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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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2
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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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3
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McDiarmid AH, Gospodinova KO, Elliott RJR, Dawson JC, Graham RE, El-Daher MT, Anderson SM, Glen SC, Glerup S, Carragher NO, Evans KL. Morphological profiling in human neural progenitor cells classifies hits in a pilot drug screen for Alzheimer's disease. Brain Commun 2024; 6:fcae101. [PMID: 38576795 PMCID: PMC10994270 DOI: 10.1093/braincomms/fcae101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 12/15/2023] [Accepted: 03/27/2024] [Indexed: 04/06/2024] Open
Abstract
Alzheimer's disease accounts for 60-70% of dementia cases. Current treatments are inadequate and there is a need to develop new approaches to drug discovery. Recently, in cancer, morphological profiling has been used in combination with high-throughput screening of small-molecule libraries in human cells in vitro. To test feasibility of this approach for Alzheimer's disease, we developed a cell morphology-based drug screen centred on the risk gene, SORL1 (which encodes the protein SORLA). Increased Alzheimer's disease risk has been repeatedly linked to variants in SORL1, particularly those conferring loss or decreased expression of SORLA, and lower SORL1 levels are observed in post-mortem brain samples from individuals with Alzheimer's disease. Consistent with its role in the endolysosomal pathway, SORL1 deletion is associated with enlarged endosomes in neural progenitor cells and neurons. We, therefore, hypothesized that multi-parametric, image-based cell phenotyping would identify features characteristic of SORL1 deletion. An automated morphological profiling method (Cell Painting) was adapted to neural progenitor cells and used to determine the phenotypic response of SORL1-/- neural progenitor cells to treatment with compounds from a small internationally approved drug library (TargetMol, 330 compounds). We detected distinct phenotypic signatures for SORL1-/- neural progenitor cells compared to isogenic wild-type controls. Furthermore, we identified 16 compounds (representing 14 drugs) that reversed the mutant morphological signatures in neural progenitor cells derived from three SORL1-/- induced pluripotent stem cell sub-clones. Network pharmacology analysis revealed the 16 compounds belonged to five mechanistic groups: 20S proteasome, aldehyde dehydrogenase, topoisomerase I and II, and DNA synthesis inhibitors. Enrichment analysis identified DNA synthesis/damage/repair, proteases/proteasome and metabolism as key pathways/biological processes. Prediction of novel targets revealed enrichment in pathways associated with neural cell function and Alzheimer's disease. Overall, this work suggests that (i) a quantitative phenotypic metric can distinguish induced pluripotent stem cell-derived SORL1-/- neural progenitor cells from isogenic wild-type controls and (ii) phenotypic screening combined with multi-parametric high-content image analysis is a viable option for drug repurposing and discovery in this human neural cell model of Alzheimer's disease.
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Affiliation(s)
- Amina H McDiarmid
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Katerina O Gospodinova
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Richard J R Elliott
- Cancer Research UK Scotland Centre, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - John C Dawson
- Cancer Research UK Scotland Centre, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Rebecca E Graham
- Cancer Research UK Scotland Centre, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Marie-Therese El-Daher
- Medical Research Council Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Susan M Anderson
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Sophie C Glen
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Simon Glerup
- Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark
| | - Neil O Carragher
- Cancer Research UK Scotland Centre, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
| | - Kathryn L Evans
- Centre for Genomic & Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh EH4 2XU, UK
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4
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Ostovich E, Klaper R. Using a Novel Multiplexed Algal Cytological Imaging (MACI) Assay and Machine Learning as a Way to Characterize Complex Phenotypes in Plant-Type Organisms. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:4894-4903. [PMID: 38446593 DOI: 10.1021/acs.est.3c07733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
High-throughput phenotypic profiling assays, popular for their ability to characterize alternations in single-cell morphological feature data, have been useful in recent years for predicting cellular targets and mechanisms of action (MoAs) for different chemicals and novel drugs. However, this approach has not been extensively used in environmental toxicology due to the lack of studies and established methods for performing this kind of assay in environmentally relevant species. Here, we developed a multiplexed algal cytological imaging (MACI) assay, based on the subcellular structures of the unicellular microalgae, Raphidocelis subcapitata, a toxicology and ecological model species. Several different herbicides and antibiotics with unique MoAs were exposed to R. subcapitata cells, and MACI was used to characterize cellular impacts by measuring subtle changes in their morphological features, including metrics of area, shape, quantity, fluorescence intensity, and granularity of individual subcellular components. This study demonstrates that MACI offers a quick and effective framework for characterizing complex phenotypic responses to environmental chemicals that can be used for determining their MoAs and identifying their cellular targets in plant-type organisms.
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Affiliation(s)
- Eric Ostovich
- School of Freshwater Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53204, United States
| | - Rebecca Klaper
- School of Freshwater Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53204, United States
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5
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Cirinciani M, Da Pozzo E, Trincavelli ML, Milazzo P, Martini C. Drug Mechanism: A bioinformatic update. Biochem Pharmacol 2024:116078. [PMID: 38402909 DOI: 10.1016/j.bcp.2024.116078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/01/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024]
Abstract
A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.
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Affiliation(s)
- Martina Cirinciani
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy
| | - Eleonora Da Pozzo
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy
| | - Maria Letizia Trincavelli
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy
| | - Paolo Milazzo
- Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy; Department of Computer Science, University of Pisa, Largo Pontecorvo, 3, 56127 Pisa, Italy
| | - Claudia Martini
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy.
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6
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Baillache DJ, Valero T, Lorente-Macías Á, Bennett DJ, Elliott RJR, Carragher NO, Unciti-Broceta A. Discovery of pyrazolopyrimidines that selectively inhibit CSF-1R kinase by iterative design, synthesis and screening against glioblastoma cells. RSC Med Chem 2023; 14:2611-2624. [PMID: 38099057 PMCID: PMC10718585 DOI: 10.1039/d3md00454f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/09/2023] [Indexed: 12/17/2023] Open
Abstract
Glioblastoma multiforme (GBM) is the most aggressive type of brain cancer in adults, with an average life expectancy under treatment of approx. 15 months. GBM is characterised by a complex set of genetic alterations that results in significant disruption of receptor tyrosine kinase (RTK) signaling. We report here an exploration of the pyrazolo[3,4-d]pyrimidine scaffold in search for antiproliferative compounds directed to GBM treatment. Small compound libraries were synthesised and screened against GBM cells to build up structure-antiproliferative activity-relationships (SAARs) and inform further rounds of design, synthesis and screening. 76 novel compounds were generated through this iterative process that found low micromolar potencies against selected GBM lines, including patient-derived stem cells. Phenomics analysis demonstrated preferential activity against glioma cells of the mesenchymal subtype, whereas kinome screening identified colony stimulating factor-1 receptor (CSF-1R) as the lead's target, a RTK implicated in the tumourigenesis and progression of different cancers and the immunoregulation of the GBM microenvironment.
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Affiliation(s)
- Daniel J Baillache
- Edinburgh Cancer Research, Institute of Genetics & Cancer, University of Edinburgh Crewe Road South Edinburgh EH4 2XR UK
- Cancer Research UK Scotland Centre UK
| | - Teresa Valero
- Edinburgh Cancer Research, Institute of Genetics & Cancer, University of Edinburgh Crewe Road South Edinburgh EH4 2XR UK
- Cancer Research UK Scotland Centre UK
| | - Álvaro Lorente-Macías
- Edinburgh Cancer Research, Institute of Genetics & Cancer, University of Edinburgh Crewe Road South Edinburgh EH4 2XR UK
- Cancer Research UK Scotland Centre UK
| | | | - Richard J R Elliott
- Edinburgh Cancer Research, Institute of Genetics & Cancer, University of Edinburgh Crewe Road South Edinburgh EH4 2XR UK
- Cancer Research UK Scotland Centre UK
| | - Neil O Carragher
- Edinburgh Cancer Research, Institute of Genetics & Cancer, University of Edinburgh Crewe Road South Edinburgh EH4 2XR UK
- Cancer Research UK Scotland Centre UK
| | - Asier Unciti-Broceta
- Edinburgh Cancer Research, Institute of Genetics & Cancer, University of Edinburgh Crewe Road South Edinburgh EH4 2XR UK
- Cancer Research UK Scotland Centre UK
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7
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Boyang H, Yangyanqiu W, Wenting R, Chenxin Y, Jian C, Zhanbo Q, Yanjun Y, Qiang Y, Shuwen H. Application and progress of highcontent imaging in molecular biology. Biotechnol J 2023; 18:e2300170. [PMID: 37639283 DOI: 10.1002/biot.202300170] [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: 04/19/2023] [Revised: 08/03/2023] [Accepted: 08/22/2023] [Indexed: 08/29/2023]
Abstract
Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.
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Affiliation(s)
- Hu Boyang
- Huzhou Hospital of Zhejiang University, Affiliated Central Hospital Huzhou University, Huzhou, China
| | - Wang Yangyanqiu
- Huzhou Hospital of Zhejiang University, Affiliated Central Hospital Huzhou University, Huzhou, China
| | - Rui Wenting
- Huzhou Hospital of Zhejiang University, Affiliated Central Hospital Huzhou University, Huzhou, China
| | - Yan Chenxin
- Shulan International Medical School, Zhejiang Shuren University, Hangzhou, China
| | - Chu Jian
- Fifth Affiliated Clinical Medical College of Zhejiang Chinese Medical University, Huzhou Central Hospital, Huzhou, China
| | - Qu Zhanbo
- Fifth Affiliated Clinical Medical College of Zhejiang Chinese Medical University, Huzhou Central Hospital, Huzhou, China
| | - Yao Yanjun
- Huzhou Hospital of Zhejiang University, Affiliated Central Hospital Huzhou University, Huzhou, China
| | - Yan Qiang
- Huzhou Hospital of Zhejiang University, Affiliated Central Hospital Huzhou University, Huzhou, China
| | - Han Shuwen
- Huzhou Hospital of Zhejiang University, Affiliated Central Hospital Huzhou University, Huzhou, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, China
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8
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Stossi F, Singh PK, Safari K, Marini M, Labate D, Mancini MA. High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery. Biochem Pharmacol 2023; 216:115770. [PMID: 37660829 DOI: 10.1016/j.bcp.2023.115770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA.
| | - Pankaj K Singh
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Kazem Safari
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Michela Marini
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Demetrio Labate
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Michael A Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
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9
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Athanasiadis P, Ravikumar B, Elliott RJ, Dawson JC, Carragher NO, Clemons PA, Johanssen T, Ebner D, Aittokallio T. Chemogenomic library design strategies for precision oncology, applied to phenotypic profiling of glioblastoma patient cells. iScience 2023; 26:107209. [PMID: 37485377 PMCID: PMC10359939 DOI: 10.1016/j.isci.2023.107209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/21/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023] Open
Abstract
Designing a targeted screening library of bioactive small molecules is a challenging task since most compounds modulate their effects through multiple protein targets with varying degrees of potency and selectivity. We implemented analytic procedures for designing anticancer compound libraries adjusted for library size, cellular activity, chemical diversity and availability, and target selectivity. The resulting compound collections cover a wide range of protein targets and biological pathways implicated in various cancers, making them widely applicable to precision oncology. We characterized the compound and target spaces of the virtual libraries, in comparison with a minimal screening library of 1,211 compounds for targeting 1,386 anticancer proteins. In a pilot screening study, we identified patient-specific vulnerabilities by imaging glioma stem cells from patients with glioblastoma (GBM), using a physical library of 789 compounds that cover 1,320 of the anticancer targets. The cell survival profiling revealed highly heterogeneous phenotypic responses across the patients and GBM subtypes.
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Affiliation(s)
- Paschalis Athanasiadis
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
| | - Balaguru Ravikumar
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
| | - Richard J.R. Elliott
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - John C. Dawson
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Neil O. Carragher
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Paul A. Clemons
- Chemical Biology and Therapeutics Science Program, Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, United States
| | - Timothy Johanssen
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Daniel Ebner
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Tero Aittokallio
- Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, 0310 Oslo, Norway
- Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, 0317 Oslo, Norway
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, 20520 00290 Helsinki, Finland
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10
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Dahlin JL, Hua BK, Zucconi BE, Nelson SD, Singh S, Carpenter AE, Shrimp JH, Lima-Fernandes E, Wawer MJ, Chung LPW, Agrawal A, O'Reilly M, Barsyte-Lovejoy D, Szewczyk M, Li F, Lak P, Cuellar M, Cole PA, Meier JL, Thomas T, Baell JB, Brown PJ, Walters MA, Clemons PA, Schreiber SL, Wagner BK. Reference compounds for characterizing cellular injury in high-content cellular morphology assays. Nat Commun 2023; 14:1364. [PMID: 36914634 PMCID: PMC10011410 DOI: 10.1038/s41467-023-36829-x] [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: 08/01/2022] [Accepted: 02/20/2023] [Indexed: 03/16/2023] Open
Abstract
Robust, generalizable approaches to identify compounds efficiently with undesirable mechanisms of action in complex cellular assays remain elusive. Such a process would be useful for hit triage during high-throughput screening and, ultimately, predictive toxicology during drug development. Here we generate cell painting and cellular health profiles for 218 prototypical cytotoxic and nuisance compounds in U-2 OS cells in a concentration-response format. A diversity of compounds that cause cellular damage produces bioactive cell painting morphologies, including cytoskeletal poisons, genotoxins, nonspecific electrophiles, and redox-active compounds. Further, we show that lower quality lysine acetyltransferase inhibitors and nonspecific electrophiles can be distinguished from more selective counterparts. We propose that the purposeful inclusion of cytotoxic and nuisance reference compounds such as those profiled in this resource will help with assay optimization and compound prioritization in complex cellular assays like cell painting.
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Grants
- R35 GM127045 NIGMS NIH HHS
- U01 CA272612 NCI NIH HHS
- T32 HL007627 NHLBI NIH HHS
- R37 GM062437 NIGMS NIH HHS
- S10 OD026839 NIH HHS
- R35 GM122481 NIGMS NIH HHS
- U01 DK123717 NIDDK NIH HHS
- Wellcome Trust
- R35 GM122547 NIGMS NIH HHS
- U01 CA217848 NCI NIH HHS
- K99 GM124357 NIGMS NIH HHS
- R35 GM149229 NIGMS NIH HHS
- This study was supported by the Ono Pharma Breakthrough Science Initiative Award (to BKW). Authors acknowledge the following financial support: JLD (NIH NHLBI, T32-HL007627); BKH (National Science Foundation, DGE1144152 and DGE1745303); BEZ (NIH NIGMS, K99-GM124357); SDN (Harvard University’s Graduate Prize Fellowship, Eli Lilly Graduate Fellowship in Chemistry); PA Cole (NIH NIGMS, R37-GM62437); SLS (NIGMS, R35-GM127045); BKW (Ono Pharma Foundation; NIH NIDDK, U01-DK123717); SS (NIH NIGMS, R35-GM122547). The authors gratefully acknowledge the use of the Opera Phenix High-Content/High-Throughput imaging system at the Broad Institute, funded by the NIH S10 grant OD026839. This research was supported in part by the Intramural/Extramural research program of the NCATS, NIH.
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Affiliation(s)
- Jayme L Dahlin
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA.
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA.
| | - Bruce K Hua
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Beth E Zucconi
- Division of Genetics, Departments of Medicine and Biological Chemistry and Molecular Pharmacology, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | | | | | | | - Jonathan H Shrimp
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | | | - Mathias J Wawer
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Lawrence P W Chung
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Ayushi Agrawal
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | | | | | - Magdalena Szewczyk
- Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada
| | - Fengling Li
- Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada
| | - Parnian Lak
- Department of Pharmaceutical Chemistry and Quantitative Biology Institute, University of California San Francisco, San Francisco, CA, USA
| | - Matthew Cuellar
- Institute for Therapeutics Discovery and Development, University of Minnesota, Minneapolis, MN, USA
| | - Philip A Cole
- Division of Genetics, Departments of Medicine and Biological Chemistry and Molecular Pharmacology, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA
| | - Jordan L Meier
- Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD, USA
| | - Tim Thomas
- Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia
| | - Jonathan B Baell
- Medicinal Chemistry Theme, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
| | - Peter J Brown
- Structural Genomics Consortium, University of Toronto, Toronto, ON, Canada
| | - Michael A Walters
- Institute for Therapeutics Discovery and Development, University of Minnesota, Minneapolis, MN, USA
| | - Paul A Clemons
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Stuart L Schreiber
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA
| | - Bridget K Wagner
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA.
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11
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Rietdijk J, Aggarwal T, Georgieva P, Lapins M, Carreras-Puigvert J, Spjuth O. Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 832:155058. [PMID: 35390365 DOI: 10.1016/j.scitotenv.2022.155058] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 04/01/2022] [Accepted: 04/01/2022] [Indexed: 06/14/2023]
Abstract
Environmental chemicals are commonly studied one at a time, and there is a need to advance our understanding of the effect of exposure to their combinations. Here we apply high-content microscopy imaging of cells stained with multiplexed dyes (Cell Painting) to profile the effects of Cetyltrimethylammonium bromide (CTAB), Bisphenol A (BPA), and Dibutyltin dilaurate (DBTDL) exposure on four human cell lines; both individually and in all combinations. We show that morphological features can be used with multivariate data analysis to discern between exposures from individual compounds, concentrations, and combinations. CTAB and DBTDL induced concentration-dependent morphological changes across the four cell lines, and BPA exacerbated morphological effects when combined with CTAB and DBTDL. Combined exposure to CTAB and BPA induced changes in the ER, Golgi apparatus, nucleoli and cytoplasmic RNA in one of the cell lines. Different responses between cell lines indicate that multiple cell types are needed when assessing combination effects. The rapid and relatively low-cost experiments combined with high information content make Cell Painting an attractive methodology for future studies of combination effects. All data in the study is made publicly available on Figshare.
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Affiliation(s)
- Jonne Rietdijk
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Sweden
| | - Tanya Aggarwal
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Sweden
| | - Polina Georgieva
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Sweden
| | - Maris Lapins
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Sweden
| | - Jordi Carreras-Puigvert
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Sweden.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Sweden.
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12
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Hughes R, Elliott RJR, Li X, Munro AF, Makda A, Carter RN, Morton NM, Fujihara K, Clemons NJ, Fitzgerald R, O’Neill JR, Hupp T, Carragher NO. Multiparametric High-Content Cell Painting Identifies Copper Ionophores as Selective Modulators of Esophageal Cancer Phenotypes. ACS Chem Biol 2022; 17:1876-1889. [PMID: 35696676 PMCID: PMC9295120 DOI: 10.1021/acschembio.2c00301] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Esophageal adenocarcinoma is of increasing global concern due to increasing incidence, a lack of effective treatments, and poor prognosis. Therapeutic target discovery and clinical trials have been hindered by the heterogeneity of the disease, the lack of "druggable" driver mutations, and the dominance of large-scale genomic rearrangements. We have previously undertaken a comprehensive small-molecule phenotypic screen using the high-content Cell Painting assay to quantify the morphological response to a total of 19,555 small molecules across a panel of genetically distinct human esophageal cell lines to identify new therapeutic targets and small molecules for the treatment of esophageal adenocarcinoma. In this current study, we report for the first time the dose-response validation studies for the 72 screening hits from the target-annotated LOPAC and Prestwick FDA-approved compound libraries and the full list of 51 validated esophageal adenocarcinoma-selective small molecules (71% validation rate). We then focus on the most potent and selective hit molecules, elesclomol, disulfiram, and ammonium pyrrolidinedithiocarbamate. Using a multipronged, multitechnology approach, we uncover a unified mechanism of action and a vulnerability in esophageal adenocarcinoma toward copper-dependent cell death that could be targeted in the future.
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Affiliation(s)
- Rebecca
E. Hughes
- Cancer
Research UK Edinburgh Centre, Institute of Genetics & Cancer, The University of Edinburgh, Western General Hospital, Edinburgh EH4 2XR, U.K.
| | - Richard J. R. Elliott
- Cancer
Research UK Edinburgh Centre, Institute of Genetics & Cancer, The University of Edinburgh, Western General Hospital, Edinburgh EH4 2XR, U.K.
| | - Xiaodun Li
- MRC
Cancer Unit, Hutchison-MRC Research Centre, University of Cambridge, Cambridge CB2 0XZ, U.K.
| | - Alison F. Munro
- Cancer
Research UK Edinburgh Centre, Institute of Genetics & Cancer, The University of Edinburgh, Western General Hospital, Edinburgh EH4 2XR, U.K.
| | - Ashraff Makda
- Cancer
Research UK Edinburgh Centre, Institute of Genetics & Cancer, The University of Edinburgh, Western General Hospital, Edinburgh EH4 2XR, U.K.
| | - Roderick N. Carter
- Centre
for Clinical Brain Sciences, Chancellors Building, University of Edinburgh, Edinburgh EH16 4SB, U.K.
- Centre
for Cardiovascular Science, The Queen’s
Medical Research Institute, Edinburgh BioQuarter, Edinburgh EH16 4TJ, U.K.
| | - Nicholas M. Morton
- Centre
for Cardiovascular Science, The Queen’s
Medical Research Institute, Edinburgh BioQuarter, Edinburgh EH16 4TJ, U.K.
| | - Kenji Fujihara
- Gastrointestinal
Cancer Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne 3000, Victoria, Australia
- Sir Peter
MacCallum Department of Oncology, The University
of Melbourne, Parkville 3010, Victoria, Australia
| | - Nicholas J. Clemons
- Gastrointestinal
Cancer Program, Cancer Research Division, Peter MacCallum Cancer Centre, Melbourne 3000, Victoria, Australia
- Sir Peter
MacCallum Department of Oncology, The University
of Melbourne, Parkville 3010, Victoria, Australia
| | - Rebecca Fitzgerald
- Early
Cancer Institute, Hutchison Research Centre, University of Cambridge, Cambridge CB2 0XZ, U.K.
| | - J. Robert O’Neill
- Cambridge
Oesophagogastric Centre, Cambridge University
Hospitals Foundation Trust, Cambridge CB2 2QQ, U.K.
| | - Ted Hupp
- Cancer
Research UK Edinburgh Centre, Institute of Genetics & Cancer, The University of Edinburgh, Western General Hospital, Edinburgh EH4 2XR, U.K.
| | - Neil O. Carragher
- Cancer
Research UK Edinburgh Centre, Institute of Genetics & Cancer, The University of Edinburgh, Western General Hospital, Edinburgh EH4 2XR, U.K.
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13
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Fennell M, Johnston PA. High-Content Imaging and Informatics: A Joint Special Issue with Society for Biomolecular Imaging and Informatics and SLAS. SLAS DISCOVERY 2021; 25:668-671. [PMID: 32687017 DOI: 10.1177/2472555220937652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
| | - Paul A Johnston
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, UPMC Hillman Cancer Center, Pittsburgh, PA, USA
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14
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Hughes RE, Elliott RJR, Dawson JC, Carragher NO. High-content phenotypic and pathway profiling to advance drug discovery in diseases of unmet need. Cell Chem Biol 2021; 28:338-355. [PMID: 33740435 DOI: 10.1016/j.chembiol.2021.02.015] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/10/2020] [Accepted: 02/18/2021] [Indexed: 02/07/2023]
Abstract
Conventional thinking in modern drug discovery postulates that the design of highly selective molecules which act on a single disease-associated target will yield safer and more effective drugs. However, high clinical attrition rates and the lack of progress in developing new effective treatments for many important diseases of unmet therapeutic need challenge this hypothesis. This assumption also impinges upon the efficiency of target agnostic phenotypic drug discovery strategies, where early target deconvolution is seen as a critical step to progress phenotypic hits. In this review we provide an overview of how emerging phenotypic and pathway-profiling technologies integrate to deconvolute the mechanism-of-action of phenotypic hits. We propose that such in-depth mechanistic profiling may support more efficient phenotypic drug discovery strategies that are designed to more appropriately address complex heterogeneous diseases of unmet need.
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Affiliation(s)
- Rebecca E Hughes
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Richard J R Elliott
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - John C Dawson
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XR, UK.
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15
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Dahlin JL, Auld DS, Rothenaigner I, Haney S, Sexton JZ, Nissink JWM, Walsh J, Lee JA, Strelow JM, Willard FS, Ferrins L, Baell JB, Walters MA, Hua BK, Hadian K, Wagner BK. Nuisance compounds in cellular assays. Cell Chem Biol 2021; 28:356-370. [PMID: 33592188 PMCID: PMC7979533 DOI: 10.1016/j.chembiol.2021.01.021] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 01/02/2021] [Accepted: 01/27/2021] [Indexed: 12/17/2022]
Abstract
Compounds that exhibit assay interference or undesirable mechanisms of bioactivity ("nuisance compounds") are routinely encountered in cellular assays, including phenotypic and high-content screening assays. Much is known regarding compound-dependent assay interferences in cell-free assays. However, despite the essential role of cellular assays in chemical biology and drug discovery, there is considerably less known about nuisance compounds in more complex cell-based assays. In our view, a major obstacle to realizing the full potential of chemical biology will not just be difficult-to-drug targets or even the sheer number of targets, but rather nuisance compounds, due to their ability to waste significant resources and erode scientific trust. In this review, we summarize our collective academic, government, and industry experiences regarding cellular nuisance compounds. We describe assay design strategies to mitigate the impact of nuisance compounds and suggest best practices to efficiently address these compounds in complex biological settings.
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Affiliation(s)
- Jayme L Dahlin
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD 20850, USA.
| | - Douglas S Auld
- Novartis Institutes for Biomedical Research, Cambridge, MA 02139, USA
| | - Ina Rothenaigner
- Assay Development and Screening Platform, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany
| | - Steve Haney
- Indiana Biosciences Research Institute, Indianapolis, IN 46202, USA
| | - Jonathan Z Sexton
- Department of Internal Medicine, Gastroenterology, Michigan Medicine at the University of Michigan, Ann Arbor, MI 48109, USA
| | | | - Jarrod Walsh
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Alderley Park SK10 4TG, UK
| | | | | | | | - Lori Ferrins
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115, USA
| | - Jonathan B Baell
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC 3052, Australia; School of Pharmaceutical Sciences, Nanjing Tech University, Nanjing 211816, People's Republic of China
| | - Michael A Walters
- Institute for Therapeutics Discovery and Development, University of Minnesota, Minneapolis, MN 55414, USA
| | - Bruce K Hua
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA 02140, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02140, USA
| | - Kamyar Hadian
- Assay Development and Screening Platform, Helmholtz Zentrum Muenchen, 85764 Neuherberg, Germany
| | - Bridget K Wagner
- Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA 02140, USA
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16
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Ziegler S, Sievers S, Waldmann H. Morphological profiling of small molecules. Cell Chem Biol 2021; 28:300-319. [PMID: 33740434 DOI: 10.1016/j.chembiol.2021.02.012] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 01/22/2021] [Accepted: 02/17/2021] [Indexed: 12/30/2022]
Abstract
Profiling approaches such as gene expression or proteome profiling generate small-molecule bioactivity profiles that describe a perturbed cellular state in a rather unbiased manner and have become indispensable tools in the evaluation of bioactive small molecules. Automated imaging and image analysis can record morphological alterations that are induced by small molecules through the detection of hundreds of morphological features in high-throughput experiments. Thus, morphological profiling has gained growing attention in academia and the pharmaceutical industry as it enables detection of bioactivity in compound collections in a broader biological context in the early stages of compound development and the drug-discovery process. Profiling may be used successfully to predict mode of action or targets of unexplored compounds and to uncover unanticipated activity for already characterized small molecules. Here, we review the reported approaches to morphological profiling and the kind of bioactivity that can be detected so far and, thus, predicted.
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Affiliation(s)
- Slava Ziegler
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, 44227 Dortmund, Germany.
| | - Sonja Sievers
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, 44227 Dortmund, Germany
| | - Herbert Waldmann
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, 44227 Dortmund, Germany; Technical University Dortmund, Faculty of Chemistry and Chemical Biology, Otto-Hahn-Strasse 6, 44227 Dortmund, Germany.
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17
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Way GP, Kost-Alimova M, Shibue T, Harrington WF, Gill S, Piccioni F, Becker T, Shafqat-Abbasi H, Hahn WC, Carpenter AE, Vazquez F, Singh S. Predicting cell health phenotypes using image-based morphology profiling. Mol Biol Cell 2021; 32:995-1005. [PMID: 33534641 PMCID: PMC8108524 DOI: 10.1091/mbc.e20-12-0784] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Genetic and chemical perturbations impact diverse cellular phenotypes, including multiple indicators of cell health. These readouts reveal toxicity and antitumorigenic effects relevant to drug discovery and personalized medicine. We developed two customized microscopy assays, one using four targeted reagents and the other three targeted reagents, to collectively measure 70 specific cell health phenotypes including proliferation, apoptosis, reactive oxygen species, DNA damage, and cell cycle stage. We then tested an approach to predict multiple cell health phenotypes using Cell Painting, an inexpensive and scalable image-based morphology assay. In matched CRISPR perturbations of three cancer cell lines, we collected both Cell Painting and cell health data. We found that simple machine learning algorithms can predict many cell health readouts directly from Cell Painting images, at less than half the cost. We hypothesized that these models can be applied to accurately predict cell health assay outcomes for any future or existing Cell Painting dataset. For Cell Painting images from a set of 1500+ compound perturbations across multiple doses, we validated predictions by orthogonal assay readouts. We provide a web app to browse predictions: http://broad.io/cell-health-app. Our approach can be used to add cell health annotations to Cell Painting datasets.
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Affiliation(s)
| | | | | | | | - Stanley Gill
- Cancer Program, Cambridge, MA 02142.,Dana-Farber Cancer Institute, Department of Medical Oncology, Harvard Medical School, Boston, MA 02215
| | - Federica Piccioni
- Genetic Perturbation Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142
| | | | | | - William C Hahn
- Cancer Program, Cambridge, MA 02142.,Dana-Farber Cancer Institute, Department of Medical Oncology, Harvard Medical School, Boston, MA 02215
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18
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Chandrasekaran SN, Ceulemans H, Boyd JD, Carpenter AE. Image-based profiling for drug discovery: due for a machine-learning upgrade? Nat Rev Drug Discov 2021; 20:145-159. [PMID: 33353986 PMCID: PMC7754181 DOI: 10.1038/s41573-020-00117-w] [Citation(s) in RCA: 131] [Impact Index Per Article: 43.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2020] [Indexed: 12/20/2022]
Abstract
Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.
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Affiliation(s)
| | - Hugo Ceulemans
- Discovery Data Sciences, Janssen Pharmaceutica NV, Beerse, Belgium
| | - Justin D Boyd
- High Content Imaging Technology Center, Internal Medicine Research Unit, Pfizer Inc., Cambridge, MA, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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