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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)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Generative Modeling ; High-throughput Screening ; Machine Learning ; Perturbation Prediction ; Single-cell Transcriptomics; Seq; Cancer; Mechanisms; Therapy
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1744-4292
e-ISSN 1744-4292
Zeitschrift Molecular Systems Biology
Quellenangaben Band: 19, Heft: 6, Seiten: , Artikelnummer: e11517 Supplement: ,
Verlag EMBO Press
Verlagsort 111 River St, Hoboken 07030-5774, Nj Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen 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
DOI 10.15252/msb.202211517
WOS ID 000982786500001
Scopus ID 85158160308
PubMed ID 37154091
Erfassungsdatum 2023-10-06
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