qSR: a quantitative super-resolution analysis tool reveals the cell-cycle dependent organization of RNA Polymerase I in live human cells
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Author(s)
Andrews, James Owen
Conway, W.
Cho, Won-ki
Narayanan, Arjun
Spille, Jan Hendrik
Jayanth, Namrata
Inoue, Takuma
Thaler, Jesse
Cisse, Ibrahim I
Mullen, Susan
Date Issued
May 2018
Journal
Scientific Reports
Publisher
Nature Publishing Group
Citation
Andrews, J. O., et al. “QSR: A Quantitative Super-Resolution Analysis Tool Reveals the Cell-Cycle Dependent Organization of RNA Polymerase I in Live Human Cells.” Scientific Reports, vol. 8, no. 1, Dec. 2018. © 2018 The Authors
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Final published version
Abstract
We present qSR, an analytical tool for the quantitative analysis of single molecule based super-resolution data. The software is created as an open-source platform integrating multiple algorithms for rigorous spatial and temporal characterizations of protein clusters in super-resolution data of living cells. First, we illustrate qSR using a sample live cell data of RNA Polymerase II (Pol II) as an example of highly dynamic sub-diffractive clusters. Then we utilize qSR to investigate the organization and dynamics of endogenous RNA Polymerase I (Pol I) in live human cells, throughout the cell cycle. Our analysis reveals a previously uncharacterized transient clustering of Pol I. Both stable and transient populations of Pol I clusters co-exist in individual living cells, and their relative fraction vary during cell cycle, in a manner correlating with global gene expression. Thus, qSR serves to facilitate the study of protein organization and dynamics with very high spatial and temporal resolutions directly in live cell.
MIT Department
Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences
Massachusetts Institute of Technology. Department of Physics
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Creative Commons Attribution 4.0 International License
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DOI of Published Version
https://doi.org/10.1038/s41598-018-25454-0
