Comparative Transcriptomic Analysis Validates iPSC Derived In-Vitro Progressive Fibrosis Model As A Screening Tool For Drug Discovery and Development in SSc

Comparative Transcriptomic Analysis Validates iPSC Derived In-Vitro Progressive Fibrosis Model As A Screening Tool For Drug Discovery and Development in SSc | 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 Comparative Transcriptomic Analysis Validates iPSC Derived In-Vitro Progressive Fibrosis Model As A Screening Tool For Drug Discovery and Development in SSc Yifei Wang, Shyam Nathan, Matthew D’ambrosio, Reeba Paul, Huimin Lyu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4546782/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Systemic sclerosis (SSc) is an autoimmune disease characterized by vasculopathy, immune dysregulation, and systemic fibrosis. Research on SSc has been hindered largely by lack of relevant models to study the progressive nature of the disease and to recapitulate the cell plasticity that is observed in this disease context. Generation of models for fibrotic disease using pluripotent stem cells is important for recapitulating the heterogeneity of the fibrotic tissue and are a potential platform for screening anti-fibrotic drugs. We previously reported a novel in-vitro model for fibrosis using induced pluripotent stem cell-derived mesenchymal cells (iSCAR). Here we report the generation of a “scar-like phenotype” when iPSC derived mesenchymal cells are cultured on hydrogel that mimicks a wound healing/scarring response (iSCAR). First, we performed RNA sequencing (RNA-seq) based transcriptome profiling of iSCAR culture at 48 hr and 13 days to characterize early and late-stage scarring phenotypes. The next generation RNA sequencing (RNA-seq) of these iSCAR culture at different timepoints detected expression 92% of early “scar associated” genes and 85% late “scar associated” genes, respectively. Comparative transcriptomic analysis of a gene level SSc compendium matrix to the iSCAR wound associated model revealed genes common in both data sets. Early scar formation genes showed biological processes of hypoxia (27.5%), vascular development (13.7%) and glycolysis (27.5), while late scar formation showed genes associated with senescence (22.6%). Next we show the effects of two different antifibrotic compounds to validate the utility of the model as an screening tool to study early and late stage fibrosis. An autotaxin inhibitor was used to validate the iSCAR late stage fibrotic model (iSCAR-T) and an antifibrotic tool screening compound of unknown mechanism (EX00015097) was used to study and validate both early (iSCAR-P) and late stage (iSCAR-T) fibrosis in the iSCAR model. Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery In vitro Models Systemic Sclerosis Progressive Fibrosis Cell Plasticity Gene Expression Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Systemic sclerosis (SSc) is a complex autoimmune disease involving vasculopathy, immune dysregulation, and systemic fibrosis. Affecting 240 patients per million in the US, SSc has a 30% mortality rate within 10 years of diagnosis. SSc is classified into limited cutaneous (lSSc) and diffuse cutaneous (dSSc) subtypes based on skin involvement 1 . Both genetic and environmental factors contribute to SSc, with key regulators including pro-fibrotic cytokines such as transforming growth factor-β (TGFβ), IL-1, IL-6 and IL-17, angiogenic and growth factors driving fibrosis and collagen deposition 2 . Collagen accumulation can cause severe organ damage and failure. Research on SSc has been hindered by disease heterogeneity, limited patient-derived biomaterials, and insufficient understanding of multiple pathogenic pathways and their interplay 3 . Induced pluripotent stem cells (iPSCs) differentiated into key disease-affected cells in vitro is a promising approach to address these biomaterial challenges 4 . A prior study generated a unique in-vitro human model using iPSC-derived heterogeneous mesenchymal cells, which mimicked progressive fibrosis and was amenable for drug screening to identify potential anti-fibrotic compounds 5 . Such a model also lends itself as an effective tool to model and study a complex disease such as SSc. We have recently reported on a novel pathway driven meta-analysis which identified 8 unique SSc subsets and 5 pathway modules that describe the patient subsets based on molecular function and cellular components 6 . This revealed a mesenchymal cell involvement among other cell types within a complex disease network 6 . In this study, we now describe a comparative analysis of iPSC derived in vitro fibrosis model with SSc bulk RNA-seq compendium and single cell atlas. Our in vitro iPSC model of self-perpetuating progressive fibrosis closely resembles human disease and can be characterized into early and late fibrotic disease states. Because of the opportunity of early diagnosis of certain fibrotic diseases such as SSc 7 , this model provides a unique platform to study biological process involved in progress to later stage disease. The model’s intrinsic inflammatory state and 3D hydrogel substrate contributes to a fibrotic phenotype and pathological effects associated with cell plasticity 7 . Here we validate our iPSC-derived mesenchymal model for progressive fibrosis as an investigative tool to study the pathological cell state of fibroblasts, which is the most important cell type of the systemic fibrosis element of SSc, as well as to investigate/validate known and novel anti-fibrotic compounds utilizing the endpoints of this model. We use a computational pipeline to analyze RNA-seq data from iPSC-derived mesenchymal cells in a 3D in-vitro system, comparing it to a compendium generated from published RNAseq and single cell seq datasets from SSc patients 6 . Methods Work-flow All experiments were performed in accordance to Boehringer Ingelheim’s compliance committee and relevant guidelines and regulations were followed. All the experimental protocols were approved by Boehringer Ingelheim’s licensing committee. iPSC from BJ skin fibroblasts were purchased from Genome Engineering and Stem Cell Center, Wahington University, St.Louis to generate the iSCAR model. In this iSCAR-SSc comparative study, we mapped gene sets from an iPSC-derived “iSCAR model” onto a gene level SSc compendium matrix to validate the utility of the iSCAR model as in-vitro antifibrotic screening tool in context of SSc ( Fig. 1a and 1b ). The iSCAR model involves skin induced pluripotent stem cell (iPSC)-derived mesenchymal-like cells (iMCs) cultured on a stiff 12-kPa polyacrylamide hydrogel to model the complex phenotype found in progressive fibrotic diseases ( Fig. 1a ) as previously published 5 . When cultured on the hydrogel, the iMCs develop a scar-like phenotype that mimics a wound-healing response involving a scarring phase 5 . The overall work flow consists of two major steps: A. Characterizing the iSCAR model in the context of early- and late- stage scar formation and B. Validating the utility of the model as a fibrotic in-vitro screening tool by testing both an anti-fibrotic compound with a known mechanism and a novel compound that has not been fully characterized but shows promising anti-fibrotic potential 8 . We conducted three iSCAR assays to mimic early and late-stage scarring. In the first (iSCAR-Ctrl), iMCs from Skin iPSCs (a gift from the Genome Engineering & iPSC Center at Washington University in St. Louis) were cultured on plastic for 48 hrs in the presence of either DMSO, 3 µM autotaxin inhibitor or 5 µM of a novel anti-fibrotic compound EX00015097 [AA5] 5 , to simulate “no scar” formation. The compound concentrations for analysis were selected at IC 80 concentrations in all the iSCAR assays, and each treatment was performed in triplicate. In the second (iSCAR-P), iMCs were cultured on 12kPa easy-coat hydrogels (Matrigen) coated with 0.1% gelatin (Stem cell technologies) for 48hrs in the presence of the above compounds to simulate “early scar” formation and preventative treatment. In the third (iSCAR-T), iMCs were cultured on hydrogels for 13 days after which compounds were added after scar establishment for 48 hrs to simulate “late scar” formation and therapeutic treatment. RNA was isolated at the end of each of the assays for sequencing. To validate the utility of iSCAR-P or iSCAR-T as an early or late in-vitro antifibrotic screening tool in context of SSc, we studied the enrichment of significant genes following treatment with the autotaxin inhibitor or EX00015097 within the SSc bulk RNA-seq compendium created from 12 publicly available sources 6 and an SSc single cell atlas created from 3 publicly available sources. After validating that the autotaxin inhibitor or EX00015097 treated iSCAR gene sets were significantly and positively enriched in dSSc and lSSc, we studied the differential effect of the autotaxin inhibitor and EX00015097 within the iSCAR model ( Fig. 1b ). iSCAR data analysis RNA-seq reads from iSCAR experiments were mapped to human reference genome GRCh38 using RNA STAR 9 . The mapped reads were run through featureCounts 10 for read summarization. featureCounts returned a counts matrix, which was then subject to differential gene expression analysis in DESeq2 with design factor “~ 0 + Endpoint_Source_Treatment + Replicate_Number” 11 . In DESeq2, the following contrasts were made to study early and late scar formation: iSCAR-P DMSO vs iSCAR-Ctrl DMSO, iSCAR-T DMSO vs iSCAR-Ctrl DMSO, and iSCAR-P DMSO vs iSCAR-T DMSO. Contrasts were made for each of the 3 iSCAR experiment endpoints to study the antifibrotics: EX00015097 vs DMSO and Autotaxin inhibitor vs DMSO. The criteria for selection of differentially expressed genes significantly increased (“UP”) or decreased (“DOWN”) compared to DMSO control is p < 0.05 and logFC 1.5. This returned 4 different groups (EX00015097 vs DMSO DOWN, EX00015097 vs DMSO UP, Autotaxin inhibitor vs DMSO DOWN, and Autotaxin inhibitor vs DMSO UP) for each iSCAR experiment. The selected genes in each group would comprise the iSCAR gene set list for that group. SSc study collection and the creation of the SSc bulk RNA-seq compendium matrix We used 12 datasets for our analysis (GSE9285, GSE32413, GSE45485, GSE58095, GSE59785, GSE76885, EMEXP1214, GSE76807, GSE76806, GSE66321, GSE65405, and GSE130955). All the datasets are from microarray, with one exception of bulk RNA-seq data GSE130955. We focused on baseline SSc patients’ skin biopsy samples and major SSc phenotypes. A ‘leave-one-out’ summary was performed to balance the number of studies and shared genes. After excluding GSE9285 dataset for optimal shared genes, Combat method using sva 12 and quantile normalization using preprocessCore 13 were performed to remove batch effect and baseline differences across the samples. The complete gene level compendium matrix contained 13616 genes x 380 samples from 239 SSc samples and 141 controls. The SSc compendium matrix The SSc bulk compendium ( Supplementary Table 1 ), which contains 224 dSSc, 15 lSSc, and 141 healthy control samples from 12 publicly available sources, was ranked in terms of gene expression level for dSSc and lSSc versus control respectively using LIMMA 14 ( Fig. 1b ). For each pairwise contrast, LIMMA generated a table of logFC values and adjusted p-values for all genes in the compendium matrix. Positive/negative logFC values correlate to up/down regulation relative to control in both the dSSc-control and lSSc-control contrast. From LIMMA, we obtained a total of 300 upregulated and significant DEGs ( 0.5 logFC) in dSSc and 144 upregulated and significant DEGs ( 0.5 logFC) in lSSc. 115 DEGs were shared between dSSc and lSSc. Further gene ontology pathway analysis, utilizing DEGs, for both dSSc (300 DEGs) and lSSc (144 DEGs) shows that genes differentially expressed in each SSc disease phenotype yielded many similar biologically relevant pathways such as ‘ECM organization’, ‘Negative regulation of immune processes’, ‘inflammation and wound healing’, ‘cell matrix/cell-cell adhesion’, and others ( Supplementary Fig. 1a and 1b ). Many of these genes are verified in the dSSc and lSSc phenotypes and are associated with disease severity, as well as MRSS scores and SSc relevant biomarkers. LIMMA to GSEA In order to assess the distribution of genes in both dSSc-control and lSSc-control, GSEA analysis 15 was subsequently performed, in which all genes from each contrast were ranked by their logFC values, from the most positive to the most negative. This ranked list of genes along with each pre-defined iSCAR gene set were used to obtain the enrichment score (ES), the statistical significance of ES, and lead genes associated with the enrichment of each iSCAR gene set in either dSSc vs control or lSSc vs control contrast. GSEA to pathway analysis For each iSCAR experiment (iSCAR-Ctrl, iSCAR-P, and iSCAR-T), there were 4 pre-defined gene sets (2 antifibrotic vs DMSO control treatment groups x UP or DOWN), each mapped to either dSSc vs control or lSSc vs control. Only iSCAR gene sets that contained genes downregulated with antifibrotic treatment compared to control (‘DOWN’) and had a positive normalized enrichment score with a < 0.05 adj. p-value in the GSEA analysis were kept for subsequent analysis. Pathway enrichment analysis was performed on genes from selected iSCAR gene sets. For pathway analysis, org.Hs.eg.db was used with gene sets from all three different domains in gene ontology (GO) including: biological processes (BP), molecular functions (MF), and cellular components (CC). Comparative pathway analysis was performed using the ‘enrichGO’ and ‘compareCluster’ functions of clusterProfiler 16 with a <0.05 p-value using the Benjamini-Hochberg method for adjusted P value. Gene sets were also pasted into Metascape 17 for enrichment analysis. Single cell atlas and module score Single cell data from 3 studies (GSE195452, GSE209635, and GSE138669) were used to create a comprehensive single cell atlas presenting healthy or SSc skin tissues ( Supplementary Table 2 ). Together, they comprise 120 SSc samples and 64 healthy control samples. Each study was normalized in scanpy.pp.normalize_total with a 1e4 target_sum setting and sc.pp.log1p with all default parameters. After initial normalization, “low quality cells” which include cells with low or high gene count and cells with high percent mitochondria were subsequently removed. We then used scVI 18 to merge and batch correct technical differences across different studies and samples, while keeping biological variations intact. scVI integrated single cell atlas was then subject to Leiden clustering 19 , which identified communities of cells that tend to cluster together. We identified relevant cell types (epithelial cells, endothelial cells, fibroblast cells, and myeloid cells) in the integrated atlas based on the gene rank of each cluster and preliminary knowledge of marker genes of different cell types. We subsequently split the atlas into SSc and control atlas. We then, using the expression values of the genes included in each iSCAR gene signature taken from the respective iSCAR experiment, scored each cell in the SSc or control atlas using the ‘AddModuleScore’ Seurat function 20 in order to identify cell populations that highly express the genes in each iSCAR gene set. LPA measurement Supernatants from iSCAR-T Autotaxin inhibitor treated cells were collected and LPA-species were quantitated via liquid chromatography based mass spectrometry according to a published protocol 21 . Multiplex analysis of cytokines The Milliplex human cytokine/chemokine Panel IV, was used per manufacturer’s instructions. iMCs seeded either on plastic or hydrogels were serum starved for 24 hours, and the supernatant collected for the assay. Results Early vs late scar formation in iPSC model In a previous study, iPSC-derived iSCAR cells formed scar-like aggregates on stiff 13-kPa hydrogels 5 . We confirmed iMC cells’ intrinsic inflammatory nature, and cytokine/chemokine distribution was similar on plastic and on hydrogel ( Supplementary Fig. 2 ). We hypothesize that the intrinsic inflammatory nature of the iMC along with mechano-forces may influence the culture conditions of the iMC in hydrogel as it applies to biological processes associated with ‘early’ and ‘late’ scar formation. DESeq analysis comparing DMSO-treated control from iSCAR-Ctrl and from iSCAR-P or iSCAR-T revealed 204 DEGs unique to early scar formation, 164 DEGs unique to late scar formation, and 332 shared DEGs ( Fig. 2a, 2b, and Supplementary Table 3 ). Processes present in both stages include vascular remodeling, cytoskeletal re-organization, and ion channel activation ( Table 1 and Table 2 ). Hypoxia-induced vascular remodeling more prominent in early scar formation and cell senescence plus positive regulation of programmed cell death more prominent in late scar formation ( Table 1, Table 2, and Fig. 2c ). Upregulated signaling pathways/genes unique to early scar formation compared to DMSO-treated control from iSCAR-Ctrl involve hypoxia-induced pathogenesis such as HIF-1a, TGFβ, PKC/ERK, PI3K/Akt, NF-kB, IL-6 [7]. These pathways can affect fibrotic phenotypes, vascular remodeling, EMT, and ECM. We found genes related to vascular remodeling, ion channel activation, and cytoskeletal remodeling, indicative of mechano-responses to mechano-transduction ( Table 1 ). We further directly assessed differential biology in early and late scar formation by comparing DMSO-treated iSCAR-P and iSCAR-T. DESeq comparison revealed 51 DEGs upregulated in early scar formation and 53 in late scar formation consistent with the prior analysis ( Supplementary Table 4 ). Early scar formation genes showed similar biological processes as iSCAR-P DMSO-treated control, such as response to hypoxia (logP=-16.6504), vasculature development (logP=-4.53389), and glycolysis and gluconeogenesis (logP=-7.93662) ( Fig. 2d, Fig. 2e, and Table 3 ). Similarly, late scar formation analysis revealed a greater role of senescence, possibly due to epigenic modifications, as indicated by increased genes in cell cycle checkpoints (logP=-8.7002), cellular senescence (logP=-5.91767), and senescence-associated secretory phenotype (logP=-5.82336) ( Fig. 2d, Fig. 2e, and Table 4 ). This suggests long-term culture in a stiff matrix may result in a senescence phenotype influenced by mechano-sensing. Additonal GSEA analysis showed significant enrichment of dSSc genes in both early (adj. p-value=4.37E-15) and late scar formation (adj. p-value=1.06E-14), indicating a dSSc phenotype capture. Enrichment analysis revealed hypoxia-induced angiogenesis in early scar formation and cell senescence in late scar formation ( Supplementary Table 5 ). These pathways are relevant to SSc disease progression, as cellular senescence plays a pathogenic role in fibrosis and senescence-associated secretory phenotype (SASP) can increase inflammatory secretory proteins like MMPs, TGFB1 and interleukins in fibrotic tissues. Effects of Autotaxin inhibitor In the context of SSc fibrotic disease, we validated the iSCAR model’s ability to differentiate treatment responses in early vs late scar phenotypes using an inhibitor of autotaxin. Autotaxin is an enzyme responsible for the production of lysophospatidic acid (LPA), the downstream signaling of which mediates responses to tissue injury and has been implicated in the pathogenesis of fibrotic conditions such as SSc. We examined the autotaxin inhibitor iSCAR-P (100 genes) and iSCAR-T (121 genes) gene sets, finding ECM, EndoMT, and EMT-related genes enriched in the both gene sets, but with a higher number of genes within the iSCAR-T set (Table 5) . GO enrichment analysis showed fibrosis-related terms for iSCAR-T (e.g. ECM structural constituent, growth factor binding and type I TGFb receptor binding), but none significant for iSCAR-P (p-value = 0.05; Benjamini-Hochberg method for adjusted P value) ( Fig. 3a ). The gene changes observed in the autotaxin inhibitor iSCAR-P and iSCAR-T models were compared to the SSc single cell compendium, and showed higher enrichment in macrophage and epithelial populations for iSCAR-P, and higher enrichment in fibroblast, smooth muscle, and pericyte populations for iSCAR-T ( Fig. 3c ). This agrees with the enrichment of autotaxin inhibitor iSCAR-P or iSCAR-T gene set in the SSc bulk RNA-seq result, since only autotaxin inhibitor iSCAR-T gene set is significantly and positively enriched in dSSc (NES = 2.1465 and FDR q-val = 0) and lSSc (NES = 2.1593 and FDR q-val = 8.88E-5) ( Fig. 3b ). SSc bulk RNA-seq results agree with iSCAR-T gene set enrichment in dSSc and lSSc, showing significant GO terms like ECM and growth factor binding. The iSCAR-P gene set had few significant GO terms. Since autotaxin functions upstream of TGFβ, the addition of an autotaxin inhibitor to an iSCAR assay should downregulate processes downstream of both LPA and TGFβ. Consistently, autotaxin inhibitor treatment reduced LPA species 18:1 and 16:1 in our iSCAR-T in-vitro model ( Fig. 3d ). Additionally, lead genes from iSCAR-T (autotaxin inhibitor) enrichment in dSSc are mostly downstream of LPA-receptor activation ( Supplementary Table 6 ), influencing fibroblast differentiation (e.g. TNC 22 ), Smad2/3 activation (e.g. MMP11 23 ), ECM remodeling (e.g ANGPTL2 24 ), and collagen production through various pathways 25,26,27 . Overall, the data aligns with the LPA/autotaxin axis-influenced phenotype, showing autotaxin inhibitor’s modulation of fibrosis-related genes through long-term mechanotransduction and metabolic effects. Effects of EX00015097 We screened the antifibrotic compound EX00015097 (AA5, unknown mechanism) 5 in iSCAR-T and iSCAR-P models, examining 360 and 420 DEGs respectively. Both models shared ECM-related and transition genes. Comparative GO analysis revealed significant terms like ECM structural constituent, growth factor binding shared in both models and specific activities for each model such as transmembrane receptor protein kinase activity in iSCAR-P, and DNA-binding transcription activator activity in iSCAR-T (p-value = 0.05; Benjamini-Hochberg method for adjusted P value) ( Fig. 4a, Table 6 ), potentially due to intrinsic effects of hydrogel. EX00015097 iSCAR-P and iSCAR-T gene sets show similar enrichment in fibroblast, smooth muscle, and pericyte cell populations ( Fig. 4c ), and in SSc bulk RNA-seq where both EX00015097 iSCAR-P and iSCAR-T gene sets are significantly and positively enriched in dSSc (Prevention: NES=1.8449, FDR q-val=0.004015; Therapeutic: NES=1.8685, FDR q-val=0.00169) and lSSc (Prevention: NES=2.048, FDR q-val=0.000712; Therapeutic: NES=1.8857, FDR q-val=0.001751) ( Fig. 4b ). Lead genes from each set highlight different processes: iSCAR-P emphasizes hypoxia-induced angiogenesis and cell migration, while iSCAR-T focuses on immune regulation and cell death/senescence ( Supplementary Table 7 ). UNC5B, a lead gene from EX00015097 iSCAR-P and iSCAR-T enriched in dSSc, has been suggested to promote vascular endothelial cell senescence via ROS-mediated P53 pathway 28 . Discussion The lack of relevant mouse models for studying progressive fibrotic diseases is a challenge, mainly due to the inability of current models to replicate the diverse cell characteristics and plasticity observed in these diseases. This study aims to validate the use of iPSC-derived mesenchymal cells as an in-vitro system to study the progressive nature of diseases like SSc. Unlike other models, these cells can mimic disease progression without the need for supplementation of additional profibrotic modulators. They can also be cultured for extended periods, allowing for the perpetuation of a significant feed-forward fibrotic response. We have demonstrated that the iSCAR-SSc comparative study validates the utility of the iSCAR model as a drug screening tool that can evaluate anti-fibrotic candidates within the context of early and late stage disease in fibroblasts within SSc. A key feature of the iSCAR is the inherent cellular plasticity of the model which lends itself to recapitulate a progressive disease with multiple cell types that drive the fibrotic phenotype. Our pipeline’s analysis of the iSCAR model within the single cell compendium demonstrates this heterogeneity. Our comparative study suggests that the interplay of hydrogel mechano-forces and inflammatory nature of the iMC/iSCAR cells could drive scar formation. The iMC’s inherent inflammatory characteristics and force-induced stretch may activate biological and mechanistic pathways influencing early scar formation, leading to late scar formation. This is evidenced by upregulated inflammatory cytokines like IL-6 and MCP-1 in early iSCAR models, driving differential gene expression in fibrotic stages. These cytokines may stimulate adhesion molecules like ICAM1, VCAM-1, and E-selectin 29 , leading to cellular stiffening and enhanced RhoA signaling 30 , mediating changes in cell motility, adhesion, and proliferation. STAT3, which is responsible for the endothelial expression of ICAM-1, is also a well-known transcriptional activator for VEGF and HIF1a and plays a key role in controlling angiogenesis 31 . ICAM, VCAM-1, Hif1a, VEGF are genes and pathways significant enriched within the early iSCAR model. This is supported by the enrichment of angiogenesis and VEGFR pathways in iSCAR-P, potentially contributing to later disease progression. This mechano force-induced stretch of the iSCAR system may also force a triggered opening of mechanosensitive ion channels which may lead to increased cytosolic Ca2+ and K+ levels that promotes the expression and activation of further inflammation, cytoskeletal re-arrangements and senescence. For example, we identified a significant upregulation of genes (eg. KCNJ15 and KCNJ6) that encode inward-rectifier type potassium channels in cells on hydrogel. The biophysical cues from stretch applied to cells on hydrogel are also transmitted to the cells via integrin-actin cytoskeleton, which activates the PI3K-Akt pathway, which drives both NF-kB and HIF1 32 . Active NF-kB and HIF1a can then translocate to the nucleus to further drive metabolic and inflammatory gene expression. Studies have shown that there is positive feedback between these inflammatory molecules/pathways and mechano force-induced stretch: the long-term presence of NF-kB and inflammatory molecules like IL-6 induce the expression of ICAM-1, which coupled with force-induced stretch can reinforce the same pathways/inflammatory molecules. These concepts are supported by evidence within our comparative study in the iSCAR model showing that hypoxia and PI3K/AKT signaling are related to the mechanical force-induced pathophysiological processes 33 , with Rho/ROCK signaling and NF-kB being pathways known to act downstream of PI3K/AKT. Further, during mechanotransduction, mechano stretch is transmitted to the cytosol via integrin-actin cytoskeleton and the opening of mechanosensitive ion channels, and then to the nucleus via YAP/TAZ dephosphorylation and subsequent translocation into the nucleus to promote gene expression. YAP/TAZ responds to mechanical cues like ECM stiffness, cell density, substrate adhesion, regulating fibrosis progression. It also upregulates the expression of ECM-related genes through the TGFb/Smad signaling pathway, and plays a crucial role in EMT and promotes angiogenesis, glycolysis, and metabolism 34 . Studies have shown that when YAP/TAZ translocate to the nucleus, it binds TEA domains and induces transcription of genes that boost glycolysis, the pentose phosphate pathway and the TCA cycle 34 . This mediates cell proliferation and further increases inflammatory cytokines like IL1a, IL1b, IL6, and CCL2. This supports our finding that glycolysis, glucose metabolism, gluconeogenesis, cellular hexose transport are all significantly enriched pathways in early scar formation compared to late scar formation. The altered cell state of the iSCAR cells in late scar formation seems to be influenced by both mechano force and inflammatory effects within the model, as we show that LPA/autotaxin axis modulated by the autotaxin inhibitor seems to be only active during iSCAR-T or late stage scar formation. From our comparative study, we can look at the differentially expressed genes and significant pathways that can lead to this altered state within the iSCAR model. For example, the long-term presence of Rho/ROCK, PI3K/Akt, NF-kB, JAK2/STAT3 and inflammatory cytokines such as IL-6 and MCP-1 can stimulate adipocytes to produce ATX and LPA 35 . LPA signaling increases and this further increases the production of more cytokines in a feedforward cycle. We also see an upregulation of cellular senescence pathways during late scar formation vs early scar formation. This may be explained by NF-kB being essential in inducing SASP factors, especially by inducing an autocrine feedback through IL-6 and IL-8 36 . The chronic increment of production of inflammatory pathways and molecules can also induce SASP in these cells. Numerous studies have confirmed the involvement of HIF-1a/VEGF/hypoxia axis and elevated autotaxin/LPA levels in SSc patients. Previous studies have confirmed elevated serum levels of HIF1A in dSSc and lSSc patients 37 , induced transformation of endothelial cells by modulating the HIF1a/VEGF axis in SSc 38 , elevated expression of autotaxin and IL-6 in skin fibroblasts from SSc patients in a humanized mouse bleo skin model 39 . Furthermore, hypoxia-induced angiogenesis and cell migration, processes found in iSCAR-P treated with EX00015097, are known to be regulated by PI3K/Akt 40,41 . NF-kB may also have influenced the formation of inflammatory immune mediators and senescence observed in iSCAR-T treated with EX00015097 36 . It is worth noting that this study has identified molecular targets and biological pathways also found in other studies in SSc patients and in-vivo model systems. Conclusions Our study not only provides a research basis for characterizing iPSC derived cells to study SSc, but also opens up a new direction to value iPSC derived cells for drug screening tools for progressive fibrosis in SSc research. It should be noted, this study in mainly based on bioinformatics speculation, which needs to be confirmed by further basic experiments. Declarations Author Contributions Y.W contributed to data analysis and interpretation and manuscript preparation. S. N contributed to conception and design, data analysis and interpretation and manuscript preparation. M.D’A, R.P and H.L, contributed to data interpretation and analysis. K.F contributed to data acquisition. D.D and T.B contributed to data acquisition and manuscript review. F.L contributed to data interpretation and manuscript review. P.V contributed to conception and design, data acquisition, data interpretation, manuscript preparation and final review. Data Availability Statement All relevant data are within the paper and its Supporting Information files. Competing Interests None References Hachulla, E., & Launay, D. Diagnosis and classification of systemic sclerosis. Clinical reviews in allergy & immunology. 40, 78-83 (2011). O’Reilly, S. Innate immunity in systemic sclerosis pathogenesis. Clinical Science. 126, 329-337 (2014). Sargent, J. L., & Whitfield, M. L. Capturing the heterogeneity in systemic sclerosis with genome-wide expression profiling. Expert review of clinical immunology. 7, 463-473 (2011). Liu, C., Oikonomopoulos, A., Sayed, N., & Wu, J. C. Modeling human diseases with induced pluripotent stem cells: from 2D to 3D and beyond. Development. 145, dev156166 (2018). Vijayaraj, P. et al. Modeling progressive fibrosis with pluripotent stem cells identifies an anti-fibrotic small molecule. Cell reports. 29, 3488-3505 (2019). Xu, X. et al. 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Single-cell metabolic imaging reveals a SLC2A3-dependent glycolytic burst in motile endothelial cells. Nature metabolism. 3, 714-727 (2021). Tables Table 1. Early Scar Formation (iSCAR-P DMSO vs iSCAR-Ctrl DMSO) DEGs: Hypoxia-induced vascular remodeling IL-6 42 , VEGFA 43 , PDGFRA 4445 , MDK 46 , ANGPTL4 47,48 , NDRG1 49 , DDIT4 50 , ANGPTL2 51,52 Ion channel activation KCNH5, KCNN3, KCNJ6, KCNJ15, KCNK3 Cytoskeletal remodeling COL9A2, COL5A3, COL9A3, COL21A1, COL14A1, COL15A1, COL10A1, COL22A1, MMP11, MMP9, MMP13, MMP1, MMP8, ADAMTS9, ADAMTS10, LAMA4, LAMB3, MFAP2, MFAP4 Table 2. Late Scar Formation (iSCAR-T DMSO vs iSCAR-Ctrl DMSO) DEGs: Hypoxia-induced vascular remodeling PDGFD 53 , ANGPTL2 Ion channel activation KCNH5, KCNN3, KCNJ6, KCNJ15, KCNK3 Cytoskeletal remodeling COL9A2, COL5A3, COL9A3, COL21A1, COL14A1, COL15A1, COL8A2, MMP11, MMP9, MMP13, MMP1, MMP28, ADAMTS9, LAMA4, LAMA3, LAMA2, LAMB3, MFAP2, MFAP4 Cell senescence and positive regulation of programmed cell death DAPK2, GAL, HMOX1 54 , MMP9, NTSR1, BMF 55 , HOXA5, SFRP4 56 , NEURL1, TGFB3, NR4A1, G0S2, PTGIS, SOX4, BMP2, PHLDA1, CTSK, FOXO1, BCL2L11, PLA2R1, ACE, GBP2, CTSS, GRIK2, FOXL2 57 Table 3. Early Scar Formation (iSCAR-P DMSO vs iSCAR-T DMSO) DEGs: Hypoxia-induced vascular remodeling PGK1, NDRG1, BNIP3L, VEGFA, FAM162A, PDK1, HK2, AK4, VCAM1, ANGPTL4, DDIT4, BNIP3, ERO1A, HSPB6, MDK, WARS1 Metabolic reprogramming ALDOC, ENO2, HK2, PFKFB4, PGK1, SLC2A3 58 Table 4. Late Scar Formation (iSCAR-T DMSO vs iSCAR-P DMSO) DEGs: Cellular senescence H1-5, H2AX, H4C9, H2BC7, H2BC17, UBE2S Table 5. DEGs in Autotaxin Inhibitor iSCAR-T and iSCAR-P ECM COL1A1 (iSCAR-T), NR4A1 (both), A2M (iSCAR-T), LAMB3 (both), COL1A2 (iSCAR-T), COL6A3 (iSCAR-T), TGFB3 (iSCAR-T), HMCN1 (iSCAR-T) EndoMT/EMT SNAI1 (both), SOX4 (iSCAR-T), SOX9 (iSCAR-P), DUSP4 (both), HES1 (iSCAR-T), TNC (iSCAR-T) Table 6. DEGs in EX00015097 iSCAR-T and iSCAR-P ECM NR4A1 (both), COL6A3 (both), EMILIN1 (both) EndoMT/EMT SOX4 (both), HEY1 (both), HES1 (both), CDH11 (both), TNC (both), SNAI1 (both) Additional Declarations No competing interests reported. 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In each volcano plot, the downregulated and upregulated genes met the logFC \u0026lt; -1.5 or \u0026gt; 1.5 and adjusted p-value \u0026lt; 0.05 cutoff with Benjamini-Hochberg method for adjusted p-value. \u003cstrong\u003e(b)\u003c/strong\u003e Venn diagram for individual and shared DEGs from iSCAR-P DMSO vs iSCAR-Ctrl DMSO and iSCAR-T DMSO vs iSCAR-Ctrl DMSO. \u003cstrong\u003e(c)\u003c/strong\u003e Metascape GO analysis for iSCAR-P DMSO vs iSCAR-Ctrl DMSO and iSCAR-T DMSO vs iSCAR-Ctrl DMSO. \u003cstrong\u003e(d)\u003c/strong\u003e Metascape GO analysis for early vs late scar formation. \u003cstrong\u003e(e)\u003c/strong\u003e Circos plot for significant GO terms (p-value\u0026lt;0.05 with Benjamini-Hochberg method for adjusted p-value) from early vs late scar formation logFC values are based on the logFC values of the same genes from dSSc from the SSc compendium.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4546782/v1/245702f5a7ca5f6b7025e1c1.png"},{"id":59409835,"identity":"e37222f0-34d2-49cb-8970-a814a69227f2","added_by":"auto","created_at":"2024-07-01 12:21:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":422089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAutotaxin inhibitor in iSCAR-P vs iSCAR-T\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Dot plot for GO analysis of leading-edge genes from iSCAR-P or iSCAR-T enrichment in dSSc or lSSc. \u003cstrong\u003e(b)\u003c/strong\u003e GSEA rank plot for iSCAR-P or iSCAR-T Autotaxin inhibitor enrichment in either dSSc or lSSc. All GSEA were significant with positive NES scores and \u0026lt; 0.05 adjusted p-value. \u003cstrong\u003e(c)\u003c/strong\u003e UMAP for enrichment of iSCAR-P or iSCAR-T Autotaxin inhibitor in SSc or control single cell atlas. \u003cstrong\u003e(d)\u003c/strong\u003e Different LPA species detected by MS are decreased in a response to the concentration-dependent increase \u0026nbsp;to the Autotaxin inhibitor in the iSCAR-T model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4546782/v1/91d2882cdd738dcebd7a6115.png"},{"id":59410225,"identity":"6946a1dd-792f-4d17-95d4-74e035a4e7e3","added_by":"auto","created_at":"2024-07-01 12:29:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":446715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEX00015097 in iSCAR-P vs iSCAR-T\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Dot plot for GO analysis of leading-edge genes from iSCAR-P or iSCAR-T enrichment in dSSc or lSSc. \u003cstrong\u003e(b)\u003c/strong\u003eGSEA rank plot for iSCAR-P or iSCAR-T EX00015097 enrichment in either dSSc or lSSc. All GSEA were significant with positive NES scores and \u0026lt; 0.05 adjusted p-value. \u003cstrong\u003e(c)\u003c/strong\u003e UMAP for enrichment of iSCAR-P or iSCAR-T EX00015097 in SSc or control single cell atlas.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4546782/v1/c86a25b692be55e367545309.png"},{"id":67148984,"identity":"bcdac365-8618-486f-8f64-319dddea59cb","added_by":"auto","created_at":"2024-10-21 16:10:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3558987,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4546782/v1/c34a1a75-47a4-4c94-8764-0403f1a021d9.pdf"},{"id":59409841,"identity":"ec1732b2-5720-42e2-9c01-abc26e5046c9","added_by":"auto","created_at":"2024-07-01 12:21:08","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":363722,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureswithFigureLegends.pptx","url":"https://assets-eu.researchsquare.com/files/rs-4546782/v1/d1bb05cd4388e0916cd46353.pptx"},{"id":59409836,"identity":"65658e42-c2bc-4be9-8b9b-94c604cd4b24","added_by":"auto","created_at":"2024-07-01 12:21:07","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":545676,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstarct.png","url":"https://assets-eu.researchsquare.com/files/rs-4546782/v1/5079e090b0c00dc47af3e33a.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Transcriptomic Analysis Validates iPSC Derived In-Vitro Progressive Fibrosis Model As A Screening Tool For Drug Discovery and Development in SSc","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSystemic sclerosis (SSc) is a complex autoimmune disease involving vasculopathy, immune dysregulation, and systemic fibrosis. Affecting 240 patients per million in the US, SSc has a 30% mortality rate within 10 years of diagnosis. SSc is classified into limited cutaneous (lSSc) and diffuse cutaneous (dSSc) subtypes based on skin involvement\u003csup\u003e1\u003c/sup\u003e. \u0026nbsp;Both genetic and environmental factors contribute to SSc, with key regulators including pro-fibrotic cytokines such as\u0026nbsp;transforming growth factor-\u0026beta; (TGF\u0026beta;), IL-1, IL-6 and IL-17, angiogenic and growth factors driving fibrosis and collagen deposition\u003csup\u003e2\u003c/sup\u003e. Collagen accumulation can cause severe organ damage and failure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch on SSc has been hindered by disease heterogeneity, limited patient-derived biomaterials, and insufficient understanding of multiple pathogenic pathways and their interplay\u003csup\u003e3\u003c/sup\u003e. Induced pluripotent stem cells (iPSCs) differentiated into key disease-affected cells in vitro is a promising approach to address these biomaterial challenges\u003csup\u003e4\u003c/sup\u003e. A prior study generated a unique in-vitro human model using iPSC-derived heterogeneous mesenchymal cells, which mimicked progressive fibrosis and was amenable for drug screening to identify potential anti-fibrotic compounds\u003csup\u003e5\u003c/sup\u003e. Such a model also lends itself as an effective tool to model and study a complex disease such as SSc. We have recently reported on a novel pathway driven meta-analysis which identified 8 unique SSc subsets and 5 pathway modules that describe the patient subsets based on molecular function and cellular components\u003csup\u003e6\u003c/sup\u003e. This revealed a mesenchymal cell involvement among other cell types within a complex disease network\u003csup\u003e6\u003c/sup\u003e. In this study, we now describe a comparative\u0026nbsp;analysis of iPSC derived in vitro fibrosis model with SSc bulk RNA-seq compendium and single cell atlas.\u003c/p\u003e\n\u003cp\u003eOur in vitro iPSC model of self-perpetuating progressive fibrosis closely resembles human disease and can be characterized into early and late fibrotic disease states. \u0026nbsp;Because of the opportunity of early diagnosis of certain fibrotic diseases such as SSc\u003csup\u003e7\u003c/sup\u003e, this model provides a unique platform to study biological process involved in progress to later stage disease.\u0026nbsp;The model\u0026rsquo;s intrinsic inflammatory state and 3D hydrogel substrate contributes to a fibrotic phenotype and pathological effects associated with cell plasticity\u003csup\u003e7\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere we validate our iPSC-derived mesenchymal model for progressive fibrosis as an investigative tool to study the pathological cell state of fibroblasts,\u0026nbsp;which is the most important cell type of the systemic fibrosis element of SSc, as well as to investigate/validate known and novel anti-fibrotic compounds utilizing the endpoints of this model. We use a computational pipeline to analyze RNA-seq data from iPSC-derived mesenchymal cells in a 3D in-vitro system, comparing it to a compendium generated from published\u0026nbsp;RNAseq and single cell seq datasets from SSc patients\u003csup\u003e6\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eWork-flow\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments were performed in accordance to Boehringer Ingelheim\u0026rsquo;s compliance committee and relevant guidelines and regulations were followed. All the experimental protocols were approved by Boehringer Ingelheim\u0026rsquo;s licensing committee. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eiPSC from BJ skin fibroblasts were purchased from Genome Engineering and Stem Cell Center, Wahington University, St.Louis to generate the iSCAR model.\u003c/p\u003e\n\u003cp\u003eIn this iSCAR-SSc comparative study, we mapped gene sets from an iPSC-derived \u0026ldquo;iSCAR model\u0026rdquo; onto a gene level SSc compendium matrix to validate the utility of the iSCAR model as in-vitro antifibrotic screening tool in context of SSc (\u003cstrong\u003eFig. 1a and 1b\u003c/strong\u003e). The iSCAR model involves skin induced pluripotent stem cell (iPSC)-derived mesenchymal-like cells (iMCs) cultured on a stiff 12-kPa polyacrylamide hydrogel to model the complex phenotype found in progressive fibrotic diseases (\u003cstrong\u003eFig. 1a\u003c/strong\u003e) as previously published\u003csup\u003e5\u003c/sup\u003e. When cultured on the hydrogel, the iMCs develop a scar-like phenotype that mimics a wound-healing response involving a scarring phase\u003csup\u003e5\u003c/sup\u003e. The overall work flow consists of two major steps: A. Characterizing the iSCAR model in the context of early- and late- stage scar formation and B. Validating the utility of the model as a fibrotic in-vitro screening tool by testing both an anti-fibrotic compound with a known mechanism and a novel compound that has not been fully characterized but shows promising anti-fibrotic potential\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe conducted three iSCAR assays to mimic early and late-stage scarring. In the first (iSCAR-Ctrl), iMCs from Skin iPSCs (a gift from the Genome Engineering \u0026amp; iPSC Center at Washington University in St. Louis) were cultured on plastic for 48 hrs in the presence of either DMSO, 3 \u0026micro;M autotaxin inhibitor or 5 \u0026micro;M of a novel anti-fibrotic compound EX00015097 [AA5]\u003csup\u003e5\u003c/sup\u003e, to simulate \u0026ldquo;no scar\u0026rdquo; formation. The compound concentrations for analysis were selected at IC\u003csub\u003e80\u003c/sub\u003e concentrations in all the iSCAR assays, and each treatment was performed in triplicate. In the second (iSCAR-P), iMCs were cultured on 12kPa easy-coat hydrogels (Matrigen) coated with 0.1% gelatin (Stem cell technologies) for 48hrs in the presence of the above compounds to simulate \u0026ldquo;early scar\u0026rdquo; formation and preventative treatment. In the third (iSCAR-T), iMCs were cultured on hydrogels for 13 days after which compounds were added after scar establishment for 48 hrs to simulate \u0026ldquo;late scar\u0026rdquo; formation and therapeutic treatment. RNA was isolated at the end of each of the assays for sequencing. To validate the utility of iSCAR-P or iSCAR-T as an early or late in-vitro antifibrotic screening tool in context of SSc, we studied the enrichment of significant genes following treatment with the autotaxin inhibitor or EX00015097 within the SSc bulk RNA-seq compendium created from 12 publicly available sources\u003csup\u003e6\u0026nbsp;\u003c/sup\u003eand an SSc single cell atlas created from 3 publicly available sources. After validating that the autotaxin inhibitor or EX00015097 treated iSCAR gene sets were significantly and positively enriched in dSSc and lSSc, we studied the differential effect of the autotaxin inhibitor and EX00015097 within the iSCAR model (\u003cstrong\u003eFig. 1b\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eiSCAR data analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-seq reads from iSCAR experiments were mapped to human reference genome GRCh38 using RNA STAR\u003csup\u003e9\u003c/sup\u003e. The mapped reads were run through featureCounts\u003csup\u003e10\u003c/sup\u003e for read summarization. featureCounts returned a counts matrix, which was then subject to differential gene expression analysis in DESeq2 with design factor \u0026ldquo;~ 0 + Endpoint_Source_Treatment + Replicate_Number\u0026rdquo;\u003csup\u003e11\u003c/sup\u003e. In DESeq2, the following contrasts were made to study early and late scar formation: iSCAR-P DMSO vs iSCAR-Ctrl DMSO, iSCAR-T DMSO vs iSCAR-Ctrl DMSO, and iSCAR-P DMSO vs iSCAR-T DMSO. Contrasts were made for each of the 3 iSCAR experiment endpoints to study the antifibrotics: EX00015097 vs DMSO and Autotaxin inhibitor vs DMSO. The criteria for selection of differentially expressed genes significantly increased (\u0026ldquo;UP\u0026rdquo;) or decreased (\u0026ldquo;DOWN\u0026rdquo;) compared to DMSO control is p \u0026lt; 0.05 and logFC \u0026lt; -1.5/ logFC \u0026gt; 1.5. This returned 4 different groups (EX00015097 vs DMSO DOWN, EX00015097 vs DMSO UP, Autotaxin inhibitor vs DMSO DOWN, and Autotaxin inhibitor vs DMSO UP) for each iSCAR experiment. The selected genes in each group would comprise the iSCAR gene set list for that group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSSc study collection and the creation of the SSc bulk RNA-seq compendium matrix\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used 12 datasets for our analysis (GSE9285, GSE32413, GSE45485, GSE58095, GSE59785, GSE76885, EMEXP1214, GSE76807, GSE76806, GSE66321, GSE65405, and GSE130955). All the datasets are from microarray, with one exception of bulk RNA-seq data GSE130955. We focused on baseline SSc patients\u0026rsquo; skin biopsy samples and major SSc phenotypes. A \u0026lsquo;leave-one-out\u0026rsquo; summary was performed to balance the number of studies and shared genes. After excluding\u0026nbsp;GSE9285 dataset for optimal shared genes,\u0026nbsp;Combat method using sva\u003csup\u003e12\u003c/sup\u003e and quantile normalization using preprocessCore\u003csup\u003e13\u003c/sup\u003e were performed to remove batch effect and baseline differences across the samples. The complete gene level compendium matrix contained 13616 genes x 380 samples\u0026nbsp;from 239 SSc samples and 141 controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe SSc compendium matrix\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SSc bulk compendium (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e), which contains 224 dSSc, 15 lSSc, and 141 healthy control samples from 12 publicly available sources, was ranked in terms of gene expression level for dSSc and lSSc versus control respectively using LIMMA\u003csup\u003e14\u003c/sup\u003e\u0026nbsp; (\u003cstrong\u003eFig. 1b\u003c/strong\u003e). For each pairwise contrast, LIMMA generated a table of logFC values and adjusted p-values for all genes in the compendium matrix. Positive/negative logFC values correlate to up/down regulation relative to control in both the dSSc-control and lSSc-control contrast.\u003c/p\u003e\n\u003cp\u003eFrom LIMMA, we obtained a total of 300 upregulated and significant DEGs (\u0026lt; 0.05 adjusted p value and \u0026gt; 0.5 logFC) in dSSc and 144 upregulated and significant DEGs (\u0026lt; 0.05 adjusted p value and \u0026gt; 0.5 logFC) in lSSc. 115 DEGs were shared between dSSc and lSSc. Further gene ontology pathway analysis, utilizing DEGs, for both dSSc (300 DEGs) and lSSc (144 DEGs) shows that genes differentially expressed in each SSc disease phenotype yielded many similar biologically relevant pathways such as \u0026lsquo;ECM organization\u0026rsquo;, \u0026lsquo;Negative regulation of immune processes\u0026rsquo;, \u0026lsquo;inflammation and wound healing\u0026rsquo;, \u0026lsquo;cell matrix/cell-cell adhesion\u0026rsquo;, and others (\u003cstrong\u003eSupplementary Fig. 1a and 1b\u003c/strong\u003e). Many of these genes are verified in the dSSc and lSSc phenotypes and are associated with disease severity, as well as MRSS scores and SSc relevant biomarkers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLIMMA to GSEA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to assess the distribution of genes in both dSSc-control and lSSc-control, GSEA analysis\u003csup\u003e15\u003c/sup\u003e was subsequently performed, in which all genes from each contrast were ranked by their logFC values, from the most positive to the most negative. This ranked list of genes along with each pre-defined iSCAR gene set were used to obtain the enrichment score (ES), the statistical significance of ES, and lead genes associated with the enrichment of each iSCAR gene set in either dSSc vs control or lSSc vs control contrast.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGSEA to pathway analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each iSCAR experiment (iSCAR-Ctrl, iSCAR-P, and iSCAR-T), there were 4 pre-defined gene sets (2 antifibrotic vs DMSO control treatment groups x UP or DOWN), each mapped to either dSSc vs control or lSSc vs control. Only iSCAR gene sets that contained genes downregulated with antifibrotic treatment compared to control (\u0026lsquo;DOWN\u0026rsquo;) and had a positive normalized enrichment score with a \u0026lt; 0.05 adj. p-value in the GSEA analysis were kept for subsequent analysis. Pathway enrichment analysis was performed on genes from selected iSCAR gene sets. For pathway analysis, org.Hs.eg.db was used with gene sets from all three different domains in gene ontology (GO) including: biological processes (BP), molecular functions (MF), and cellular components (CC). Comparative pathway analysis was performed using the \u0026lsquo;enrichGO\u0026rsquo; and \u0026lsquo;compareCluster\u0026rsquo; functions of clusterProfiler\u003csup\u003e16\u003c/sup\u003e with a \u0026lt;0.05 p-value using the Benjamini-Hochberg method for adjusted P value. Gene sets were also pasted into Metascape\u003csup\u003e17\u003c/sup\u003e for enrichment analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSingle cell atlas and module score\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle cell data from 3 studies (GSE195452, GSE209635, and GSE138669) were used to create a comprehensive single cell atlas presenting healthy or SSc skin tissues (\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e). Together, they comprise 120 SSc samples and 64 healthy control samples. Each study was normalized in scanpy.pp.normalize_total with a 1e4 target_sum setting and sc.pp.log1p with all default parameters. After initial normalization, \u0026ldquo;low quality cells\u0026rdquo; which include cells with low or high gene count and cells with high percent mitochondria were subsequently removed. We then used scVI\u003csup\u003e18\u003c/sup\u003e to merge and batch correct technical differences across different studies and samples, while keeping biological variations intact. scVI integrated single cell atlas was then subject to Leiden clustering\u003csup\u003e19\u003c/sup\u003e, which identified communities of cells that tend to cluster together. We identified relevant cell types (epithelial cells, endothelial cells, fibroblast cells, and myeloid cells) in the integrated atlas based on the gene rank of each cluster and preliminary knowledge of marker genes of different cell types. We subsequently split the atlas into SSc and control atlas. We then, using the expression values of the genes included in each iSCAR gene signature taken from the respective iSCAR experiment, scored each cell in the SSc or control atlas using the \u0026lsquo;AddModuleScore\u0026rsquo; Seurat function\u003csup\u003e20\u003c/sup\u003e in order to identify cell populations that highly express the genes in each iSCAR gene set.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLPA measurement\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupernatants from iSCAR-T Autotaxin inhibitor treated cells were collected and LPA-species were quantitated via liquid chromatography based mass spectrometry according to a published protocol\u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMultiplex analysis of cytokines\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The Milliplex human cytokine/chemokine Panel IV, was used per manufacturer\u0026rsquo;s instructions. iMCs seeded either on plastic or hydrogels were serum starved for 24 hours, and the supernatant collected for the assay. \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEarly vs late scar formation in iPSC model\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a previous study, iPSC-derived iSCAR cells formed scar-like aggregates on stiff 13-kPa hydrogels\u003csup\u003e5\u003c/sup\u003e. \u0026nbsp;We confirmed iMC cells\u0026rsquo; intrinsic inflammatory nature, and cytokine/chemokine distribution was similar on plastic and on hydrogel (\u003cstrong\u003eSupplementary Fig. 2\u003c/strong\u003e).\u0026nbsp;We hypothesize that the intrinsic inflammatory nature of the iMC along with mechano-forces may influence the culture conditions of the iMC in hydrogel as it applies to biological processes associated with \u0026lsquo;early\u0026rsquo; and \u0026lsquo;late\u0026rsquo; scar formation. DESeq analysis comparing DMSO-treated control from iSCAR-Ctrl and from iSCAR-P or iSCAR-T revealed 204 DEGs unique to early scar formation, 164 DEGs unique to late scar formation, and 332 shared DEGs (\u003cstrong\u003eFig. 2a, 2b, and Supplementary Table 3\u003c/strong\u003e). Processes present in both stages include vascular remodeling, cytoskeletal re-organization, and ion channel activation (\u003cstrong\u003eTable 1 and Table 2\u003c/strong\u003e). Hypoxia-induced vascular remodeling more prominent in early scar formation and cell senescence plus positive regulation of programmed cell death more prominent in late scar formation (\u003cstrong\u003eTable 1, Table 2, and Fig. 2c\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eUpregulated signaling pathways/genes unique to early scar formation compared to DMSO-treated control from iSCAR-Ctrl involve hypoxia-induced pathogenesis such as HIF-1a, TGF\u0026beta;, PKC/ERK, PI3K/Akt, NF-kB, IL-6 [7]. These pathways can affect fibrotic phenotypes, vascular remodeling, EMT, and ECM. We found genes related to vascular remodeling, ion channel activation, and cytoskeletal remodeling, indicative of mechano-responses to mechano-transduction (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eWe further directly assessed differential biology in early and late scar formation by comparing DMSO-treated iSCAR-P and iSCAR-T. DESeq comparison revealed 51 DEGs upregulated in early scar formation and 53 in late scar formation consistent with the prior analysis\u0026nbsp;(\u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e). Early scar formation genes showed similar biological processes as iSCAR-P DMSO-treated control, such as\u0026nbsp;response to hypoxia (logP=-16.6504), vasculature development (logP=-4.53389), and glycolysis and gluconeogenesis (logP=-7.93662) (\u003cstrong\u003eFig. 2d, Fig. 2e, and Table 3\u003c/strong\u003e).\u0026nbsp;Similarly, late scar formation analysis revealed a greater role of senescence, possibly due to epigenic modifications, as indicated by increased genes in cell cycle checkpoints (logP=-8.7002), cellular senescence (logP=-5.91767), and senescence-associated secretory phenotype (logP=-5.82336)\u0026nbsp;(\u003cstrong\u003eFig. 2d, Fig. 2e, and Table 4\u003c/strong\u003e).\u0026nbsp;This suggests long-term culture in a stiff matrix may result in a senescence phenotype influenced by mechano-sensing.\u003c/p\u003e\n\u003cp\u003eAdditonal GSEA analysis showed significant enrichment of dSSc genes in both early (adj. p-value=4.37E-15) and late scar formation (adj. p-value=1.06E-14), indicating a dSSc phenotype capture. Enrichment analysis revealed hypoxia-induced angiogenesis in early scar formation and cell senescence in late scar formation (\u003cstrong\u003eSupplementary Table 5\u003c/strong\u003e). These pathways are relevant to SSc disease progression, as cellular senescence plays a pathogenic role in fibrosis and senescence-associated secretory phenotype (SASP) can increase inflammatory secretory proteins like MMPs, TGFB1 and interleukins in fibrotic tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEffects of Autotaxin inhibitor\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the context of SSc fibrotic disease, we validated the iSCAR model\u0026rsquo;s ability to differentiate treatment responses in early vs late scar phenotypes using an inhibitor of autotaxin. \u0026nbsp;Autotaxin is an enzyme responsible for the production of lysophospatidic acid (LPA), the downstream signaling of which mediates responses to tissue injury and has been implicated in the pathogenesis of fibrotic conditions such as SSc.\u003c/p\u003e\n\u003cp\u003eWe examined the autotaxin inhibitor iSCAR-P (100 genes) and iSCAR-T (121 genes) gene sets, finding ECM, EndoMT, and EMT-related genes enriched in the both gene sets, but with a higher number of genes within the iSCAR-T set \u003cstrong\u003e(Table 5)\u003c/strong\u003e. GO enrichment analysis showed fibrosis-related terms for iSCAR-T (e.g. \u0026nbsp;ECM structural constituent, growth factor binding and type I TGFb receptor binding), but none significant for iSCAR-P (p-value = 0.05; Benjamini-Hochberg method for adjusted P value) (\u003cstrong\u003eFig. 3a\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe gene changes observed in the autotaxin inhibitor iSCAR-P and iSCAR-T models were compared to the SSc single cell compendium, and showed higher enrichment in macrophage and epithelial populations for iSCAR-P, and higher enrichment in fibroblast, smooth muscle, and pericyte populations for iSCAR-T (\u003cstrong\u003eFig. 3c\u003c/strong\u003e). This agrees with the enrichment of autotaxin inhibitor iSCAR-P or iSCAR-T gene set in the SSc bulk RNA-seq result, since only autotaxin inhibitor iSCAR-T gene set is significantly and positively enriched in dSSc (NES = 2.1465 and FDR q-val = 0) and lSSc (NES = 2.1593 and FDR q-val = 8.88E-5) (\u003cstrong\u003eFig. 3b\u003c/strong\u003e). SSc bulk RNA-seq results agree with iSCAR-T gene set enrichment in dSSc and lSSc, showing significant GO terms like ECM and growth factor binding. The iSCAR-P gene set had few significant GO terms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince autotaxin functions upstream of TGF\u0026beta;, the addition of an autotaxin inhibitor to an iSCAR assay should downregulate processes downstream of both LPA and TGF\u0026beta;. Consistently, autotaxin inhibitor treatment reduced LPA species 18:1 and 16:1 in our iSCAR-T in-vitro model\u0026nbsp;(\u003cstrong\u003eFig. 3d\u003c/strong\u003e).\u0026nbsp;Additionally, lead genes from iSCAR-T (autotaxin inhibitor) enrichment in dSSc are mostly downstream of LPA-receptor activation (\u003cstrong\u003eSupplementary Table 6\u003c/strong\u003e), influencing fibroblast differentiation (e.g. TNC\u003csup\u003e22\u003c/sup\u003e), Smad2/3 activation (e.g. MMP11\u003csup\u003e23\u003c/sup\u003e), ECM remodeling (e.g ANGPTL2\u003csup\u003e24\u003c/sup\u003e), and collagen production through various pathways\u003csup\u003e25,26,27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOverall, the data aligns with the LPA/autotaxin axis-influenced phenotype, showing autotaxin inhibitor\u0026rsquo;s modulation of fibrosis-related genes through long-term mechanotransduction and metabolic effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEffects of EX00015097\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe screened the antifibrotic compound EX00015097 (AA5, unknown mechanism)\u003csup\u003e5\u003c/sup\u003e in iSCAR-T and iSCAR-P models, examining 360 and 420 DEGs respectively. Both models shared ECM-related and transition genes. Comparative GO analysis revealed significant terms like ECM structural constituent, growth factor binding shared in both models and specific activities for each model such as\u0026nbsp;transmembrane receptor protein kinase activity in iSCAR-P, and DNA-binding transcription activator activity in iSCAR-T (p-value = 0.05; Benjamini-Hochberg method for adjusted P value) (\u003cstrong\u003eFig. 4a, Table 6\u003c/strong\u003e), potentially due to intrinsic effects of hydrogel.\u003c/p\u003e\n\u003cp\u003eEX00015097 iSCAR-P and iSCAR-T gene sets show similar enrichment in fibroblast, smooth muscle, and pericyte cell populations (\u003cstrong\u003eFig. 4c\u003c/strong\u003e), and in SSc bulk RNA-seq where both EX00015097 iSCAR-P and iSCAR-T gene sets are significantly and positively enriched in dSSc (Prevention: NES=1.8449, FDR q-val=0.004015; Therapeutic: NES=1.8685, FDR q-val=0.00169) and lSSc (Prevention: NES=2.048, FDR q-val=0.000712; Therapeutic: NES=1.8857, FDR q-val=0.001751) (\u003cstrong\u003eFig. 4b\u003c/strong\u003e). Lead genes from each set highlight different processes: iSCAR-P emphasizes hypoxia-induced angiogenesis and cell migration, while iSCAR-T focuses on immune regulation and cell death/senescence (\u003cstrong\u003eSupplementary Table 7\u003c/strong\u003e). UNC5B, a lead gene from EX00015097 iSCAR-P and iSCAR-T enriched in dSSc, has been suggested to promote vascular endothelial cell senescence via ROS-mediated P53 pathway\u003csup\u003e28\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe lack of relevant mouse models for studying progressive fibrotic diseases is a challenge, mainly due to the inability of current models to replicate the diverse cell characteristics and plasticity observed in these diseases. This study aims to validate the use of iPSC-derived mesenchymal cells as an in-vitro system to study the progressive nature of diseases like SSc. Unlike other models, these cells can mimic disease progression without the need for supplementation of additional profibrotic modulators. They can also be cultured for extended periods, allowing for the perpetuation of a significant feed-forward fibrotic response.\u003c/p\u003e\n\u003cp\u003eWe have demonstrated that the iSCAR-SSc comparative study validates the utility of the iSCAR model as a drug screening tool that can evaluate anti-fibrotic candidates within the context of early and late stage disease in fibroblasts within SSc. A key feature of the iSCAR is the inherent cellular plasticity of the model which lends itself to recapitulate a progressive disease with multiple cell types that drive the fibrotic phenotype. Our pipeline\u0026rsquo;s analysis of the iSCAR model within the single cell compendium demonstrates this heterogeneity.\u003c/p\u003e\n\u003cp\u003eOur comparative study suggests that the interplay of hydrogel mechano-forces and inflammatory nature of the iMC/iSCAR cells could drive scar formation. The iMC\u0026rsquo;s inherent inflammatory characteristics and force-induced stretch may activate biological and mechanistic pathways influencing early scar formation, leading to late scar formation. This is evidenced by upregulated inflammatory cytokines like IL-6 and MCP-1 in early iSCAR models, driving differential gene expression in fibrotic stages. These cytokines may stimulate adhesion molecules like ICAM1, VCAM-1, and E-selectin\u003csup\u003e29\u003c/sup\u003e, leading to cellular stiffening and enhanced RhoA signaling\u003csup\u003e30\u003c/sup\u003e, mediating changes in cell motility, adhesion, and proliferation. STAT3, which is responsible for the endothelial expression of ICAM-1, is also a well-known transcriptional activator for VEGF and HIF1a and plays a key role in controlling angiogenesis\u003csup\u003e31\u003c/sup\u003e. ICAM, VCAM-1, Hif1a, VEGF are genes and pathways significant enriched within the early iSCAR model. This is supported by the enrichment of angiogenesis and VEGFR pathways in iSCAR-P, potentially contributing to later disease progression.\u003c/p\u003e\n\u003cp\u003eThis mechano force-induced stretch of the iSCAR system may also force a triggered opening of mechanosensitive ion channels which may lead to increased cytosolic Ca2+ and K+ levels that promotes the expression and activation of further inflammation, cytoskeletal re-arrangements and senescence. For example, we identified a significant upregulation of genes (eg. KCNJ15 and KCNJ6) that encode inward-rectifier type potassium channels in cells on hydrogel. The biophysical cues from stretch applied to cells on hydrogel are also transmitted to the cells via integrin-actin cytoskeleton, which activates the PI3K-Akt pathway, which drives both NF-kB and HIF1\u003csup\u003e32\u003c/sup\u003e. Active NF-kB and HIF1a can then translocate to the nucleus to further drive metabolic and inflammatory gene expression. Studies have shown that there is positive feedback between these inflammatory molecules/pathways and mechano force-induced stretch: the long-term presence of NF-kB and inflammatory molecules like IL-6 induce the expression of ICAM-1, which coupled with force-induced stretch can reinforce the same pathways/inflammatory molecules. These concepts are supported by evidence within our comparative study in the iSCAR model showing that hypoxia and PI3K/AKT signaling are related to the mechanical force-induced pathophysiological processes\u003csup\u003e33\u003c/sup\u003e, with Rho/ROCK signaling and NF-kB being pathways known to act downstream of PI3K/AKT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther, during mechanotransduction, mechano stretch is transmitted to the cytosol via integrin-actin cytoskeleton and the opening of mechanosensitive ion channels, and then to the nucleus via YAP/TAZ dephosphorylation and subsequent translocation into the nucleus to promote gene expression. YAP/TAZ responds to mechanical cues like ECM stiffness, cell density, substrate adhesion, regulating fibrosis progression. It also upregulates the expression of ECM-related genes through the TGFb/Smad signaling pathway, and plays a crucial role in EMT and promotes angiogenesis, glycolysis, and metabolism\u003csup\u003e34\u003c/sup\u003e. Studies have shown that when YAP/TAZ translocate to the nucleus, it\u0026nbsp;binds TEA domains\u0026nbsp;and induces transcription of genes that boost glycolysis, the pentose phosphate pathway and the TCA cycle\u003csup\u003e34\u003c/sup\u003e. This mediates cell proliferation and further increases inflammatory cytokines like IL1a, IL1b, IL6, and CCL2. This supports our finding that glycolysis, glucose metabolism, gluconeogenesis, cellular hexose transport are all significantly enriched pathways in early scar formation compared to late scar formation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe altered cell state of the iSCAR cells in late scar formation seems to be influenced by both mechano force and inflammatory effects within the model, as we show that LPA/autotaxin axis modulated by the autotaxin inhibitor seems to be only active during iSCAR-T or late stage scar formation. From our comparative study, we can look at the differentially expressed genes and significant pathways that can lead to this altered state within the iSCAR model. For example, the long-term presence of Rho/ROCK, PI3K/Akt, NF-kB, JAK2/STAT3 and inflammatory cytokines such as IL-6 and MCP-1 can stimulate adipocytes to produce ATX and LPA\u003csup\u003e35\u003c/sup\u003e. LPA signaling increases and this further increases the production of more cytokines in a feedforward cycle. We also see an upregulation of cellular senescence pathways during late scar formation vs early scar formation. This may be explained by NF-kB being essential in inducing SASP factors, especially by inducing an autocrine feedback through IL-6 and IL-8\u003csup\u003e36\u003c/sup\u003e. The chronic increment of production of inflammatory pathways and molecules can also induce SASP in these cells. Numerous studies have confirmed the involvement of HIF-1a/VEGF/hypoxia axis and elevated autotaxin/LPA levels in SSc patients. Previous studies have confirmed elevated serum levels of HIF1A in dSSc and lSSc patients\u003csup\u003e37\u003c/sup\u003e, induced transformation of endothelial cells by modulating the HIF1a/VEGF axis in SSc\u003csup\u003e38\u003c/sup\u003e, elevated expression of autotaxin and IL-6 in skin fibroblasts from SSc patients in a humanized mouse bleo skin model\u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, hypoxia-induced angiogenesis and cell migration, processes found in iSCAR-P treated with EX00015097, are known to be regulated by PI3K/Akt\u003csup\u003e40,41\u003c/sup\u003e. NF-kB may also have influenced the formation of inflammatory immune mediators and senescence observed in iSCAR-T treated with EX00015097\u003csup\u003e36\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is worth noting that this study has identified molecular targets and biological pathways also found in other studies in SSc patients and in-vivo model systems.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study not only provides a research basis for characterizing iPSC derived cells to study SSc, but also opens up a new direction to value iPSC derived cells for drug screening tools for progressive fibrosis in SSc research. It should be noted, this study in mainly based on bioinformatics speculation, which needs to be confirmed by further basic experiments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.W contributed to data analysis and interpretation and manuscript preparation. S. N contributed to conception and design, data analysis and interpretation and manuscript preparation. M.D\u0026rsquo;A, R.P and \u0026nbsp;H.L, contributed to data interpretation and analysis. K.F \u0026nbsp;contributed to data acquisition. D.D and T.B contributed to data acquisition and manuscript review. F.L contributed to data interpretation and manuscript review. P.V contributed to conception and design, data acquisition, data interpretation, manuscript preparation and final review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are within the paper and its Supporting Information files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHachulla, E., \u0026amp; Launay, D. Diagnosis and classification of systemic sclerosis. Clinical reviews in allergy \u0026amp; immunology. 40, 78-83 (2011).\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Reilly, S. Innate immunity in systemic sclerosis pathogenesis. Clinical Science. 126, 329-337 (2014).\u003c/li\u003e\n\u003cli\u003eSargent, J. L., \u0026amp; Whitfield, M. L. 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Angiopoietin‐like protein 2 renders colorectal cancer cells resistant to chemotherapy by activating spleen tyrosine kinase\u0026ndash;phosphoinositide 3‐kinase‐dependent anti‐apoptotic signaling. Cancer Science. 105, 1550-1559 (2014).\u003c/li\u003e\n\u003cli\u003ePeng, G. et al. The HIF1\u0026alpha;-PDGFD-PDGFR\u0026alpha; axis controls glioblastoma growth at normoxia/mild-hypoxia and confers sensitivity to targeted therapy by echinomycin. Journal of Experimental \u0026amp; Clinical Cancer Research. 40, 1-16 (2021).\u003c/li\u003e\n\u003cli\u003eLimbad, C. et al. Senolysis induced by 25-hydroxycholesterol targets CRYAB in multiple cell types. Iscience. 25 (2022).\u003c/li\u003e\n\u003cli\u003eLin, X. et al. lncRNA-ES3/miR-34c-5p/BMF axis is involved in regulating high-glucose-induced calcification/senescence of VSMCs. Aging (Albany NY). 11, 523 (2019).\u003c/li\u003e\n\u003cli\u003eTakaya, K., Asou, T., \u0026amp; Kishi, K. Downregulation of senescence-associated secretory phenotype by knockdown of secreted frizzled-related protein 4 contributes to the prevention of skin aging. Aging (Albany NY). 14, 8167 (2022).\u003c/li\u003e\n\u003cli\u003eLiu, X. L. et al. FOXL2 suppresses proliferation, invasion and promotes apoptosis of cervical cancer cells. International journal of clinical and experimental pathology. 7, 1534 (2014).\u003c/li\u003e\n\u003cli\u003eWu, D. et al. Single-cell metabolic imaging reveals a SLC2A3-dependent glycolytic burst in motile endothelial cells. Nature metabolism. 3, 714-727 (2021).\u003c/li\u003e\n\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Early Scar Formation (iSCAR-P DMSO vs iSCAR-Ctrl DMSO) DEGs:\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eHypoxia-induced vascular remodeling\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eIL-6\u003csup\u003e42\u003c/sup\u003e, VEGFA\u003csup\u003e43\u003c/sup\u003e, PDGFRA\u003csup\u003e4445\u003c/sup\u003e, MDK\u003csup\u003e46\u003c/sup\u003e, ANGPTL4\u003csup\u003e47,48\u003c/sup\u003e, NDRG1\u003csup\u003e49\u003c/sup\u003e, DDIT4\u003csup\u003e50\u003c/sup\u003e, ANGPTL2\u003csup\u003e51,52\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eIon channel activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eKCNH5, KCNN3, KCNJ6, KCNJ15, KCNK3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eCytoskeletal remodeling\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCOL9A2, COL5A3, COL9A3, COL21A1, COL14A1, COL15A1, COL10A1, COL22A1, MMP11, MMP9, MMP13, MMP1, MMP8, ADAMTS9, ADAMTS10, LAMA4, LAMB3, MFAP2, MFAP4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Late Scar Formation (iSCAR-T DMSO vs iSCAR-Ctrl DMSO) DEGs:\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eHypoxia-induced vascular remodeling\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003ePDGFD\u003csup\u003e53\u003c/sup\u003e, ANGPTL2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eIon channel activation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eKCNH5, KCNN3, KCNJ6, KCNJ15, KCNK3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eCytoskeletal remodeling\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCOL9A2, COL5A3, COL9A3, COL21A1, COL14A1, COL15A1, COL8A2, MMP11, MMP9, MMP13, MMP1, MMP28, ADAMTS9, LAMA4, LAMA3, LAMA2, LAMB3, MFAP2, MFAP4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eCell senescence and positive regulation of programmed cell death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eDAPK2, GAL, HMOX1\u003csup\u003e54\u003c/sup\u003e, MMP9, NTSR1, BMF\u003csup\u003e55\u003c/sup\u003e, HOXA5, SFRP4\u003csup\u003e56\u003c/sup\u003e, NEURL1, TGFB3, NR4A1, G0S2, PTGIS, SOX4, BMP2, PHLDA1, CTSK, FOXO1, BCL2L11, PLA2R1, ACE, GBP2, CTSS, GRIK2, FOXL2\u003csup\u003e57\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Early Scar Formation (iSCAR-P DMSO vs iSCAR-T DMSO) DEGs:\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eHypoxia-induced vascular remodeling\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003ePGK1, NDRG1, BNIP3L, VEGFA, FAM162A, PDK1, HK2, AK4, VCAM1, ANGPTL4, DDIT4, BNIP3, ERO1A, HSPB6, MDK, WARS1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eMetabolic reprogramming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eALDOC, ENO2, HK2, PFKFB4, PGK1, SLC2A3\u003csup\u003e58\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Late Scar Formation (iSCAR-T DMSO vs iSCAR-P DMSO) DEGs:\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eCellular senescence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;H1-5, H2AX, H4C9, H2BC7, H2BC17, UBE2S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. DEGs in Autotaxin Inhibitor iSCAR-T and iSCAR-P\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eECM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCOL1A1 (iSCAR-T), NR4A1 (both), A2M (iSCAR-T), LAMB3 (both), COL1A2 (iSCAR-T), COL6A3 (iSCAR-T), TGFB3 (iSCAR-T), HMCN1 (iSCAR-T)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eEndoMT/EMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eSNAI1 (both), SOX4 (iSCAR-T), SOX9 (iSCAR-P), DUSP4 (both), HES1 (iSCAR-T), TNC (iSCAR-T)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. DEGs in EX00015097 iSCAR-T and iSCAR-P\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eECM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eNR4A1 (both), COL6A3 (both), EMILIN1 (both)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.192307692307693%\" valign=\"top\"\u003e\n \u003cp\u003eEndoMT/EMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"79.8076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eSOX4 (both), HEY1 (both), HES1 (both), CDH11 (both), TNC (both), SNAI1 (both)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"In vitro Models, Systemic Sclerosis, Progressive Fibrosis, Cell Plasticity, Gene Expression Analysis","lastPublishedDoi":"10.21203/rs.3.rs-4546782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4546782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSystemic sclerosis (SSc) is an autoimmune disease characterized by vasculopathy, immune dysregulation, and systemic fibrosis. Research on SSc has been hindered largely by lack of relevant models to study the progressive nature of the disease and to recapitulate the cell plasticity that is observed in this disease context. Generation of models for fibrotic disease using pluripotent stem cells is important for recapitulating the heterogeneity of the fibrotic tissue and are a potential platform for screening anti-fibrotic drugs. We previously reported a novel in-vitro model for fibrosis using induced pluripotent stem cell-derived mesenchymal cells (iSCAR). Here we report the generation of a \u0026ldquo;scar-like phenotype\u0026rdquo; when iPSC derived mesenchymal cells are cultured on hydrogel that mimicks a wound healing/scarring response (iSCAR). First, we performed RNA sequencing (RNA-seq) based transcriptome profiling of iSCAR culture at 48 hr and 13 days to characterize early and late-stage scarring phenotypes. The next generation RNA sequencing (RNA-seq) of these iSCAR culture at different timepoints detected expression 92% of early \u0026ldquo;scar associated\u0026rdquo; genes and 85% late \u0026ldquo;scar associated\u0026rdquo; genes, respectively. Comparative transcriptomic analysis of a gene level SSc compendium matrix to the iSCAR wound associated model revealed genes common in both data sets. Early scar formation genes showed biological processes of hypoxia (27.5%), vascular development (13.7%) and glycolysis (27.5), while late scar formation showed genes associated with senescence (22.6%). Next we show the effects of two different antifibrotic compounds to validate the utility of the model as an screening tool to study early and late stage fibrosis. An autotaxin inhibitor was used to validate the iSCAR late stage fibrotic model (iSCAR-T) and an antifibrotic tool screening compound of unknown mechanism (EX00015097) was used to study and validate both early (iSCAR-P) and late stage (iSCAR-T) fibrosis in the iSCAR model.\u003c/p\u003e","manuscriptTitle":"Comparative Transcriptomic Analysis Validates iPSC Derived In-Vitro Progressive Fibrosis Model As A Screening Tool For Drug Discovery and Development in SSc","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-01 12:21:02","doi":"10.21203/rs.3.rs-4546782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-24T04:55:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-24T02:23:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53857961362998533170753673962983947159","date":"2024-07-15T13:46:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-27T16:01:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102283016173889280738575466212736062231","date":"2024-06-25T09:32:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152970213829140458900536585495916681827","date":"2024-06-17T14:50:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-17T14:30:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-17T12:33:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-17T11:50:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-13T13:29:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-07T14:41:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"737337d5-4800-4dd9-9dae-5f7450bcd929","owner":[],"postedDate":"July 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33824773,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":33824774,"name":"Biological sciences/Drug discovery"}],"tags":[],"updatedAt":"2024-10-21T16:02:12+00:00","versionOfRecord":{"articleIdentity":"rs-4546782","link":"https://doi.org/10.1038/s41598-024-74610-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-18 15:57:35","publishedOnDateReadable":"October 18th, 2024"},"versionCreatedAt":"2024-07-01 12:21:02","video":"","vorDoi":"10.1038/s41598-024-74610-2","vorDoiUrl":"https://doi.org/10.1038/s41598-024-74610-2","workflowStages":[]},"version":"v1","identity":"rs-4546782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4546782","identity":"rs-4546782","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}