Multiparameter Analysis Identifies Heterogeneity in Knee Osteoarthritis Synovial Responses

Objective Synovial membrane inflammation is common in osteoarthritis (OA) and increases cartilage injury. However, synovial fluid and histology studies suggest that OA inflammatory responses are not homogeneous. Greater understanding of these responses may provide new insights into OA disease mechan...

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Veröffentlicht in:Arthritis & rheumatology (Hoboken, N.J.) N.J.), 2020-04, Vol.72 (4), p.598-608
Hauptverfasser: Labinsky, Hannah, Panipinto, Paul M., Ly, Kaytlyn A., Khuat, Deric K., Madarampalli, Bhanupriya, Mahajan, Vineet, Clabeaux, Jonathan, MacDonald, Kevin, Verdin, Peter J., Buckner, Jane H., Noss, Erika H.
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container_end_page 608
container_issue 4
container_start_page 598
container_title Arthritis & rheumatology (Hoboken, N.J.)
container_volume 72
creator Labinsky, Hannah
Panipinto, Paul M.
Ly, Kaytlyn A.
Khuat, Deric K.
Madarampalli, Bhanupriya
Mahajan, Vineet
Clabeaux, Jonathan
MacDonald, Kevin
Verdin, Peter J.
Buckner, Jane H.
Noss, Erika H.
description Objective Synovial membrane inflammation is common in osteoarthritis (OA) and increases cartilage injury. However, synovial fluid and histology studies suggest that OA inflammatory responses are not homogeneous. Greater understanding of these responses may provide new insights into OA disease mechanisms. We undertook this study to develop a novel multiparameter approach to phenotype synovial responses in knee OA. Methods Cell composition and soluble protein production were measured by flow cytometry and multiplex enzyme‐linked immunosorbent assay in synovium collected from OA patients undergoing knee replacement surgery (n = 35). Results Testing disaggregation conditions showed that aggressive digestion improved synovial cell yield and mesenchymal staining by flow cytometry, but it negatively impacted CD4+ T cell and CD56+ natural killer cell staining. Less aggressive digestion preserved these markers and showed highly variable T cell infiltration (range 0–43%; n = 32). Correlation analysis identified mesenchymal subpopulations associated with different nonmesenchymal populations, including macrophages and T cells (CD45+CD11b+HLA−DR+ myeloid cells with PDPN+CD73+CD90−CD34− mesenchymal cells [r = 0.65, P < 0.0001]; and CD45+CD3+ T cells with PDPN+CD73+CD90+CD34+ mesenchymal cells [r = 0.50, P = 0.003]). Interleukin‐6 (IL‐6) measured by flow cytometry strongly correlated with IL‐6 released by ex vivo culture of synovial tissue (r = 0.59, P = 0.0012) and was highest in mesenchymal cells coexpressing CD90 and CD34. IL‐6, IL‐8, complement factor D, and IL‐10 release correlated positively with tissue cellularity (P = 0.0042, P = 0.018, P = 0.0012, and P = 0.038, respectively). Additionally, increased CD8+ T cell numbers correlated with retinol binding protein 4 (P = 0.033). Finally, combining flow cytometry and multiplex data identified patient clusters with different types of inflammatory responses. Conclusion We used a novel approach to analyze OA synovium, identifying patient‐specific inflammatory clusters. Our findings indicate that phenotyping synovial inflammation may provide new insights into OA patient heterogeneity and biomarker development.
doi_str_mv 10.1002/art.41161
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However, synovial fluid and histology studies suggest that OA inflammatory responses are not homogeneous. Greater understanding of these responses may provide new insights into OA disease mechanisms. We undertook this study to develop a novel multiparameter approach to phenotype synovial responses in knee OA. Methods Cell composition and soluble protein production were measured by flow cytometry and multiplex enzyme‐linked immunosorbent assay in synovium collected from OA patients undergoing knee replacement surgery (n = 35). Results Testing disaggregation conditions showed that aggressive digestion improved synovial cell yield and mesenchymal staining by flow cytometry, but it negatively impacted CD4+ T cell and CD56+ natural killer cell staining. Less aggressive digestion preserved these markers and showed highly variable T cell infiltration (range 0–43%; n = 32). Correlation analysis identified mesenchymal subpopulations associated with different nonmesenchymal populations, including macrophages and T cells (CD45+CD11b+HLA−DR+ myeloid cells with PDPN+CD73+CD90−CD34− mesenchymal cells [r = 0.65, P &lt; 0.0001]; and CD45+CD3+ T cells with PDPN+CD73+CD90+CD34+ mesenchymal cells [r = 0.50, P = 0.003]). Interleukin‐6 (IL‐6) measured by flow cytometry strongly correlated with IL‐6 released by ex vivo culture of synovial tissue (r = 0.59, P = 0.0012) and was highest in mesenchymal cells coexpressing CD90 and CD34. IL‐6, IL‐8, complement factor D, and IL‐10 release correlated positively with tissue cellularity (P = 0.0042, P = 0.018, P = 0.0012, and P = 0.038, respectively). Additionally, increased CD8+ T cell numbers correlated with retinol binding protein 4 (P = 0.033). Finally, combining flow cytometry and multiplex data identified patient clusters with different types of inflammatory responses. Conclusion We used a novel approach to analyze OA synovium, identifying patient‐specific inflammatory clusters. Our findings indicate that phenotyping synovial inflammation may provide new insights into OA patient heterogeneity and biomarker development.</description><identifier>ISSN: 2326-5191</identifier><identifier>EISSN: 2326-5205</identifier><identifier>DOI: 10.1002/art.41161</identifier><identifier>PMID: 31702112</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Aged ; Arthritis ; Arthroplasty, Replacement, Knee ; Biomarkers ; Biomarkers - metabolism ; Biomedical materials ; Cartilage ; Cartilage diseases ; CD11b antigen ; CD3 antigen ; CD34 antigen ; CD4 antigen ; CD4-Positive T-Lymphocytes - metabolism ; CD4-Positive T-Lymphocytes - pathology ; CD45 antigen ; CD56 antigen ; CD73 antigen ; CD8 antigen ; CD90 antigen ; Cell culture ; Clusters ; Complement factor D ; Correlation analysis ; Digestion ; Disaggregation ; Female ; Flow Cytometry ; Heterogeneity ; Histocompatibility antigen HLA ; Histology ; Humans ; Inflammation ; Inflammation - metabolism ; Inflammation - pathology ; Interleukin-6 - metabolism ; Interleukins ; Killer Cells, Natural - metabolism ; Killer Cells, Natural - pathology ; Knee ; Knee Joint - metabolism ; Knee Joint - pathology ; Knee Joint - surgery ; Lymphocytes ; Lymphocytes T ; Macrophages ; Male ; Mesenchyme ; Middle Aged ; Multiplexing ; Osteoarthritis ; Osteoarthritis, Knee - metabolism ; Osteoarthritis, Knee - pathology ; Osteoarthritis, Knee - surgery ; Phenotypes ; Phenotyping ; Protein composition ; Proteins ; Staining ; Subpopulations ; Surgery ; Surgical implants ; Synovial Membrane - metabolism ; Synovial Membrane - pathology ; Tissue culture</subject><ispartof>Arthritis &amp; rheumatology (Hoboken, N.J.), 2020-04, Vol.72 (4), p.598-608</ispartof><rights>2019, American College of Rheumatology</rights><rights>2019, American College of Rheumatology.</rights><rights>2020, American College of Rheumatology</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4431-9f14b3a1d1651edfbf169473a2010847a2d33534f9b78d31e3f84c5b5c775d2e3</citedby><cites>FETCH-LOGICAL-c4431-9f14b3a1d1651edfbf169473a2010847a2d33534f9b78d31e3f84c5b5c775d2e3</cites><orcidid>0000-0001-5712-3579</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fart.41161$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fart.41161$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31702112$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Labinsky, Hannah</creatorcontrib><creatorcontrib>Panipinto, Paul M.</creatorcontrib><creatorcontrib>Ly, Kaytlyn A.</creatorcontrib><creatorcontrib>Khuat, Deric K.</creatorcontrib><creatorcontrib>Madarampalli, Bhanupriya</creatorcontrib><creatorcontrib>Mahajan, Vineet</creatorcontrib><creatorcontrib>Clabeaux, Jonathan</creatorcontrib><creatorcontrib>MacDonald, Kevin</creatorcontrib><creatorcontrib>Verdin, Peter J.</creatorcontrib><creatorcontrib>Buckner, Jane H.</creatorcontrib><creatorcontrib>Noss, Erika H.</creatorcontrib><title>Multiparameter Analysis Identifies Heterogeneity in Knee Osteoarthritis Synovial Responses</title><title>Arthritis &amp; rheumatology (Hoboken, N.J.)</title><addtitle>Arthritis Rheumatol</addtitle><description>Objective Synovial membrane inflammation is common in osteoarthritis (OA) and increases cartilage injury. However, synovial fluid and histology studies suggest that OA inflammatory responses are not homogeneous. Greater understanding of these responses may provide new insights into OA disease mechanisms. We undertook this study to develop a novel multiparameter approach to phenotype synovial responses in knee OA. Methods Cell composition and soluble protein production were measured by flow cytometry and multiplex enzyme‐linked immunosorbent assay in synovium collected from OA patients undergoing knee replacement surgery (n = 35). Results Testing disaggregation conditions showed that aggressive digestion improved synovial cell yield and mesenchymal staining by flow cytometry, but it negatively impacted CD4+ T cell and CD56+ natural killer cell staining. Less aggressive digestion preserved these markers and showed highly variable T cell infiltration (range 0–43%; n = 32). Correlation analysis identified mesenchymal subpopulations associated with different nonmesenchymal populations, including macrophages and T cells (CD45+CD11b+HLA−DR+ myeloid cells with PDPN+CD73+CD90−CD34− mesenchymal cells [r = 0.65, P &lt; 0.0001]; and CD45+CD3+ T cells with PDPN+CD73+CD90+CD34+ mesenchymal cells [r = 0.50, P = 0.003]). Interleukin‐6 (IL‐6) measured by flow cytometry strongly correlated with IL‐6 released by ex vivo culture of synovial tissue (r = 0.59, P = 0.0012) and was highest in mesenchymal cells coexpressing CD90 and CD34. IL‐6, IL‐8, complement factor D, and IL‐10 release correlated positively with tissue cellularity (P = 0.0042, P = 0.018, P = 0.0012, and P = 0.038, respectively). Additionally, increased CD8+ T cell numbers correlated with retinol binding protein 4 (P = 0.033). Finally, combining flow cytometry and multiplex data identified patient clusters with different types of inflammatory responses. Conclusion We used a novel approach to analyze OA synovium, identifying patient‐specific inflammatory clusters. Our findings indicate that phenotyping synovial inflammation may provide new insights into OA patient heterogeneity and biomarker development.</description><subject>Aged</subject><subject>Arthritis</subject><subject>Arthroplasty, Replacement, Knee</subject><subject>Biomarkers</subject><subject>Biomarkers - metabolism</subject><subject>Biomedical materials</subject><subject>Cartilage</subject><subject>Cartilage diseases</subject><subject>CD11b antigen</subject><subject>CD3 antigen</subject><subject>CD34 antigen</subject><subject>CD4 antigen</subject><subject>CD4-Positive T-Lymphocytes - metabolism</subject><subject>CD4-Positive T-Lymphocytes - pathology</subject><subject>CD45 antigen</subject><subject>CD56 antigen</subject><subject>CD73 antigen</subject><subject>CD8 antigen</subject><subject>CD90 antigen</subject><subject>Cell culture</subject><subject>Clusters</subject><subject>Complement factor D</subject><subject>Correlation analysis</subject><subject>Digestion</subject><subject>Disaggregation</subject><subject>Female</subject><subject>Flow Cytometry</subject><subject>Heterogeneity</subject><subject>Histocompatibility antigen HLA</subject><subject>Histology</subject><subject>Humans</subject><subject>Inflammation</subject><subject>Inflammation - metabolism</subject><subject>Inflammation - pathology</subject><subject>Interleukin-6 - metabolism</subject><subject>Interleukins</subject><subject>Killer Cells, Natural - metabolism</subject><subject>Killer Cells, Natural - pathology</subject><subject>Knee</subject><subject>Knee Joint - metabolism</subject><subject>Knee Joint - pathology</subject><subject>Knee Joint - surgery</subject><subject>Lymphocytes</subject><subject>Lymphocytes T</subject><subject>Macrophages</subject><subject>Male</subject><subject>Mesenchyme</subject><subject>Middle Aged</subject><subject>Multiplexing</subject><subject>Osteoarthritis</subject><subject>Osteoarthritis, Knee - metabolism</subject><subject>Osteoarthritis, Knee - pathology</subject><subject>Osteoarthritis, Knee - surgery</subject><subject>Phenotypes</subject><subject>Phenotyping</subject><subject>Protein composition</subject><subject>Proteins</subject><subject>Staining</subject><subject>Subpopulations</subject><subject>Surgery</subject><subject>Surgical implants</subject><subject>Synovial Membrane - metabolism</subject><subject>Synovial Membrane - pathology</subject><subject>Tissue culture</subject><issn>2326-5191</issn><issn>2326-5205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kU1LxDAURYMoKurCPyAFN7oYzUvSpt0Iw-AXKoIfGzchbV810knGpFX67804KiqYTQLvcHJ5l5BtoAdAKTvUvjsQABkskXXGWTZKGU2Xv95QwBrZCuGZxlNImtF0laxxkJQBsHXycNW3nZlpr6fYoU_GVrdDMCE5r9F2pjEYkrP5xD2iRdMNibHJhUVMrkOHLn7-5E0X-dvBulej2-QGw8zZgGGTrDS6Dbj1eW-Q-5Pju8nZ6PL69HwyvhxVQnAYFQ2IkmuoIUsB66ZsICuE5JpRoLmQmtWcp1w0RSnzmgPyJhdVWqaVlGnNkG-Qo4V31pdTrKuY2-tWzbyZaj8op436PbHmST26VyUBOFAZBXufAu9eegydmppQYdtqi64PinHgXGZFziK6-wd9dr2PO5tTuYCcUT6n9hdU5V0IHpvvMEDVvDQV96Y-Sovszs_03-RXRRE4XABvpsXhf5Ma39wtlO_CrqGx</recordid><startdate>202004</startdate><enddate>202004</enddate><creator>Labinsky, Hannah</creator><creator>Panipinto, Paul M.</creator><creator>Ly, Kaytlyn A.</creator><creator>Khuat, Deric K.</creator><creator>Madarampalli, Bhanupriya</creator><creator>Mahajan, Vineet</creator><creator>Clabeaux, Jonathan</creator><creator>MacDonald, Kevin</creator><creator>Verdin, Peter J.</creator><creator>Buckner, Jane H.</creator><creator>Noss, Erika H.</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7QP</scope><scope>7T5</scope><scope>7TM</scope><scope>7U7</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-5712-3579</orcidid></search><sort><creationdate>202004</creationdate><title>Multiparameter Analysis Identifies Heterogeneity in Knee Osteoarthritis Synovial Responses</title><author>Labinsky, Hannah ; Panipinto, Paul M. ; Ly, Kaytlyn A. ; Khuat, Deric K. ; Madarampalli, Bhanupriya ; Mahajan, Vineet ; Clabeaux, Jonathan ; MacDonald, Kevin ; Verdin, Peter J. ; Buckner, Jane H. ; Noss, Erika H.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4431-9f14b3a1d1651edfbf169473a2010847a2d33534f9b78d31e3f84c5b5c775d2e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aged</topic><topic>Arthritis</topic><topic>Arthroplasty, Replacement, Knee</topic><topic>Biomarkers</topic><topic>Biomarkers - metabolism</topic><topic>Biomedical materials</topic><topic>Cartilage</topic><topic>Cartilage diseases</topic><topic>CD11b antigen</topic><topic>CD3 antigen</topic><topic>CD34 antigen</topic><topic>CD4 antigen</topic><topic>CD4-Positive T-Lymphocytes - metabolism</topic><topic>CD4-Positive T-Lymphocytes - pathology</topic><topic>CD45 antigen</topic><topic>CD56 antigen</topic><topic>CD73 antigen</topic><topic>CD8 antigen</topic><topic>CD90 antigen</topic><topic>Cell culture</topic><topic>Clusters</topic><topic>Complement factor D</topic><topic>Correlation analysis</topic><topic>Digestion</topic><topic>Disaggregation</topic><topic>Female</topic><topic>Flow Cytometry</topic><topic>Heterogeneity</topic><topic>Histocompatibility antigen HLA</topic><topic>Histology</topic><topic>Humans</topic><topic>Inflammation</topic><topic>Inflammation - metabolism</topic><topic>Inflammation - pathology</topic><topic>Interleukin-6 - metabolism</topic><topic>Interleukins</topic><topic>Killer Cells, Natural - metabolism</topic><topic>Killer Cells, Natural - pathology</topic><topic>Knee</topic><topic>Knee Joint - metabolism</topic><topic>Knee Joint - pathology</topic><topic>Knee Joint - surgery</topic><topic>Lymphocytes</topic><topic>Lymphocytes T</topic><topic>Macrophages</topic><topic>Male</topic><topic>Mesenchyme</topic><topic>Middle Aged</topic><topic>Multiplexing</topic><topic>Osteoarthritis</topic><topic>Osteoarthritis, Knee - metabolism</topic><topic>Osteoarthritis, Knee - pathology</topic><topic>Osteoarthritis, Knee - surgery</topic><topic>Phenotypes</topic><topic>Phenotyping</topic><topic>Protein composition</topic><topic>Proteins</topic><topic>Staining</topic><topic>Subpopulations</topic><topic>Surgery</topic><topic>Surgical implants</topic><topic>Synovial Membrane - metabolism</topic><topic>Synovial Membrane - pathology</topic><topic>Tissue culture</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Labinsky, Hannah</creatorcontrib><creatorcontrib>Panipinto, Paul M.</creatorcontrib><creatorcontrib>Ly, Kaytlyn A.</creatorcontrib><creatorcontrib>Khuat, Deric K.</creatorcontrib><creatorcontrib>Madarampalli, Bhanupriya</creatorcontrib><creatorcontrib>Mahajan, Vineet</creatorcontrib><creatorcontrib>Clabeaux, Jonathan</creatorcontrib><creatorcontrib>MacDonald, Kevin</creatorcontrib><creatorcontrib>Verdin, Peter J.</creatorcontrib><creatorcontrib>Buckner, Jane H.</creatorcontrib><creatorcontrib>Noss, Erika H.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Calcium &amp; 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rheumatology (Hoboken, N.J.)</jtitle><addtitle>Arthritis Rheumatol</addtitle><date>2020-04</date><risdate>2020</risdate><volume>72</volume><issue>4</issue><spage>598</spage><epage>608</epage><pages>598-608</pages><issn>2326-5191</issn><eissn>2326-5205</eissn><abstract>Objective Synovial membrane inflammation is common in osteoarthritis (OA) and increases cartilage injury. However, synovial fluid and histology studies suggest that OA inflammatory responses are not homogeneous. Greater understanding of these responses may provide new insights into OA disease mechanisms. We undertook this study to develop a novel multiparameter approach to phenotype synovial responses in knee OA. Methods Cell composition and soluble protein production were measured by flow cytometry and multiplex enzyme‐linked immunosorbent assay in synovium collected from OA patients undergoing knee replacement surgery (n = 35). Results Testing disaggregation conditions showed that aggressive digestion improved synovial cell yield and mesenchymal staining by flow cytometry, but it negatively impacted CD4+ T cell and CD56+ natural killer cell staining. Less aggressive digestion preserved these markers and showed highly variable T cell infiltration (range 0–43%; n = 32). Correlation analysis identified mesenchymal subpopulations associated with different nonmesenchymal populations, including macrophages and T cells (CD45+CD11b+HLA−DR+ myeloid cells with PDPN+CD73+CD90−CD34− mesenchymal cells [r = 0.65, P &lt; 0.0001]; and CD45+CD3+ T cells with PDPN+CD73+CD90+CD34+ mesenchymal cells [r = 0.50, P = 0.003]). Interleukin‐6 (IL‐6) measured by flow cytometry strongly correlated with IL‐6 released by ex vivo culture of synovial tissue (r = 0.59, P = 0.0012) and was highest in mesenchymal cells coexpressing CD90 and CD34. IL‐6, IL‐8, complement factor D, and IL‐10 release correlated positively with tissue cellularity (P = 0.0042, P = 0.018, P = 0.0012, and P = 0.038, respectively). Additionally, increased CD8+ T cell numbers correlated with retinol binding protein 4 (P = 0.033). Finally, combining flow cytometry and multiplex data identified patient clusters with different types of inflammatory responses. Conclusion We used a novel approach to analyze OA synovium, identifying patient‐specific inflammatory clusters. Our findings indicate that phenotyping synovial inflammation may provide new insights into OA patient heterogeneity and biomarker development.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>31702112</pmid><doi>10.1002/art.41161</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0001-5712-3579</orcidid><oa>free_for_read</oa></addata></record>
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subjects Aged
Arthritis
Arthroplasty, Replacement, Knee
Biomarkers
Biomarkers - metabolism
Biomedical materials
Cartilage
Cartilage diseases
CD11b antigen
CD3 antigen
CD34 antigen
CD4 antigen
CD4-Positive T-Lymphocytes - metabolism
CD4-Positive T-Lymphocytes - pathology
CD45 antigen
CD56 antigen
CD73 antigen
CD8 antigen
CD90 antigen
Cell culture
Clusters
Complement factor D
Correlation analysis
Digestion
Disaggregation
Female
Flow Cytometry
Heterogeneity
Histocompatibility antigen HLA
Histology
Humans
Inflammation
Inflammation - metabolism
Inflammation - pathology
Interleukin-6 - metabolism
Interleukins
Killer Cells, Natural - metabolism
Killer Cells, Natural - pathology
Knee
Knee Joint - metabolism
Knee Joint - pathology
Knee Joint - surgery
Lymphocytes
Lymphocytes T
Macrophages
Male
Mesenchyme
Middle Aged
Multiplexing
Osteoarthritis
Osteoarthritis, Knee - metabolism
Osteoarthritis, Knee - pathology
Osteoarthritis, Knee - surgery
Phenotypes
Phenotyping
Protein composition
Proteins
Staining
Subpopulations
Surgery
Surgical implants
Synovial Membrane - metabolism
Synovial Membrane - pathology
Tissue culture
title Multiparameter Analysis Identifies Heterogeneity in Knee Osteoarthritis Synovial Responses
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