Meta-analysis of single-cell RNA-sequencing data for depicting the transcriptomic landscape of chronic obstructive pulmonary disease
Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by airflow limitation and chronic inflammation of the lungs that is a leading cause of death worldwide. Since the complete pathological mechanisms at the single-cell level are not fully understood yet, an integrative...
Gespeichert in:
Veröffentlicht in: | Computers in biology and medicine 2023-12, Vol.167, p.107685-107685, Article 107685 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 107685 |
---|---|
container_issue | |
container_start_page | 107685 |
container_title | Computers in biology and medicine |
container_volume | 167 |
creator | Lee, Yubin Song, Jaeseung Jeong, Yeonbin Choi, Eunyoung Ahn, Chulwoo Jang, Wonhee |
description | Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by airflow limitation and chronic inflammation of the lungs that is a leading cause of death worldwide. Since the complete pathological mechanisms at the single-cell level are not fully understood yet, an integrative approach to characterizing the single-cell-resolution landscape of COPD is required. To identify the cell types and mechanisms associated with the development of COPD, we conducted a meta-analysis using three single-cell RNA-sequencing datasets of COPD. Among the 154,011 cells from 16 COPD patients and 18 healthy subjects, 17 distinct cell types were observed. Of the 17 cell types, monocytes, mast cells, and alveolar type 2 cells (AT2 cells) were found to be etiologically implicated in COPD based on genetic and transcriptomic features. The most transcriptomically diversified states of the three etiological cell types showed significant enrichment in immune/inflammatory responses (monocytes and mast cells) and/or mitochondrial dysfunction (monocytes and AT2 cells). We then identified three chemical candidates that may potentially induce COPD by modulating gene expression patterns in the three etiological cell types. Overall, our study suggests the single-cell level mechanisms underlying the pathogenesis of COPD and may provide information on toxic compounds that could be potential risk factors for COPD. |
doi_str_mv | 10.1016/j.compbiomed.2023.107685 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2891751526</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2892265543</sourcerecordid><originalsourceid>FETCH-LOGICAL-c393t-dd016bf4d65546be56af0d40564a161295e5bab3c1e659ad52942710b2f3c13a3</originalsourceid><addsrcrecordid>eNpdkU9rFTEUxYNU7Gv1K0igGzfzzJ9JZmZZStVCVRBdhzvJHZvHzGRMMoXu_eBm-lqErgInv3NzTw4hlLM9Z1x_POxtmJbehwndXjAhi9zoVr0iO942XcWUrE_IjjHOqroV6pScpXRgjNVMsjfkVDZdwUW3I3-_YoYKZhgfkk80DDT5-feIlcVxpD--XVYJ_6w426JSBxnoECJ1uHibNynfIc0R5mSjX3KYvKUjzC5ZWHCbZu9imIsY-pTjWjz3SJd1nMIM8YE6nxASviWvBxgTvns6z8mvT9c_r75Ut98_31xd3lZWdjJXzpXs_VA7rVSte1QaBuZqpnQNXHPRKVQ99NJy1KoDp0RXi4azXgxFkyDPyYfj3CWGEiplM_m0BYUZw5qMaDveKK6ELujFC_QQ1li-6ZESYltBFqo9UjaGlCIOZol-KskMZ2ZryhzM_6bM1pQ5NlWs758eWPvt7tn4XI38B3bulOE</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2892265543</pqid></control><display><type>article</type><title>Meta-analysis of single-cell RNA-sequencing data for depicting the transcriptomic landscape of chronic obstructive pulmonary disease</title><source>MEDLINE</source><source>Access via ScienceDirect (Elsevier)</source><source>ProQuest Central UK/Ireland</source><creator>Lee, Yubin ; Song, Jaeseung ; Jeong, Yeonbin ; Choi, Eunyoung ; Ahn, Chulwoo ; Jang, Wonhee</creator><creatorcontrib>Lee, Yubin ; Song, Jaeseung ; Jeong, Yeonbin ; Choi, Eunyoung ; Ahn, Chulwoo ; Jang, Wonhee</creatorcontrib><description>Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by airflow limitation and chronic inflammation of the lungs that is a leading cause of death worldwide. Since the complete pathological mechanisms at the single-cell level are not fully understood yet, an integrative approach to characterizing the single-cell-resolution landscape of COPD is required. To identify the cell types and mechanisms associated with the development of COPD, we conducted a meta-analysis using three single-cell RNA-sequencing datasets of COPD. Among the 154,011 cells from 16 COPD patients and 18 healthy subjects, 17 distinct cell types were observed. Of the 17 cell types, monocytes, mast cells, and alveolar type 2 cells (AT2 cells) were found to be etiologically implicated in COPD based on genetic and transcriptomic features. The most transcriptomically diversified states of the three etiological cell types showed significant enrichment in immune/inflammatory responses (monocytes and mast cells) and/or mitochondrial dysfunction (monocytes and AT2 cells). We then identified three chemical candidates that may potentially induce COPD by modulating gene expression patterns in the three etiological cell types. Overall, our study suggests the single-cell level mechanisms underlying the pathogenesis of COPD and may provide information on toxic compounds that could be potential risk factors for COPD.</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2023.107685</identifier><identifier>PMID: 37976829</identifier><language>eng</language><publisher>United States: Elsevier Limited</publisher><subject>Air flow ; Alveoli ; Annotations ; Cells ; Chronic obstructive pulmonary disease ; Datasets ; Etiology ; Gene expression ; Gene sequencing ; Health care ; Humans ; Inflammation ; Lung ; Lung diseases ; Lungs ; Mast cells ; Meta-analysis ; Monocytes ; Obstructive lung disease ; Pathogenesis ; Pulmonary Disease, Chronic Obstructive - genetics ; Respiratory diseases ; Ribonucleic acid ; Risk Factors ; RNA ; Transcriptome - genetics ; Transcriptomics</subject><ispartof>Computers in biology and medicine, 2023-12, Vol.167, p.107685-107685, Article 107685</ispartof><rights>Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.</rights><rights>2023. The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c393t-dd016bf4d65546be56af0d40564a161295e5bab3c1e659ad52942710b2f3c13a3</citedby><cites>FETCH-LOGICAL-c393t-dd016bf4d65546be56af0d40564a161295e5bab3c1e659ad52942710b2f3c13a3</cites><orcidid>0000-0003-2247-3194 ; 0009-0004-5639-3760 ; 0000-0003-2470-8137</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2892265543?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37976829$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Yubin</creatorcontrib><creatorcontrib>Song, Jaeseung</creatorcontrib><creatorcontrib>Jeong, Yeonbin</creatorcontrib><creatorcontrib>Choi, Eunyoung</creatorcontrib><creatorcontrib>Ahn, Chulwoo</creatorcontrib><creatorcontrib>Jang, Wonhee</creatorcontrib><title>Meta-analysis of single-cell RNA-sequencing data for depicting the transcriptomic landscape of chronic obstructive pulmonary disease</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by airflow limitation and chronic inflammation of the lungs that is a leading cause of death worldwide. Since the complete pathological mechanisms at the single-cell level are not fully understood yet, an integrative approach to characterizing the single-cell-resolution landscape of COPD is required. To identify the cell types and mechanisms associated with the development of COPD, we conducted a meta-analysis using three single-cell RNA-sequencing datasets of COPD. Among the 154,011 cells from 16 COPD patients and 18 healthy subjects, 17 distinct cell types were observed. Of the 17 cell types, monocytes, mast cells, and alveolar type 2 cells (AT2 cells) were found to be etiologically implicated in COPD based on genetic and transcriptomic features. The most transcriptomically diversified states of the three etiological cell types showed significant enrichment in immune/inflammatory responses (monocytes and mast cells) and/or mitochondrial dysfunction (monocytes and AT2 cells). We then identified three chemical candidates that may potentially induce COPD by modulating gene expression patterns in the three etiological cell types. Overall, our study suggests the single-cell level mechanisms underlying the pathogenesis of COPD and may provide information on toxic compounds that could be potential risk factors for COPD.</description><subject>Air flow</subject><subject>Alveoli</subject><subject>Annotations</subject><subject>Cells</subject><subject>Chronic obstructive pulmonary disease</subject><subject>Datasets</subject><subject>Etiology</subject><subject>Gene expression</subject><subject>Gene sequencing</subject><subject>Health care</subject><subject>Humans</subject><subject>Inflammation</subject><subject>Lung</subject><subject>Lung diseases</subject><subject>Lungs</subject><subject>Mast cells</subject><subject>Meta-analysis</subject><subject>Monocytes</subject><subject>Obstructive lung disease</subject><subject>Pathogenesis</subject><subject>Pulmonary Disease, Chronic Obstructive - genetics</subject><subject>Respiratory diseases</subject><subject>Ribonucleic acid</subject><subject>Risk Factors</subject><subject>RNA</subject><subject>Transcriptome - genetics</subject><subject>Transcriptomics</subject><issn>0010-4825</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNpdkU9rFTEUxYNU7Gv1K0igGzfzzJ9JZmZZStVCVRBdhzvJHZvHzGRMMoXu_eBm-lqErgInv3NzTw4hlLM9Z1x_POxtmJbehwndXjAhi9zoVr0iO942XcWUrE_IjjHOqroV6pScpXRgjNVMsjfkVDZdwUW3I3-_YoYKZhgfkk80DDT5-feIlcVxpD--XVYJ_6w426JSBxnoECJ1uHibNynfIc0R5mSjX3KYvKUjzC5ZWHCbZu9imIsY-pTjWjz3SJd1nMIM8YE6nxASviWvBxgTvns6z8mvT9c_r75Ut98_31xd3lZWdjJXzpXs_VA7rVSte1QaBuZqpnQNXHPRKVQ99NJy1KoDp0RXi4azXgxFkyDPyYfj3CWGEiplM_m0BYUZw5qMaDveKK6ELujFC_QQ1li-6ZESYltBFqo9UjaGlCIOZol-KskMZ2ZryhzM_6bM1pQ5NlWs758eWPvt7tn4XI38B3bulOE</recordid><startdate>202312</startdate><enddate>202312</enddate><creator>Lee, Yubin</creator><creator>Song, Jaeseung</creator><creator>Jeong, Yeonbin</creator><creator>Choi, Eunyoung</creator><creator>Ahn, Chulwoo</creator><creator>Jang, Wonhee</creator><general>Elsevier Limited</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>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>M7Z</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-2247-3194</orcidid><orcidid>https://orcid.org/0009-0004-5639-3760</orcidid><orcidid>https://orcid.org/0000-0003-2470-8137</orcidid></search><sort><creationdate>202312</creationdate><title>Meta-analysis of single-cell RNA-sequencing data for depicting the transcriptomic landscape of chronic obstructive pulmonary disease</title><author>Lee, Yubin ; Song, Jaeseung ; Jeong, Yeonbin ; Choi, Eunyoung ; Ahn, Chulwoo ; Jang, Wonhee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-dd016bf4d65546be56af0d40564a161295e5bab3c1e659ad52942710b2f3c13a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Air flow</topic><topic>Alveoli</topic><topic>Annotations</topic><topic>Cells</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Datasets</topic><topic>Etiology</topic><topic>Gene expression</topic><topic>Gene sequencing</topic><topic>Health care</topic><topic>Humans</topic><topic>Inflammation</topic><topic>Lung</topic><topic>Lung diseases</topic><topic>Lungs</topic><topic>Mast cells</topic><topic>Meta-analysis</topic><topic>Monocytes</topic><topic>Obstructive lung disease</topic><topic>Pathogenesis</topic><topic>Pulmonary Disease, Chronic Obstructive - genetics</topic><topic>Respiratory diseases</topic><topic>Ribonucleic acid</topic><topic>Risk Factors</topic><topic>RNA</topic><topic>Transcriptome - genetics</topic><topic>Transcriptomics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Yubin</creatorcontrib><creatorcontrib>Song, Jaeseung</creatorcontrib><creatorcontrib>Jeong, Yeonbin</creatorcontrib><creatorcontrib>Choi, Eunyoung</creatorcontrib><creatorcontrib>Ahn, Chulwoo</creatorcontrib><creatorcontrib>Jang, Wonhee</creatorcontrib><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>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology 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>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</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>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Yubin</au><au>Song, Jaeseung</au><au>Jeong, Yeonbin</au><au>Choi, Eunyoung</au><au>Ahn, Chulwoo</au><au>Jang, Wonhee</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Meta-analysis of single-cell RNA-sequencing data for depicting the transcriptomic landscape of chronic obstructive pulmonary disease</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2023-12</date><risdate>2023</risdate><volume>167</volume><spage>107685</spage><epage>107685</epage><pages>107685-107685</pages><artnum>107685</artnum><issn>0010-4825</issn><eissn>1879-0534</eissn><abstract>Chronic obstructive pulmonary disease (COPD) is a respiratory disease characterized by airflow limitation and chronic inflammation of the lungs that is a leading cause of death worldwide. Since the complete pathological mechanisms at the single-cell level are not fully understood yet, an integrative approach to characterizing the single-cell-resolution landscape of COPD is required. To identify the cell types and mechanisms associated with the development of COPD, we conducted a meta-analysis using three single-cell RNA-sequencing datasets of COPD. Among the 154,011 cells from 16 COPD patients and 18 healthy subjects, 17 distinct cell types were observed. Of the 17 cell types, monocytes, mast cells, and alveolar type 2 cells (AT2 cells) were found to be etiologically implicated in COPD based on genetic and transcriptomic features. The most transcriptomically diversified states of the three etiological cell types showed significant enrichment in immune/inflammatory responses (monocytes and mast cells) and/or mitochondrial dysfunction (monocytes and AT2 cells). We then identified three chemical candidates that may potentially induce COPD by modulating gene expression patterns in the three etiological cell types. Overall, our study suggests the single-cell level mechanisms underlying the pathogenesis of COPD and may provide information on toxic compounds that could be potential risk factors for COPD.</abstract><cop>United States</cop><pub>Elsevier Limited</pub><pmid>37976829</pmid><doi>10.1016/j.compbiomed.2023.107685</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-2247-3194</orcidid><orcidid>https://orcid.org/0009-0004-5639-3760</orcidid><orcidid>https://orcid.org/0000-0003-2470-8137</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0010-4825 |
ispartof | Computers in biology and medicine, 2023-12, Vol.167, p.107685-107685, Article 107685 |
issn | 0010-4825 1879-0534 |
language | eng |
recordid | cdi_proquest_miscellaneous_2891751526 |
source | MEDLINE; Access via ScienceDirect (Elsevier); ProQuest Central UK/Ireland |
subjects | Air flow Alveoli Annotations Cells Chronic obstructive pulmonary disease Datasets Etiology Gene expression Gene sequencing Health care Humans Inflammation Lung Lung diseases Lungs Mast cells Meta-analysis Monocytes Obstructive lung disease Pathogenesis Pulmonary Disease, Chronic Obstructive - genetics Respiratory diseases Ribonucleic acid Risk Factors RNA Transcriptome - genetics Transcriptomics |
title | Meta-analysis of single-cell RNA-sequencing data for depicting the transcriptomic landscape of chronic obstructive pulmonary disease |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T18%3A42%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Meta-analysis%20of%20single-cell%20RNA-sequencing%20data%20for%20depicting%20the%20transcriptomic%20landscape%20of%20chronic%20obstructive%20pulmonary%20disease&rft.jtitle=Computers%20in%20biology%20and%20medicine&rft.au=Lee,%20Yubin&rft.date=2023-12&rft.volume=167&rft.spage=107685&rft.epage=107685&rft.pages=107685-107685&rft.artnum=107685&rft.issn=0010-4825&rft.eissn=1879-0534&rft_id=info:doi/10.1016/j.compbiomed.2023.107685&rft_dat=%3Cproquest_cross%3E2892265543%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2892265543&rft_id=info:pmid/37976829&rfr_iscdi=true |