Transcriptional landscape of canine hematopoiesis and cross-species comparisons revealed by single-cell RNA sequencing

Transcriptional landscape of canine hematopoiesis and cross-species comparisons revealed by single-cell RNA sequencing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptional landscape of canine hematopoiesis and cross-species comparisons revealed by single-cell RNA sequencing Dylan T. Ammons, Christopher Contursi, McKenzie Olsen, Janna A. Yoshimoto, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6299609/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Hematopoiesis is a complex process that begins with hematopoietic stem cells (HSCs) that progressively differentiate into all the cellular components found in blood. Numerous cell states exist along this continuum and alterations along the differentiation trajectory can lead to disorders, including hematopoietic neoplasms. Dogs are a valuable model for several human diseases, including a variety of leukemias/lymphomas, but comparative studies investigating the transcriptomic programs of canine hematopoiesis are lacking. The goals of this study were to (1) identify bone marrow hematopoietic cell type gene signatures, (2) characterize temporal gene expression patterns, and (3) complete integrated comparisons to human bone marrow. As such, we performed single cell RNA-sequencing (scRNA-seq) on canine bone marrow and fluorescence activated cell sorted CD34 + hematopoietic precursors. Unsupervised clustering of normal canine bone marrow revealed 29 transcriptomically distinct cell populations across 4 major lineages (erythroid, monocytic/dendritic cell, granulocytic, lymphocytic) arising from HSCs. Pseudotemporal analysis enabled the classification of gene expression patterns (as early, intermediate, and late) along lymphocytic, erythroid, and granulocytic lineages. Investigation of transcription factor activity and gene expression during the branch point between granulocytic and monocytic lineages identified features important in cell fate decision, including CIITA and LMO1. Transcriptomic divisions of the granulocytic lineage were subsequently corroborated with immunophenotyping, and the lineage was subjected to comparative analysis with human granulocytes. Comparative transcriptomics revealed 1,522 (78%) conserved and 417 (22%) divergent gene expression patterns along granulopoiesis. Taken together, our analysis characterizes the single-cell transcriptional landscape of canine hematopoiesis which can serve as a reference for the study of hematopoietic malignancies. Hematology Translational Medicine Immunology Dog Bone marrow Hematology Translational Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Additional Declarations The authors declare no competing interests. Supplementary Files supplementaldata1.csv Supplemental Data 1 supplementaldata2.csv Supplemental Data 2 supplementaldata3.csv Supplemental Data 3 supplementaldata4.csv Supplemental Data 4 supplementaldata51.csv Supplemental Data 5 supplementaldata6.csv Supplemental Data 6 SupplementalFiguresSCS.docx Supplemental Figures SupplementalDataDescriptions.docx Supplemental Data 1-6 Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6299609","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435146318,"identity":"3857f113-ff95-4b59-8201-a7f973eb7d9c","order_by":0,"name":"Dylan T. Ammons","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIie2QP0vDQBiH33DgdOQcL0TqJxBOAkEx+EVcGgKdGnAMGGikEJe6x2/hlDnHQbMcdj1wUfwCgSyFgthToYI56ehwz/b+eXj5vQAWy3+kQcWu6ACOfoxMirNTnAoAA6Dv7X0UhPdRXBXf9tdweXVydy/6KI8wISvedxmMXDUeVDwVz/0KkrSWTxN/upxgr0oQbSQEnkFhKi58DCit1TREaSEwUwgoLyF+NCvzDYaZVoL+XCsrgdb8HWZ/KOX2itAK8x2tNMkB5QWMmSmLfC0vMGu3WWToLb6yhGdySU8f5Mvwx9pEPOPsJq3bRdCt82hECH9TWR4du+3wFTjUffa7T4fXNaQxzywWi8XyyQe7w2ZIybbZxgAAAABJRU5ErkJggg==","orcid":"","institution":"Department: Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA","correspondingAuthor":true,"prefix":"","firstName":"Dylan","middleName":"T.","lastName":"Ammons","suffix":""},{"id":435146319,"identity":"89b49ac6-c16d-4acd-bebb-360778e187cd","order_by":1,"name":"Christopher Contursi","email":"","orcid":"","institution":"Department: Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Contursi","suffix":""},{"id":435146320,"identity":"dded47e7-d063-456c-8ff6-3df94ff75d4a","order_by":2,"name":"McKenzie Olsen","email":"","orcid":"","institution":"Department: Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA","correspondingAuthor":false,"prefix":"","firstName":"McKenzie","middleName":"","lastName":"Olsen","suffix":""},{"id":435146321,"identity":"35a2cf3d-7425-4fbb-aae2-3f896b4b8e51","order_by":3,"name":"Janna A. Yoshimoto","email":"","orcid":"","institution":"Department: Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA","correspondingAuthor":false,"prefix":"","firstName":"Janna","middleName":"A.","lastName":"Yoshimoto","suffix":""},{"id":435146322,"identity":"1fe7c761-a53b-46e4-bade-de4a1a22d5c8","order_by":4,"name":"Eileen Owens","email":"","orcid":"","institution":"Department: Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA","correspondingAuthor":false,"prefix":"","firstName":"Eileen","middleName":"","lastName":"Owens","suffix":""},{"id":435146323,"identity":"6fffd490-0e0a-4425-938f-99352724e4f1","order_by":5,"name":"Macallister Harris","email":"","orcid":"","institution":"Department: Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA","correspondingAuthor":false,"prefix":"","firstName":"Macallister","middleName":"","lastName":"Harris","suffix":""},{"id":435146324,"identity":"69f0fb0b-f891-4dce-b1cd-528862e811e3","order_by":6,"name":"Steven Dow","email":"","orcid":"","institution":"Department: Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA","correspondingAuthor":false,"prefix":"","firstName":"Steven","middleName":"","lastName":"Dow","suffix":""},{"id":435146325,"identity":"4085968f-2b65-4fc1-8749-c451a235fe5a","order_by":7,"name":"Anne. C. Avery","email":"","orcid":"","institution":"Department: Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA","correspondingAuthor":false,"prefix":"","firstName":"Anne.","middleName":"C.","lastName":"Avery","suffix":""},{"id":435146326,"identity":"22e9a817-2fb5-4bd2-9a2f-265206ee6f90","order_by":8,"name":"R. Adam Harris","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYFCCHMYHCRUMDHwIkQSCWpgNHpxhYGBjYGBsgKgmrIVN8mEbKVr423OPSSTOs5NjY29//uDnDxsGfvYcA7xaJM68S7ZI3JZszMZzxrCxJyGNQbLnDX4tDDdyDG8kbjuQ2CaRw9jAk3CYweAGAVvkgQokEueAtKQ/bPyT8J/BnpAWoJlGEokNIC0Jhs08CQcYDCQIaDE888bYIOEYxC+zZdKSeSTOPCvAq0XueI7hwx81dnL87O0PPr6xATLakzfg1YIBeEhTPgpGwSgYBaMAKwAAhgpH7M6XyTMAAAAASUVORK5CYII=","orcid":"","institution":"Department: Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA","correspondingAuthor":true,"prefix":"","firstName":"R.","middleName":"Adam","lastName":"Harris","suffix":""}],"badges":[],"createdAt":"2025-03-25 03:32:31","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6299609/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-6299609/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84426086,"identity":"1c74af3a-6497-46aa-b51f-34b13ec0fa51","added_by":"auto","created_at":"2025-06-11 20:05:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1625494,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptomic profiling of canine hematopoiesis identifies four major hematopoietic lineages. \u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) UMAP embedding of 14,293 cells from whole bone marrow and 8,695 cells from CD34\u003csup\u003e+\u003c/sup\u003e sorted bone marrow isolated from 3 healthy dogs. UMAP is colorized by cell type as determined through unsupervised clustering. (\u003cstrong\u003eb\u003c/strong\u003e) Dot plot depicting average, scaled expression for features that define each of the 29 identified cell types. (\u003cstrong\u003ec\u003c/strong\u003e) STREAM plot colorized by annotated cell type depicting the flow of cells over pseudotime. (\u003cstrong\u003ed\u003c/strong\u003e) Heatmap depicting transcription factor (TF) activity scores for each lineage determine using pySCENIC. Values in the heatmap represent the scaled, mean area under the curve (AUC) value for each TF (rows) within each lineage (columns). (\u003cstrong\u003ee\u003c/strong\u003e) Feature plots depicting raw AUC values used to infer TF activity for each cell. Abbreviations: Hematopoietic stem and progenitor cells (HSPC), Hematopoietic stem cell (HSC), Multipotent progenitor-Common myeloid progenitor (MPP-CMP), Granulocytic (Gran), Granulocyte-monocyte progenitor (GMP), Erythroid (Eryth), Megakaryocyte-erythroid progenitor (MEP), Monocytic-Dendritic (Mono/DC), Monocyte precursor (MP), Dendritic cell (DC) Monocyte-dendritic progenitor (MDP), Granulocyte precursor (GP), Cycling granulocyte precursor (G_cycling), Lymphoid (Lymph), Common lymphoid progenitor (CLP), Pro-B cell (Pro-B), Pre-B cell (Pre-B), Immature B cell (Immature_B), and Cycling lymphoid progenitor (B_cycling).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6299609/v2/0eb40d2d07e9ea024d62c327.png"},{"id":84426090,"identity":"7987505b-e4e1-4736-adcf-5feb8b493eca","added_by":"auto","created_at":"2025-06-11 20:05:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":868890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression profiles of erythroid lineage during differentiation.\u003c/strong\u003e UMAP embedding of the erythroid (1,345 cells) lineage colorized by pseudotime (rescaled to range from 0 [early] to 1 [late]). (\u003cstrong\u003eb\u003c/strong\u003e) Ridge plot depicting cell abundance by cluster over pseudotime. Scatter plot depicting (\u003cstrong\u003ec\u003c/strong\u003e) raw transcription factor (TF) area under the curve (AUC) values and (\u003cstrong\u003ed\u003c/strong\u003e) log normalized gene expression counts over pseudotime. TF and gene expression profiles are fitted with generalized additive models (GAMs) to model expression over pseudotime. (\u003cstrong\u003ee\u003c/strong\u003e) Heatmap depicting smoothed (smoothing spline, df = 4), scaled gene expression of genes determined to be differentially expressed over pseudotime. Genes exhibiting unimodal distributions were classified 3 groups based decreasing slope (“Early”), convex pattern (“Intermediate”), or increasing slope (“Late”) over pseudotime. The “Other” genes were those that did not exhibit a unimodal distribution (exhibited a concave expression pattern). Abbreviations: Megakaryocyte-erythroid progenitor (MEP).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6299609/v2/27beaa66f941dc53c1a206f7.png"},{"id":84426501,"identity":"49d802cd-2ff4-4538-bfa4-1ba8856d3595","added_by":"auto","created_at":"2025-06-11 20:13:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":969353,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression profiles of lymphocytic lineage during differentiation.\u003c/strong\u003e UMAP embedding of the lymphoid (5,784 cells) lineage colorized by pseudotime (rescaled to range from 0 [early] to 1 [late]). (\u003cstrong\u003eb\u003c/strong\u003e) Ridge plot depicting cell abundance by cluster over pseudotime. Scatter plot depicting (\u003cstrong\u003ec\u003c/strong\u003e) raw transcription factor (TF) area under the curve (AUC) values and (\u003cstrong\u003ed\u003c/strong\u003e) log normalized gene expression counts over pseudotime. TF and gene expression profiles are fitted with generalized additive models (GAMs) to model expression over pseudotime. (\u003cstrong\u003ee\u003c/strong\u003e) Heatmap depicting smoothed (smoothing spline, df = 4), scaled gene expression of genes determined to be differentially expressed over pseudotime. Genes exhibiting unimodal distributions were classified 3 groups based decreasing slope (“Early”), convex pattern (“Intermediate”), or increasing slope (“Late”) over pseudotime. The “Other” genes were those that did not exhibit a unimodal distribution (exhibited a concave expression pattern). Abbreviations: Common lymphoid progenitor (CLP), Pro-B cell (Pro-B), Pre-B cell (Pre-B), Immature B cell (Immature_B), and Cycling lymphoid progenitor (B_cycling).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6299609/v2/949b33c97776fd236d0cf50d.png"},{"id":84426502,"identity":"76865a51-72bf-433c-84e2-a5ce30daaa3d","added_by":"auto","created_at":"2025-06-11 20:13:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":887975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression profiles of granulocytic lineage during differentiation.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) UMAP embedding of the granulocytic (10,305 cells) lineage colorized by pseudotime (rescaled to range from 0 [early] to 1 [late]). (\u003cstrong\u003eb\u003c/strong\u003e) Ridge plot depicting cell abundance by cluster over pseudotime. Scatter plot depicting (\u003cstrong\u003ec\u003c/strong\u003e) raw transcription factor (TF) area under the curve (AUC) values and (\u003cstrong\u003ed\u003c/strong\u003e) log normalized gene expression counts over pseudotime. TF and gene expression profiles are fitted with generalized additive models (GAMs) to model expression over pseudotime. (\u003cstrong\u003ee\u003c/strong\u003e) Heatmap depicting smoothed (smoothing spline, df = 4), scaled gene expression of genes determined to be differentially expressed over pseudotime. Genes exhibiting unimodal distributions were classified 3 groups based decreasing slope (“Early”), convex pattern (“Intermediate”), or increasing slope (“Late”) over pseudotime. The “Other” genes were those that did not exhibit a unimodal distribution (exhibited a concave expression pattern). Abbreviations: Granulocyte precursor (GP) and Cycling granulocyte precursor (G_cycling).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6299609/v2/6c5cdb53a91f15b25089b72b.png"},{"id":84426503,"identity":"3279cb76-fe07-47cf-828d-6a69e67dfc1b","added_by":"auto","created_at":"2025-06-11 20:13:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1272606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of\u003c/strong\u003e \u003cstrong\u003egene expression patterns during differentiation of GMP into granulocytic and monocytic lineages\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) UMAP embedding highlighting the 3 clusters involved in the GMP-granulocytic/monocytic branch point. (\u003cstrong\u003eb\u003c/strong\u003e) Scatter plot depicting regulon specificity score (rss), as determined using pySCENIC, for active transcription factors identified to be enriched within each branch point cluster. (\u003cstrong\u003ec\u003c/strong\u003e) Feature plots depicting raw AUC value for each cell. (\u003cstrong\u003ed\u003c/strong\u003e) Heatmap depicting smoothed (smoothing spline, df = 4), scaled gene expression of features determined to be differentially expressed over 30 pseudotime bins within the 3 GMP branchpoint clusters. Genes differentially expressed over pseudotime were ordered by peak gene expression over pseudotime and select features with peak expression during transition points are labeled. Genes exhibiting expression in granulocytic and monocytic lineages (concave expression patterns) are presented in the bottom of the heatmap. (\u003cstrong\u003ee\u003c/strong\u003e) Feature plots depicting log normalized expression of features identified to have peak expression during the transition from GMP to granulocytic (MPO, PLPPR3) or monocytic (CIITA, LMO1) lineages. (\u003cstrong\u003ef\u003c/strong\u003e) Line plots depicting smoothed, scaled expression along pseudotime (rescaled to range from 0 [early] to 1 [late]) for early granulocytic-monocytic precursors (GMPs) to late granulocytic precursors (GP, blue) and late monocytic precursors (MP, green). Abbreviations: Hematopoietic stem and progenitor cells (HSPC), Erythroid (Eryth), Lymphoid (Lymph), Monocytic-Dendritic (Mono/DC), Granulocytic (Gran).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6299609/v2/383592631f7ec336ce689267.png"},{"id":84426506,"identity":"b9244f0e-67c2-4470-9e56-88760f9a0ccb","added_by":"auto","created_at":"2025-06-11 20:13:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":916679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunophenotyping of canine bone marrow enables the identification of granulocytic precursors. \u003c/strong\u003ePhotomicrographs of cells isolated from canine bone marrow and sorted to isolate cells consistent with (\u003cstrong\u003ea\u003c/strong\u003e) GMPs, (\u003cstrong\u003eb\u003c/strong\u003e) early granulocytic precursors (early GP), (\u003cstrong\u003ec\u003c/strong\u003e) late granulocytic precursors (late GP) and (\u003cstrong\u003ed\u003c/strong\u003e) mature neutrophils. Violin plots (\u003cstrong\u003ee\u003c/strong\u003e) show gene expression levels, while box plots (\u003cstrong\u003ef\u003c/strong\u003e) display mean fluorescence intensity for molecules included in the immunophenotyping panel used to isolate granulocytic subtypes. Abbreviations: Hematopoietic stem and progenitor cells (HSPC), Granulocyte-monocyte progenitor (GMP), and Granulocyte precursor (GP).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6299609/v2/515e11dae9182cbff36279c0.png"},{"id":84426504,"identity":"1cb9c262-4eb5-4e9d-a791-7a9733ed28b4","added_by":"auto","created_at":"2025-06-11 20:13:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2465990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHematopoietic cell type gene signatures and granulocytic gene expression patterns are largely conserved in dogs and humans.\u003c/strong\u003e (\u003cstrong\u003ea\u003c/strong\u003e) Workflow summary and results of label transfer from the annotated canine dataset to a human bone marrow dataset. (\u003cstrong\u003eb\u003c/strong\u003e) Heatmap depicting the percentage overlap of the predicted cell type annotations within the original cell type annotations presented in the previously published annotated human dataset. (\u003cstrong\u003ec\u003c/strong\u003e) Heatmap depicting smoothed (smoothing spline, df = 4), scaled gene expression of genes determined to be differentially expressed within the granulocytic lineage along pseudotime in both humans (left) and dogs (right). The top portion of the heatmap depicts “conserved” genes which had a Pearson correlation coefficient \u0026gt; 0.5 and genes are ordered by peak expression. The lower portion of the heatmap depicts “divergent” genes which had a Pearson correlation coefficient ≤ 0.5 and genes are ordered based on hierarchical clustering. Plotting on the right side of the heatmap depicts scaled expression over pseudotime for humans and dogs, as well as log normalized expression in the UMAP embeddings for select conserved (CD177, SPI1) and divergent (CD4, OSM) features. Abbreviations: Hematopoietic stem cell (HSC), Multipotent progenitor–Common myeloid progenitor (MPP-CMP), Megakaryocyte–erythroid progenitor (MEP), Red blood cell (RBC), Granulocyte–monocyte progenitor (GMP), Granulocyte precursor (GP), Cycling granulocyte precursor (G_cycling), Multipotent progenitor (MPP), Monocyte precursor (MP), Dendritic cell precursor, Monocyte–dendritic progenitor (MDP), Common lymphoid progenitor (CLP), Cycling B cell (B_cycling), Progenitor B cell (Pro-B), Precursor B cell (Pre-B), Immature B cell (Immature_B), Mature B cell (Mature_B), Dendritic cell (DCs), and Plasmacytoid dendritic cell (pDC).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6299609/v2/ebfc0989a4ff57e0a9dc85d9.png"},{"id":84427173,"identity":"08e87794-c06b-41e8-9585-09b0b7fcf6bb","added_by":"auto","created_at":"2025-06-11 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cells (HSCs) that progressively differentiate into all the cellular components found in blood. Numerous cell states exist along this continuum and alterations along the differentiation trajectory can lead to disorders, including hematopoietic neoplasms. Dogs are a valuable model for several human diseases, including a variety of leukemias/lymphomas, but comparative studies investigating the transcriptomic programs of canine hematopoiesis are lacking.\u003c/p\u003e\n\u003cp\u003eThe goals of this study were to (1) identify bone marrow hematopoietic cell type gene signatures, (2) characterize temporal gene expression patterns, and (3) complete integrated comparisons to human bone marrow. As such, we performed single cell RNA-sequencing (scRNA-seq) on canine bone marrow and fluorescence activated cell sorted CD34\u003csup\u003e+\u003c/sup\u003e hematopoietic precursors.\u003c/p\u003e\n\u003cp\u003eUnsupervised clustering of normal canine bone marrow revealed 29 transcriptomically distinct cell populations across 4 major lineages (erythroid, monocytic/dendritic cell, granulocytic, lymphocytic) arising from HSCs. Pseudotemporal analysis enabled the classification of gene expression patterns (as early, intermediate, and late) along lymphocytic, erythroid, and granulocytic lineages. Investigation of transcription factor activity and gene expression during the branch point between granulocytic and monocytic lineages identified features important in cell fate decision, including CIITA and LMO1. Transcriptomic divisions of the granulocytic lineage were subsequently corroborated with immunophenotyping, and the lineage was subjected to comparative analysis with human granulocytes. Comparative transcriptomics revealed 1,522 (78%) conserved and 417 (22%) divergent gene expression patterns along granulopoiesis.\u003c/p\u003e\n\u003cp\u003eTaken together, our analysis characterizes the single-cell transcriptional landscape of canine hematopoiesis which can serve as a reference for the study of hematopoietic malignancies.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Transcriptional landscape of canine hematopoiesis and cross-species comparisons revealed by single-cell RNA sequencing","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-06-11 20:05:37","doi":"10.21203/rs.3.rs-6299609/v2","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}},{"code":1,"date":"2025-03-28 05:32:52","doi":"10.21203/rs.3.rs-6299609/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":"d9557426-739d-4f9e-9899-792b14187808","owner":[],"postedDate":"June 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46335437,"name":"Hematology"},{"id":46335438,"name":"Translational Medicine"},{"id":46335439,"name":"Immunology"}],"tags":[],"updatedAt":"2025-03-28T05:32:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-11 20:05:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-6299609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6299609","identity":"rs-6299609","version":["v2"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}