Abstract
Stochastic gene expression causes phenotypic heterogeneity in a population of genetically identical bacterial cells. Such non-genetic heterogeneity can have important consequences for the population fitness, and therefore cells implement regulation strategies to either suppress or exploit such heterogeneity to adapt to their circumstances. By employing time-lapse microscopy of single cells, the fluctuation dynamics of gene expression may be analysed, and their regulatory mechanisms thus deciphered. However, a careful consideration of the experimental design and data-analysis is needed to produce useful data for deriving meaningful insights from them. In the present paper, the individual steps and challenges involved in a time-lapse experiment are discussed, and a rigorous framework for designing, performing, and extracting single-cell gene expression dynamics data from such experiments is outlined.
Highlights
In the present paper the steps which lead from the basics of a biological process
an intuition for the abundances and fluctuation timescales can help the user in choosing fluorescent protein (FP)
here that monitoring fluctuation dynamics of reporters to infer the dynamics of the underlying process is still an indirect method
Summary
(Figure 3E) where the ‘mother’ cells stay at the closed end of the trench for many generations while its progeny are washed away. The high-throughput nature of time-lapse experiments in microfluidic devices [34,36,32] coupled with the long-term imaging at high time resolutions generates a vast amount of data Analysing these data by hand is an impossible task, and accurate automated analysis pipelines are the only option. The phase-contrast point spread function has alternating phases, causing it to have alternating bright and dark concentric rings This limits our abilities to segment cells from an image, especially when they are trapped in microfluidic devices. By training a model specific to the data, or specific to datasets which are similar, the model can: be robust to changes in contrast, be specific to cell shape, recognise regions where cells touch each other, and recognise the walls of the mother machine
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