Literature DB >> 32552291

Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals.

Fabio Stossi1,2,3, Ragini M Mistry3, Pankaj K Singh3,4, Hannah L Johnson2,3, Maureen G Mancini1, Adam T Szafran1, Michael A Mancini1,2,3,4,5,6.   

Abstract

Cell-to-cell variation of protein expression in genetically homogeneous populations is a common biological trait often neglected during analysis of high-throughput (HT) screens and is rarely used as a metric to characterize chemicals. We have captured single-cell distributions of androgen receptor (AR) nuclear levels after perturbations as a means to evaluate assay reproducibility and characterize a small subset of chemicals. AR, a member of the nuclear receptor family of transcription factors, is the central regulator of male reproduction and is involved in many pathophysiological processes. AR protein levels and nuclear localization often increase following ligand binding, with dihydrotestosterone (DHT) being the natural agonist. HT AR immunofluorescence imaging was used in multiple cell lines to define single-cell nuclear values extracted from thousands of cells per condition treated with DHT or DMSO (control). Analysis of numerous biological replicates led to a quality control metric that takes into account the distribution of single-cell data, and how it changes upon treatments. Dose-response experiments across several cell lines showed a large range of sensitivity to DHT, prompting us to treat selected cell lines with 45 Environmental Protection Agency (EPA)-provided chemicals that include many endocrine-disrupting chemicals (EDCs); data from six of the compounds were then integrated with orthogonal assays. Our comprehensive results indicate that quantitative single-cell distribution analysis of AR protein levels is a valid method to detect the potential androgenic and antiandrogenic actions of environmentally relevant chemicals in a sensitive and reproducible manner.

Entities:  

Keywords:  androgen receptor; endocrine disruptor chemicals; high-throughput microscopy

Mesh:

Substances:

Year:  2020        PMID: 32552291      PMCID: PMC7430197          DOI: 10.1177/2472555220934420

Source DB:  PubMed          Journal:  SLAS Discov        ISSN: 2472-5552            Impact factor:   3.341


  25 in total

1.  A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens.

Authors:  Albert Gough; Tong Ying Shun; D Lansing Taylor; Mark Schurdak
Journal:  Methods       Date:  2015-11-04       Impact factor: 3.608

2.  Identifying environmental chemicals as agonists of the androgen receptor by using a quantitative high-throughput screening platform.

Authors:  Caitlin Lynch; Srilatha Sakamuru; Ruili Huang; Diana A Stavreva; Lyuba Varticovski; Gordon L Hager; Richard S Judson; Keith A Houck; Nicole C Kleinstreuer; Warren Casey; Richard S Paules; Anton Simeonov; Menghang Xia
Journal:  Toxicology       Date:  2017-05-04       Impact factor: 4.221

3.  Quantifying effects of ligands on androgen receptor nuclear translocation, intranuclear dynamics, and solubility.

Authors:  Marco Marcelli; David L Stenoien; Adam T Szafran; Silvia Simeoni; Irina U Agoulnik; Nancy L Weigel; Tim Moran; Ivana Mikic; Jeffrey H Price; Michael A Mancini
Journal:  J Cell Biochem       Date:  2006-07-01       Impact factor: 4.429

Review 4.  Biologically Relevant Heterogeneity: Metrics and Practical Insights.

Authors:  Albert Gough; Andrew M Stern; John Maier; Timothy Lezon; Tong-Ying Shun; Chakra Chennubhotla; Mark E Schurdak; Steven A Haney; D Lansing Taylor
Journal:  SLAS Discov       Date:  2017-01-06       Impact factor: 3.341

5.  On the Utility of ToxCast™ and ToxPi as Methods for Identifying New Obesogens.

Authors:  Amanda Shaine Janesick; Giorgio Dimastrogiovanni; Lenka Vanek; Christy Boulos; Raquel Chamorro-García; Weiyi Tang; Bruce Blumberg
Journal:  Environ Health Perspect       Date:  2016-01-13       Impact factor: 9.031

6.  CERAPP: Collaborative Estrogen Receptor Activity Prediction Project.

Authors:  Kamel Mansouri; Ahmed Abdelaziz; Aleksandra Rybacka; Alessandra Roncaglioni; Alexander Tropsha; Alexandre Varnek; Alexey Zakharov; Andrew Worth; Ann M Richard; Christopher M Grulke; Daniela Trisciuzzi; Denis Fourches; Dragos Horvath; Emilio Benfenati; Eugene Muratov; Eva Bay Wedebye; Francesca Grisoni; Giuseppe F Mangiatordi; Giuseppina M Incisivo; Huixiao Hong; Hui W Ng; Igor V Tetko; Ilya Balabin; Jayaram Kancherla; Jie Shen; Julien Burton; Marc Nicklaus; Matteo Cassotti; Nikolai G Nikolov; Orazio Nicolotti; Patrik L Andersson; Qingda Zang; Regina Politi; Richard D Beger; Roberto Todeschini; Ruili Huang; Sherif Farag; Sine A Rosenberg; Svetoslav Slavov; Xin Hu; Richard S Judson
Journal:  Environ Health Perspect       Date:  2016-02-23       Impact factor: 9.031

7.  High throughput microscopy identifies bisphenol AP, a bisphenol A analog, as a novel AR down-regulator.

Authors:  Fabio Stossi; Radhika D Dandekar; Michael J Bolt; Justin Y Newberg; Maureen G Mancini; Akash K Kaushik; Vasanta Putluri; Arun Sreekumar; Michael A Mancini
Journal:  Oncotarget       Date:  2016-03-29

8.  ToxPi Graphical User Interface 2.0: Dynamic exploration, visualization, and sharing of integrated data models.

Authors:  Skylar W Marvel; Kimberly To; Fabian A Grimm; Fred A Wright; Ivan Rusyn; David M Reif
Journal:  BMC Bioinformatics       Date:  2018-03-05       Impact factor: 3.169

9.  Limited Chemical Structural Diversity Found to Modulate Thyroid Hormone Receptor in the Tox21 Chemical Library.

Authors:  Katie Paul-Friedman; Matt Martin; Kevin M Crofton; Chia-Wen Hsu; Srilatha Sakamuru; Jinghua Zhao; Menghang Xia; Ruili Huang; Diana A Stavreva; Vikas Soni; Lyuba Varticovski; Razi Raziuddin; Gordon L Hager; Keith A Houck
Journal:  Environ Health Perspect       Date:  2019-09-30       Impact factor: 9.031

10.  Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.

Authors:  Albert H Gough; Ning Chen; Tong Ying Shun; Timothy R Lezon; Robert C Boltz; Celeste E Reese; Jacob Wagner; Lawrence A Vernetti; Jennifer R Grandis; Adrian V Lee; Andrew M Stern; Mark E Schurdak; D Lansing Taylor
Journal:  PLoS One       Date:  2014-07-18       Impact factor: 3.240

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  1 in total

1.  Quality Control for Single Cell Imaging Analytics Using Endocrine Disruptor-Induced Changes in Estrogen Receptor Expression.

Authors:  Fabio Stossi; Pankaj K Singh; Ragini M Mistry; Hannah L Johnson; Radhika D Dandekar; Maureen G Mancini; Adam T Szafran; Arvind U Rao; Michael A Mancini
Journal:  Environ Health Perspect       Date:  2022-02-15       Impact factor: 9.031

  1 in total

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