qSR: a quantitative super-resolution analysis tool reveals the cell-cycle dependent organization of RNA Polymerase I in live human cells
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.
© The Author(s) 2018. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 14 December 2017. Accepted 13 April 2018. Published 09 May 2018. The work is supported by the National Institutes of Health, the National Cancer Institutes through the NIH Director's New Innovator Award, DP2-CA195769 to IIC and funds from MIT Department of Physics. Further support for this work was provided by NIH 4D Nucleome program, grant U54-DK107980, the DeFlorez Endowment Fund, and the MIT Undergraduate Research Opportunities Program (UROP). JOA is supported by the National Science Foundation Graduate Research Fellowship. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health nor the National Science Foundation. The software is distributed open-source under the GNU General Public License (GPLv2/LGPLv2) Version 2. qSR is maintained and distributed for free on www.github.com/cisselab/qSR. J. O. Andrews and W. Conway contributed equally to this work. Author Contributions. The software was conceived by J.O.A. and I.I.C. and the Pol I experiments were conceived by W.C. and I.I.C. J.T. provided expertise in spatial clustering and implemented the initial version of FastJet-based spatial clustering. Initial versions of the other algorithms were implemented by J.O.A., A.N., and J.H.S. The rest of the software was developed by J.O.A. T.I. and I.I.C. designed and built the homebuilt super-resolution microscope. W.-K.C., N.J. and S.M. provided assistance in the generation of the endogenous Dendra2-RPA190 cell line. W.-K.C. acquired the Pol II data, and W.C. acquired and analyzed the Pol I data. J.O.A., W.C., W.-K.C., and I.I.C. wrote the manuscript. The authors declare no competing interests.
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