PuSH - Publication Server of Helmholtz Zentrum München: Predicting cell morphological responses to perturbations using generative modeling.

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... Journal

... Publication Type

... Research Data

... Publication Year

Publication overview

Support & Contact

Contact persons

Help

Data protection

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

Predicting cell morphological responses to perturbations using generative modeling.

Nat. Commun. 16:505 (2025)

Publ. Version/Full Text

DOI

PMC

	Open Access Gold

Abstract
Metrics
Extra information

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.

Altmetric

Additional Metrics?

[➜Log in]

Edit extra informations Login

Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Image Style Transfer; Dna-replication

ISSN (print) / ISBN 2041-1723

e-ISSN 2041-1723

Journal Nature Communications

Quellenangaben Volume: 16, Issue: 1, Article Number: 505

Publisher Nature Publishing Group

Publishing Place London

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)

Grants - 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)