qSR: a quantitative super-resolution analysis tool reveals the cell-cycle dependent organization of RNA Polymerase I in live human cells.
J O AndrewsW ConwayWon-Ki ChoA NarayananJan-Hendrik SpilleN JayanthT InoueS MullenJ ThalerIbrahim I CissePublished in: Scientific reports (2018)
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.
Keyphrases
- cell cycle
- living cells
- single molecule
- fluorescent probe
- cell proliferation
- gene expression
- electronic health record
- big data
- machine learning
- high resolution
- atomic force microscopy
- data analysis
- cerebral ischemia
- protein protein
- amino acid
- high throughput
- blood brain barrier
- artificial intelligence
- rna seq
- small molecule
- binding protein