Precise Characterization of Cellular States and Spatial Variable Patterns within Spatial Transcriptomics

Precise Characterization of Cellular States and Spatial Variable Patterns within Spatial Transcriptomics | 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 Precise Characterization of Cellular States and Spatial Variable Patterns within Spatial Transcriptomics Guangchuang Yu, Shuangbin Xu, Qianwen Wang, Junrui Li, Jiaxing Li, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5965581/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 Spatial transcriptomics enables the simultaneous characterization of gene expression and spatial organization, providing transformative insights into tissue architecture and function. While numerous tools have been developed to identify spatial variable genes as proxies for functional variation, none can directly detect spatial variable patterns, such as celluar states and biological pathways. To address this gap, we introduce SVP (Spatially Variable Pathways), a computational framework for predicting functional cell states and analyzing their spatial variation. By generalizing spatial variable features to genes, cellular states, and biological pathways, SVP facilitates the co-distribution analysis of spatial features, enhancing biological interpretation and mechanistic exploration. Integrating graph propagation, hypergeometric testing, and advanced spatial statistics, SVP identifies spatially variable functions and uncover spatially resolved interactions. Evaluations on benchmark and real-world datasets demonstrate its accuracy and scalability. SVP has broad applications, including elucidating immune evasion in pancreatic cancer, tracking cardiac developmental dynamics, and investigating neurodegeneration in Alzheimer's disease model. Overall, SVP provides a robust framework for dissecting cellular and tissue-level organization. Biological sciences/Computational biology and bioinformatics Health sciences/Medical research Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryFile.pdf Supplementary File 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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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-5965581","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":423443843,"identity":"9f056f85-5c25-4cdb-86ee-5c8bac67a369","order_by":0,"name":"Guangchuang 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12:11:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5965581/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5965581/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80193530,"identity":"bdc9aa5f-33d2-4725-8ba7-66b7a115b783","added_by":"auto","created_at":"2025-04-09 05:05:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverview of SVP.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e The flowchart on the topoutlines the process for assessing cell states using SVP. \u003cstrong\u003e(B)\u003c/strong\u003e The flowchart in the middleillustrates global or local spatial autocorrelation algorithms for identifying spatially variable cell functions and features of cell-clusters or pathology domains. \u003cstrong\u003e(C)\u003c/strong\u003e The bottom flowchart depicts global or local spatial bivariate autocorrelation methods for analyzing the spatial co-distribution of different features, which can be used to explore the cell-to-cell or other features co-location in the physical space.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5965581/v1/944730dac271b1998d33babc.png"},{"id":80194739,"identity":"c5dd616f-cf81-4629-8e8c-c5256c838764","added_by":"auto","created_at":"2025-04-09 05:29:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":297747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of cell type predictions from simulated data using various functional evaluation tools.\u003c/strong\u003e Simulation data were generated based on STARMap data from the mouse primary visual cortex, with cell data aggregated into 55μm diameter circles to simulate low-resolution 10X platform data. \u003cstrong\u003e(A)\u003c/strong\u003e Cell type predictions: Pie charts comparing actual cell types to those predicted by different software tools. \u003cstrong\u003e(B)\u003c/strong\u003e Abundance comparison: Predicted versus actual cell abundance is evaluated. The lower dot plot compares results across different cell types, while the upper bar chart provides overall comparisons. Metrics include: 1-JSD (1-Jensen-Shannon Divergence): Higher values indicate better alignment of abundance distributions; 1-RMSE (1-Root Mean Square Error): Higher values reflect closer alignment of predicted and actual abundances; PCC (Pearson Correlation Coefficient): Higher values signify stronger predictive accuracy. \u003cstrong\u003e(C)\u003c/strong\u003e Local spatial autocorrelation: The local spatial autocorrelation coefficients of each method are compared with the ground truth. The bar chart above provides overall comparisons, while box plots assess each prediction method for individual cell types against the ground truth. Metrics are analogous to those in \u003cstrong\u003e(B)\u003c/strong\u003e. \u003cstrong\u003e(D) \u003c/strong\u003eThe evaluation is based on 161 functional gene sets. The left chart shows runtime, and the right chart shows peak memory usage. The findings reveal that SVP consistently completes calculations quickly and requires relatively little memory, irrespective of the number of cells.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5965581/v1/d30e87b0966095ec8d40039f.png"},{"id":80193531,"identity":"7a4c6410-4fb0-40f7-990a-ba0c38c9fee3","added_by":"auto","created_at":"2025-04-09 05:05:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":169239,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe robustness and scalability of SVP for the identification of spatial variable features via simulation data. (A)\u003c/strong\u003e and\u003cstrong\u003e (B) \u003c/strong\u003eillustrate the spatial distribution of spatially variable features in two types of simulated data: High and Low. Panel \u003cstrong\u003e(A)\u003c/strong\u003e represents the High type, characterized by 3,000 features with high expression clustering in regions B and C. In contrast, panel \u003cstrong\u003e(B)\u003c/strong\u003e depicts the Low type, where 3,000 features show low expression clustering in regions B and C. In the mixed type dataset, 1,500 features exhibit high expression clustering in regions B and C, while another 1,500 features display low expression clustering in the same regions. The remaining 17,000 features are randomly distributed across the three data types.\u003cstrong\u003e (C)\u003c/strong\u003e illustrates the accuracy performance of different tools for predicting spatially variable features across all simulated datasets. The autocorrelation method implemented by SVP and SpatialDE demonstrates strong robustness.\u003cstrong\u003e (D)\u003c/strong\u003e The computation time and peak memory usage for different software tools at 3,000 genes with varying numbers of cells are compared. The results indicate that the spatial variability feature identification method implemented by SVP offers significant performance advantages. \u003cstrong\u003e(E)\u003c/strong\u003e The runtime and memory requirements for various software tools were evaluated using the mouse SlideSeq V2 HPC dataset, which, after filtering, contains 53,208 cells and 23,264 genes. Some software, such as Seurat, SpatialDE, SPARK, and MERINGUE, were unable to deliver final results due to extremely high memory consumption (\u0026gt;3000 Gb) and an inability to complete within a reasonable time frame (\u0026gt;24 hours). Once again, the findings highlight that the method implemented by SVP offers considerable performance advantages.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5965581/v1/2c687a48903494bcaa44b1e3.png"},{"id":80194740,"identity":"665f2645-ff8a-4ff2-8cbe-adb4df511c89","added_by":"auto","created_at":"2025-04-09 05:29:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1178371,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSVP accurately predicts cell types and uncovers immune evasion in the pancreatic ductal carcinoma (PDAC) tumor microenvironment.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eHistological overview: Published HE-stained sections of PDAC with annotated regions, including tumors, ducts, acini, and stroma. \u003cstrong\u003e(B)\u003c/strong\u003e Clustering analysis: Clustering results derived from spatial transcriptomic data, highlighting distinct cellular distributions. \u003cstrong\u003e(C)\u003c/strong\u003e Cell type distribution: Pie chart showing the proportions of cell types in the PDAC tumor microenvironment as predicted by SVP. \u003cstrong\u003e(D)\u003c/strong\u003e Tumor vs. Non-Tumor comparison: Boxplot (Wilcoxon rank-sum test) depict differences in the spatial transcriptomic features between tumor and non-tumor regions for tumor cells, four types of pancreatic ductal cells, endothelial cells, fibroblasts, and associated immune cells. \u003cstrong\u003e(E)\u003c/strong\u003e Spatial co-distribution patterns: Point plot shows the spatial co-distribution of tumor cells, ductal cells, fibroblasts, and immune cells, with an index range from -1 to 1. Larger positive values (circles) indicate strong clustering between features, values near zero indicate no spatial autocorrelation, and larger negative values (squares) indicate mutual spatial exclusion. Central ribbon heatmap represents the clustering degree of corresponding cell types in tumor, ductal, acini, and stroma regions after local spatial autocorrelation analysis, with deeper colors indicating stronger clustering in specific area. The outer bar graph shows the Moran’s I index for corresponding features, representing their spatial clustering degree, ranging from -1 (extreme dispersion) to 1 (strong clustering), with values near zero indicating random spatial distribution. \u003cstrong\u003e(F)\u003c/strong\u003e Functional abundance and clustering in tumor microenvironment: Boxplots illustrate the abundance and clustering F1 values for GO and Reactome pathways in tumor, ductal, acinar, and stroma regions as predicted by SVP. The F1 value to measure the feature enrichment in a cell cluster or spatial domain the after local spatial autocorrelation analysis (see methods) \u003cstrong\u003e(G)\u003c/strong\u003e Spatial distribution of GO and Reactome pathways: Spatial mapping of GO and Reactome pathways on tissue sections, with highlighted regions denoting areas of high affinity. \u003cstrong\u003e(H)\u003c/strong\u003e Network of spatial co-distribution: The network showing the spatial co-distribution of cell type activity, transcription factor activity, and their target genes as predicted by SVP. Line thickness reflects the degree of spatial co-distribution between features. Red lines between transcription factors and genes indicate regulatory relationships, with the genes serving as transcription factor targets. \u003cstrong\u003e(I)\u003c/strong\u003e Co-distribution and clustering analysis of transcription factors and targets: Heatmaps display the co-distribution of transcription factor activity with their target genes and cell type activity in space, along with clustering in tumor, acinar, ductal, and stroma regions. The far-right dot plot illustrates the spatial co-distribution degree between transcription factors and genes, with red highlights indicating regulatory relationships. The middle heatmap shows clustering of transcription factors across regions. The middle dot plot indicates spatial co-distribution between transcription factors and tumor cells, hypoxic ductal cells, endothelial cells, and fibroblasts. The left bar chart illustrates clustering of corresponding transcription factors in space. The far-left hierarchical clustering tree represents clustering based on spatial co-distribution of transcription factors and cell types, mainly divided into two branches for distinguishing co-clustering with endothelial cells.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5965581/v1/c14d0f2b6f8bcdb6718fdee1.png"},{"id":80194807,"identity":"852b576a-3ebc-4a25-aac7-fec0ac636145","added_by":"auto","created_at":"2025-04-09 05:30:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":419101,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSVP accurately predicts dynamic cell type changes during human heart development.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Cell type composition across developmental stages: The predicted cell type composition of the human heart at different development stages using SVP. The left dot plot illustrates the spatial distribution of heart cell types at 6.5 PCW, as annotated by ISS in the original study. \u003cstrong\u003e(B)\u003c/strong\u003e Spatial distribution of heart cell types: The right side of each subfigure shows the spatial distribution of atrial cells, epicardium-derived cells, fibroblast-like 1 cells related to cardiac skeletal muscle, and ventricular cells predicted by SVP at different developmental stages. The left side shows the corresponding 6.5 PCW heart cell distributions as annotated by ISS in the original study. \u003cstrong\u003e(C)\u003c/strong\u003eSpatial aggregation of heart cells: The x-axis represents different developmental stages and the y-axis represents the Moran’s I index. Different colors indicate different heart cells, with asterisks showing significant spatial clustering of particular cell types at specific developmental stages. \u003cstrong\u003e(D)\u003c/strong\u003e Co-distribution of heart cell types across developmental stages: The dot plot indicates the degree of spatial co-distribution among heart cell types, with an index ranging from -1 to 1. Larger positive values (larger circles) suggest a higher degree of spatial co-clustering between two features, values near zero indicate no spatial autocorrelation, and larger negative values (larger squares) imply mutual spatial exclusion.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5965581/v1/1194f60a219ecdfa109b5dda.png"},{"id":80193533,"identity":"692df02f-b409-47fd-a0d7-9bd5d54cb104","added_by":"auto","created_at":"2025-04-09 05:05:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1053423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSVP identifies cells that are co-located with Aβ plaques and elucidates their specific functions and associated genes. (A)\u003c/strong\u003e Cell annotation results for hippocampal slices of AD model mice, utilizing SVP to identify the dominant cell type in each cell. \u003cstrong\u003e(B)\u003c/strong\u003e The spatial distribution of Aβ plaques in paired mouse hippocampal slices is depicted, with the black highlighted regions indicating areas of high Aβ plaque aggregation as predicted by the local autocorrelation algorithm implemented by SVP. \u003cstrong\u003e(C)\u003c/strong\u003e Heatmap illustrating the spatial co-aggregation of Aβ plaques and various cell types, alongside the enrichment score F1 for each cell type within the Aβ plaque aggregation area, as calculated by the local autocorrelation algorithm (refer to the Methods section). \u003cstrong\u003e(D)\u003c/strong\u003e The abundance distribution of the main cell types significantly co-aggregating with Aβ plaques in this section is shown, with red indicating spots where cells and Aβ plaques co-aggregate, and blue indicating spots where only cells aggregate.\u003cstrong\u003e (E) \u003c/strong\u003eComparison of Aβ plaque density between spots where cells and Aβ plaques co-aggregate and spots where only cells aggregate. \u003cstrong\u003e(F)\u003c/strong\u003e Volcano plot depicting differentially expressed genes between spots co-aggregating with Aβ plaques and spots aggregated with each cell type alone. \u003cstrong\u003e(G)\u003c/strong\u003e Differential functional spatial distribution between spots co-aggregating with Aβ plaques and spots aggregated with each cell type alone.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5965581/v1/4a6d4478d7a932ed8bc60d81.png"},{"id":80195416,"identity":"8167d927-e587-4bbb-937e-203fdb8ad7f1","added_by":"auto","created_at":"2025-04-09 05:38:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4560979,"visible":true,"origin":"","legend":"","description":"","filename":"SVPmanuscriptv2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5965581/v1_covered_2cd07252-592e-458c-8b38-7983352f9e59.pdf"},{"id":80193544,"identity":"a535f6dd-1d90-473e-b3c6-5bc7b5ff1b83","added_by":"auto","created_at":"2025-04-09 05:05:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":97231576,"visible":true,"origin":"","legend":"Supplementary File","description":"","filename":"SupplementaryFile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5965581/v1/7a3e22ebd1c28b7e9532ff93.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Precise Characterization of Cellular States and Spatial Variable Patterns within Spatial Transcriptomics","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":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":"","lastPublishedDoi":"10.21203/rs.3.rs-5965581/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5965581/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpatial transcriptomics enables the simultaneous characterization of gene expression and spatial organization, providing transformative insights into tissue architecture and function. While numerous tools have been developed to identify spatial variable genes as proxies for functional variation, none can directly detect spatial variable patterns, such as celluar states and biological pathways. To address this gap, we introduce SVP (Spatially Variable Pathways), a computational framework for predicting functional cell states and analyzing their spatial variation. By generalizing spatial variable features to genes, cellular states, and biological pathways, SVP facilitates the co-distribution analysis of spatial features, enhancing biological interpretation and mechanistic exploration. Integrating graph propagation, hypergeometric testing, and advanced spatial statistics, SVP identifies spatially variable functions and uncover spatially resolved interactions. Evaluations on benchmark and real-world datasets demonstrate its accuracy and scalability. SVP has broad applications, including elucidating immune evasion in pancreatic cancer, tracking cardiac developmental dynamics, and investigating neurodegeneration in Alzheimer's disease model. Overall, SVP provides a robust framework for dissecting cellular and tissue-level organization.\u003c/p\u003e","manuscriptTitle":"Precise Characterization of Cellular States and Spatial Variable Patterns within Spatial Transcriptomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-09 05:05:48","doi":"10.21203/rs.3.rs-5965581/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":"d599d301-4d4b-4474-aea9-91fd34d9d6ff","owner":[],"postedDate":"April 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45125260,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":45125261,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-01-06T05:55:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-09 05:05:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5965581","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5965581","identity":"rs-5965581","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}