A machine learning-based analysis method for small molecule high content screening of three-dimensional cancer spheroid morphology.

ABSTRACT: Although multiparameter cellular morphological profiling methods and three-dimensional (3D) biological model systems can potentially provide complex insights for pharmaceutical discovery campaigns, there have been relatively few reports combining these experimental approaches. In this study, we used the U87 glioblastoma cell line grown in a 3D spheroid format to validate a multiparameter cellular morphological profiling screening method. The steps of this approach include 3D spheroid treatment, cell staining, fully automated digital image acquisition, image segmentation, numerical feature extraction, and multiple machine learning approaches for cellular profiling. For comparison, we measured the same samples after live-cell microscopy with an endpoint CellTiter-Glo cell viability assay. Th

SUBMITTER: Goyal V 

PROVIDER: S-EPMC12597564 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature