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Seal S, Trapotsi MA, Spjuth O, Singh S, Carreras-Puigvert J, Greene N, Bender A, Carpenter AE. A Decade in a Systematic Review: The Evolution and Impact of Cell Painting. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.04.592531. [PMID: 38766203 PMCID: PMC11100607 DOI: 10.1101/2024.05.04.592531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.
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Affiliation(s)
- Srijit Seal
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom
| | - Maria-Anna Trapotsi
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, 1 Francis Crick Avenue, Cambridge, CB2 0AA, United Kingdom
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-75124, Uppsala, Sweden
| | - Shantanu Singh
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, 1 Francis Crick Avenue, Cambridge, CB2 0AA, United Kingdom
| | - Jordi Carreras-Puigvert
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-75124, Uppsala, Sweden
| | - Nigel Greene
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, 35 Gatehouse Drive, Waltham, MA 02451, USA
| | - Andreas Bender
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom
| | - Anne E. Carpenter
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States
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Seal S, Spjuth O, Hosseini-Gerami L, García-Ortegón M, Singh S, Bender A, Carpenter AE. Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank. J Chem Inf Model 2024; 64:1172-1186. [PMID: 38300851 PMCID: PMC10900289 DOI: 10.1021/acs.jcim.3c01834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024]
Abstract
Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.
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Affiliation(s)
- Srijit Seal
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Ola Spjuth
- Department
of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box
591, SE-75124 Uppsala, Sweden
| | - Layla Hosseini-Gerami
- Ignota
Labs, The Bradfield Centre, Cambridge Science Park, County Hall, Westminster Bridge Road, Cambridge CB4 0GA, U.K.
| | - Miguel García-Ortegón
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Shantanu Singh
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Andreas Bender
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Anne E. Carpenter
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
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