TY - JOUR
T1 - Computational methods and biomarker discovery strategies for spatial proteomics
T2 - a review in immuno-oncology
AU - Mi, Haoyang
AU - Sivagnanam, Shamilene
AU - Ho, Won Jin
AU - Zhang, Shuming
AU - Bergman, Daniel
AU - Deshpande, Atul
AU - Baras, Alexander S.
AU - Jaffee, Elizabeth M.
AU - Coussens, Lisa M.
AU - Fertig, Elana J.
AU - Popel, Aleksander S.
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/9/1
Y1 - 2024/9/1
N2 - 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.
AB - 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.
KW - biomarker discovery
KW - computational pathology
KW - immuno-oncology
KW - medical image analysis
KW - single cell
KW - spatial proteomics
UR - http://www.scopus.com/inward/record.url?scp=85202267343&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85202267343&partnerID=8YFLogxK
U2 - 10.1093/bib/bbae421
DO - 10.1093/bib/bbae421
M3 - Review article
C2 - 39179248
AN - SCOPUS:85202267343
SN - 1467-5463
VL - 25
JO - Briefings in bioinformatics
JF - Briefings in bioinformatics
IS - 5
M1 - bbae421
ER -