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Spitzer, H. ; Berry, S.* ; Donoghoe, M.* ; Pelkmans, L.* ; Theis, F.J.

Learning consistent subcellular landmarks to quantify changes in multiplexed protein maps.

Nat. Methods 20, 1058-1069 (2023)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Rna-polymerase-ii; Transcription; Pml
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Quellenangaben Band: 20, Heft: 7, Seiten: 1058-1069 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Verlagsort New York, NY
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen 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