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