Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology
Abstract Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into...
Gespeichert in:
| Veröffentlicht in: | Briefings in bioinformatics 2024-07, Vol.25 (5) |
|---|---|
| Hauptverfasser: | , , , , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 5 |
| container_start_page | |
| container_title | Briefings in bioinformatics |
| container_volume | 25 |
| creator | Mi, Haoyang Sivagnanam, Shamilene Ho, Won Jin Zhang, Shuming Bergman, Daniel Deshpande, Atul Baras, Alexander S Jaffee, Elizabeth M Coussens, Lisa M Fertig, Elana J Popel, Aleksander S |
| description | Abstract
Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology. |
| doi_str_mv | 10.1093/bib/bbae421 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3096666129</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><oup_id>10.1093/bib/bbae421</oup_id><sourcerecordid>3096666129</sourcerecordid><originalsourceid>FETCH-LOGICAL-c273t-816742e83b2c2e6970a89c6b38bf59365c0812c2c5e58554406c43ef5507d8123</originalsourceid><addsrcrecordid>eNp9kc1r3DAQxUVJ6G6TnHoPgkAoFCf6tKzcwpIvCOSSnI0kj7fa2pYr2Qn730dhtzn00LloYH7zGL2H0HdKLijR_NJ6e2mtAcHoF7SkQqlCECkOPvpSFVKUfIG-pbQhhBFV0a9owTVVmolqibpV6Md5MpMPg-lwD9Ov0CRshgZbH3oTf0PEjU8uvELc4jRFM8HaQ8JtiDiNeTGvjTFMEHrv0hU2OMKrhzfsB-z7fh5CEQYXurDeHqPD1nQJTvbvEXq5vXle3RePT3cPq-vHwjHFp6KipRIMKm6ZY1BqRUylXWl5ZVupeSkdqWgeOQmyklIIUjrBoZWSqCZP-BH6sdPNd_2ZIU11n38AXWcGCHOqOdFlLsp0Rs_-QTdhjtmKTFHCtM6G0Uz93FEuhpQitPUYfTZnW1NSf4RQ5xDqfQiZPt1rzraH5pP963oGzndAmMf_Kr0DrQWQcQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3102997811</pqid></control><display><type>article</type><title>Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology</title><source>MEDLINE</source><source>Oxford Journals Open Access Collection</source><source>PubMed Central</source><creator>Mi, Haoyang ; Sivagnanam, Shamilene ; Ho, Won Jin ; Zhang, Shuming ; Bergman, Daniel ; Deshpande, Atul ; Baras, Alexander S ; Jaffee, Elizabeth M ; Coussens, Lisa M ; Fertig, Elana J ; Popel, Aleksander S</creator><creatorcontrib>Mi, Haoyang ; Sivagnanam, Shamilene ; Ho, Won Jin ; Zhang, Shuming ; Bergman, Daniel ; Deshpande, Atul ; Baras, Alexander S ; Jaffee, Elizabeth M ; Coussens, Lisa M ; Fertig, Elana J ; Popel, Aleksander S</creatorcontrib><description>Abstract
Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.</description><identifier>ISSN: 1467-5463</identifier><identifier>ISSN: 1477-4054</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbae421</identifier><identifier>PMID: 39179248</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Algorithms ; Biomarkers ; Biomarkers, Tumor - metabolism ; Cancer immunotherapy ; Computational Biology - methods ; Computer applications ; Data collection ; Humans ; Image processing ; Image Processing, Computer-Assisted - methods ; Medical imaging ; Neoplasms - immunology ; Neoplasms - metabolism ; Oncology ; Proteomics ; Proteomics - methods ; Spatial data ; Spatial discrimination ; Spatial resolution ; Statistical methods</subject><ispartof>Briefings in bioinformatics, 2024-07, Vol.25 (5)</ispartof><rights>The Author(s) 2024. Published by Oxford University Press. 2024</rights><rights>The Author(s) 2024. Published by Oxford University Press.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c273t-816742e83b2c2e6970a89c6b38bf59365c0812c2c5e58554406c43ef5507d8123</cites><orcidid>0000-0001-8388-008X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1604,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39179248$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Mi, Haoyang</creatorcontrib><creatorcontrib>Sivagnanam, Shamilene</creatorcontrib><creatorcontrib>Ho, Won Jin</creatorcontrib><creatorcontrib>Zhang, Shuming</creatorcontrib><creatorcontrib>Bergman, Daniel</creatorcontrib><creatorcontrib>Deshpande, Atul</creatorcontrib><creatorcontrib>Baras, Alexander S</creatorcontrib><creatorcontrib>Jaffee, Elizabeth M</creatorcontrib><creatorcontrib>Coussens, Lisa M</creatorcontrib><creatorcontrib>Fertig, Elana J</creatorcontrib><creatorcontrib>Popel, Aleksander S</creatorcontrib><title>Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Abstract
Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.</description><subject>Algorithms</subject><subject>Biomarkers</subject><subject>Biomarkers, Tumor - metabolism</subject><subject>Cancer immunotherapy</subject><subject>Computational Biology - methods</subject><subject>Computer applications</subject><subject>Data collection</subject><subject>Humans</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Medical imaging</subject><subject>Neoplasms - immunology</subject><subject>Neoplasms - metabolism</subject><subject>Oncology</subject><subject>Proteomics</subject><subject>Proteomics - methods</subject><subject>Spatial data</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Statistical methods</subject><issn>1467-5463</issn><issn>1477-4054</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>TOX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kc1r3DAQxUVJ6G6TnHoPgkAoFCf6tKzcwpIvCOSSnI0kj7fa2pYr2Qn730dhtzn00LloYH7zGL2H0HdKLijR_NJ6e2mtAcHoF7SkQqlCECkOPvpSFVKUfIG-pbQhhBFV0a9owTVVmolqibpV6Md5MpMPg-lwD9Ov0CRshgZbH3oTf0PEjU8uvELc4jRFM8HaQ8JtiDiNeTGvjTFMEHrv0hU2OMKrhzfsB-z7fh5CEQYXurDeHqPD1nQJTvbvEXq5vXle3RePT3cPq-vHwjHFp6KipRIMKm6ZY1BqRUylXWl5ZVupeSkdqWgeOQmyklIIUjrBoZWSqCZP-BH6sdPNd_2ZIU11n38AXWcGCHOqOdFlLsp0Rs_-QTdhjtmKTFHCtM6G0Uz93FEuhpQitPUYfTZnW1NSf4RQ5xDqfQiZPt1rzraH5pP963oGzndAmMf_Kr0DrQWQcQ</recordid><startdate>20240725</startdate><enddate>20240725</enddate><creator>Mi, Haoyang</creator><creator>Sivagnanam, Shamilene</creator><creator>Ho, Won Jin</creator><creator>Zhang, Shuming</creator><creator>Bergman, Daniel</creator><creator>Deshpande, Atul</creator><creator>Baras, Alexander S</creator><creator>Jaffee, Elizabeth M</creator><creator>Coussens, Lisa M</creator><creator>Fertig, Elana J</creator><creator>Popel, Aleksander S</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>TOX</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>7QO</scope><scope>7SC</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-8388-008X</orcidid></search><sort><creationdate>20240725</creationdate><title>Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology</title><author>Mi, Haoyang ; Sivagnanam, Shamilene ; Ho, Won Jin ; Zhang, Shuming ; Bergman, Daniel ; Deshpande, Atul ; Baras, Alexander S ; Jaffee, Elizabeth M ; Coussens, Lisa M ; Fertig, Elana J ; Popel, Aleksander S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c273t-816742e83b2c2e6970a89c6b38bf59365c0812c2c5e58554406c43ef5507d8123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Biomarkers</topic><topic>Biomarkers, Tumor - metabolism</topic><topic>Cancer immunotherapy</topic><topic>Computational Biology - methods</topic><topic>Computer applications</topic><topic>Data collection</topic><topic>Humans</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Medical imaging</topic><topic>Neoplasms - immunology</topic><topic>Neoplasms - metabolism</topic><topic>Oncology</topic><topic>Proteomics</topic><topic>Proteomics - methods</topic><topic>Spatial data</topic><topic>Spatial discrimination</topic><topic>Spatial resolution</topic><topic>Statistical methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mi, Haoyang</creatorcontrib><creatorcontrib>Sivagnanam, Shamilene</creatorcontrib><creatorcontrib>Ho, Won Jin</creatorcontrib><creatorcontrib>Zhang, Shuming</creatorcontrib><creatorcontrib>Bergman, Daniel</creatorcontrib><creatorcontrib>Deshpande, Atul</creatorcontrib><creatorcontrib>Baras, Alexander S</creatorcontrib><creatorcontrib>Jaffee, Elizabeth M</creatorcontrib><creatorcontrib>Coussens, Lisa M</creatorcontrib><creatorcontrib>Fertig, Elana J</creatorcontrib><creatorcontrib>Popel, Aleksander S</creatorcontrib><collection>Oxford Journals Open Access Collection</collection><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>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mi, Haoyang</au><au>Sivagnanam, Shamilene</au><au>Ho, Won Jin</au><au>Zhang, Shuming</au><au>Bergman, Daniel</au><au>Deshpande, Atul</au><au>Baras, Alexander S</au><au>Jaffee, Elizabeth M</au><au>Coussens, Lisa M</au><au>Fertig, Elana J</au><au>Popel, Aleksander S</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2024-07-25</date><risdate>2024</risdate><volume>25</volume><issue>5</issue><issn>1467-5463</issn><issn>1477-4054</issn><eissn>1477-4054</eissn><abstract>Abstract
Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>39179248</pmid><doi>10.1093/bib/bbae421</doi><orcidid>https://orcid.org/0000-0001-8388-008X</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1467-5463 |
| ispartof | Briefings in bioinformatics, 2024-07, Vol.25 (5) |
| issn | 1467-5463 1477-4054 1477-4054 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3096666129 |
| source | MEDLINE; Oxford Journals Open Access Collection; PubMed Central |
| subjects | Algorithms Biomarkers Biomarkers, Tumor - metabolism Cancer immunotherapy Computational Biology - methods Computer applications Data collection Humans Image processing Image Processing, Computer-Assisted - methods Medical imaging Neoplasms - immunology Neoplasms - metabolism Oncology Proteomics Proteomics - methods Spatial data Spatial discrimination Spatial resolution Statistical methods |
| title | Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T12%3A21%3A30IST&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=Computational%20methods%20and%20biomarker%20discovery%20strategies%20for%20spatial%20proteomics:%20a%20review%20in%20immuno-oncology&rft.jtitle=Briefings%20in%20bioinformatics&rft.au=Mi,%20Haoyang&rft.date=2024-07-25&rft.volume=25&rft.issue=5&rft.issn=1467-5463&rft.eissn=1477-4054&rft_id=info:doi/10.1093/bib/bbae421&rft_dat=%3Cproquest_cross%3E3096666129%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=3102997811&rft_id=info:pmid/39179248&rft_oup_id=10.1093/bib/bbae421&rfr_iscdi=true |