Predicting cellular responses to complex perturbations in high‐throughput screens
Abstract 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...
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| Format: | Article |
| Language: | English |
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Springer Nature
2023-05-01
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| Series: | Molecular Systems Biology |
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| Online Access: | https://doi.org/10.15252/msb.202211517 |
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| author | Mohammad Lotfollahi Anna Klimovskaia Susmelj Carlo De Donno Leon Hetzel Yuge Ji Ignacio L Ibarra Sanjay R Srivatsan Mohsen Naghipourfar Riza M Daza Beth Martin Jay Shendure Jose L McFaline‐Figueroa Pierre Boyeau F Alexander Wolf Nafissa Yakubova Stephan Günnemann Cole Trapnell David Lopez‐Paz Fabian J Theis |
| author_facet | Mohammad Lotfollahi Anna Klimovskaia Susmelj Carlo De Donno Leon Hetzel Yuge Ji Ignacio L Ibarra Sanjay R Srivatsan Mohsen Naghipourfar Riza M Daza Beth Martin Jay Shendure Jose L McFaline‐Figueroa Pierre Boyeau F Alexander Wolf Nafissa Yakubova Stephan Günnemann Cole Trapnell David Lopez‐Paz Fabian J Theis |
| author_sort | Mohammad Lotfollahi |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-418ea36376f0413cb228551ff196f2db |
| institution | Kabale University |
| issn | 1744-4292 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj-art-418ea36376f0413cb228551ff196f2db2025-08-20T04:02:41ZengSpringer NatureMolecular Systems Biology1744-42922023-05-0119611910.15252/msb.202211517Predicting cellular responses to complex perturbations in high‐throughput screensMohammad Lotfollahi0Anna Klimovskaia Susmelj1Carlo De Donno2Leon Hetzel3Yuge Ji4Ignacio L Ibarra5Sanjay R Srivatsan6Mohsen Naghipourfar7Riza M Daza8Beth Martin9Jay Shendure10Jose L McFaline‐Figueroa11Pierre Boyeau12F Alexander Wolf13Nafissa Yakubova14Stephan Günnemann15Cole Trapnell16David Lopez‐Paz17Fabian J Theis18Helmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational BiologyMeta AIHelmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational BiologyHelmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational BiologyHelmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational BiologyHelmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational BiologyDepartment of Genome Sciences, University of WashingtonDepartment of Bioengineering, University of CaliforniaDepartment of Genome Sciences, University of WashingtonDepartment of Genome Sciences, University of WashingtonDepartment of Genome Sciences, University of WashingtonDepartment of Biomedical Engineering, Columbia UniversityDepartment of Electrical Engineering and Computer Sciences, University of CaliforniaHelmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational BiologyMeta AIDepartment of Computer Science, Technical University of MunichDepartment of Genome Sciences, University of WashingtonWellcome Trust Sanger Institute, Wellcome Genome Campus, HinxtonHelmholtz Center Munich – German Research Center for Environmental Health, Institute of Computational BiologyAbstract 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.https://doi.org/10.15252/msb.202211517generative modelinghigh‐throughput screeningmachine learningperturbation predictionsingle‐cell transcriptomics |
| spellingShingle | Mohammad Lotfollahi Anna Klimovskaia Susmelj Carlo De Donno Leon Hetzel Yuge Ji Ignacio L Ibarra Sanjay R Srivatsan Mohsen Naghipourfar Riza M Daza Beth Martin Jay Shendure Jose L McFaline‐Figueroa Pierre Boyeau F Alexander Wolf Nafissa Yakubova Stephan Günnemann Cole Trapnell David Lopez‐Paz Fabian J Theis Predicting cellular responses to complex perturbations in high‐throughput screens Molecular Systems Biology generative modeling high‐throughput screening machine learning perturbation prediction single‐cell transcriptomics |
| title | Predicting cellular responses to complex perturbations in high‐throughput screens |
| title_full | Predicting cellular responses to complex perturbations in high‐throughput screens |
| title_fullStr | Predicting cellular responses to complex perturbations in high‐throughput screens |
| title_full_unstemmed | Predicting cellular responses to complex perturbations in high‐throughput screens |
| title_short | Predicting cellular responses to complex perturbations in high‐throughput screens |
| title_sort | predicting cellular responses to complex perturbations in high throughput screens |
| topic | generative modeling high‐throughput screening machine learning perturbation prediction single‐cell transcriptomics |
| url | https://doi.org/10.15252/msb.202211517 |
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