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 livi...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Published: |
Nature Publishing Group
2018
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| Online Access: | http://hdl.handle.net/1721.1/118788 https://orcid.org/0000-0003-1867-4380 https://orcid.org/0000-0001-9336-0686 https://orcid.org/0000-0002-2269-3253 https://orcid.org/0000-0001-8493-4721 https://orcid.org/0000-0001-9746-6007 https://orcid.org/0000-0003-0362-0072 https://orcid.org/0000-0002-2406-8160 https://orcid.org/0000-0002-8764-1809 |
| Summary: | 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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