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Multiplex immunofluorescence and single‐cell transcriptomic profiling reveal the spatial cell interaction networks in the non‐small cell lung cancer microenvironment
Background Conventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology. Methods Tissue slices of primary...
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Published in: | Clinical and translational medicine 2023-01, Vol.13 (1), p.e1155-n/a |
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creator | Peng, Haoxin Wu, Xiangrong Liu, Shaopeng He, Miao Xie, Chao Zhong, Ran Liu, Jun Tang, Chenshuo Li, Caichen Xiong, Shan Zheng, Hongbo He, Jianxing Lu, Xu Liang, Wenhua |
description | Background
Conventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology.
Methods
Tissue slices of primary tumours from 553 IA∼IIIB non‐small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD‐L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single‐cell RNA sequencing (scRNA‐seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks.
Results
Spatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells (r2 = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells (r2 = −0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease‐free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA‐seq further showed that spatially adjacent N1‐like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2‐like macrophages showed negative effects. An immune‐related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high‐IRRS patients had significantly worse DFS than low‐IRRS ones (HR 2.72, 95% CI 1.87–3.94, p |
doi_str_mv | 10.1002/ctm2.1155 |
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Conventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology.
Methods
Tissue slices of primary tumours from 553 IA∼IIIB non‐small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD‐L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single‐cell RNA sequencing (scRNA‐seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks.
Results
Spatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells (r2 = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells (r2 = −0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease‐free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA‐seq further showed that spatially adjacent N1‐like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2‐like macrophages showed negative effects. An immune‐related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high‐IRRS patients had significantly worse DFS than low‐IRRS ones (HR 2.72, 95% CI 1.87–3.94, p < .001).
Conclusions
We developed a framework to analyse the cell interaction networks in tumour microenvironment, revealing the spatial architecture and intricate interplays between immune and tumour cells.
Deep learning algorithm on multiplex immunofluorescence images identified cell spatial patterns with prognostic effects in the lung cancer microenvironment.
Single‐cell RNA‐sequencing revealed the cell interaction networks suggested by spatial paradigm analyses.
Proximity among CD4+ T cells, CD20+ B cells and N1‐like neutrophils, and spatial compartmentalisation between cancer stem cells and CD8+ T cells, were associated with significantly longer disease‐free survival.</description><identifier>ISSN: 2001-1326</identifier><identifier>EISSN: 2001-1326</identifier><identifier>DOI: 10.1002/ctm2.1155</identifier><identifier>PMID: 36588094</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Antibodies ; Cancer therapies ; Carcinoma, Non-Small-Cell Lung - genetics ; Carcinoma, Non-Small-Cell Lung - pathology ; cell interaction networks ; Cells ; Clinical medicine ; Deep learning ; deep learning algorithm ; Fluorescent Antibody Technique ; Humans ; Lung cancer ; Lung Neoplasms - pathology ; multiplex immunofluorescence ; Patients ; Proteins ; single‐cell RNA sequencing ; Thoracic surgery ; Transcriptome ; Tumor Microenvironment - genetics ; Tumors ; tumour microenvironment</subject><ispartof>Clinical and translational medicine, 2023-01, Vol.13 (1), p.e1155-n/a</ispartof><rights>2022 The Authors. published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.</rights><rights>2022 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.</rights><rights>2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5065-9d17da0125af3776585feabb9a2cc5335eea663e96c146fc0d8d6ec3c1d7bdc3</citedby><cites>FETCH-LOGICAL-c5065-9d17da0125af3776585feabb9a2cc5335eea663e96c146fc0d8d6ec3c1d7bdc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2890096425/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2890096425?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,11562,25753,27924,27925,37012,37013,44590,46052,46476,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36588094$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Peng, Haoxin</creatorcontrib><creatorcontrib>Wu, Xiangrong</creatorcontrib><creatorcontrib>Liu, Shaopeng</creatorcontrib><creatorcontrib>He, Miao</creatorcontrib><creatorcontrib>Xie, Chao</creatorcontrib><creatorcontrib>Zhong, Ran</creatorcontrib><creatorcontrib>Liu, Jun</creatorcontrib><creatorcontrib>Tang, Chenshuo</creatorcontrib><creatorcontrib>Li, Caichen</creatorcontrib><creatorcontrib>Xiong, Shan</creatorcontrib><creatorcontrib>Zheng, Hongbo</creatorcontrib><creatorcontrib>He, Jianxing</creatorcontrib><creatorcontrib>Lu, Xu</creatorcontrib><creatorcontrib>Liang, Wenhua</creatorcontrib><title>Multiplex immunofluorescence and single‐cell transcriptomic profiling reveal the spatial cell interaction networks in the non‐small cell lung cancer microenvironment</title><title>Clinical and translational medicine</title><addtitle>Clin Transl Med</addtitle><description>Background
Conventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology.
Methods
Tissue slices of primary tumours from 553 IA∼IIIB non‐small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD‐L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single‐cell RNA sequencing (scRNA‐seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks.
Results
Spatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells (r2 = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells (r2 = −0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease‐free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA‐seq further showed that spatially adjacent N1‐like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2‐like macrophages showed negative effects. An immune‐related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high‐IRRS patients had significantly worse DFS than low‐IRRS ones (HR 2.72, 95% CI 1.87–3.94, p < .001).
Conclusions
We developed a framework to analyse the cell interaction networks in tumour microenvironment, revealing the spatial architecture and intricate interplays between immune and tumour cells.
Deep learning algorithm on multiplex immunofluorescence images identified cell spatial patterns with prognostic effects in the lung cancer microenvironment.
Single‐cell RNA‐sequencing revealed the cell interaction networks suggested by spatial paradigm analyses.
Proximity among CD4+ T cells, CD20+ B cells and N1‐like neutrophils, and spatial compartmentalisation between cancer stem cells and CD8+ T cells, were associated with significantly longer disease‐free survival.</description><subject>Antibodies</subject><subject>Cancer therapies</subject><subject>Carcinoma, Non-Small-Cell Lung - genetics</subject><subject>Carcinoma, Non-Small-Cell Lung - pathology</subject><subject>cell interaction networks</subject><subject>Cells</subject><subject>Clinical medicine</subject><subject>Deep learning</subject><subject>deep learning algorithm</subject><subject>Fluorescent Antibody Technique</subject><subject>Humans</subject><subject>Lung cancer</subject><subject>Lung Neoplasms - pathology</subject><subject>multiplex immunofluorescence</subject><subject>Patients</subject><subject>Proteins</subject><subject>single‐cell RNA sequencing</subject><subject>Thoracic surgery</subject><subject>Transcriptome</subject><subject>Tumor Microenvironment - genetics</subject><subject>Tumors</subject><subject>tumour microenvironment</subject><issn>2001-1326</issn><issn>2001-1326</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp1ksFuFCEYxydGY5vagy9gJvGih22BGRjmYmI2Vpu08bJ3wsA3W1YGRmC29uYj9DV8LZ9EZndtWhO5QD5-_IB_vqJ4jdEZRoicqzSQM4wpfVYcE4TwAleEPX-0PipOY9ygPHjdtg15WRxVjHKO2vq4-HU92WRGCz9KMwyT872dfICowCkopdNlNG5t4ffPewXWlilIF1UwY_KDUeUYfG9sJsoAW5B5_wbKOMpk8np3wLgEQapkvCsdpFsfvsVc3IHOu-yNg7QH2E7ZpGS-OpRZHzy4rQneDeDSq-JFL22E08N8UqwuPq2WXxZXXz9fLj9eLRRFjC5ajRstESZU9lXT5I_SHmTXtZIoRauKAkjGKmiZwjXrFdJcM1CVwrrptKpOisu9Vnu5EWMwgwx3wksjdgUf1kKGZJQFwQnnXccp0h2utUJSc1blgImuO80Ryq4Pe9c4dQPonGmOzz6RPt1x5kas_Va0HDGEaRa8OwiC_z5BTGIwcU5KOvBTFKTJWFMhQjL69h9046fgclKC8BahltVkFr7fUznbGAP0D4_BSMztJOZ2EnM7ZfbN49c_kH-bJwPne-DWWLj7v0ksV9dkp_wDeQjcEg</recordid><startdate>202301</startdate><enddate>202301</enddate><creator>Peng, Haoxin</creator><creator>Wu, Xiangrong</creator><creator>Liu, Shaopeng</creator><creator>He, Miao</creator><creator>Xie, Chao</creator><creator>Zhong, Ran</creator><creator>Liu, Jun</creator><creator>Tang, Chenshuo</creator><creator>Li, Caichen</creator><creator>Xiong, Shan</creator><creator>Zheng, Hongbo</creator><creator>He, Jianxing</creator><creator>Lu, Xu</creator><creator>Liang, Wenhua</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><general>Wiley</general><scope>24P</scope><scope>WIN</scope><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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>202301</creationdate><title>Multiplex immunofluorescence and single‐cell transcriptomic profiling reveal the spatial cell interaction networks in the non‐small cell lung cancer microenvironment</title><author>Peng, Haoxin ; Wu, Xiangrong ; Liu, Shaopeng ; He, Miao ; Xie, Chao ; Zhong, Ran ; Liu, Jun ; Tang, Chenshuo ; Li, Caichen ; Xiong, Shan ; Zheng, Hongbo ; He, Jianxing ; Lu, Xu ; Liang, Wenhua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5065-9d17da0125af3776585feabb9a2cc5335eea663e96c146fc0d8d6ec3c1d7bdc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Antibodies</topic><topic>Cancer therapies</topic><topic>Carcinoma, Non-Small-Cell Lung - genetics</topic><topic>Carcinoma, Non-Small-Cell Lung - pathology</topic><topic>cell interaction networks</topic><topic>Cells</topic><topic>Clinical medicine</topic><topic>Deep learning</topic><topic>deep learning algorithm</topic><topic>Fluorescent Antibody Technique</topic><topic>Humans</topic><topic>Lung cancer</topic><topic>Lung Neoplasms - pathology</topic><topic>multiplex immunofluorescence</topic><topic>Patients</topic><topic>Proteins</topic><topic>single‐cell RNA sequencing</topic><topic>Thoracic surgery</topic><topic>Transcriptome</topic><topic>Tumor Microenvironment - genetics</topic><topic>Tumors</topic><topic>tumour microenvironment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Peng, Haoxin</creatorcontrib><creatorcontrib>Wu, Xiangrong</creatorcontrib><creatorcontrib>Liu, Shaopeng</creatorcontrib><creatorcontrib>He, Miao</creatorcontrib><creatorcontrib>Xie, Chao</creatorcontrib><creatorcontrib>Zhong, Ran</creatorcontrib><creatorcontrib>Liu, Jun</creatorcontrib><creatorcontrib>Tang, Chenshuo</creatorcontrib><creatorcontrib>Li, Caichen</creatorcontrib><creatorcontrib>Xiong, Shan</creatorcontrib><creatorcontrib>Zheng, Hongbo</creatorcontrib><creatorcontrib>He, Jianxing</creatorcontrib><creatorcontrib>Lu, Xu</creatorcontrib><creatorcontrib>Liang, Wenhua</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library Journals</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma 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 Central</collection><collection>British Nursing Database</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>Clinical and translational medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Peng, Haoxin</au><au>Wu, Xiangrong</au><au>Liu, Shaopeng</au><au>He, Miao</au><au>Xie, Chao</au><au>Zhong, Ran</au><au>Liu, Jun</au><au>Tang, Chenshuo</au><au>Li, Caichen</au><au>Xiong, Shan</au><au>Zheng, Hongbo</au><au>He, Jianxing</au><au>Lu, Xu</au><au>Liang, Wenhua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiplex immunofluorescence and single‐cell transcriptomic profiling reveal the spatial cell interaction networks in the non‐small cell lung cancer microenvironment</atitle><jtitle>Clinical and translational medicine</jtitle><addtitle>Clin Transl Med</addtitle><date>2023-01</date><risdate>2023</risdate><volume>13</volume><issue>1</issue><spage>e1155</spage><epage>n/a</epage><pages>e1155-n/a</pages><issn>2001-1326</issn><eissn>2001-1326</eissn><abstract>Background
Conventional immunohistochemistry technologies were limited by the inability to simultaneously detect multiple markers and the lack of identifying spatial relationships among cells, hindering understanding of the biological processes in cancer immunology.
Methods
Tissue slices of primary tumours from 553 IA∼IIIB non‐small cell lung cancer (NSCLC) cases were stained by multiplex immunofluorescence (mIF) assay for 10 markers, including CD4, CD38, CD20, FOXP3, CD66b, CD8, CD68, PD‐L1, CD133 and CD163, evaluating the amounts of 26 phenotypes of cells in tumour nest and tumour stroma. StarDist depth learning model was utilised to determine the spatial location of cells based on mIF graphs. Single‐cell RNA sequencing (scRNA‐seq) on four primary NSCLC cases was conducted to investigate the putative cell interaction networks.
Results
Spatial proximity among CD20+ B cells, CD4+ T cells and CD38+ T cells (r2 = 0.41) was observed, whereas the distribution of regulatory T cells was associated with decreased infiltration levels of CD20+ B cells and CD38+ T cells (r2 = −0.45). Univariate Cox analyses identified closer proximity between CD8+ T cells predicted longer disease‐free survival (DFS). In contrast, closer proximity between CD133+ cancer stem cells (CSCs), longer distances between CD4+ T cells and CD20+ B cells, CD4+ T cells and neutrophils, and CD20+ B cells and neutrophils were correlated with dismal DFS. Data from scRNA‐seq further showed that spatially adjacent N1‐like neutrophils could boost the proliferation and activation of T and B lymphocytes, whereas spatially neighbouring M2‐like macrophages showed negative effects. An immune‐related risk score (IRRS) system aggregating robust quantitative and spatial prognosticators showed that high‐IRRS patients had significantly worse DFS than low‐IRRS ones (HR 2.72, 95% CI 1.87–3.94, p < .001).
Conclusions
We developed a framework to analyse the cell interaction networks in tumour microenvironment, revealing the spatial architecture and intricate interplays between immune and tumour cells.
Deep learning algorithm on multiplex immunofluorescence images identified cell spatial patterns with prognostic effects in the lung cancer microenvironment.
Single‐cell RNA‐sequencing revealed the cell interaction networks suggested by spatial paradigm analyses.
Proximity among CD4+ T cells, CD20+ B cells and N1‐like neutrophils, and spatial compartmentalisation between cancer stem cells and CD8+ T cells, were associated with significantly longer disease‐free survival.</abstract><cop>United States</cop><pub>John Wiley & Sons, Inc</pub><pmid>36588094</pmid><doi>10.1002/ctm2.1155</doi><tpages>23</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Antibodies Cancer therapies Carcinoma, Non-Small-Cell Lung - genetics Carcinoma, Non-Small-Cell Lung - pathology cell interaction networks Cells Clinical medicine Deep learning deep learning algorithm Fluorescent Antibody Technique Humans Lung cancer Lung Neoplasms - pathology multiplex immunofluorescence Patients Proteins single‐cell RNA sequencing Thoracic surgery Transcriptome Tumor Microenvironment - genetics Tumors tumour microenvironment |
title | Multiplex immunofluorescence and single‐cell transcriptomic profiling reveal the spatial cell interaction networks in the non‐small cell lung cancer microenvironment |
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