Single-cell analysis of transcription kinetics across the cell cycle.
ABSTRACT: Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation.
Project description:Single-cell measurements of mRNA copy numbers inform our understanding of stochastic gene expression1-3, but these measurements coarse-grain over the individual copies of the gene, where transcription and its regulation take place stochastically4,5. Here, we combine single-molecule quantification of mRNA and gene loci to measure the transcriptional activity of an endogenous gene in individual Escherichia coli bacteria. When interpreted using a theoretical model for mRNA dynamics, the single-cell data allow us to obtain the probabilistic rates of promoter switching, transcription initiation and elongation, mRNA release and degradation. Unexpectedly, we find that gene activity can be strongly coupled to the transcriptional state of another copy of the same gene present in the cell, and to the event of gene replication during the bacterial cell cycle. These gene-copy and cell-cycle correlations demonstrate the limits of mapping whole-cell mRNA numbers to the underlying stochastic gene activity and highlight the contribution of previously hidden variables to the observed population heterogeneity.
Project description:Many organisms possess both a cell cycle to control DNA replication and a circadian clock to anticipate changes between day and night. In some cases, these two rhythmic systems are known to be coupled by specific, cross-regulatory interactions. Here, we use mathematical modeling to show that, additionally, the cell cycle generically influences circadian clocks in a nonspecific fashion: The regular, discrete jumps in gene-copy number arising from DNA replication during the cell cycle cause a periodic driving of the circadian clock, which can dramatically alter its behavior and impair its function. A clock built on negative transcriptional feedback either phase-locks to the cell cycle, so that the clock period tracks the cell division time, or exhibits erratic behavior. We argue that the cyanobacterium Synechococcus elongatus has evolved two features that protect its clock from such disturbances, both of which are needed to fully insulate it from the cell cycle and give it its observed robustness: a phosphorylation-based protein modification oscillator, together with its accompanying push-pull read-out circuit that responds primarily to the ratios of different phosphoform concentrations, makes the clock less susceptible to perturbations in protein synthesis; the presence of multiple, asynchronously replicating copies of the same chromosome diminishes the effect of replicating any single copy of a gene.
Project description:The haploid genome of Saccharomyces cerevisiae contains two nonallelic sets of histone H3 and H4 gene pairs, termed the copy I and copy II loci. The structures of the mRNA transcripts from each of these four genes were examined by nuclease protection and primer extension mapping. For each gene, several species of mRNAs were identified that differed in the lengths of their 5' and 3' untranslated regions. The cell cycle accumulation pattern of the H3 and H4 mRNAs was determined in cells from early-exponential-growth cultures fractionated by centrifugal elutriation. The RNA transcripts from all four genes were regulated with the cell division cycle, and transcripts from the nonallelic gene copies showed tight temporal coordination. Cell cycle regulation did not depend on selection of a particular histone mRNA transcript since the ratio of the multiple species from each gene remained the same across the division cycle. Quantitative measurements showed significant differences in the amounts of mRNA expressed from the two nonallelic gene sets. The mRNAs from the copy II H3 and H4 genes were five to seven times more abundant than the mRNAs from the copy I genes. There was no dosage compensation in the steady-state levels of mRNA when either set of genes was deleted. In particular, there was no increase in the amount of copy I H3 or H4 transcripts in cells in which the high-abundance copy II genes were deleted.
Project description:Background:Results of previous studies suggest that NANOG may be an important prognostic biomarker in oral squamous cell carcinoma (OSCC), but there are contradictory results regarding NANOG expression patterns on mRNA and protein levels, and the mechanisms of its regulation are poorly understood. Our aim was to analyze the expression and diagnostic significance of NANOG in OSCC, and the possible mechanisms of its regulation, i.e., protein regulators on mRNA level (OCT4, SOX2, KLF4, AGR2, and NOTCH1), methylation status, copy number variation, and regulatory miRNAs, miR-145, miR-335, miR-150, miR-34a, miR-128, and miR-27a. Methods:Our study included 120 patients with OSCC. Expression of NANOG protein and mRNA was analyzed using immunohistochemistry and qPCR. Expression of regulatory factors, miRNAs, and copy number variation was performed using qPCR. Methylation status of NANOG promoter was determined using PCR and Sanger sequencing. Results:We detected upregulation of NANOG and OCT4 and downregulation of NOTCH1 and AGR2 mRNA in OSCC with lymph node metastases compared to OSCC without lymph node metastases. We observed a strong positive correlation between mRNAs of NANOG and those of its protein regulators OCT4, SOX2, NOTCH1, AGR2, and KLF4. The expression of NANOG was in positive correlation with the expression of miR-34a. There was also a correlation between T status of OSCC and the expression of miR-335 and miR-150 and a correlation of miR-150 with the N status of T2 OSCC. NANOG promoter methylation and copy number variation were only observed in a small proportion of samples. Conclusions:Our findings confirm the diagnostic significance of NANOG in OSCC and provide information on NANOG expression patterns on both mRNA and protein levels. They also suggest that protein regulators and microRNAs might play a crucial role, whereas methylation of its promoter and copy number variation probably have a minor role in the regulation of NANOG expression in OSCC.
Project description:Gene expression is intrinsically a stochastic (noisy) process with important implications for cellular functions. Deciphering the underlying mechanisms of gene expression noise remains one of the key challenges of regulatory biology. Theoretical models of transcription often incorporate the kinetics of how transcription factors (TFs) interact with a single promoter to impact gene expression noise. However, inside single cells multiple identical gene copies as well as additional binding sites can compete for a limiting pool of TFs. Here we develop a simple kinetic model of transcription, which explicitly incorporates this interplay between TF copy number and its binding sites. We show that TF sharing enhances noise in mRNA distribution across an isogenic population of cells. Moreover, when a single gene copy shares it's TFs with multiple competitor sites, the mRNA variance as a function of the mean remains unaltered by their presence. Hence, all the data for variance as a function of mean expression collapse onto a single master curve independent of the strength and number of competitor sites. However, this result does not hold true when the competition stems from multiple copies of the same gene. Therefore, although previous studies showed that the mean expression follows a universal master curve, our findings suggest that different scenarios of competition bear distinct signatures at the level of variance. Intriguingly, the introduction of competitor sites can transform a unimodal mRNA distribution into a multimodal distribution. These results demonstrate the impact of limited availability of TF resource on the regulation of noise in gene expression.
Project description:An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non-secreted proteins based on parallel reaction monitoring to measure, at steady-state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene-specific RNA-to-protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics.
Project description:In prokaryotes and eukaryotes, genes are transcribed stochastically according to various temporal patterns that range from simple first-order kinetics to marked bursts, resulting in temporal and cell-to-cell variations of mRNA and protein levels. Here, we consider the effect of the transport of regulatory molecules on the noise in gene expression by taking into account explicitly the dynamics of a finite number of transcription factors confined in the cell. We calculate analytically time-dependent correlation functions of mRNA levels for a wide range of transport mechanisms and find that in the limit of small-transcription-factor copy number, the results differ significantly from standard approaches, which ignore confinement. It is shown how such dynamical quantities, which can now be obtained experimentally, can be used to identify the underlying mechanisms of transcription. Of particular importance, it is demonstrated that the geometry of transcription-factor trajectories in the cellular environment plays a key role in transcription kinetics, and can intrinsically generate the observed various transcription patterns ranging from simple first-order kinetics to bursts.
Project description:Recent advances in single-molecule fluorescent imaging have enabled quantitative measurements of transcription at a single gene copy, yet an accurate understanding of transcriptional kinetics is still lacking due to the difficulty of solving detailed biophysical models. Here we introduce a stochastic simulation and statistical inference platform for modeling detailed transcriptional kinetics in prokaryotic systems, which has not been solved analytically. The model includes stochastic two-state gene activation, mRNA synthesis initiation and stepwise elongation, release to the cytoplasm, and stepwise co-transcriptional degradation. Using the Gillespie algorithm, the platform simulates nascent and mature mRNA kinetics of a single gene copy and predicts fluorescent signals measurable by time-lapse single-cell mRNA imaging, for different experimental conditions. To approach the inverse problem of estimating the kinetic parameters of the model from experimental data, we develop a heuristic optimization method based on the genetic algorithm and the empirical distribution of mRNA generated by simulation. As a demonstration, we show that the optimization algorithm can successfully recover the transcriptional kinetics of simulated and experimental gene expression data. The platform is available as a MATLAB software package at https://data.caltech.edu/records/1287.
Project description:A comprehensive analysis of the molecular network of cellular factors establishing and maintaining pluripotency as well as self renewal of pluripotent stem cells is key for further progress in understanding basic stem cell biology. Nanog is necessary for the natural induction of pluripotency in early mammalian development but dispensable for both its maintenance and its artificial induction. To gain further insight into the molecular activity of Nanog, we analyzed the outcomes of Nanog gain-of-function in various cell models employing a recently developed biologically active recombinant cell-permeant protein, Nanog-TAT. We found that Nanog enhances the proliferation of both NIH 3T3 and primary fibroblast cells. Nanog transduction into primary fibroblasts results in suppression of senescence-associated ?-galactosidase activity. Investigation of cell cycle factors revealed that transient activation of Nanog correlates with consistent downregulation of the cell cycle inhibitor p27(KIP1) (also known as CDKN1B). By performing chromatin immunoprecipitation analysis, we confirmed bona fide Nanog-binding sites upstream of the p27(KIP1) gene, establishing a direct link between physical occupancy and functional regulation. Our data demonstrates that Nanog enhances proliferation of fibroblasts through transcriptional regulation of cell cycle inhibitor p27 gene.
Project description:The integration of genes into the nuclear genome of Chlamydomonas reinhardtii is mediated by Non-Homologous-End-Joining, thus resulting in unpredicted insertion locations. This phenomenon defines 'the position-effect', which is used to explain the variation of expression levels between different clones transformed with the same DNA fragment. Likewise, nuclear transgenes often undergo epigenetic silencing that reduces their expression; hence, nuclear transformations require high-throughput screening methods to isolate clones that express the foreign gene at a desirable level. Here, we show that the number of integration sites of heterologous genes results in higher mRNA levels. By transforming both a synthetic ferredoxin-hydrogenase fusion enzyme and a Gaussia-Luciferase reporter protein, we were able to obtain 33 positive clones that exhibit a wide range of synthetic expression. We then performed a droplet-digital polymerase-chain-reaction for these lines to measure their transgene DNA copy-number and mRNA levels. Surprisingly, most clones contain two integration sites of the synthetic gene (45.5%), whilst 33.3% contain one, 18.1% include three and 3.1% encompass four. Remarkably, we observed a positive correlation between the raw DNA copy-number values to the mRNA levels, suggesting a general effect of which transcription of transgenes is partially modulated by their number of copies in the genome. However, our data indicate that only clones harboring at least three copies of the target amplicon show a significant increment in mRNA levels of the reporter transgene. Lastly, we measured protein activity for each of the reporter genes to elucidate the effect of copy-number variation on heterologous expression.