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In Toxicological sciences : an official journal of the Society of Toxicology ; h5-index 0.0

Emerging data indicate that structural analogs of Bisphenol A (BPA) such as Bisphenol S (BPS), tetrabromobisphenol A (TBBPA), and bisphenol AF (BPAF) have been introduced into the market as substitutes for BPA. Our previous study compared in vitro testicular toxicity using murine C18-4 spermatogonial cells and found that BPAF and TBBPA exhibited higher spermatogonial toxicities as compared with BPA and BPS. Recently, we developed a novel in vitro three-dimensional (3D) testicular cell co-culture model, enabling the classification of reproductive toxic substances. In this study, we applied the testicular cell co-culture model and employed a High-content image (HCA) based single-cell analysis to further compare the testicular toxicities of BPA and its analogs. We also developed a machine learning-based HCA pipeline to examine the complex phenotypic changes associated with testicular toxicities. We found dose and time-dependent changes in a wide spectrum of adverse endpoints, including nuclear morphology, DNA synthesis, DNA damage, and cytoskeletal structure in a single-cell-based analysis. The co-cultured testicular cells were more sensitive than the C18 spermatogonial cells in response to BPA and its analogs. Unlike conventional population-averaged assays, single-cell-based assays not only showed the levels of the averaged population, but also revealed changes in the sub-population. Machine learning-based phenotypic analysis revealed that treatment of BPA and its analogs resulted in the loss of spatial cytoskeletal structure, and an accumulation of M phase cells in a dose- and time-dependent manner. Furthermore, treatment of BPAF induced multinucleated cells, which were associated with altered DNA damage response and impaired cellular F-actin filaments. Overall, we demonstrated a new and effective means to evaluate multiple toxic endpoints in the testicular co-culture model through the combination of machine learning and high-content image-based single cell analysis. This approach provided an in-depth analysis of the multi-dimensional HCA data and provided an unbiased quantitative analysis of the phenotypes of interest.

Yin Lei, Siracusa Jacob, Measel Emily, Guan Xueling, Edenfield Clayton, Liang Shenxuan, Yu Xiaozhong

2019-Nov-21

\n In vitro co-culture model, Bisphenol A, Bisphenol AF, Bisphenol S, High-content image, Machine learning, Single cell analysis, Testicular toxicity, Tetrabromobisphenol A