1
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Haratipour Z, Foutch D, Blind RD. A novel heuristic of rigid docking scores positively correlates with full-length nuclear receptor LRH-1 regulation. Comput Struct Biotechnol J 2024; 23:3065-3080. [PMID: 39185441 PMCID: PMC11342790 DOI: 10.1016/j.csbj.2024.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 07/26/2024] [Accepted: 07/26/2024] [Indexed: 08/27/2024] Open
Abstract
The nuclear receptor Liver Receptor Homolog-1 (LRH-1, NR5A2) is a ligand-regulated transcription factor and validated drug target for several human diseases. LRH-1 activation is regulated by small molecule ligands, which bind to the ligand binding domain (LBD) within the full-length LRH-1. We recently identified 57 compounds that bind LRH-1, and unexpectedly found these compounds regulated either the isolated LBD, or the full-length LRH-1 in cells, with little overlap. Here, we correlated compound binding energy from a single rigid-body scoring function with full-length LRH-1 activity in cells. Although docking scores of the 57 hit compounds did not correlate with LRH-1 regulation in wet lab assays, a subset of the compounds had large differences in binding energy docked to the isolated LBD vs. full-length LRH-1, which we used to empirically derive a new metric of the docking scores we call "ΔΔG". Initial regressions, correlations and contingency analyses all suggest compounds with high ΔΔG values more frequently regulated LRH-1 in wet lab assays. We then docked all 57 compounds to 18 separate crystal structures of LRH-1 to obtain averaged ΔΔG values for each compound, which robustly and reproducibly associated with full-length LRH-1 activity in cells. Network analyses on the 18 crystal structures of LRH-1 suggest unique communication paths exist between the subsets of LRH-1 crystal structures that produced high vs. low ΔΔG values, identifying a structural relationship between ΔΔG and the position of Helix 6, a previously established regulatory helix important for LRH-1 regulation. Together, these data suggest rigid-body computational docking can be used to quickly calculate ΔΔG, which positively correlated with the ability of these 57 hit compounds to regulate full-length LRH-1 in cell-based assays. We propose ΔΔG as a novel computational tool that can be applied to LRH-1 drug screens to prioritize compounds for resource-intense secondary screening.
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Affiliation(s)
- Zeinab Haratipour
- Vanderbilt University Medical Center, Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Nashville, TN 37232, USA
- Austin Peay State University, Department of Chemistry
| | - David Foutch
- Vanderbilt University Medical Center, Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Nashville, TN 37232, USA
| | - Raymond D. Blind
- Vanderbilt University Medical Center, Department of Medicine, Division of Diabetes, Endocrinology and Metabolism, Nashville, TN 37232, USA
- Vanderbilt University School of Medicine, Departments of Biochemistry and Pharmacology, Nashville, TN 37232, USA
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2
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Chandrasekaran SN, Cimini BA, Goodale A, Miller L, Kost-Alimova M, Jamali N, Doench JG, Fritchman B, Skepner A, Melanson M, Kalinin AA, Arevalo J, Haghighi M, Caicedo JC, Kuhn D, Hernandez D, Berstler J, Shafqat-Abbasi H, Root DE, Swalley SE, Garg S, Singh S, Carpenter AE. Three million images and morphological profiles of cells treated with matched chemical and genetic perturbations. Nat Methods 2024; 21:1114-1121. [PMID: 38594452 PMCID: PMC11166567 DOI: 10.1038/s41592-024-02241-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 03/11/2024] [Indexed: 04/11/2024]
Abstract
The identification of genetic and chemical perturbations with similar impacts on cell morphology can elucidate compounds' mechanisms of action or novel regulators of genetic pathways. Research on methods for identifying such similarities has lagged due to a lack of carefully designed and well-annotated image sets of cells treated with chemical and genetic perturbations. Here we create such a Resource dataset, CPJUMP1, in which each perturbed gene's product is a known target of at least two chemical compounds in the dataset. We systematically explore the directionality of correlations among perturbations that target the same protein encoded by a given gene, and we find that identifying matches between chemical and genetic perturbations is a challenging task. Our dataset and baseline analyses provide a benchmark for evaluating methods that measure perturbation similarities and impact, and more generally, learn effective representations of cellular state from microscopy images. Such advancements would accelerate the applications of image-based profiling of cellular states, such as uncovering drug mode of action or probing functional genomics.
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Affiliation(s)
| | - Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Goodale
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lisa Miller
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Nasim Jamali
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - John G Doench
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Adam Skepner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - John Arevalo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | | | | | | | - David E Root
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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3
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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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4
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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. ARXIV 2024:arXiv:2405.02767v1. [PMID: 38745696 PMCID: PMC11092692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
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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5
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Moshkov N, Bornholdt M, Benoit S, Smith M, McQuin C, Goodman A, Senft RA, Han Y, Babadi M, Horvath P, Cimini BA, Carpenter AE, Singh S, Caicedo JC. Learning representations for image-based profiling of perturbations. Nat Commun 2024; 15:1594. [PMID: 38383513 PMCID: PMC10881515 DOI: 10.1038/s41467-024-45999-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data. Here, we present an improved strategy for learning representations of treatment effects from high-throughput imaging, following a causal interpretation. We use weakly supervised learning for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN. We evaluated our strategy on three publicly available Cell Painting datasets, and observed that the Cell Painting CNN improves performance in downstream analysis up to 30% with respect to classical features, while also being more computationally efficient.
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Affiliation(s)
- Nikita Moshkov
- HUN-REN Biological Research Centre, 62 Temesvári krt, Szeged, 6726, Hungary
| | - Michael Bornholdt
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Santiago Benoit
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
- Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA
| | - Matthew Smith
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
- Harvard College, 86 Brattle Street Cambridge, Cambridge, MA, 02138, USA
| | - Claire McQuin
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Allen Goodman
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Rebecca A Senft
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Yu Han
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Mehrtash Babadi
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Peter Horvath
- HUN-REN Biological Research Centre, 62 Temesvári krt, Szeged, 6726, Hungary
| | - Beth A Cimini
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Anne E Carpenter
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Shantanu Singh
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA
| | - Juan C Caicedo
- Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA, 02141, USA.
- Morgridge Institute for Research, 330 N Orchard St, Madison, WI, 53715, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 1300 University Ave, Madison, WI, 53706, USA.
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6
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Tromans-Coia C, Jamali N, Abbasi HS, Giuliano KA, Hagimoto M, Jan K, Kaneko E, Letzsch S, Schreiner A, Sexton JZ, Suzuki M, Trask OJ, Yamaguchi M, Yanagawa F, Yang M, Carpenter AE, Cimini BA. Assessing the performance of the Cell Painting assay across different imaging systems. Cytometry A 2023; 103:915-926. [PMID: 37789738 PMCID: PMC10841730 DOI: 10.1002/cyto.a.24786] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/16/2023] [Accepted: 08/08/2023] [Indexed: 10/05/2023]
Abstract
Quantitative microscopy is a powerful method for performing phenotypic screens from which image-based profiling can extract a wealth of information, termed profiles. These profiles can be used to elucidate the changes in cellular phenotypes across cell populations from different patient samples or following genetic or chemical perturbations. One such image-based profiling method is the Cell Painting assay, which provides morphological insight through the imaging of eight cellular compartments. Here, we examine the performance of the Cell Painting assay across multiple high-throughput microscope systems and find that all are compatible with this assay. Furthermore, we determine independently for each microscope system the best performing settings, providing those who wish to adopt this assay an ideal starting point for their own assays. We also explore the impact of microscopy setting changes in the Cell Painting assay and find that few dramatically reduce the quality of a Cell Painting profile, regardless of the microscope used.
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Affiliation(s)
- Callum Tromans-Coia
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
| | - Nasim Jamali
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Anne E. Carpenter
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
| | - Beth A. Cimini
- Broad Institute of MIT and Harvard, Cambridge MA, USA; Department: Imaging Platform
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7
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Postma RJ, Broekhoven AG, Verspaget HW, de Boer H, Hankemeier T, Coenraad MJ, van Duinen V, van Zonneveld AJ. Novel Morphological Profiling Assay Connects ex Vivo Endothelial Cell Responses to Disease Severity in Liver Cirrhosis. GASTRO HEP ADVANCES 2023; 3:238-249. [PMID: 39129954 PMCID: PMC11307659 DOI: 10.1016/j.gastha.2023.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/16/2023] [Indexed: 08/13/2024]
Abstract
Background and Aims Endothelial cell (EC) dysfunction in response to circulating plasma factors is a known causal factor in many systemic diseases. However, no appropriate assay is available to investigate this causality ex vivo. In liver cirrhosis, systemic inflammation is identified as central mechanism in progression from compensated to decompensated cirrhosis (DC), but the role of ECs therein is unknown. We aimed to develop a novel ex vivo assay for assessing EC responses to patient-derived plasma (PDP) and assess the potential of this assay in a cohort of liver cirrhosis patients. Methods Image-based morphological profiling was utilized to assess the impact of PDP on cultured ECs. Endothelial cell (EC) monolayers were exposed to 25% stabilized PDP (20 compensated cirrhoses, 20 DCs, and 20 healthy controls (HCs). Single-cell morphological profiles were extracted by automated image-analysis following staining of multiple cellular components and high-content imaging. Patient profiles were created by dimension reduction and cell-to-patient data aggregation, followed by multivariate-analysis to stratify patients and identify discriminating features. Results Patient-derived plasma (PDP) exposure induced profound changes in EC morphology, displaying clear differences between controls and DC patients. Compensated cirrhosis patients showed overlap with healthy controls and DC patients. Supervised analysis showed Child-Pugh (CP) class could be predicted from EC morphology. Most importantly, CP-C patients displayed distinct EC phenotypes, in which mitochondrial changes were most discriminative. Conclusion Morphological profiling presents a viable tool to assess the endothelium ex vivo. We demonstrated that the EC phenotype corresponds with disease severity in liver cirrhosis. Moreover, our results suggest the presence of mitochondrial dysfunction in ECs of CP-C patient.
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Affiliation(s)
- Rudmer J. Postma
- Department of Internal Medicine (Nephrology) and the Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Annelotte G.C. Broekhoven
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hein W. Verspaget
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hetty de Boer
- Department of Internal Medicine (Nephrology) and the Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Thomas Hankemeier
- Department of Analytical BioSciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Minneke J. Coenraad
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Vincent van Duinen
- Department of Internal Medicine (Nephrology) and the Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
- MIMETAS B.V., Oegstgeest, The Netherlands
| | - Anton Jan van Zonneveld
- Department of Internal Medicine (Nephrology) and the Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
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8
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Way GP, Sailem H, Shave S, Kasprowicz R, Carragher NO. Evolution and impact of high content imaging. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2023; 28:292-305. [PMID: 37666456 DOI: 10.1016/j.slasd.2023.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/09/2023] [Accepted: 08/29/2023] [Indexed: 09/06/2023]
Abstract
The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.
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Affiliation(s)
- Gregory P Way
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Heba Sailem
- School of Cancer and Pharmaceutical Sciences, King's College London, UK
| | - Steven Shave
- GlaxoSmithKline Medicines Research Centre, Gunnels Wood Rd, Stevenage SG1 2NY, UK; Edinburgh Cancer Research, Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, UK
| | - Richard Kasprowicz
- GlaxoSmithKline Medicines Research Centre, Gunnels Wood Rd, Stevenage SG1 2NY, UK
| | - Neil O Carragher
- Edinburgh Cancer Research, Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, UK.
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9
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Cimini BA, Chandrasekaran SN, Kost-Alimova M, Miller L, Goodale A, Fritchman B, Byrne P, Garg S, Jamali N, Logan DJ, Concannon JB, Lardeau CH, Mouchet E, Singh S, Shafqat Abbasi H, Aspesi P, Boyd JD, Gilbert T, Gnutt D, Hariharan S, Hernandez D, Hormel G, Juhani K, Melanson M, Mervin LH, Monteverde T, Pilling JE, Skepner A, Swalley SE, Vrcic A, Weisbart E, Williams G, Yu S, Zapiec B, Carpenter AE. Optimizing the Cell Painting assay for image-based profiling. Nat Protoc 2023; 18:1981-2013. [PMID: 37344608 PMCID: PMC10536784 DOI: 10.1038/s41596-023-00840-9] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 03/28/2023] [Indexed: 06/23/2023]
Abstract
In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations on the basis of their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells and predicting assay outcomes by using machine learning, among many others. Here, we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum and mitochondria. The original protocol was updated in 2016 on the basis of several years' experience running it at two sites, after optimizing it by visual stain quality. Here, we describe the work of the Joint Undertaking for Morphological Profiling Cell Painting Consortium, to improve upon the assay via quantitative optimization by measuring the assay's ability to detect morphological phenotypes and group similar perturbations together. The assay gives very robust outputs despite various changes to the protocol, and two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for typically sized batches of ≤20 plates; feature extraction and data analysis take an additional 1-2 weeks.This protocol is an update to Nat. Protoc. 11, 1757-1774 (2016): https://doi.org/10.1038/nprot.2016.105.
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Affiliation(s)
- Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Maria Kost-Alimova
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lisa Miller
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Goodale
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Briana Fritchman
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Patrick Byrne
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Nasim Jamali
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David J Logan
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - John B Concannon
- Chemical Biology & Therapeutics Department, Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | | | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Peter Aspesi
- Chemical Biology & Therapeutics Department, Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Justin D Boyd
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - Tamara Gilbert
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA, USA
| | - David Gnutt
- Bayer AG, Research & Development, Pharmaceuticals, Wuppertal, Germany
| | | | - Desiree Hernandez
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Michelle Melanson
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Adam Skepner
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Anita Vrcic
- Center for Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Guy Williams
- AstraZeneca BioPharmaceuticals R&D, Cambridge, UK
| | - Shan Yu
- Takeda Development Center Americas, Inc., San Diego, CA, USA
| | | | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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10
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Tromans-Coia C, Jamali N, Abbasi HS, Giuliano KA, Hagimoto M, Jan K, Kaneko E, Letzsch S, Schreiner A, Sexton JZ, Suzuki M, Trask OJ, Yamaguchi M, Yanagawa F, Yang M, Carpenter AE, Cimini BA. Assessing the performance of the Cell Painting assay across different imaging systems. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528711. [PMID: 36824835 PMCID: PMC9949001 DOI: 10.1101/2023.02.15.528711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Quantitative microscopy is a powerful method for performing phenotypic screens from which image-based profiling can extract a wealth of information, termed profiles. These profiles can be used to elucidate the changes in cellular phenotypes across cell populations from different patient samples or following genetic or chemical perturbations. One such image-based profiling method is the Cell Painting assay, which provides morphological insight through the imaging of eight cellular compartments. Here, we examine the performance of the Cell Painting assay across multiple high-throughput microscope systems and find that all are compatible with this assay. Furthermore, we determine independently for each microscope system the best performing settings, providing those who wish to adopt this assay an ideal starting point for their own assays. We also explore the impact of microscopy setting changes in the Cell Painting assay and find that few dramatically reduce the quality of a Cell Painting profile, regardless of the microscope used.
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11
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Pruteanu LL, Bender A. Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice. ACS Med Chem Lett 2023; 14:386-395. [PMID: 37077392 PMCID: PMC10107910 DOI: 10.1021/acsmedchemlett.3c00015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/10/2023] [Indexed: 04/21/2023] Open
Abstract
Gene expression and cell morphology data are high-dimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.
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Affiliation(s)
- Lavinia-Lorena Pruteanu
- Department
of Chemistry and Biology, North University
Center at Baia Mare, Technical University of Cluj-Napoca, Victoriei 76, 430122 Baia Mare, Romania
- Research
Center for Functional Genomics, Biomedicine, and Translational Medicine, “Iuliu Haţieganu” University
of Medicine and Pharmacy, 400337 Cluj-Napoca, Romania
| | - Andreas Bender
- Centre
for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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12
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Garcia-Fossa F, Cruz MC, Haghighi M, de Jesus MB, Singh S, Carpenter AE, Cimini BA. Interpreting Image-based Profiles using Similarity Clustering and Single-Cell Visualization. Curr Protoc 2023; 3:e713. [PMID: 36921124 PMCID: PMC10027367 DOI: 10.1002/cpz1.713] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
Abstract
Image-based profiling quantitatively assesses the effects of perturbations on cells by capturing a breadth of changes via microscopy. Here, we provide two complementary protocols to help explore and interpret data from image-based profiling experiments. In the first protocol, we examine the similarity among perturbed cell samples using data from compounds that cluster by their mechanisms of action. The protocol includes steps to examine feature-driving differences between samples and to visualize correlations between features and treatments to create interpretable heatmaps using the open-source web tool Morpheus. In the second protocol, we show how to interactively explore images together with the numerical data, and we provide scripts to create visualizations of representative single cells and image sites to understand how changes in features are reflected in the images. Together, these two tutorials help researchers interpret image-based data to speed up research. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory analysis of profile similarities and driving features Basic Protocol 2: Image and single-cell visualization following profile interpretation.
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Affiliation(s)
- Fernanda Garcia-Fossa
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
| | - Mario Costa Cruz
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Marzieh Haghighi
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Marcelo Bispo de Jesus
- Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
| | - Shantanu Singh
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Anne E. Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Beth A. Cimini
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
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Duran-Frigola M, Cigler M, Winter GE. Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence. J Am Chem Soc 2023; 145:2711-2732. [PMID: 36706315 PMCID: PMC9912273 DOI: 10.1021/jacs.2c11098] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Indexed: 01/28/2023]
Abstract
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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Affiliation(s)
- Miquel Duran-Frigola
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
- Ersilia
Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom
| | - Marko Cigler
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
| | - Georg E. Winter
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
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Haghighi M, Caicedo JC, Cimini BA, Carpenter AE, Singh S. High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations. Nat Methods 2022; 19:1550-1557. [PMID: 36344834 PMCID: PMC10012424 DOI: 10.1038/s41592-022-01667-0] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 09/28/2022] [Indexed: 11/09/2022]
Abstract
Cells can be perturbed by various chemical and genetic treatments and the impact on gene expression and morphology can be measured via transcriptomic profiling and image-based assays, respectively. The patterns observed in these high-dimensional profile data can power a dozen applications in drug discovery and basic biology research, but both types of profiles are rarely available for large-scale experiments. Here, we provide a collection of four datasets with both gene expression and morphological profile data useful for developing and testing multimodal methodologies. Roughly a thousand features are measured for each of the two data types, across more than 28,000 chemical and genetic perturbations. We define biological problems that use the shared and complementary information in these two data modalities, provide baseline analysis and evaluation metrics for multi-omic applications, and make the data resource publicly available ( https://broad.io/rosetta/ ).
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Affiliation(s)
| | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
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