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Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system
The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages, and dendritic cells (DCs) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected...
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Published in: | PLoS biology 2020-10, Vol.18 (10), p.e3000859-e3000859 |
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description | The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages, and dendritic cells (DCs) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) a total of 466 quality RNA sequencing (RNA-seq) data sets generated from mouse MPS cells isolated from bone marrow, blood, and multiple tissues. The primary data were randomly downsized to a depth of 10 million reads and requantified. The resulting data set was clustered using the network analysis tool BioLayout. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1s and cDC2s, and lacking Fcgr1 (encoding the protein CD64) were contained within the MPS cluster, no more distinct than other MPS cells. A gene-to-gene correlation matrix identified large generic coexpression clusters associated with MPS maturation and innate immune function. Smaller coexpression gene clusters, including the transcription factors that drive them, showed higher expression within defined isolated cells, including monocytes, macrophages, and DCs isolated from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell (EC) homeostasis, a cluster of transcripts enriched in intestinal macrophages, and a generic lymphoid tissue cDC cluster associated with Ccr7. However, transcripts encoding Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Mrc1, Retnla, and H2-a/e (encoding class II major histocompatibility complex [MHC] proteins) and many other proposed macrophage subset and DC lineage markers each had idiosyncratic expression profiles. Coexpression of immediate early genes (for example, Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (tumour necrosis factor [Tnf], Il1b, Ccl3/4) indicated that all tissue disaggregation and separation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of RNA-seq and single-cell RNA-seq (scRNA-seq) data from the same lung cell populations indicated that MPS heterogeneity implied by global cluster analysis may be even greater at a single-cell level. This analysis highlig |
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To test the relationships between markers and transcriptomic diversity in the MPS, we collected from National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) a total of 466 quality RNA sequencing (RNA-seq) data sets generated from mouse MPS cells isolated from bone marrow, blood, and multiple tissues. The primary data were randomly downsized to a depth of 10 million reads and requantified. The resulting data set was clustered using the network analysis tool BioLayout. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1s and cDC2s, and lacking Fcgr1 (encoding the protein CD64) were contained within the MPS cluster, no more distinct than other MPS cells. A gene-to-gene correlation matrix identified large generic coexpression clusters associated with MPS maturation and innate immune function. Smaller coexpression gene clusters, including the transcription factors that drive them, showed higher expression within defined isolated cells, including monocytes, macrophages, and DCs isolated from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell (EC) homeostasis, a cluster of transcripts enriched in intestinal macrophages, and a generic lymphoid tissue cDC cluster associated with Ccr7. However, transcripts encoding Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Mrc1, Retnla, and H2-a/e (encoding class II major histocompatibility complex [MHC] proteins) and many other proposed macrophage subset and DC lineage markers each had idiosyncratic expression profiles. Coexpression of immediate early genes (for example, Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (tumour necrosis factor [Tnf], Il1b, Ccl3/4) indicated that all tissue disaggregation and separation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of RNA-seq and single-cell RNA-seq (scRNA-seq) data from the same lung cell populations indicated that MPS heterogeneity implied by global cluster analysis may be even greater at a single-cell level. This analysis highlights the power of large data sets to identify the diversity of MPS cellular phenotypes and the limited predictive value of surface markers to define lineages, functions, or subpopulations.</description><identifier>ISSN: 1545-7885</identifier><identifier>ISSN: 1544-9173</identifier><identifier>EISSN: 1545-7885</identifier><identifier>DOI: 10.1371/journal.pbio.3000859</identifier><identifier>PMID: 33031383</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Animals ; Antigens ; Archives & records ; Biological diversity ; Biology and Life Sciences ; Biomarkers ; Biomarkers - metabolism ; Biotechnology ; Blood ; Blood circulation ; Bone marrow ; CCL3 protein ; CCR7 protein ; CD163 antigen ; Cell Separation ; Chemokines ; Cluster analysis ; Comparative analysis ; Computer and Information Sciences ; Correlation analysis ; Cytokines ; Databases as Topic ; Datasets ; Dendritic cells ; Dendritic Cells - cytology ; Dendritic Cells - metabolism ; Disaggregation ; EGR-1 protein ; Endothelial cells ; Fc receptors ; Gene clusters ; Gene expression ; Gene Expression Regulation ; Gene Regulatory Networks ; Gene sequencing ; Genes, Essential ; Genetic aspects ; Hemopoiesis ; Heterogeneity ; Homeostasis ; Immune response ; In vivo methods and tests ; Inflammation ; Interleukin 1 ; Intestine ; Kidney - metabolism ; Lymphoid tissue ; Macrophage Activation - genetics ; Macrophages ; Macrophages - cytology ; Macrophages - metabolism ; Medicine and Health Sciences ; Mice ; Network analysis ; Organ Specificity - genetics ; Phenotypes ; Physiological aspects ; Populations ; Proteins ; Protocol (computers) ; Rats ; Rattus ; Reproducibility of Results ; Ribonucleic acid ; RNA ; RNA, Messenger - genetics ; RNA, Messenger - metabolism ; Spleen - metabolism ; Subpopulations ; Surface markers ; Tissues ; Transcription factors ; Transcription Factors - metabolism ; Transcriptome - genetics ; Transcriptomics ; Tumors</subject><ispartof>PLoS biology, 2020-10, Vol.18 (10), p.e3000859-e3000859</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Summers et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Summers et al 2020 Summers et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c812t-b3dcdcba57ee882bef49cbdca84bffc847f0d0375e7d594585088b272516d7fe3</citedby><cites>FETCH-LOGICAL-c812t-b3dcdcba57ee882bef49cbdca84bffc847f0d0375e7d594585088b272516d7fe3</cites><orcidid>0000-0002-2615-1478 ; 0000-0002-7084-4386 ; 0000-0001-9341-2562</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2460095555/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2460095555?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,725,778,782,883,25736,27907,27908,36995,36996,44573,53774,53776,74877</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33031383$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Marrack, Philippa</contributor><creatorcontrib>Summers, Kim M</creatorcontrib><creatorcontrib>Bush, Stephen J</creatorcontrib><creatorcontrib>Hume, David A</creatorcontrib><title>Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system</title><title>PLoS biology</title><addtitle>PLoS Biol</addtitle><description>The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages, and dendritic cells (DCs) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) a total of 466 quality RNA sequencing (RNA-seq) data sets generated from mouse MPS cells isolated from bone marrow, blood, and multiple tissues. The primary data were randomly downsized to a depth of 10 million reads and requantified. The resulting data set was clustered using the network analysis tool BioLayout. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1s and cDC2s, and lacking Fcgr1 (encoding the protein CD64) were contained within the MPS cluster, no more distinct than other MPS cells. A gene-to-gene correlation matrix identified large generic coexpression clusters associated with MPS maturation and innate immune function. Smaller coexpression gene clusters, including the transcription factors that drive them, showed higher expression within defined isolated cells, including monocytes, macrophages, and DCs isolated from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell (EC) homeostasis, a cluster of transcripts enriched in intestinal macrophages, and a generic lymphoid tissue cDC cluster associated with Ccr7. However, transcripts encoding Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Mrc1, Retnla, and H2-a/e (encoding class II major histocompatibility complex [MHC] proteins) and many other proposed macrophage subset and DC lineage markers each had idiosyncratic expression profiles. Coexpression of immediate early genes (for example, Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (tumour necrosis factor [Tnf], Il1b, Ccl3/4) indicated that all tissue disaggregation and separation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of RNA-seq and single-cell RNA-seq (scRNA-seq) data from the same lung cell populations indicated that MPS heterogeneity implied by global cluster analysis may be even greater at a single-cell level. This analysis highlights the power of large data sets to identify the diversity of MPS cellular phenotypes and the limited predictive value of surface markers to define lineages, functions, or subpopulations.</description><subject>Analysis</subject><subject>Animals</subject><subject>Antigens</subject><subject>Archives & records</subject><subject>Biological diversity</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers - metabolism</subject><subject>Biotechnology</subject><subject>Blood</subject><subject>Blood circulation</subject><subject>Bone marrow</subject><subject>CCL3 protein</subject><subject>CCR7 protein</subject><subject>CD163 antigen</subject><subject>Cell Separation</subject><subject>Chemokines</subject><subject>Cluster analysis</subject><subject>Comparative analysis</subject><subject>Computer and Information Sciences</subject><subject>Correlation analysis</subject><subject>Cytokines</subject><subject>Databases as Topic</subject><subject>Datasets</subject><subject>Dendritic cells</subject><subject>Dendritic Cells - cytology</subject><subject>Dendritic Cells - metabolism</subject><subject>Disaggregation</subject><subject>EGR-1 protein</subject><subject>Endothelial cells</subject><subject>Fc receptors</subject><subject>Gene clusters</subject><subject>Gene expression</subject><subject>Gene Expression Regulation</subject><subject>Gene Regulatory Networks</subject><subject>Gene sequencing</subject><subject>Genes, Essential</subject><subject>Genetic aspects</subject><subject>Hemopoiesis</subject><subject>Heterogeneity</subject><subject>Homeostasis</subject><subject>Immune response</subject><subject>In vivo methods and tests</subject><subject>Inflammation</subject><subject>Interleukin 1</subject><subject>Intestine</subject><subject>Kidney - metabolism</subject><subject>Lymphoid tissue</subject><subject>Macrophage Activation - genetics</subject><subject>Macrophages</subject><subject>Macrophages - cytology</subject><subject>Macrophages - metabolism</subject><subject>Medicine and Health Sciences</subject><subject>Mice</subject><subject>Network analysis</subject><subject>Organ Specificity - 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metabolism</topic><topic>Biotechnology</topic><topic>Blood</topic><topic>Blood circulation</topic><topic>Bone marrow</topic><topic>CCL3 protein</topic><topic>CCR7 protein</topic><topic>CD163 antigen</topic><topic>Cell Separation</topic><topic>Chemokines</topic><topic>Cluster analysis</topic><topic>Comparative analysis</topic><topic>Computer and Information Sciences</topic><topic>Correlation analysis</topic><topic>Cytokines</topic><topic>Databases as Topic</topic><topic>Datasets</topic><topic>Dendritic cells</topic><topic>Dendritic Cells - cytology</topic><topic>Dendritic Cells - metabolism</topic><topic>Disaggregation</topic><topic>EGR-1 protein</topic><topic>Endothelial cells</topic><topic>Fc receptors</topic><topic>Gene clusters</topic><topic>Gene expression</topic><topic>Gene Expression Regulation</topic><topic>Gene Regulatory Networks</topic><topic>Gene sequencing</topic><topic>Genes, Essential</topic><topic>Genetic aspects</topic><topic>Hemopoiesis</topic><topic>Heterogeneity</topic><topic>Homeostasis</topic><topic>Immune response</topic><topic>In vivo methods and tests</topic><topic>Inflammation</topic><topic>Interleukin 1</topic><topic>Intestine</topic><topic>Kidney - metabolism</topic><topic>Lymphoid tissue</topic><topic>Macrophage Activation - genetics</topic><topic>Macrophages</topic><topic>Macrophages - cytology</topic><topic>Macrophages - metabolism</topic><topic>Medicine and Health Sciences</topic><topic>Mice</topic><topic>Network analysis</topic><topic>Organ Specificity - genetics</topic><topic>Phenotypes</topic><topic>Physiological aspects</topic><topic>Populations</topic><topic>Proteins</topic><topic>Protocol (computers)</topic><topic>Rats</topic><topic>Rattus</topic><topic>Reproducibility of Results</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>RNA, Messenger - genetics</topic><topic>RNA, Messenger - metabolism</topic><topic>Spleen - metabolism</topic><topic>Subpopulations</topic><topic>Surface markers</topic><topic>Tissues</topic><topic>Transcription factors</topic><topic>Transcription Factors - metabolism</topic><topic>Transcriptome - genetics</topic><topic>Transcriptomics</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Summers, Kim M</creatorcontrib><creatorcontrib>Bush, Stephen J</creatorcontrib><creatorcontrib>Hume, David A</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Opposing Viewpoints in Context (Gale)</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><collection>PLoS Biology</collection><jtitle>PLoS biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Summers, Kim M</au><au>Bush, Stephen J</au><au>Hume, David A</au><au>Marrack, Philippa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system</atitle><jtitle>PLoS biology</jtitle><addtitle>PLoS Biol</addtitle><date>2020-10-08</date><risdate>2020</risdate><volume>18</volume><issue>10</issue><spage>e3000859</spage><epage>e3000859</epage><pages>e3000859-e3000859</pages><issn>1545-7885</issn><issn>1544-9173</issn><eissn>1545-7885</eissn><abstract>The mononuclear phagocyte system (MPS) is a family of cells including progenitors, circulating blood monocytes, resident tissue macrophages, and dendritic cells (DCs) present in every tissue in the body. To test the relationships between markers and transcriptomic diversity in the MPS, we collected from National Center for Biotechnology Information Gene Expression Omnibus (NCBI-GEO) a total of 466 quality RNA sequencing (RNA-seq) data sets generated from mouse MPS cells isolated from bone marrow, blood, and multiple tissues. The primary data were randomly downsized to a depth of 10 million reads and requantified. The resulting data set was clustered using the network analysis tool BioLayout. A sample-to-sample matrix revealed that MPS populations could be separated based upon tissue of origin. Cells identified as classical DC subsets, cDC1s and cDC2s, and lacking Fcgr1 (encoding the protein CD64) were contained within the MPS cluster, no more distinct than other MPS cells. A gene-to-gene correlation matrix identified large generic coexpression clusters associated with MPS maturation and innate immune function. Smaller coexpression gene clusters, including the transcription factors that drive them, showed higher expression within defined isolated cells, including monocytes, macrophages, and DCs isolated from specific tissues. They include a cluster containing Lyve1 that implies a function in endothelial cell (EC) homeostasis, a cluster of transcripts enriched in intestinal macrophages, and a generic lymphoid tissue cDC cluster associated with Ccr7. However, transcripts encoding Adgre1, Itgax, Itgam, Clec9a, Cd163, Mertk, Mrc1, Retnla, and H2-a/e (encoding class II major histocompatibility complex [MHC] proteins) and many other proposed macrophage subset and DC lineage markers each had idiosyncratic expression profiles. Coexpression of immediate early genes (for example, Egr1, Fos, Dusp1) and inflammatory cytokines and chemokines (tumour necrosis factor [Tnf], Il1b, Ccl3/4) indicated that all tissue disaggregation and separation protocols activate MPS cells. Tissue-specific expression clusters indicated that all cell isolation procedures also co-purify other unrelated cell types that may interact with MPS cells in vivo. Comparative analysis of RNA-seq and single-cell RNA-seq (scRNA-seq) data from the same lung cell populations indicated that MPS heterogeneity implied by global cluster analysis may be even greater at a single-cell level. This analysis highlights the power of large data sets to identify the diversity of MPS cellular phenotypes and the limited predictive value of surface markers to define lineages, functions, or subpopulations.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33031383</pmid><doi>10.1371/journal.pbio.3000859</doi><orcidid>https://orcid.org/0000-0002-2615-1478</orcidid><orcidid>https://orcid.org/0000-0002-7084-4386</orcidid><orcidid>https://orcid.org/0000-0001-9341-2562</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1545-7885 |
ispartof | PLoS biology, 2020-10, Vol.18 (10), p.e3000859-e3000859 |
issn | 1545-7885 1544-9173 1545-7885 |
language | eng |
recordid | cdi_plos_journals_2460095555 |
source | Open Access: PubMed Central; Publicly Available Content Database |
subjects | Analysis Animals Antigens Archives & records Biological diversity Biology and Life Sciences Biomarkers Biomarkers - metabolism Biotechnology Blood Blood circulation Bone marrow CCL3 protein CCR7 protein CD163 antigen Cell Separation Chemokines Cluster analysis Comparative analysis Computer and Information Sciences Correlation analysis Cytokines Databases as Topic Datasets Dendritic cells Dendritic Cells - cytology Dendritic Cells - metabolism Disaggregation EGR-1 protein Endothelial cells Fc receptors Gene clusters Gene expression Gene Expression Regulation Gene Regulatory Networks Gene sequencing Genes, Essential Genetic aspects Hemopoiesis Heterogeneity Homeostasis Immune response In vivo methods and tests Inflammation Interleukin 1 Intestine Kidney - metabolism Lymphoid tissue Macrophage Activation - genetics Macrophages Macrophages - cytology Macrophages - metabolism Medicine and Health Sciences Mice Network analysis Organ Specificity - genetics Phenotypes Physiological aspects Populations Proteins Protocol (computers) Rats Rattus Reproducibility of Results Ribonucleic acid RNA RNA, Messenger - genetics RNA, Messenger - metabolism Spleen - metabolism Subpopulations Surface markers Tissues Transcription factors Transcription Factors - metabolism Transcriptome - genetics Transcriptomics Tumors |
title | Network analysis of transcriptomic diversity amongst resident tissue macrophages and dendritic cells in the mouse mononuclear phagocyte system |
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