A machine learning pipeline revealing heterogeneous responses to drug perturbations on vascular smooth muscle cell spheroid morphology and formation
| Autor: | Yongho Bae, Moses Choi, Junbong Jang, Kwonmoo Lee, Yudong Yu, Amanda Krajnik, Kalyanaraman Vaidyanathan, Su Jin Heo, John Kolega, Chuangqi Wang, Bolun Lin |
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| Rok vydání: | 2020 |
| Předmět: |
Drug
Neointima Cell biology Vascular smooth muscle Science media_common.quotation_subject Cell CDC42 Biology Cellular imaging Machine learning computer.software_genre Muscle Smooth Vascular Article Machine Learning Functional clustering Cell growth Image processing In vivo Spheroids Cellular medicine Humans Computational models cdc42 GTP-Binding Protein Cells Cultured media_common Multidisciplinary business.industry Spheroid Cell adhesion Vascular System Injuries musculoskeletal system Atherosclerosis In vitro rac GTP-Binding Proteins Computational biology and bioinformatics medicine.anatomical_structure Mechanisms of disease Focal Adhesion Kinase 1 embryonic structures cardiovascular system Medicine Artificial intelligence business computer Cell signalling |
| Zdroj: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021) |
| ISSN: | 2045-2322 |
| Popis: | Machine learning approaches have shown great promise in biology and medicine discovering hidden information to further understand complex biological and pathological processes. In this study, we developed a deep learning-based machine learning algorithm to meaningfully process image data and facilitate studies in vascular biology and pathology. Vascular injury and atherosclerosis 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 VSMC behaviors would promote the development of therapeutic targets to treat vascular diseases. However, the response to drug treatments among VSMCs with the same diseased vascular condition is often heterogeneous. Here, to identify the heterogeneous responses of drug treatments, we created an in vitro experimental model system using VSMC spheroids and developed a machine learning-based computational method called HETEROID (heterogeneous spheroid). First, we established a VSMC spheroid model that mimics neointima-like formation and the structure of arteries. Then, to identify the morphological subpopulations of drug-treated VSMC spheroids, we used a machine learning framework that combines deep learning-based spheroid segmentation and morphological clustering analysis. Our machine learning approach successfully showed that FAK, Rac, Rho, and Cdc42 inhibitors differentially affect spheroid morphology, suggesting that multiple drug responses of VSMC spheroid formation exist. Overall, our HETEROID pipeline enables detailed quantitative drug characterization of morphological changes in neointima formation, that occurs in vivo, by single-spheroid analysis. |
| Databáze: | OpenAIRE |
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