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Wang D, Chen Y, Li J, Wu E, Tang T, Singla RK, Shen B, Zhang M. Natural products for the treatment of age-related macular degeneration. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 130:155522. [PMID: 38820665 DOI: 10.1016/j.phymed.2024.155522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/08/2024] [Accepted: 03/07/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUND Age-related macular degeneration (AMD) is a chronic retinal disease that significantly influences the vision of the elderly. PURPOSE There is no effective treatment and prevention method. The pathogenic process behind AMD is complex, including oxidative stress, inflammation, and neovascularization. It has been demonstrated that several natural products can be used to manage AMD, but systematic summaries are lacking. STUDY DESIGN AND METHODS PubMed, Web of Science, and ClinicalTrials.gov were searched using the keywords "Biological Products" AND "Macular Degeneration" for studies published within the last decade until May 2023 to summarize the latest findings on the prevention and treatment of age-related macular degeneration through the herbal medicines and functional foods. RESULTS The eligible studies were screened, and the relevant information about the therapeutic action and mechanism of natural products used to treat AMD was extracted. Our findings demonstrate that natural substances, including retinol, phenols, and other natural products, prevent the development of new blood vessels and protect the retina from oxidative stress in cells and animal models. However, they have barely been examined in clinical studies. CONCLUSION Natural products could be highly prospective candidate drugs used to treat AMD, and further preclinical and clinical research is required to validate it to control the disease.
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Affiliation(s)
- Dongyue Wang
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China
| | - Yi Chen
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China
| | - Jiakun Li
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, PR China
| | - Erman Wu
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, PR China
| | - Tong Tang
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, PR China
| | - Rajeev K Singla
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, PR China; School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab-144411, India.
| | - Bairong Shen
- Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, PR China.
| | - Ming Zhang
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China.
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Shukla H, John D, Banerjee S, Tiwari AK. Drug repurposing for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:249-319. [PMID: 38942541 DOI: 10.1016/bs.pmbts.2024.03.035] [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: 06/30/2024]
Abstract
Neurodegenerative diseases (NDDs) are neuronal problems that include the brain and spinal cord and result in loss of sensory and motor dysfunction. Common NDDs include Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), Multiple Sclerosis (MS), and Amyotrophic Lateral Sclerosis (ALS) etc. The occurrence of these diseases increases with age and is one of the challenging problems among elderly people. Though, several scientific research has demonstrated the key pathologies associated with NDDs still the underlying mechanisms and molecular details are not well understood and need to be explored and this poses a lack of effective treatments for NDDs. Several lines of evidence have shown that NDDs have a high prevalence and affect more than a billion individuals globally but still, researchers need to work forward in identifying the best therapeutic target for NDDs. Thus, several researchers are working in the directions to find potential therapeutic targets to alter the disease pathology and treat the diseases. Several steps have been taken to identify the early detection of the disease and drug repurposing for effective treatment of NDDs. Moreover, it is logical that current medications are being evaluated for their efficacy in treating such disorders; therefore, drug repurposing would be an efficient, safe, and cost-effective way in finding out better medication. In the current manuscript we discussed the utilization of drugs that have been repurposed for the treatment of AD, PD, HD, MS, and ALS.
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Affiliation(s)
- Halak Shukla
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Diana John
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Shuvomoy Banerjee
- Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India
| | - Anand Krishna Tiwari
- Genetics and Developmental Biology Laboratory, Department of Biotechnology and Bioengineering, Institute of Advanced Research (IAR), Gandhinagar, Gujarat, India.
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3
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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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4
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Wang S, Oliveira-Silveira J, Fang G, Kang J. High-content analysis identified synergistic drug interactions between INK128, an mTOR inhibitor, and HDAC inhibitors in a non-small cell lung cancer cell line. BMC Cancer 2024; 24:335. [PMID: 38475728 DOI: 10.1186/s12885-024-12057-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 02/26/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND The development of drug resistance is a major cause of cancer therapy failures. To inhibit drug resistance, multiple drugs are often treated together as a combinatorial therapy. In particular, synergistic drug combinations, which kill cancer cells at a lower concentration, guarantee a better prognosis and fewer side effects in cancer patients. Many studies have sought out synergistic combinations by small-scale function-based targeted growth assays or large-scale nontargeted growth assays, but their discoveries are always challenging due to technical problems such as a large number of possible test combinations. METHODS To address this issue, we carried out a medium-scale optical drug synergy screening in a non-small cell lung cancer cell line and further investigated individual drug interactions in combination drug responses by high-content image analysis. Optical high-content analysis of cellular responses has recently attracted much interest in the field of drug discovery, functional genomics, and toxicology. Here, we adopted a similar approach to study combinatorial drug responses. RESULTS By examining all possible combinations of 12 drug compounds in 6 different drug classes, such as mTOR inhibitors, HDAC inhibitors, HSP90 inhibitors, MT inhibitors, DNA inhibitors, and proteasome inhibitors, we successfully identified synergism between INK128, an mTOR inhibitor, and HDAC inhibitors, which has also been reported elsewhere. Our high-content analysis further showed that HDAC inhibitors, HSP90 inhibitors, and proteasome inhibitors played a dominant role in combinatorial drug responses when they were mixed with MT inhibitors, DNA inhibitors, or mTOR inhibitors, suggesting that recessive drugs could be less prioritized as components of multidrug cocktails. CONCLUSIONS In conclusion, our optical drug screening platform efficiently identified synergistic drug combinations in a non-small cell lung cancer cell line, and our high-content analysis further revealed how individual drugs in the drug mix interact with each other to generate combinatorial drug response.
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Affiliation(s)
- Sijiao Wang
- School of Chemistry and Molecular Engineering at East China Normal University, Shanghai, 200062, China
| | - Juliano Oliveira-Silveira
- Center of Biotechnology, PPGBCM, Federal University of Rio Grande Do Sul (UFRGS), Porto Alegre, Rio Grande Do Sul, 91501970, Brazil
| | - Gang Fang
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China
- Arts and Science, New York University at Shanghai, Shanghai, 200122, China
| | - Jungseog Kang
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai, 200062, China.
- Arts and Science, New York University at Shanghai, Shanghai, 200122, China.
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5
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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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6
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Postma RJ, Broekhoven AG, Verspaget HW, de Boer H, Hankemeier T, Coenraad MJ, van Duinen V, van Zonneveld AJ. Novel Morphological Profiling Assay Connects ex Vivo Endothelial Cell Responses to Disease Severity in Liver Cirrhosis. GASTRO HEP ADVANCES 2023; 3:238-249. [PMID: 39129954 PMCID: PMC11307659 DOI: 10.1016/j.gastha.2023.10.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/16/2023] [Indexed: 08/13/2024]
Abstract
Background and Aims Endothelial cell (EC) dysfunction in response to circulating plasma factors is a known causal factor in many systemic diseases. However, no appropriate assay is available to investigate this causality ex vivo. In liver cirrhosis, systemic inflammation is identified as central mechanism in progression from compensated to decompensated cirrhosis (DC), but the role of ECs therein is unknown. We aimed to develop a novel ex vivo assay for assessing EC responses to patient-derived plasma (PDP) and assess the potential of this assay in a cohort of liver cirrhosis patients. Methods Image-based morphological profiling was utilized to assess the impact of PDP on cultured ECs. Endothelial cell (EC) monolayers were exposed to 25% stabilized PDP (20 compensated cirrhoses, 20 DCs, and 20 healthy controls (HCs). Single-cell morphological profiles were extracted by automated image-analysis following staining of multiple cellular components and high-content imaging. Patient profiles were created by dimension reduction and cell-to-patient data aggregation, followed by multivariate-analysis to stratify patients and identify discriminating features. Results Patient-derived plasma (PDP) exposure induced profound changes in EC morphology, displaying clear differences between controls and DC patients. Compensated cirrhosis patients showed overlap with healthy controls and DC patients. Supervised analysis showed Child-Pugh (CP) class could be predicted from EC morphology. Most importantly, CP-C patients displayed distinct EC phenotypes, in which mitochondrial changes were most discriminative. Conclusion Morphological profiling presents a viable tool to assess the endothelium ex vivo. We demonstrated that the EC phenotype corresponds with disease severity in liver cirrhosis. Moreover, our results suggest the presence of mitochondrial dysfunction in ECs of CP-C patient.
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Affiliation(s)
- Rudmer J. Postma
- Department of Internal Medicine (Nephrology) and the Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Annelotte G.C. Broekhoven
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hein W. Verspaget
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Hetty de Boer
- Department of Internal Medicine (Nephrology) and the Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Thomas Hankemeier
- Department of Analytical BioSciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Minneke J. Coenraad
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Vincent van Duinen
- Department of Internal Medicine (Nephrology) and the Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
- MIMETAS B.V., Oegstgeest, The Netherlands
| | - Anton Jan van Zonneveld
- Department of Internal Medicine (Nephrology) and the Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
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7
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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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8
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Hughes BK, Wallis R, Bishop CL. Yearning for machine learning: applications for the classification and characterisation of senescence. Cell Tissue Res 2023; 394:1-16. [PMID: 37016180 PMCID: PMC10558380 DOI: 10.1007/s00441-023-03768-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/05/2023] [Indexed: 04/06/2023]
Abstract
Senescence is a widely appreciated tumour suppressive mechanism, which acts as a barrier to cancer development by arresting cell cycle progression in response to harmful stimuli. However, senescent cell accumulation becomes deleterious in aging and contributes to a wide range of age-related pathologies. Furthermore, senescence has beneficial roles and is associated with a growing list of normal physiological processes including wound healing and embryonic development. Therefore, the biological role of senescent cells has become increasingly nuanced and complex. The emergence of sophisticated, next-generation profiling technologies, such as single-cell RNA sequencing, has accelerated our understanding of the heterogeneity of senescence, with distinct final cell states emerging within models as well as between cell types and tissues. In order to explore data sets of increasing size and complexity, the senescence field has begun to employ machine learning (ML) methodologies to probe these intricacies. Most notably, ML has been used to aid the classification of cells as senescent, as well as to characterise the final senescence phenotypes. Here, we provide a background to the principles of ML tasks, as well as some of the most commonly used methodologies from both traditional and deep ML. We focus on the application of these within the context of senescence research, by addressing the utility of ML for the analysis of data from different laboratory technologies (microscopy, transcriptomics, proteomics, methylomics), as well as the potential within senolytic drug discovery. Together, we aim to highlight both the progress and potential for the application of ML within senescence research.
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Affiliation(s)
- Bethany K Hughes
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Ryan Wallis
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK
| | - Cleo L Bishop
- Blizard Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, E1 2AT, UK.
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9
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Heinrich L, Kumbier K, Li L, Altschuler SJ, Wu LF. Selection of Optimal Cell Lines for High-Content Phenotypic Screening. ACS Chem Biol 2023; 18:679-685. [PMID: 36920184 PMCID: PMC10127200 DOI: 10.1021/acschembio.2c00878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/07/2023] [Indexed: 03/16/2023]
Abstract
High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity ("phenoactivity") and grouping compounds with similar MOA by similar phenotype ("phenosimilarity"). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g., detecting phenoactivity vs inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and a set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.
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Affiliation(s)
- Louise Heinrich
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Fancisco, California 94158, United States
| | - Karl Kumbier
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Fancisco, California 94158, United States
| | - Li Li
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Fancisco, California 94158, United States
| | - Steven J. Altschuler
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Fancisco, California 94158, United States
| | - Lani F. Wu
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Fancisco, California 94158, United States
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10
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Pruteanu LL, Bender A. Using Transcriptomics and Cell Morphology Data in Drug Discovery: The Long Road to Practice. ACS Med Chem Lett 2023; 14:386-395. [PMID: 37077392 PMCID: PMC10107910 DOI: 10.1021/acsmedchemlett.3c00015] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/10/2023] [Indexed: 04/21/2023] Open
Abstract
Gene expression and cell morphology data are high-dimensional biological readouts of much recent interest for drug discovery. They are able to describe biological systems in different states (e.g., healthy and diseased), as well as biological systems before and after compound treatment, and they are hence useful for matching both spaces (e.g., for drug repurposing) as well as for characterizing compounds with respect to efficacy and safety endpoints. This Microperspective describes recent advances in this direction with a focus on applied drug discovery and drug repurposing, as well as outlining what else is needed to advance further, with a particular focus on better understanding the applicability domain of readouts and their relevance for decision making, which is currently often still unclear.
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Affiliation(s)
- Lavinia-Lorena Pruteanu
- Department
of Chemistry and Biology, North University
Center at Baia Mare, Technical University of Cluj-Napoca, Victoriei 76, 430122 Baia Mare, Romania
- Research
Center for Functional Genomics, Biomedicine, and Translational Medicine, “Iuliu Haţieganu” University
of Medicine and Pharmacy, 400337 Cluj-Napoca, Romania
| | - Andreas Bender
- Centre
for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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11
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Nyffeler J, Willis C, Harris FR, Foster MJ, Chambers B, Culbreth M, Brockway RE, Davidson-Fritz S, Dawson D, Shah I, Friedman KP, Chang D, Everett LJ, Wambaugh JF, Patlewicz G, Harrill JA. Application of cell painting for chemical hazard evaluation in support of screening-level chemical assessments. Toxicol Appl Pharmacol 2023; 468:116513. [PMID: 37044265 DOI: 10.1016/j.taap.2023.116513] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 04/03/2023] [Accepted: 04/08/2023] [Indexed: 04/14/2023]
Abstract
'Cell Painting' is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ~100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.
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Affiliation(s)
- Jo 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
| | - M J Foster
- 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
| | - Bryant Chambers
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Megan Culbreth
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Richard E Brockway
- 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
| | - Sarah Davidson-Fritz
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Daniel Dawson
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Imran Shah
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Katie Paul Friedman
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Dan Chang
- 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
| | - John F Wambaugh
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Grace Patlewicz
- 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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12
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Huang B, Wu Y, Li C, Tang Q, Zhang Y. Molecular basis and mechanism of action of Albizia julibrissin in depression treatment and clinical application of its formulae. CHINESE HERBAL MEDICINES 2023; 15:201-213. [PMID: 37265761 PMCID: PMC10230641 DOI: 10.1016/j.chmed.2022.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 07/21/2022] [Accepted: 10/11/2022] [Indexed: 03/17/2023] Open
Abstract
Albizzia julibrissin is empirically used as an antidepressant in clinical practice. Preclinical studies have indicated that its total extracts or bioactive constituents exerted antidepressant-like responses in animal models, providing the molecular basis to reveal its underlying mechanism of action. While attempts have been made to understand the antidepressant effect of A. julibrissin, many fundamental questions regarding its mechanism of action remain to be addressed at the molecular and systems levels. In this review, we conclusively discussed the mechanism of action of A. julibrissin and A. julibrissin formulae by reviewing recent preclinical and clinical studies conducted by using depressive animal models and depressive patients. Several representative bioactive constituents and formulae were highlighted as examples, and their mechanisms of action were discussed. In addition, some representative A. julibrissin formulae that have been shown to be compatible with conventional antidepressants in clinical practice were also reviewed. Furthermore, we discussed the future research directions to reveal the underlying mechanism of A. julibrissin at the molecular and systems levels in depression treatment. The integrated study using both the molecular and systematic approaches is required not only for improving our understanding of its molecular basis and mechanisms of action, but also for providing a way to discover novel agents or approaches for the effective and systematic treatment of depression.
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Affiliation(s)
- Bishan Huang
- School of Life Sciences, Guangzhou University, Guangzhou 510006, China
| | - Yingyao Wu
- School of Life Sciences, Guangzhou University, Guangzhou 510006, China
| | - Chan Li
- School of Life Sciences, Guangzhou University, Guangzhou 510006, China
| | - Qingfa Tang
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou 510515, China
| | - Yuanwei Zhang
- School of Life Sciences, Guangzhou University, Guangzhou 510006, China
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13
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Zhang D, Shimokawa T, Guo Q, Dan S, Miki Y, Sunada S. Discovery of novel DNA-damaging agents through phenotypic screening for DNA double-strand break. Cancer Sci 2023; 114:1108-1117. [PMID: 36385507 PMCID: PMC9986057 DOI: 10.1111/cas.15659] [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: 09/12/2022] [Revised: 11/07/2022] [Accepted: 11/10/2022] [Indexed: 11/18/2022] Open
Abstract
DNA double-strand breaks (DSBs) seriously damage DNA and promote genomic instability that can lead to cell death. They are the source of conditions such as carcinogenesis and aging, but also have important applications in cancer therapy. Therefore, rapid detection and quantification of DSBs in cells are necessary for identifying carcinogenic and anticancer factors. In this study, we detected DSBs using a flow cytometry-based high-throughput method to analyze γH2AX intensity. We screened a chemical library containing 9600 compounds and detected multiple DNA-damaging compounds, although we could not identify mechanisms of action through this procedure. Thus, we also profiled a representative compound with the highest DSB potential, DNA-damaging agent-1 (DDA-1), using a bioinformatics-based method we termed "molecular profiling." Prediction and verification analysis revealed DDA-1 as a potential inhibitor of topoisomerase IIα, different from known inhibitors such as etoposide and doxorubicin. Additional investigation of DDA-1 analogs and xenograft models suggested that DDA-1 is a potential anticancer drug. In conclusion, our findings established that combining high-throughput DSB detection and molecular profiling to undertake phenotypic analysis is a viable method for efficient identification of novel DNA-damaging compounds for clinical applications.
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Affiliation(s)
- Doudou Zhang
- Department of Molecular Genetics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Takashi Shimokawa
- Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Qianqian Guo
- Department of Molecular Genetics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.,Department of Oncology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shingo Dan
- Division of Molecular Pharmacology, Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Yoshio Miki
- Department of Molecular Genetics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Shigeaki Sunada
- Department of Molecular Genetics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan.,Department of Oncology, Juntendo University School of Medicine, Tokyo, Japan.,Juntendo Advanced Research Institute for Health Science, Juntendo University, Tokyo, Japan
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14
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Zhu Y, Yang H, Han L, Mervin LH, Hosseini-Gerami L, Li P, Wright P, Trapotsi MA, Liu K, Fan TP, Bender A. In silico prediction and biological assessment of novel angiogenesis modulators from traditional Chinese medicine. Front Pharmacol 2023; 14:1116081. [PMID: 36817116 PMCID: PMC9937659 DOI: 10.3389/fphar.2023.1116081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/20/2023] [Indexed: 02/05/2023] Open
Abstract
Uncontrolled angiogenesis is a common denominator underlying many deadly and debilitating diseases such as myocardial infarction, chronic wounds, cancer, and age-related macular degeneration. As the current range of FDA-approved angiogenesis-based medicines are far from meeting clinical demands, the vast reserve of natural products from traditional Chinese medicine (TCM) offers an alternative source for developing pro-angiogenic or anti-angiogenic modulators. Here, we investigated 100 traditional Chinese medicine-derived individual metabolites which had reported gene expression in MCF7 cell lines in the Gene Expression Omnibus (GSE85871). We extracted literature angiogenic activities for 51 individual metabolites, and subsequently analysed their predicted targets and differentially expressed genes to understand their mechanisms of action. The angiogenesis phenotype was used to generate decision trees for rationalising the poly-pharmacology of known angiogenesis modulators such as ferulic acid and curculigoside and validated by an in vitro endothelial tube formation assay and a zebrafish model of angiogenesis. Moreover, using an in silico model we prospectively examined the angiogenesis-modulating activities of the remaining 49 individual metabolites. In vitro, tetrahydropalmatine and 1 beta-hydroxyalantolactone stimulated, while cinobufotalin and isoalantolactone inhibited endothelial tube formation. In vivo, ginsenosides Rb3 and Rc, 1 beta-hydroxyalantolactone and surprisingly cinobufotalin, restored angiogenesis against PTK787-induced impairment in zebrafish. In the absence of PTK787, deoxycholic acid and ursodeoxycholic acid did not affect angiogenesis. Despite some limitations, these results suggest further refinements of in silico prediction combined with biological assessment will be a valuable platform for accelerating the research and development of natural products from traditional Chinese medicine and understanding their mechanisms of action, and also for other traditional medicines for the prevention and treatment of angiogenic diseases.
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Affiliation(s)
- Yingli Zhu
- Department of Clinical Chinese Pharmacy, School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing, China,Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom,Department of Pharmacology, University of Cambridge, Cambridge, United Kingdom
| | - Hongbin Yang
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Liwen Han
- Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening of Shandong Province, Biology Institute, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China,School of Pharmacy and Pharmaceutical Science, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - Lewis H. Mervin
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Layla Hosseini-Gerami
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Peihai Li
- Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening of Shandong Province, Biology Institute, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Peter Wright
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Maria-Anna Trapotsi
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Kechun Liu
- Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening of Shandong Province, Biology Institute, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Tai-Ping Fan
- Department of Pharmacology, University of Cambridge, Cambridge, United Kingdom,*Correspondence: Tai-Ping Fan, ; Andreas Bender,
| | - Andreas Bender
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom,*Correspondence: Tai-Ping Fan, ; Andreas Bender,
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15
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Heinrich L, Kumbier K, Li L, Altschuler SP, Wu LF. Selection of optimal cell lines for high-content phenotypic screening. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.11.523662. [PMID: 36711978 PMCID: PMC9882115 DOI: 10.1101/2023.01.11.523662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity ("phenoactivity") and grouping compounds with similar MOA by similar phenotype ("phenosimilarity"). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g. detecting phenoactivity vs. inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.
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Affiliation(s)
- Louise Heinrich
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Fancisco, California, 94158, United States
| | - Karl Kumbier
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Fancisco, California, 94158, United States
| | - Li Li
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Fancisco, California, 94158, United States
| | - Steven P Altschuler
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Fancisco, California, 94158, United States
| | - Lani F Wu
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Fancisco, California, 94158, United States
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16
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Pearson YE, Kremb S, Butterfoss GL, Xie X, Fahs H, Gunsalus KC. A statistical framework for high-content phenotypic profiling using cellular feature distributions. Commun Biol 2022; 5:1409. [PMID: 36550289 PMCID: PMC9780213 DOI: 10.1038/s42003-022-04343-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 12/05/2022] [Indexed: 12/24/2022] Open
Abstract
High-content screening (HCS) uses microscopy images to generate phenotypic profiles of cell morphological data in high-dimensional feature space. While HCS provides detailed cytological information at single-cell resolution, these complex datasets are usually aggregated into summary statistics that do not leverage patterns of biological variability within cell populations. Here we present a broad-spectrum HCS analysis system that measures image-based cell features from 10 cellular compartments across multiple assay panels. We introduce quality control measures and statistical strategies to streamline and harmonize the data analysis workflow, including positional and plate effect detection, biological replicates analysis and feature reduction. We also demonstrate that the Wasserstein distance metric is superior over other measures to detect differences between cell feature distributions. With this workflow, we define per-dose phenotypic fingerprints for 65 mechanistically diverse compounds, provide phenotypic path visualizations for each compound and classify compounds into different activity groups.
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Affiliation(s)
- Yanthe E. Pearson
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Stephan Kremb
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Glenn L. Butterfoss
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Xin Xie
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Hala Fahs
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE
| | - Kristin C. Gunsalus
- grid.440573.10000 0004 1755 5934Center for Genomics and Systems Biology, New York University Abu Dhabi, P. O. Box 129188, Abu Dhabi, UAE ,grid.137628.90000 0004 1936 8753Department of Biology and Center for Genomics and Systems Biology, New York University, New York, NY 10003 USA
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17
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Severin Y, Hale BD, Mena J, Goslings D, Frey BM, Snijder B. Multiplexed high-throughput immune cell imaging reveals molecular health-associated phenotypes. SCIENCE ADVANCES 2022; 8:eabn5631. [PMID: 36322666 PMCID: PMC9629716 DOI: 10.1126/sciadv.abn5631] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Phenotypic plasticity is essential to the immune system, yet the factors that shape it are not fully understood. Here, we comprehensively analyze immune cell phenotypes including morphology across human cohorts by single-round multiplexed immunofluorescence, automated microscopy, and deep learning. Using the uncertainty of convolutional neural networks to cluster the phenotypes of eight distinct immune cell subsets, we find that the resulting maps are influenced by donor age, gender, and blood pressure, revealing distinct polarization and activation-associated phenotypes across immune cell classes. We further associate T cell morphology to transcriptional state based on their joint donor variability and validate an inflammation-associated polarized T cell morphology and an age-associated loss of mitochondria in CD4+ T cells. Together, we show that immune cell phenotypes reflect both molecular and personal health information, opening new perspectives into the deep immune phenotyping of individual people in health and disease.
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Affiliation(s)
- Yannik Severin
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
| | - Benjamin D. Hale
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
| | - Julien Mena
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
| | - David Goslings
- Blood Transfusion Service Zürich, SRC, 8952 Schlieren, Switzerland
| | - Beat M. Frey
- Blood Transfusion Service Zürich, SRC, 8952 Schlieren, Switzerland
| | - Berend Snijder
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, 8049 Zürich, Switzerland
- Corresponding author.
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18
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Mizuno T. [Development of Decomposition Approach for Comprehensive Understanding of Drug Effects]. YAKUGAKU ZASSHI 2022; 142:1077-1082. [PMID: 36184442 DOI: 10.1248/yakushi.22-00123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
As the term polypharmacology suggests, there are multiple actions of small-molecule compounds. We proposed a decomposition and understanding concept that sheds light on the small effects in comparison to the large effects by decomposing these multiple effects. This concept was embodied by describing the effects of the compounds in a transcriptome profile, followed by factor analysis to extract latent variables as decomposed effects. Application of this approach to public datasets resulted in the inferences of compound effects consistent with existing knowledge such as gene ontologies and pathways. In one experimental validation, the potential inducibility of endoplasmic reticulum stress of several commercial drugs was detected by decomposition. Another study successfully discriminated the effects of a natural product and its derivatives despite their structural similarity. In the era of big data, it is important to infer conceptual elements composed of measurable elements as a higher layer than the given data of a specimen, which can expand our perception and understanding of the specimen. This review introduces an example of such a philosophy by applying it to the multiple effects of drugs to contribute to the understanding.
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Affiliation(s)
- Tadahaya Mizuno
- Graduate School of Pharmaceutical Sciences, The University of Tokyo
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19
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Orthogonally-tunable and ER-targeting fluorophores detect avian influenza virus early infection. Nat Commun 2022; 13:5841. [PMID: 36192426 PMCID: PMC9529605 DOI: 10.1038/s41467-022-33586-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 09/23/2022] [Indexed: 11/08/2022] Open
Abstract
Cell-based assays can monitor virus infection at a single-cell level with high sensitivity and cost-efficiency. For this purpose, it is crucial to develop molecular probes that respond selectively to physiological changes in live cells. We report stimuli-responsive light-emitters built on a T-shaped benzimidazole platform, and consecutive borylation reactions to produce a library of homologs displaying systematic changes in fluorescence quantum yield and environmental sensitivity. We find that certain fluorophores localize selectively at the endoplasmic reticulum, and interact with proteins involved in the stress signaling pathways. Notably, the mono-borylated compound responds selectively to the stress conditions by enhancing fluorescence, and detects avian influenza virus infection at the single-cell level. Our findings demonstrate the unprecedented practical utility of the stress-responsive molecular probes to differentiate cellular states for early diagnosis. Methods to detect and distinguish the early stage of viral infection often involve complicated and time-consuming protocols. Here, the authors disclose a class of fluorescent molecules that enable fast detection of avian influenza virus infection by selectively localizing at the endoplasmic reticulum in the cell.
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20
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Moreno-Andrés D, Bhattacharyya A, Scheufen A, Stegmaier J. LiveCellMiner: A new tool to analyze mitotic progression. PLoS One 2022; 17:e0270923. [PMID: 35797385 PMCID: PMC9262191 DOI: 10.1371/journal.pone.0270923] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/17/2022] [Indexed: 11/21/2022] Open
Abstract
Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.
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Affiliation(s)
- Daniel Moreno-Andrés
- Institute of Biochemistry and Molecular Cell Biology, Medical School, RWTH Aachen University, Aachen, Germany
- * E-mail: (DMA), (JS)
| | - Anuk Bhattacharyya
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - Anja Scheufen
- Institute of Biochemistry and Molecular Cell Biology, Medical School, RWTH Aachen University, Aachen, Germany
| | - Johannes Stegmaier
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
- * E-mail: (DMA), (JS)
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21
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Trapotsi MA, Mouchet E, Williams G, Monteverde T, Juhani K, Turkki R, Miljković F, Martinsson A, Mervin L, Pryde KR, Müllers E, Barrett I, Engkvist O, Bender A, Moreau K. Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature. ACS Chem Biol 2022; 17:1733-1744. [PMID: 35793809 PMCID: PMC9295119 DOI: 10.1021/acschembio.2c00076] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
PROteolysis TArgeting Chimeras (PROTACs) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances made in targeted protein degradation technology have been remarkable, with several molecules having moved into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step toward delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high-content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design.
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Affiliation(s)
- Maria-Anna Trapotsi
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.,Data Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Elizabeth Mouchet
- High Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K
| | - Guy Williams
- High Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K
| | - Tiziana Monteverde
- High Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K
| | - Karolina Juhani
- High Throughput Screening, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Macclesfield SK10 4TF, U.K
| | - Riku Turkki
- Data Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Filip Miljković
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Anton Martinsson
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Lewis Mervin
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Kenneth R Pryde
- Oncology Safety, Clinical Pharmacology and Safety Sciences R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Erik Müllers
- Bioscience Cardiovascular, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Ian Barrett
- Data Sciences & Quantitative Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge CB2 0AA, U.K
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Kevin Moreau
- Safety Innovation, Clinical Pharmacology and Safety Sciences R&D, AstraZeneca, Cambridge CB2 0AA, U.K
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22
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Wallis R, Milligan D, Hughes B, Mizen H, López-Domínguez JA, Eduputa U, Tyler EJ, Serrano M, Bishop CL. Senescence-associated morphological profiles (SAMPs): an image-based phenotypic profiling method for evaluating the inter and intra model heterogeneity of senescence. Aging (Albany NY) 2022; 14:4220-4246. [PMID: 35580013 PMCID: PMC9186762 DOI: 10.18632/aging.204072] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 04/22/2022] [Indexed: 01/10/2023]
Abstract
Senescence occurs in response to a number of damaging stimuli to limit oncogenic transformation and cancer development. As no single, universal senescence marker has been discovered, the confident classification of senescence induction requires the parallel assessment of a series of hallmarks. Therefore, there is a growing need for “first-pass” tools of senescence identification to streamline experimental workflows and complement conventional markers. Here, we utilise a high content, multidimensional phenotypic profiling-based approach, to assess the morphological profiles of senescent cells induced via a range of stimuli. In the context of senescence, we refer to these as senescence-associated morphological profiles (SAMPs), as they facilitate distinction between senescent and proliferating cells. The complexity of the profiles generated also allows exploration of the heterogeneity both between models of senescence and within an individual senescence model, providing a level of insight at the single cell level. Furthermore, we also demonstrate that these models are applicable to the assessment of senescence in vivo, which remains a key challenge for the field. Therefore, we believe SAMPs has the potential to serve as a useful addition in the repertoire of senescence researchers, either as a first-pass tool or as part of the established senescence hallmarks.
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Affiliation(s)
- Ryan Wallis
- Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Deborah Milligan
- Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Bethany Hughes
- Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Hannah Mizen
- Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - José Alberto López-Domínguez
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Ugochim Eduputa
- Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Eleanor J Tyler
- Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Manuel Serrano
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Cleo L Bishop
- Blizard Institute of Cell and Molecular Science, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
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23
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Manuela J, David ZJ, Nicole S, Nicole C, Paul B, Erich K, Lisa SP, Claudia M, Marcel L, Stefan K. Optimization of the TeraTox assay for preclinical teratogenicity assessment. Toxicol Sci 2022; 188:17-33. [PMID: 35485993 PMCID: PMC9237991 DOI: 10.1093/toxsci/kfac046] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Current animal-free methods to assess teratogenicity of drugs under development still deliver high numbers of false negatives. To improve the sensitivity of human teratogenicity prediction, we characterized the TeraTox test, a newly developed multilineage differentiation assay using 3D human-induced pluripotent stem cells. TeraTox produces primary output concentration-dependent cytotoxicity and altered gene expression induced by each test compound. These data are fed into an interpretable machine-learning model to perform prediction, which relates to the concentration-dependent human teratogenicity potential of drug candidates. We applied TeraTox to profile 33 approved pharmaceuticals and 12 proprietary drug candidates with known in vivo data. Comparing TeraTox predictions with known human or animal toxicity, we report an accuracy of 69% (specificity: 53%, sensitivity: 79%). TeraTox performed better than 2 quantitative structure-activity relationship models and had a higher sensitivity than the murine embryonic stem cell test (accuracy: 58%, specificity: 76%, and sensitivity: 46%) run in the same laboratory. The overall prediction accuracy could be further improved by combining TeraTox and mouse embryonic stem cell test results. Furthermore, patterns of altered gene expression revealed by TeraTox may help grouping toxicologically similar compounds and possibly deducing common modes of action. The TeraTox assay and the dataset described here therefore represent a new tool and a valuable resource for drug teratogenicity assessment.
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Affiliation(s)
- Jaklin Manuela
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche, Switzerland.,Department for In Vitro Toxicology and Biomedicine Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Germany
| | - Zhang Jitao David
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche, Switzerland
| | - Schäfer Nicole
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche, Switzerland
| | - Clemann Nicole
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche, Switzerland
| | - Barrow Paul
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche, Switzerland
| | - Küng Erich
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche, Switzerland
| | - Sach-Peltason Lisa
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche, Switzerland
| | | | - Leist Marcel
- Department for In Vitro Toxicology and Biomedicine Inaugurated by the Doerenkamp-Zbinden Foundation, University of Konstanz, Germany
| | - Kustermann Stefan
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche, Switzerland
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24
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Alqahtani A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:6201067. [PMID: 35509623 PMCID: PMC9060979 DOI: 10.1155/2022/6201067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/17/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022]
Abstract
Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.
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Affiliation(s)
- Amal Alqahtani
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, 31541, Saudi Arabia
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
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25
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Ghanegolmohammadi F, Ohnuki S, Ohya Y. Assignment of unimodal probability distribution models for quantitative morphological phenotyping. BMC Biol 2022; 20:81. [PMID: 35361198 PMCID: PMC8969357 DOI: 10.1186/s12915-022-01283-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 03/17/2022] [Indexed: 01/02/2023] Open
Abstract
Background Cell morphology is a complex and integrative readout, and therefore, an attractive measurement for assessing the effects of genetic and chemical perturbations to cells. Microscopic images provide rich information on cell morphology; therefore, subjective morphological features are frequently extracted from digital images. However, measured datasets are fundamentally noisy; thus, estimation of the true values is an ultimate goal in quantitative morphological phenotyping. Ideal image analyses require precision, such as proper probability distribution analyses to detect subtle morphological changes, recall to minimize artifacts due to experimental error, and reproducibility to confirm the results. Results Here, we present UNIMO (UNImodal MOrphological data), a reliable pipeline for precise detection of subtle morphological changes by assigning unimodal probability distributions to morphological features of the budding yeast cells. By defining the data type, followed by validation using the model selection method, examination of 33 probability distributions revealed nine best-fitting probability distributions. The modality of the distribution was then clarified for each morphological feature using a probabilistic mixture model. Using a reliable and detailed set of experimental log data of wild-type morphological replicates, we considered the effects of confounding factors. As a result, most of the yeast morphological parameters exhibited unimodal distributions that can be used as basic tools for powerful downstream parametric analyses. The power of the proposed pipeline was confirmed by reanalyzing morphological changes in non-essential yeast mutants and detecting 1284 more mutants with morphological defects compared with a conventional approach (Box–Cox transformation). Furthermore, the combined use of canonical correlation analysis permitted global views on the cellular network as well as new insights into possible gene functions. Conclusions Based on statistical principles, we showed that UNIMO offers better predictions of the true values of morphological measurements. We also demonstrated how these concepts can provide biologically important information. This study draws attention to the necessity of employing a proper approach to do more with less. Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01283-6.
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Affiliation(s)
- Farzan Ghanegolmohammadi
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan.,Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Bldg. FSB-101, 5-1-5 Kashiwanoha, Kashiwa, Chiba Prefecture, 277-8562, Japan. .,Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan.
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26
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Coram MA, Wang L, Godinez WJ, Barkan DT, Armstrong Z, Ando DM, Feng BY. Morphological Characterization of Antibiotic Combinations. ACS Infect Dis 2022; 8:66-77. [PMID: 34937332 DOI: 10.1021/acsinfecdis.1c00312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Combination therapies are common in many therapeutic contexts, including infectious diseases and cancer. A common approach for evaluating combinations in vitro is to assess effects on cell growth as synergistic, antagonistic, or neutral using "checkerboard" experiments to systematically sample combinations of agents in multiple doses. To further understand the effects of antibiotic combinations, we employed high-content imaging to study the morphological changes caused by combination treatments in checkerboard experiments. Using an automated, unsupervised image analysis approach to group morphologies, and an "expert-in-the-loop" to annotate them, we attributed the heterogeneous morphological changes ofEscherichia coli cells to varying doses of both single-agent and combination treatments. We identified patterns of morphological change, including morphological potentiation, competition, and the emergence of unexpected morphologies. We found these frequently did not correlate with synergistic or antagonistic effects on viability, suggesting morphological approaches may provide a distinctive signature of the biological interaction between compounds over a range of conditions. Among the unexpected morphologies we observed, there were transitional changes associated with intermediate doses of compounds and uncharacterized phenotypes associated with well-studied antibiotics. Our approach exemplifies how unsupervised image analysis and expert knowledge can be combined to reckon with complex phenotypic changes arising from combination screening, dose titrations, or polypharmacology. In this way, quantification of morphological diversity across treatment conditions could aid in analysis and prioritization of complementary pairings of antibiotic agents by more closely capturing the true signature of the integrated cellular response.
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Affiliation(s)
- Marc A. Coram
- Google Research Applied Science, Mountain View, California 94043, United States
| | - Lisha Wang
- Infectious Diseases, Novartis Institutes for BioMedical Research, Inc., Emeryville, California 94608, United States
| | - William J. Godinez
- Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Inc., Emeryville, California 94608, United States
| | - David T. Barkan
- Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Inc., Emeryville, California 94608, United States
| | - Zan Armstrong
- Google Research Applied Science, Mountain View, California 94043, United States
| | - D. Michael Ando
- Google Research Applied Science, Mountain View, California 94043, United States
| | - Brian Y. Feng
- Infectious Diseases, Novartis Institutes for BioMedical Research, Inc., Emeryville, California 94608, United States
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Stingray Venom Proteins: Mechanisms of Action Revealed Using a Novel Network Pharmacology Approach. Mar Drugs 2021; 20:md20010027. [PMID: 35049882 PMCID: PMC8781517 DOI: 10.3390/md20010027] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/20/2021] [Accepted: 12/22/2021] [Indexed: 01/02/2023] Open
Abstract
Animal venoms offer a valuable source of potent new drug leads, but their mechanisms of action are largely unknown. We therefore developed a novel network pharmacology approach based on multi-omics functional data integration to predict how stingray venom disrupts the physiological systems of target animals. We integrated 10 million transcripts from five stingray venom transcriptomes and 848,640 records from three high-content venom bioactivity datasets into a large functional data network. The network featured 216 signaling pathways, 29 of which were shared and targeted by 70 transcripts and 70 bioactivity hits. The network revealed clusters for single envenomation outcomes, such as pain, cardiotoxicity and hemorrhage. We carried out a detailed analysis of the pain cluster representing a primary envenomation symptom, revealing bibrotoxin and cholecystotoxin-like transcripts encoding pain-inducing candidate proteins in stingray venom. The cluster also suggested that such pain-inducing toxins primarily activate the inositol-3-phosphate receptor cascade, inducing intracellular calcium release. We also found strong evidence for synergistic activity among these candidates, with nerve growth factors cooperating with the most abundant translationally-controlled tumor proteins to activate pain signaling pathways. Our network pharmacology approach, here applied to stingray venom, can be used as a template for drug discovery in neglected venomous species.
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28
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Morphological profiling by means of the Cell Painting assay enables identification of tubulin-targeting compounds. Cell Chem Biol 2021; 29:1053-1064.e3. [PMID: 34968420 DOI: 10.1016/j.chembiol.2021.12.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 09/27/2021] [Accepted: 12/06/2021] [Indexed: 11/23/2022]
Abstract
In phenotypic compound discovery, conclusive identification of cellular targets and mode of action are often impaired by off-target binding. In particular, microtubules are frequently targeted in cellular assays. However, in vitro tubulin binding assays do not correctly reflect the cellular context, and conclusive high-throughput phenotypic assays monitoring tubulin binding are scarce, such that tubulin binding is rarely identified. We report that morphological profiling using the Cell Painting assay (CPA) can efficiently detect tubulin modulators in compound collections with a high throughput, including annotated reference compounds and unannotated compound classes with unrelated chemotypes and scaffolds. Small-molecule tubulin binders share similar CPA fingerprints, which enables prediction and experimental validation of microtubule-binding activity. Our findings suggest that CPA or a related morphological profiling approach will be an invaluable addition to small-molecule discovery programs in chemical biology and medicinal chemistry, enabling early identification of one of the most frequently observed off-target activities.
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29
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Fernández-Torras A, Comajuncosa-Creus A, Duran-Frigola M, Aloy P. Connecting chemistry and biology through molecular descriptors. Curr Opin Chem Biol 2021; 66:102090. [PMID: 34626922 DOI: 10.1016/j.cbpa.2021.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 08/23/2021] [Accepted: 09/03/2021] [Indexed: 01/14/2023]
Abstract
Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.
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Affiliation(s)
- Adrià Fernández-Torras
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Arnau Comajuncosa-Creus
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Miquel Duran-Frigola
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Ersilia Open Source Initiative, Cambridge, United Kingdom
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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30
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Klein VG, Bray WM, Wang HY, Edmondson Q, Schwochert J, Ono S, Naylor MR, Turmon AC, Faris JH, Okada O, Taunton J, Lokey RS. Identifying the Cellular Target of Cordyheptapeptide A and Synthetic Derivatives. ACS Chem Biol 2021; 16:1354-1364. [PMID: 34251165 DOI: 10.1021/acschembio.1c00094] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Cordyheptapeptide A is a lipophilic cyclic peptide from the prized Cordyceps fungal genus that shows potent cytotoxicity in multiple cancer cell lines. To better understand the bioactivity and physicochemical properties of cordyheptapeptide A with the ultimate goal of identifying its cellular target, we developed a solid-phase synthesis of this multiply N-methylated cyclic heptapeptide which enabled rapid access to both side chain- and backbone-modified derivatives. Removal of one of the backbone amide N-methyl (N-Me) groups maintained bioactivity, while membrane permeability was also preserved due to the formation of a new intramolecular hydrogen bond in a low dielectric solvent. Based on its cytotoxicity profile in the NCI-60 cell line panel, as well as its phenotype in a microscopy-based cytological assay, we hypothesized that cordyheptapeptide was acting on cells as a protein synthesis inhibitor. Further studies revealed the molecular target of cordyheptapeptide A to be the eukaryotic translation elongation factor 1A (eEF1A), a target shared by other lipophilic cyclic peptide natural products. This work offers a strategy to study and improve cyclic peptide natural products while highlighting the ability of these lipophilic compounds to effectively inhibit intracellular disease targets.
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Affiliation(s)
- Victoria G. Klein
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California 95064, United States
| | - Walter M. Bray
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California 95064, United States
| | - Hao-Yuan Wang
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, 94158, United States
| | - Quinn Edmondson
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California 95064, United States
| | - Joshua Schwochert
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California 95064, United States
| | - Satoshi Ono
- Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa 227-0033, Japan
| | - Matthew R. Naylor
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California 95064, United States
| | - Alexandra C. Turmon
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California 95064, United States
| | - Justin H. Faris
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California 95064, United States
| | - Okimasa Okada
- Innovative Research Division, Mitsubishi Tanabe Pharma Corporation, Kanagawa 227-0033, Japan
| | - Jack Taunton
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, 94158, United States
| | - R. Scott Lokey
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California 95064, United States
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31
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Saed B, Munaweera R, Anderson J, O'Neill WD, Hu YS. Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions. Sci Rep 2021; 11:15488. [PMID: 34326382 PMCID: PMC8322097 DOI: 10.1038/s41598-021-94730-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/15/2021] [Indexed: 11/24/2022] Open
Abstract
The spatial organization of T cell receptors (TCRs) correlates with membrane-associated signal amplification, dispersion, and regulation during T cell activation. Despite its potential clinical importance, quantitative analysis of the spatial arrangement of TCRs from standard fluorescence images remains difficult. Here, we report Statistical Classification Analyses of Membrane Protein Images or SCAMPI as a technique capable of analyzing the spatial arrangement of TCRs on the plasma membrane of T cells. We leveraged medical image analysis techniques that utilize pixel-based values. We transformed grayscale pixel values from fluorescence images of TCRs into estimated model parameters of partial differential equations. The estimated model parameters enabled an accurate classification using linear discrimination techniques, including Fisher Linear Discriminant (FLD) and Logistic Regression (LR). In a proof-of-principle study, we modeled and discriminated images of fluorescently tagged TCRs from Jurkat T cells on uncoated cover glass surfaces (Null) or coated cover glass surfaces with either positively charged poly-L-lysine (PLL) or TCR cross-linking anti-CD3 antibodies (OKT3). Using 80 training images and 20 test images per class, our statistical technique achieved 85% discrimination accuracy for both OKT3 versus PLL and OKT3 versus Null conditions. The run time of image data download, model construction, and image discrimination was 21.89 s on a laptop computer, comprised of 20.43 s for image data download, 1.30 s on the FLD-SCAMPI analysis, and 0.16 s on the LR-SCAMPI analysis. SCAMPI represents an alternative approach to morphology-based qualifications for discriminating complex patterns of membrane proteins conditioned on a small sample size and fast runtime. The technique paves pathways to characterize various physiological and pathological conditions using the spatial organization of TCRs from patient T cells.
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Affiliation(s)
- Badeia Saed
- Department of Chemistry, College of Liberal Arts and Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Rangika Munaweera
- Department of Chemistry, College of Liberal Arts and Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Jesse Anderson
- Department of Chemical Engineering, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - William D O'Neill
- Department of Bioengineering, Colleges of Engineering and Medicine, University of Illinois at Chicago, Chicago, IL, 60607, USA.
| | - Ying S Hu
- Department of Chemistry, College of Liberal Arts and Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA.
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32
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Schneidewind T, Brause A, Schölermann B, Sievers S, Pahl A, Sankar MG, Winzker M, Janning P, Kumar K, Ziegler S, Waldmann H. Combined morphological and proteome profiling reveals target-independent impairment of cholesterol homeostasis. Cell Chem Biol 2021; 28:1780-1794.e5. [PMID: 34214450 DOI: 10.1016/j.chembiol.2021.06.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/11/2021] [Accepted: 06/08/2021] [Indexed: 12/19/2022]
Abstract
Unbiased profiling approaches are powerful tools for small-molecule target or mode-of-action deconvolution as they generate a holistic view of the bioactivity space. This is particularly important for non-protein targets that are difficult to identify with commonly applied target identification methods. Thereby, unbiased profiling can enable identification of novel bioactivity even for annotated compounds. We report the identification of a large bioactivity cluster comprised of numerous well-characterized drugs with different primary targets using a combination of the morphological Cell Painting Assay and proteome profiling. Cluster members alter cholesterol homeostasis and localization due to their physicochemical properties that lead to protonation and accumulation in lysosomes, an increase in lysosomal pH, and a disturbed cholesterol homeostasis. The identified cluster enables identification of modulators of cholesterol homeostasis and links regulation of genes or proteins involved in cholesterol synthesis or trafficking to physicochemical properties rather than to nominal targets.
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Affiliation(s)
- Tabea Schneidewind
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany; Technical University Dortmund, Faculty of Chemistry and Chemical Biology, Otto-Hahn-Strasse 6, Dortmund 44227, Germany
| | - Alexandra Brause
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany
| | - Beate Schölermann
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany
| | - Sonja Sievers
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany
| | - Axel Pahl
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany
| | - Muthukumar G Sankar
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany
| | - Michael Winzker
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany
| | - Petra Janning
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany
| | - Kamal Kumar
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany
| | - Slava Ziegler
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany
| | - Herbert Waldmann
- Max-Planck Institute of Molecular Physiology, Department of Chemical Biology, Otto-Hahn-Strasse 11, Dortmund 44227, Germany; Technical University Dortmund, Faculty of Chemistry and Chemical Biology, Otto-Hahn-Strasse 6, Dortmund 44227, Germany.
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33
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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: 19] [Impact Index Per Article: 6.3] [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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34
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Trapotsi MA, Mervin LH, Afzal AM, Sturm N, Engkvist O, Barrett IP, Bender A. Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions. J Chem Inf Model 2021; 61:1444-1456. [PMID: 33661004 DOI: 10.1021/acs.jcim.0c00864] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.8) with ECFP data and image data as side information, respectively. There were targets better predicted by image data as side information, such as β-catenin, and others better predicted by fingerprint-based side information, such as proteins belonging to the G-protein-Coupled Receptor 1 family, which could be rationalized from the underlying data distributions in each descriptor domain. In conclusion, both cell morphology changes and chemical structure information contain information about compound bioactivity, which is also partially complementary, and can hence contribute to in silico MoA analysis.
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Affiliation(s)
- Maria-Anna Trapotsi
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Lewis H Mervin
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Avid M Afzal
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Noé Sturm
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Ola Engkvist
- Hit Discovery, Discovery Sciences, R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Ian P Barrett
- Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, U.K
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
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35
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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: 28] [Impact Index Per Article: 9.3] [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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36
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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: 151] [Impact Index Per Article: 50.3] [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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37
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Malandraki-Miller S, Riley PR. Use of artificial intelligence to enhance phenotypic drug discovery. Drug Discov Today 2021; 26:887-901. [PMID: 33484947 DOI: 10.1016/j.drudis.2021.01.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 12/28/2020] [Accepted: 01/15/2021] [Indexed: 01/17/2023]
Abstract
Research and development (R&D) productivity across the pharmaceutical industry has received close scrutiny over the past two decades, especially taking into consideration reports of attrition rates and the colossal cost for drug development. The respective merits of the two main drug discovery approaches, phenotypic and target based, have divided opinion across the research community, because each hold different advantages for identifying novel molecular entities with a successful path to the market. Nevertheless, both have low translatability in the clinic. Artificial intelligence (AI) and adoption of machine learning (ML) tools offer the promise of revolutionising drug development, and overcoming obstacles in the drug discovery pipeline. Here, we assess the potential of target-driven and phenotypic-based approaches and offer a holistic description of the current state of the field, from both a scientific and industry perspective. With the emerging partnerships between AI/ML and pharma still in their relative infancy, we investigate the potential and current limitations with a particular focus on phenotypic drug discovery. Finally, we emphasise the value of public-private partnerships (PPPs) and cross-disciplinary collaborations to foster innovation and facilitate efficient drug discovery programmes.
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Affiliation(s)
| | - Paul R Riley
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
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38
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Biswas S. High Content Analysis Across Signaling Modulation Treatments for Subcellular Target Identification Reveals Heterogeneity in Cellular Response. Front Cell Dev Biol 2021; 8:594750. [PMID: 33490062 PMCID: PMC7817946 DOI: 10.3389/fcell.2020.594750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 11/09/2020] [Indexed: 11/13/2022] Open
Abstract
Cellular phenotypes on bioactive compound treatment are a result of the downstream targets of the respective treatment. Here, a computational approach is taken for downstream subcellular target identification to understand the basis of the cellular response. This response is a readout of cellular phenotypes captured from cell-painting-based light microscopy images. The readouts are morphological profiles measured simultaneously from multiple cellular organelles. Cellular profiles generated from roughly 270 diverse treatments on bone cancer cell line form the high content screen used in this study. Phenotypic diversity across these treatments is demonstrated, depending on the image-based phenotypic profiles. Furthermore, the impact of the treatments on specific organelles and associated organelle sensitivities are determined. This revealed that endoplasmic reticulum has a higher likelihood of being targeted. Employing multivariate regression overall cellular response is predicted based on fewer organelle responses. This prediction model is validated against 1,000 new candidate compounds. Different compounds despite driving specific modulation outcomes elicit a varying effect on cellular integrity. Strikingly, this confirms that phenotypic responses are not conserved that enables quantification of signaling heterogeneity. Agonist-antagonist signaling pairs demonstrate switch of the targets in the cascades hinting toward evidence of signaling plasticity. Quantitative analysis of the screen has enabled the identification of these underlying signatures. Together, these image-based profiling approaches can be employed for target identification in drug and diseased states and understand the hallmark of cellular response.
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Affiliation(s)
- Sayan Biswas
- Department of Biochemistry, School of Life Sciences, University of Hyderabad, Hyderabad, India
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39
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Veschini L, Sailem H, Malani D, Pietiäinen V, Stojiljkovic A, Wiseman E, Danovi D. High-Content Imaging to Phenotype Human Primary and iPSC-Derived Cells. Methods Mol Biol 2021; 2185:423-445. [PMID: 33165865 DOI: 10.1007/978-1-0716-0810-4_27] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Increasingly powerful microscopy, liquid handling, and computational techniques have enabled cell imaging in high throughput. Microscopy images are quantified using high-content analysis platforms linking object features to cell behavior. This can be attempted on physiologically relevant cell models, including stem cells and primary cells, in complex environments, and conceivably in the presence of perturbations. Recently, substantial focus has been devoted to cell profiling for cell therapy, assays for drug discovery or biomarker identification for clinical decision-making protocols, bringing this wealth of information into translational applications. In this chapter, we focus on two protocols enabling to (1) benchmark human cells, in particular human endothelial cells as a case study and (2) extract cells from blood for follow-up experiments including image-based drug testing. We also present concepts of high-content imaging and discuss the benefits and challenges, with the aim of enabling readers to tailor existing pipelines and bring such approaches closer to translational research and the clinic.
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Affiliation(s)
- Lorenzo Veschini
- Academic Centre of Reconstructive Science, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - Heba Sailem
- The Institute of Biomedical Engineering, Oxford, UK
| | - Disha Malani
- Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland
| | - Vilja Pietiäinen
- Institute for Molecular Medicine Finland-FIMM, Helsinki Institute of Life Science-HiLIFE, University of Helsinki, Helsinki, Finland
| | - Ana Stojiljkovic
- Division of Veterinary Anatomy, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Erika Wiseman
- Stem Cell Hotel, Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK
| | - Davide Danovi
- Stem Cell Hotel, Centre for Stem Cells and Regenerative Medicine, King's College London, London, UK.
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40
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Vincent F, Loria PM, Weston AD, Steppan CM, Doyonnas R, Wang YM, Rockwell KL, Peakman MC. Hit Triage and Validation in Phenotypic Screening: Considerations and Strategies. Cell Chem Biol 2020; 27:1332-1346. [DOI: 10.1016/j.chembiol.2020.08.009] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 05/31/2020] [Accepted: 08/14/2020] [Indexed: 02/06/2023]
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41
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Richaud AD, Roche SP. Structure-Property Relationship Study of N-(Hydroxy)Peptides for the Design of Self-Assembled Parallel β-Sheets. J Org Chem 2020; 85:12329-12342. [PMID: 32881524 DOI: 10.1021/acs.joc.0c01441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
The design of novel and functional biomimetic foldamers remains a major challenge in creating mimics of native protein structures. Herein, we report the stabilization of a remarkably short β-sheet by incorporating N-(hydroxy)glycine (Hyg) residues into the backbone of peptides. These peptide-peptoid hybrids form unique parallel β-sheet structures by self-assembly upon hydrogenation. Our spectroscopic and crystallographic data suggest that the local conformational perturbations induced by N-(hydroxy)amides are outweighed by a network of strong interstrand hydrogen bonds.
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Affiliation(s)
- Alexis D Richaud
- Department of Chemistry and Biochemistry, Florida Atlantic University, Boca Raton, Florida 33431, United States
| | - Stéphane P Roche
- Department of Chemistry and Biochemistry, Florida Atlantic University, Boca Raton, Florida 33431, United States.,Center for Molecular Biology and Biotechnology, Florida Atlantic University, Jupiter, Florida 33458, United States
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42
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Mizuno T, Morita K, Kusuhara H. Interesting Properties of Profile Data Analysis in the Understanding and Utilization of the Effects of Drugs. Biol Pharm Bull 2020; 43:1435-1442. [DOI: 10.1248/bpb.b20-00301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Tadahaya Mizuno
- Graduate School of Pharmaceutical Sciences, the University of Tokyo
| | - Katsuhisa Morita
- Graduate School of Pharmaceutical Sciences, the University of Tokyo
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43
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Hussain S, Anees A, Das A, Nguyen BP, Marzuki M, Lin S, Wright G, Singhal A. High-content image generation for drug discovery using generative adversarial networks. Neural Netw 2020; 132:353-363. [PMID: 32977280 DOI: 10.1016/j.neunet.2020.09.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 06/11/2020] [Accepted: 09/07/2020] [Indexed: 10/23/2022]
Abstract
Immense amount of high-content image data generated in drug discovery screening requires computationally driven automated analysis. Emergence of advanced machine learning algorithms, like deep learning models, has transformed the interpretation and analysis of imaging data. However, deep learning methods generally require large number of high-quality data samples, which could be limited during preclinical investigations. To address this issue, we propose a generative modeling based computational framework to synthesize images, which can be used for phenotypic profiling of perturbations induced by drug compounds. We investigated the use of three variants of Generative Adversarial Network (GAN) in our framework, viz., a basic Vanilla GAN, Deep Convolutional GAN (DCGAN) and Progressive GAN (ProGAN), and found DCGAN to be most efficient in generating realistic synthetic images. A pre-trained convolutional neural network (CNN) was used to extract features of both real and synthetic images, followed by a classification model trained on real and synthetic images. The quality of synthesized images was evaluated by comparing their feature distributions with that of real images. The DCGAN-based framework was applied to high-content image data from a drug screen to synthesize high-quality cellular images, which were used to augment the real image data. The augmented dataset was shown to yield better classification performance compared with that obtained using only real images. We also demonstrated the application of proposed method on the generation of bacterial images and computed feature distributions for bacterial images specific to different drug treatments. In summary, our results showed that the proposed DCGAN-based framework can be utilized to generate realistic synthetic high-content images, thus enabling the study of drug-induced effects on cells and bacteria.
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Affiliation(s)
- Shaista Hussain
- Institute of High Performance Computing, A*STAR, 138673, Singapore.
| | - Ayesha Anees
- Institute of High Performance Computing, A*STAR, 138673, Singapore
| | - Ankit Das
- Institute of High Performance Computing, A*STAR, 138673, Singapore
| | - Binh P Nguyen
- School of Mathematics and Statistics, VUW, 6140, New Zealand
| | | | - Shuping Lin
- Skin Research Institute of Singapore, A*STAR, 138648, Singapore
| | - Graham Wright
- Skin Research Institute of Singapore, A*STAR, 138648, Singapore
| | - Amit Singhal
- Singapore Immunology Network, A*STAR, 138648, Singapore.
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44
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Zhang L, Song J, Kong L, Yuan T, Li W, Zhang W, Hou B, Lu Y, Du G. The strategies and techniques of drug discovery from natural products. Pharmacol Ther 2020; 216:107686. [PMID: 32961262 DOI: 10.1016/j.pharmthera.2020.107686] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/17/2020] [Accepted: 09/17/2020] [Indexed: 12/15/2022]
Abstract
Natural products have been the main sources of new drugs. The different strategies have been developed to find the new drugs based on natural products. The traditional and ethic medicines have provided information on the therapeutic effects and resulted in some notable drug discovery of natural products. The special activities of the medicine plants such as the side effects have inspired scientists to develop the novel small molecular. The microorganisms and the endogenous active substances from human or animal also become the important approaches to the drug discovery. The tremendous progress in technology led to the new strategies in drug discovery from natural products. The bioinformation and artificial intelligence have facilitated the research and development of natural products. We will provide a scene of strategies and technologies for drug discovery from natural products in this review.
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Affiliation(s)
- Li Zhang
- Beijing Key Laboratory of Drug Target Research and Drug Screening, State Key Laboratory for Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China; General Office, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Junke Song
- Beijing Key Laboratory of Drug Target Research and Drug Screening, State Key Laboratory for Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Linglei Kong
- Beijing Key Laboratory of Drug Target Research and Drug Screening, State Key Laboratory for Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Tianyi Yuan
- Beijing Key Laboratory of Drug Target Research and Drug Screening, State Key Laboratory for Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Wan Li
- Beijing Key Laboratory of Drug Target Research and Drug Screening, State Key Laboratory for Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Wen Zhang
- Beijing Key Laboratory of Drug Target Research and Drug Screening, State Key Laboratory for Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Biyu Hou
- Beijing Key Laboratory of Drug Target Research and Drug Screening, State Key Laboratory for Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Yang Lu
- Beijing Key Laboratory of Polymorphic Drug, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Guanhua Du
- Beijing Key Laboratory of Drug Target Research and Drug Screening, State Key Laboratory for Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China.
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45
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Chavan S, Scherbak N, Engwall M, Repsilber D. Predicting Chemical-Induced Liver Toxicity Using High-Content Imaging Phenotypes and Chemical Descriptors: A Random Forest Approach. Chem Res Toxicol 2020; 33:2261-2275. [DOI: 10.1021/acs.chemrestox.9b00459] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Swapnil Chavan
- School of Science and Technology, Örebro University, 70112 Örebro, Sweden
| | - Nikolai Scherbak
- School of Science and Technology, Örebro University, 70112 Örebro, Sweden
| | - Magnus Engwall
- School of Science and Technology, Örebro University, 70112 Örebro, Sweden
| | - Dirk Repsilber
- School of Medical Sciences, Örebro University, 70185 Örebro, Sweden
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46
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Hughes RE, Elliott RJR, Munro AF, Makda A, O’Neill JR, Hupp T, Carragher NO. High-Content Phenotypic Profiling in Esophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery. SLAS DISCOVERY : ADVANCING LIFE SCIENCES R & D 2020; 25:770-782. [PMID: 32441181 PMCID: PMC7372582 DOI: 10.1177/2472555220917115] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 02/26/2020] [Accepted: 03/16/2020] [Indexed: 12/11/2022]
Abstract
Esophageal adenocarcinoma (EAC) is a highly heterogeneous disease, dominated by large-scale genomic rearrangements and copy number alterations. Such characteristics have hampered conventional target-directed drug discovery and personalized medicine strategies, contributing to poor outcomes for patients. We describe the application of a high-content Cell Painting assay to profile the phenotypic response of 19,555 compounds across a panel of six EAC cell lines and two tissue-matched control lines. We built an automated high-content image analysis pipeline to identify compounds that selectively modified the phenotype of EAC cell lines. We further trained a machine-learning model to predict the mechanism of action of EAC selective compounds using phenotypic fingerprints from a library of reference compounds. We identified a number of phenotypic clusters enriched with similar pharmacological classes, including methotrexate and three other antimetabolites that are highly selective for EAC cell lines. We further identify a small number of hits from our diverse chemical library that show potent and selective activity for EAC cell lines and that do not cluster with the reference library of compounds, indicating they may be selectively targeting novel esophageal cancer biology. Overall, our results demonstrate that our EAC phenotypic screening platform can identify existing pharmacologic classes and novel compounds with selective activity for EAC cell phenotypes.
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Affiliation(s)
- Rebecca E. Hughes
- MRC Institute of Genetics &
Molecular Medicine, The University of Edinburgh, Western General Hospital,
Edinburgh, UK
| | - Richard J. R. Elliott
- MRC Institute of Genetics &
Molecular Medicine, The University of Edinburgh, Western General Hospital,
Edinburgh, UK
| | - Alison F. Munro
- MRC Institute of Genetics &
Molecular Medicine, The University of Edinburgh, Western General Hospital,
Edinburgh, UK
| | - Ashraff Makda
- MRC Institute of Genetics &
Molecular Medicine, The University of Edinburgh, Western General Hospital,
Edinburgh, UK
| | - J. Robert O’Neill
- Cambridge Oesophagogastric Centre,
Cambridge University Hospitals NHS Foundation Trust, Cambridge, Cambridgeshire,
UK
| | - Ted Hupp
- MRC Institute of Genetics &
Molecular Medicine, The University of Edinburgh, Western General Hospital,
Edinburgh, UK
| | - Neil O. Carragher
- MRC Institute of Genetics &
Molecular Medicine, The University of Edinburgh, Western General Hospital,
Edinburgh, UK
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47
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Omta WA, van Heesbeen RG, Shen I, de Nobel J, Robers D, van der Velden LM, Medema RH, Siebes APJM, Feelders AJ, Brinkkemper S, Klumperman JS, Spruit MR, Brinkhuis MJS, Egan DA. Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS DISCOVERY 2020; 25:655-664. [PMID: 32400262 DOI: 10.1177/2472555220919345] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for building the machine learning models. We propose that unsupervised exploratory methods should first be applied to the data set to gain a better insight into the quality of the data. This improves the selection and labeling of data for creating training sets before the application of machine learning. We demonstrate this using a high-content genome-wide small interfering RNA screen. We perform an unsupervised exploratory data analysis to facilitate the identification of four robust phenotypes, which we subsequently use as a training set for building a high-quality random forest machine learning model to differentiate four phenotypes with an accuracy of 91.1% and a kappa of 0.85. Our approach enhanced our ability to extract new knowledge from the screen when compared with the use of unsupervised methods alone.
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Affiliation(s)
- Wienand A Omta
- Department of Cell Biology, Centre for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands.,Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands.,Core Life Analytics B.V., Utrecht, The Netherlands
| | - Roy G van Heesbeen
- Department of Cell Biology, NKI-AVL, Amsterdam, Noord-Holland, The Netherlands
| | - Ian Shen
- Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
| | - Jacob de Nobel
- Department of Cell Biology, Centre for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands
| | - Desmond Robers
- Department of Cell Biology, Centre for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands
| | - Lieke M van der Velden
- Department of Cell Biology, Centre for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands
| | - René H Medema
- Department of Cell Biology, NKI-AVL, Amsterdam, Noord-Holland, The Netherlands
| | - Arno P J M Siebes
- Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
| | - Ad J Feelders
- Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
| | - Sjaak Brinkkemper
- Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
| | - Judith S Klumperman
- Department of Cell Biology, Centre for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands
| | - Marco René Spruit
- Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
| | - Matthieu J S Brinkhuis
- Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
| | - David A Egan
- Core Life Analytics B.V., Utrecht, The Netherlands
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48
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Lau TA, Bray WM, Lokey RS. Macrophage Cytological Profiling and Anti-Inflammatory Drug Discovery. Assay Drug Dev Technol 2020; 17:14-16. [PMID: 30657701 DOI: 10.1089/adt.2018.894] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Millions of people are affected by diseases and conditions related to the immune system. Unfortunately, our current supply of approved anti-inflammatory medicine is very limited and only treats a small fraction of inflammatory diseases. Nearly half of the drugs on the market today are natural products and natural product derivatives. The long-term objective of my research is to continue efforts toward the discovery of diverse chemical compounds and their mechanism of action (MOA) to inspire the next generation of novel therapeutics. This project approaches this objective by creating a robust platform for the in-depth phenotypic profiling of complex natural product samples with respect to their effect on pathways related to the innate immune response. This approach has the potential to elucidate the MOAs of novel natural products relevant to inflammation and accelerate the pace of drug discovery in this therapeutic area.
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Affiliation(s)
- Tannia A Lau
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California
| | - Walter M Bray
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California
| | - R Scott Lokey
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, California.,Tannia Lau from the Department of Chemistry and Biochemistry, University of California Santa Cruz, was awarded First Place Poster award at the annual Society of Biomolecular Imaging and Informatics (SBI2) meeting held in Boston, September 2018
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49
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Boyd J, Fennell M, Carpenter A. Harnessing the power of microscopy images to accelerate drug discovery: what are the possibilities? Expert Opin Drug Discov 2020; 15:639-642. [PMID: 32200648 DOI: 10.1080/17460441.2020.1743675] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Justin Boyd
- Internal Medicines Research Unit, Pfizer Inc ., Cambridge, MA, USA
| | - Myles Fennell
- Neuroscience and Platform Biology, Arvinas , New Haven, CT, USA
| | - Anne Carpenter
- Imaging Platform, Broad Institute of MIT and Harvard , Cambridge, MA, USA
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50
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Advanced identification of global bioactivity hotspots via screening of the metabolic fingerprint of entire ecosystems. Sci Rep 2020; 10:1319. [PMID: 31992728 PMCID: PMC6987164 DOI: 10.1038/s41598-020-57709-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 01/02/2020] [Indexed: 11/29/2022] Open
Abstract
Natural products (NP) are a valuable drug resource. However, NP-inspired drug leads are declining, among other reasons due to high re-discovery rates. We developed a conceptual framework using the metabolic fingerprint of entire ecosystems (MeE) to facilitate the discovery of global bioactivity hotspots. We assessed the MeE of 305 sites of diverse aquatic ecosystems, worldwide. All samples were tested for antiviral effects against the human immunodeficiency virus (HIV), followed by a comprehensive screening for cell-modulatory activity by High-Content Screening (HCS). We discovered a very strong HIV-1 inhibition mainly in samples taken from fjords with a strong terrestrial input. Multivariate data integration demonstrated an association of a set of polyphenols with specific biological alterations (endoplasmic reticulum, lysosomes, and NFkB) caused by these samples. Moreover, we found strong HIV-1 inhibition in one unrelated oceanic sample closely matching to HIV-1-inhibitory drugs on a cytological and a chemical level. Taken together, we demonstrate that even without physical purification, a sophisticated strategy of differential filtering, correlation analysis, and multivariate statistics can be employed to guide chemical analysis, to improve de-replication, and to identify ecosystems with promising characteristics as sources for NP discovery.
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