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Progress Reports: Texas A&M University: Single Cell, Multi-Parametric High Throughput Platform to Classify Endocrine Disruptor Potential of Mixtures

Superfund Research Program

Single Cell, Multi-Parametric High Throughput Platform to Classify Endocrine Disruptor Potential of Mixtures

Project Leader: Michael A. Mancini (Baylor College of Medicine)
Co-Investigators: Fabio Stossi (Baylor College of Medicine), Adam T. Szafran (Baylor College of Medicine)
Grant Number: P42ES027704
Funding Period: 2017-2022
View this project in the NIH Research Portfolio Online Reporting Tools (RePORT)

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Progress Reports

Year:   2018  2017 

In Year Two, Principal Investigator Michael A. Mancini, Ph.D., and his researchers validated the androgen receptor (AR) chimera cell line and are in the process of rebuilding the thyroid hormone receptor (TR) chimera, which proved quite challenging. The researchers completed the reference compounds (EPA estrogen receptor (ER) set and the Inter-tissue and -Individual Variability in Response to Mixtures Project set) and lab-made mixtures (from Exposure Science Core) dose-response experiments for the ER and AR stable engineered cell lines and provided metrics to the Data Science Core to begin working on machine learning modeling to identify active vs. inactive endocrine disrupting chemicals (EDCs) and mixtures. The same compound libraries are being run on the endogenous cell models for ER and AR (TR is on standby as Mancini and his researchers are in the final stages of validation of their newly-generated, imaging-compatible TR antibody) and they have developed a completely novel quality control pipeline for single cell analysis. This new method considers metrics to describe single cell distributions and the researchers are using them to determine experiment's quality control and compound effects. Mancini and his researchers also developed a FUCCI (cell-cycle sensors for imaging) set of stable cell lines to add another layer of phenotypic measurements to EDC detection. Integration of all these end points with EPA orthogonal assays (publicly available) will be performed by the Data Science Core.

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