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Updated: Sep 25, 2026

A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation
Published on: October 4, 2024
Imaging methods to monitor and quantify cell differentiation
Kevin Cheah1, Pingjin Chu1, Greta Schmidt1
1Department of Chemistry and Biochemistry, Worcester Polytechnic Institute, Worcester, MA, United States.
Abstract:
The transition of a cell from a stem to a differentiated state involves an interrelated and complex series of events. These events include dynamic changes in cellular nucleic acid and protein content that are mediated by both intrinsic and extrinsic factors which ultimately lead to differentiation into specific lineage. Quantifying the parameters associated with differentiation and their changes under different conditions would not only allow for a better understanding of this process but also would enable the development of approaches that control differentiation. Here, we describe processes associated with the differentiation of two types of cultured cells, neurons and fibroblasts, and the tools to follow changes in real time. Specifically, we discuss methods to the identify cell lineage, changes in morphology, shifts in specific mRNA and miRNA levels as well as the changes in protein localization, interactions and assemblies that accompany differentiation.

