The volumes and transcript counts of single cells reveal concentration homeostasis and capture biological noise.

Transcriptional stochasticity can be measured by counting the number of mRNA molecules per cell. Cell-to-cell variability is best captured in terms of concentration rather than molecule counts, because reaction rates depend on concentrations. We combined single-molecule mRNA counting with single-cell volume measurements to quantify the statistics of both transcript numbers and concentrations in human cells. We compared three cell clones that differ only in the genomic integration site of an identical constitutively expressed reporter gene. The transcript number per cell varied proportionally with cell volume in all three clones, indicating concentration homeostasis. We found that the cell-to-cell variability in the mRNA concentration is almost exclusively due to cell-to-cell variation in gene expression activity, whereas the cell-to-cell variation in mRNA number is larger, due to a significant contribution of cell volume variability. We concluded that the precise relationship between transcript number and cell volume sets the biological stochasticity of living cells. This study highlights the importance of the quantitative measurement of transcript concentrations in studies of cell-to-cell variability in biology.

Single yeast cells vary in transcription activity not in delay time after a metabolic shift.

Individual cells respond very differently to changes in environmental conditions. Stochasticity causes cells to respond at different times, magnitudes or both. Here we disentangle and quantify these two sources of heterogeneity. We track the adaptation dynamics of single Saccharomyces cerevisiae cells exposed to a nutrient shift from methionine to sulphate and back. Using single-molecule RNA fluorescence in situ hybridization, we count the number of transcripts of a methionine-biosynthesis enzyme in single cells during adaptation. The variation of response times between cells is small, yet we find a high transient variability in the messenger RNA copy numbers. Surprisingly, single cells display strongly delayed transcription induction, as we could induce transcription fourfold quicker by direct activation and bypassing the cellular control circuitry. Transcription repression occurs rapidly within several minutes. This study indicates that small variability in response timing combined with high, stochastic transcription activity can cause large cell-to-cell variability in dynamic adaptation responses.

Contributions of cell growth and biochemical reactions to nongenetic variability of cells.

Cell-to-cell variability in the molecular composition of isogenic, steady-state growing cells arises spontaneously from the inherent stochasticity of intracellular biochemical reactions and cell growth. Here, we present a general decomposition of the total variance in the copy number per cell of a particular molecule. It quantifies the individual contributions made by processes associated with cell growth, biochemical reactions, and their control. We decompose the growth contribution further into variance contributions of random partitioning of molecules at cell division, mother-cell heterogeneity, and variation in cell-cycle progression. The contribution made by biochemical reactions is expressed in variance generated by molecule synthesis, degradation, and their regulation. We use this theory to study the influence of different growth and reaction-related processes, such as DNA replication, variable molecule-partitioning probability, and synthesis bursts, on stochastic cell-to-cell variability. Using simulations, we characterize the impact of noise in the generation-time on cell-to-cell variability. This article offers a widely-applicable theory on the influence of biochemical reactions and cellular growth on the phenotypic variability of growing, isogenic cells. The theory aids the design and interpretation of experiments involving single-molecule counting or real-time imaging of fluorescent reporter constructs.

Exploration of the spontaneous fluctuating activity of single enzyme molecules.

Single enzyme molecules display inevitable, stochastic fluctuations in their catalytic activity. In metabolism, for instance, the stochastic activity of individual enzymes is averaged out due to their high copy numbers per single cell. However, many processes inside cells rely on single enzyme activity, such as transcription, replication, translation, and histone modifications. Here we introduce the main theoretical concepts of stochastic single-enzyme activity starting from the Michaelis-Menten enzyme mechanism. Next, we discuss stochasticity of multi-substrate enzymes, of enzymes and receptors with multiple conformational states and finally, how fluctuations in receptor activity arise from fluctuations in signal concentration. This paper aims to introduce the exciting field of single-molecule enzyme kinetics and stochasticity to a wider audience of biochemists and systems biologists.

Transcription stochasticity of complex gene regulation models.

Transcription is regulated by a multitude of factors that concertedly induce genes to switch between activity states. Eukaryotic transcription involves a multitude of complexes that sequentially assemble on chromatin under the influence of transcription factors and the dynamic state of chromatin. Prokaryotic transcription depends on transcription factors, sigma-factors, and, in some cases, on DNA looping. We present a stochastic model of transcription that considers these complex regulatory mechanisms. We coarse-grain the molecular details in such a way that the model can describe a broad class of gene-regulation mechanisms. We solve this model analytically for various measures of stochastic transcription and compare alternative gene-regulation designs. We find that genes with complex multiprotein regulation can have peaked burst-size distributions in contrast to the geometric distributions found for simple models of transcription regulation. Burst-size distributions are, in addition, shaped by mRNA degradation during transcription bursts. We derive the stochastic properties of genes in the limit of deterministic switch times. These genes typically have reduced transcription noise. Severe timescale separation between gene regulation and transcription initiation enhances noise and leads to bimodal mRNA copy number distributions. In general, complex mechanisms for gene regulation lead to nonexponential waiting-time distributions for gene switching and transcription initiation, which typically reduce noise in mRNA copy numbers and burst size. Finally, we discuss that qualitatively different gene regulation models can often fit the same experimental data on single-cell mRNA abundance even though they have qualitatively different burst-size statistics and regulatory parameters.

The role of DNA methylation, nucleosome occupancy and histone modifications in paramutation.

Paramutation is the transfer of epigenetic information between alleles that leads to a heritable change in expression of one of these alleles. Paramutation at the tissue-specifically expressed maize (Zea mays) b1 locus involves the low-expressing B' and high-expressing B-I allele. Combined in the same nucleus, B' heritably changes B-I into B'. A hepta-repeat located 100-kb upstream of the b1 coding region is required for paramutation and for high b1 expression. The role of epigenetic modifications in paramutation is currently not well understood. In this study, we show that the B' hepta-repeat is DNA-hypermethylated in all tissues analyzed. Importantly, combining B' and B-I in one nucleus results in de novo methylation of the B-I repeats early in plant development. These findings indicate a role for hepta-repeat DNA methylation in the establishment and maintenance of the silenced B' state. In contrast, nucleosome occupancy, H3 acetylation, and H3K9 and H3K27 methylation are mainly involved in tissue-specific regulation of the hepta-repeat. Nucleosome depletion and H3 acetylation are tissue-specifically regulated at the B-I hepta-repeat and associated with enhancement of b1 expression. H3K9 and H3K27 methylation are tissue-specifically localized at the B' hepta-repeat and reinforce the silenced B' chromatin state. The B' coding region is H3K27 dimethylated in all tissues analyzed, indicating a role in the maintenance of the silenced B' state. Taken together, these findings provide insight into the mechanisms underlying paramutation and tissue-specific regulation of b1 at the level of chromatin structure.

Microtubule depolymerization by the Kinesin-8 motor Kip3p: a mathematical model.

Proteins from the kinesin-8 family promote microtubule (MT) depolymerization, a process thought to be important for the control of microtubule length in living cells. In addition to this MT shortening activity, kinesin 8s are motors that show plus-end directed motility on MTs. Here we describe a simple model that incorporates directional motion and destabilization of the MT plus-end by kinesin 8. Our model quantitatively reproduces the key features of length-versus-time traces for stabilized MTs in the presence of purified kinesin 8, including length-dependent depolymerization. Comparison of model predictions with experiments suggests that kinesin 8 depolymerizes processively, i.e., one motor can remove multiple tubulin dimers from a stabilized MT. Fluctuations in MT length as a function of time are related to depolymerization processivity. We have also determined the parameter regime in which the rate of MT depolymerization is length dependent: length-dependent depolymerization occurs only when MTs are sufficiently short; this crossover is sensitive to the bulk motor concentration.