriptional reprogramming in the different cell subpopulations induced by exposure to enzalutamide. According to this analysis, specific subpopulations of PC cells are more subjected than others to TFs activity reprogramming. This is consistent with recent studies showing simultaneous detection of multiple transcription factors on single DNA molecules and TFs co-occupancy frequently occurring at sites of competition with nucleosomes (Strickfaden et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe show that treatment-persistent cells have high cell cycle turnover, compatible with high regenerative potential (Poli et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), and assume states of stemness from their transcriptional profiles. As these features have been associated with more aggressive tumors, we developed transcriptional signatures derived from two states in particular: one state derived in ADT-treated prostate cells by Karthaus et al. (Karthaus et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), which we renamed PROSGenesis, tightly associated with initial and enzalutamide-induced clusters in our model of enzalutamide resistance, and one state that we called \u0026ldquo;Stem-Like\u0026rdquo;, associated with persistent cells during the emergence of enzalutamide resistance. PROSGenesis, Stem-Like, and associated signatures can capture different tumor types and stratify ARSI-exposed CRPC patients' outcome. Moreover, we show that in primary PC patients undergoing ADT treatment, high signature scores in treatment-naive specimens are associated with shorter time to progression (biochemical recurrence). Interestingly, in primary treatment-naive patients, high score for PROSGenesis is associated with longer response to ADT, possibly due to the stronger contribution of AR activity in these tumors. Overall, we have identified and characterized gene signatures that can be used to profile subpopulations of treatment-persistent cells with stem-like and regenerative properties that foster alternative trajectories of AR-targeted treatment resistant PCs.\u003c/p\u003e"},{"header":"Methods","content":"\n\u003cp\u003e\u003cstrong\u003eCell lines and culture\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003cp\u003eLNCaP and VCaP cell lines were obtained from American Type Culture Collection (ATCC; LGC Standards) and authenticated periodically (HPA cultures or Eurofins). RES-A and RES-B cells were generated by prolonged exposure to the second-generation anti-androgens enzalutamide and RD-162 as described earlier (Handle et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). LNCaP parental (ATCC), RES-A, and RES-B cells were cultured in RPMI 1640 (Sigma R0883) supplemented with 10% FBS (Sigma F7524), 2 mM Alanyl-glutamine (Sigma G8541), 1 mM sodium pyruvate (Merck TMS-005-C), 2.5 g/L glucose (Sigma G8769), and 1x Antibiotic-Antimycotic (Gibco, 15240062) in a humidified 37\u0026deg;C incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e. RES-A and RES-B cells additionally received 10 \u0026micro;M enzalutamide (MedChemExpress HY-70002) with each cell splitting/feeding. VCaP cells were cultured in DMEM (Gibco) supplemented with 10% FBS in a humidified 37\u0026deg;C incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eFor experimental treatments, ~1x10\u003csup\u003e6\u003c/sup\u003e cells were seeded into 5 cm culture plate dishes, and allowed to settle before exposure to 10 \u0026micro;M enzalutamide or DMSO vehicle control (0.1%) for 48 h or 168 h. The additional LNCaP cells (ATCC) and RES-C cells were cultured in a humidified CO2-incubator at 37\u0026deg;C in Gibco\u0026trade; RPMI 1640 (1X) media (Thermo Fisher Scientific) supplemented with 10% FBS (Gibco standard FBS, Thermo Fisher Scientific), 2 mM L-Glutamine (Gibco\u0026reg;, Thermo Fisher Scientific), and a combination of 100 U/ml Penicillin and 100 \u0026micro;g/ml Streptomycin (Gibco\u0026reg; Pen Strep, Thermo Fisher Scientific). The enzalutamide resistant LNCaP RES-C cell line was generated by passaging of LNCaP cells with continuous treatment with 10 \u0026micro;M enzalutamide for 9 months and maintained in the same medium as LNCaP except for the supplementation with 10 \u0026micro;M enzalutamide.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eGeneration of resistant VCaP subline derivatives and RNA-seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAndrogen-sensitive VCaP prostate cancer cell line (passage (p.) 15.) was a gift from Dr. Tapio Visakorpi, Tampere University, Finland. Cells were cultured in RPMI 1640 supplemented with 10 % DCC-FBS, 1 % L-glutamine, 1 % A/A, and 10 nM testosterone (T) for seven months to establish T-dependent subclone VCaP-T. VCaP-T cells were then cultured at low testosterone (0.1 nM) for 10 months to establish VCaP-CT, an androgen-independent cell line able to grow despite low testosterone. VCaP-CT were then cultured at 10 \u0026micro;M enzalutamide until the cells regained ability to grow despite enzalutamide, creating enzalutamide resistant cell line VCaP-CT-ET. Another cell line was created by incubating first VCaP-CT cells with bicalutamide and subsequently with enzalutamide upon reaching bicalutamide insensitivity. Ultimately these cells also gained the ability to grow despite enzalutamide, creating the multiresistant cell line VCaP-CT-Br-ER.\u003c/p\u003e\n\u003cp\u003eRNA sequencing was performed with Illumina HiSeq 3000. We sequenced 3 replicates, obtaining an average of 111\u0026nbsp;million paired-end reads per sample. Reads were aligned using STAR aligner v2.5.4b (Dobin et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Ensembl reference genome GRCh38. Genewise read counts were quantified using featureCounts v1.6.2 (Liao et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Gencode annotations release 28.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell samples preparation and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLNCaP parental (treated for 48 hours with enzalutamide or DMSO), RES-A, and, RES-B cells were harvested with 0.05% Trypsin-EDTA (Sigma T3924). After neutralization with complete medium, centrifugation (300 x g for 5 min), and resuspension in PBS/0.5% BSA, the cells were filtered through a 35 \u0026micro;m Cell Strainer (Corning 352235) and a single-cell suspension of living cells was acquired through sorting on a FACS Aria II cell sorter. The cell concentration of the single-cell suspension was assessed with a Countess II FL Automated Cell Counter and ~\u0026thinsp;3 x 10\u003csup\u003e4\u003c/sup\u003e cells were pelleted (300 x g for 5 min) for further processing for using the Chromium Single Cell 3\u0026rsquo; Library, Gel Bead \u0026amp; Multiplex Kit, and Chip Kit (v3, 10x Genomics).\u003c/p\u003e\n\u003cp\u003eFor the additional LNCaP parental and RES-C cells, 1\u0026nbsp;million cells were thawed in RPMI (Gibco) with 10% FBS (Gibco) and centrifuged at 300g for 5 min. The cells were then suspended in PBS with 0.04% BSA (Ambion) and filtered with Flowmi\u0026trade; cell strainer (Bel-Art). Before loading, the cells\u0026rsquo; viability and concentration was determined using Trypan blue with Cellometer Mini Automated Cell Counter (Nexcelom Bioscience). Chromium Single Cell 5` RNA-seq was performed using the 10X Genomics Chromium technology, according to the Chromium Next GEM Single Cell V(D)J Reagent Kits v1.1 kit User guide CG000208 Rev D with loading concentration of 1000\u0026thinsp;\u0026minus;\u0026thinsp;200 cells/\u0026micro;l.\u003c/p\u003e\n\u003cp\u003eThe LNCaP-ENZ168, VCaP, and VCaP-ENZ48 single-cell RNA-seq samples were prepared with Drop-seq (Macosko et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) using the Dolomite cell encapsulation system (Dolomite Bio). Cells were trypsinized with TrypLE\u0026trade; Express Enzyme (ThermoFisher Scientific, #12604021), spun down (5 min at 300xg) and washed with 0.1% BSA-PBS. After pelleting, the cells were resuspended in plain PBS and passed through a 40-micron filter. The number of viable cells was estimated with the use of trypan blue staining and Fuchs-Rosenthal hemocytometer chamber. The concentration of cells was brought down to 3x105 cells/mL in 0.1% BSA-PBS. For single-cell encapsulation, single-cell suspension, beads in lysis buffer and oil were connected with the loops and tubing to the Mitos P pumps and run through the glass microfluidic chip at the following flow rates: 100\u0026micro;L/min (Oil channel), 20\u0026micro;L/min (Bead channel); 350 mbar (Cell channel). Droplets were separated by centrifugation and beads counted with the use of Fuchs-Rosenthal hemocytometer chamber and up to 90000 beads were collected into one tube for Reverse Transcription reaction, exonuclease treatment, and amplification of cDNA library according to the original protocol (Macosko et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Tagmentation of cDNA was performed with the Nextera XT DNA Library Preparation Kit (Illumina, #FC-131-1024). The PCR product was cleaned-up with AMPure XP beads, eluted in 10\u0026micro;L H2O and sequenced using Illumina HiSeq 2500 Rapid run.\u003c/p\u003e\n\u003cp\u003eFor scATAC-seq, cell nuclei were isolated following the 10x Genomics Demonstrated Protocol for Single Cell ATAC Sequencing (CG000169-Rev C). Briefly, the cell suspension was washed once in PBS/0.04%BSA, and 2 x 10\u003csup\u003e5\u003c/sup\u003e cells were pelleted (300 x g for 5 min), resuspended in 100 \u0026micro;l freshly prepared Lysis Buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.1% Tween-20, 0.1% NP40 Substitute, 0.01% Digitonin, 1% BSA), and incubated on ice for 4 min (LNCaP parental cells), 6 min (RES-A), or 5 min (RES-B). The lysates were diluted with 1 ml wash buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.1% Tween-20, 1% BSA), and the nuclei were pelleted (500 x g for 5 min) and resuspended in 30 \u0026micro;l 1x Nuclei Buffer (10x Genomics PN-2000153). Successful preparation of intact, isolated nuclei was confirmed through visual inspection in a phase-contrast microscopy, and nuclei concentration was assessed with a Countess II FL Automated Cell Counter, before proceeding immediately to processing for Single Cell ATAC sequencing using 10x Chromium, 10x Genomics library preparation and the Chromium Single Cell ATAC Reagent Kits (v1) User Guide (CG000168 Rev D).\u003c/p\u003e\n\u003cp\u003eSequencing was performed on the Illumina NextSeq500 instrument at the genomics core facility at the Oslo University Hospital, while sequencing of the additional LNCaP parental and RES-C was performed with Novogene Company Limited, Cambridge, UK\u0026acute;s sequencing core facility was used with a PE150 NovaSeq sequencer, aiming at 50000 reads per cell.\u003c/p\u003e\n\u003cp\u003eFor scRNA-seq, sequencing reads were processed into FASTQ format and single-cell feature counts using Cell Ranger v3.0.2 (Zheng et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, Cell Ranger ATAC v1.1.0 (Satpathy et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used to process sequencing reads from scATAC-seq into FASTQ format and peak-barcode counts. In both cases, we used the Cell Ranger pre-built GRCh38 reference. The LNCaP-ENZ168, VCaP, and VCaP-ENZ48 Drop-seq samples were pre-processed, aligned, and processed to cell count matrices with the Drop-seq tools v2.3.0 pipeline (as described in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/broadinstitute/Drop-seq/blob/master/doc/\u003c/span\u003e\u003c/span\u003e Drop-seq_Alignment_Cookbook.pdf) using default parameters and with the expectation that each sample contained 1000 cells (Macosko et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). The pipeline uses the STAR aligner v2.7.3a (Dobin et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Picard Tools v2.18.22 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://broadinstitute.github.io/picard/\u003c/span\u003e\u003c/span\u003e). We utilized the human reference genome version GRCh38, along with Gencode annotations version 33.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eFormaldehyde-assisted isolation of regulatory elements (FAIRE) sequencing and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFAIRE was performed on parental and LNCaP-ResA cells in biological triplicate according to the standard protocol (Simon et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Prior to FAIRE-seq, cells were cultured for three days in RPMI medium supplemented with 5% DCC FBS and 10 \u0026micro;M enzalutamide was added only to the resistant cell line. Both sublines were then treated with DMSO (control), DHT (10nM; Sigma Aldrich), enzalutamide (10\u0026micro;M, Selleckchem), or a combination of DHT and enzalutamide for 18 hours. The DNA fragments isolated by FAIRE were used for library preparation with the Roche KAPA library prep kit according to the manual and sequenced on the Illumina HiSeq 2500 to produce 50 bp single-end reads at the Genomics core (KU Leuven) and aligned using bwa v0.7.8-r455 (Li and Durbin, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) against hg19. Duplicates were marked and realigned using Picard 1.118. Peak calling was performed on the aligned files using MACS2 v2.1.0 (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e). MSPC v4.0.2 (Jalili et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used to jointly analyze the peaks called in the three technical replicates from each sample and to derive a common peak set. DiffBind v2.14.0 (Stark et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) was used to explore peak overlaps and differential accessibility between samples. Read distribution analysis around transcription start sites, MYC binding sites, and AR binding sites was performed by counting the average number of reads across replicates for each sample condition in 100bp bins extending 1kb up- and downstream of the sites. The value at the center (position 0) of the resulting distributions was compared between samples using the \u003cem\u003et\u003c/em\u003e-test to assess for differences in chromatin openness at these sites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware and statistical testing\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003cp\u003eAnalyses were performed using R v3.6.3 or Python v3.7.0. Statistical testing was performed using R v3.6.3. Statistical tests used are indicated in the text and in figure legends. The Shapiro-Wilk test was used to test for normality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA pre-processing and quality control\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003cp\u003eThe Cell Ranger output was used as the input to Seurat v3.2.0 (Butler et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stuart et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) for further analysis of the scRNA-seq samples. For each sample, poor quality cells were filtered based on the number of detected genes, the total number of molecules detected, and the percentage of reads arising from the mitochondrial genome. Specific thresholds for each filtering criterium were adjusted per sample to preserve a maximal number of cells. To address the effects of cell cycle heterogeneity in the data, each cell was scored for its expression of genes associated with S or G2/M phases (gene sets provided within Seurat) using the Seurat CellCycleScoring function. The difference between the G2/M and S phase scores was regressed out using sctransform (Hafemeister and Satija, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell RNA clustering\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003cp\u003eThe mutual nearest neighbor approach fastMNN (Haghverdi et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) was used to integrate the four LNCaP samples using 2000 integration features and account for batch effect. Clustering and UMAP non-linear dimensionality reduction were performed using Seurat v3.2.0, and we refer to the result as our integrated clusters. The marker genes of each cluster were determined by identifying genes differentially expressed in each cluster compared to all other clusters based on the generalized linear model MAST framework v1.12.0 (Finak et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), using the number of RNA reads as a latent variable. A gene was considered to be differentially expressed with Bonferroni corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, at least 10% of the cells in the cluster expressing the gene, and an average log-fold change of at least 0.25.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCluster and sample characterization\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003cp\u003eWe utilized hallmark gene sets from the Molecular Signatures Database (MSigDB) v7.2 (Liberzon et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Subramanian et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e) to characterize clusters and samples based on their differentially expressed genes. Gene set variation analysis (GSVA) was performed using the GSVA package v1.36.2 to characterize the average expression profile of each cluster. See the \u0026ldquo;Bulk RNA-seq and clinical data analysis\u0026rdquo; section for a more detailed description of the method. To characterize the gene expression changes within each cluster between samples, all genes were ranked based on their average log-fold change. The fgsea package v1.14.0 was then used to perform gene set enrichment analysis for the MSigDB hallmark gene sets using 1000 permutations. Differentiation states of each cell in each sample were predicted using cytoTRACE v0.3.3 (Gulati et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The RNA velocities of single cells in the scRNA-seq samples were assessed using scVelo v0.2.2 (Bergen et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). Loom input files for scVelo were generated from the FASTQ files of each sample using loompy v3.0.0, and the metadata for running scVelo (filtered cell identifiers, UMAP coordinates, and cluster information) were extracted from the integrated Seurat object and integrated with the Loom files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell ATAC pre-processing and quality control\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003cp\u003eThe output of the Cell Ranger ATAC pipeline was used as the input to Signac package v0.2.5 (Stuart et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) for further analysis of the scATAC-seq samples. For each sample, poor quality cells were filtered based on the following features: strength of nucleosome binding pattern, transcription start site enrichment score as defined by ENCODE, total number of fragments in peaks, fraction of fragments in peaks, and percentage of reads in ENCODE blacklisted genomic regions. Specific thresholds for each adjusted per sample to preserve the maximal number of cells. Data normalization and dimensionality reduction was performed using Signac with latent semantic indexing (LSI), consisting of term frequency-inverse document frequency (TF-IDF) normalization and singular value decomposition (SVD) for dimensionality reduction, using the top 50% of peaks in terms of their variability across the samples. The first LSI component reflected sequencing depth across the samples and was not utilized in downstream analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-cell ATAC clustering\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003cp\u003eIntegrated clustering of the scATAC-seq samples was performed with harmony v1.0 (Korsunsky et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) using LSI embeddings. The resulting harmony-adjusted cell embeddings were used as input for UMAP non-linear dimensionality reduction and clustering using default parameters and the smart local moving (SLM) algorithm for modularity optimization.\u003c/p\u003e\n\u003cp\u003eA \u0026ldquo;pseudo-bulk\u0026rdquo; analysis of changes in chromatin accessibility in the scATAC-seq samples was performed by pooling the reads from all good-quality cells in each sample. Visualization of peak overlap between samples was generated using R package ggradar v0.2. Differentially accessible regions in the clusters were identified using logistic regression with the total number of peaks as a latent variable. Regions were considered differentially accessible with Bonferroni corrected p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, at least 10% of the cells showing accessibility in the region, and an average log-fold change of at least 0.25. Differentially accessible regions were annotated with their closest gene using the Signac ClosestFeature function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscription factor motif enrichment\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003cp\u003eTranscription factor motif enrichment was performed using Signac in differentially accessible regions between sample conditions and between clusters in each sample with R package TFBSTools v1.26.0 and JASPAR database position frequency matrices retrieved from the R JASPAR2018 data package v1.1.1. The hypergeometric test was used to test for significant motif enrichments, taking into account sequence characteristics of the peaks (e.g. GC-frequency). P-values were adjusted with the Benjamini-Hochberg method and motifs with adjusted p-values less than 0.05 were considered to be enriched. Transcription factors that are known to play a role in PC were filtered based on their expression in the single cell dataset. Chromatin states in scATAC-seq (as defined by the enriched TFs in differentially open chromatin regions) were connected to transcriptional outputs in the scRNA-seq by assessing for overlap between the target genes of enriched transcription factors and differentially expressed genes in the scRNA-seq clusters. Transcription factor target genes were obtained using the GTRD database v18.06 (Kolmykov et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and selecting those with differentially accessible regions observed between castration-resistant prostate cancer and prostate cancer patients in Uusi-M\u0026auml;kel\u0026auml; \u003cem\u003eet al\u003c/em\u003e (Uusi-M\u0026auml;kel\u0026auml; et al.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of scRNA-seq datasets and scRNA- and scATAC-seq datasets using label transfer\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003cp\u003eThe clusters identified from the integrated clustering of scRNA-seq from LNCaP, LNCaP-ENZ48, RES-A, and RES-B (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA) were queried in additional scRNA-seq samples (alternative LNCaP parental, LNCaP-ENZ168, RES-C, VCaP parental, and VCaP-ENZ48) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA) using the label transfer approach implemented in Seurat v3.2.0 (Stuart et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The additional scRNA-seq samples were individually clustered and anchors were identified for each additional scRNA-seq sample (the query) and the LNCaP integrated clusters (the reference). This was done using the FindTransferAnchors function with principal component analysis (PCA). The anchors were used to transfer cluster label identifiers between the two data types using the TransferData function. Each cell in the query was assigned the cluster label with the highest confidence score, and only query cells with confidence scores above 0.5 were considered to have been successfully label transferred.\u003c/p\u003e\n\u003cp\u003eLNCaP, LNCaP-ENZ48, RES-A, and RES-B had scRNA-seq and scATAC-seq data available from each sample (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). These data types were integrated using the cluster label transfer procedure as implemented in Signac v0.2.5 and Seurat v3.2.0. Each scRNA-seq sample was clustered individually and its cluster labels were projected onto the matching, individually clustered scATAC-seq sample, or vice versa. The clustering resolution of each sample was assessed and decided using clustree v0.4.3 (Zappia and Oshlack, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Briefly, RNA-seq expression levels were imputed from the scATAC-seq data by defining for each gene a genomic region including the gene body and 2kb upstream of the transcription start site and taking the sum of scATAC-seq fragments within the region. Anchors were identified for condition-matched scRNA- and scATAC-seq samples using the FindTransferAnchors function and canonical correlation analysis (CCA) was performed on the scRNA expression values and the scATAC imputed gene expression values. The anchors were used to transfer cluster label identifiers between the two data types using the TransferData function. Each cell in the query was assigned the cluster label with the highest confidence score, and only query cells with confidence scores above 0.4 were considered to have been successfully label transferred.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignature gene selection\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n\u003cp\u003eTo generate the PROSGenesis signature, we extracted the gene expression profile associated with the regenerative mouse prostate luminal 2 cells reported in Karthaus \u003cem\u003eet al\u003c/em\u003e (Karthaus et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and found 78 genes with homologues in humans that were profiled in our scRNA-seq dataset. The mTORC1 signaling and MYC target gene signatures were obtained from the hallmark gene sets from the Molecular Signatures Database (MSigDB). Other signature gene sets were retrieved from previous publications or from our scRNA-seq data analysis as indicated in the main text.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBulk RNA-seq and clinical data analysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n\u003cp\u003eEach gene signature or set was assessed for enrichment and scored in a sample using the GSVA package v1.36.2, which is a non-parametric, unsupervised method for estimating gene set enrichment of each sample from gene expression data. For GSVA analysis, first, scale normalization at the seventy-fifth percentile based on the DSS package (Wu et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) was applied to the raw read counts from samples in datasets where these counts were available. For the TCGA and ICGC cohorts, we then filtered out genes with a zero count in any of the tumor samples. For each gene, GSVA performed a Poisson kernel transformation based on its empirical cumulative density function (CDF) across all samples. For RNA-sequencing datasets where only log-normalized expression values rather than raw counts were available, Gaussian kernels were utilized instead of Poisson kernels in the GSVA calculation. The kernel transformed expression values were then converted to ranks for each sample across all genes and the ranks were normalized to centered at zero. Next, for a given gene signature or set, following a similar procedure as GSEA (Subramanian et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e), the Kolmogorov-Smirnov-like random walk statistics were calculated using the normalized ranks based on two statistics: 1) a running sum of the genes which belong to the gene set. It is denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{1}\\)\u003c/span\u003e\u003c/span\u003e. 2) a running sum for the genes which do not belong to the gene set. It is denoted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{2}\\)\u003c/span\u003e\u003c/span\u003e. For sample \u003cem\u003ej\u003c/em\u003e, and gene signature \u003cem\u003ek\u003c/em\u003e, we define \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ES}_{jk}^{+}\\)\u003c/span\u003e\u003c/span\u003e as the largest positive deviations from zero of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{1}\\)\u003c/span\u003e\u003c/span\u003e- \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{2}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ES}_{jk}^{-}\\)\u003c/span\u003e\u003c/span\u003e as the smallest negative deviations from zero of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{1}\\)\u003c/span\u003e\u003c/span\u003e- \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{2}\\)\u003c/span\u003e\u003c/span\u003e. The final GSVA enrichment score of sample \u003cem\u003ej\u003c/em\u003e and gene signature \u003cem\u003ek\u003c/em\u003e is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left|{ES}_{jk}^{+}\\right|\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left|{ES}_{jk}^{-}\\right|\\)\u003c/span\u003e\u003c/span\u003e. The GSVA enrichment score emphasizes genes in pathways that are concordantly activated in one direction only, either over-expressed or under-expressed relative to the overall population. For pathways containing genes strongly acting in both directions, the deviations of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left|{ES}_{jk}^{+}\\right|\\)\u003c/span\u003e\u003c/span\u003eand\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left|{ES}_{jk}^{-}\\right|\\)\u003c/span\u003e\u003c/span\u003ewill cancel each other out and show little or no enrichment.\u003c/p\u003e\n\u003cp\u003eIn cases where the expression of a gene set was assessed at the single-cell level, the GetModuleScore function in Seurat was used to generate an average expression score per cell. Survival analyses were performed using the survival package v3.2-3 and Kaplan-Meier curves were plotted using the survminer package v0.4.8. For single signature survival analyses, median GSVA score was used to stratify patients into low and high expressing groups for the signature. For survival analyses of multiple signatures, samples were clustered using their GSVA enrichment scores for each signature using Euclidean distance and hierarchical clustering. The clustering result was then used to define the two-group split of samples for the survival analysis.\u003c/p\u003e\n\u003cp\u003eWe utilized a published scRNA-seq dataset of prostate cancer patient tumor samples from Chen \u003cem\u003eet al\u003c/em\u003e (Chen et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) to assess for the presence of our gene signatures in different cell types. The data was processed and visualized according to the code provided as part of the publication (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/chensujun/scRNA\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"Underline\"\u003e)\u003c/span\u003e using Seurat v3.2.0. Cell types were identified from the data using the marker genes reported in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial transcriptomics analysis of primary prostate cancer tissue\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n\u003cp\u003eTwo sections of cryopreserved prostate cancer tissue from one patient (pT\u0026thinsp;=\u0026thinsp;2b, T1c, Gleason 6, PSA 3.5 ng/mL) were profiled for spatial transcriptomics using the Visium Spatial library preparation protocol from 10x Genomics with a resolution of 55 \u0026micro;m (1\u0026ndash;10 cells) per spot. The tissues were cryosectioned at 10 \u0026micro;m thickness to Visium library preparation slide, fixed in ice-cold 100% methanol for 30 min, H\u0026amp;E stained with KEDEE KD-RS3 automatic slide stainer and the whole-slide was imaged using Hamamatsu NanoZoomer S60 digital slide scanner.\u003c/p\u003e\n\u003cp\u003eSequencing library preparation was performed according to Visium Spatial Gene Expression user guide (CG000239 Rev D, 10x Genomics), using 24 min tissue permeabilization time. Sequencing was done on the Illumina NovaSeq PE150 sequencer at Novogene Company Limited, Cambridge, UK\u0026acute;s sequencing core facility, aiming at 50,000 read pairs per tissue covered spot.\u003c/p\u003e\n\u003cp\u003eSequenced data was first processed using Space Ranger v1.2.0 from 10x Genomics to obtain per-spot expression matrices for both sections. Downstream processing and clustering was then performed using Seurat v3.2.0. Normalization of the data was performed with sctransform to account for differences in sequencing depth across spots. Clustering was performed using the FindClusters function using a resolution parameter value of 0.8. The resulting clusters were found to correspond to histological characteristics of the tissue. The GetModuleScore function of Seurat was used to score the spots for our scRNA-seq derived gene signatures, as well as length-matched random housekeeping gene signatures from the Housekeeping and Reference Transcript Atlas v1.0 (Hounkpe et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The distributions of the gene expression scores for the housekeeping gene sets and our scRNA-seq signatures were compared to determine the 90th percentile as a score cutoff at which we considered a spot to have high expression of the scRNA-seq signature, allowing for 5% false positives (spots scoring above the threshold for housekeeping gene sets).\u003c/p\u003e\n\u003cp\u003eTo validate our spatial transcriptomics findings, we utilized prostate Sect.\u0026nbsp;1.2, 2.4, and 3.3 from the spatial transcriptomics publication by Berglund \u003cem\u003eet al\u003c/em\u003e (Berglund et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). H\u0026amp;E images and spot count matrices were provided by the Lundeberg lab. Processing, clustering, and signature scoring of the data was performed identically to sections of Prostate A and B, but requiring that each spot would have a minimum of 500 reads counts. Similar to the analysis for Prostate A and B, the 90th percentile cutoff for high versus low scoring spots for the gene sets enrichment was assessed and confirmed using comparisons to housekeeping gene set scores for each spot.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Genomics Core Facility at Institute for Cancer Research, OUH, for the support during preparation of the scATAC- and RNA-seq, the Tampere University histology core facility and Sari Toivola for skillful assistance during Visium experiments, and the UEF Bioinformatics Center, University of Eastern Finland, Finland. The study was financially supported by the Finnish Cultural Foundation (ST), Academy of Finland (#312043 (MN), #310829 (MN), #324009 (KK), #328928 (KK), #333545 (KG, EMV, SH), #3121330724 (TM)), Finnish Cancer Society\u0026nbsp; #3122800563 (TM) Cancer Foundation Finland (MN, KK, KG), Sigrid Jus\u0026eacute;lius Foundation (MN, KK, KG), Emil Aaltonen Foundation (KG), Finnish Cancer Institute (MN), Norwegian Cancer Society (#198016-2018)(AU, NE), Competitive State Research Financing of the Expert Responsibility area of Tampere University Hospital (MN, TLJT, TV, TM), The Norman Jaffe Professorship in Pediatrics Endowment Fund (SC), MD Anderson Colorectal Cancer Moon Shot Program (SC), Oncode Institute (SP), Finnish Cultural Foundation North Savo Regional fund (KK, RK), University of Eastern Finland Doctoral Programme in Molecular Medicine (RK), K. Albin Johansson Foundation (KK), John Black Foundation (IM), Human Cell Atlas Seed Network - Retina (WW), Chan Zuckerberg Institute (WW), NIH R01CA183793 (WW, SC), NIH R01CA239342 (WW), NIH R01CA158113 (WW), P30CA016672 (WW), the Fonds Wetenschappelijk Onderzoek-Vlaanderen #GOA9816N (FC), KU Leuven #C14/19/100 (FC), Kom op tegen Kanker #KOTK (FC, WD), Cancer Research UK # A22744 (GA, DW, KN, PC), Cancer Research UK #C57899/A25812 (AE, ADL). The results published here are in part based upon data generated by The Cancer Genome Atlas project established by the NCI and NHGRI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGA receives a reward from the Institute of Cancer Research for his role as an inventor of abiraterone. GA has received honoraria, consulting fees, or travel support from Janssen, Astellas, Pfizer, Novartis, Bayer, Amgen, AstraZeneca, Sanofi, and Sapience, grant support from Janssen and Astellas, and is a principal investigator for clinical trials sponsored by Janssen, Pfizer, and Astellas. TM receives consultant fees from Astellas, Janssen, and Bayer; lecture fees from Novartis, Janssen, and Sanofi. He is a stockholder of Arocell ab.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbida, W., Cyrta, J., Heller, G., Prandi, D., Armenia, J., Coleman, I., Cieslik, M., Benelli, M., Robinson, D., Van Allen, E.M., et al. (2019). Genomic correlates of clinical outcome in advanced prostate cancer. Proc. Natl. Acad. Sci. U. S. 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Commun. \u003cem\u003e8\u003c/em\u003e, 14049.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cstrong\u003e\u003cspan style=\"font-family: Calibri, sans-serif; font-size: 15px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);\"\u003eData and code availability\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"width: 4.5e+2pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, 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initial;border-left: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003cstrong\u003eIdentifier\u003c/strong\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSingle-cell RNA- and ATAC-sequencing of LNCaP\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eThis study\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGEO: GSE168669\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eFAIRE-seq of LNCaP\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eThis study\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGEO: GSE168669\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSpatial transcriptomics data from prostate sections Prostate A and Prostate B\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eThis study\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003cspan style=\"background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"\u003eEuropean Genome-phenome Archive: EGAS00001000526\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSingle cell RNA-sequencing of LNCaP and VCaP from group of Prof. Gerhardt Attard, UCL\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eThis study\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGEO: GSE168733\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eRNA-sequencing of VCaP models of CRPC and resistance to AR signaling-targeted treatments from group of Prof. Teemu Murtola, Tampere University\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eThis study\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGEO: GSE168669\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eAR and c-MYC binding sites map\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/A4EV\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Barfeld et al., 2017)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGEO: GSE73994\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eBulk RNA-sequencing of LNCaP samples\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/2uTU\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Handle et al., 2019)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGEO: GSE130534\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eXenografts of AR-positive / NE-negative and AR-negative / NE-positive CRPC tumors\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/NnYA+dVY4\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Labrecque et al., 2019; Lam et al., 2020)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGEO: GSE124704\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGEO: GSE126078\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eLNCaP xenograft model of CRPC\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/Kgy3\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(King et al., 2017)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSupplementary File 1 in publication\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ePatient RNA-sequencing from enzalutamide responders and non-responders\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/n10r\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Alumkal et al., 2020)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eReceived from authors\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eRNA-sequencing from SU2C CRPC patient samples\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/q3oc\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Abida et al., 2019)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eReceived from authors\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eRNA-sequencing from SU2C West Coast DT patient samples\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/q9pK\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Quigley et al., 2018)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eReceived from authors\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSingle cell RNA-sequencing of LNCaP from group of Dr Kirsi Ketola, University of Eastern Finland\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eUnpublished\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eReceived from Ketola lab\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003escRNA-seq of 12 treatment-naive prostate cancer patient tissue samples\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/0Yk9\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Chen et al., 2021)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGEO: GSE141445\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSpatial transcriptomics data from prostate tissue sections 1.2, 2.4, and 3.3\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/w4n2\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Berglund et al., 2018)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eReceived from authors\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eTCGA-PRAD RNA-seq\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eICGC-EOPC RNA-seq\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/aQma\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Gerhauser et al., 2018)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eReceived from authors\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eHallmark gene sets from the Molecular Signatures Database (MSigDB) v7.2\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/Uikm+Xebd\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Liberzon et al., 2015; Subramanian et al., 2005)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttp://www.gsea-msigdb.org/gsea/msigdb/index.jsp\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;margin-bottom:4.0pt;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGTRD database v18.06\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/MC3y\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Kolmykov et al., 2021)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://gtrd.biouml.org/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162.75pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eHousekeeping and Reference Transcript Atlas v1.0\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144.75pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/n038\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Hounkpe et al., 2021)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttp://www.housekeeping.unicamp.br/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003cstrong\u003eMethods Table 1\u003c/strong\u003e. Details of datasets utilized as part of the study.\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ctable style=\"width: 4.5e+2pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003cstrong\u003eResource\u003c/strong\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: 1pt solid black;border-right: 1pt solid black;border-bottom: 1pt solid black;border-image: initial;border-left: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: 1pt solid black;border-right: 1pt solid black;border-bottom: 1pt solid black;border-image: initial;border-left: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003cstrong\u003eIdentifier\u003c/strong\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eCell Ranger (version 3.0.2)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/ryV5\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Zheng et al., 2017)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e10x Genomics\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eCell Ranger ATAC (version 1.1.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/LYap\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Satpathy et al., 2019)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e10x Genomics\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSpace Ranger (version 1.2.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e-\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e10x Genomics\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSeurat (version 3.2.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/LPVQ+A2m4\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Butler et al., 2018; Stuart et al., 2019)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://cran.r-project.org/web/packages/Seurat/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003esctransform (version 0.3.1)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/L1Sg\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Hafemeister and Satija, 2019)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://cran.r-project.org/web/packages/sctransform/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003efastMNN / batchelor\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e(version 1.2.4)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/8N1w\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Haghverdi et al., 2018)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://bioconductor.org/packages/release/bioc/html/batchelor.html\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eMAST (version 1.12.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/SfvD\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Finak et al., 2015)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://www.bioconductor.org/packages/release/bioc/html/MAST.html\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eGSVA (version 1.36.2)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/ycoe\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(H\u0026auml;nzelmann et al., 2013)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://bioconductor.org/packages/release/bioc/html/GSVA.html\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003efgsea (version 1.14.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/aaEd\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Korotkevich et al.)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://bioconductor.org/packages/release/bioc/html/fgsea.html\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003escVelo (version 0.2.2)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/i5ba\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Bergen et al., 2020)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://pypi.org/project/scvelo/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ecytoTRACE (version 0.3.3)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/V3zw\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Gulati et al., 2020)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://cytotrace.stanford.edu/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSignac (version 0.2.5)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/9TiF\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Stuart et al., 2020)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://github.com/timoast/signac\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eharmony (version 1.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/6B55\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Korsunsky et al., 2019)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://github.com/immunogenomics/harmony\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eggradar (version 0.2)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e-\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://github.com/ricardo-bion/ggradar\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eTFBSTools (version 1.26.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/7UuU\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Tan and Lenhard, 2016)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttp://bioconductor.org/packages/release/bioc/html/TFBSTools.html\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eclustree (version 0.4.3)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/BX6O\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Zappia and Oshlack, 2018)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://github.com/lazappi/clustree\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003esurvival (version 3.2-3)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/bNOx\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Therneau and Grambsch, 2000)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://cran.r-project.org/package=survival/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003esurvminer (version 0.4.8)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e-\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://cran.r-project.org/we/packages/survminer/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eDrop-seq tools (version 2.3.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/ww4Q\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Macosko et al., 2015)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://github.com/broadinstitute/Drop-seq\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ebwa (version 0.7.8-r455)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/Qadx\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Li and Durbin, 2010)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttp://bio-bwa.sourceforge.net/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ePicard (versions 1.118 and 2.18.22)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e-\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://broadinstitute.githu.io/picard/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eMACS2 (version 2.1.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/nikI\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Zhang et al., 2008)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://github.com/jsh58/MACS\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eMSPC (version 4.0.2)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/Elrx\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Jalili et al., 2018)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://genometric.github.io/MSPC/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eDiffBind (version 2.14.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/kjVh\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Stark et al., 2011)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://www.bioconductor.org/packages/release/bioc/html/DiffBind.html\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003efeatureCounts (version 1.6.2)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/ujYy\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Liao et al., 2014)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttp://subread.sourceforge.net/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eSTAR (versions 2.5.4b and 2.7.3a)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003ca href=\"https://paperpile.com/c/uIHtgp/wJWB\"\u003e\u003cspan style=\"text-decoration: none;\"\u003e(Dobin et al., 2013)\u003c/span\u003e\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://github.com/alexdobin/STAR\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eJASPAR2018 (version 1.1.1)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e-\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttps://bioconductor.org/packages/release/data/annotation/html/JASPAR2018.html\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 163.5pt;border-right: 1pt solid black;border-bottom: 1pt solid black;border-left: 1pt solid black;border-image: initial;border-top: none;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003eloompy (version 3.0.0)\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2in;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e-\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143.25pt;border-top: none;border-left: none;border-bottom: 1pt solid black;border-right: 1pt solid black;padding: 5pt;vertical-align: top;\"\u003e\n \u003cp style='margin:0in;line-height:normal;font-size:15px;font-family:\"Arial\",sans-serif;text-align:center;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003ehttp://loompy.org/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u003cstrong\u003eMethods Table 2\u003c/strong\u003e. Software and tools used in the study. The version and download location of each tool is indicated.\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"background-color: rgb(255, 255, 255);\"\u003e\u003cspan style=\"color: rgb(0, 0, 0);\"\u003e\u003cspan style=\"font-size: 15px;\"\u003e\u003cspan style=\"font-family: Calibri, sans-serif;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;line-height:115%;font-size:15px;font-family:\"Arial\",sans-serif;'\u003e\u003cspan style=\"font-family: Calibri, sans-serif; font-size: 15px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"single-cell ATAC sequencing, single-cell RNA sequencing, cancer genomics, gene signature score, prostate cancer; enzalutamide, cancer treatment resistance, treatment response prediction, chromatin reprogramming","lastPublishedDoi":"10.21203/rs.3.rs-384422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-384422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProstate cancer is heterogeneous and patients would benefit from methods that stratify those who are likely to respond to systemic therapy. We employed single-cell assays for transposase-accessible chromatin (ATAC) and RNA sequencing in models of early treatment response and resistance to enzalutamide. In doing so, we identified pre-existing and treatment-persistent cell subpopulations that possess transcriptional stem-like features and regenerative potential when subjected to treatment. We found distinct chromatin landscapes associated with enzalutamide treatment and resistance that were linked to alternative transcriptional programs. Transcriptional profiles characteristic of persistent stem-like cells were able to stratify the treatment response of patients. Ultimately, we show that defining changes in chromatin and gene expression in single-cell populations from pre-clinical models can reveal hitherto unrecognized molecular predictors of treatment response. This suggests that the high analytical resolution of pre-clinical models enabled by single-cell methods may powerfully inform clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Single-cell ATAC and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-12 22:29:15","doi":"10.21203/rs.3.rs-384422/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"699125b4-e198-43ef-9382-a1a0b4d0e055","owner":[],"postedDate":"April 12th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":3575734,"name":"Cancer Biology"},{"id":3575735,"name":"Oncology"},{"id":3575736,"name":"Urology \u0026 Nephrology"}],"tags":[],"updatedAt":"2021-11-22T15:35:27+00:00","versionOfRecord":{"articleIdentity":"rs-384422","link":"https://doi.org/10.1038/s41467-021-25624-1","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2021-09-06 04:00:00","publishedOnDateReadable":"September 6th, 2021"},"versionCreatedAt":"2021-04-12 22:29:15","video":"","vorDoi":"10.1038/s41467-021-25624-1","vorDoiUrl":"https://doi.org/10.1038/s41467-021-25624-1","workflowStages":[]},"version":"v1","identity":"rs-384422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-384422","identity":"rs-384422","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}