Machine learning approach reveals heterogeneous responses to FAK and Rho GTPases inhibition on smooth muscle spheroid formation

Hyper proliferation of vascular smooth muscle cells (VSMCs) contributes to neointima formation in atherosclerosis and the response to vascular injury. Understanding how to control VSMC proliferation would advance the effort to treat vascular disease. Drug responses are often different among patients with the same vascular disease condition, making it difficult to cater patient-specific treatments (existence of heterogeneity). Thus, we examined variations in response to drug treatments using VSMC spheroids that mimic vascular disease condition in vivo. FAK and its downstream Rho GTPases (Rac, Rho, and Cdc42) control cell-cell contact and play a key role in vascular pathology. Here, we tested the importance of FAK and Rho GTPases in spheroid formation. VSMC spheroids were made using a hanging-drop method with either inhibitors or vehicle control. Changes in morphology were used as a key indicator of spheroid response to drug treatment. A machine learning (ML) image segmentation (VGG16-U-net) was used to segment the spheroid images. After the morphological features were extracted from the segmented images, a two-level cluster framework was used to cluster VSMC spheroids into different morphologies. We found that FAK and Rho GTPases are required for normal spheroid formation. Next, we analyzed the various morphologies of disrupted spheroids resulting from drug treatment using our ML pipeline. The first-level clustering analysis showed the presence of 4 clusters of spheroids with rounded and disrupted morphologies. The subsequent second-level clustering analysis identified the presence of four distinct morphological clusters among these disrupted spheroids, and they exhibited differential responses to FAK and Rho GTPases inhibition. Particularly, we found FAK and Rho specific morphological phenotypes, thus suggesting that there may be two distinct pathways governing VSMC spheroid formation. Collectively, we revealed there are significant heterogeneities in the drug responses of spheroid formation, which were overlooked in previous analyses. This is our first step towards developing the ML-based method that can be used to assess the effects of different drugs on the VSMC spheroid model for better characterization of pathologic progression of vascular disease.