Emerging machine learning approaches to phenotyping cellular motility and morphodynamics. (17th June 2021)

• Record Type: Journal Article
• Authors: Choi, Hee June; Wang, Chuangqi; Pan, Xiang; Jang, Junbong; Cao, Mengzhi; Brazzo, Joseph A; Bae, Yongho; Lee, Kwonmoo
• Is Part Of: Physical biology. Volume 18:Number 4(2021)
• Publication Date: 2021-06-17
• Subjects: machine learning -- live cell imaging -- deep learning -- phenotyping -- cell motility -- cell morphodynamics; Biophysics -- Periodicals; Biochemistry -- Periodicals; Biology -- Data processing -- Periodicals; 570.5
• DOI: 10.1088/1478-3975/abffbe
• Languages: English
• ISSNs: 1478-3967

Abstract

Abstract: Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.