Multiregion Cross-species Single-cell Multimodal Study of Prefrontal Cortex Reveals Cell-type Divergence and PTSD-associated Regulatory Landscapes

Multiregion Cross-species Single-cell Multimodal Study of Prefrontal Cortex Reveals Cell-type Divergence and PTSD-associated Regulatory Landscapes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multiregion Cross-species Single-cell Multimodal Study of Prefrontal Cortex Reveals Cell-type Divergence and PTSD-associated Regulatory Landscapes Xiang Li, Feiyang Zhang, Kaixin huang, Zechen Liu, Xin Liu, Qiongyi Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7451905/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The prefrontal cortex subregions—particularly the prelimbic (PLPFC) and infralimbic (ILPFC) cortices in rodents and dorsal anterior cingulate (dACC) and ventromedial prefrontal cortex (vmPFC) in humans—exhibit functionally specialized yet interconnected roles in PTSD pathogenesis. While PLPFC/dACC are implicated in fear memory, ILPFC/vmPFC are associated with fear extinction. However, the inherent difference and cross-species molecular signatures underlying these functional parallels remain unresolved. To bridge the gap, we integrate single-nucleus RNA-seq, ATAC-seq, and spatial transcriptomics across mouse PLPFC/ILPFC and human dACC/vmPFC to construct a cross-species, multi-omic atlas. We then delineate conserved/divergent gene regulatory networks (GRNs), with emphasis on excitatory neuron evolution. By incorporating PTSD GWAS data and gene expression changes from vmPFC of PTSD patients, we identify cell-type-specific PTSD risks, SNP-anchored GRNs linked to PTSD heritability, and stress-induced chromatin-primed genes. This work provides a multiregion atlas and advances translational understanding of PTSD-related gene regulation divergence from mouse transition to human and complement the present multi-omic research of PTSD. Biological sciences/Genetics/Gene regulation Biological sciences/Genetics/Genomics Health sciences/Diseases/Psychiatric disorders/Post-traumatic stress disorder Biological sciences/Genetics/Sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files ExtendedDataFiguresv3.pdf Extended_Data_Figures Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7451905","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":513520841,"identity":"cbe1384d-2771-4b5f-92e2-02f776e30373","order_by":0,"name":"Xiang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACPmYgkQDE/Aw8ID4zYS1sMC2SDURrgTEMDhCthZ2BTeJBzR27zTdyj0kwVFgnNrCfPUDIYcwGCceeJW+7kZcmwXAmPbGBJy+BkBbGBwlsh5PNbueYSTC2HU5skOAxIKSF4UDCv8PJxrNBWv4Rp4XxQWLbYTsDaZCWBqK0MDYbJPYdTpC4/8bYIuFYunEbTw5+Lfz8h49J/vh22J6/54zhjQ811rL97Gfwa2FgYGwAkYlgMoEBKaYIAXtiFY6CUTAKRsEIBAAA2zy86+1pWgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6849-353X","institution":"Zhongnan Hospital of Wuhan University","correspondingAuthor":true,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""},{"id":513520842,"identity":"d165c85a-55d8-4cc5-86bd-77f2641fe8ee","order_by":1,"name":"Feiyang Zhang","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Feiyang","middleName":"","lastName":"Zhang","suffix":""},{"id":513520843,"identity":"480fc1c8-3caf-42b3-a2a9-b9a567163697","order_by":2,"name":"Kaixin huang","email":"","orcid":"","institution":"Berlin Institute of Health at Charité","correspondingAuthor":false,"prefix":"","firstName":"Kaixin","middleName":"","lastName":"huang","suffix":""},{"id":513520844,"identity":"23d2fc5e-1e40-4442-9bb5-d343c13c9c74","order_by":3,"name":"Zechen Liu","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Zechen","middleName":"","lastName":"Liu","suffix":""},{"id":513520845,"identity":"fbd48f49-e8ea-4e5c-9c7f-1087590ceacc","order_by":4,"name":"Xin Liu","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":513520846,"identity":"31008475-a313-4244-a236-15c75efb600e","order_by":5,"name":"Qiongyi Zhao","email":"","orcid":"https://orcid.org/0000-0002-6341-0416","institution":"The University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Qiongyi","middleName":"","lastName":"Zhao","suffix":""},{"id":513520847,"identity":"9a323a52-7150-46c1-aae5-5e30019f3699","order_by":6,"name":"Xinyan Li","email":"","orcid":"","institution":"Tongji Medical College, Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinyan","middleName":"","lastName":"Li","suffix":""},{"id":513520848,"identity":"20da76ee-7a57-4a1f-95d6-76d18d0fac75","order_by":7,"name":"Wei Wei","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2025-08-25 09:10:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7451905/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7451905/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91079849,"identity":"692e3837-f4ed-4907-8f43-6cbc07d80f6b","added_by":"auto","created_at":"2025-09-11 11:27:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10987069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell multi-omic profiling of mouse prefrontal subregions. Integrates gene expression, chromatin accessibility, regulatory relationships, and spatial transcriptomics\u003c/strong\u003e. \u003cstrong\u003ea\u003c/strong\u003e, Schematic of the experimental workflow of this research. The study utilized both mouse and human subjects. Mouse prelimbic (PLPFC) and infralimbic (ILPFC) prefrontal cortex (PFC) subregions were microdissected, collected separately, and subjected to snRNA-seq and snATAC-seq. A coronal section encompassing is included for spatial transcriptomics. For human donors, dorsal anterior cingulate cortex (dACC) and ventromedial PFC (vmPFC) tissues were collected from each individual. Experiments are divided into Within Species, Regional Difference; Cross-species Evolution; PTSD-GWAS, PTSD Related Chromatin Primed Genes. \u003cstrong\u003eb\u003c/strong\u003e, UMAP projection of mouse snRNA-seq data. Cell types were annotated, revealing 7 excitatory neuron types, 5 inhibitory interneuron types, and 4 glial cell types. Bar plots below show the proportion of each major cell type within the PLPFC and ILPFC subregions. \u003cstrong\u003ec\u003c/strong\u003e, Proportions of major cell types identified in mouse snRNA-seq. \u003cstrong\u003ed\u003c/strong\u003e, UMAP colored by the relative enrichment of cells in PLPFC versus ILPFC subregions. \u003cstrong\u003ee-g\u003c/strong\u003e, UMAP projection (e), cell type proportions (f), and UMAP colored by relative enrichment in ILPFC versus PLPFC (g) for mouse snATAC-seq data. \u003cstrong\u003eh\u003c/strong\u003e, Relative distribution of each major cell type between PLPFC and ILPFC subregions in snRNA-seq (top) and snATAC-seq (bottom). X-axis: Major cell types; Y-axis: Ratio of cell type proportion in PLPFC to that in ILPFC. Dashed line indicates equal proportion (ratio=1). \u003cstrong\u003ei\u003c/strong\u003e, Identification of differentially expressed genes (DEGs) between PLPFC and ILPFC within each cell type. Points represent genes; X-axis incorporates random jitter within each cell type; Y-axis: log2 fold change (FC) of expression in PLPFC relative to ILPFC. \u003cstrong\u003ej\u003c/strong\u003e, Bubble plot of Gene Ontology (GO) enrichment for DEGs identified in panel i. X-axis: GO terms; Y-axis: Major cell types; Color: Adjusted p-value; Size: Gene Ratio (percentage of DEGs in the cell type associated with the GO term). \u003cstrong\u003ek-l\u003c/strong\u003e, Top regulon sets by regulon specificity score (RSS) identified through SCENIC+ integrative analysis of snATAC-seq and snRNA-seq. \u003cstrong\u003em-n\u003c/strong\u003e, Schematic of the mouse spatial transcriptomics region (black box). Boundaries between PLPFC and ILPFC were defined using histological staining from the Allen Brain Atlas. n-p, Identification and functional annotation of spatial gene expression signatures specific to PLPFC (o), ILPFC (q).\u003c/p\u003e","description":"","filename":"Figure111.png","url":"https://assets-eu.researchsquare.com/files/rs-7451905/v1/10709d7090a4261f5da95ad5.png"},{"id":91078341,"identity":"7dcd3249-3968-408b-8d71-88dbd5356646","added_by":"auto","created_at":"2025-09-11 11:19:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1870991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulatory network analysis in mouse and human.\u003c/strong\u003e \u003cstrong\u003ea-b\u003c/strong\u003e, Heatmaps of high-scoring cell-type-specific regulons identified in mouse PLPFC (a) and ILPFC (b). Left bar plot: Information entropy per regulon. Black marks: SCENIC+-identified direct regulon; Red/Blue marks: Activator/Repressor. Upper left triangle: Expression level of regulated genes; Lower right triangle: Chromatin accessibility of TF-bound regions. \u003cstrong\u003ec\u003c/strong\u003e, Example of an activator regulon: Klf10 binds an open region upstream of Fndc9 (functioning as an enhancer), promoting higher expression of Fndc9 in PLPFC within the excitatory L2/3IT neuron type. \u003cstrong\u003ed\u003c/strong\u003e, Detailed regulatory network for mouse module 3 in PLPFC and ILPFC. Triangles: TFs; Squares: TF-bound regions; Circles: Target genes. Blue and red boxes highlight region sets correlating within PLPFC and ILPFC networks, respectively. \u003cstrong\u003ee-f\u003c/strong\u003e, Functional annotation (GO analysis) of genes regulated by neuron-associated modules, analyzed separately for PLPFC and ILPFC.\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-7451905/v1/1c0e077388bb4d7a4d60bf28.png"},{"id":91078345,"identity":"f01c28d6-5d2c-4c58-ada2-d29f3c57a3c8","added_by":"auto","created_at":"2025-09-11 11:19:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4850691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell multi-omic profiling of human cortical subregions. Integrates gene expression, chromatin accessibility, and regulatory relationships.\u003c/strong\u003e \u003cstrong\u003ea\u003c/strong\u003e, Integrated UMAP projection of snRNA-seq data from all human samples across dACC and vmPFC subregions. Cell types annotated include 8 excitatory neuron types, 5 inhibitory interneuron types, and 4 glial cell types. Bar plots below show the proportion of each major cell type within vmPFC and dACC. \u003cstrong\u003eb\u003c/strong\u003e, Cell type proportions across all donors and subregions. \u003cstrong\u003ec\u003c/strong\u003e, UMAP colored by the relative enrichment of cells in dACC versus vmPFC. \u003cstrong\u003ed-f\u003c/strong\u003e, Integrated UMAP projection (d), cell type proportions (e), and UMAP colored by relative enrichment in dACC versus vmPFC (f) for human snATAC-seq data. \u003cstrong\u003eg\u003c/strong\u003e, Relative distribution of each major cell type between PLPFC and ILPFC subregions in snRNA-seq (top) and snATAC-seq (bottom). \u003cstrong\u003ei\u003c/strong\u003e, Relative distribution of each major cell type between dACC and vmPFC in snRNA-seq (top) and snATAC-seq (bottom). Format as in Fig. 1h. Dashed line indicates equal proportion. \u003cstrong\u003eh-i\u003c/strong\u003e, Identification of DEGs and DARs between dACC and vmPFC within each cell type. Format as in Fig. 1i. \u003cstrong\u003ej\u003c/strong\u003e, Bubble plot of GO enrichment for DEGs identified in panel h. Format as in Fig. 1j. m, Proportion of the excitatory neuron subtype Exc-NF within each donor's dACC and vmPFC. Donor IDs: 005-009. \u003cstrong\u003ek\u003c/strong\u003e. Number of intersected DEGs and DARs (measured using the gene the region located) for excitatory neurons, inhibitory neurons and glia cells. \u003cstrong\u003el\u003c/strong\u003e, Ratio of Exc-NF sub-cluster from all donors. \u003cstrong\u003em\u003c/strong\u003e, Bubble plot the marker genes from Exc-NF with color as average expression value in the cluster and size represents the percentage expressed in the cluster. \u003cstrong\u003en\u003c/strong\u003e, Functional annotation of marker genes for the Exc-NF neuronal subtype. \u003cstrong\u003eo-p\u003c/strong\u003e, Heatmaps of high-scoring cell-type-specific regulons identified in human dACC (o) and vmPFC (p). \u003cstrong\u003eq\u003c/strong\u003e, Detailed regulatory network for human module 45 in vmPFC and dACC. Triangles: TFs; Squares: TF-bound regions; Circles: Target genes. Blue and red boxes highlight region sets correlating within PLPFC and ILPFC networks, respectively.\u003c/p\u003e","description":"","filename":"Figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-7451905/v1/6d52432269ed07831b5008df.png"},{"id":91078339,"identity":"d5117760-3a3c-401f-8911-c134e2bf50e0","added_by":"auto","created_at":"2025-09-11 11:19:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3650438,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of cell type homology and gene expression divergence between mouse and human.\u003c/strong\u003e \u003cstrong\u003ea-c\u003c/strong\u003e, Integrated co-embedding of representative human-cell and mouse cells (a). Heatmap depicting similarity between homologous mouse (x-axis) and human (y-axis) major cell types (b), Transcription factor expression in human and mouse (c). \u003cstrong\u003ed-e\u003c/strong\u003e, Jaccard index (JI) assessing similarity using marker genes (d) or marker chromatin accessibility peaks (peaks) (e) identified by starTracer. Upper panel: JI comparing pooled dACC/vmPFC to pooled PLPFC/ILPFC; Middle panel: PLPFC vs dACC; Lower panel: ILPFC vs vmPFC. Higher JI indicates greater similarity. \u003cstrong\u003ef\u003c/strong\u003e, Divergence in expression levels of conserved genes within homologous cell types. Y-axis (left): Log1p expression in mouse; X-axis (right): Log1p expression in human. Dashed lines indicate Deviation Score (DS) = ±2. Genes outside these lines exhibit significant expression divergence. Genes are categorized into five elements: Housekeeping genes, Mouse marker genes, Mouse PLPFC/ILPFC DEGs, Human marker genes, Human dACC/vmPFC DEGs. Color denotes the combination of elements assigned to each gene. \u003cstrong\u003eg-i\u003c/strong\u003e, Bubble plots of GO enrichment for genes with |DS| \u0026gt; 2 within each cell type. DEGs from excitatory and inhibitory neurons were annotated together (g-h), as were those from the four glial cell types (i). \u003cstrong\u003ej\u003c/strong\u003e, Left: Cumulative distribution functions (CDFs) of DS for each element type. Right: CDFs of DS for genes regulated by regulons classified by specificity: Non-specific (Rg for regulon), Cell-type-specific (Rg.Spe), and highly specific/Most specific (Rg.Spe.Most). \u003cstrong\u003ek\u003c/strong\u003e, Distribution of DS values closely follow a Laplacian distribution rather than a normal distribution. \u003cstrong\u003el\u003c/strong\u003e, Kolmogorov-Smirnov (K-S) test results comparing DS distributions across element types and regulon specificity classes. Due to universally significant p-values (p\u0026lt;\u0026lt;0.05), the effect size (D statistic) is shown. Larger D indicates greater distribution difference. \u003cstrong\u003em-n\u003c/strong\u003e, Bar plots showing the proportion of highly divergent genes (|DS| \u0026gt; 2) among the different elements (m) and among genes regulated by regulons of varying specificity levels (n).\u003c/p\u003e","description":"","filename":"figure42.png","url":"https://assets-eu.researchsquare.com/files/rs-7451905/v1/a67487d95a809121949c84a8.png"},{"id":91078347,"identity":"ba7d9a7f-1219-4a2a-89f6-9d999fc9f8bd","added_by":"auto","created_at":"2025-09-11 11:19:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2940246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrajectory analysis of L2/3IT excitatory neurons from mouse PLPFC to human dACC. a\u003c/strong\u003e, UMAP of integrated mouse and human L2/3IT neurons, colored by pseudotime inferred from defined Start Points 1 \u0026amp; 2. Top right: Species origin (human/mouse). Bottom right: L2/3IT subclusters identified by MultiK. \u003cstrong\u003eb\u003c/strong\u003e, Module activity scores derived from Non-negative Matrix Factorization (NMF) of trajectory DEGs across L2/3IT subclusters. \u003cstrong\u003ec\u003c/strong\u003e, Proportion of human cells within each of 50 pseudotime bins. Red line: Linear fit. \u003cstrong\u003ed\u003c/strong\u003e. Change in scaled Euclidean and Manhattan distances between human and mouse cells in the high-dimensional space upon sequential removal of each NMF module. Red dashed box highlights the module whose removal maximally increases distance. \u003cstrong\u003ee\u003c/strong\u003e, Permutation test p-value (p=0) for the effect of module 3 removal (from d). \u003cstrong\u003ef\u003c/strong\u003e, Module 3 activity score across pseudotime bins. Red line: Linear fit. \u003cstrong\u003eg\u003c/strong\u003e, Bubble plot of GO enrichment for genes within module 3. \u003cstrong\u003eh\u003c/strong\u003e, Scatter plot of spatial distribution metric (Moran's I) for trajectory DEGs versus their DS. X-axis: Moran's I; Y-axis: DS. Positive correlation is observed. \u003cstrong\u003ei\u003c/strong\u003e, Feature UMAP plot showing expression of \u003cem\u003eSTXBP6\u003c/em\u003e (a module 3 gene) in L2/3IT neurons. \u003cstrong\u003ej-k\u003c/strong\u003e, Expression of \u003cem\u003eSTXBP6\u003c/em\u003e along pseudotime (j) and violin/box plots comparing expression in human (h.s.) versus mouse (m.m.) (k). Boxplot center line: Median; Box limits: Upper/lower quartiles.\u003c/p\u003e","description":"","filename":"Figure51.png","url":"https://assets-eu.researchsquare.com/files/rs-7451905/v1/32dbd22b2c8e5719624fcef9.png"},{"id":91078343,"identity":"8389b795-76c1-4129-9414-3dd10567d7a2","added_by":"auto","created_at":"2025-09-11 11:19:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3122065,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrajectory analysis of L2/3IT excitatory neurons from mouse ILPFC to human vmPFC. a\u003c/strong\u003e, UMAP of integrated mouse and human L2/3IT neurons, colored by pseudotime inferred from defined Start Points 1 \u0026amp; 2. Top right: Species origin. Bottom right: L2/3IT subclusters. \u003cstrong\u003eb\u003c/strong\u003e, Module activity scores from NMF of trajectory DEGs across subclusters. \u003cstrong\u003ec\u003c/strong\u003e, Proportion of human cells within pseudotime bins. Red line: Linear fit. \u003cstrong\u003ed\u003c/strong\u003e. Change in scaled Euclidean/Manhattan distances upon module removal. Red dashed box highlights module 4. \u003cstrong\u003ee\u003c/strong\u003e, Permutation test p-value (p=0) for module 4 removal effect. \u003cstrong\u003ef\u003c/strong\u003e, Module 4 activity score across pseudotime bins. Red line: Linear fit. \u003cstrong\u003eg\u003c/strong\u003e, Bubble plot of GO enrichment for genes within module 4. \u003cstrong\u003eh\u003c/strong\u003e, Scatter plot of Moran's I versus DS for trajectory DEGs. \u003cstrong\u003ei\u003c/strong\u003e, Feature UMAP plot showing expression of \u003cem\u003eLDB2\u003c/em\u003e (a module 4 gene) in L2/3IT neurons. \u003cstrong\u003ej-k\u003c/strong\u003e, Expression of \u003cem\u003eLDB2\u003c/em\u003e along pseudotime (j) and in human vs mouse (k). Format as in Fig 5j-k.\u003c/p\u003e","description":"","filename":"Figure61.png","url":"https://assets-eu.researchsquare.com/files/rs-7451905/v1/5c9521e2d660002b123a990d.png"},{"id":91079850,"identity":"00ec4154-5e87-4612-948b-5a68d94f7045","added_by":"auto","created_at":"2025-09-11 11:27:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1820250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePTSD-associated regulatory networks in human vmPFC and dACC. a\u003c/strong\u003e, MAGMA analysis assessing the contribution of cell-type-specific subregional (dACC/vmPFC) marker genes and peaks to PTSD risk using East Asian GWAS summary statistics. X-axis: -log10(adjusted p-value). \u003cstrong\u003eb\u003c/strong\u003e, Histogram of the number of SNPs within cell-type-specific subregional marker genes and peaks across all cell types. \u003cstrong\u003ec\u003c/strong\u003e, Scatter plot of SNP count versus ZSTAT (a measure of association strength; ZSTAT \u0026gt; 2 indicates significance) for PTSD-associated loci. Red: Significant genes; Blue: TFs. \u003cstrong\u003ed-e\u003c/strong\u003e, Number of SNPs within genes regulated by regulons of varying specificity levels (Rg for regulon, Rg.Spe, Rg.Spe.Most) in dACC (d) and vmPFC (e). Significance: * p \u0026lt; 0.05; **** p \u0026lt; 0.00005. \u003cstrong\u003ef\u003c/strong\u003e, Bubble plot of GO enrichment for genes significantly associated with PTSD risk within each cell type. Format as in Fig. 1j. \u003cstrong\u003eg\u003c/strong\u003e, Potential PTSD-associated regulatory network. Nodes: Triangles (TFs), Squares (Regulatory regions), Circles (Target genes). Color: Cell type origin of the regulon. Purple TFs: Function in multiple cell types. Stroke color: Subregion origin (dACC/vmPFC). Circle size: Number of GWAS SNPs within the gene.\u003c/p\u003e","description":"","filename":"Figure71.png","url":"https://assets-eu.researchsquare.com/files/rs-7451905/v1/3218e67abdda28385d7df976.png"},{"id":91078340,"identity":"1d52702c-d693-4da9-a195-900a886708a2","added_by":"auto","created_at":"2025-09-11 11:19:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":284366,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChromatin Primed Genes in human PTSD. a\u003c/strong\u003e. Gene expression value and the accessibility of TF binding regions (bin1~5) in dACC and vmPFC. \u003cstrong\u003eb\u003c/strong\u003e. Gene expression value and the accessibility of gene (bin1~5) in dACC and vmPFC. \u003cstrong\u003ec\u003c/strong\u003e. The profiling of gene’s accessibility, TF binding region accessibility and gene expression (all scaled). Each vertical column represents three attributes of a single gene: overall chromatin accessibility (ACTV, circles), gene expression level (EXP, triangles), and enhancer accessibility (ACC, squares). For every cell type, the values of these three attributes are min–max scaled to a 0–1 range. Genes are divided into two groups according to the relative values of ACTV and EXP: genes with ACTV \u0026gt; EXP are placed in group 1, whereas genes with ACTV \u0026lt; EXP are placed in group 2. Consequently, genes in group 1 are considered candidate chromatin-primed genes (CPGs). Columns are color-coded by the scaled difference (ACTV − EXP); deeper red indicates a higher degree of chromatin priming. \u003cstrong\u003ed\u003c/strong\u003e. Barplot of the number of unique CPGs in each major cell type. \u003cstrong\u003ee\u003c/strong\u003e. Similarity measured by Jaccard Index of the CPGs in each major cell type. \u003cstrong\u003ef\u003c/strong\u003e. VennDiagram of the intersection of GWAS significant genes, CPGs and PTSD up-regulated genes found by PTSD patients vmPFC bulk-RNA-seq. \u003cstrong\u003eg\u003c/strong\u003e. Integrated analysis of CPGs and PTSD patients vmPFC bulk-RNA-seq, x-axis represents the beta value (derived from fold-change) while y-axis represents the PrimedScore.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7451905/v1/d4fe3ec8db8deb1b4a2a743e.png"},{"id":91078344,"identity":"bab4e576-edf5-4fac-9c82-1b4f59a8c855","added_by":"auto","created_at":"2025-09-11 11:19:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14280141,"visible":true,"origin":"","legend":"Extended_Data_Figures","description":"","filename":"ExtendedDataFiguresv3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7451905/v1/937325c39d919b6a5c00fa91.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Multiregion Cross-species Single-cell Multimodal Study of Prefrontal Cortex Reveals Cell-type Divergence and PTSD-associated Regulatory Landscapes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7451905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7451905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The prefrontal cortex subregions—particularly the prelimbic (PLPFC) and infralimbic (ILPFC) cortices in rodents and dorsal anterior cingulate (dACC) and ventromedial prefrontal cortex (vmPFC) in humans—exhibit functionally specialized yet interconnected roles in PTSD pathogenesis. While PLPFC/dACC are implicated in fear memory, ILPFC/vmPFC are associated with fear extinction. However, the inherent difference and cross-species molecular signatures underlying these functional parallels remain unresolved. To bridge the gap, we integrate single-nucleus RNA-seq, ATAC-seq, and spatial transcriptomics across mouse PLPFC/ILPFC and human dACC/vmPFC to construct a cross-species, multi-omic atlas. We then delineate conserved/divergent gene regulatory networks (GRNs), with emphasis on excitatory neuron evolution. By incorporating PTSD GWAS data and gene expression changes from vmPFC of PTSD patients, we identify cell-type-specific PTSD risks, SNP-anchored GRNs linked to PTSD heritability, and stress-induced chromatin-primed genes. This work provides a multiregion atlas and advances translational understanding of PTSD-related gene regulation divergence from mouse transition to human and complement the present multi-omic research of PTSD.","manuscriptTitle":"Multiregion Cross-species Single-cell Multimodal Study of Prefrontal Cortex Reveals Cell-type Divergence and PTSD-associated Regulatory Landscapes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 11:19:54","doi":"10.21203/rs.3.rs-7451905/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"626ff402-23d4-47eb-a381-1838a351a764","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54552279,"name":"Biological sciences/Genetics/Gene regulation"},{"id":54552280,"name":"Biological sciences/Genetics/Genomics"},{"id":54552281,"name":"Health sciences/Diseases/Psychiatric disorders/Post-traumatic stress disorder"},{"id":54552282,"name":"Biological sciences/Genetics/Sequencing"}],"tags":[],"updatedAt":"2025-10-19T17:25:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 11:19:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7451905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7451905","identity":"rs-7451905","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}