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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] [What about the content of this article? (0)] [Affiliation(s)] [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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2
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Arevalo J, Su E, van Dijk R, Carpenter AE, Singh S. Evaluating batch correction methods for image-based cell profiling. bioRxiv 2024:2023.09.15.558001. [PMID: 37745478 PMCID: PMC10516049 DOI: 10.1101/2023.09.15.558001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
High-throughput image-based profiling platforms are powerful technologies capable of collecting data from billions of cells exposed to thousands of perturbations in a time- and cost-effective manner. Therefore, image-based profiling data has been increasingly used for diverse biological applications, such as predicting drug mechanism of action or gene function. However, batch effects pose severe limitations to community-wide efforts to integrate and interpret image-based profiling data collected across different laboratories and equipment. To address this problem, we benchmarked seven high-performing scRNA-seq batch correction techniques, representing diverse approaches, using a newly released Cell Painting dataset, the largest publicly accessible image-based dataset. We focused on five different scenarios with varying complexity, and we found that Harmony, a mixture-model based method, consistently outperformed the other tested methods. Our proposed framework, benchmark, and metrics can additionally be used to assess new batch correction methods in the future. Overall, this work paves the way for improvements that allow the community to make best use of public Cell Painting data for scientific discovery.
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Affiliation(s)
- John Arevalo
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Ellen Su
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Robert van Dijk
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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3
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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4
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Yu S, Kalinin AA, Paraskevopoulou MD, Maruggi M, Cheng J, Tang J, Icke I, Luo Y, Wei Q, Scheibe D, Hunter J, Singh S, Nguyen D, Carpenter AE, Horman SR. Integrating inflammatory biomarker analysis and artificial-intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis. Cell Chem Biol 2023; 30:1169-1182.e8. [PMID: 37437569 PMCID: PMC10529501 DOI: 10.1016/j.chembiol.2023.06.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 03/11/2023] [Accepted: 06/13/2023] [Indexed: 07/14/2023]
Abstract
Intestinal fibrosis, often caused by inflammatory bowel disease, can lead to intestinal stenosis and obstruction, but there are no approved treatments. Drug discovery has been hindered by the lack of screenable cellular phenotypes. To address this, we used a scalable image-based morphology assay called Cell Painting, augmented with machine learning algorithms, to identify small molecules that could reverse the activated fibrotic phenotype of intestinal myofibroblasts. We then conducted a high-throughput small molecule chemogenomics screen of approximately 5,000 compounds with known targets or mechanisms, which have achieved clinical stage or approval by the FDA. By integrating morphological analyses and AI using pathologically relevant cells and disease-relevant stimuli, we identified several compounds and target classes that are potentially able to treat intestinal fibrosis. This phenotypic screening platform offers significant improvements over conventional methods for identifying a wide range of drug targets.
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Affiliation(s)
- Shan Yu
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA.
| | | | | | - Marco Maruggi
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA
| | - Jie Cheng
- Takeda Development Center Americas, Inc., Cambridge, MA 02142, USA
| | - Jie Tang
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA
| | - Ilknur Icke
- Takeda Development Center Americas, Inc., Cambridge, MA 02142, USA
| | - Yi Luo
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA
| | - Qun Wei
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA
| | - Dan Scheibe
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA
| | - Joel Hunter
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA
| | - Shantanu Singh
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Deborah Nguyen
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA
| | | | - Shane R Horman
- Takeda Development Center Americas, Inc., San Diego, CA 92121, USA.
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5
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Seal S, Yang H, Trapotsi MA, Singh S, Carreras-Puigvert J, Spjuth O, Bender A. Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data. J Cheminform 2023; 15:56. [PMID: 37268960 DOI: 10.1186/s13321-023-00723-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/20/2023] [Indexed: 06/04/2023] Open
Abstract
The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the compounds in the test dataset to compounds in the training dataset. We applied these similarity-based merger models using logistic regression models on the predictions and similarities as features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute (where the required Cell Painting annotations were available). We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC > 0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrated that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and further expanded the applicability domain by better extrapolating to new structural and morphology spaces.
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Affiliation(s)
- Srijit Seal
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Hongbin Yang
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Maria-Anna Trapotsi
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Satvik Singh
- Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Cambridge, UK
| | - Jordi Carreras-Puigvert
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Andreas Bender
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
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Way GP, Natoli T, Adeboye A, Litichevskiy L, Yang A, Lu X, Caicedo JC, Cimini BA, Karhohs K, Logan DJ, Rohban MH, Kost-Alimova M, Hartland K, Bornholdt M, Chandrasekaran SN, Haghighi M, Weisbart E, Singh S, Subramanian A, Carpenter AE. Morphology and gene expression profiling provide complementary information for mapping cell state. Cell Syst 2022; 13:911-923.e9. [PMID: 36395727 PMCID: PMC10246468 DOI: 10.1016/j.cels.2022.10.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 05/12/2022] [Accepted: 09/28/2022] [Indexed: 01/26/2023]
Abstract
Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.
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Affiliation(s)
- Gregory P Way
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Ted Natoli
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Adeniyi Adeboye
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Lev Litichevskiy
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Andrew Yang
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xiaodong Lu
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Juan C Caicedo
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kyle Karhohs
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - David J Logan
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Mohammad H Rohban
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Maria Kost-Alimova
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kate Hartland
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Michael Bornholdt
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Marzieh Haghighi
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Aravind Subramanian
- Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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7
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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. Sci Total Environ 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Nyffeler J, Willis C, Harris FR, Taylor LW, Judson R, Everett LJ, Harrill JA. Combining phenotypic profiling and targeted RNA-Seq reveals linkages between transcriptional perturbations and chemical effects on cell morphology: Retinoic acid as an example. Toxicol Appl Pharmacol 2022; 444:116032. [PMID: 35483669 PMCID: PMC10894461 DOI: 10.1016/j.taap.2022.116032] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 10/18/2022]
Abstract
The United States Environmental Protection Agency has proposed a tiered testing strategy for chemical hazard evaluation based on new approach methods (NAMs). The first tier includes in vitro profiling assays applicable to many (human) cell types, such as high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). The goals of this study were to: (1) harmonize the seeding density of U-2 OS human osteosarcoma cells for use in both assays; (2) compare HTTr- versus HTPP-derived potency estimates for 11 mechanistically diverse chemicals; (3) identify candidate reference chemicals for monitoring assay performance in future screens; and (4) characterize the transcriptional and phenotypic changes in detail for all-trans retinoic acid (ATRA) as a model compound known for its adverse effects on osteoblast differentiation. The results of this evaluation showed that (1) HTPP conducted at low (400 cells/well) and high (3000 cells/well) seeding densities yielded comparable potency estimates and similar phenotypic profiles for the tested chemicals; (2) HTPP and HTTr resulted in comparable potency estimates for changes in cellular morphology and gene expression, respectively; (3) three test chemicals (etoposide, ATRA, dexamethasone) produced concentration-dependent effects on cellular morphology and gene expression that were consistent with known modes-of-action, demonstrating their suitability for use as reference chemicals for monitoring assay performance; and (4) ATRA produced phenotypic changes that were highly similar to other retinoic acid receptor activators (AM580, arotinoid acid) and some retinoid X receptor activators (bexarotene, methoprene acid). This phenotype was observed concurrently with autoregulation of the RARB gene. Both effects were prevented by pre-treating U-2 OS cells with pharmacological antagonists of their respective receptors. Thus, the observed phenotype could be considered characteristic of retinoic acid pathway activation in U-2 OS cells. These findings lay the groundwork for combinatorial screening of chemicals using HTTr and HTPP to generate complementary information for the first tier of a NAM-based chemical hazard evaluation strategy.
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Affiliation(s)
- Johanna Nyffeler
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Fellow, Oak Ridge, TN 37831, United States of America
| | - Clinton Willis
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Felix R Harris
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU) National Student Services Contractor, Oak Ridge, TN 37831, United States of America
| | - Laura W Taylor
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Richard Judson
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Logan J Everett
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Joshua A Harrill
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America.
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Palliyil Sreekumar S, Palanisamy R, Swaminathan R. Differentiation of Cell Painted Organelles Using Non Local Texture Descriptor and Random Forest Approach. Stud Health Technol Inform 2022; 294:925-929. [PMID: 35612244 DOI: 10.3233/shti220626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Discriminating the cell organelles from microscopic images is a challenging task due to their high similarity in image appearance. In this work, an attempt has been made to differentiate nuclei, Endoplasmic Reticulum (ER) and cytoplasm using a texture pattern descriptor and Random Forest classifier. For this, Cell Painted public dataset from Broad Bioimage Benchmark collection are considered. Texture features are extracted from each image using Non Local Binary Pattern (NLBP) that captures the relationship between global pixels and sampling instances in a local neighborhood. Non local central pixels called anchors are derived from central pixels of image patches and compared with sampling instances. Binary string generated from this is encoded into 29 patterns. Statistical one-way analysis of variance (ANOVA) is performed to select significant features and are validated using Random Forest classifier. The dependency of classifier performance on the local patch radius (R) and the number of anchors (K) are also evaluated. The results indicate that 8 patterns out of 29 are showing strong inter class variability with high F value. Classification accuracy of 84% is achieved with R=3 and K=5. Experimental results demonstrate that the proposed work captures complex patterns in cell structure useful for differentiating cell components which can be employed for evaluating the cytotoxic effects in cell lines.
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Affiliation(s)
- Sreelekshmi Palliyil Sreekumar
- Biomedical Engineering Group, Centre of Excellence in Medical Device Regulations and Standards, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Rohini Palanisamy
- Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, Tamil Nadu, India
| | - Ramakrishnan Swaminathan
- Biomedical Engineering Group, Centre of Excellence in Medical Device Regulations and Standards, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
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Rietdijk J, Tampere M, Pettke A, Georgiev P, Lapins M, Warpman-Berglund U, Spjuth O, Puumalainen MR, Carreras-Puigvert J. A phenomics approach for antiviral drug discovery. BMC Biol 2021; 19:156. [PMID: 34334126 PMCID: PMC8325993 DOI: 10.1186/s12915-021-01086-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 07/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections. RESULTS We combine Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. We show that this methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state. CONCLUSIONS The phenomics approach presented here, which makes use of a modified Cell Painting protocol by incorporating an anti-virus antibody stain, can be used for the unbiased morphological profiling of virus infection on host cells. The method can identify antiviral reference compounds, as well as novel antivirals, demonstrating its suitability to be implemented as a strategy for antiviral drug repurposing and drug discovery.
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Affiliation(s)
- Jonne Rietdijk
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-751 24, Uppsala, Sweden
| | - Marianna Tampere
- Department of Oncology and Pathology and Science for Life Laboratory, Karolinska Institutet, SE-171 76, Stockholm, Sweden
- National Veterinary Institute, SE-756 51, Uppsala, Sweden
| | - Aleksandra Pettke
- Department of Oncology and Pathology and Science for Life Laboratory, Karolinska Institutet, SE-171 76, Stockholm, Sweden
| | - Polina Georgiev
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-751 24, Uppsala, Sweden
| | - Maris Lapins
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-751 24, Uppsala, Sweden
| | - Ulrika Warpman-Berglund
- Department of Oncology and Pathology and Science for Life Laboratory, Karolinska Institutet, SE-171 76, Stockholm, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-751 24, Uppsala, Sweden
| | - Marjo-Riitta Puumalainen
- Department of Oncology and Pathology and Science for Life Laboratory, Karolinska Institutet, SE-171 76, Stockholm, Sweden
| | - Jordi Carreras-Puigvert
- Department of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box 591, SE-751 24, Uppsala, Sweden.
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11
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Nyffeler J, Haggard DE, Willis C, Setzer RW, Judson R, Paul-Friedman K, Everett LJ, Harrill JA. Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data. SLAS Discov 2020; 26:292-308. [PMID: 32862757 DOI: 10.1177/2472555220950245] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Phenotypic profiling assays are untargeted screening assays that measure a large number (hundreds to thousands) of cellular features in response to a stimulus and often yield diverse and unanticipated profiles of phenotypic effects, leading to challenges in distinguishing active from inactive treatments. Here, we compare a variety of different strategies for hit identification in imaging-based phenotypic profiling assays using a previously published Cell Painting data set. Hit identification strategies based on multiconcentration analysis involve curve fitting at several levels of data aggregation (e.g., individual feature level, aggregation of similarly derived features into categories, and global modeling of all features) and on computed metrics (e.g., Euclidean and Mahalanobis distance metrics and eigenfeatures). Hit identification strategies based on single-concentration analysis included measurement of signal strength (e.g., total effect magnitude) and correlation of profiles among biological replicates. Modeling parameters for each approach were optimized to retain the ability to detect a reference chemical with subtle phenotypic effects while limiting the false-positive rate to 10%. The percentage of test chemicals identified as hits was highest for feature-level and category-based approaches, followed by global fitting, whereas signal strength and profile correlation approaches detected the fewest number of active hits at the fixed false-positive rate. Approaches involving fitting of distance metrics had the lowest likelihood for identifying high-potency false-positive hits that may be associated with assay noise. Most of the methods achieved a 100% hit rate for the reference chemical and high concordance for 82% of test chemicals, indicating that hit calls are robust across different analysis approaches.
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Affiliation(s)
- Johanna Nyffeler
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Derik E Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, USA
| | - Clinton Willis
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA.,Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, USA
| | - R Woodrow Setzer
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Richard Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Katie Paul-Friedman
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Logan J Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
| | - Joshua A Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC, USA
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12
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Warchal SJ, Dawson JC, Shepherd E, Munro AF, Hughes RE, Makda A, Carragher NO. High content phenotypic screening identifies serotonin receptor modulators with selective activity upon breast cancer cell cycle and cytokine signaling pathways. Bioorg Med Chem 2019; 28:115209. [PMID: 31757681 PMCID: PMC6961118 DOI: 10.1016/j.bmc.2019.115209] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 10/31/2019] [Accepted: 11/04/2019] [Indexed: 02/08/2023]
Abstract
Heterogeneity in disease mechanisms between genetically distinct patients contributes to high attrition rates in late stage clinical drug development. New personalized medicine strategies aim to identify predictive biomarkers which stratify patients most likely to respond to a particular therapy. However, for complex multifactorial diseases not characterized by a single genetic driver, empirical approaches to identifying predictive biomarkers and the most promising therapies for personalized medicine are required. In vitro pharmacogenomics seeks to correlate in vitro drug sensitivity testing across panels of genetically distinct cell models with genomic, gene expression or proteomic data to identify predictive biomarkers of drug response. However, the vast majority of in vitro pharmacogenomic studies performed to date are limited to dose-response screening upon a single viability assay endpoint. In this article we describe the application of multiparametric high content phenotypic screening and the theta comparative cell scoring method to quantify and rank compound hits, screened at a single concentration, which induce a broad variety of divergent phenotypic responses between distinct breast cancer cell lines. High content screening followed by transcriptomic pathway analysis identified serotonin receptor modulators which display selective activity upon breast cancer cell cycle and cytokine signaling pathways correlating with inhibition of cell growth and survival. These methods describe a new evidence-led approach to rapidly identify compounds which display distinct response between different cell types. The results presented also warrant further investigation of the selective activity of serotonin receptor modulators upon breast cancer cell growth and survival as a potential drug repurposing opportunity.
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Affiliation(s)
- Scott J Warchal
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XR Edinburgh, United Kingdom
| | - John C Dawson
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XR Edinburgh, United Kingdom.
| | - Emelie Shepherd
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XR Edinburgh, United Kingdom
| | - Alison F Munro
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XR Edinburgh, United Kingdom
| | - Rebecca E Hughes
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XR Edinburgh, United Kingdom
| | - Ashraff Makda
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XR Edinburgh, United Kingdom
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, EH4 2XR Edinburgh, United Kingdom.
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