From observing to predicting single-cell structure and function with high-throughput/high-content microscopy. Issue 2 (26th June 2019)

• Record Type: Journal Article
• Authors: Chessel, Anatole; Carazo Salas, Rafael E.
• Editors: Szczelkun, Mark
• Is Part Of: Essays in biochemistry. Volume 63:Issue 2(2019)
• Page Start: 197
• Page End: 208
• Publication Date: 2019-06-26
• Subjects: causal cell behaviour -- gene regulatory networks -- genome-wide screening -- high-content microscopy -- high-throughput microscopy -- Machine Learning; Biochemistry -- Periodicals; 572
• DOI: 10.1042/EBC20180044
• Languages: English
• ISSNs: 0071-1365

Abstract

Abstract : In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular—powered by breakthroughs in computer vision, large-scale image analysis and machine learning—high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.