Machine learning-assisted high-content imaging analysis of 3D MCF7 microtissues for estrogenic effect prediction.

ABSTRACT: Endocrine-disrupting chemicals (EDCs) pose a significant threat to human well-being and the ecosystem. However, in managing the many thousands of uncharacterized chemical entities, the high-throughput screening of EDCs using relevant biological endpoints remains challenging. Three-dimensional (3D) culture technology enables the development of more physiologically relevant systems in more realistic biochemical microenvironments. The high-content and quantitative imaging techniques enable quantifying endpoints associated with cell morphology, cell-cell interaction, and microtissue organization. In the present study, 3D microtissues formed by MCF-7 breast cancer cells were exposed to the model EDCs estradiol (E2) and propyl pyrazole triol (PPT). A 3D imaging and image analysis pipeline was es

SUBMITTER: Li H 

PROVIDER: S-EPMC10844358 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature