Machine learning-assisted Repli-Histo labeling reveals distinct transcription-dependent constraints on chromatin motion in living cells

Avatar
Poster
Voice is AI-generated
Connected to paperThis paper is a preprint and has not been certified by peer review

Machine learning-assisted Repli-Histo labeling reveals distinct transcription-dependent constraints on chromatin motion in living cells

Authors

Minami, K.; Nakazato, K.; Tamura, S.; Ashwin, S. S.; Maeshima, K.

Abstract

Genomic DNA is wrapped around core histones to form nucleosomes, which are organized in cells from euchromatin to heterochromatin with distinct genome functions. Although transcription is known to shape chromatin behavior in live cells, it remains unclear how different transcription systems shape chromatin classes and nuclear subcompartments. We developed machine learning-assisted Repli-Histo labeling to classify euchromatin and heterochromatin classes (Classes IA, IB, II, and III) and combined it with single-nucleosome imaging in live cells. Nucleosome motion was progressively constrained from euchromatin to heterochromatin. RNA polymerase II inhibition by THZ1, DRB, or -amanitin increased nucleosome motion in euchromatic Classes IA and IB and in heterochromatin around nucleoli, but not at the nuclear periphery. In contrast, RNA polymerase I inhibition by CX-5461 selectively increased nucleosome motion in Class III heterochromatin around nucleoli. Our study reveals that Pol II and Pol I transcription shape chromatin behavior in distinct chromatin classes and nuclear subcompartments. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=143 SRC="FIGDIR/small/736477v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): [email protected]@fd2c13org.highwire.dtl.DTLVardef@158c210org.highwire.dtl.DTLVardef@2cba91_HPS_FORMAT_FIGEXP M_FIG C_FIG

Follow Us on

0 comments

Add comment