Tracking induced pluripotent stem cell differentiation with a fluorescent genetically encoded epigenetic probe

AI Stepanov, AA Shuvaeva, LV Putlyaeva… - Cellular and Molecular …, 2024 - Springer
AI Stepanov, AA Shuvaeva, LV Putlyaeva, DK Lukyanov, AA Galiakberova, DA Gorbachev
Cellular and Molecular Life Sciences, 2024Springer
Epigenetic modifications (methylation, acetylation, etc.) of core histones play a key role in
regulation of gene expression. Thus, the epigenome changes strongly during various
biological processes such as cell differentiation and dedifferentiation. Classical methods of
analysis of epigenetic modifications such as mass-spectrometry and chromatin immuno-
precipitation, work with fixed cells only. Here we present a genetically encoded fluorescent
probe, MPP8-Green, for detecting H3K9me3, a histone modification associated with inactive …
Abstract
Epigenetic modifications (methylation, acetylation, etc.) of core histones play a key role in regulation of gene expression. Thus, the epigenome changes strongly during various biological processes such as cell differentiation and dedifferentiation. Classical methods of analysis of epigenetic modifications such as mass-spectrometry and chromatin immuno-precipitation, work with fixed cells only. Here we present a genetically encoded fluorescent probe, MPP8-Green, for detecting H3K9me3, a histone modification associated with inactive chromatin. This probe, based on the chromodomain of MPP8, allows for visualization of H3K9me3 epigenetic landscapes in single living cells. We used this probe to track changes in H3K9me3 landscapes during the differentiation of induced pluripotent stem cells (iPSCs) into induced neurons. Our findings revealed two major waves of global H3K9me3 reorganization during 4-day differentiation, namely on the first and third days, whereas nearly no changes occurred on the second and fourth days. The proposed method LiveMIEL (Live-cell Microscopic Imaging of Epigenetic Landscapes), which combines genetically encoded epigenetic probes and machine learning approaches, enables classification of multiparametric epigenetic signatures of single cells during stem cell differentiation and potentially in other biological models.
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