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Lotfollahi, M. ; Klimovskaia Susmelj, A.* ; De Donno, C. ; Hetzel, L. ; Ji, Y. ; Ibarra Del Rio, I.A. ; Srivatsan, S.R.* ; Naghipourfar, M.* ; Daza, R.M.* ; Martin, B.* ; Shendure, J.* ; McFaline-Figueroa, J.L.* ; Boyeau, P.* ; Wolf, F.A. ; Yakubova, N.* ; Günnemann, S.* ; Trapnell, C.* ; Lopez-Paz, D.* ; Theis, F.J.

Predicting cellular responses to complex perturbations in high-throughput screens.

Mol. Syst. Biol. 19:e11517 (2023)
DOI PMC
Creative Commons Lizenzvertrag
Open Access Gold as soon as Publ. Version/Full Text is submitted to ZB.
Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Generative Modeling ; High-throughput Screening ; Machine Learning ; Perturbation Prediction ; Single-cell Transcriptomics; Seq; Cancer; Mechanisms; Therapy
ISSN (print) / ISBN 1744-4292
e-ISSN 1744-4292
Journal Molecular Systems Biology
Quellenangaben Volume: 19, Issue: 6, Pages: , Article Number: e11517 Supplement: ,
Publisher EMBO Press
Publishing Place 111 River St, Hoboken 07030-5774, Nj Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants Projekt DEAL
European Union
Helmholtz Association's Initiative and Networking Fund through Helmholtz AI
Chan Zuckerberg Initiative DAF (advised fund of Silicon Valley Community Foundation)
Helmholtz Association
BMBF