Myeloid-derived immunosuppression of Chimeric Antigen Receptor T cells in the neuronal microenvironment of Glioblastoma

Myeloid-derived immunosuppression of Chimeric Antigen Receptor T cells in the neuronal microenvironment of Glioblastoma | 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 Research Article Myeloid-derived immunosuppression of Chimeric Antigen Receptor T cells in the neuronal microenvironment of Glioblastoma Junyi Zhang, Jasmin Ehr, Thomas Look, Jasim Kada Benotmane, Nicolas Neidert, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7240692/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in BMC Medicine → Version 1 posted 9 You are reading this latest preprint version Abstract Background Chimeric antigen receptor (CAR)-T cell therapy remains largely ineffective in glioblastoma (GB), where a highly immunosuppressive microenvironment and tumor heterogeneity impair therapeutic durability. Methods Using a human neocortical brain slice model that preserves the complex GB microenvironment, we profiled interactions between natural killer group 2D ( NKG2D ) CAR-T cells and tumor ecosystems via PIC-seq, spatial transcriptomics, and gene regulatory network reconstruction. Results CAR-T cells initially suppressed tumor growth but rapidly transitioned to a dysfunctional state marked by exhaustion-associated transcriptional programs. This shift was driven by signaling interactions between CAR-T cells and myeloid cells. Tumor-associated macrophages displayed enhanced phagocytic activity and spatially colocalize with mesenchymal-like GB cells within hypoxic regions. Our gene regulatory network analysis identified MAF and BACH2 as key transcriptional regulators, with MAF promoting CAR CD8 exhaustion and BACH2 preserving CD8 T cells effector function. In silico perturbation confirmed the reciprocal effect of MAF and BACH2 on CD8⁺ T cell fate. Conclusions These findings reveal mechanisms of rapid CAR-T cell dysfunction in GB and identify actionable targets for engineering more durable cellular therapies. Chimeric Antigen Receptor T cell exhaustion Immunosuppression Glioblastoma Brain slices Transcriptional regulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Chimeric antigen receptor (CAR)-T cell therapy has shown remarkable success in certain hematologic malignancies, however, this success has not yet been translated to most solid tumors including glioblastoma (GB) 1 – 6 . GB, characterized by its inherent genetic and cellular heterogeneity as well as its immunosuppressive microenvironment, so far presents an invincible barrier to the efficacy of CAR-T cell therapy 7 . In contrast to its successes in other malignancies, the limited efficacy of CAR-T cell therapy in GB has raised critical questions regarding its adaptability to the unique challenges posed by the tumor microenvironment (TME) in GB 8 . This immunosuppressive TME dampens the therapeutic potential of CAR-T cells, undermining their ability to mount an effective immune response against the tumor. In GB, T cells transit into a progressive dysfunctional state 9 . This state is characterized by the upregulation of inhibitory receptors, such as PD-1, TIM-3, and LAG-3, which impair the ability of exhausted T cells to recognize and eliminate target cells 10 . Immunotherapeutic approaches, such as immune checkpoint blockade, aimed to revitalize exhausted T cells and restore their anti-tumor activity. However, the persistence of an exhausted phenotype undermines the success of immunotherapies, limiting the durability and strength of the immune response. In recent studies, a deeper exploration of the GB microenvironment has revealed the importance of T cell-myeloid interaction in driving an immunosuppressive environment. Spatial and single-cell transcriptomic analyses highlight the important role of CD163-positive myeloid cells in promoting this transcriptional transformation in T cells 11 . The mechanism of this phenomenon lies in the HMOX1-IL10 axis and the interplay within mesenchymal tumor regions that influence immune responses in gliomas 10 . CAR-T cell therapy has shown notable efficacy in cell lines, murine and patient-derived xenograft (PDX) models, with no apparent signs of T cell exhaustion 12 – 15 . However, in human clinical reports, T cell exhaustion has emerged as a significant impediment to the effectiveness of CAR-T therapy. Thus, a comprehensive and detailed exploration of the regulatory mechanisms governing CAR-T cell functionality within the intricate GB ecosystem is necessary in a model closer to human GB. Here, we compared CAR-T efficacy with normal T cells (which lack the CAR construct) in vitro using a novel human neocortical slice model, in which we used the access cortex from neurosurgery and implanted mesenchymal primary-derived tumor cells in the brain slices 16 . Additionally, CAR and control T cells were added to the tumor-implanted brain slices and profiled using PIC-seq. We hypothesize that the human neural environment, particularly in conjunction with mesenchymal tumors, will recapitulate the human GB TME and may manifest distinct responses compared to murine models. Further, we hypothesize that these distinct responses mask the effects of immune suppression observed in PDX models which in turn necessitates the need for a more comprehensive understanding of the interplay between CAR-T cell therapy, neural environments, and tumor subtypes in patients 17 . Our integrative analysis of CART and Mock-treated brain slices revealed that the brain slice model recapitulates the GB tumor microenvironment. We revealed that, upon interaction with GB cells, CAR T cells exert an initial antitumor effect followed by a decline in effectivity. We identified a subpopulation of tumor-associated macrophages (TAMs) with enhanced phagocytic and scavenger activity spatially located in proximity to mesenchymal-like GB cells, within hypoxic tumor niches potentially contributing to CD8 T cell dysfunction. Both myeloid-tumor and myeloid-T cell interactions collectively promote an immunosuppressive microenvironment in GB that drives transcriptional shift towards T cells exhaustion. Gene regulatory network analysis identified MAF and BACH2 as key transcriptional regulators of CAR-T cell function. Our findings provide insights into the molecular mechanisms governing the functionality of CAR-T cells and highlight BACH2 and MAF as potential targets for optimizing and enhancing the efficacy of CAR-T cell therapy. Gene regulatory network construction Before the construction of the GRNs, only CD8 + cells were extracted from the seurat object and converted to H5ad format. CellOracle (version 0.18.0) was used to infer cell-specific gene regulatory networks and predict the activity of transcription factors within individual cells using scRNA-seq. TFs controlling the gene regulation in both groups were identified based on the calculated centrality scores. The selected TFs were knocked out to simulate cell state transitions in each group. Pseudotime trajectory was used to identify the normal developmental path of the cells. The gene regulatory interactions were filtered to allow only connections where the JUN or FOS was either a source or target gene. Filtered TFs were visualized in a network showing the 20 most significant TFs linked to JUN and FOS within each group using NetworkX. Spatial Transcriptomics Data Analysis Spatial transcriptomics data from multiple samples were processed and analyzed to infer the adjacency and relationship between different cell types. For each dataset, a correlation matrix was computed to quantify the adjacency between the selected cell types. The individual correlation matrices were combined into a single matrix representing the mean correlation values. The resulting mean correlation matrix was filtered to retain only significant correlations, defined as those with a value greater than 0.4. The processed data served as the basis for constructing a network graph, where nodes represent cell types, and edges indicate significant correlations between them. The graph was constructed using the `igraph` package, with edges directed and properties such as magnitude and color derived from the correlation data. Visualization of the network graph was performed using `ggraph`. Methods Ethical approval and resource sharing Human sample preparation and analysis was approved by the local ethics committees of the Universities of Freiburg and Ulm (Freiburg: protocol 100020/09 and 472/15_160880; Ulm: 162/10) with written informed consent obtained from all subjects. The studies were approved by the respective institutional review board. Human organotypic slice culture We used a human neocortical slice model recently described in detail 16 . In the case of tumors deeply localized without infiltrating the cortex, we used cortical regions that were removed to access the tumors, followed by an intraoperative evaluation using Stimulated Raman Histology to confirm the non-tumor infiltration status. The cortical tissue was collected and transported to the laboratory in a pre-cooled preparation medium within 10 minutes of the resection procedure. In brief, the tissue block was processed on a pre-cooled platform. All visibly cauterized and damaged parts were dissected away before trimming the tissue into smaller cubes. These cortex cubes were fixed on a magnetic platform and re-sliced into 300 µm thick slices using a Leica VT1200 semi-automatic vibratome (14912000001, Leica, Germany) and cultured for this study. Growth medium was used as slice culture medium, and was refreshed every 24 hours throughout the culture period. Brain slices were allowed to recover for the initial 24 hours post-collection. Tumor inoculation To establish the GB brain slice model, 1 µL of ZsGreen-tagged BTSC233 GB cells (20,000 cells/µL) was injected into each brain slice. Tumor growth was monitored every 24 hours using an EVOS M7000 fluorescent microscope paired with an on-stage incubation system. On day 3 post-tumor injection (DPI3), CAR T-cell, Mock T-cell, or control treatment was applied by injecting Cell Trace Far Red (CTFR)-tagged single T-cell suspensions. A total of 2 million cells in 4 µL were injected per brain slice. Tumor growth was assessed at DPI3 to confirm model reliability. Slices that did not exhibit established tumor patterns at DPI3 were excluded from further experiments; no such exclusions occurred in this study. Generation of CAR-T cells Human T cells were isolated from Peripheral Blood Mononuclear Cell (PBMCs) using the EasySep™ Release Human CD3 Positive Selection Kit (Stemcell Technologies, #17751). Enriched T cells were activated with Dynabeads™ Human T-Activator CD3/CD28 for T Cell Expansion and Activation (Thermo Fisher, #11131D) for 3 days and kept at a density of 1 x 10 6 cells/ml. The medium was supplemented with 100 U/ml IL-2 throughout the culture. T cells were expanded for 11 days and then electrophoresed with human NKG2D-CAR messenger ribonucleic acid (mRNA) using a NEON transfection system (Invitrogen). In vitro transcription and electroporation of human NKG2D-CAR mRNA have recently been described 18 . For CAR-T cell generation 10 µg mRNA was used per 100 µl electroporation reaction. Live imaging and tumor growth monitoring Imaging was performed using an upright two-photon microscope Olympus FV1000 using an excitation wavelength of 870–890 nm with an emission filter of 515–560 nm to image the green fluorescent protein (GFP) signal. Optical magnification was set to 20x. Images were acquired with 1- to 2-µm z-axis increments and 800 × 800-pixel resolution. Simple neurite tracer (SNT) plugin 42 implemented in ImageJ was used to visualize cell-cell connections. We transformed the traces into a 3D matrix and visualized the network pattern in plotly (R). Temporally-resolved live tissue imaging was performed using an EVOS M7000 microscope (AMF7000, Thermo Fisher Scientific, USA) with an on-stage incubator (AMC1000, Thermo Fisher Scientific, USA) coupled with an automated temperature, humidity, O2, and CO2 controlling system. Image acquisition interval was 24 hours. GFP and Cy5 filter cubes were used respectively for acquiring images for tumor cells or CAR-T/Mock cells under an Olympus UPLXAPO 4X objective (#14–905, EVIDENT Olympus, Japan). Tumor growth was quantified as below: Tumor volume = Tumor growth area x Mean fluorescent intensity of the tumor growth area A t-test was used to compare the daily tumor volume increase between CAR T-cell treated and Mock T-cell treated groups. For day (n) post treatment: Daily tumor volume increase = [Tumor volume(n) - Tumor volume(n-1)] / Tumor volume(n-1) * 100% Tissue dissociation For tissue dissociation and preparation of single-cell suspensions, samples were processed using the Neural Tissue Dissociation Kit (Miltenyi Biotec) according to the manufacturer’s protocol with slight modifications. Briefly, enzyme mix 1 was prepared by combining 200 µL of Enzyme T with 1750 µ of Buffer X per 500 mg of tissue, and enzyme mix 2 was prepared by mixing 10 µL of Enzyme A with 20 µL of Buffer Y per 500 mg of tissue. Tissue samples were transferred into C-tubes, supplemented with enzyme mix 1, and incubated at 37°C for 5 minutes. Mechanical dissociation was then performed within the C-tube for 2 minutes at 37°C under slow, continuous rotation. Subsequently, enzyme mix 2 was added and the sample was incubated for an additional 5 minutes at 37°C, followed by a second round of mechanical dissociation under the same conditions. The dissociated suspension was centrifuged at 350 g for 1 minute at 4°C, the supernatant was removed, and the cell pellet was resuspended in 9 mL of growth medium. The suspension was then filtered through a 100 µm cell strainer (pre-moistened with 1 mL growth medium) placed atop a 50 mL falcon tube. The filtrate was collected and transferred to a 15 mL falcon tube for downstream applications. Fluorescence-activated cell sorting Fluorescence-activated cell sorting (FACS) for brain slice culture was recently described by us 11 , 16 . Briefly, single-cell suspensions were centrifuged at 300 g for 10 minutes at 4°C. Following centrifugation, the supernatant was aspirated, and the pellet was gently resuspended. The resuspended cells were transferred to FACS tubes containing FACS buffer and centrifuged again at 300 g for 5 minutes at 4°C. Washed samples were resuspended in 450 µL DAPI-containing FACS incubation buffer (DFIB) and incubated on ice, protected from light. A total of 70,000 DAPI-negative, ZsGreen-positive cells were sorted by collecting two sets of 35,000 cells into pre-coated Eppendorf tubes for downstream analysis. The pellets were then gated and separated into different compartments based on the fluorescence of either Alexa488 or Alexa647 or both. scRNA-seq was performed on the different compartments of interest. Single-cell RNA sequencing PIC-seq, a modified scRNA-seq, was employed in this study using 10X Genomics Chromium Next GEM Single Cell 3’ Reagent Kits User Guide (v3.1 Chemistry). Amplified cDNA and constructed single-cell libraries were evaluated using Fragment Analyzer 5200 (M5310AA, Agilent, USA) and Qubit™ 4 Fluorometer (Q33238, Thermo Fisher Scientific, USA) for quality assessment of base pair length and concentration. The constructed libraries were indexed, denatured, and pooled based on Illumina NextSeq 500 and 550 System Denature and Dilute Libraries Guide. NextSeq High Output kit v2.5 (20024906, Illumina, USA) flowcell chip (75 cycles) was used. Sequencing cycles were set as follows: read1 -- i7 -- i5 -- read2: 28–8 -- 0–56. scRNA-seq data analyses Gene expression matrix from the CellRanger pipeline was analyzed using the Seurat package in R. Low-quality cells (cells expressing less than 200 genes, and genes expressed in less than 5 cells plus cells with mitochondrial reads greater than 10%) were filtered out. Data were log-transformed to mitigate technical noise and gene expression variability. The identification and removal of potential doublets were performed using the DoubletFinder algorithm. Cells were clustered using the FindNeighbors and FindClusters functions in Seurat. Principal Component Analysis (PCA) was applied to reduce the dimensionality of the data, and cell clusters were identified using shared nearest neighbor (SNN) modularity optimization. Clusters were visualized using Uniform Manifold Approximation and Projection (UMAP) to group cells with shared gene expression profiles. To account for batch effects resulting from data generated from different conditions, the Mutual Nearest Neighbors (MNN) correction method was applied. This method aligns the data across batches to enable the comparison of gene expression profiles. MNN-corrected data were used for downstream analysis. Differentially expressed genes (DEGs) were determined using the Wilcoxon rank-sum test, to identify genes associated with specific cell types and experimental conditions. Cell types were identified by the expression of key marker genes and by mapping the scRNA-seq data to a reference dataset. Cell-to-cell communications Cell-to-cell interaction inference was done using the CellChat package (v1.6.0). In this analysis, the 'CellChatDB.human' database was used as a reference source for understanding cell-to-cell communication pathways between distinct cell types in various experimental conditions (CAR-T vs mock). We first identified activated signaling pathways in each specific experimental condition. Subsequently, the 2 conditions were merged to compare the signaling pathways between them as described in the CellChat tutorial on comparing "multiple datasets with different cell type composition”. We identified dysfunctional signaling by comparing the communication probabilities and differential expression analyses between CAR-T and mock groups. Results CAR-T cells display an initial anti-tumor effect on glioblastoma cells in a complex human-like microenvironment To understand GB-CAR T cells interaction in a human-like model with intact neuro-glial microenvironment, we employed a GB-injected brain slice culture model treated with CAR T-cells, coupled with high-throughput transcriptomic analysis. Slices of 300µm thickness were prepared and cultured in a growth medium. The medium was refreshed every 24 hours throughout the culture period. Brain slices were allowed to recover for the initial 24 hours post-collection before tumor cells injection 11 , 16 ( Fig. 1 a ) . We inoculated the brain slices with the heterogeneous primary cell line (#BTSC233), which has already demonstrated a high level of plasticity in previous studies 11 . To ensure the consistency of the model, we generated and inoculated brain slices across three different patients with the same cell line (#BTSC233). Tumor cell proliferation and growth was monitored microscopically. After 3 days of tumor growth, the brain slices were treated with either T cells with a CAR construct (CART) ( Fig. 1 b ) or control PBMC-derived T cells without CAR construct (Mock). Slices injected with only GB cells without T cells treatment showed a progressive growth and infiltration of the GB cell in the slices ( Fig. 1 c ) . We observed a decline in GB cells growth based on the area covered by the tumor cells in the slices in both the CART and Mock groups. In both the Mock and CART treated slices, a tumor-suppressing local effect during the early phase following CART cell inoculation was observed ( Fig. 1 d-e ) . Quantification of the tumor area revealed significant decrease (p = 0.0421) in tumor growth in CART treated brain slices in the initial phase post-treatment, in comparison to Mock treated and untreated conditions ( Fig. 1 f ) . These observations indicate a potential therapeutic local effect. However, the subsequent decline in T cell levels with an increase in GB cells growth after this initial phase of CART hyperactivity suggests that the CART cells became rapidly exhausted following the initial local effect. Single-cell analyses of glioblastoma cells and the tumor microenvironment in neocortical slices To identify the cellular diversity within the brain slices injected with CAR-T cells and normal T cells, at day 7 post-CAR-T injection, the slices were dissociated and FAC (Fluorescence-activated cell)-sorted into 3 major channels, T cells (Cy5 + ), tumor cells (ZsGreen + ) and double positive cells considered as physical interacting cells (Cy5 + ZsGreen + ) 19 (Fig. 1 a ) . We performed droplet-based physically interacting cells sequencing (PIC-seq) that combines cell sorting combined with scRNA-seq on the different groups. The resulting scRNA-seq data from different samples (Supplementary Fig. 1a) were merged and horizontally integrated using the mutual nearest neighbor algorithm (MNN) with the top 2,000 most variable features as anchors. After quality control, a total of 35,085 cells remained (Fig. 2 a ) . We identified malignant cells through inferred copy-number alterations (Fig. 2 c, Supplementary Fig. 1b) . Next, we identified the major cell types by the top markers expressed by each cluster (Fig. 2 d ) from a list of top differentially expressed genes per cluster ( Supplementary Table 1 ). We then mapped our data to the reference GBmap dataset to infer cell types using Azimuth 20 Supplementary Fig. 1c) . Our initial cell type identification corresponds to the major GBmap cell types 21 (Fig. 2 e, Supplementary Fig. 1d ). To explore the T cell diversity, we performed subclustering of the T cell subsets in our dataset (Fig. 2 f ) . The T cell subsets consist of clusters enriched for CD8A , CD4 , TOP2A , CTLA4 , and JUN , representing CD8 T cells, CD4 T cells, proliferating T cells, regulatory T cells, and T cells with stress signatures respectively (Fig. 2 f, Supplementary Fig. 1e, Supplementary Table 2) . A comparison of T cell subpopulation between the CART and Mock populations revealed the presence of the identified T cell subtypes in all groups. CD4 T cells were however absent in the Mock group (Fig. 2 g ). Since CD8 T cells are known to exhibit rapid exhaustion after an initial antitumor effect in comparison in GB 22 and to allow proper comparison between the CART cell and Mock T cell groups, we focused on CD8 T cells in our downstream analysis. To understand the phenotypic diversity of the CD8 cells in the different groups, we first identified the differentially expressed genes (DEGs) between CART and Mock CD8 T cells (Supplementary Table 3) . Gene ontology analysis of these DEGs revealed an enrichment of metabolic processes in the CART group while T cell differentiation and cell cycling were enriched in the Mock group (Supplementary Fig. 1f). This suggests T cell dysfunction in the CART group and normal T cell differentiation and proliferation in the Mock group. We then checked the expression of different T cell markers in the CD8 + subpopulation in the CART and the Mock T cell groups to understand the cause of T cell dysfunction in the CART group. Naive T cell markers ( SELL , CCR7 , CD8A ) and activation markers ( GZMB , NKG7, GZMA, EOMES ) were highly expressed in Mock CD8 T cells in comparison to the CART group, while T cells exhaustion markers ( LAG3, CD3D, CTLA4 ) were expressed in the CART group (Fig. 2 h ) . Taken together, the rapid decline in antitumor effect we observed in the brain slices treated with CAR-T cells may be due to the dysfunction and possible exhaustion of the CAR CD8 T cell subpopulation as previously reported 22 . Cell-cell signaling reveals CAR CD8 T cells dysfunction upon interaction with the glioblastoma microenvironment To understand the role of the TME in CART cells function or dysfunction during therapy in GB, we used CellChatDB 23 to identify the communication patterns between all cells within the GB TME in both CART and Mock-treated GB cells. First, we identified all the signaling pathways activated in CART and Mock groups. Our analysis revealed a significant difference in ligand-receptor interactions and the strength of the interactions between the CART and the Mock group ( Fig. 3 a ). The CART group also had a higher activation of signaling pathways indicating enhanced cellular communication between CAR-T cells and other cell types within the TME (Supplementary Fig. 2b) . Thus, we hypothesized that this increased cellular signaling within the TME of CAR-T treated GB cells contributes to T cell dysfunction and CAR-T treatment failure. Interestingly, among the pathways enhanced in the CART group when compared to the Mock group are TIGIT, PD-L1, SELPLG, NOTCH, that are strongly linked to T cell exhaustion (Supplementary Fig. 2a) . Given our initial observation that CD8 T cells are the most abundant T cell subpopulation and are present in all the groups in our dataset, we focused our analysis on the signaling between CD8 T cells and other cells within the GB TME. We confirmed the upregulation of T cell exhaustion pathways in the CART group compared to the Mock group by the significant activation of SELPLG and TIGIT pathways that indicate T cell exhaustion ( Fig. 3 c and Fig. 3 d ) . Additionally, we found an upregulation of signaling pathways (CLEC, MHC-II, and MIF) specific to CD8 CAR-T cells ( Fig. 3 b ) . Notably, the CLEC pathway involving the ligands CLEC2B , CLEC3C , and CLEC3D expressed by several immune cells interact with the CD161 ( KLRB1 ) receptor expressed by exhausted CD8 T cells ( Fig. 3 e ). Interestingly, regulatory T cells, proliferating T cells, and CD4 T cells all interact with CD8 T cells via the CLEC-KLRB1 pathway in the CART group. Surprisingly, we observed an upregulation of MHC-II signaling ( supplementary Fig. 2c ) in the CART group involving the CD8 cells, and a downregulation of the canonical MHC-I pathway ( Supplementary Fig. 2d ) further indicating the dysfunctional signaling in CD8 CAR-T cells. Taken together, these observations imply an increased signaling cascades associated with T cell exhaustion in the CART group upon interaction with GB cells in the GB microenvironment. Myeloid doublets exhibit enhanced phagocytic activity in proximity to glioblastoma cells A subpopulation of cells in our dataset were both Cy5 and ZsGreen positive ( Fig. 4 a ) . scRNA-seq analysis revealed that a large fraction of these cells with double positive signals are classified as doublets and are within the myeloid cells cluster ( Fig. 4 b ) . However, not all myeloid cells were classified as doublets. Sub-clustering analysis of the myeloid cluster revealed that most of the myeloid cells with a doublet score are classified as monocytes and tumor-associated macrophages (TAMs) of bone marrow origin (Supplementary Fig. 3a and 3b, Supplementary Table 4) . The doublet myeloid cells show strong expression of both classical myeloid markers ( AIF1 , HEXB , PTPRC ) and GB markers ( PTEN , ATRX , PIK3CA ) with no expression of classical T cell, oligodendrocyte, or astrocyte markers ( Fig. 4 c, Supplementary Table 5) . Myeloid cells with both myeloid and GB signals have been previously described 24 , 25 and were shown to promote an immunosuppressive TME in GB. Since these myeloid cells with doublet score express both myeloid and tumor cell markers, we hypothesize that the myeloid cells are in physical contact with the GB cells and may be engulfing the GB tumor cells attacked by CAR-T cells. To assess the phagocytosis hypothesis, we scored all the cells in the myeloid cluster for the expression of phagocytic and scavenger markers previously described in myeloid cells 26 , 27 . Doublet myeloid cells scored significantly higher for phagocytic and scavenger activity compared to non-doublets ( p = 2.2e-16 for both) ( Fig. 4 d and 4 e ) . This indicates that the myeloid cells with a doublet score are either in the process of engulfing the GB cells or have already engulfed the cells to clear dying GB cells. We then asked whether the myeloid cells share spatial proximity with GB cells in patient samples. To investigate the spatial relationship between myeloid cells and tumor cells, we leveraged our publicly available spatial transcriptomic datasets. We selected only IDHwt GBs with corresponding spatial TRC quantification 11 , 28 (n = 5) to compute the spatial proximity between GB cells and other cell types within the TME using minimal spanning trees. On a broader cell type classification level, the adjacency matrix revealed a higher proximity of tumor cells with myeloid cells compared to lymphoid or neuronal cell types (Supplementary Fig. 3d) . A correlation analysis of cell types proximity inferred at sub-cellular level, showed a close proximity of mesenchymal-like tumor cells, TAM-BDM, and monocytes that are linked to hypoxic conditions ( Fig. 4 f, Supplementary Fig. 3c) . Our results are in line with previous studies that showed a direct interaction between myeloid cells and Mes-like GB cells 17 , 29 , 30 . These results suggest that myeloid cells are actively engaged in the engulfment process, potentially targeting dying GB cells within the hypoxic niche. MAF drives CAR CD8 cytotoxic T cell exhaustion To understand the key regulators of CAR-T cell dysfunction we extracted only CD8 T cells from our scRNA-seq dataset representing CD8 T cells in CART and Mock groups. We reconstructed the gene regulatory networks (GRN) of these CD8 subsets using CellOracle 31 and identified the most highly active transcription factors (TFs) related to CART and Mock groups ( Fig. 5 a ) . TFs with the highest centrality scores were identified as the top essential TFs driving CD8 T cell function. A high degree centrality score indicates TFs controlling cellular functions such as cytotoxicity and exhaustion. While a high betweenness centrality score denotes essential TFs responsible for the flow of transcriptional information within the GRN. Among the top 30 TFs with the highest degree centrality score, BACH2 and MAF have the highest betweenness centrality score in CD8 CAR-T cells when compared to CD8 mock T cells (Supplementary Fig. 4a) , indicating that these TFs are central to the maintenance and stabilization of CD8 T cell functional state. Since BACH2 and MAF are inferred to control the CD8 functional state, we checked their expression in CD8 T cell subsets of our scRNA-seq data. We found the expression of BACH2 and MAF to be consistent with the TFs activity ( Fig. 5 b ) . BACH2 is highly expressed in CD8 Mock T cells while MAF is expressed mainly by the CD8 CAR-T cells, indicating an alternate expression pattern between the different groups. A pseudotime analysis of the expression of MAF and BACH2 confirmed an alternate expression of these genes ( Fig. 5 c, Supplementary Fig. 4b) . While MAF is upregulated in CD8 T cells, BACH2 is downregulated and vice versa ( Supplementary Fig. 4c) . This indicates regulation of different functions. We asked if MAF and BACH2 expression dynamics are present in T cells within the patient GB samples and other CAR T cells. Interestingly, we found that while MAF was found to be upregulated in exhausted CD8 T cells in GB patient samples 32 ( Supplementary Fig. 4d) and CAR constructs with an exhaustion phenotype in mouse models 33 (Supplementary Fig. 4e) , BACH2 is elevated in naïve CD8 T cells in patient samples and in normal CD8 T cells used as control while comparing several CART constructs in mouse GB models. To further understand the effect of these TFs in driving and maintaining CD8 T cell exhaustion, we performed in silico perturbation of MAF and BACH2 using our scRNA-seq dataset and the GRNs constructed using CellOracle. We inferred the normal developmental flow by pseudotime analysis. This inference analysis indicates potentially two different developmental pathways or cell states within our mixed CD8 populations ( Fig. 5 d ). Each of the developmental axes correlates with either CART or Mock groups as shown in ( Fig. 5 a ). Upon MAF simulation of knock-out (KO), the developmental trajectory revealed a differentiation shift towards a non-exhausted CD8 state (from CART to the mock group) ( Fig. 5 e ) , while upon simulation of BACH2 KO, the trajectory shows a differentiation shift towards an exhausted CD8 state (from Mock to CART group) ( Fig. 5 f ) . These indicate that the activity of MAF drives CD8 T cells towards exhaustion, while the activity of BACH2 drives CD8 T cells towards the normal CD8 T cell developmental trajectory. Thus, the activity of these TFs upon perturbation is in line with our observation of an initial rapid loss of cytotoxic effect observed in the engineered CAR-T cells and a shift towards exhaustion. Discussion Resistance to CAR-T cell therapy in GB arises from tumor-intrinsic or extrinsic factors such as an immunosuppressive microenvironment. Using a systems biology approach, we integrated PIC-seq and spatial transcriptomics to investigate CAR-T cell dysfunction. Comparative analysis of CAR-T and mock T cells revealed distinct gene regulatory programs in the GB TME. Neocortical brain slices recapitulated the cellular architecture of patient tumors and supported tumor progression. Both Mock and CAR-T cells engaged with tumor and stromal components in the microenvironment. Interactions between CAR-T cells and myeloid cells fostered an immunosuppressive niche that impaired CD8⁺ T cell function. A spatially localized myeloid subpopulation with phagocytic traits was enriched near tumor cells. We identified MAF and BACH2 as key transcriptional regulators in CAR-T cells, with MAF driving CAR-T cells to a dysfunctional state. CAR-T cell therapy has been successful in other malignancies but has so far failed to cure GB patients. The reasons for the low efficacy in GB and other solid tumors have not been fully understood 1 . Recent results from early clinical trials reported a rapid but shortened antitumor effect in recurrent GB 3 , 4 . Unlike the results from human clinical trials, CAR-T cell therapy in murine models shows a remarkably positive and sustained response across different treatment strategies 12 , 13 , 18 , 34 . We show that CAR-T cells demonstrated a strong initial cytotoxic effect but were unable to maintain their cytotoxic activity due to the activation of CAR CD8 specific signaling pathways, myeloid cells mobilization, and the suppressive microenvironment leading to CD8 T cell exhaustion. Exhausted cytotoxic T cells represent a distinct phenotypic state of T cells following intense activation in the context of chronic infections or cancer 35 . While initially responding to antigens, these T cells experience a progressive loss of function, marked by reduced cytotoxicity and diminished cytokine production 36 . This dysfunctional state is a consequence of multiple factors, including impaired regulation, an immunosuppressive TME, and tumor-specific adaptations. These circumstances collectively drive T cells into a state of exhaustion and dysfunction, hindering their ability to effectively eliminate the tumor cells 36 , 37 . Crosstalk between myeloid cells and T cells in GB is known to promote immunosuppression and T cell dysfunction 17 , 38 , 39 . The dysfunctionality of CD8 T-cells which seems to result from interaction with myeloid cells of the TME highlights the important role of the TME in driving and maintaining T cell exhaustion. In line with our observations, CD8 T cells have been reported to physically interact with TAMs forming a long-lasting synapse that initiates and maintains CD8 exhaustion 38 , 40 . Interestingly, the initiation of CD8 T cell exhaustion by TAMs cells that is accelerated in the hypoxic tumor niche as reported by Kersten et al., aligns with our observation of spatial proximity of Monocytes, Mes-like GB cells and TAMs in a hypoxic environment. We have previously shown that myeloid cells are spatially located in tumor-T cell neighbourhood 17 . In addition, our study further highlights the specific ligands-receptors involved in this interaction. Although further research is needed to fully describe and validate the signaling between TAMs and CD8 T cells, we identified the CLEC pathway as a potential target in interfering myeloid-GB interaction driving CD8 T cell dysfunction and subsequent hyporesponsiveness. In recent studies, myeloid-tumor doublets have also been identified and linked to immunosuppressive function in GB, however their spatial localization remains unclear 17 , 24 , 29 . Our results indicating the spatial proximity of myeloid cells with Mes-like GB cells within the hypoxic tumor niche and having enhanced phagocytic and scavenger signatures may indicate the clearance of apoptotic GB cells to avoid the engulfment and processing of the GB cells by antigen-presenting cells (APCs). This prevents the APCs from presenting tumor antigens to CAR-T cells hence contributing to the rapid exhaustion of the CAR-T cells and sustaining an immunosuppressed TME. Both our data and previous studies 11 , 24 , 29 indicate that the engulfment of GB cells by myeloid cells plays an important role in shaping the GB microenvironment by driving immunosuppression. More experimental studies are needed to fully understand the nature and activity of these myeloid-tumor doublets. We identified MAF and BACH2 as key transcriptional regulators driving CD8 T cell exhaustion. While MAF is upregulated in the CAR CD8 T cells, BACH2 is upregulated in Mock CD8 T cells. MAF , a member of the AP-1 (Activator Protein-1) family, is involved in the regulation of immune cells, including T cells 41 . Activation of MAF and its overexpression in tumor-associated exhausted CD8 T cells under the influence of TGFb and IL6, drive CD8 T cells into exhaustion 42 , 43 . BACH2 regulates cytotoxic CD8 T cell and other immune cell development and function 44 – 46 . Overexpression of BACH2 limits T cell exhaustion and maintains T cell effector function by inhibiting IRF4 and AP-1 family TFs known to drive and maintain CD8 T cell exhaustion 44 , 47 . BACH2 's association with the formation of memory in T cells highlights the potential consequence of its downregulation on the capacity of CD8 T cells to form an enduring memory which is crucial for sustained protective immune responses 46 , 48 . BACH2 directly binds to the MAF locus, suppressing MAF gene expression and the BACH2-MAF heterodimer inhibits AP-1-dependent gene activation 44 , 49 , 50 . This repression by BACH2 is important in maintaining normal T cell function. Hence, in the absence of BACH2 , MAF is upregulated and influences T cell transition to a dysfunctional state. Our study defines both intrinsic and extrinsic mechanisms driving CD8⁺ CAR-T cell exhaustion in GB. We highlight the role of myeloid-T cell and myeloid–tumor interactions in shaping an immunosuppressive microenvironment. Key signaling pathways and transcriptional regulators identified are potential targets to enhance CAR-T cell design and therapeutic efficacy. Limitations of the study The CAR construct used in this study was delivered via mRNA electroporation, which offers high transfection efficiency but results in transient protein expression. Alternative delivery methods that support prolonged CAR expression may yield different outcomes. Additionally, there was an imbalance in T cell subpopulations between CAR-T and Mock-treated slices, with CD4⁺ and regulatory T cells absent in the Mock group. Given the known importance of CD4⁺ T cells in sustaining antitumor responses and the differential susceptibility of CD8⁺ T cells to exhaustion, this discrepancy may influence comparative interpretations. Prior studies have shown that CD4⁺ CAR-T cells can mediate more durable responses, whereas CD8⁺ T cells may dampen CD4⁺-driven efficacy in GB. Therefore, future designs of CAR-T studies and therapies should consider the CD4:CD8 ratio as a critical variable in optimizing therapeutic efficacy. Abbreviations CAR Chimeric antigen receptor GB Glioblastoma NKG2D Natural killer group 2D MAF Musculoaponeurotic fibrosarcoma BACH2 BTB Domain and CNC Homolog 2 TME Tumor microenvironment PDX Patient-derived xenograft PIC seq-Physically interacting cells sequencing scRNA seq-single-cell RNA sequencing TAMs Tumor-associated macrophages DPI3 Post-tumor injection CTFR Cell trace far red PBMC Peripheral Blood Mononuclear Cell SNT Simple neurite tracer GFP Green fluorescent protein FACS Fluorescence-activated cell sorting PCA Principal component analysis SNN Shared nearest neighbor UMAP Uniform manifold approximation and projection MNN Mutual nearest neighbors DEGs Differentially expressed genes Gbmap Glioblastoma map TFs Transcription factors GRN Gene regulatory network KO Knock-out APCs antigen-presenting cells AP 1-Activator Protein-1 mRNA messenger ribonucleic acid Declarations Competing Interests T.W. received honoraria for a lecture and advisory board participation from Philogen. Authorship JVE, TW, DHH designed the experiments JVE, JZ, TL conducted the experiments YAY, JZ, JKB, NN and JK analyzed the data YAY, TW, DHH supervised experiments and data analysis YAY, JZ, TL, TW, DHH wrote the manuscript All authors read and approved the manuscript Funding: DHH received funding from the TRANSCAN (BMBF: 01KT2328), German Research Foundation (Heisenberg Program: DFG HE 8145/6 − 1, Funding: HE 8145/5 − 1), and the DKTK partner side Freiburg (DKTK-PI) and Joint Funding Program (HematoTrac). TW received support from Promedica Foundation, EMPIRIS Foundation, C3Z Precision Oncology Funding Program, Sophien Foundation, Baasch-Medicus Foundation, the Helmut Horten Foundation and the Swiss Cancer League (KFS-5763-02-2023). Author Contribution JVE, TW, DHH designed the experimentsJVE, JZ, TL conducted the experiments YAY, JZ, JKB, NN and JK analyzed the dataYAY, TW, DHH supervised experiments and data analysisYAY, JZ, TL, TW, DHH wrote the manuscriptAll authors read and approved the manuscript Acknowledgement We thank Jorge Andres Ibanez and Giedre Krenciute for sharing their processed single- cell RNA sequencing data. Data Availability Data from this paper is available from the corresponding authors upon reasonable request. References Majzner RG, Mackall CL. Clinical lessons learned from the first leg of the CAR T cell journey. Nat Med. 2019;25:1341–55. 10.1038/s41591-019-0564-6 . Guzman G, Pellot K, Reed MR, Rodriguez A. CAR T-cells to treat brain tumors. Brain Res Bull. 2023;196:76–98. 10.1016/j.brainresbull.2023.02.014 . Bagley SJ, Logun M, Fraietta JA, Wang X, Desai AS, Bagley LJ, Nabavizadeh A, Jarocha D, Martins R, Maloney E, et al. 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Bove C, Arcangeli S, Falcone L, Camisa B, El Khoury R, Greco B, De Lucia A, Bergamini A, Bondanza A, Ciceri F et al. (2023). CD4 CAR-T cells targeting CD19 play a key role in exacerbating cytokine release syndrome, while maintaining long-term responses. J. Immunother. Cancer 11 . 10.1136/jitc-2022-005878 Additional Declarations Competing interest reported. T.W. received honoraria for a lecture and advisory board participation from Philogen. 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University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Dieter","middleName":"Henrik","lastName":"Heiland","suffix":""},{"id":496306918,"identity":"43b0ca60-753e-4817-8a37-93517a8aebd6","order_by":8,"name":"Yahaya Abubakar Yabo","email":"data:image/png;base64,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","orcid":"","institution":"Medical Center - University of Freiburg","correspondingAuthor":true,"prefix":"","firstName":"Yahaya","middleName":"Abubakar","lastName":"Yabo","suffix":""}],"badges":[],"createdAt":"2025-07-29 08:08:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7240692/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7240692/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12916-026-04783-2","type":"published","date":"2026-03-13T15:58:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88655914,"identity":"e7265a66-4ef1-4707-8a16-86ec639d5e2d","added_by":"auto","created_at":"2025-08-08 19:08:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":566139,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of CAR-T cell treatment on tumor growth in glioblastoma inoculated brain slices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eIllustration of the experimental workflow showing brain slice culture, CAR-T cell treatment, and droplet-based scRNA-seq \u003cstrong\u003e(b)\u003c/strong\u003e Illustration of the NKG2D CART construct \u003cstrong\u003e(c, d and e)\u003c/strong\u003e Representative tumor growth pattern respectively in control, CAR T cell, and mock conditions (DPI = day post-inoculation, scale bar = 1 mm) (\u003cstrong\u003ef)\u003c/strong\u003eQuantification of tumor growth under CAR-T/mock treatment, as well as the respective control condition. (Tumor growth = Area x mean_intensity). Error bar = mean ± SEM.\u003cstrong\u003e \u003c/strong\u003ePICs = physically interacting cells.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7240692/v1/93f3d7efb6c9bfd6a747d834.png"},{"id":88654775,"identity":"ca4eb161-4334-40a1-9dd6-9e9c34cd7a1f","added_by":"auto","created_at":"2025-08-08 18:52:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":595049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003escRNA-seq analysis of brain slices treated with CAR and normal T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eUMAP representation showing major cell types identified by marker expression \u003cstrong\u003e(b)\u003c/strong\u003e Barplot showing the proportions of cell types in each channel \u003cstrong\u003e(c)\u003c/strong\u003eHeatmap of inferred copy number alterations, on the Y-axis are cells and on the X-axis are the chromosomes \u003cstrong\u003e(d)\u003c/strong\u003e Violin plot of key marker genes for myeloid, T cells, oligodendrocytes, and tumor cells \u003cstrong\u003e(e) \u003c/strong\u003eUMAP showing cell types mapped to GBmap reference dataset \u003cstrong\u003e(f)\u003c/strong\u003e Phate map of T cells subpopulation showing the identified T cell populations \u003cstrong\u003e(g)\u003c/strong\u003e Stacked bar chart of T cell subsets showing the different T cell subpopulation and their proportions in CAR T cell and mock T cell groups \u003cstrong\u003e(h)\u003c/strong\u003e Dotplot of markers expressed by naive, activated and exhausted T cells in CAR-T and mock T cell groups. MES = mesenchymal-like, AC = astrocytic-like, NPC = neural progenitor cell-like, OPC = oligodendrocyte progenitor cell-like, NK= natural killer cell, Prolif_T = proliferating T cells, Reg_T = regulatory T cells, Stress_sig = T cells with stress signature, Mono= monocyte, TAM_BDM = bone marrow-derived tumor-associated macrophages, TAM_MG = microglia derived tumor-associated macrophages, RG = radial glia, SMC= smooth muscle cells.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7240692/v1/a5e2468155ae6e82faa054f0.png"},{"id":88654779,"identity":"e4ce808b-d4c6-4d36-ad03-0091bcc7f975","added_by":"auto","created_at":"2025-08-08 18:52:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":368241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell-cell communication between CD8 T cells and cells within the GB microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Total number of interactions and the strength of the interactions in mock and CAR-T cell groups \u003cstrong\u003e(b)\u003c/strong\u003e Signaling changes of CD8 cells in mock compared to CAR-T cell group \u003cstrong\u003e(c, d and e)\u003c/strong\u003eCircle plots showing the signaling directions of key signaling pathways upregulated in the CAR-T cell group, SELPLG, TIGIT and CLEC respectively.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7240692/v1/e40e4502a9d56751ac234d34.png"},{"id":88654785,"identity":"b2c71398-5ea4-4c1e-b368-1e2906551e18","added_by":"auto","created_at":"2025-08-08 18:52:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":331611,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization and spatial analysis of myeloid doublets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eUMAP representation of all cells per channel \u003cstrong\u003e(b)\u003c/strong\u003e Subclustering and doublet analysis of myeloid subsets showing mixed cells predicted to have a doublet profile \u003cstrong\u003e(c)\u003c/strong\u003e Dotplot of average expression of key marker genes for different GB TME cell types \u003cstrong\u003e(d)\u003c/strong\u003e Violin plot showing the scores of phagocytic and \u003cstrong\u003e(e)\u003c/strong\u003escavenger signature scores in myeloid doublets and singlets \u003cstrong\u003e(f) \u003c/strong\u003eNetwork graph of adjacency matrix showing inferred proximity of different cell types in spatial transcriptomics datasets, nodes represent cell types, and edges indicate significant spatial correlations between nodes. Blue colors represent different GB cell states, red colors represent immune cell types, and green colors represent TAMs and dendritic cells. Other cell types are shown in different colors.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7240692/v1/8ef21473b86946bffd3b9656.png"},{"id":88655117,"identity":"f01ef81f-8848-439f-b812-7469f016eb7c","added_by":"auto","created_at":"2025-08-08 19:00:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":433550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptional regulators driving CAR CD8 T cells dysregulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e UMAP representation of CD8\u003csup\u003e+\u003c/sup\u003e cells from CAR-T and mock groups. \u003cstrong\u003e(b)\u003c/strong\u003e Violin plot showing the expression levels of MAF and BACH2 genes in CD8\u003csup\u003e+\u003c/sup\u003e cells\u003cstrong\u003e (c) \u003c/strong\u003eLineplot showing alternate expression of BACH2 and MAF along pseudotime trajectory\u003cstrong\u003e (d)\u003c/strong\u003e Normal developmental flow of CD8 T cells based on pseudotime analyses (arrows indicating the direction of developmental trajectory). \u003cstrong\u003e(e)\u003c/strong\u003e Inferred perturbation analysis showing a change of trajectory upon MAF KO (arrows indicating the direction of potential cellular state switch towards normal CD8 T cell state) and \u003cstrong\u003e(f)\u003c/strong\u003e and upon BACH2 KO (arrows indicating the direction of potential cellular state switch towards CD8 T cell exhaustion). The green color in \u003cstrong\u003ee\u003c/strong\u003e and \u003cstrong\u003ef\u003c/strong\u003e shows similarity between the developmental trajectory and perturbation vectors while pink color shows dissimilarity between the vectors. The deeper the color the stronger the similarity or dissimilarity.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7240692/v1/91e232f3752641747ac7d688.png"},{"id":104739924,"identity":"d63bc399-4b5f-4cc9-bb3f-5562e62c21be","added_by":"auto","created_at":"2026-03-16 16:13:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3302958,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7240692/v1/5aaea368-d6bb-4d80-b2bd-e1555d0527af.pdf"},{"id":88655114,"identity":"986ef6c1-125b-4b69-868c-9b93deb77027","added_by":"auto","created_at":"2025-08-08 19:00:40","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1111949,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytablesfinal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7240692/v1/290556ead686e99344303021.xlsx"}],"financialInterests":"Competing interest reported. T.W. received honoraria for a lecture and advisory board participation from Philogen.","formattedTitle":"Myeloid-derived immunosuppression of Chimeric Antigen Receptor T cells in the neuronal microenvironment of Glioblastoma","fulltext":[{"header":"Background","content":"\u003cp\u003eChimeric antigen receptor (CAR)-T cell therapy has shown remarkable success in certain hematologic malignancies, however, this success has not yet been translated to most solid tumors including glioblastoma (GB)\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. GB, characterized by its inherent genetic and cellular heterogeneity as well as its immunosuppressive microenvironment, so far presents an invincible barrier to the efficacy of CAR-T cell therapy\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In contrast to its successes in other malignancies, the limited efficacy of CAR-T cell therapy in GB has raised critical questions regarding its adaptability to the unique challenges posed by the tumor microenvironment (TME) in GB\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This immunosuppressive TME dampens the therapeutic potential of CAR-T cells, undermining their ability to mount an effective immune response against the tumor. In GB, T cells transit into a progressive dysfunctional state\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This state is characterized by the upregulation of inhibitory receptors, such as PD-1, TIM-3, and LAG-3, which impair the ability of exhausted T cells to recognize and eliminate target cells\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Immunotherapeutic approaches, such as immune checkpoint blockade, aimed to revitalize exhausted T cells and restore their anti-tumor activity. However, the persistence of an exhausted phenotype undermines the success of immunotherapies, limiting the durability and strength of the immune response.\u003c/p\u003e\u003cp\u003eIn recent studies, a deeper exploration of the GB microenvironment has revealed the importance of T cell-myeloid interaction in driving an immunosuppressive environment. Spatial and single-cell transcriptomic analyses highlight the important role of CD163-positive myeloid cells in promoting this transcriptional transformation in T cells\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The mechanism of this phenomenon lies in the HMOX1-IL10 axis and the interplay within mesenchymal tumor regions that influence immune responses in gliomas\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. CAR-T cell therapy has shown notable efficacy in cell lines, murine and patient-derived xenograft (PDX) models, with no apparent signs of T cell exhaustion\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. However, in human clinical reports, T cell exhaustion has emerged as a significant impediment to the effectiveness of CAR-T therapy. Thus, a comprehensive and detailed exploration of the regulatory mechanisms governing CAR-T cell functionality within the intricate GB ecosystem is necessary in a model closer to human GB. Here, we compared CAR-T efficacy with normal T cells (which lack the CAR construct) \u003cem\u003ein vitro\u003c/em\u003e using a novel human neocortical slice model, in which we used the access cortex from neurosurgery and implanted mesenchymal primary-derived tumor cells in the brain slices\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Additionally, CAR and control T cells were added to the tumor-implanted brain slices and profiled using PIC-seq.\u003c/p\u003e\u003cp\u003eWe hypothesize that the human neural environment, particularly in conjunction with mesenchymal tumors, will recapitulate the human GB TME and may manifest distinct responses compared to murine models. Further, we hypothesize that these distinct responses mask the effects of immune suppression observed in PDX models which in turn necessitates the need for a more comprehensive understanding of the interplay between CAR-T cell therapy, neural environments, and tumor subtypes in patients\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur integrative analysis of CART and Mock-treated brain slices revealed that the brain slice model recapitulates the GB tumor microenvironment. We revealed that, upon interaction with GB cells, CAR T cells exert an initial antitumor effect followed by a decline in effectivity. We identified a subpopulation of tumor-associated macrophages (TAMs) with enhanced phagocytic and scavenger activity spatially located in proximity to mesenchymal-like GB cells, within hypoxic tumor niches potentially contributing to CD8 T cell dysfunction. Both myeloid-tumor and myeloid-T cell interactions collectively promote an immunosuppressive microenvironment in GB that drives transcriptional shift towards T cells exhaustion. Gene regulatory network analysis identified \u003cem\u003eMAF\u003c/em\u003e and \u003cem\u003eBACH2\u003c/em\u003e as key transcriptional regulators of CAR-T cell function. Our findings provide insights into the molecular mechanisms governing the functionality of CAR-T cells and highlight BACH2 and MAF as potential targets for optimizing and enhancing the efficacy of CAR-T cell therapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene regulatory network construction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBefore the construction of the GRNs, only CD8\u003csup\u003e+\u003c/sup\u003e cells were extracted from the seurat object and converted to H5ad format. CellOracle (version 0.18.0) was used to infer cell-specific gene regulatory networks and predict the activity of transcription factors within individual cells using scRNA-seq.\u0026nbsp;TFs controlling the gene regulation in both groups were identified based on the calculated centrality scores. The selected TFs were knocked out to simulate cell state transitions in each group. Pseudotime trajectory was used to identify the normal developmental path of the cells. The gene regulatory interactions were filtered to allow only connections where the \u003cem\u003eJUN\u003c/em\u003e or \u003cem\u003eFOS\u003c/em\u003e was either a source or target gene. Filtered TFs were visualized in a network showing the 20 most significant TFs linked to \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003eFOS\u003c/em\u003e within each group using NetworkX.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpatial Transcriptomics Data Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSpatial transcriptomics data from multiple samples were processed and analyzed to infer the adjacency and relationship between different cell types. For each dataset, a correlation matrix was computed to quantify the adjacency between the selected cell types. The individual correlation matrices were combined into a single matrix representing the mean correlation values. The resulting mean correlation matrix was filtered to retain only significant correlations, defined as those with a value greater than 0.4. The processed data served as the basis for constructing a network graph, where nodes represent cell types, and edges indicate significant correlations between them. The graph was constructed using the `igraph` package, with edges directed and properties such as magnitude and color derived from the correlation data. Visualization of the network graph was performed using `ggraph`.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eand resource sharing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHuman sample preparation and analysis was approved by the local ethics committees of the Universities of Freiburg and Ulm (Freiburg: protocol 100020/09 and 472/15_160880; Ulm: 162/10) with written informed consent obtained from all subjects. The studies were approved by the respective institutional review board.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHuman organotypic slice culture\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe used a human neocortical slice model recently described in detail\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In the case of tumors deeply localized without infiltrating the cortex, we used cortical regions that were removed to access the tumors, followed by an intraoperative evaluation using Stimulated Raman Histology to confirm the non-tumor infiltration status. The cortical tissue was collected and transported to the laboratory in a pre-cooled preparation medium within 10 minutes of the resection procedure. In brief, the tissue block was processed on a pre-cooled platform. All visibly cauterized and damaged parts were dissected away before trimming the tissue into smaller cubes. These cortex cubes were fixed on a magnetic platform and re-sliced into 300 µm thick slices using a Leica VT1200 semi-automatic vibratome (14912000001, Leica, Germany) and cultured for this study. Growth medium was used as slice culture medium, and was refreshed every 24 hours throughout the culture period. Brain slices were allowed to recover for the initial 24 hours post-collection.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTumor inoculation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo establish the GB brain slice model, 1 µL of ZsGreen-tagged BTSC233 GB cells (20,000 cells/µL) was injected into each brain slice. Tumor growth was monitored every 24 hours using an EVOS M7000 fluorescent microscope paired with an on-stage incubation system. On day 3 post-tumor injection (DPI3), CAR T-cell, Mock T-cell, or control treatment was applied by injecting Cell Trace Far Red (CTFR)-tagged single T-cell suspensions. A total of 2\u0026nbsp;million cells in 4 µL were injected per brain slice. Tumor growth was assessed at DPI3 to confirm model reliability. Slices that did not exhibit established tumor patterns at DPI3 were excluded from further experiments; no such exclusions occurred in this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeneration of CAR-T cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHuman T cells were isolated from Peripheral Blood Mononuclear Cell (PBMCs) using the EasySep™ Release Human CD3 Positive Selection Kit (Stemcell Technologies, #17751). Enriched T cells were activated with Dynabeads™ Human T-Activator CD3/CD28 for T Cell Expansion and Activation (Thermo Fisher, #11131D) for 3 days and kept at a density of 1 x 10\u003csup\u003e6\u003c/sup\u003e cells/ml. The medium was supplemented with 100 U/ml IL-2 throughout the culture. T cells were expanded for 11 days and then electrophoresed with human NKG2D-CAR messenger ribonucleic acid (mRNA) using a NEON transfection system (Invitrogen). \u003cem\u003eIn vitro\u003c/em\u003e transcription and electroporation of human NKG2D-CAR mRNA have recently been described\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. For CAR-T cell generation 10 µg mRNA was used per 100 µl electroporation reaction.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLive imaging and tumor growth monitoring\u003c/b\u003e\u003c/p\u003e\u003cp\u003eImaging was performed using an upright two-photon microscope Olympus FV1000 using an excitation wavelength of 870–890 nm with an emission filter of 515–560 nm to image the green fluorescent protein (GFP) signal. Optical magnification was set to 20x. Images were acquired with 1- to 2-µm z-axis increments and 800 × 800-pixel resolution. Simple neurite tracer (SNT) plugin\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e implemented in ImageJ was used to visualize cell-cell connections. We transformed the traces into a 3D matrix and visualized the network pattern in plotly (R). Temporally-resolved live tissue imaging was performed using an EVOS M7000 microscope (AMF7000, Thermo Fisher Scientific, USA) with an on-stage incubator (AMC1000, Thermo Fisher Scientific, USA) coupled with an automated temperature, humidity, O2, and CO2 controlling system. Image acquisition interval was 24 hours. GFP and Cy5 filter cubes were used respectively for acquiring images for tumor cells or CAR-T/Mock cells under an Olympus UPLXAPO 4X objective (#14–905, EVIDENT Olympus, Japan). Tumor growth was quantified as below:\u003c/p\u003e\u003cp\u003e\u003cem\u003eTumor volume = Tumor growth area x Mean fluorescent intensity of the tumor growth area\u003c/em\u003e\u003c/p\u003e\u003cp\u003eA t-test was used to compare the daily tumor volume increase between CAR T-cell treated and Mock T-cell treated groups. For day (n) post treatment:\u003c/p\u003e\u003cp\u003e\u003cem\u003eDaily tumor volume increase = [Tumor volume(n) - Tumor volume(n-1)] / Tumor volume(n-1) * 100%\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTissue dissociation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor tissue dissociation and preparation of single-cell suspensions, samples were processed using the Neural Tissue Dissociation Kit (Miltenyi Biotec) according to the manufacturer’s protocol with slight modifications. Briefly, enzyme mix 1 was prepared by combining 200 µL of Enzyme T with 1750 µ of Buffer X per 500 mg of tissue, and enzyme mix 2 was prepared by mixing 10 µL of Enzyme A with 20 µL of Buffer Y per 500 mg of tissue. Tissue samples were transferred into C-tubes, supplemented with enzyme mix 1, and incubated at 37°C for 5 minutes. Mechanical dissociation was then performed within the C-tube for 2 minutes at 37°C under slow, continuous rotation. Subsequently, enzyme mix 2 was added and the sample was incubated for an additional 5 minutes at 37°C, followed by a second round of mechanical dissociation under the same conditions. The dissociated suspension was centrifuged at 350 g for 1 minute at 4°C, the supernatant was removed, and the cell pellet was resuspended in 9 mL of growth medium. The suspension was then filtered through a 100 µm cell strainer (pre-moistened with 1 mL growth medium) placed atop a 50 mL falcon tube. The filtrate was collected and transferred to a 15 mL falcon tube for downstream applications.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFluorescence-activated cell sorting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFluorescence-activated cell sorting (FACS) for brain slice culture was recently described by us\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Briefly, single-cell suspensions were centrifuged at 300 g for 10 minutes at 4°C. Following centrifugation, the supernatant was aspirated, and the pellet was gently resuspended. The resuspended cells were transferred to FACS tubes containing FACS buffer and centrifuged again at 300 g for 5 minutes at 4°C. Washed samples were resuspended in 450 µL DAPI-containing FACS incubation buffer (DFIB) and incubated on ice, protected from light. A total of 70,000 DAPI-negative, ZsGreen-positive cells were sorted by collecting two sets of 35,000 cells into pre-coated Eppendorf tubes for downstream analysis. The pellets were then gated and separated into different compartments based on the fluorescence of either Alexa488 or Alexa647 or both. scRNA-seq was performed on the different compartments of interest.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-cell RNA sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePIC-seq, a modified scRNA-seq, was employed in this study using 10X Genomics Chromium Next GEM Single Cell 3’ Reagent Kits User Guide (v3.1 Chemistry). Amplified cDNA and constructed single-cell libraries were evaluated using Fragment Analyzer 5200 (M5310AA, Agilent, USA) and Qubit™ 4 Fluorometer (Q33238, Thermo Fisher Scientific, USA) for quality assessment of base pair length and concentration. The constructed libraries were indexed, denatured, and pooled based on Illumina NextSeq 500 and 550 System Denature and Dilute Libraries Guide. NextSeq High Output kit v2.5 (20024906, Illumina, USA) flowcell chip (75 cycles) was used. Sequencing cycles were set as follows: read1 -- i7 -- i5 -- read2: 28–8 -- 0–56.\u003c/p\u003e\u003cp\u003e\u003cb\u003escRNA-seq data analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGene expression matrix from the CellRanger pipeline was analyzed using the Seurat package in R. Low-quality cells (cells expressing less than 200 genes, and genes expressed in less than 5 cells plus cells with mitochondrial reads greater than 10%) were filtered out. Data were log-transformed to mitigate technical noise and gene expression variability. The identification and removal of potential doublets were performed using the DoubletFinder algorithm. Cells were clustered using the FindNeighbors and FindClusters functions in Seurat. Principal Component Analysis (PCA) was applied to reduce the dimensionality of the data, and cell clusters were identified using shared nearest neighbor (SNN) modularity optimization. Clusters were visualized using Uniform Manifold Approximation and Projection (UMAP) to group cells with shared gene expression profiles. To account for batch effects resulting from data generated from different conditions, the Mutual Nearest Neighbors (MNN) correction method was applied. This method aligns the data across batches to enable the comparison of gene expression profiles. MNN-corrected data were used for downstream analysis. Differentially expressed genes (DEGs) were determined using the Wilcoxon rank-sum test, to identify genes associated with specific cell types and experimental conditions. Cell types were identified by the expression of key marker genes and by mapping the scRNA-seq data to a reference dataset.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell-to-cell communications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCell-to-cell interaction inference was done using the CellChat package (v1.6.0). In this analysis, the 'CellChatDB.human' database was used as a reference source for understanding cell-to-cell communication pathways between distinct cell types in various experimental conditions (CAR-T vs mock). We first identified activated signaling pathways in each specific experimental condition. Subsequently, the 2 conditions were merged to compare the signaling pathways between them as described in the CellChat tutorial on comparing \"multiple datasets with different cell type composition”. We identified dysfunctional signaling by comparing the communication probabilities and differential expression analyses between CAR-T and mock groups.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eCAR-T cells display an initial anti-tumor effect on glioblastoma cells in a complex human-like microenvironment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo understand GB-CAR T cells interaction in a human-like model with intact neuro-glial microenvironment, we employed a GB-injected brain slice culture model treated with CAR T-cells, coupled with high-throughput transcriptomic analysis. Slices of 300\u0026micro;m thickness were prepared and cultured in a growth medium. The medium was refreshed every 24 hours throughout the culture period. Brain slices were allowed to recover for the initial 24 hours post-collection before tumor cells injection\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. We inoculated the brain slices with the heterogeneous primary cell line (#BTSC233), which has already demonstrated a high level of plasticity in previous studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. To ensure the consistency of the model, we generated and inoculated brain slices across three different patients with the same cell line (#BTSC233). Tumor cell proliferation and growth was monitored microscopically. After 3 days of tumor growth, the brain slices were treated with either T cells with a CAR construct (CART) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e or control PBMC-derived T cells without CAR construct (Mock). Slices injected with only GB cells without T cells treatment showed a progressive growth and infiltration of the GB cell in the slices \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec\u003cb\u003e)\u003c/b\u003e. We observed a decline in GB cells growth based on the area covered by the tumor cells in the slices in both the CART and Mock groups. In both the Mock and CART treated slices, a tumor-suppressing local effect during the early phase following CART cell inoculation was observed \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed-e\u003cb\u003e)\u003c/b\u003e. Quantification of the tumor area revealed significant decrease (p\u0026thinsp;=\u0026thinsp;0.0421) in tumor growth in CART treated brain slices in the initial phase post-treatment, in comparison to Mock treated and untreated conditions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. These observations indicate a potential therapeutic local effect. However, the subsequent decline in T cell levels with an increase in GB cells growth after this initial phase of CART hyperactivity suggests that the CART cells became rapidly exhausted following the initial local effect.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSingle-cell analyses of glioblastoma cells and the tumor microenvironment in neocortical slices\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo identify the cellular diversity within the brain slices injected with CAR-T cells and normal T cells, at day 7 post-CAR-T injection, the slices were dissociated and FAC (Fluorescence-activated cell)-sorted into 3 major channels, T cells (Cy5\u003csup\u003e+\u003c/sup\u003e), tumor cells (ZsGreen\u003csup\u003e+\u003c/sup\u003e) and double positive cells considered as physical interacting cells (Cy5\u003csup\u003e+\u003c/sup\u003eZsGreen\u003csup\u003e+\u003c/sup\u003e)\u003csup\u003e19\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. We performed droplet-based physically interacting cells sequencing (PIC-seq) that combines cell sorting combined with scRNA-seq on the different groups. The resulting scRNA-seq data from different samples \u003cb\u003e(Supplementary Fig.\u0026nbsp;1a)\u003c/b\u003e were merged and horizontally integrated using the mutual nearest neighbor algorithm (MNN) with the top 2,000 most variable features as anchors. After quality control, a total of 35,085 cells remained (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. We identified malignant cells through inferred copy-number alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec, \u003cb\u003eSupplementary Fig.\u0026nbsp;1b)\u003c/b\u003e. Next, we identified the major cell types by the top markers expressed by each cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e from a list of top differentially expressed genes per cluster (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). We then mapped our data to the reference GBmap dataset to infer cell types using Azimuth\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e \u003cb\u003eSupplementary Fig.\u0026nbsp;1c)\u003c/b\u003e. Our initial cell type identification corresponds to the major GBmap cell types\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee, \u003cb\u003eSupplementary Fig.\u0026nbsp;1d\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo explore the T cell diversity, we performed subclustering of the T cell subsets in our dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. The T cell subsets consist of clusters enriched for \u003cem\u003eCD8A\u003c/em\u003e, \u003cem\u003eCD4\u003c/em\u003e, \u003cem\u003eTOP2A\u003c/em\u003e, \u003cem\u003eCTLA4\u003c/em\u003e, and \u003cem\u003eJUN\u003c/em\u003e, representing CD8 T cells, CD4 T cells, proliferating T cells, regulatory T cells, and T cells with stress signatures respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, \u003cb\u003eSupplementary Fig.\u0026nbsp;1e, Supplementary Table\u0026nbsp;2)\u003c/b\u003e. A comparison of T cell subpopulation between the CART and Mock populations revealed the presence of the identified T cell subtypes in all groups. CD4 T cells were however absent in the Mock group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg\u003cb\u003e).\u003c/b\u003e Since CD8 T cells are known to exhibit rapid exhaustion after an initial antitumor effect in comparison in GB\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and to allow proper comparison between the CART cell and Mock T cell groups, we focused on CD8 T cells in our downstream analysis.\u003c/p\u003e\u003cp\u003eTo understand the phenotypic diversity of the CD8 cells in the different groups, we first identified the differentially expressed genes (DEGs) between CART and Mock CD8 T cells \u003cb\u003e(Supplementary Table\u0026nbsp;3)\u003c/b\u003e. Gene ontology analysis of these DEGs revealed an enrichment of metabolic processes in the CART group while T cell differentiation and cell cycling were enriched in the Mock group \u003cb\u003e(Supplementary Fig.\u0026nbsp;1f).\u003c/b\u003e This suggests T cell dysfunction in the CART group and normal T cell differentiation and proliferation in the Mock group. We then checked the expression of different T cell markers in the CD8\u003csup\u003e+\u003c/sup\u003e subpopulation in the CART and the Mock T cell groups to understand the cause of T cell dysfunction in the CART group. Naive T cell markers (\u003cem\u003eSELL\u003c/em\u003e, \u003cem\u003eCCR7\u003c/em\u003e, \u003cem\u003eCD8A\u003c/em\u003e) and activation markers (\u003cem\u003eGZMB\u003c/em\u003e, \u003cem\u003eNKG7, GZMA, EOMES\u003c/em\u003e) were highly expressed in Mock CD8 T cells in comparison to the CART group, while T cells exhaustion markers (\u003cem\u003eLAG3, CD3D, CTLA4\u003c/em\u003e) were expressed in the CART group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh\u003cb\u003e)\u003c/b\u003e. Taken together, the rapid decline in antitumor effect we observed in the brain slices treated with CAR-T cells may be due to the dysfunction and possible exhaustion of the CAR CD8 T cell subpopulation as previously reported\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCell-cell signaling reveals CAR CD8 T cells dysfunction upon interaction with the glioblastoma microenvironment\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo understand the role of the TME in CART cells function or dysfunction during therapy in GB, we used CellChatDB\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e to identify the communication patterns between all cells within the GB TME in both CART and Mock-treated GB cells. First, we identified all the signaling pathways activated in CART and Mock groups. Our analysis revealed a significant difference in ligand-receptor interactions and the strength of the interactions between the CART and the Mock group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cb\u003e).\u003c/b\u003e The CART group also had a higher activation of signaling pathways indicating enhanced cellular communication between CAR-T cells and other cell types within the TME \u003cb\u003e(Supplementary Fig.\u0026nbsp;2b)\u003c/b\u003e. Thus, we hypothesized that this increased cellular signaling within the TME of CAR-T treated GB cells contributes to T cell dysfunction and CAR-T treatment failure. Interestingly, among the pathways enhanced in the CART group when compared to the Mock group are TIGIT, PD-L1, SELPLG, NOTCH, that are strongly linked to T cell exhaustion \u003cb\u003e(Supplementary Fig.\u0026nbsp;2a)\u003c/b\u003e. Given our initial observation that CD8 T cells are the most abundant T cell subpopulation and are present in all the groups in our dataset, we focused our analysis on the signaling between CD8 T cells and other cells within the GB TME. We confirmed the upregulation of T cell exhaustion pathways in the CART group compared to the Mock group by the significant activation of SELPLG and TIGIT pathways that indicate T cell exhaustion \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e. Additionally, we found an upregulation of signaling pathways (CLEC, MHC-II, and MIF) specific to CD8 CAR-T cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. Notably, the CLEC pathway involving the ligands \u003cem\u003eCLEC2B\u003c/em\u003e, \u003cem\u003eCLEC3C\u003c/em\u003e, and \u003cem\u003eCLEC3D\u003c/em\u003e expressed by several immune cells interact with the CD161 (\u003cem\u003eKLRB1\u003c/em\u003e) receptor expressed by exhausted CD8 T cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee\u003cb\u003e).\u003c/b\u003e Interestingly, regulatory T cells, proliferating T cells, and CD4 T cells all interact with CD8 T cells via the CLEC-KLRB1 pathway in the CART group. Surprisingly, we observed an upregulation of MHC-II signaling (\u003cb\u003esupplementary Fig.\u0026nbsp;2c\u003c/b\u003e) in the CART group involving the CD8 cells, and a downregulation of the canonical MHC-I pathway (\u003cb\u003eSupplementary Fig.\u0026nbsp;2d\u003c/b\u003e) further indicating the dysfunctional signaling in CD8 CAR-T cells. Taken together, these observations imply an increased signaling cascades associated with T cell exhaustion in the CART group upon interaction with GB cells in the GB microenvironment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMyeloid doublets exhibit enhanced phagocytic activity in proximity to glioblastoma cells\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA subpopulation of cells in our dataset were both Cy5 and ZsGreen positive \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. scRNA-seq analysis revealed that a large fraction of these cells with double positive signals are classified as doublets and are within the myeloid cells cluster \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. However, not all myeloid cells were classified as doublets. Sub-clustering analysis of the myeloid cluster revealed that most of the myeloid cells with a doublet score are classified as monocytes and tumor-associated macrophages (TAMs) of bone marrow origin \u003cb\u003e(Supplementary Fig.\u0026nbsp;3a and 3b, Supplementary Table\u0026nbsp;4)\u003c/b\u003e. The doublet myeloid cells show strong expression of both classical myeloid markers (\u003cem\u003eAIF1\u003c/em\u003e, \u003cem\u003eHEXB\u003c/em\u003e, \u003cem\u003ePTPRC\u003c/em\u003e) and GB markers (\u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eATRX\u003c/em\u003e, \u003cem\u003ePIK3CA\u003c/em\u003e) with no expression of classical T cell, oligodendrocyte, or astrocyte markers \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, \u003cb\u003eSupplementary Table\u0026nbsp;5)\u003c/b\u003e. Myeloid cells with both myeloid and GB signals have been previously described\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and were shown to promote an immunosuppressive TME in GB. Since these myeloid cells with doublet score express both myeloid and tumor cell markers, we hypothesize that the myeloid cells are in physical contact with the GB cells and may be engulfing the GB tumor cells attacked by CAR-T cells. To assess the phagocytosis hypothesis, we scored all the cells in the myeloid cluster for the expression of phagocytic and scavenger markers previously described in myeloid cells\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Doublet myeloid cells scored significantly higher for phagocytic and scavenger activity compared to non-doublets (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.2e-16 for both) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e. This indicates that the myeloid cells with a doublet score are either in the process of engulfing the GB cells or have already engulfed the cells to clear dying GB cells. We then asked whether the myeloid cells share spatial proximity with GB cells in patient samples. To investigate the spatial relationship between myeloid cells and tumor cells, we leveraged our publicly available spatial transcriptomic datasets. We selected only IDHwt GBs with corresponding spatial TRC quantification\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;5) to compute the spatial proximity between GB cells and other cell types within the TME using minimal spanning trees. On a broader cell type classification level, the adjacency matrix revealed a higher proximity of tumor cells with myeloid cells compared to lymphoid or neuronal cell types \u003cb\u003e(Supplementary Fig.\u0026nbsp;3d)\u003c/b\u003e. A correlation analysis of cell types proximity inferred at sub-cellular level, showed a close proximity of mesenchymal-like tumor cells, TAM-BDM, and monocytes that are linked to hypoxic conditions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef, \u003cb\u003eSupplementary Fig.\u0026nbsp;3c)\u003c/b\u003e. Our results are in line with previous studies that showed a direct interaction between myeloid cells and Mes-like GB cells\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These results suggest that myeloid cells are actively engaged in the engulfment process, potentially targeting dying GB cells within the hypoxic niche.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMAF drives CAR CD8 cytotoxic T cell exhaustion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo understand the key regulators of CAR-T cell dysfunction we extracted only CD8 T cells from our scRNA-seq dataset representing CD8 T cells in CART and Mock groups. We reconstructed the gene regulatory networks (GRN) of these CD8 subsets using CellOracle\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and identified the most highly active transcription factors (TFs) related to CART and Mock groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. TFs with the highest centrality scores were identified as the top essential TFs driving CD8 T cell function. A high degree centrality score indicates TFs controlling cellular functions such as cytotoxicity and exhaustion. While a high betweenness centrality score denotes essential TFs responsible for the flow of transcriptional information within the GRN. Among the top 30 TFs with the highest degree centrality score, BACH2 and MAF have the highest betweenness centrality score in CD8 CAR-T cells when compared to CD8 mock T cells \u003cb\u003e(Supplementary Fig.\u0026nbsp;4a)\u003c/b\u003e, indicating that these TFs are central to the maintenance and stabilization of CD8 T cell functional state. Since \u003cem\u003eBACH2\u003c/em\u003e and \u003cem\u003eMAF\u003c/em\u003e are inferred to control the CD8 functional state, we checked their expression in CD8 T cell subsets of our scRNA-seq data. We found the expression of \u003cem\u003eBACH2\u003c/em\u003e and \u003cem\u003eMAF\u003c/em\u003e to be consistent with the TFs activity \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e. \u003cem\u003eBACH2\u003c/em\u003e is highly expressed in CD8 Mock T cells while \u003cem\u003eMAF\u003c/em\u003e is expressed mainly by the CD8 CAR-T cells, indicating an alternate expression pattern between the different groups. A pseudotime analysis of the expression of \u003cem\u003eMAF\u003c/em\u003e and \u003cem\u003eBACH2\u003c/em\u003e confirmed an alternate expression of these genes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, \u003cb\u003eSupplementary Fig.\u0026nbsp;4b)\u003c/b\u003e. While \u003cem\u003eMAF\u003c/em\u003e is upregulated in CD8 T cells, \u003cem\u003eBACH2\u003c/em\u003e is downregulated and vice versa (\u003cb\u003eSupplementary Fig.\u0026nbsp;4c)\u003c/b\u003e. This indicates regulation of different functions. We asked if \u003cem\u003eMAF\u003c/em\u003e and \u003cem\u003eBACH2\u003c/em\u003e expression dynamics are present in T cells within the patient GB samples and other CAR T cells. Interestingly, we found that while \u003cem\u003eMAF\u003c/em\u003e was found to be upregulated in exhausted CD8 T cells in GB patient samples\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;4d)\u003c/b\u003e and CAR constructs with an exhaustion phenotype in mouse models\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e \u003cb\u003e(Supplementary Fig.\u0026nbsp;4e)\u003c/b\u003e, \u003cem\u003eBACH2\u003c/em\u003e is elevated in na\u0026iuml;ve CD8 T cells in patient samples and in normal CD8 T cells used as control while comparing several CART constructs in mouse GB models. To further understand the effect of these TFs in driving and maintaining CD8 T cell exhaustion, we performed \u003cem\u003ein silico\u003c/em\u003e perturbation of \u003cem\u003eMAF\u003c/em\u003e and \u003cem\u003eBACH2\u003c/em\u003e using our scRNA-seq dataset and the GRNs constructed using CellOracle. We inferred the normal developmental flow by pseudotime analysis. This inference analysis indicates potentially two different developmental pathways or cell states within our mixed CD8 populations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed\u003cb\u003e).\u003c/b\u003e Each of the developmental axes correlates with either CART or Mock groups as shown in \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea\u003cb\u003e).\u003c/b\u003e Upon \u003cem\u003eMAF\u003c/em\u003e simulation of knock-out (KO), the developmental trajectory revealed a differentiation shift towards a non-exhausted CD8 state (from CART to the mock group) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee\u003cb\u003e)\u003c/b\u003e, while upon simulation of \u003cem\u003eBACH2\u003c/em\u003e KO, the trajectory shows a differentiation shift towards an exhausted CD8 state (from Mock to CART group) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef\u003cb\u003e)\u003c/b\u003e. These indicate that the activity of \u003cem\u003eMAF\u003c/em\u003e drives CD8 T cells towards exhaustion, while the activity of \u003cem\u003eBACH2\u003c/em\u003e drives CD8 T cells towards the normal CD8 T cell developmental trajectory. Thus, the activity of these TFs upon perturbation is in line with our observation of an initial rapid loss of cytotoxic effect observed in the engineered CAR-T cells and a shift towards exhaustion.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eResistance to CAR-T cell therapy in GB arises from tumor-intrinsic or extrinsic factors such as an immunosuppressive microenvironment. Using a systems biology approach, we integrated PIC-seq and spatial transcriptomics to investigate CAR-T cell dysfunction. Comparative analysis of CAR-T and mock T cells revealed distinct gene regulatory programs in the GB TME. Neocortical brain slices recapitulated the cellular architecture of patient tumors and supported tumor progression. Both Mock and CAR-T cells engaged with tumor and stromal components in the microenvironment. Interactions between CAR-T cells and myeloid cells fostered an immunosuppressive niche that impaired CD8⁺ T cell function. A spatially localized myeloid subpopulation with phagocytic traits was enriched near tumor cells. We identified \u003cem\u003eMAF\u003c/em\u003e and \u003cem\u003eBACH2\u003c/em\u003e as key transcriptional regulators in CAR-T cells, with \u003cem\u003eMAF\u003c/em\u003e driving CAR-T cells to a dysfunctional state. CAR-T cell therapy has been successful in other malignancies but has so far failed to cure GB patients. The reasons for the low efficacy in GB and other solid tumors have not been fully understood\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Recent results from early clinical trials reported a rapid but shortened antitumor effect in recurrent GB\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Unlike the results from human clinical trials, CAR-T cell therapy in murine models shows a remarkably positive and sustained response across different treatment strategies\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. We show that CAR-T cells demonstrated a strong initial cytotoxic effect but were unable to maintain their cytotoxic activity due to the activation of CAR CD8 specific signaling pathways, myeloid cells mobilization, and the suppressive microenvironment leading to CD8 T cell exhaustion.\u003c/p\u003e\u003cp\u003eExhausted cytotoxic T cells represent a distinct phenotypic state of T cells following intense activation in the context of chronic infections or cancer\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. While initially responding to antigens, these T cells experience a progressive loss of function, marked by reduced cytotoxicity and diminished cytokine production\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This dysfunctional state is a consequence of multiple factors, including impaired regulation, an immunosuppressive TME, and tumor-specific adaptations. These circumstances collectively drive T cells into a state of exhaustion and dysfunction, hindering their ability to effectively eliminate the tumor cells\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCrosstalk between myeloid cells and T cells in GB is known to promote immunosuppression and T cell dysfunction\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The dysfunctionality of CD8 T-cells which seems to result from interaction with myeloid cells of the TME highlights the important role of the TME in driving and maintaining T cell exhaustion. In line with our observations, CD8 T cells have been reported to physically interact with TAMs forming a long-lasting synapse that initiates and maintains CD8 exhaustion\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Interestingly, the initiation of CD8 T cell exhaustion by TAMs cells that is accelerated in the hypoxic tumor niche as reported by Kersten et al., aligns with our observation of spatial proximity of Monocytes, Mes-like GB cells and TAMs in a hypoxic environment. We have previously shown that myeloid cells are spatially located in tumor-T cell neighbourhood\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In addition, our study further highlights the specific ligands-receptors involved in this interaction. Although further research is needed to fully describe and validate the signaling between TAMs and CD8 T cells, we identified the CLEC pathway as a potential target in interfering myeloid-GB interaction driving CD8 T cell dysfunction and subsequent hyporesponsiveness.\u003c/p\u003e\u003cp\u003eIn recent studies, myeloid-tumor doublets have also been identified and linked to immunosuppressive function in GB, however their spatial localization remains unclear\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Our results indicating the spatial proximity of myeloid cells with Mes-like GB cells within the hypoxic tumor niche and having enhanced phagocytic and scavenger signatures may indicate the clearance of apoptotic GB cells to avoid the engulfment and processing of the GB cells by antigen-presenting cells (APCs). This prevents the APCs from presenting tumor antigens to CAR-T cells hence contributing to the rapid exhaustion of the CAR-T cells and sustaining an immunosuppressed TME. Both our data and previous studies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e indicate that the engulfment of GB cells by myeloid cells plays an important role in shaping the GB microenvironment by driving immunosuppression. More experimental studies are needed to fully understand the nature and activity of these myeloid-tumor doublets.\u003c/p\u003e\u003cp\u003eWe identified \u003cem\u003eMAF\u003c/em\u003e and \u003cem\u003eBACH2\u003c/em\u003e as key transcriptional regulators driving CD8 T cell exhaustion. While \u003cem\u003eMAF\u003c/em\u003e is upregulated in the CAR CD8 T cells, \u003cem\u003eBACH2\u003c/em\u003e is upregulated in Mock CD8 T cells. \u003cem\u003eMAF\u003c/em\u003e, a member of the AP-1 (Activator Protein-1) family, is involved in the regulation of immune cells, including T cells\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Activation of \u003cem\u003eMAF\u003c/em\u003e and its overexpression in tumor-associated exhausted CD8 T cells under the influence of TGFb and IL6, drive CD8 T cells into exhaustion\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eBACH2\u003c/em\u003e regulates cytotoxic CD8 T cell and other immune cell development and function\u003csup\u003e\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Overexpression of \u003cem\u003eBACH2\u003c/em\u003e limits T cell exhaustion and maintains T cell effector function by inhibiting \u003cem\u003eIRF4\u003c/em\u003e and AP-1 family TFs known to drive and maintain CD8 T cell exhaustion\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eBACH2\u003c/em\u003e's association with the formation of memory in T cells highlights the potential consequence of its downregulation on the capacity of CD8 T cells to form an enduring memory which is crucial for sustained protective immune responses\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eBACH2\u003c/em\u003e directly binds to the \u003cem\u003eMAF\u003c/em\u003e locus, suppressing \u003cem\u003eMAF\u003c/em\u003e gene expression and the \u003cem\u003eBACH2-MAF\u003c/em\u003e heterodimer inhibits AP-1-dependent gene activation\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. This repression by \u003cem\u003eBACH2\u003c/em\u003e is important in maintaining normal T cell function. Hence, in the absence of \u003cem\u003eBACH2\u003c/em\u003e, \u003cem\u003eMAF\u003c/em\u003e is upregulated and influences T cell transition to a dysfunctional state. Our study defines both intrinsic and extrinsic mechanisms driving CD8⁺ CAR-T cell exhaustion in GB. We highlight the role of myeloid-T cell and myeloid\u0026ndash;tumor interactions in shaping an immunosuppressive microenvironment. Key signaling pathways and transcriptional regulators identified are potential targets to enhance CAR-T cell design and therapeutic efficacy.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations of the study\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe CAR construct used in this study was delivered via mRNA electroporation, which offers high transfection efficiency but results in transient protein expression. Alternative delivery methods that support prolonged CAR expression may yield different outcomes. Additionally, there was an imbalance in T cell subpopulations between CAR-T and Mock-treated slices, with CD4⁺ and regulatory T cells absent in the Mock group. Given the known importance of CD4⁺ T cells in sustaining antitumor responses and the differential susceptibility of CD8⁺ T cells to exhaustion, this discrepancy may influence comparative interpretations. Prior studies have shown that CD4⁺ CAR-T cells can mediate more durable responses, whereas CD8⁺ T cells may dampen CD4⁺-driven efficacy in GB. Therefore, future designs of CAR-T studies and therapies should consider the CD4:CD8 ratio as a critical variable in optimizing therapeutic efficacy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCAR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eChimeric antigen receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGB\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlioblastoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNKG2D\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNatural killer group 2D\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMusculoaponeurotic fibrosarcoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBACH2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBTB Domain and CNC Homolog 2\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTME\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor microenvironment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePDX\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePatient-derived xenograft\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePIC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eseq-Physically interacting cells sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003escRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eseq-single-cell RNA sequencing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTAMs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor-associated macrophages\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDPI3\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePost-tumor injection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCTFR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCell trace far red\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePBMC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePeripheral Blood Mononuclear Cell\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSimple neurite tracer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGFP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGreen fluorescent protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFACS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFluorescence-activated cell sorting\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePrincipal component analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSNN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eShared nearest neighbor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUMAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUniform manifold approximation and projection\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMNN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMutual nearest neighbors\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDifferentially expressed genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGbmap\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlioblastoma map\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTFs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTranscription factors\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGRN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene regulatory network\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKnock-out\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAPCs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eantigen-presenting cells\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e1-Activator Protein-1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003emRNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emessenger ribonucleic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eT.W. received honoraria for a lecture and advisory board participation from Philogen.\u003c/p\u003e\u003ch2\u003eAuthorship\u003c/h2\u003e\u003cp\u003eJVE, TW, DHH designed the experiments\u003c/p\u003e\u003cp\u003eJVE, JZ, TL conducted the experiments\u003c/p\u003e\u003cp\u003eYAY, JZ, JKB, NN and JK analyzed the data\u003c/p\u003e\u003cp\u003eYAY, TW, DHH supervised experiments and data analysis\u003c/p\u003e\u003cp\u003eYAY, JZ, TL, TW, DHH wrote the manuscript\u003c/p\u003e\u003cp\u003eAll authors read and approved the manuscript\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eDHH received funding from the TRANSCAN (BMBF: 01KT2328), German Research Foundation (Heisenberg Program: DFG HE 8145/6\u0026thinsp;\u0026minus;\u0026thinsp;1, Funding: HE 8145/5\u0026thinsp;\u0026minus;\u0026thinsp;1), and the DKTK partner side Freiburg (DKTK-PI) and Joint Funding Program (HematoTrac). TW received support from Promedica Foundation, EMPIRIS Foundation, C3Z Precision Oncology Funding Program, Sophien Foundation, Baasch-Medicus Foundation, the Helmut Horten Foundation and the Swiss Cancer League (KFS-5763-02-2023).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJVE, TW, DHH designed the experimentsJVE, JZ, TL conducted the experiments YAY, JZ, JKB, NN and JK analyzed the dataYAY, TW, DHH supervised experiments and data analysisYAY, JZ, TL, TW, DHH wrote the manuscriptAll authors read and approved the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Jorge Andres Ibanez and Giedre Krenciute for sharing their processed single- cell RNA sequencing data.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData from this paper is available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMajzner RG, Mackall CL. 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Cancer \u003cem\u003e11\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jitc-2022-005878\u003c/span\u003e\u003cspan address=\"10.1136/jitc-2022-005878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chimeric Antigen Receptor, T cell exhaustion, Immunosuppression, Glioblastoma, Brain slices, Transcriptional regulation","lastPublishedDoi":"10.21203/rs.3.rs-7240692/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7240692/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eChimeric antigen receptor (CAR)-T cell therapy remains largely ineffective in glioblastoma (GB), where a highly immunosuppressive microenvironment and tumor heterogeneity impair therapeutic durability.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing a human neocortical brain slice model that preserves the complex GB microenvironment, we profiled interactions between natural killer group 2D (\u003cem\u003eNKG2D\u003c/em\u003e) CAR-T cells and tumor ecosystems via PIC-seq, spatial transcriptomics, and gene regulatory network reconstruction.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCAR-T cells initially suppressed tumor growth but rapidly transitioned to a dysfunctional state marked by exhaustion-associated transcriptional programs. This shift was driven by signaling interactions between CAR-T cells and myeloid cells. Tumor-associated macrophages displayed enhanced phagocytic activity and spatially colocalize with mesenchymal-like GB cells within hypoxic regions. Our gene regulatory network analysis identified \u003cem\u003eMAF\u003c/em\u003e and \u003cem\u003eBACH2\u003c/em\u003e as key transcriptional regulators, with \u003cem\u003eMAF\u003c/em\u003e promoting CAR CD8 exhaustion and \u003cem\u003eBACH2\u003c/em\u003e preserving CD8 T cells effector function. In silico perturbation confirmed the reciprocal effect of \u003cem\u003eMAF\u003c/em\u003e and \u003cem\u003eBACH2\u003c/em\u003e on CD8⁺ T cell fate.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese findings reveal mechanisms of rapid CAR-T cell dysfunction in GB and identify actionable targets for engineering more durable cellular therapies.\u003c/p\u003e","manuscriptTitle":"Myeloid-derived immunosuppression of Chimeric Antigen Receptor T cells in the neuronal microenvironment of Glioblastoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 18:52:35","doi":"10.21203/rs.3.rs-7240692/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-17T09:20:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-15T19:19:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-05T07:52:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217420957906897648261197711900613607450","date":"2025-09-02T08:55:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334495624710649292341747491999837671397","date":"2025-08-13T19:50:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T19:43:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-30T05:34:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-30T05:06:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2025-07-29T07:58:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18b443b3-a476-4d98-91e4-a5c2a9622a7a","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:08:44+00:00","versionOfRecord":{"articleIdentity":"rs-7240692","link":"https://doi.org/10.1186/s12916-026-04783-2","journal":{"identity":"bmc-medicine","isVorOnly":false,"title":"BMC Medicine"},"publishedOn":"2026-03-13 15:58:27","publishedOnDateReadable":"March 13th, 2026"},"versionCreatedAt":"2025-08-08 18:52:35","video":"","vorDoi":"10.1186/s12916-026-04783-2","vorDoiUrl":"https://doi.org/10.1186/s12916-026-04783-2","workflowStages":[]},"version":"v1","identity":"rs-7240692","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7240692","identity":"rs-7240692","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}