QSR: A quantitative super-resolution analysis tool reveals the cell-cycle dependent organization of RNA Polymerase i in live human cells

J. O. Andrews; W. Conway; W. K. Cho; A. Narayanan; J. H. Spille; N. Jayanth; T. Inoue; S. Mullen; J. Thaler; I. I. Cissé

doi:10.1038/s41598-018-25454-0

QSR: A quantitative super-resolution analysis tool reveals the cell-cycle dependent organization of RNA Polymerase i in live human cells

J. O. Andrews, W. Conway, W. K. Cho, A. Narayanan, J. H. Spille, N. Jayanth, T. Inoue, S. Mullen, J. Thaler, I. I. Cissé

Physics

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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.

Original language	English (US)
Article number	7424
Journal	Scientific reports
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41598-018-25454-0
State	Published - Dec 1 2018

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title = "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 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.",

author = "Andrews, {J. O.} and W. Conway and Cho, {W. K.} and A. Narayanan and Spille, {J. H.} and N. Jayanth and T. Inoue and S. Mullen and J. Thaler and Ciss{\'e}, {I. I.}",

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doi = "10.1038/s41598-018-25454-0",

language = "English (US)",

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T2 - A quantitative super-resolution analysis tool reveals the cell-cycle dependent organization of RNA Polymerase i in live human cells

AU - Andrews, J. O.

AU - Conway, W.

AU - Cho, W. K.

AU - Narayanan, A.

AU - Spille, J. H.

AU - Jayanth, N.

AU - Inoue, T.

AU - Mullen, S.

AU - Thaler, J.

AU - Cissé, I. I.

PY - 2018/12/1

Y1 - 2018/12/1

N2 - 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.

AB - 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.

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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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