Estimating RNA numbers in single cells by RNA fluorescent tagging and flow cytometry
Research output: Contribution to journal › Article › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Article number | 105745 |
| Journal | Journal of Microbiological Methods |
| Volume | 166 |
| DOIs | |
| Publication status | Published - 22 Oct 2019 |
| Publication type | A1 Journal article-refereed |
Abstract
Estimating the statistics of single-cell RNA numbers has become a key source of information on gene expression dynamics. One of the most informative methods of in vivo single-RNA detection is MS2d-GFP tagging. So far, it requires microscopy and laborious semi-manual image analysis, which hampers the amount of collectable data. To overcome this limitation, we present a new methodology for quantifying the mean, standard deviation, and skewness of single-cell distributions of RNA numbers, from flow cytometry data on cells expressing RNA tagged with MS2d-GFP. The quantification method, based on scaling flow-cytometry data from microscopy single-cell data on integer-valued RNA numbers, is shown to readily produce precise, big data on in vivo single-cell distributions of RNA numbers and, thus, can assist in studies of transcription dynamics.
ASJC Scopus subject areas
Keywords
- Flow cytometry, MS2d-GFP RNA tagging, Single-cell RNA numbers, Time-lapse microscopy