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Palma, A. ; Theis, F.J. ; Lotfollahi, M.

Predicting cell morphological responses to perturbations using generative modeling.

Nat. Commun. 16:505 (2025)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Advancements in high-throughput screenings enable the exploration of rich phenotypic readouts through high-content microscopy, expediting the development of phenotype-based drug discovery. However, analyzing large and complex high-content imaging screenings remains challenging due to incomplete sampling of perturbations and the presence of technical variations between experiments. To tackle these shortcomings, we present IMage Perturbation Autoencoder (IMPA), a generative style-transfer model predicting morphological changes of perturbations across genetic and chemical interventions. We show that IMPA accurately captures morphological and population-level changes of both seen and unseen perturbations on breast cancer and osteosarcoma cells. Additionally, IMPA accounts for batch effects and can model perturbations across various sources of technical variation, further enhancing its robustness in diverse experimental conditions. With the increasing availability of large-scale high-content imaging screens generated by academic and industrial consortia, we envision that IMPA will facilitate the analysis of microscopy data and enable efficient experimental design via in-silico perturbation prediction.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Image Style Transfer; Dna-replication
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 16, Heft: 1, Seiten: , Artikelnummer: 505 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen - German Federal Ministry of Education and Research (BMBF) through - HOPARL project (grant number 031L0289A) - European Union (ERC, DeepCell - grant number 101054957)
German Federal Ministry of Education and Research (BMBF)
European Union (ERC)
Helmholtz Association under the joint research school Munich School for Data Science
Wellcome Sanger Institute
Wellcome Trust
Open Targets (Drug2Cell Grant)