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Abbott DA, Mancini MG, Bolt MJ, Szafran AT, Neugebauer KA, Stossi F, Gorelick DA, Mancini MA. A novel ERβ high throughput microscopy platform for testing endocrine disrupting chemicals. Heliyon 2024; 10:e23119. [PMID: 38169792 PMCID: PMC10758781 DOI: 10.1016/j.heliyon.2023.e23119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 11/24/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024] Open
Abstract
In this study we present an inducible biosensor model for the Estrogen Receptor Beta (ERβ), GFP-ERβ:PRL-HeLa, a single-cell-based high throughput (HT) in vitro assay that allows direct visualization and measurement of GFP-tagged ERβ binding to ER-specific DNA response elements (EREs), ERβ-induced chromatin remodeling, and monitor transcriptional alterations via mRNA fluorescence in situ hybridization for a prolactin (PRL)-dsRED2 reporter gene. The model was used to accurately (Z' = 0.58-0.8) differentiate ERβ-selective ligands from ERα ligands when treated with a panel of selective agonists and antagonists. Next, we tested an Environmental Protection Agency (EPA)-provided set of 45 estrogenic reference chemicals with known ERα in vivo activity and identified several that activated ERβ as well, with varying sensitivity, including a subset that is completely novel. We then used an orthogonal ERE-containing transgenic zebrafish (ZF) model to cross validate ERβ and ERα selective activities at the organism level. Using this environmentally relevant ZF assay, some compounds were confirmed to have ERβ activity, validating the GFP-ERβ:PRL-HeLa assay as a screening tool for potential ERβ active endocrine disruptors (EDCs). These data demonstrate the value of sensitive multiplex mechanistic data gathered by the GFP-ERβ:PRL-HeLa assay coupled with an orthogonal zebrafish model to rapidly identify environmentally relevant ERβ EDCs and improve upon currently available screening tools for this understudied nuclear receptor.
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Affiliation(s)
- Derek A. Abbott
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Maureen G. 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
| | - Michael J. Bolt
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA
- Center for Translational Cancer Research, Institute of Biosciences & Technology, Texas A&M University, Houston, TX, USA
| | - Adam T. Szafran
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA
| | - Kaley A. Neugebauer
- Center for Precision Environmental Health, Baylor College of Medicine, Houston, TX, USA
| | - 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
| | - Daniel A. Gorelick
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
- Center for Precision Environmental Health, Baylor College of Medicine, 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 & Technology, Texas A&M University, Houston, TX, USA
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, USA
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Aghayev Z, Szafran AT, Tran A, Ganesh HS, Stossi F, Zhou L, Mancini MA, Pistikopoulos EN, Beykal B. Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data. Chem Eng Sci 2023; 281:119086. [PMID: 37637227 PMCID: PMC10448728 DOI: 10.1016/j.ces.2023.119086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.
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Affiliation(s)
- Zahir Aghayev
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT
| | - Adam T. Szafran
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX
| | - Anh Tran
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX
- Texas A&M Energy Institute, Texas A&M University, College Station, TX
| | - Hari S. Ganesh
- Discipline of Chemical Engineering, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar, Gujarat - 382055, India
| | - Fabio Stossi
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX
| | - Lan Zhou
- Department of Statistics, Texas A&M University, College Station, TX
| | - Michael A. Mancini
- Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX
| | - Efstratios N. Pistikopoulos
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX
- Texas A&M Energy Institute, Texas A&M University, College Station, TX
| | - Burcu Beykal
- Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT
- Center for Clean Energy Engineering, University of Connecticut, Storrs, CT
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Schaufele F. Maximizing the quantitative accuracy and reproducibility of Förster resonance energy transfer measurement for screening by high throughput widefield microscopy. Methods 2013; 66:188-99. [PMID: 23927839 DOI: 10.1016/j.ymeth.2013.07.040] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Revised: 07/27/2013] [Accepted: 07/30/2013] [Indexed: 11/16/2022] Open
Abstract
Förster resonance energy transfer (FRET) between fluorescent proteins (FPs) provides insights into the proximities and orientations of FPs as surrogates of the biochemical interactions and structures of the factors to which the FPs are genetically fused. As powerful as FRET methods are, technical issues have impeded their broad adoption in the biologic sciences. One hurdle to accurate and reproducible FRET microscopy measurement stems from variable fluorescence backgrounds both within a field and between different fields. Those variations introduce errors into the precise quantification of fluorescence levels on which the quantitative accuracy of FRET measurement is highly dependent. This measurement error is particularly problematic for screening campaigns since minimal well-to-well variation is necessary to faithfully identify wells with altered values. High content screening depends also upon maximizing the numbers of cells imaged, which is best achieved by low magnification high throughput microscopy. But, low magnification introduces flat-field correction issues that degrade the accuracy of background correction to cause poor reproducibility in FRET measurement. For live cell imaging, fluorescence of cell culture media in the fluorescence collection channels for the FPs commonly used for FRET analysis is a high source of background error. These signal-to-noise problems are compounded by the desire to express proteins at biologically meaningful levels that may only be marginally above the strong fluorescence background. Here, techniques are presented that correct for background fluctuations. Accurate calculation of FRET is realized even from images in which a non-flat background is 10-fold higher than the signal.
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Affiliation(s)
- Fred Schaufele
- Center for Reproductive Science, University of California San Francisco, 513 Parnassus, HSE-1622, San Francisco, CA 94143-0556, United States.
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