Image-based cell phenotyping with deep learning. (December 2021)

• Record Type: Journal Article
• Authors: Pratapa, Aditya; Doron, Michael; Caicedo, Juan C.
• Is Part Of: Current opinion in chemical biology. Volume 65(2021)
• Page Start: 9
• Page End: 17
• Publication Date: 2021-12
• Subjects: Deep learning -- Cell phenotyping -- Phenotypic screening -- Image analysis; Bioorganic chemistry -- Periodicals; Biology -- Periodicals; Biochemistry -- Periodicals; Clinical biochemistry -- Periodicals; Biochemistry -- Periodicals; Chimie bio-organique -- Périodiques; Biologie -- Périodiques; 572.05
• DOI: 10.1016/j.cbpa.2021.04.001
• Languages: English
• ISSNs: 1367-5931

Abstract

Abstract: A cell's phenotype is the culmination of several cellular processes through a complex network of molecular interactions that ultimately result in a unique morphological signature. Visual cell phenotyping is the characterization and quantification of these observable cellular traits in images. Recently, cellular phenotyping has undergone a massive overhaul in terms of scale, resolution, and throughput, which is attributable to advances across electronic, optical, and chemical technologies for imaging cells. Coupled with the rapid acceleration of deep learning–based computational tools, these advances have opened up new avenues for innovation across a wide variety of high-throughput cell biology applications. Here, we review applications wherein deep learning is powering the recognition, profiling, and prediction of visual phenotypes to answer important biological questions. As the complexity and scale of imaging assays increase, deep learning offers computational solutions to elucidate the details of previously unexplored cellular phenotypes.