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Open Access Green as soon as Postprint is submitted to ZB.
Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps.
Nat. Methods 20, 1058-1069 (2023)
Highly multiplexed imaging holds enormous promise for understanding how spatial context shapes the activity of the genome and its products at multiple length scales. Here, we introduce a deep learning framework called CAMPA (Conditional Autoencoder for Multiplexed Pixel Analysis), which uses a conditional variational autoencoder to learn representations of molecular pixel profiles that are consistent across heterogeneous cell populations and experimental perturbations. Clustering these pixel-level representations identifies consistent subcellular landmarks, which can be quantitatively compared in terms of their size, shape, molecular composition and relative spatial organization. Using high-resolution multiplexed immunofluorescence, this reveals how subcellular organization changes upon perturbation of RNA synthesis, RNA processing or cell size, and uncovers links between the molecular composition of membraneless organelles and cell-to-cell variability in bulk RNA synthesis rates. By capturing interpretable cellular phenotypes, we anticipate that CAMPA will greatly accelerate the systematic mapping of multiscale atlases of biological organization to identify the rules by which context shapes physiology and disease.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Rna-polymerase-ii; Transcription; Pml
ISSN (print) / ISBN
1548-7091
e-ISSN
1548-7105
Journal
Nature Methods
Quellenangaben
Volume: 20,
Issue: 7,
Pages: 1058-1069
Publisher
Nature Publishing Group
Publishing Place
New York, NY
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)
Grants
Swiss National Science Foundation (SNF)
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
German Federal Ministry of Education and Research (BMBF)
University of Zurich
Swiss National Science Foundation (SNSF)
European Research Council
University of New South Wales
Australian Research Council Discovery Early Career Researcher Award
Human Frontiers Science Programme long-term fellowship
European Molecular Biology Organisation long-term fellowship
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
German Federal Ministry of Education and Research (BMBF)
University of Zurich
Swiss National Science Foundation (SNSF)
European Research Council
University of New South Wales
Australian Research Council Discovery Early Career Researcher Award
Human Frontiers Science Programme long-term fellowship
European Molecular Biology Organisation long-term fellowship