Measuring single-cell gene expression dynamics in bacteria using fluorescence time-lapse microscopy
Abstract
Quantitative single-cell time-lapse microscopy is a powerful method for analyzing gene circuit dynamics and heterogeneous cell behavior. We describe the application of this method to imaging bacteria by using an automated microscopy system. This protocol has been used to analyze sporulation and competence differentiation in Bacillus subtilis, and to quantify gene regulation and its fluctuations in individual Escherichia coli cells. The protocol involves seeding and growing bacteria on small agarose pads and imaging the resulting microcolonies. Images are then reviewed and analyzed using our laboratory's custom MATLAB analysis code, which segments and tracks cells in a frame-to-frame method. This process yields quantitative expression data on cell lineages, which can illustrate dynamic expression profiles and facilitate mathematical models of gene circuits. With fast-growing bacteria, such as E. coli or B. subtilis, image acquisition can be completed in 1 d, with an additional 1–2 d for progressing through the analysis procedure.
Additional Information
© 2011 Nature America, Inc. Published online 15 December 2011. We thank J. Park and additional Elowitz Lab members (former and present) for helpful comments regarding the manuscript. The authors declare no competing financial interests. This research was supported by US National Institutes of Health grant R01GM07977, the National Science Foundation CAREER Award 0644463 and the Packard Foundation. Author Contributions: J.W.Y., J.C.W.L. and M.B.E. wrote and developed the protocol. A.A. helped with developing a website and modifying the Schnitzcells software package for public release. N.R., P.S.S. and M.B.E. were major original developers of Schnitzcells, and T.B. and E.M. optimized the tracking algorithm.Attached Files
Accepted Version - nihms605711.pdf
Files
Name | Size | Download all |
---|---|---|
md5:dc485357dbfb19112917e65ac5ef9069
|
273.9 kB | Preview Download |
Additional details
- PMCID
- PMC4161363
- Eprint ID
- 29362
- Resolver ID
- CaltechAUTHORS:20120217-105727788
- NIH
- R01GM07977
- NSF
- MCB-0644463
- David and Lucile Packard Foundation
- Created
-
2012-02-17Created from EPrint's datestamp field
- Updated
-
2021-11-09Created from EPrint's last_modified field