Single-molecule approaches to stochastic gene expression

Abstract

Both the transcription of mRNAs from genes and their subsequent translation into proteins are inherently stochastic biochemical events, and this randomness can lead to substantial cell-to-cell variability in mRNA and protein numbers in otherwise identical cells. Recently, a number of studies have greatly enhanced our understanding of stochastic processes in gene expression by utilizing new methods capable of counting individual mRNAs and proteins in cells. In this review, we examine the insights that these studies have yielded in the field of stochastic gene expression. In particular, we discuss how these studies have played in understanding the properties of bursts in gene expression. We also compare the array of different methods that have arisen for single mRNA and protein detection, highlighting their relative strengths and weaknesses. In conclusion, we point out further areas where single-molecule techniques applied to gene expression may lead to new discoveries.

Figure 1

(a) Promoter dynamics for a gene that is always in the active state (i.e., nonbursting) versus (b) promoter dynamics for a gene that switches between active and inactive states (i.e., bursty dynamics). (c) mRNA dynamics for nonbursting and (d) bursting genes. In the nonbursting case, one obtains a Poisson distribution of mRNAs per cell across the population, as shown in the marginal histogram, whereas the distribution of mRNAs per cell in the bursting case is much wider than a Poisson distribution despite having the same mean. Protein dynamics for (e) nonbursting and (f) bursting genes, again with the same mean. Although the underlying gene expression dynamics are bursty, the relatively long half-life of the protein results in a wide but Gaussian-looking population distribution, pointing out the need for single-molecule mRNA-counting approaches when studying bursty gene expression. The marginal histograms on the right of the time courses show the distribution of the promoter states, mRNAs, and proteins across a population.

Figure 2

Distributions resulting from different values of the parameters in the gene activation/inactivation model of Peccoud & Ycart (28). The top row corresponds to the parameter γ being larger than the mRNA decay rate δ. The left side of the figure corresponds to high burst frequency compared with δ, whereas the right side corresponds to low burst frequency. The transcription rate γ was also altered as indicated. As mentioned in the text, the burst approximation is only valid when the burst frequency is low and the inactivation rate is faster than the mRNA decay rate. In particular, the bimodal expression pattern that appears with high μ and small λ, and γ cannot appear when one uses the burst approximation.