Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences

Raj, Arjun; Oudenaarden, Alexander van

doi:10.1016/j.cell.2008.09.050

Cited by 2,388 publications

(2,266 citation statements)

References 109 publications

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“…r ¼ const) and therefore S / v(rDC) (see Main Text for further discussion of r variation with v), where we have interpreted f / rDC. Using S ¼ D results in the prediction that rDC / v À1/4 , in contrast to the model we suggest in Equation (3).…”

Section: Box 1 Allometric Scaling Hypothesis Of Cellular Power Demandscontrasting

confidence: 65%

Mitochondrial heterogeneity, metabolic scaling and cell death

et al. 2017

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Heterogeneity in mitochondrial content has been previously suggested as a major contributor to cellular noise, with multiple studies indicating its direct involvement in biomedically important cellular phenomena. A recently published dataset explored the connection between mitochondrial functionality and cell physiology, where a non-linearity between mitochondrial functionality and cell size was found. Using mathematical models, we suggest that a combination of metabolic scaling and a simple model of cell death may account for these observations. However, our findings also suggest the existence of alternative competing hypotheses, such as a non-linearity between cell death and cell size. While we find that the proposed non-linear coupling between mitochondrial functionality and cell size provides a compelling alternative to previous attempts to link mitochondrial heterogeneity and cell physiology, we emphasise the need to account for alternative causal variables, including cell cycle, size, mitochondrial density and death, in future studies of mitochondrial physiology.

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Section: Box 1 Allometric Scaling Hypothesis Of Cellular Power Demandscontrasting

confidence: 65%

Mitochondrial heterogeneity, metabolic scaling and cell death

et al. 2017

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“…The power of metabolomics can be exploited in population-level measurements, but as cell populations are not necessarily homogeneous, it will be even more informative to measure at the single cell level. In addition to the abovementioned sources for heterogeneity, stochasticity-induced phenotypic heterogeneity was identified a few years ago as an additional source of cell-to-cell variability [18,19]. Because of low copy numbers of specific biomolecules, certain processes at the gene and protein expression level are inherently stochastic and can cause random fluctuations in the abundance of biomolecules.…”

Section: Why Single Cell Metabolomics?mentioning

confidence: 99%

Single cell metabolomics

Heinemann

Zenobi

2011

Current Opinion in Biotechnology

116

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Recent discoveries suggest that cells of a clonal population often display multiple metabolic phenotypes at the same time. Motivated by the success of mass spectrometry (MS) in the investigation of population-level metabolomics, the analytical community has initiated efforts towards MS-based single cell metabolomics to investigate metabolic phenomena that are buried under the population average. Here, we review the current approaches and illustrate their advantages and disadvantages. Because of significant advances in the field, different technologies are now at the verge of generating data that are useful for exploring and investigating metabolic heterogeneity. IntroductionQuantitative metabolomics, the technology for large-scale quantification of intracellular metabolite concentrations, is a powerful tool in systems biology research that has recently led to a series of interesting findings (e.g. [1][2][3][4]). Because of the metabolome's chemical diversity, mass spectrometry (MS) is the analytical method of choice [5]. In addition to analytical challenges, quantitative metabolomics as required for addressing (systems) biology questions poses significant challenges in sample processing. One important challenge is the need to preserve the original metabolome during sample processing, which is often difficult because of the presence of enzymes in the sample and the fast metabolic turnover rates.For sensitivity reasons, current metabolomics methods require samples that contain a large number of cells. However, cell populations are not necessarily homogeneous. Besides genetic differences, several other sources for population heterogeneity exist, of which several are also known to cause metabolic differences. Today, methods capable of resolving differences in metabolite levels on the single cell level are provided, within limits, by molecular sensors such as FRET sensors [6,7] or aptamer-based technology [8 ,9]. Both types of molecular sensors, however, are difficult to develop, are limited to specific analytes, and quantitative analyses (e.g. in terms of mol/L) are hardly possible with them. Laserinduced fluorescence, as introduced by Dovichi for single cell proteomics, is limited to fluorescent compounds or labelled species [10,11]. In addition, all these existing methods share the limitation that they can never be extended to the ''-omics'' level, that is to measuring a large number of metabolites at the same time. They will thus not be applicable to discovery type research and research that requires a large number of metabolites to be measured in the same cell.Because of the success of mass spectrometry in population-level metabolome analyses, the analytical community has recently made great strides towards single cell level metabolite analyses (for a review, see [12]). So far, however, hardly any new biological insight has been generated from these endeavours. In this Current Opinion paper, we will thus not only review the current status of MS-based single cell metabolomics but also discuss which of the differ...

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“…Circadian neurons in the brain are organized in a network that synchronizes and likely amplifies the individual neuronal oscillators thereby contributing to a coherent and robust behavioural output 2,[37][38][39] . Circadian clocks must also control or buffer transcriptional noise and its consequences, especially for genes that are expressed at limiting levels (that is, Clk), as it is well known that transcriptional noise is inversely correlated with transcriptional rate 40 .…”

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confidence: 99%

Clk post-transcriptional control denoises circadian transcription both temporally and spatially

et al. 2015

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The transcription factor CLOCK (CLK) is essential for the development and maintenance of circadian rhythms in Drosophila. However, little is known about how CLK levels are controlled. Here we show that Clk mRNA is strongly regulated post-transcriptionally through its 3 0 UTR. Flies expressing Clk transgenes without normal 3 0 UTR exhibit variable CLK-driven transcription and circadian behaviour as well as ectopic expression of CLK-target genes in the brain. In these flies, the number of the key circadian neurons differs stochastically between individuals and within the two hemispheres of the same brain. Moreover, flies carrying Clk transgenes with deletions in the binding sites for the miRNA bantam have stochastic number of pacemaker neurons, suggesting that this miRNA mediates the deterministic expression of CLK. Overall our results demonstrate a key role of Clk post-transcriptional control in stabilizing circadian transcription, which is essential for proper development and maintenance of circadian rhythms in Drosophila.

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Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences

Cited by 2,388 publications

References 109 publications

Mitochondrial heterogeneity, metabolic scaling and cell death

Mitochondrial heterogeneity, metabolic scaling and cell death

Single cell metabolomics

Clk post-transcriptional control denoises circadian transcription both temporally and spatially

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