Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.
Journal:
Communications biology
PMID:
40000764
Abstract
Single-cell image analysis is crucial for studying drug effects on cellular morphology and phenotypic changes. Most studies focus on single cell types, overlooking the complexity of cellular interactions. Here, we establish an analysis pipeline to extract phenotypic features of cancer cells cultured with fibroblasts. Using high-content imaging, we analyze an oncology drug library across five cancer and fibroblast cell line co-culture combinations, generating 61,440 images and ∼170 million single-cell objects. Traditional phenotyping with CellProfiler achieves an average enrichment score of 62.6% for mechanisms of action, while pre-trained neural networks (EfficientNetB0 and MobileNetV2) reach 61.0% and 62.0%, respectively. Variability in enrichment scores may reflect the use of multiple drug concentrations since not all induce significant morphological changes, as well as the cellular and genetic context of the treatment. Our study highlights nuanced drug-induced phenotypic variations and underscores the morphological heterogeneity of ovarian cancer cell lines and their response to complex co-culture environments.