Probing Pluripotency Gene Regulatory Networks with Live Cell Imaging

Published

September 20, 2020

Author(s)

Anne Plant, Michael Halter, Jeffrey R. Stinson

Abstract

Live cell imaging uniquely enables the measurement of dynamic events in single cells, but it has not been used often in the study of gene regulatory networks. Network components can be examined in relation to one another by quantitative live cell imaging of fluorescent protein reporter cell lines that simultaneously report on more than one network component. A series of dual-reporter cell lines would allow different combinations of network components to be examined in individual cells. Dynamical information about interacting network components in individual cells is critical to predictive modeling of gene regulatory networks, and such information is not accessible through omics and other end point techniques. Achieving this requires that gene-edited cell lines are appropriately designed and adequately characterized to assure the validity of the biological conclusions derived from the expression of the reporters. In this brief review we discuss what is known about the importance of dynamics to network modeling and review some recent advances in optical microscopy methods and image analysis approaches that are making the use of quantitative live cell imaging for network analysis possible. We also discuss how strategies for genetic engineering of reporter cell lines can influence the biological relevance of the data.

Citation

Computational and Structural Biotechnology Journal

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.csbj.2020.09.025

Local Download

Keywords

systems biology, live cell imaging, gene regulatory networks, Quantitative light microscopy, Fluorescent reporter cell lines, Phenotype heterogeneity, Dynamic fluctuations, Dynamic cell response

Citation

Plant, A. , Halter, M. and Stinson, J. (2020), Probing Pluripotency Gene Regulatory Networks with Live Cell Imaging, Computational and Structural Biotechnology Journal, [online], https://doi.org/10.1016/j.csbj.2020.09.025, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930631 (Accessed December 11, 2024)