qSR: a quantitative super-resolution analysis tool reveals the cell-cycle dependent organization of RNA Polymerase I in live human cells

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 dat...

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Autores principales: J. O. Andrews, W. Conway, W -K. Cho, A. Narayanan, J -H. Spille, N. Jayanth, T. Inoue, S. Mullen, J. Thaler, I. I. Cissé
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2018
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Acceso en línea:https://doaj.org/article/9142ddcc0bf44d48baf4957c07989089
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Sumario: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.