An integrated single-cell transcriptomic dataset for Mouse cortex

An integrated single-cell transcriptomic dataset for Mouse cortex | 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 An integrated single-cell transcriptomic dataset for Mouse cortex Xuefeng Shi, Zhihui Qi, Hong Huang, Zhiming Ye, YuMin Wu, Kahei Chan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8195268/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 As the central functional hub of the central nervous system, the cerebral cortex has long been a major frontier in neuroscience research. With the increasing maturity and widespread application of single-cell RNA sequencing technologies, multiple studies leveraging this technology have been conducted to systematically decipher the complexity and diversity of cortical cellular composition. However, substantial variations in sequencing platforms, cohort sizes, and sequencing depth have impeded deeper investigation into the functions of cortical cells and their underlying molecular mechanisms. Here we present a comprehensive cortical transcriptome dataset integrating multimodal data from 9 mouse datasets, encompassing 10x and Drop-seq (single-cell/nucleus) profiling. Following rigorous quality control, we systematically analyzed 173,081 high-quality cells, providing a comprehensive characterization of cellular composition, intercellular communication networks, chromatin accessibility, and functional properties. To evaluate the cross-species relevance of our findings, we performed comparative analyses with single-cell datasets of mixed cortical tissues from humans, chimpanzees, bonobos, and macaques (n = 29,353). This integrated resource provides a foundational reference for cortical transcriptomes and a standardized framework for cross-platform integration. Cortex transcriptome sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background & Summary The cerebral cortex represents the most intricate and integrative structure of the central nervous system, orchestrating higher-order functions such as sensory perception, consciousness, decision-making, and motor coordination 1 . Distinct cortical regions exhibit specialized functional architectures shaped by differences in cytoarchitecture and synaptic connectivity 2 . For instance, the primary motor cortex is enriched in projection neurons that initiate voluntary movements, whereas the primary somatosensory cortex decodes peripheral sensory inputs. The prefrontal cortex, through its highly interconnected microcircuits, integrates multimodal information to mediate executive control, cognitive processing, and memory regulation 3 . This hierarchical and functionally diversified organization underpins the neural basis of complex behaviors and higher cognition. Over the past decades, extensive studies have elucidated the fundamental principles governing laminar organization and neural circuit formation 4 . However, the intricate cellular heterogeneity and dynamic state transitions within the cortex remain incompletely resolved 5 . The advent of single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) has enabled high-resolution reconstruction of the cortical transcriptomic landscape, uncovering profound molecular diversity and functional specialization across neuronal and glial lineages 6 – 8 . Nevertheless, discrepancies in sequencing platforms, sample processing, and data analysis strategies across studies have resulted in fragmented datasets and inconsistent outcomes, hindering the systematic integration of cortical cell fate determination, transcriptional regulatory networks, and other critical processes 9 . To overcome the limitations imposed by data heterogeneity and technical discrepancies across studies, it is essential to establish a unified, high-resolution cortical transcriptome framework to lay the foundation for systematic research in cortical transcriptomics. In response to this need, we have developed a comprehensive cortical transcriptome resource integrating multimodal sequencing data. This dataset compiles single-cell and single-nucleus transcriptomic information from multiple mouse cortical samples, covering major sequencing platforms such as 10x Genomics and Drop-seq. Through rigorous data standardization, quality control, and multimodal integration, we have created a systematic reference atlas of the cortical transcriptome, providing a standardized benchmark for in-depth analysis of cortical cell composition and functional states 10 . Additionally, utilizing this resource, we systematically evaluated the core functions and intercellular communication networks of cortical cells 11 . To validate the consistency and complementarity of different sequencing technologies in cell type identification and functional state characterization, we performed integrated analyses with mouse cortical ATAC-seq data and multi-species cortical transcriptomes. The results show that it has strong applicability in deciphering chromatin accessibility and assessing evolutionary conservation. Methods RNA-seq Data preprocessing and integration A total of 10 publicly available scRNA-seq and snRNA-seq datasets were collected, comprising 9 mouse cerebral cortex samples and a mixed sample containing data from humans, chimpanzees, bonobos, and macaques. Detailed information on the datasets included in the analysis is summarized in Table S1. The standard workflow for data preprocessing and cell clustering was followed using the Seurat package (v4.1.0) in R (v4.3.0). Each RNA-seq dataset was analyzed uniformly through a pipeline that included quality control (QC), normalization, feature selection, data scaling, principal component analysis (PCA) for dimension reduction, Harmony integration, clustering, and Uniform Manifold Approximation and Projection (UMAP) projection for visualization 12 . QC for each dataset involved filtering cells to retain only those with mitochondrial gene counts below 20% and feature counts between 200 and 3000. Log-normalization was applied to normalize gene expression in each dataset 13 . The Elbow plot generated for each dataset was used to determine the number of PCs to include in further analysis. Harmony was used to integrate data from different sequencing batches 14 . To evaluate the potential influence of the cell cycle, cell cycle scores were assigned to each cell based on known cell cycle–related genes. Cell clustering was performed using the FindNeighbors and FindClusters functions, and nonlinear dimensionality reduction was conducted using the RunUMAP function. Cluster-specific marker genes were identified using FindAllMarkers, and clusters were annotated based on these marker genes. For visualization, UMAP plots displaying the annotated clusters were generated. scATAC-seq data processing For scATAC-seq data, we applied quality thresholds of 1e05 ≥ nFrags ≥ 1000 and TSS enrichment score ≥ 5 to retain high-quality nuclei, while excluding the chrM and chrY chromosomes 15 . We performed iterative latent semantic indexing (LSI) dimensionality reduction using the addIterativeLSI function, followed by unsupervised clustering via the addClusters function with Seurat's Leiden algorithm 16 . Cell populations were visualized using UMAP. Cell Type Annotation and Cross-Dataset Cross-Validation We annotated all identified clusters with cell types based on the expression or co-expression patterns of canonical marker genes. For the ATAC-seq datasets, cell type annotation was performed by integrating chromatin accessibility profiles at key genomic loci with label transfer from reference single-cell RNA-seq datasets. To validate the robustness of the annotations, we performed non-negative least squares (NNLS) regression analysis to assess the similarity between the identified clusters and reference cell types across multiple independently generated datasets 17 . All gene expression visualizations were generated using the ggplot2 package. Gene activity scores of the scATAC-seq datasets The gene activity matrix was computed using the GeneActivity function in Signac, which quantifies accessibility in gene bodies and promoter regions. To mitigate technical noise inherent in sparse scATAC-seq data, the resulting gene activity scores were normalized via the NormalizeData function and scaled using the ScaleData function. We then performed dimensionality reduction using RunPCA on the gene activity matrix. Cell clusters were identified based on chromatin accessibility profiles, and the FindAllMarkers function was subsequently applied to identify marker genes for each cluster. Finally, cell types were annotated by integrating with a reference single-cell RNA-seq dataset via label transfer using the FindTransferAnchors and TransferData functions 15 . Identification of cell-type-specific regulatory regions Cell-type-specific peaks were identified using the FindAllMarkers function (test.use = "LR") on the peak assay, with a log-fold change threshold of 0.25 and a minimum fraction threshold of 0.25. Significant differential peaks (adjusted p-value ≤ 0.05) were annotated to their nearest genes using the ClosestFeature function. To visualize chromatin accessibility patterns, we generated coverage plots for key marker genes across all major cell types using the CoveragePlot function. Chromatin Co-accessibility Analysis To identify coordinately regulated cis-regulatory elements, we performed chromatin co-accessibility analysis using ArchR on the ATAC-seq datasets 18 . We constructed a genome-wide co-accessibility network based on correlations of chromatin accessibility across all cells 19 . Genomic loci demonstrating tight connectivity within this network were defined as chromatin co-accessibility modules, representing elements with potential synergistic regulatory functions. By linking these modules to annotated gene promoters, we inferred their potential roles in cell type-specific transcriptional regulation. Cell–Cell Communication analysis To systematically investigate intercellular signaling networks across distinct datasets, we performed a computational analysis of cell-cell communication using CellChat (v1.6.1) on annotated cell populations. The gene expression matrix was used to calculate potential ligand-receptor interaction probabilities between all pairwise combinations of cell types. This quantitative framework enabled the identification of significantly enriched signaling pathways and supported the systematic inference of intercellular communication probabilities 11 . scWGCNA Analysis We performed single-cell weighted gene co-expression network analysis (scWGCNA) using the hdWGCNA package (v0.2.5) 20 . The merged single-cell RNA-seq object was initialized with genes expressed in at least 5% of cells. Metacells were constructed by grouping cells within each annotated subcluster using k-nearest neighbors (k =10) with a maximum shared-cell limit of 10. Following metacell normalization, we generated the expression matrix for network construction. The soft power-threshold was determined through scale-free topology analysis (signed network type), selecting the lowest power that achieved a scale-free fit index >0.8. The gene co-expression network was constructed using this optimized power value. Module eigengenes were computed with batch correction for sample origin. We assessed module connectivity by calculating kME values within each subcluster and assigned modules with systematic identifiers. Hub genes were defined as the top 25 genes ranked by kME within each module. Module expression scores were computed using both Seurat and UCell algorithms, based on the top 25 genes per module 21 . Finally, we visualized module eigengene patterns using feature plotting and exported all results for downstream analysis. Data Records The single-cell RNA-seq and ATAC-seq data in this study were all sourced from the Gene Expression Omnibus (GEO) under the accession codes listed in Table S1. Additionally, All data were uploaded to Figshare (https://figshare.com/articles/dataset/An_integrated_single-cell_transcriptomic_dataset_for_Mouse_cortex/30672836). Technical Validation Based on data obtained from the GEO platform, we collected transcriptomic data from adult mouse cortical tissues and a mixed dataset from human, chimpanzee, bonobo, and macaque cortical tissues. Additionally, we analyzed publicly available scATAC-seq data from the cortex. The reliability of the transcriptomic data was supported by standard quality metrics including nFeature_RNA, nCount_RNA, mitochondrial gene percentage (percent.mt), and hemoglobin gene percentage (HB_percent), while the scATAC-seq data quality was confirmed by high fragment counts, transcription start site (TSS) enrichment, and the fraction of reads in peaks (FRiP) (Fig. 1A-C). According to the different sequencing platforms and experimental methods, a total of 11 libraries were analyzed and annotated 22,23,23–31 . Detailed information on the cell type marker genes used for all transcriptomic analyses in this study is provided in Fig. 1D-E. A total of 173,081 high-quality RNA-seq cells were obtained from adult mouse brain tissue, and a total of 8 cell types were annotated. In addition, cell cycle scoring analysis indicated that cell clustering was not driven by cell cycle effects 32 , and cell density visualization further confirmed that no single cluster was disproportionately influenced by variations in cell number (Fig. 2A). We applied Harmony to remove batch effects arising from differences in sequencing depth. Through cross-dataset non-negative least squares regression analysis, we demonstrated that all annotated cell types exhibit highly consistent similarity across different technological platforms, confirming the robustness of our cell type annotations (Fig. 2B). To further explore the function of these cells under different sequencing methods, we used WGCNA to identify the functional hub genes of 8 cells. A total of 33 co-expression modules were identified: 7 in drop-sn, 8 in drop-sc, 6 in 10x-sc, and 12 in 10x-sn. We subsequently performed functional enrichment analysis on each gene co-expression module to systematically identify the dominant functional characteristics of different cell types (Fig. 3A). Modules that did not yield any meaningful functional enrichment results were excluded from subsequent analyses. For each module, we calculated the intramodular connectivity of every gene, selected the top 25 genes with the highest connectivity as hub genes, and subsequently determined the correlation between module gene expression levels and cell types (Fig. 3B). Specifically, the results revealed that both glial cells and neuronal populations play significant roles in synaptic-related functions and metabolic support pathways, while exhibiting distinct functional specializations: neuronal functions primarily focus on synaptic signaling and neuromodulation, whereas glial cells mainly govern the assembly, maintenance, and fine-tuned regulation of synaptic structures. Additionally, to investigate the core functional genes within co-expression modules, we extracted the top 40 genes from each module and further partitioned them using the Louvain community detection algorithm. The results indicated that core genes identified across four datasets predominantly involved protein synthesis (Rpl23a, Rpl35, Rps23), energy metabolism (Aldoa, Slc25a4, Ftl1) 33 , synaptic signaling (Gria4, Dlg2) 34 , and glial support functions (Slc1a3, Apoe, Mbp) (Fig. 3C-F). Overall, these findings collectively reveal the key regulatory mechanisms of neurons and glial cells. To gain deeper insights into the cellular interaction network within the cortex, we performed cell-cell communication analysis on the datasets using CellChat. The results revealed significant and strong interactions among excitatory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs), except in the dataset in which OPCs were not detected (Fig. 4A). Further pattern recognition analysis identified that the core signaling pathways shared across the four datasets were primarily enriched in key biological processes, including cell adhesion, neuron-glia interactions, synaptogenesis, and axon guidance (Fig. 4B-C). Notably, we found that the communication among these three cell types is predominantly mediated by the NRXN and PTN signaling pathways. Neurexin (NRXN), primarily localized at the neuronal presynaptic membrane, functions by trans-synaptically organizing and stabilizing both excitatory and inhibitory synapses through interactions with postsynaptic ligands such as Neuroligin, serving as a central molecule in synapse formation and specificity regulation 35 . Pleiotrophin (PTN), a signaling molecule secreted by astrocytes, binds to its receptors (e.g., Syndecan-3, RPTPβ/Z) and concurrently promotes neuronal survival, dendritic spine formation, and synapse maturation, while also regulating the migration and differentiation of OPCs 36 . Therefore, investigating the NRXN and PTN signaling pathways is likely to provide valuable insights into cortical cellular interactions. Furthermore, we discovered that signaling pathways related to cell adhesion molecules—including NEGR, CADM, and CDH—serve as essential communication mediators between astrocytes and excitatory neurons 37 . These molecules form specific trans-synaptic adhesion complexes that directly facilitate the physical envelopment and spatial positioning of synaptic structures by astrocytes. This finding highlights the critical role of intercellular physical contact in neuron-glia interactions and provides a new molecular theoretical foundation for understanding nervous system development and synaptic plasticity (Fig. 4D). To systematically evaluate the performance of transcriptomic datasets in scATAC-seq data integration, we collected 10,055 high-quality cortical scATAC-seq cells for cross-modal integration with RNA-seq data (Fig. 5A) 38 . Our integration consistency assessment revealed that 4,139 cells (41.1%) obtained consistent cell-type annotations across at least three datasets, indicating that most cells achieve stable annotations across different reference datasets. Further analysis demonstrated that excitatory and inhibitory neurons were prone to mutual misclassification during cross-dataset integration. Surprisingly, despite their substantial heterogeneity, astrocytes exhibited remarkable stability, suggesting that astrocytes may maintain more conserved regulatory patterns at the chromatin accessibility level (Fig. 5B) 39 . To delineate the regulatory architecture underlying cell type-specific chromatin landscapes in cortex, we extracted 4139 cells with consistent cell type annotations in at least three datasets for subsequent analysis(Fig. 5C).We identified 6 major cell types, namely, Astrocytes, OPCs, Excitatory neurons, Inhibitory neurons, Oligodendrocytes, and Microglial cells, each exhibiting distinct chromatin accessibility patterns at established marker loci (Fig. 5D). Subsequently, we systematically constructed chromatin accessibility networks using Cicero co-accessibility analysis. Network topology analysis based on betweenness centrality (threshold: >80th percentile) identified 18 genes as topologically central hubs (Fig. 5E–F). Among them, Cacna1b encodes the pore-forming subunit of neuronal N-type voltage-gated calcium channels, directly mediating presynaptic calcium influx and triggering neurotransmitter release 40 . The axonal guidance molecule Ntng2 (Netrin-G2) orchestrates excitatory synaptogenesis via mechanisms involving specific cell adhesion 41 . Notably, we discovered the central hub positions of metabolism-related genes (Rapgef1, Ass1), which suggests the intrinsic metabolic state of a cell may serves as a key driver in reshaping the chromatin accessibility landscape 42 . To investigate the performance of the four datasets in cross-species analysis, we collected a publicly available single-nucleus transcriptomic dataset of mixed cortical tissues from humans, chimpanzees, bonobos, and macaques (n = 29,353). Due to variations in sequencing depth, only four major cortical cell types were reliably detected (astrocytes, oligodendrocytes, inhibitory neurons, and excitatory neurons) (Fig. 6A–B). We then performed a correlation analysis based on the average expression of conserved highly variable genes across species within each cluster. The results demonstrated highly consistent patterns among the four datasets across all cell types (similarity > 0.7). Furthermore, we analyzed the evolutionary rate of gene expression across cell types, which suggested that astrocytes exhibit lower interspecies divergence compared with other cell types, potentially reflecting their highly conserved functional architecture (Fig. 6C) 43 . To capture subtle species-specific differences in gene expression, we further analyzed significantly upregulated differentially expressed genes (DEGs) within each cluster across species. Surprisingly, astrocytes showed fewer overlapping DEGs (Fig. 6D). Further principal component analysis of shared gene expression patterns revealed that oligodendrocytes, inhibitory neurons, and excitatory neurons displayed similar cross-species expression profiles, whereas astrocytes exhibited highly species-specific expression patterns (Fig. 6E). Specifically, in addition to canonical astrocyte markers such as AQP4, SLC1A3, and GFAP, astrocytes were enriched for genes involved in core functional pathways 44 . These include genes regulating immune response and signaling (e.g., PON2, ID3) 45 , lipid metabolism and transport (e.g., ETNPPL, CLU, FADS2, APOE) 46 , as well as specialized molecular transport (e.g., SLC39A12, SLCO1C1) 47 . In contrast, although oligodendrocytes are also glial cells, their core myelin structural components (MOG, PLP1, MAG, MBP) 48 , signaling-related factors (LPAR1, EFNB3, S1PR5) 49 , and cytoskeletal regulatory proteins (ERMN, CRYAB, GSN) exhibit conserved expression patterns across species that are highly consistent with neurons. This finding suggests that oligodendrocytes, as glial cells closely coupled with neuronal function, may have undergone evolutionary conservation pressure similar to those of neurons for their core functional modules, thereby forming a coordinated and unified cross-species expression pattern. This study constructed multiple transcriptomic atlases of mouse cortical cells across different sequencing platforms through systematic technical validation. We rigorously evaluated data reliability and uncovered core gene modules, key signaling pathways, and chromatin regulatory networks that coordinate between neurons and glial cells to maintain cortical function. In addition, through cross-omics, and cross-species analyses, we confirmed the robustness of data integration across technologies. In summary, we provide a high-quality data resource and analytical framework for advancing the exploration of cellular heterogeneity, molecular regulatory mechanisms, and evolutionary conservation in the cerebral cortex. Declarations Usage Notes All data processing pipeline, including cell filtering, clustering and annotating, which were run using R version 4.3.1. The Python/R codes used for relative analyses are provided online. (https://figshare.com/articles/dataset/An_integrated_single-cell_transcriptomic_dataset_for_Mouse_cortex/30672836) Funding Maojin Yao, National Natural Science Foundation of China, General Program, "Mechanism of Airway GFAP+ Glial Cells in Promoting Epithelial Barrier Repair Following Viral Infection", Grant Nos.32470888 Maojin Yao, Guangzhou National Laboratory - State Key Laboratory of Respiratory Disease (Guangzhou Medical University) Joint Funding Project 2024, "Study on the Mechanisms and Intervention Strategies for Small Cell Lung Cancer Development", Project No. GZNL2024B01004, Grant Nos.GZNL2024B01004 Zhongxing Wang, National Natural Science Foundation of China, Grant Nos.82272224 Zhongxing Wang, the Basic and Applied Basic Research Foundation of Guangdong Province, Grant Nos. 2021A1515220042 Zhongxing Wang, Natural Science Foundation of Guangdong Province, Grant Nos. 2022A1515012475 Data Availability Among the input data processed in this reanalysis, twelve datasets were all acquired from the NCBI GEO database, with specific accession codes including: GSE255405, GSE137665, GSE276683, GSE273765, GSE239477, GSE172382, GSE160519, GSE106678, GSE126074,and GSE127774. The set of samples used in this study is summarized in Table S1. Seurat objects for all datasets have been deposited in the Figshare repository (https://figshare.com/articles/dataset/An_integrated_single-cell_transcriptomic_dataset_for_Mouse_cortex/30672836). Code availability The code used to analyze the data in this study is available online (https://figshare.com/articles/dataset/An_integrated_single-cell_transcriptomic_dataset_for_Mouse_cortex/30672836) Author Contribution Conceptualization, Xuefeng Shi and Zhihui Qi; Methodology, YuMin Wu, Kahei Chan, and Zhihui Qi; Formal Analysis, Zhihui Qi, Hong Huang, and Zhiming Ye; Resources, Maojin Yao and Zhongxing Wang; Data Curation, Xuefeng Shi, Kahei Chan, and Hong Huang; Writing – Original Draft, Xuefeng Shi and Zhihui Qi; Writing – Review & Editing, Hong Huang, Zhiming Ye, YuMin Wu, Kahei Chan, Maojin Yao, and Zhongxing Wang; Visualization, Xuefeng Shi and Hong Huang; Supervision, Maojin Yao and Zhongxing Wang. 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Proc Natl Acad Sci U S A . 2013;110(24):9903-9908. doi:10.1073/pnas.1222142110 Matsumoto Y, Kaneko K, Takahashi T, et al. Diagnostic implications of MOG-IgG detection in sera and cerebrospinal fluids. Brain . 2023;146(9):3938-3948. doi:10.1093/brain/awad122 Luo YL, Li Y, Zhou W, Wang SY, Liu YQ. Inhibition of LPA-LPAR1 and VEGF-VEGFR2 Signaling in IPF Treatment. Drug Des Devel Ther . 2023;17:2679-2690. doi:10.2147/DDDT.S415453 Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx 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. 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17:08:45","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132688,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8195268/v1/0202cc1c51341325647007c4.html"},{"id":98511943,"identity":"f25674eb-a0c7-468b-949c-f7584b16dad9","added_by":"auto","created_at":"2025-12-18 11:52:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":497718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eQuality control of 12 data. (A)\u003c/strong\u003e Boxplots showing the number of detected genes per cell (nFeature_RNA), total RNA counts per cell (nCount_RNA), mitochondrial gene percentage (\u003ca href=\"https://percent.mt/\" target=\"_blank\"\u003epercent.mt\u003c/a\u003e), and hemoglobin gene percentage (HB_percent) across 9 adult mouse cortical datasets.\u003cstrong\u003e (B)\u003c/strong\u003e Boxplots showing nFeature_RNA and nCount_RNA distributions across mixed samples from human, chimpanzee, bonobo, and macaque cortical tissues.\u003cstrong\u003e (C) \u003c/strong\u003eBoxplot showing the number of unique nuclear fragments, the distribution of the transcription start site (TSS) enrichment score, and the fraction of reads in peak (FRiP) of mouse cortical ATAC datasets. \u003cstrong\u003e(D)\u003c/strong\u003e Heatmap of cell type-specific marker genes from a mixed cortical dataset of human, chimpanzee, bonobo, and macaque. \u003cstrong\u003e(E)\u003c/strong\u003e Heatmap of cell type-specific marker genes from 9 adult mouse cortical datasets\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8195268/v1/555fba68982b6d1949b69c04.png"},{"id":98511946,"identity":"ace5ffcc-6f94-4b71-9452-4e4573b547cb","added_by":"auto","created_at":"2025-12-18 11:52:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2074035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegration and similarity assessment of the Mouse datasets (A) \u003c/strong\u003eUMAP embeddings of the 4 datasets, colored by cell type, data source, cell cycle phase, and cell density, respectively.\u003cstrong\u003e (B) \u003c/strong\u003eHeatmap of similarity across the four datasets based on Non-negative Matrix Least Squares (NMLF).\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8195268/v1/4f3e35cacf365e90209eb98f.png"},{"id":98624976,"identity":"7be174eb-c9f7-4dfd-a3e7-3b4c61464525","added_by":"auto","created_at":"2025-12-19 17:08:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2699069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWGCNA Analysis of 4 Datasets. (A)\u003c/strong\u003e GO Enrichment Heatmap of Co-expression Modules (top 10 terms ordered by log_padjust).\u003cstrong\u003e (B) \u003c/strong\u003eHeatmap of correlations between cell types and co-expression modules (based on the top 10 genes by expression level) \u003cstrong\u003e(C)\u003c/strong\u003e Core Gene Interaction Network of Co-expression Modules from 10xsc Data\u003cstrong\u003e. (D)\u003c/strong\u003e, \u003cstrong\u003e(E)\u003c/strong\u003e, and \u003cstrong\u003e(F)\u003c/strong\u003e are the same as\u003cstrong\u003e(C) \u003c/strong\u003ebut derived from 10xsn, dropsc, and dropsn data, respectively.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8195268/v1/b3e47a42ba0bc859b3f5916a.png"},{"id":98511947,"identity":"6b4fc988-8178-460b-85f5-4c5a742456e8","added_by":"auto","created_at":"2025-12-18 11:52:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1711189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntercellular Communication Analysis of 4 Datasets. (A)\u003c/strong\u003e Cellular communication networks across 4 datasets.\u003cstrong\u003e (B)\u003c/strong\u003e Upset plot of signaling pathways across four datasets. \u003cstrong\u003e(C)\u003c/strong\u003e Ligand-receptor interaction network of conserved signaling pathways across four datasets.\u003cstrong\u003e (D)\u003c/strong\u003e Sankey diagram depicting conserved signaling pathway activity among three cell types.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8195268/v1/890512f42098ac7b3dff816b.png"},{"id":98511949,"identity":"9a972a0c-5e3d-4c43-bdfa-fbb19ca2ddf2","added_by":"auto","created_at":"2025-12-18 11:52:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1482411,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell chromatin accessibility landscape of the cerebral cortex. (A)\u003c/strong\u003e UMAP embedding of scATAC-seq data from cortical regions, with dashed lines indicating misprojected cell types. \u003cstrong\u003e(B) \u003c/strong\u003eHeatmap of cell type misclassification probabilities in scATAC-seq data.\u003cstrong\u003e (C) \u003c/strong\u003eUMAP embedding of filtered scATAC-seq data.\u003cstrong\u003e(D)\u003c/strong\u003e The marker peak features corresponding to the 6 cortical celltypes. \u003cstrong\u003e(E)\u003c/strong\u003e Chromatin accessibility co-accessibility network.\u003cstrong\u003e (F)\u003c/strong\u003e Peak accessibility features of topologically central hub genes.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8195268/v1/b7af85403842380fad84dcc3.png"},{"id":98624840,"identity":"e5d08642-e273-4c81-b6db-d9c97daabc55","added_by":"auto","created_at":"2025-12-19 17:08:45","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1847548,"visible":true,"origin":"","legend":"\u003cp\u003eCross-species evolutionary analysis of cortical cell types (A) UMAP visualization of integrated cross-species data, colored by cell type. (B) Same UMAP as (A), colored by species (B: Bonobo; C: Chimpanzee; H: Human; M: Macaque). (C) Left: Spearman correlation of per-cluster mean normalized gene counts between 4 mouse datasets and different species. Center: Similarity heatmap of correlation results from the 4 datasets. Right: Evolutionary rate of gene expression across cell types in mouse (y-axis: variance of log-ratio of gene expression levels). (D) Volcano plots of DEGs between 4 datasets and different species, with connecting lines indicating co-upregulated orthologs across species in the same cell type. (E) PCA of cross-species expression patterns for DEGs identified in four datasets.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8195268/v1/a19ffb85fe9a1af166d2d80e.png"},{"id":99788107,"identity":"c8afdcd0-70a4-499e-bdad-70ffcd13ca37","added_by":"auto","created_at":"2026-01-08 12:44:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7731096,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8195268/v1/43050c3a-ebd3-4401-82dc-5607d8780143.pdf"},{"id":98625572,"identity":"dcd27e0d-91d6-43ba-92ac-2d9eeab9304d","added_by":"auto","created_at":"2025-12-19 17:09:11","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12937,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8195268/v1/c19e1b2fae84965036dc6479.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"An integrated single-cell transcriptomic dataset for Mouse cortex","fulltext":[{"header":"Background \u0026 Summary","content":"\u003cp\u003eThe cerebral cortex represents the most intricate and integrative structure of the central nervous system, orchestrating higher-order functions such as sensory perception, consciousness, decision-making, and motor coordination\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Distinct cortical regions exhibit specialized functional architectures shaped by differences in cytoarchitecture and synaptic connectivity\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. For instance, the primary motor cortex is enriched in projection neurons that initiate voluntary movements, whereas the primary somatosensory cortex decodes peripheral sensory inputs. The prefrontal cortex, through its highly interconnected microcircuits, integrates multimodal information to mediate executive control, cognitive processing, and memory regulation\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This hierarchical and functionally diversified organization underpins the neural basis of complex behaviors and higher cognition.\u003c/p\u003e \u003cp\u003eOver the past decades, extensive studies have elucidated the fundamental principles governing laminar organization and neural circuit formation\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, the intricate cellular heterogeneity and dynamic state transitions within the cortex remain incompletely resolved\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The advent of single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq) has enabled high-resolution reconstruction of the cortical transcriptomic landscape, uncovering profound molecular diversity and functional specialization across neuronal and glial lineages\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Nevertheless, discrepancies in sequencing platforms, sample processing, and data analysis strategies across studies have resulted in fragmented datasets and inconsistent outcomes, hindering the systematic integration of cortical cell fate determination, transcriptional regulatory networks, and other critical processes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo overcome the limitations imposed by data heterogeneity and technical discrepancies across studies, it is essential to establish a unified, high-resolution cortical transcriptome framework to lay the foundation for systematic research in cortical transcriptomics. In response to this need, we have developed a comprehensive cortical transcriptome resource integrating multimodal sequencing data. This dataset compiles single-cell and single-nucleus transcriptomic information from multiple mouse cortical samples, covering major sequencing platforms such as 10x Genomics and Drop-seq.\u0026nbsp;Through rigorous data standardization, quality control, and multimodal integration, we have created a systematic reference atlas of the cortical transcriptome, providing a standardized benchmark for in-depth analysis of cortical cell composition and functional states\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, utilizing this resource, we systematically evaluated the core functions and intercellular communication networks of cortical cells\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. To validate the consistency and complementarity of different sequencing technologies in cell type identification and functional state characterization, we performed integrated analyses with mouse cortical ATAC-seq data and multi-species cortical transcriptomes. The results show that it has strong applicability in deciphering chromatin accessibility and assessing evolutionary conservation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eRNA-seq Data preprocessing and integration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 10 publicly available scRNA-seq and snRNA-seq datasets were collected, comprising 9 mouse cerebral cortex samples and a mixed sample containing data from humans, chimpanzees, bonobos, and macaques. Detailed information on the datasets included in the analysis is summarized in Table S1. The standard workflow for data preprocessing and cell clustering was followed using the Seurat package (v4.1.0) in R (v4.3.0). Each RNA-seq dataset was analyzed uniformly through a pipeline that included quality control (QC), normalization, feature selection, data scaling, principal component analysis (PCA) for dimension reduction, Harmony integration, clustering, and Uniform Manifold Approximation and Projection (UMAP) projection for visualization\u003csup\u003e12\u003c/sup\u003e. QC for each dataset involved filtering cells to retain only those with mitochondrial gene counts below 20% and feature counts between 200 and 3000. Log-normalization was applied to normalize gene expression in each dataset\u003csup\u003e13\u003c/sup\u003e. The Elbow plot generated for each dataset was used to determine the number of PCs to include in further analysis. Harmony was used to integrate data from different sequencing batches\u003csup\u003e14\u003c/sup\u003e. To evaluate the potential influence of the cell cycle, cell cycle scores were assigned to each cell based on known cell cycle\u0026ndash;related genes. Cell clustering was performed using the FindNeighbors and FindClusters functions, and nonlinear dimensionality reduction was conducted using the RunUMAP function. Cluster-specific marker genes were identified using FindAllMarkers, and clusters were annotated based on these marker genes. For visualization, UMAP plots displaying the annotated clusters were generated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escATAC-seq data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor scATAC-seq data, we applied quality thresholds of 1e05 \u0026ge; nFrags \u0026ge; 1000 and TSS enrichment score \u0026ge; 5 to retain high-quality nuclei, while excluding\u0026nbsp;the chrM and chrY chromosomes\u003csup\u003e15\u003c/sup\u003e. We performed iterative latent semantic indexing (LSI) dimensionality reduction using the addIterativeLSI function, followed by unsupervised clustering via the addClusters function with Seurat\u0026apos;s Leiden algorithm\u003csup\u003e16\u003c/sup\u003e. Cell populations were visualized using UMAP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Type Annotation and Cross-Dataset Cross-Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe annotated all identified clusters with cell types based on the expression or co-expression patterns of canonical marker genes. For the ATAC-seq datasets, cell type annotation was performed by integrating chromatin accessibility profiles at key genomic loci with label transfer from reference single-cell RNA-seq datasets. To validate the robustness of the annotations, we performed non-negative least squares (NNLS) regression analysis to assess the similarity between\u0026nbsp;the identified clusters and reference cell types across multiple independently generated datasets\u003csup\u003e17\u003c/sup\u003e. All gene expression visualizations were generated using the ggplot2 package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene activity scores of the scATAC-seq datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe gene activity matrix was computed using the GeneActivity function in Signac, which quantifies accessibility in gene bodies and promoter regions. To mitigate technical noise inherent in sparse scATAC-seq data, the resulting gene activity scores were normalized via the NormalizeData function and scaled using the ScaleData function. We then performed dimensionality reduction using RunPCA on the gene activity matrix. Cell clusters were identified based on chromatin accessibility profiles, and the FindAllMarkers function was subsequently applied to identify marker genes for each cluster. Finally, cell types were annotated by integrating with a reference single-cell RNA-seq dataset via label transfer using the FindTransferAnchors and TransferData functions\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of cell-type-specific regulatory regions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell-type-specific peaks were identified using the FindAllMarkers function (test.use = \u0026quot;LR\u0026quot;) on the peak assay, with a log-fold change threshold of 0.25 and a minimum fraction threshold of 0.25. Significant differential peaks (adjusted p-value \u0026le; 0.05) were annotated to their nearest genes using the ClosestFeature function. To visualize chromatin accessibility patterns, we generated coverage plots for key marker genes across all major cell types using the CoveragePlot function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChromatin Co-accessibility Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify coordinately regulated cis-regulatory elements, we performed chromatin co-accessibility analysis using ArchR on the ATAC-seq datasets\u003csup\u003e18\u003c/sup\u003e. We constructed a genome-wide co-accessibility network based on correlations of chromatin accessibility across all cells\u003csup\u003e19\u003c/sup\u003e. Genomic loci demonstrating tight connectivity within this network were defined as chromatin co-accessibility modules, representing elements with potential synergistic regulatory functions. By linking these modules to annotated gene promoters, we inferred their potential roles in cell type-specific transcriptional regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell\u0026ndash;Cell Communication analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo systematically investigate intercellular signaling networks across distinct datasets, we performed a computational analysis of cell-cell communication using CellChat (v1.6.1) on annotated cell populations. The gene expression matrix was used to calculate potential ligand-receptor interaction probabilities between all pairwise combinations of cell types. This quantitative framework enabled the identification of significantly enriched signaling pathways and supported the systematic inference of intercellular communication probabilities\u003csup\u003e11\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003escWGCNA Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed single-cell weighted gene co-expression network analysis (scWGCNA) using the hdWGCNA package (v0.2.5)\u003csup\u003e20\u003c/sup\u003e. The merged single-cell RNA-seq object was initialized with genes expressed in at least 5% of cells. Metacells were constructed by grouping cells within each annotated subcluster using k-nearest neighbors (k =10) with a maximum shared-cell limit of 10. Following metacell normalization, we generated the expression matrix for network construction.\u003c/p\u003e\n\u003cp\u003eThe soft power-threshold was determined through scale-free topology analysis (signed network type), selecting the lowest power that achieved a scale-free fit index \u0026gt;0.8. The gene co-expression network was constructed using this optimized power value. Module eigengenes were computed with batch correction for sample origin. We assessed module connectivity by calculating kME values within each subcluster and assigned modules with systematic identifiers.\u003c/p\u003e\n\u003cp\u003eHub genes were defined as the top 25 genes ranked by kME within each module. Module expression scores were computed using both Seurat and UCell algorithms, based on the top 25 genes per module\u003csup\u003e21\u003c/sup\u003e. Finally, we visualized module eigengene patterns using feature plotting and exported all results for downstream analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Records\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe single-cell RNA-seq and ATAC-seq data in this study were all sourced from the Gene Expression Omnibus (GEO) under the accession codes listed in Table S1. Additionally, All data were uploaded to Figshare\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(https://figshare.com/articles/dataset/An_integrated_single-cell_transcriptomic_dataset_for_Mouse_cortex/30672836).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTechnical Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on data obtained from the GEO platform, we collected transcriptomic data from adult mouse cortical tissues and a mixed dataset from human, chimpanzee, bonobo, and macaque cortical tissues. Additionally, we analyzed publicly available scATAC-seq data from the cortex. The reliability of the transcriptomic data was supported by standard quality metrics including nFeature_RNA, nCount_RNA, mitochondrial gene percentage (percent.mt), and hemoglobin gene percentage (HB_percent), while the scATAC-seq data quality was confirmed by high fragment counts, transcription start site (TSS) enrichment, and the fraction of reads in peaks (FRiP) (Fig. 1A-C). According to the different sequencing platforms and experimental methods, a total of 11 libraries were analyzed and annotated \u003csup\u003e22,23,23\u0026ndash;31\u003c/sup\u003e. Detailed information on the cell type marker genes used for all transcriptomic analyses in this study is provided in Fig. 1D-E.\u003c/p\u003e\n\u003cp\u003eA total of 173,081 high-quality RNA-seq cells were obtained from adult mouse brain tissue, and a total of 8 cell types were annotated. In addition, cell cycle scoring analysis indicated that cell clustering was not driven by cell cycle effects\u003csup\u003e32\u003c/sup\u003e, and cell density visualization further confirmed that no single cluster was disproportionately influenced by variations in cell number (Fig. 2A). We applied Harmony to remove batch effects arising from differences in sequencing depth. Through cross-dataset non-negative least squares regression analysis, we demonstrated that all annotated cell types exhibit highly consistent similarity across different technological platforms, confirming the robustness of our cell type annotations (Fig. 2B).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To further explore the function of these cells under different sequencing methods, we used WGCNA to identify the functional hub genes of 8 cells. A total of 33 co-expression modules were identified: 7 in drop-sn, 8 in drop-sc, 6 in 10x-sc, and 12 in 10x-sn. We subsequently performed functional enrichment analysis on each gene co-expression module to systematically identify the dominant functional characteristics of different cell types (Fig. 3A).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eModules that did not yield any meaningful functional enrichment results were excluded from subsequent analyses. For each module, we calculated the intramodular connectivity of every gene, selected the top 25 genes with the highest connectivity as hub genes, and subsequently determined the correlation between module gene expression levels and cell types (Fig. 3B).\u0026nbsp;Specifically, the results revealed that both glial cells and neuronal populations play significant roles in synaptic-related functions and metabolic support pathways, while exhibiting distinct functional specializations: neuronal functions primarily focus on synaptic signaling and neuromodulation, whereas glial cells mainly govern the assembly, maintenance, and fine-tuned regulation of synaptic structures.\u0026nbsp;Additionally, to investigate the core functional genes within co-expression modules, we extracted the top 40 genes from each module and further partitioned them using the Louvain community detection algorithm. The results indicated that core genes identified across four datasets predominantly involved protein synthesis (Rpl23a, Rpl35, Rps23), energy metabolism (Aldoa, Slc25a4, Ftl1)\u003csup\u003e33\u003c/sup\u003e, synaptic signaling (Gria4, Dlg2)\u003csup\u003e34\u003c/sup\u003e, and glial support functions (Slc1a3, Apoe, Mbp) (Fig. 3C-F). Overall, these findings collectively reveal the key regulatory mechanisms of neurons and glial cells.\u003c/p\u003e\n\u003cp\u003eTo gain deeper insights into the cellular interaction network within the cortex, we performed cell-cell communication analysis on the datasets using CellChat. The results revealed significant and strong interactions among excitatory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs), except in the dataset in which OPCs were not detected (Fig. 4A). Further pattern recognition analysis identified that the core signaling pathways shared across the four datasets were primarily enriched in key biological processes, including cell adhesion, neuron-glia interactions, synaptogenesis, and axon guidance (Fig. 4B-C). Notably, we found that the communication among these three cell types is predominantly mediated by the NRXN and PTN signaling pathways. Neurexin (NRXN), primarily localized at the neuronal presynaptic membrane, functions by trans-synaptically organizing and stabilizing both excitatory and inhibitory synapses through interactions with postsynaptic ligands such as Neuroligin, serving as a central molecule in synapse formation and specificity regulation\u003csup\u003e35\u003c/sup\u003e. Pleiotrophin (PTN), a signaling molecule secreted by astrocytes, binds to its receptors (e.g., Syndecan-3, RPTP\u0026beta;/Z) and concurrently promotes neuronal survival, dendritic spine formation, and synapse maturation, while also regulating the migration and differentiation of OPCs\u003csup\u003e36\u003c/sup\u003e. Therefore, investigating the NRXN and PTN signaling pathways is likely to provide valuable insights into cortical cellular interactions. Furthermore, we discovered that signaling pathways related to cell adhesion molecules\u0026mdash;including NEGR, CADM, and CDH\u0026mdash;serve as essential communication mediators between astrocytes and excitatory neurons\u003csup\u003e37\u003c/sup\u003e. These molecules form specific trans-synaptic adhesion complexes that directly facilitate the physical envelopment and spatial positioning of synaptic structures by astrocytes. This finding highlights the critical role of intercellular physical contact in neuron-glia interactions and provides a new molecular theoretical foundation for understanding nervous system development and synaptic plasticity (Fig. 4D).\u003c/p\u003e\n\u003cp\u003eTo systematically evaluate the performance of transcriptomic datasets in scATAC-seq data integration, we collected 10,055 high-quality cortical scATAC-seq cells for cross-modal integration with RNA-seq data (Fig. 5A)\u003csup\u003e38\u003c/sup\u003e. Our integration consistency assessment revealed that 4,139 cells (41.1%) obtained consistent cell-type annotations across at least three datasets, indicating that most cells achieve stable annotations across different reference datasets. Further analysis demonstrated that excitatory and inhibitory neurons were prone to mutual misclassification during cross-dataset integration. Surprisingly, despite their substantial heterogeneity, astrocytes exhibited remarkable stability, suggesting that astrocytes may maintain more conserved regulatory patterns at the chromatin accessibility level (Fig. 5B)\u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo delineate the regulatory architecture underlying cell type-specific chromatin landscapes in cortex, we extracted 4139 cells with consistent cell type annotations in at least three datasets for subsequent analysis(Fig. 5C).We identified 6 major cell types, namely, Astrocytes, OPCs, Excitatory neurons, Inhibitory neurons, Oligodendrocytes, and Microglial cells, each exhibiting distinct chromatin accessibility patterns at established marker loci (Fig. 5D). Subsequently, we systematically constructed chromatin accessibility networks using Cicero co-accessibility analysis. Network topology analysis based on betweenness centrality (threshold: \u0026gt;80th percentile) identified 18 genes as topologically central hubs (Fig. 5E\u0026ndash;F). Among them, Cacna1b encodes the pore-forming subunit of neuronal N-type voltage-gated calcium channels, directly mediating presynaptic calcium influx and triggering neurotransmitter release\u003csup\u003e40\u003c/sup\u003e. The axonal guidance molecule Ntng2 (Netrin-G2) orchestrates excitatory synaptogenesis via mechanisms involving specific cell adhesion\u003csup\u003e41\u003c/sup\u003e. Notably, we discovered the central hub positions of metabolism-related genes (Rapgef1, Ass1), which suggests the intrinsic metabolic state of a cell may serves as a key driver in reshaping the chromatin accessibility landscape\u003csup\u003e42\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo investigate the performance of the four datasets in cross-species analysis, we collected a publicly available single-nucleus transcriptomic dataset of mixed cortical tissues from humans, chimpanzees, bonobos, and macaques (n = 29,353). Due to variations in sequencing depth, only four major cortical cell types were reliably detected (astrocytes, oligodendrocytes, inhibitory neurons, and excitatory neurons) (Fig. 6A\u0026ndash;B). We then performed a correlation analysis based on the average expression of conserved highly variable genes across species within each cluster. The results demonstrated highly consistent patterns among the four datasets across all cell types (similarity \u0026gt; 0.7). Furthermore, we analyzed the evolutionary rate of gene expression across cell types, which suggested that astrocytes exhibit lower interspecies divergence compared with other cell types, potentially reflecting their highly conserved functional architecture (Fig. 6C)\u003csup\u003e43\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo capture subtle species-specific differences in gene expression, we further analyzed significantly upregulated differentially expressed genes (DEGs) within each cluster across species. Surprisingly, astrocytes showed fewer overlapping DEGs (Fig. 6D). Further principal component analysis of shared gene expression patterns revealed that oligodendrocytes, inhibitory neurons, and excitatory neurons displayed similar cross-species expression profiles, whereas astrocytes exhibited highly species-specific expression patterns (Fig. 6E). Specifically, in addition to canonical astrocyte markers such as AQP4, SLC1A3, and GFAP, astrocytes were enriched for genes involved in core functional pathways\u003csup\u003e44\u003c/sup\u003e. These include genes regulating immune response and signaling (e.g., PON2, ID3)\u003csup\u003e45\u003c/sup\u003e, lipid metabolism and transport (e.g., ETNPPL, CLU, FADS2, APOE)\u003csup\u003e46\u003c/sup\u003e, as well as specialized molecular transport (e.g., SLC39A12, SLCO1C1)\u003csup\u003e47\u003c/sup\u003e.\u0026nbsp;In contrast, although oligodendrocytes are also glial cells, their core myelin structural components (MOG, PLP1, MAG, MBP)\u003csup\u003e48\u003c/sup\u003e, signaling-related factors (LPAR1, EFNB3, S1PR5)\u003csup\u003e49\u003c/sup\u003e, and cytoskeletal regulatory proteins (ERMN, CRYAB, GSN) exhibit conserved expression patterns across species that are highly consistent with neurons. This finding suggests that oligodendrocytes, as glial cells closely coupled with neuronal function, may have undergone evolutionary conservation pressure similar to those of neurons for their core functional modules, thereby forming a coordinated and unified cross-species expression pattern.\u003c/p\u003e\n\u003cp\u003eThis study constructed multiple transcriptomic atlases of mouse cortical cells across different sequencing platforms through systematic technical validation. We rigorously evaluated data reliability and uncovered core gene modules, key signaling pathways, and chromatin regulatory networks that coordinate between neurons and glial cells to maintain cortical function. In addition, through cross-omics, and cross-species analyses, we confirmed the robustness of data integration across technologies. In summary, we provide a high-quality data resource and analytical framework for advancing the exploration of cellular heterogeneity, molecular regulatory mechanisms, and evolutionary conservation in the cerebral cortex.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eUsage Notes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data processing pipeline, including cell filtering, clustering and annotating, which were run using R version 4.3.1. The Python/R codes used for relative analyses are provided online. (https://figshare.com/articles/dataset/An_integrated_single-cell_transcriptomic_dataset_for_Mouse_cortex/30672836)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaojin Yao, National Natural Science Foundation of China, General Program, \u0026quot;Mechanism of Airway GFAP+ Glial Cells in Promoting Epithelial Barrier Repair Following Viral Infection\u0026quot;, Grant Nos.32470888\u003c/p\u003e\n\u003cp\u003eMaojin Yao, Guangzhou National Laboratory - State Key Laboratory of Respiratory Disease (Guangzhou Medical University) Joint Funding Project 2024, \u0026quot;Study on the Mechanisms and Intervention Strategies for Small Cell Lung Cancer Development\u0026quot;, Project No. GZNL2024B01004, Grant Nos.GZNL2024B01004\u003c/p\u003e\n\u003cp\u003eZhongxing Wang, National Natural Science Foundation of China, Grant Nos.82272224\u003c/p\u003e\n\u003cp\u003eZhongxing Wang, the Basic and Applied Basic Research Foundation of Guangdong Province, Grant Nos. 2021A1515220042\u003c/p\u003e\n\u003cp\u003eZhongxing Wang, Natural Science Foundation of Guangdong Province, Grant Nos. 2022A1515012475\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the input data processed in this reanalysis, twelve datasets were all acquired from the NCBI GEO database, with specific accession codes including: GSE255405, GSE137665, GSE276683, GSE273765, GSE239477, GSE172382, GSE160519, GSE106678, GSE126074,and GSE127774. The set of samples used in this study is summarized in Table S1. Seurat objects for all datasets have been deposited in the Figshare repository (https://figshare.com/articles/dataset/An_integrated_single-cell_transcriptomic_dataset_for_Mouse_cortex/30672836).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code used to analyze the data in this study is available online (https://figshare.com/articles/dataset/An_integrated_single-cell_transcriptomic_dataset_for_Mouse_cortex/30672836)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Xuefeng Shi and Zhihui Qi; Methodology, YuMin Wu, Kahei Chan, and Zhihui Qi; Formal Analysis, Zhihui Qi, Hong Huang, and Zhiming Ye; Resources, Maojin Yao and Zhongxing Wang; Data Curation, Xuefeng Shi, Kahei Chan, and Hong Huang; Writing \u0026ndash; Original Draft, Xuefeng Shi and Zhihui Qi; Writing \u0026ndash; Review \u0026amp; Editing, Hong Huang, Zhiming Ye, YuMin Wu, Kahei Chan, Maojin Yao, and Zhongxing Wang; Visualization, Xuefeng Shi and Hong Huang; Supervision, Maojin Yao and Zhongxing Wang.\u003c/p\u003e\n\u003ch2\u003e\u003cbr\u003e\u003c/h2\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCadwell CR, Bhaduri A, Mostajo-Radji MA, Keefe MG, Nowakowski TJ. 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Inhibition of LPA-LPAR1 and VEGF-VEGFR2 Signaling in IPF Treatment. \u003cem\u003eDrug Des Devel Ther\u003c/em\u003e. 2023;17:2679-2690. doi:10.2147/DDDT.S415453\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Cortex, transcriptome sequencing","lastPublishedDoi":"10.21203/rs.3.rs-8195268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8195268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs the central functional hub of the central nervous system, the cerebral cortex has long been a major frontier in neuroscience research. With the increasing maturity and widespread application of single-cell RNA sequencing technologies, multiple studies leveraging this technology have been conducted to systematically decipher the complexity and diversity of cortical cellular composition. However, substantial variations in sequencing platforms, cohort sizes, and sequencing depth have impeded deeper investigation into the functions of cortical cells and their underlying molecular mechanisms. Here we present a comprehensive cortical transcriptome dataset integrating multimodal data from 9 mouse datasets, encompassing 10x and Drop-seq (single-cell/nucleus) profiling. Following rigorous quality control, we systematically analyzed 173,081 high-quality cells, providing a comprehensive characterization of cellular composition, intercellular communication networks, chromatin accessibility, and functional properties. To evaluate the cross-species relevance of our findings, we performed comparative analyses with single-cell datasets of mixed cortical tissues from humans, chimpanzees, bonobos, and macaques (n = 29,353). This integrated resource provides a foundational reference for cortical transcriptomes and a standardized framework for cross-platform integration.\u003c/p\u003e","manuscriptTitle":"An integrated single-cell transcriptomic dataset for Mouse cortex","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 11:52:50","doi":"10.21203/rs.3.rs-8195268/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"fa1c05a9-b1aa-4fdc-a1b3-5411c6cb3b9b","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-31T15:54:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-18 11:52:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8195268","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8195268","identity":"rs-8195268","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}