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PreprintCited by 3Cell Image Analysis TechniquesSingle-cell and spatial transcriptomicsAdvanced Proteomics Techniques and Applications

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Cell shapes decode molecular phenotypes in image-based spatial proteomics

Open access
Le, Trang, Leineweber, William D., Viana, Matheus P., Cesnik, Anthony, Hansen, Jan N., Ouyang, Wei, Rafelski, Susanne M., Lundberg, Emma·2025·Cell Systems
DOI 10.1101/2025.05.13.653868PMC PMC13138879

Preprint — published as 10.1016/j.cels.2026.101589 · Paper page

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Abstract

Summary: The diversity of cellular and tissue structures can arise from a few basic cell shapes, which undergo various transformations based on biophysical constraints on cytoskeletal organization. While cellular geometry has been linked with selected biological processes such as polarity, signaling or morphogenesis, the orchestration of the whole proteome in association to cell shape is still poorly understood. In this study, using more than 1 million images of single cells stained for 11,998 proteins across 10 cell lines in the Human Protein Atlas database, we performed an integrated analysis of organelle, pathway and single protein levels in association to a 2D cellular shapespace. We found that cell and nuclear shapes across cell lines exist in a shared continuum. We also found that the subcellular organelle topology varies across cell lines, but remains robust within each cell line’s shapespace. At the single protein level, we found that cells of different shapes in the same cell cycle phase might be preparing for different fates, and that many non-cell cycle proteins expressed shape-based abundance variation. Using the same coordinate framework defined by shape, we could analyze the distribution shift of protein spatial localization under drug perturbation.

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Cites

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    “…of organelles is robust across shape variation, and that for specific events, like early mitosis, the reorganization of organelle topology ( i.e. change in the association between subcellular organelles) is associated with the rewiring of the cell needed to support the cell division process 7 .”
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Metadata

Authors
Le, T., Leineweber, W. D., Viana, M. P., Cesnik, A., Hansen, J. N., Ouyang, W., Rafelski, S. M., & Lundberg, E.
Affiliations
  • Science for Life Laboratory (SE)
  • Stanford University (US)
  • Allen Institute for Cell Science (US)
Corresponding author
Lundberg, Emma
ORCID
Venue
Cell Systems
Publisher
openRxiv
Published
2025-05-16
Volume
17
Issue
6
Pages
101589-101589
ISSN
2405-4712 (ISSN-L)
DOI
10.1101/2025.05.13.653868
PMC
PMC13138879
OpenAlex
W7154988717
Cited by
3
References
1
Citations by year
2026: 3
FWCI
8.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Cell Systems (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (submittedVersion, cc-by-nc-nd)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
  • Schmidt Futures
  • National Institutes of Health (OT2 OD032742)
  • National Cancer Institute (U54 CA274502),)
  • Stanford Institute for Human Centered AI
  • Param Hansa Philanthropies
  • European Molecular Biology Organization (ALTF 556-2022)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Keywords
cell shape, single-cell proteomics, nuclear shape, drug-induced perturbation, cell cycle, cell fate, Human Protein Atlas, cell lines

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