Charting spatial ligand-target activity using Renoir

Charting spatial ligand-target activity using Renoir | 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 Charting spatial ligand-target activity using Renoir Hamim Zafar, Narein Rao, Tanush Kumar, Rhea Pai, Archita Mishra, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5331951/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 May, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract The advancement of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics has enabled the inference of cellular interactions in a tissue microenvironment. Despite the development of cell-cell interaction inference methods, there is a lack of methods capable of mapping the influence of ligands on downstream target genes across a spatial topology with specific cell type composition, with the potential to shed light on niche-specific relationship between ligands and their downstream targets. Here we present Renoir for charting the ligand-target activities across a spatial topology and delineating spatial communication niches harboring specific ligand-target activities. Renoir also spatially maps pathway-level activity of ligand-target genesets and identifies domain-specific ligand-target activities. Across spatial datasets with varying resolution (spot to single-cell) ranging from development to disease, Renoir inferred cellular niches with distinct ligand-target interactions, spatially mapped pathway activities, and identified context-specific novel cell-cell interactions. Renoir uncovers biological insights and therapeutically-relevant cellular crosstalk from spatial transcriptomics data. Biological sciences/Computational biology and bioinformatics/Cellular signalling networks Biological sciences/Computational biology and bioinformatics/Statistical methods Cell-cell communication ligand-target activity spatial transcriptomics pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Renoirsupplementary.pdf Supplementary Material Cite Share Download PDF Status: Published Journal Publication published 05 May, 2026 Read the published version in Nature Communications → 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. 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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-5331951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375506255,"identity":"60d3e7a7-ac03-4474-8248-ee0aa60ba7be","order_by":0,"name":"Hamim Zafar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYBACAyA+wFAhwcPAA+ZLEKvljAQPD0laGBjbGBigWogA5uynEw/dnGchY89zgPHDDwaLPIJaLHtyNxzO3QZ0GG8Ds2QPg0QxYYcdgGnhZ2CQBvolsYGglvNvgVrmgLUw/yZOyw2QLQ1gh7ERacsNoC05x4Bazhxss+wxIMphuZs/59TU2bP3JB++8aOijrAWJMDYAI2mUTAKRsEoGAUUAwD5Ujb8a2zlCgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1617-2806","institution":"Indian Institute of Technology Kanpur","correspondingAuthor":true,"prefix":"","firstName":"Hamim","middleName":"","lastName":"Zafar","suffix":""},{"id":375506256,"identity":"bef05b43-a3eb-4f8c-85ca-3b3508f0a401","order_by":1,"name":"Narein Rao","email":"","orcid":"","institution":"Indian Institute of Technology Kanpur","correspondingAuthor":false,"prefix":"","firstName":"Narein","middleName":"","lastName":"Rao","suffix":""},{"id":375506257,"identity":"37535f52-df38-4612-b8eb-97d1a63668ab","order_by":2,"name":"Tanush Kumar","email":"","orcid":"","institution":"Indian Institute of Technology Kanpur","correspondingAuthor":false,"prefix":"","firstName":"Tanush","middleName":"","lastName":"Kumar","suffix":""},{"id":375506258,"identity":"b1352f08-3579-4a09-93a9-def31d8519b8","order_by":3,"name":"Rhea Pai","email":"","orcid":"","institution":"Curtin Medical School","correspondingAuthor":false,"prefix":"","firstName":"Rhea","middleName":"","lastName":"Pai","suffix":""},{"id":375506259,"identity":"b25feeac-2905-4865-bba3-9ec6b25438ec","order_by":4,"name":"Archita Mishra","email":"","orcid":"","institution":"University of Sydney","correspondingAuthor":false,"prefix":"","firstName":"Archita","middleName":"","lastName":"Mishra","suffix":""},{"id":375506260,"identity":"ae6784ee-1d6b-44a1-bed7-f0b58a5b1a63","order_by":5,"name":"Florent Ginhoux","email":"","orcid":"https://orcid.org/0000-0002-2857-7755","institution":"Singapore Immunology Network Agency for Science Technology and Research","correspondingAuthor":false,"prefix":"","firstName":"Florent","middleName":"","lastName":"Ginhoux","suffix":""},{"id":375506261,"identity":"c8a18598-701b-4742-baf8-94e1cc5e3289","order_by":6,"name":"Jerry Chan","email":"","orcid":"","institution":"KK Research Center, KK Women’s and Children’s Hospital, Singapore","correspondingAuthor":false,"prefix":"","firstName":"Jerry","middleName":"","lastName":"Chan","suffix":""},{"id":375506262,"identity":"abc6a335-a389-4d0c-b591-b9a22de7412e","order_by":7,"name":"Ankur Sharma","email":"","orcid":"https://orcid.org/0000-0002-6862-136X","institution":"Harry Perkins Institute of Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Ankur","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2024-10-25 11:15:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5331951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5331951/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-026-72388-7","type":"published","date":"2026-05-05T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69761051,"identity":"8d488565-4e96-4982-83d2-34359dfdf4ea","added_by":"auto","created_at":"2024-11-25 04:44:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":669568,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of Renoir. (a) Renoir utilizes either spot-resolution spatial transcriptomic data and cell type-annotated scRNA-seq data from the same tissue or single-cell resolution spatial transcriptomic data to infer spatial neighborhood activity scores for a set of curated ligand-target pairs at each spot. (b) To estimate the neighborhood activity score for a ligand-target pair l −t at a given spot S1; Renoir utilizes cell type abundance, cell type-specific mRNA abundance for each spot as inferred by a cell type deconvolution module, expression of the receptor at the target cell types, gene entropy and mutual information between l-t specific to cell types. (c) Renoir performs various downstream tasks based on the ligand-target neighborhood scores - inference of spatial communication domains and domain-specific ligand-target activities, spatial mapping of pathway activity and ranking of ligand activity.\u003c/p\u003e","description":"","filename":"Fig1Overview.png","url":"https://assets-eu.researchsquare.com/files/rs-5331951/v1/9b9922c9826cb60a0a22e2b5.png"},{"id":69761052,"identity":"a9b368ac-f1fe-4a66-9c9a-e2e6a677b143","added_by":"auto","created_at":"2024-11-25 04:44:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1263992,"visible":true,"origin":"","legend":"\u003cp\u003eRenoir outperforms state-of-the-art CCC inference methods in inferring ligand-target activities. (a) Overview of the simulation strategy used for benchmarking. (b) Comparison of Renoir, SpatialDM, COMMOT, and stLearn for the simulated dataset for the ligandtarget pair A2M-MYC simulated based on the human intestine dataset. Positive and negative reference spots are shown in the tissue. Regions 1, 2 and 3 contain negative spots for which SpatialDM, COMMOT, and stLearn falesly inferred activity of the pair but Renoir correctly inferred the absence of activity. (c) Comparison of Renoir, SpatialDM, COMMOT, and stLearn in terms of spatial activity accuracy over all the ligand-target pairs across the tissue types. (d) ARI and NMI comparison of the spatial communication domains computed based on the ligand-target activity scores inferred by Renoir, SpatialDM, COMMOT, and stLearn. (e) Qualitative comparison of the spatial communication domains inferred by Renoir, SpatialDM, COMMOT, and stLearn for DLPFC sample 151673.\u003c/p\u003e","description":"","filename":"Fig2Benchmark.png","url":"https://assets-eu.researchsquare.com/files/rs-5331951/v1/629e6f7c31cadf75f0ee7912.png"},{"id":69761049,"identity":"3b52810d-4d76-4867-b904-99ab79b55d88","added_by":"auto","created_at":"2024-11-25 04:44:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1313469,"visible":true,"origin":"","legend":"\u003cp\u003eRenoir identifies distinct communication domains in mouse brain. (a) Spatial communication domains with distinct latent embeddings inferred by Renoir based on ligand-target neighborhood scores for two mouse brain sections. (b) Distribution of spots in spatial communication domains across different anatomical regions of the mouse brain. The rows correspond to spatial communication domains (in both S1 and S2) and columns correspond to known brain regions. Each cell represents the proportion of spots of a communication domain in the corresponding anatomical brain region. (c) Differentially active ligand-target interactions across the communication domains for sample S1. (d) - (e) Characterization of ligand-target interactions between regional astrocyte subtypes and other co-localized cell types in sample S1. (d) Ligands expressed by regional astrocyte subtypes activating target genes in other cell types. (e) Target genes in regional astrocyte subtypes activated by ligands expressed by other co-localized cell types. (f) Communication domain-specific ranking of ligands based on cumulative activities over target genes expressed by major cell types in the domain for sample S1. Top three ligands for each of eight communication domains are represented. Stacked color bars represent the cell types that express the ligand (right) and target (top). (g) Spatial map of MAPK SIGNALING and NEUROTROPHIN SIGNALING pathway activity across the spots in sample S1.\u003c/p\u003e","description":"","filename":"Fig3MB.png","url":"https://assets-eu.researchsquare.com/files/rs-5331951/v1/6e52d9a9f82e4f03252904dd.png"},{"id":69761433,"identity":"c0492a5a-2da4-4947-b851-85f202351dba","added_by":"auto","created_at":"2024-11-25 04:52:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1177300,"visible":true,"origin":"","legend":"\u003cp\u003eRenoir uncovers intratumor spatial communication sub-domains in triple negative breast cancer tissue. (a) The original histopathological annotations of H\u0026amp;E-stained triple negative breast cancer tissue (CID44971 (28), n = 1322 spots) showing six pathologically distinct regions. (b) Spatial communication domains inferred by Renoir based on ligand-target neighbor activity scores. The communication domains display distinct latent embeddings as can be seen from UMAP. (c) Distribution of spots in inferred communication domains across pathological annotations as in a. Each cell represents the proportion of spots of a communication domain in the corresponding pathological region. (d) Spatial map of neighborhood activity scores for the ligandtarget pairs (e) Differentially active ligand-target pairs inferred by Renoir across all communication domains. (f) Cell type-specific ligand-target interactions for the major cell types in domains 1, 2, and 3. The inner color bar represents the cell types of the ligand (left) and target (top) and the outer color bar represents the communication domain. Each cell represents the average Pearson correlation between the ligand-target neighborhood scores and the abundances of the cell types expressing the ligand and target across the spots pertaining to the domain being considered. (g) - (h) Domain-specific ligand ranking based on their cumulative activities over target genes expressed by major cell types in the domain. (g) Top six ligands for communication domains 0, 1, 2, and 3 are represented. (h) represents the unique ligand-target pairs within each domain. Stacked color bars represent the cell types that express the ligand (right) and target (top). (i) Spatial map of four hallmark pathways - IL6 JAK STAT SIGNALING, TGF BETA SIGNALING, WNT SIGNALING and MAPK signalling pathway activity.\u003c/p\u003e","description":"","filename":"Fig4BC.png","url":"https://assets-eu.researchsquare.com/files/rs-5331951/v1/2cab50495fb98113551f2778.png"},{"id":69761434,"identity":"7e200252-9440-4f3b-91ab-4aee6e73b7f2","added_by":"auto","created_at":"2024-11-25 04:52:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1015292,"visible":true,"origin":"","legend":"\u003cp\u003eRenoir identifies hepatocyte-macrophage interactions in developing fetal liver. (a) Preparation of FFPE fetal liver tissue (b) Spatial communication domains inferred by Renoir based on ligand-target neighborhood scores. (c) Distribution of UMI count across the tissue section. (d) Spatial map of neighborhood activity scores for the ligand-target pairs (e) Ranking of ligands based on their cumulative activities over target genes expressed by major cell types in domain 0. (f) UMAP plot of latent embedding for hepatocyte population in scRNA-seq data, cells are colored as either PLG+ or PLG−. (g) Cell type abundances of Hepatocytes (PLG+ and PLG−) and FOLR2+ Macrophages overlaid onto the spots in ST data. (h) Volcano plot depicting differentially expressed genes between PLG+ and PLG− Hepatocytes (−log10P threshold = 32 and log2foldchange threshold = 0.5). (i) Spatial similarity measures between PLG:MARCO neighborhood activity scores and cell type abundances of PLG+, PLG− Hepatocytes and FOLR2+ Macrophages.\u003c/p\u003e","description":"","filename":"Fig5FL.png","url":"https://assets-eu.researchsquare.com/files/rs-5331951/v1/878802e8704d6a3441355edd.png"},{"id":69761053,"identity":"185a53a1-66f5-4323-90d5-062cf9d63778","added_by":"auto","created_at":"2024-11-25 04:44:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1214389,"visible":true,"origin":"","legend":"\u003cp\u003eRenoir identifies onco-fetal interactions in hepatocellular carcinoma. (a) Ranking of activity of ligands expressed by onco-fetal cell types with other major cell types present in the neighborhoods of the onco-fetal cells. Stacked color bars represent the cell types that express the ligand (left) and target (bottom). (b) (left) Spatial map of cell type specific neighborhood activity scores across cell types associated with the ligand and target for V EGFA : KDR and CXCL12 : CXCR4 respectively (fov 10); (right) Spatial distribution of cell types associated with the ligand and target for V EGFA : KDR and CXCL12 : CXCR4 respectively (fov 10) (c) Distribution of ligand-target interactions across target cell types. The target cell types include major non-onco fetal cell types present in the neighborhoods of the onco-fetal cell types and onco-fetal cell types (d) Top pathways in the MUC6+ bi-potent cells targeted by the oncofetal ligands (e) (From left to right) Spatial map of cell type specific neighborhood activity scores across cell types associated with IL6 : PTGS2; Spatial distribution of cell types associated with IL6 : PTGS2.\u003c/p\u003e","description":"","filename":"Fig6HCC.png","url":"https://assets-eu.researchsquare.com/files/rs-5331951/v1/42406967dd463d3f0debe1ca.png"},{"id":108575383,"identity":"b37bea54-2cd8-486c-bcfd-187d23d4ca06","added_by":"auto","created_at":"2026-05-06 07:07:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1963963,"visible":true,"origin":"","legend":"","description":"","filename":"Renoir.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5331951/v1_covered_79052d38-fcf7-4331-9172-2005c63c3269.pdf"},{"id":69761055,"identity":"d413cba8-d6bb-402f-9f96-8aea0b276dc6","added_by":"auto","created_at":"2024-11-25 04:44:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17053564,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"Renoirsupplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5331951/v1/17afe8a173faf132186db44f.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Charting spatial ligand-target activity using Renoir","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cell-cell communication, ligand-target activity, spatial transcriptomics, pathway","lastPublishedDoi":"10.21203/rs.3.rs-5331951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5331951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The advancement of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics has enabled the inference of cellular interactions in a tissue microenvironment. Despite the development of cell-cell interaction inference methods, there is a lack of methods capable of mapping the influence of ligands on downstream target genes across a spatial topology with specific cell type composition, with the potential to shed light on niche-specific relationship between ligands and their downstream targets. Here we present Renoir for charting the ligand-target activities across a spatial topology and delineating spatial communication niches harboring specific ligand-target activities. Renoir also spatially maps pathway-level activity of ligand-target genesets and identifies domain-specific ligand-target activities. Across spatial datasets with varying resolution (spot to single-cell) ranging from development to disease, Renoir inferred cellular niches with distinct ligand-target interactions, spatially mapped pathway activities, and identified context-specific novel cell-cell interactions. Renoir uncovers biological insights and therapeutically-relevant cellular crosstalk from spatial transcriptomics data.","manuscriptTitle":"Charting spatial ligand-target activity using Renoir","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 04:44:27","doi":"10.21203/rs.3.rs-5331951/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e2adb1c4-c06c-44c1-b322-b240cb91ca2a","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39976370,"name":"Biological sciences/Computational biology and bioinformatics/Cellular signalling networks"},{"id":39976371,"name":"Biological sciences/Computational biology and bioinformatics/Statistical methods"}],"tags":[],"updatedAt":"2026-05-06T07:07:32+00:00","versionOfRecord":{"articleIdentity":"rs-5331951","link":"https://doi.org/10.1038/s41467-026-72388-7","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-05-05 04:00:00","publishedOnDateReadable":"May 5th, 2026"},"versionCreatedAt":"2024-11-25 04:44:27","video":"","vorDoi":"10.1038/s41467-026-72388-7","vorDoiUrl":"https://doi.org/10.1038/s41467-026-72388-7","workflowStages":[]},"version":"v1","identity":"rs-5331951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5331951","identity":"rs-5331951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}