Cell shapes decode molecular phenotypes in image-based spatial proteomicsShow others and affiliations
2026 (English)In: Cell Systems, ISSN 2405-4712, E-ISSN 2405-4720, article id 101589Article in journal (Refereed) Epub ahead of print
Abstract [en]
Cellular and tissue structures arise from a few cell shapes, which undergo transformations based on biophysical constraints. Despite links between signaling pathways and cellular geometry, whole-proteome orchestration in association with cell shape is underexplored. In this study, over 1 million single cells stained for 11,998 proteins across 11 cell lines in the Human Protein Atlas were analyzed for organelle, pathway, and single-protein levels in association with cellular shapespace. We found that cell and nuclear shapes across cell lines exist in a shared continuum. The subcellular organelle topology varies across cell lines but remains consistent within each cell line's shapespace. At the single-protein level, cells of different shapes in the same cell-cycle phase might be preparing for different fates, and many non-cell-cycle proteins expressed shape-based abundance variation. Using a shape-based coordinate framework, we analyzed the distribution shift of protein spatial localization under drug perturbation.
Place, publisher, year, edition, pages
Elsevier BV , 2026. article id 101589
Keywords [en]
cell shape, interpretable machine learning, molecular variation, morphological variation, single cell, spatial proteomics
National Category
Cell Biology Cell and Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-381092DOI: 10.1016/j.cels.2026.101589PubMedID: 42013840Scopus ID: 2-s2.0-105036292748OAI: oai:DiVA.org:kth-381092DiVA, id: diva2:2059075
Note
QC 20260511
2026-05-112026-05-112026-05-11Bibliographically approved