Subcellular Region Morphology Reflects Cellular Identity
In multicellular organisms, various cells perform distinct physiological and structural roles. Traditionally, cell identity has been defined through morphological features and molecular markers, but these methods have limitations. Our study explores the potential of subcellular morphology to define cellular identity and predict molecular differences. We developed workflows to identify subcellular regions in different cell lines, using convolutional neural networks (CNNs) to classify these regions and finally quantify morphological distances between cell types. First, we demonstrated that subcellular regions could accurately distinguish between isolated cell lines and predict cell types in mixed cultures. We extended this approach to predict molecular differences by training networks to identify human dermal fibroblast subtypes and correlating morphological features with gene expression profiles. Further, we tested pharmacological treatments to induce controlled morphological changes, validating our approach in order to detect these changes. Our results showed that subcellular morphology could be a robust indicator of cellular identity and molecular characteristics. We observed that features learned by networks to distinguish specific cell types could be generalized to quantify distances between other cell types. Networks focusing on different subcellular regions (nucleus, cytosol, membrane) revealed distinct morphological features correlating with specific molecular changes. This study underscores the potential of combining imaging and AI-based methodologies to enhance cell classification without relying on markers or destructive sampling. By quantifying morphological distances, we provide a quantitative characterization of cell subtypes and states, offering valuable insights for regenerative medicine and other biomedical fields.