Machine learning reveals heterogeneous responses to FAK, Rac, Rho, and Cdc42 inhibition on vascular smooth muscle cell spheroid formation and morphology
Machine learning reveals heterogeneous responses to FAK, Rac, Rho, and Cdc42 inhibition on vascular smooth muscle cell spheroid formation and morphology
SUMMARY Atherosclerosis and vascular injury are characterized by neointima formation caused by the aberrant accumulation and proliferation of vascular smooth muscle cells (VSMCs) within the vessel wall. Understanding how to control VSMCs would advance the effort to treat vascular disease. However, the response to treatments aimed at VSMCs is often different among patients with the same disease condition, suggesting patient-specific heterogeneity in VSMCs. Here, we present an experimental and computational method called HETEROID (Heterogeneous Spheroid), which examines the heterogeneity of the responses to drug treatments at the single-spheroid level by combining a VSMC spheroid model and machine learning (ML) analysis. First, we established a VSMC spheroid model that mimics neointima formation induced by atherosclerosis and vascular injury. We found that FAK-Rac/Rho, but not Cdc42, pathways regulate the VSMC spheroid formation through N-cadherin. Then, to identify the morphological subpopulations of drug-perturbed spheroids, we used an ML framework that combines deep learning-based spheroid segmentation and morphological clustering analysis. Our ML approach reveals that FAK, Rac, Rho, and Cdc42 inhibitors differentially affect the spheroid morphology, suggesting there exist multiple distinct pathways governing VSMC spheroid formation. Overall, our HETEROID pipeline enables detailed quantitative characterization of morphological changes in neointima formation, that occurs in vivo, by single-spheroid analysis of various drug treatments.
Vaidyanathan Kalyanaraman、Lin Bolun、Heo Su-Jin、Lee Kwonmoo、Yu Yudong、Wang Chuangqi、Choi Moses、Krajnik Amanda、Kolega John、Bae Yongho
Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New YorkDepartment of Biomedical Engineering, Worcester Polytechnic InstituteDepartment of Orthopedic Surgery, Perelman School of Medicine, University of PennsylvaniaDepartment of Biomedical Engineering, Worcester Polytechnic Institute||Vascular Biology Program, Boston Children?ˉs HospitalDepartment of Biomedical Engineering, Worcester Polytechnic InstituteDepartment of Biomedical Engineering, Worcester Polytechnic InstituteDepartment of Biomedical Engineering, Worcester Polytechnic InstituteDepartment of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New YorkDepartment of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New YorkDepartment of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York
基础医学生物科学研究方法、生物科学研究技术生理学
vascular smooth muscle cellspheroidmorphologyneointima formationheterogeneitymachine learningclusteringsegmentationFAKsmall GTPases
Vaidyanathan Kalyanaraman,Lin Bolun,Heo Su-Jin,Lee Kwonmoo,Yu Yudong,Wang Chuangqi,Choi Moses,Krajnik Amanda,Kolega John,Bae Yongho.Machine learning reveals heterogeneous responses to FAK, Rac, Rho, and Cdc42 inhibition on vascular smooth muscle cell spheroid formation and morphology[EB/OL].(2025-03-28)[2025-07-01].https://www.biorxiv.org/content/10.1101/2020.01.30.927616.点此复制
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