Meanwhile, work has begun on systematically examining cell-to-cell variability in gene expression in higher eukaryotes. A priori, one might expect that higher eukaryotes, with their larger size and numbers of molecules, might exhibit less variability than prokaryotes and yeast. On the other hand, the prevalence of transcriptionally-silenced heterochromatin would argue that slow, random events of gene activation and inactivation would lead to much larger fluctuations than in unicellular organisms. As it happens, the latter is the case, with a growing body of evidence that fluctuations in higher eukaryotes can be remarkably large.
Many of these early experiments were limited by the difficulties inherent to measuring gene expression in single-cells in higher eukaryotes. One problem is sensitivity: owing to their large cellular volumes, even moderately expressed fluorescent proteins can be difficult to detect. Another problem is the lack of tools available to manipulate these organisms genetically. To circumvent these problems, researchers have come up with many new ways of assaying gene expression at the single-cell level to measure cell-to-cell variability.
One approach is to measure mRNAs rather than proteins. For instance, utilizing the MS2-GFP method of mRNA detection (Beach et al., 1999
; Bertrand et al., 1998
), Chubb et al. (2006)
showed that a developmental gene in Dictyostelium discoideum
is transcribed in a pulsatile fashion, directly demonstrating the burst hypothesis by watching mRNAs accumulate and dissipate from active and inactive sites of transcription in real time. In comparison with the less intense bursts observed using a similar approach in bacteria (Golding et al., 2005
), the authors found that the bursts were less frequent but longer lasting. In contrast with earlier bacterial models, this shows that bursts in gene expression are the primary intrinsic cause of cell-to-cell variability.
One can also measure mRNA numbers in single cells across a population using variants of fluorescence in situ
hybridization (FISH) capable of detecting individual mRNA molecules (Femino et al., 1998
; Raj et al., 2006
; Raj et al., 2008
). Raj et al. (2006)
combined single molecule FISH with statistical analysis to show that individual mammalian cells transcribed a stably integrated transgene in infrequent but potent bursts, resulting in large cell-to-cell variations in mRNA number () that correlated with the presence or absence of active sites of transcription [seen also by (Voss et al., 2006
)]. These bursts were correlated between genes that were located proximally to each other but not between genes that were distally located, providing another clue that chromatin remodeling may be responsible for genes transitioning between an active and inactive state: “opening” of the chromatin surrounding one gene is likely to open chromatin for neighboring genes, leading to correlations in their expression, whereas distant genes are not affected in this coordinated manner, resulting in uncorrelated expression. This behavior is also seen in globin expression (de Krom et al., 2002
) and shows that genomic position can be important in interpreting the concepts of intrinsic and extrinsic noise.
Quantitative single-cell RT-PCR methods have been used to obtain cell-by-cell counts of endogenous mRNAs, thus circumventing issues associated with generating transgenic cell lines and organisms. By simultaneously measuring the numbers of five transcripts in individual pancreatic islet cells, Bengtsson et al. (2005)
showed that the distributions of these mRNAs across the population were heavily skewed as in . Moreover, they measured correlations in the fluctuations in the expression of these genes, finding that two functionally related genes were highly correlated whereas the rest were uncorrelated, perhaps pointing to the existence of common regulators for the two genes. Such findings highlight the potential use of stochastic gene expression in uncovering the mechanisms of transcriptional regulation. One difficulty with this approach is the rigorous set of controls required to calibrate RT-PCR results in molecular units, a problem that can be obviated through the use of so-called “digital” RT-PCR. This method, in which cDNA reverse transcribed from an individual cell is fractionated into enough individual PCR reactions that each reaction will contain either 0 or 1 cDNAs, has been used to examine the expression of the PU.1 transcription factor in both hematopoetic stem cells and in myeloid progenitor cells (Warren et al., 2006
), in which the authors observed marked heterogeneity in transcript levels.
Atlhough the evidence for transcriptional bursting continues to accumulate, little is known about the source of these bursts. As mentioned earlier, one possibility often posited is that stochastic events of chromatin remodeling could underlie the bursts by causing the gene to switch between transcriptionally active and inactive states (Becskei et al., 2005
; Raj et al., 2006
; Raser and O'Shea, 2004
; Warren et al., 2006
). In support of this view, direct visualization of chromatin remodeling has shown it to be a slow process that can act over a long range on a timescale of hours (Tumbar et al., 1999
). However, there are other plausible mechanisms that might underlie transcriptional bursts. One possibility is the existence of pre-initiation complexes that form on the promoter region of the DNA and facilitate multiple rounds of RNA polymerase II transcription events (Blake et al., 2006
; Blake et al., 2003
). If such complexes exist only for short periods of time, they could also result in pulsatile transcription. Another point to consider is that transcription doesn't take place in a uniform fashion throughout the genome but is concentrated in transcriptional “factories” (Jackson et al., 1993
; Wansink et al., 1993
) to which active genes are recruited (Osborne et al., 2004
). Remarkably, it appears that a limited number of these factories (on the order of hundreds) are responsible for most mRNA transcription in the cell; thus, competition for these factories could result in the stochastic expression of any given gene. Ultimately, understanding the biochemical origins of bursting may require the application of new (or perhaps combinations of old) techniques for imaging gene expression and genome organization in real-time, as cell-to-cell variability in population “snapshots” may not be sufficient to resolve the dynamics of the bursting mechanisms (Pedraza and Paulsson, 2008
). Although difficult, the prize for such a technical feat would be a much deeper understanding of the transcriptional process.
The above studies examining mRNA copy number variation provide insights into the origins of noise, although they mostly fail to show how those mRNA fluctuations propagate to noise in protein levels. To examine noise in protein levels in human cells, Sigal et al. (2006)
used a clever strategy to fluorescently tag endogenous proteins. They transfected a cell line with DNA containing artificial YFP exons that occasionally insert themselves into an intron, YFP is included in the protein encoded by the encapsulating gene. Using time-lapse microscopy, the authors were able to show that gene expression in individual cells was variable, but that the fluctuations were slowly varying in time; that is, it took multiple cell divisions before a highly expressing cell would become a lowly expressing cell and vice versa. Interestingly, they also found correlations between genes in the same pathway, but not between unrelated genes, echoing the results of Bengtsson et al. (2005)
Yet, the variability observed at the protein level by Sigal et al. (2006)
seems generally much smaller than that observed at the mRNA level in the aforementioned studies, with the distribution of mRNAs being much more heavily skewed (). How might such a discrepancy be resolved? One answer may be methodological: by screening for cells expressing a detectable amount of YFP, the proteins with YFP insertions obtained by Sigal et al. may be biased towards heavily or constitutively expressing genes with less variability, an interpretation supported by the fact that variability in the number of GAPDH mRNAs is lower than other genes (Warren et al., 2006
). It is also possible that protein stability plays a role in the relationship between mRNA and protein variability (Raj et al., 2006
). Short-lived proteins will track mRNA levels very closely, leading to protein distributions that resemble (and correlate strongly with) mRNA distributions. However, if the proteins degrade slowly (as is the case for YFP), then the large pool of older proteins will buffer the rapid fluctuations in mRNA; that is, mRNA bursts may serve only to “top up” protein levels. In this case, mRNA and protein levels do not strongly correlate.