Identification of organ‐specific T cell populations by analysis of multiparameter flow cytometry data using DNA‐chip analysis software
Background: The analysis of cells from multiple experimental groups by multiparameter flow cytometry leads to the generation of complex data sets, for which adequate analysis tools are not commonly available. We report here that software designed for transcriptomics applications can be used in multi...
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| Veröffentlicht in: | Cytometry. Part A 2006-06, Vol.69A (6), p.533-540 |
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| container_title | Cytometry. Part A |
| container_volume | 69A |
| creator | Hofmann, Matthias Zerwes, Hans‐Günter |
| description | Background:
The analysis of cells from multiple experimental groups by multiparameter flow cytometry leads to the generation of complex data sets, for which adequate analysis tools are not commonly available. We report here that software designed for transcriptomics applications can be used in multiparameter flow cytometry.
Methods:
Lymphocytes isolated from nine different mouse organs were stained and subjected to 10‐parameter flow cytometry. The resulting data set contained 594 different T cell subsets per organ per mouse and was organized into a so‐called flow cytometry array (FCA).
Results:
Computation of a hierarchical tree revealed that lymph nodes and spleen were populated by similar T cell subsets, while T cells from peripheral organs displayed a diverse subset composition. Furthermore, organ‐specific T cell subsets were identified.
Conclusions:
This new FCA concept in flow cytomics proved to be a valuable tool for the fast and unbiased analysis of complex multiparameter flow cytometry data sets. It can be used for assessing disease progression and therapeutic intervention, and for the association of disease‐related biomarkers on the protein level. © 2006 International Society for Analytical Cytology |
| doi_str_mv | 10.1002/cyto.a.20278 |
| format | Article |
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The analysis of cells from multiple experimental groups by multiparameter flow cytometry leads to the generation of complex data sets, for which adequate analysis tools are not commonly available. We report here that software designed for transcriptomics applications can be used in multiparameter flow cytometry.
Methods:
Lymphocytes isolated from nine different mouse organs were stained and subjected to 10‐parameter flow cytometry. The resulting data set contained 594 different T cell subsets per organ per mouse and was organized into a so‐called flow cytometry array (FCA).
Results:
Computation of a hierarchical tree revealed that lymph nodes and spleen were populated by similar T cell subsets, while T cells from peripheral organs displayed a diverse subset composition. Furthermore, organ‐specific T cell subsets were identified.
Conclusions:
This new FCA concept in flow cytomics proved to be a valuable tool for the fast and unbiased analysis of complex multiparameter flow cytometry data sets. It can be used for assessing disease progression and therapeutic intervention, and for the association of disease‐related biomarkers on the protein level. © 2006 International Society for Analytical Cytology</description><identifier>ISSN: 1552-4922</identifier><identifier>EISSN: 1552-4930</identifier><identifier>DOI: 10.1002/cyto.a.20278</identifier><identifier>PMID: 16646049</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc., A Wiley Company</publisher><subject>Animals ; cell surface antigens ; Data Interpretation, Statistical ; Female ; flow cytometry ; Flow Cytometry - methods ; Mice ; Mice, Inbred C57BL ; microarray analysis ; mouse ; Multivariate Analysis ; Oligonucleotide Array Sequence Analysis - methods ; Organ Specificity ; Software ; T-Lymphocyte Subsets - classification ; tissue distribution ; T‐lymphocytes</subject><ispartof>Cytometry. Part A, 2006-06, Vol.69A (6), p.533-540</ispartof><rights>Copyright © 2006 International Society for Analytical Cytology</rights><rights>Copyright 2006 International Society for Analytical Cytology.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4028-6e7f7b259321323abf5e99ebbe2039dbbe1b981d56ec2076b0b10780134b6ae3</citedby><cites>FETCH-LOGICAL-c4028-6e7f7b259321323abf5e99ebbe2039dbbe1b981d56ec2076b0b10780134b6ae3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcyto.a.20278$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcyto.a.20278$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16646049$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hofmann, Matthias</creatorcontrib><creatorcontrib>Zerwes, Hans‐Günter</creatorcontrib><title>Identification of organ‐specific T cell populations by analysis of multiparameter flow cytometry data using DNA‐chip analysis software</title><title>Cytometry. Part A</title><addtitle>Cytometry A</addtitle><description>Background:
The analysis of cells from multiple experimental groups by multiparameter flow cytometry leads to the generation of complex data sets, for which adequate analysis tools are not commonly available. We report here that software designed for transcriptomics applications can be used in multiparameter flow cytometry.
Methods:
Lymphocytes isolated from nine different mouse organs were stained and subjected to 10‐parameter flow cytometry. The resulting data set contained 594 different T cell subsets per organ per mouse and was organized into a so‐called flow cytometry array (FCA).
Results:
Computation of a hierarchical tree revealed that lymph nodes and spleen were populated by similar T cell subsets, while T cells from peripheral organs displayed a diverse subset composition. Furthermore, organ‐specific T cell subsets were identified.
Conclusions:
This new FCA concept in flow cytomics proved to be a valuable tool for the fast and unbiased analysis of complex multiparameter flow cytometry data sets. It can be used for assessing disease progression and therapeutic intervention, and for the association of disease‐related biomarkers on the protein level. © 2006 International Society for Analytical Cytology</description><subject>Animals</subject><subject>cell surface antigens</subject><subject>Data Interpretation, Statistical</subject><subject>Female</subject><subject>flow cytometry</subject><subject>Flow Cytometry - methods</subject><subject>Mice</subject><subject>Mice, Inbred C57BL</subject><subject>microarray analysis</subject><subject>mouse</subject><subject>Multivariate Analysis</subject><subject>Oligonucleotide Array Sequence Analysis - methods</subject><subject>Organ Specificity</subject><subject>Software</subject><subject>T-Lymphocyte Subsets - classification</subject><subject>tissue distribution</subject><subject>T‐lymphocytes</subject><issn>1552-4922</issn><issn>1552-4930</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkT1PwzAQhi0E4ntjRp6YaDnbcdKMqHxKCJYuTJadXMAoiYOdqMrGzMRv5JeQ0Ao2mM7WPffIvpeQIwZTBsDPsr51Uz3lwJPZBtllUvJJlArY_DlzvkP2QngBEBIE3yY7LI6jGKJ0l7zf5li3trCZbq2rqSuo80-6_nz7CA1mY4MuaIZlSRvXdOU3Fajpqa512QcbxpGqK1vbaK8rbNHTonRLOr5ruPqe5rrVtAu2fqIX9-eDOXu2ze98cEW71B4PyFahy4CH67pPFleXi_nN5O7h-nZ-fjfJIuCzSYxJkRguU8GZ4EKbQmKaojHIQaT5UJlJZyyXMWYcktiAYZDMgInIxBrFPjlZaRvvXjsMrapsGD-oa3RdUPGASh6Jf0EOkicySQbwdAVm3oXgsVCNt5X2vWKgxozUuAul1XdGA3689namwvwXXocyAGIFLG2J_Z8yNX9cPKy0X1o7osw</recordid><startdate>200606</startdate><enddate>200606</enddate><creator>Hofmann, Matthias</creator><creator>Zerwes, Hans‐Günter</creator><general>Wiley Subscription Services, Inc., A Wiley Company</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>7QO</scope><scope>7T5</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>H94</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>200606</creationdate><title>Identification of organ‐specific T cell populations by analysis of multiparameter flow cytometry data using DNA‐chip analysis software</title><author>Hofmann, Matthias ; Zerwes, Hans‐Günter</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4028-6e7f7b259321323abf5e99ebbe2039dbbe1b981d56ec2076b0b10780134b6ae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Animals</topic><topic>cell surface antigens</topic><topic>Data Interpretation, Statistical</topic><topic>Female</topic><topic>flow cytometry</topic><topic>Flow Cytometry - methods</topic><topic>Mice</topic><topic>Mice, Inbred C57BL</topic><topic>microarray analysis</topic><topic>mouse</topic><topic>Multivariate Analysis</topic><topic>Oligonucleotide Array Sequence Analysis - methods</topic><topic>Organ Specificity</topic><topic>Software</topic><topic>T-Lymphocyte Subsets - classification</topic><topic>tissue distribution</topic><topic>T‐lymphocytes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hofmann, Matthias</creatorcontrib><creatorcontrib>Zerwes, Hans‐Günter</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Immunology Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Cytometry. Part A</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hofmann, Matthias</au><au>Zerwes, Hans‐Günter</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identification of organ‐specific T cell populations by analysis of multiparameter flow cytometry data using DNA‐chip analysis software</atitle><jtitle>Cytometry. Part A</jtitle><addtitle>Cytometry A</addtitle><date>2006-06</date><risdate>2006</risdate><volume>69A</volume><issue>6</issue><spage>533</spage><epage>540</epage><pages>533-540</pages><issn>1552-4922</issn><eissn>1552-4930</eissn><abstract>Background:
The analysis of cells from multiple experimental groups by multiparameter flow cytometry leads to the generation of complex data sets, for which adequate analysis tools are not commonly available. We report here that software designed for transcriptomics applications can be used in multiparameter flow cytometry.
Methods:
Lymphocytes isolated from nine different mouse organs were stained and subjected to 10‐parameter flow cytometry. The resulting data set contained 594 different T cell subsets per organ per mouse and was organized into a so‐called flow cytometry array (FCA).
Results:
Computation of a hierarchical tree revealed that lymph nodes and spleen were populated by similar T cell subsets, while T cells from peripheral organs displayed a diverse subset composition. Furthermore, organ‐specific T cell subsets were identified.
Conclusions:
This new FCA concept in flow cytomics proved to be a valuable tool for the fast and unbiased analysis of complex multiparameter flow cytometry data sets. It can be used for assessing disease progression and therapeutic intervention, and for the association of disease‐related biomarkers on the protein level. © 2006 International Society for Analytical Cytology</abstract><cop>Hoboken</cop><pub>Wiley Subscription Services, Inc., A Wiley Company</pub><pmid>16646049</pmid><doi>10.1002/cyto.a.20278</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Access via Wiley Online Library; EZB-FREE-00999 freely available EZB journals; Wiley Online Library (Open Access Collection); Alma/SFX Local Collection |
| subjects | Animals cell surface antigens Data Interpretation, Statistical Female flow cytometry Flow Cytometry - methods Mice Mice, Inbred C57BL microarray analysis mouse Multivariate Analysis Oligonucleotide Array Sequence Analysis - methods Organ Specificity Software T-Lymphocyte Subsets - classification tissue distribution T‐lymphocytes |
| title | Identification of organ‐specific T cell populations by analysis of multiparameter flow cytometry data using DNA‐chip analysis software |
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