Structure of silent transcription intervals and noise characteristics of mammalian genes

Exploring the central dogma through the lens of gene expression noise

Over the past two decades, cell-to-cell heterogeneity has garnered increasing attention due to its critical role in both developmental and pathological processes. This growing interest has been driven, in part, by the advancements in live-cell and single-molecule imaging techniques. These techniques have provided mechanistic insights into how processes, transcription in particular, contribute to gene expression noise and, ultimately, cell-to-cell heterogeneity. More recently, however, research has expanded to explore how downstream steps in the central dogma influence gene expression noise. In this review, we mostly examine the impact of transcriptional processes on the generation of gene expression noise but also discuss how post-transcriptional mechanisms modulate noise and its propagation to the protein level. This evaluation emphasizes the need for further investigation into how processes beyond transcription shape gene expression noise, highlighting unanswered questions that remain in the field.

Mouse promoters are characterised by low occupancy and high turnover of RNA polymerase II

The general transcription machinery and its occupancy at promoters are highly conserved across metazoans. This contrasts with the kinetics of mRNA production that considerably differ between model species such as Drosophila and mouse. The molecular basis for these kinetic differences is currently unknown. Here, we used Single-Molecule Footprinting to measure RNA Polymerase II (Pol II) occupancy, the fraction of DNA molecules bound, at promoters in mouse and Drosophila cell lines. Single-molecule data reveals that Pol II occupancy is on average 3-5 times more frequent at transcriptionally active Drosophila promoters than active mouse promoters. Kinetic modelling of the occupancy states suggests that these differences in Pol II occupancy are determined by the ratio between the transcription initiation and Pol II turnover rates. We used chemical perturbation of transcription initiation to determine Pol II turnover rate in both species. Integration of these data into the model shows that infrequent Pol II occupancy in mouse is explained by the combination of high Pol II turnover and low transcription initiation rates.

Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

What features of transcription can be learnt by fitting mathematical models of gene expression to mRNA count data? Given a suite of models, fitting to data selects an optimal one, thus identifying a probable transcriptional mechanism. Whilst attractive, the utility of this methodology remains unclear. Here, we sample steady-state, single-cell mRNA count distributions from parameters in the physiological range, and show they cannot be used to confidently estimate the number of inactive gene states, i.e. the number of rate-limiting steps in transcriptional initiation. Distributions from over 99% of the parameter space generated using models with 2, 3, or 4 inactive states can be well fit by one with a single inactive state. However, we show that for many minutes following induction, eukaryotic cells show an increase in the mean mRNA count that obeys a power law whose exponent equals the sum of the number of states visited from the initial inactive to the active state and the number of rate-limiting post-transcriptional processing steps. Our study shows that estimation of the exponent from eukaryotic data can be sufficient to determine a lower bound on the total number of regulatory steps in transcription initiation, splicing, and nuclear export.

Transcriptional bursting dynamics in gene expression

Gene transcription is a stochastic process that occurs in all organisms. Transcriptional bursting, a critical molecular dynamics mechanism, creates significant heterogeneity in mRNA and protein levels. This heterogeneity drives cellular phenotypic diversity. Currently, the lack of a comprehensive quantitative model limits the research on transcriptional bursting. This review examines various gene expression models and compares their strengths and weaknesses to guide researchers in selecting the most suitable model for their research context. We also provide a detailed summary of the key metrics related to transcriptional bursting. We compared the temporal dynamics of transcriptional bursting across species and the molecular mechanisms influencing these bursts, and highlighted the spatiotemporal patterns of gene expression differences by utilizing metrics such as burst size and burst frequency. We summarized the strategies for modeling gene expression from both biostatistical and biochemical reaction network perspectives. Single-cell sequencing data and integrated multiomics approaches drive our exploration of cutting-edge trends in transcriptional bursting mechanisms. Moreover, we examined classical methods for parameter estimation that help capture dynamic parameters in gene expression data, assessing their merits and limitations to facilitate optimal parameter estimation. Our comprehensive summary and review of the current transcriptional burst dynamics theories provide deeper insights for promoting research on the nature of cell processes, cell fate determination, and cancer diagnosis.

Nascent RNA kinetics with complex promoter architecture: Analytic results and parameter inference

Transcription is a stochastic process that involves several downstream operations which make it difficult to model and infer transcription kinetics from mature RNA numbers in individual cell. However, recent advances in single-cell technologies have enabled a more precise measurement of the fluctuations of nascent RNA that closely reflect transcription kinetics. In this paper we introduce a general stochastic model to mimic nascent RNA kinetics with complex promoter architecture. We derive the exact distribution and moments of nascent RNA using queuing theory techniques, which provide valuable insights into the effect of the molecular memory created by the multistep activation and deactivation on the stochastic kinetics of nascent RNA. Moreover, based on the analytical results, we develop a statistical method to infer the promoter memory from stationary nascent RNA distributions. Data analysis of synthetic data and a realistic example, the HIV-1 gene, verifies the validity of this inference method.

Gene activity fully predicts transcriptional bursting dynamics

Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. Here, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcriptional activities in early fly embryos, we uncover stringent relationships between bursting parameters. Specifically, we find that the durations of ON and OFF periods are linked. Regardless of the developmental stage or body-axis position, gene activity levels predict individual alleles' average ON and OFF periods. Lowly transcribing alleles predominantly modulate OFF periods (burst frequency), while highly transcribing alleles primarily tune ON periods (burst size). These relationships persist even under perturbations of cis-regulatory elements or trans-factors and account for bursting dynamics measured in other species. Our results suggest a novel mechanistic constraint governing bursting dynamics rather than a modular control of distinct parameters by distinct regulatory processes.

What can we learn when fitting a simple telegraph model to a complex gene expression model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study

Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Quantifying and correcting bias in transcriptional parameter inference from single-cell data

The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: mRNA synthesis rate, the switching on rate (the on state being the active transcriptional state), and the switching off rate. This model assumes no extrinsic noise, i.e., parameters do not vary between cells, and thus estimated parameters are to be understood as approximating the average values in a population. The accuracy of this approximation is currently unclear. Here, we develop a theory that explains the size and sign of estimation bias when inferring parameters from single-cell data using the standard telegraph model. We find specific bias signatures depending on the source of extrinsic noise (which parameter is most variable across cells) and the mode of transcriptional activity. If gene expression is not bursty then the population averages of all three parameters are overestimated if extrinsic noise is in the synthesis rate; underestimation occurs if extrinsic noise is in the switching on rate; both underestimation and overestimation can occur if extrinsic noise is in the switching off rate. We find that some estimated parameters tend to infinity as the size of extrinsic noise approaches a critical threshold. In contrast when gene expression is bursty, we find that in all cases the mean burst size (ratio of the synthesis rate to the switching off rate) is overestimated while the mean burst frequency (the switching on rate) is underestimated. We estimate the size of extrinsic noise from the covariance matrix of sequencing data and use this together with our theory to correct published estimates of transcriptional parameters for mammalian genes.

Gene transcription regulation by ER at the single cell and allele level

In this short review we discuss the current view of how the estrogen receptor (ER), a pivotal member of the nuclear receptor superfamily of transcription factors, regulates gene transcription at the single cell and allele level, focusing on in vitro cell line models. We discuss central topics and new trends in molecular biology including phenotypic heterogeneity, single cell sequencing, nuclear phase separated condensates, single cell imaging, and image analysis methods, with particular focus on the methodologies and results that have been reported in the last few years using microscopy-based techniques. These observations augment the results from biochemical assays that lead to a much more complex and dynamic view of how ER, and arguably most transcription factors, act to regulate gene transcription.

Regulatory Mechanisms for Transcriptional Bursting Revealed by an Event-Based Model

Gene transcription often occurs in discrete bursts, and it can be difficult to deduce the underlying regulatory mechanisms for transcriptional bursting with limited experimental data. Here, we categorize numerous states of single eukaryotic genes and identify 6 essential transcriptional events, each comprising a series of state transitions; transcriptional bursting is characterized as a sequence of 4 events, capable of being organized in various configurations, in addition to the beginning and ending events. By associating transcriptional kinetics with mean durations and recurrence probabilities of the events, we unravel how transcriptional bursting is modulated by various regulators including transcription factors. Through analytical derivation and numerical simulation, this study reveals key state transitions contributing to transcriptional sensitivity and specificity, typical characteristics of burst profiles, global constraints on intrinsic transcriptional noise, major regulatory modes in individual genes and across the genome, and requirements for fast gene induction upon stimulation. It is illustrated how biochemical reactions on different time scales are modulated to separately shape the durations and ordering of the events. Our results suggest that transcriptional patterns are essentially controlled by a shared set of transcriptional events occurring under specific promoter architectures and regulatory modes, the number of which is actually limited.

Analysis and Modeling of Early Estradiol-induced GREB1 Single Allele Gene Transcription at the Population Level

Single molecule fluorescence in situ hybridization (smFISH) can be used to visualize transcriptional activation at the single allele level. We and others have applied this approach to better understand the mechanisms of activation by steroid nuclear receptors. However, there is limited understanding of the interconnection between the activation of target gene alleles inside the same nucleus and within large cell populations. Using the GREB1 gene as an early estrogen receptor (ER) response target, we applied smFISH to track E2-activated GREB1 allelic transcription over early time points to evaluate potential dependencies between alleles within the same nucleus. We compared two types of experiments where we altered the initial status of GREB1 basal transcription by treating cells with and without the elongation inhibitor flavopiridol (FV). E2 stimulation changed the frequencies of active GREB1 alleles in the cell population independently of FV pre-treatment. In FV treated cells, the response time to hormone was delayed, albeit still reaching at 90 minutes the same levels as in cells not treated by FV. We show that the joint frequencies of GREB1 activated alleles observed at the cell population level imply significant dependency between pairs of alleles within the same nucleus. We identify probabilistic models of joint alleles activations by applying a principle of maximum entropy. For pairs of alleles, we have then quantified statistical dependency by computing their mutual information. We have then introduced a stochastic model compatible with allelic statistical dependencies, and we have fitted this model to our data by intensive simulations. This provided estimates of the average lifetime for degradation of GREB1 introns and of the mean time between two successive transcription rounds. Our approach informs on how to extract information on single allele regulation by ER from within a large population of cells, and should be applicable to many other genes.

AUTHOR SUMMARY

After application of a gene transcription stimulus, in this case the hormone 17 β -estradiol, on large populations of cells over a short time period, we focused on quantifying and modeling the frequencies of GREB1 single allele activations. We have established an experimental and computational pipeline to analyze large numbers of high resolution smFISH images to detect and monitor active GREB1 alleles, that can be translatable to any target gene of interest. A key result is that, at the population level, activation of individual GREB1 alleles within the same nucleus do exhibit statistically significant dependencies which we quantify by the mutual information between activation states of pairs of alleles. After noticing that frequencies of joint alleles activations observed over our large cell populations evolve smoothly in time, we have defined a population level stochastic model which we fit to the observed time course of GREB1 activation frequencies. This provided coherent estimates of the mean time between rounds of GREB1 transcription and the mean lifetime of nascent mRNAs. Our algorithmic approach and experimental methods are applicable to many other genes.

LAST-seq: single-cell RNA sequencing by direct amplification of single-stranded RNA without prior reverse transcription and second-strand synthesis

Existing single-cell RNA sequencing (scRNA-seq) methods rely on reverse transcription (RT) and second-strand synthesis (SSS) to convert single-stranded RNA into double-stranded DNA prior to amplification, with the limited RT/SSS efficiency compromising RNA detectability. Here, we develop a new scRNA-seq method, Linearly Amplified Single-stranded-RNA-derived Transcriptome sequencing (LAST-seq), which directly amplifies the original single-stranded RNA molecules without prior RT/SSS. LAST-seq offers a high single-molecule capture efficiency and a low level of technical noise for single-cell transcriptome analyses. Using LAST-seq, we characterize transcriptional bursting kinetics in human cells, revealing a role of topologically associating domains in transcription regulation.

Variability of the innate immune response is globally constrained by transcriptional bursting

Transcription of almost all mammalian genes occurs in stochastic bursts, however the fundamental control mechanisms that allow appropriate single-cell responses remain unresolved. Here we utilise single cell genomics data and stochastic models of transcription to perform global analysis of the toll-like receptor (TLR)-induced gene expression variability. Based on analysis of more than 2000 TLR-response genes across multiple experimental conditions we demonstrate that the single-cell, gene-by-gene expression variability can be empirically described by a linear function of the population mean. We show that response heterogeneity of individual genes can be characterised by the slope of the mean-variance line, which captures how cells respond to stimulus and provides insight into evolutionary differences between species. We further demonstrate that linear relationships theoretically determine the underlying transcriptional bursting kinetics, revealing different regulatory modes of TLR response heterogeneity. Stochastic modelling of temporal scRNA-seq count distributions demonstrates that increased response variability is associated with larger and more frequent transcriptional bursts, which emerge via increased complexity of transcriptional regulatory networks between genes and different species. Overall, we provide a methodology relying on inference of empirical mean-variance relationships from single cell data and new insights into control of innate immune response variability.

Transcriptional bursting: stochasticity in deterministic development

The transcription of DNA by RNA polymerase occurs as a discontinuous process described as transcriptional bursting. This bursting behavior is observed across species and has been quantified using various stochastic modeling approaches. There is a large body of evidence that suggests the bursts are actively modulated by transcriptional machinery and play a role in regulating developmental processes. Under a commonly used two-state model of transcription, various enhancer-, promoter- and chromatin microenvironment-associated features are found to differentially influence the size and frequency of bursting events - key parameters of the two-state model. Advancement of modeling and analysis tools has revealed that the simple two-state model and associated parameters may not sufficiently characterize the complex relationship between these features. The majority of experimental and modeling findings support the view of bursting as an evolutionarily conserved transcriptional control feature rather than an unintended byproduct of the transcription process. Stochastic transcriptional patterns contribute to enhanced cellular fitness and execution of proper development programs, which posit this mode of transcription as an important feature in developmental gene regulation. In this Review, we present compelling examples of the role of transcriptional bursting in development and explore the question of how stochastic transcription leads to deterministic organism development.

The impact of cell size on morphogen gradient precision

Tissue patterning during embryonic development is remarkably precise. Here, we numerically determine the impact of the cell diameter, gradient length and the morphogen source on the variability of morphogen gradients. We show that the positional error increases with the gradient length relative to the size of the morphogen source, and with the square root of the cell diameter and the readout position. We provide theoretical explanations for these relationships, and show that they enable high patterning precision over developmental time for readouts that scale with expanding tissue domains, as observed in the Drosophila wing disc. Our analysis suggests that epithelial tissues generally achieve higher patterning precision with small cross-sectional cell areas. An extensive survey of measured apical cell areas shows that they are indeed small in developing tissues that are patterned by morphogen gradients. Enhanced precision may thus have led to the emergence of pseudostratification in epithelia, a phenomenon for which the evolutionary benefit had so far remained elusive.

Transcriptional activators in the early Drosophila embryo perform different kinetic roles

Combinatorial regulation of gene expression by transcription factors (TFs) may in part arise from kinetic synergy-wherein TFs regulate different steps in the transcription cycle. Kinetic synergy requires that TFs play distinguishable kinetic roles. Here, we used live imaging to determine the kinetic roles of three TFs that activate transcription in the Drosophila embryo-Zelda, Bicoid, and Stat92E-by introducing their binding sites into the even-skipped stripe 2 enhancer. These TFs influence different sets of kinetic parameters, and their influence can change over time. All three TFs increased the fraction of transcriptionally active nuclei; Zelda also shortened the first-passage time into transcription and regulated the interval between transcription events. Stat92E also increased the lifetimes of active transcription. Different TFs can therefore play distinct kinetic roles in activating the transcription. This has consequences for understanding the composition and flexibility of regulatory DNA sequences and the biochemical function of TFs. A record of this paper's transparent peer review process is included in the supplemental information.

Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model

Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics

Gene expression in mammalian cells is highly variable and episodic, resulting in a series of discontinuous bursts of mRNAs. A challenge is to understand how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this challenge, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson's paradoxes, i.e. genome-wide burst kinetics exhibit different characteristics in two cases without and with distinguishing feedback regulations. We also showed that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.

Transcription factor binding process is the primary driver of noise in gene expression

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.

Novel biological insights revealed from the investigation of multiscale genome architecture

Gene expression and cell fate determination require precise and coordinated epigenetic regulation. The complex three-dimensional (3D) genome organization plays a critical role in transcription in myriad biological processes. A wide range of architectural features of the 3D genome, including chromatin loops, topologically associated domains (TADs), chromatin compartments, and phase separation, together regulate the chromatin state and transcriptional activity at multiple levels. With the help of 3D genome informatics, recent biochemistry and imaging approaches based on different strategies have revealed functional interactions among biomacromolecules, even at the single-cell level. Here, we review the occurrence, mechanistic basis, and functional implications of dynamic genome organization, and outline recent experimental and computational approaches for profiling multiscale genome architecture to provide robust tools for studying the 3D genome.

On-microscope staging of live cells reveals changes in the dynamics of transcriptional bursting during differentiation

Determining the mechanisms by which genes are switched on and off during development is a key aim of current biomedical research. Gene transcription has been widely observed to occur in a discontinuous fashion, with short bursts of activity interspersed with periods of inactivity. It is currently not known if or how this dynamic behaviour changes as mammalian cells differentiate. To investigate this, using an on-microscope analysis, we monitored mouse α-globin transcription in live cells throughout erythropoiesis. We find that changes in the overall levels of α-globin transcription are most closely associated with changes in the fraction of time a gene spends in the active transcriptional state. We identify differences in the patterns of transcriptional bursting throughout differentiation, with maximal transcriptional activity occurring in the mid-phase of differentiation. Early in differentiation, we observe increased fluctuation in transcriptional activity whereas at the peak of gene expression, in early erythroblasts, transcription is relatively stable. Later during differentiation as α-globin expression declines, we again observe more variability in transcription within individual cells. We propose that the observed changes in transcriptional behaviour may reflect changes in the stability of active transcriptional compartments as gene expression is regulated during differentiation.

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription

Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway.

Many genes are transcribed in infrequent bursts of mRNA production through a process called transcriptional bursting, which contributes to variability in responses between cells. Heterogeneity in cell responses can have important biological impacts, such as whether a cell supports viral replication or responds to a drug, and thus there is an effort to describe this process with mathematical models to predict biological outcomes. Previous models described bursting as a transition between an “OFF” state or an “ON” state, an elegant and simple mathematical representation of complex molecular mechanisms, but one which failed to capture how upstream activation signals affected bursting. To address this, we added an additional promoter state to better reflect biological mechanisms underlying bursting. By fitting this model to variable activation of quiescent HIV infections in T cells, we showed that our model more accurately described viral expression variability across cells in response to an upstream stimulus. Our work highlights how mathematical models can be further developed to understand complex biological mechanisms and suggests ways to connect transcriptional bursting to upstream activation pathways.

Eukaryotic gene regulation at equilibrium, or non?

Models of transcriptional regulation that assume equilibrium binding of transcription factors have been less successful at predicting gene expression from sequence in eukaryotes than in bacteria. This could be due to the non-equilibrium nature of eukaryotic regulation. Unfortunately, the space of possible non-equilibrium mechanisms is vast and predominantly uninteresting. The key question is therefore how this space can be navigated efficiently, to focus on mechanisms and models that are biologically relevant. In this review, we advocate for the normative role of theory-theory that prescribes rather than just describes-in providing such a focus. Theory should expand its remit beyond inferring mechanistic models from data, towards identifying non-equilibrium gene regulatory schemes that may have been evolutionarily selected, despite their energy consumption, because they are precise, reliable, fast, or otherwise outperform regulation at equilibrium. We illustrate our reasoning by toy examples for which we provide simulation code.

Activation domains can decouple the mean and noise of gene expression

Regulatory mechanisms set a gene's average level of expression, but a gene's expression constantly fluctuates around that average. These stochastic fluctuations, or expression noise, play a role in cell-fate transitions, bet hedging in microbes, and the development of chemotherapeutic resistance in cancer. An outstanding question is what regulatory mechanisms contribute to noise. Here, we demonstrate that, for a fixed mean level of expression, strong activation domains (ADs) at low abundance produce high expression noise, while weak ADs at high abundance generate lower expression noise. We conclude that differences in noise can be explained by the interplay between a TF's nuclear concentration and the strength of its AD's effect on mean expression, without invoking differences between classes of ADs. These results raise the possibility of engineering gene expression noise independently of mean levels in synthetic biology contexts and provide a potential mechanism for natural selection to tune the noisiness of gene expression.

Kinetic characteristics of transcriptional bursting in a complex gene model with cyclic promoter structure

While transcription often occurs in a bursty manner, various possible regulations can lead to complex promoter patterns such as promoter cycles, giving rise to an important question: How do promoter kinetics shape transcriptional bursting kinetics? Here we introduce and analyze a general model of the promoter cycle consisting of multi-OFF states and multi-ON states, focusing on the effects of multi-ON mechanisms on transcriptional bursting kinetics. The derived analytical results indicate that burst size follows a mixed geometric distribution rather than a single geometric distribution assumed in previous studies, and ON and OFF times obey their own mixed exponential distributions. In addition, we find that the multi-ON mechanism can lead to bimodal burst-size distribution, antagonistic timing of ON and OFF, and diverse burst frequencies, each further contributing to cell-to-cell variability in the mRNA expression level. These results not only reveal essential features of transcriptional bursting kinetics patterns shaped by multi-state mechanisms but also can be used to the inferences of transcriptional bursting kinetics and promoter structure based on experimental data.

Comparison and evaluation of statistical error models for scRNA-seq

BACKGROUND

Heterogeneity in single-cell RNA-seq (scRNA-seq) data is driven by multiple sources, including biological variation in cellular state as well as technical variation introduced during experimental processing. Deconvolving these effects is a key challenge for preprocessing workflows. Recent work has demonstrated the importance and utility of count models for scRNA-seq analysis, but there is a lack of consensus on which statistical distributions and parameter settings are appropriate.

RESULTS

Here, we analyze 59 scRNA-seq datasets that span a wide range of technologies, systems, and sequencing depths in order to evaluate the performance of different error models. We find that while a Poisson error model appears appropriate for sparse datasets, we observe clear evidence of overdispersion for genes with sufficient sequencing depth in all biological systems, necessitating the use of a negative binomial model. Moreover, we find that the degree of overdispersion varies widely across datasets, systems, and gene abundances, and argues for a data-driven approach for parameter estimation.

CONCLUSIONS

Based on these analyses, we provide a set of recommendations for modeling variation in scRNA-seq data, particularly when using generalized linear models or likelihood-based approaches for preprocessing and downstream analysis.

Silent transcription intervals and translational bursting lead to diverse phenotypic switching

For complex process of gene expression, we use theoretical analysis and stochastic simulations to study the phenotypic diversity induced by silent transcription intervals and translational bursting.

Heterogeneity is not always a source of noise: Stochastic gene expression in regulatory heterozygote

Zygosity of diploid genome (i.e., degree to which two parental alleles of a gene have varied genetic sequences) adds another dimension to stochastic gene expression. The allelic imbalance in chromatin accessibility or divergence in regulatory sequences leads to fitness effects but the quantitative aspects thereof are largely left unexplored. We investigate diploid gene expression systems with homozygous (the same) and heterozygous (varied) combination of alleles in cis-regulatory sequences, not in structural gene loci, and characterize the zygosity-associated stochastic fluctuations in protein abundance. An emerging feat of heterozygosity is its counterintuitive capacity for genetic noise control. Especially when the noise is dominantly contributed to by the fluctuations in duty cycle ("reliability") rather than in process speed ("productivity") of gene expression machinery, its interallelic discrepancy acts to reduce the gene expression noise. These findings offer a novel insight into the rich repertoire of balancing selection enriched in the regulatory elements of immune response genes.

Quantitative imaging of transcription in living Drosophila embryos reveals the impact of core promoter motifs on promoter state dynamics

Genes are expressed in stochastic transcriptional bursts linked to alternating active and inactive promoter states. A major challenge in transcription is understanding how promoter composition dictates bursting, particularly in multicellular organisms. We investigate two key Drosophila developmental promoter motifs, the TATA box (TATA) and the Initiator (INR). Using live imaging in Drosophila embryos and new computational methods, we demonstrate that bursting occurs on multiple timescales ranging from seconds to minutes. TATA-containing promoters and INR-containing promoters exhibit distinct dynamics, with one or two separate rate-limiting steps respectively. A TATA box is associated with long active states, high rates of polymerase initiation, and short-lived, infrequent inactive states. In contrast, the INR motif leads to two inactive states, one of which relates to promoter-proximal polymerase pausing. Surprisingly, the model suggests pausing is not obligatory, but occurs stochastically for a subset of polymerases. Overall, our results provide a rationale for promoter switching during zygotic genome activation.

A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions

Stochastic fluctuations in gene expression ("noise") are often considered detrimental, but fluctuations can also be exploited for benefit (e.g., dither). We show here that DNA base excision repair amplifies transcriptional noise to facilitate cellular reprogramming. Specifically, the DNA repair protein Apex1, which recognizes both naturally occurring and unnatural base modifications, amplifies expression noise while homeostatically maintaining mean expression levels. This amplified expression noise originates from shorter-duration, higher-intensity transcriptional bursts generated by Apex1-mediated DNA supercoiling. The remodeling of DNA topology first impedes and then accelerates transcription to maintain mean levels. This mechanism, which we refer to as "discordant transcription through repair" ("DiThR," which is pronounced "dither"), potentiates cellular reprogramming and differentiation. Our study reveals a potential functional role for transcriptional fluctuations mediated by DNA base modifications in embryonic development and disease.

Altering transcription factor binding reveals comprehensive transcriptional kinetics of a basic gene

Transcription is a vital process activated by transcription factor (TF) binding. The active gene releases a burst of transcripts before turning inactive again. While the basic course of transcription is well understood, it is unclear how binding of a TF affects the frequency, duration and size of a transcriptional burst. We systematically varied the residence time and concentration of a synthetic TF and characterized the transcription of a synthetic reporter gene by combining single molecule imaging, single molecule RNA-FISH, live transcript visualisation and analysis with a novel algorithm, Burst Inference from mRNA Distributions (BIRD). For this well-defined system, we found that TF binding solely affected burst frequency and variations in TF residence time had a stronger influence than variations in concentration. This enabled us to device a model of gene transcription, in which TF binding triggers multiple successive steps before the gene transits to the active state and actual mRNA synthesis is decoupled from TF presence. We quantified all transition times of the TF and the gene, including the TF search time and the delay between TF binding and the onset of transcription. Our quantitative measurements and analysis revealed detailed kinetic insight, which may serve as basis for a bottom-up understanding of gene regulation.

Transcription Factor Pit-1 Affects Transcriptional Timing in the Dual-Promoter Human Prolactin Gene

Gene transcription occurs in short bursts interspersed with silent periods, and these kinetics can be altered by promoter structure. The effect of alternate promoter architecture on transcription bursting is not known. We studied the human prolactin (hPRL) gene that contains 2 promoters, a pituitary-specific promoter that requires the transcription factor Pit-1 and displays dramatic transcriptional bursting activity and an alternate upstream promoter that is active in nonpituitary tissues. We studied large hPRL genomic fragments with luciferase reporters, and used bacterial artificial chromosome recombineering to manipulate critical promoter regions. Stochastic switch mathematical modelling of single-cell time-lapse luminescence image data revealed that the Pit-1-dependent promoter showed longer, higher-amplitude transcriptional bursts. Knockdown studies confirmed that the presence of Pit-1 stabilized and prolonged periods of active transcription. Pit-1 therefore plays an active role in establishing the timing of transcription cycles, in addition to its cell-specific functions.

Interplay between genetic, epigenetic, and gene expression variability: Considering complexity in evolvability

Genetic variability, epigenetic variability, and gene expression variability (noise) are generally considered independently in their relationship with phenotypic variation. However, they appear to be intrinsically interconnected and influence it in combination. The study of the interplay between genetic and epigenetic variability has the longest history. This article rather considers the introduction of gene expression variability in its relationships with the two others and reviews for the first time experimental evidences over the four relationships connected to gene expression noise. They show how introducing this third source of variability complicates the way of thinking evolvability and the emergence of biological novelty. Finally, cancer cells are proposed to be an ideal model to decipher the dynamic interplay between genetic, epigenetic, and gene expression variability when one of them is either experimentally increased or therapeutically targeted. This interplay is also discussed in an evolutionary perspective in the context of cancer cell drug resistance.

The circadian oscillator analysed at the single-transcript level

The circadian clock is an endogenous and self-sustained oscillator that anticipates daily environmental cycles. While rhythmic gene expression of circadian genes is well-described in populations of cells, the single-cell mRNA dynamics of multiple core clock genes remain largely unknown. Here we use single-molecule fluorescence in situ hybridisation (smFISH) at multiple time points to measure pairs of core clock transcripts, Rev-erbα (Nr1d1), Cry1 and Bmal1, in mouse fibroblasts. The mean mRNA level oscillates over 24 h for all three genes, but mRNA numbers show considerable spread between cells. We develop a probabilistic model for multivariate mRNA counts using mixtures of negative binomials, which accounts for transcriptional bursting, circadian time and cell-to-cell heterogeneity, notably in cell size. Decomposing the mRNA variability into distinct noise sources shows that clock time contributes a small fraction of the total variability in mRNA number between cells. Thus, our results highlight the intrinsic biological challenges in estimating circadian phase from single-cell mRNA counts and suggest that circadian phase in single cells is encoded post-transcriptionally.

TATA and paused promoters active in differentiated tissues have distinct expression characteristics

Core promoter types differ in the extent to which RNA polymerase II (Pol II) pauses after initiation, but how this affects their tissue-specific gene expression characteristics is not well understood. While promoters with Pol II pausing elements are active throughout development, TATA promoters are highly active in differentiated tissues. We therefore used a genomics approach on late-stage Drosophila embryos to analyze the properties of promoter types. Using tissue-specific Pol II ChIP-seq, we found that paused promoters have high levels of paused Pol II throughout the embryo, even in tissues where the gene is not expressed, while TATA promoters only show Pol II occupancy when the gene is active. The promoter types are associated with different chromatin accessibility in ATAC-seq data and have different expression characteristics in single-cell RNA-seq data. The two promoter types may therefore be optimized for different properties: paused promoters show more consistent expression when active, while TATA promoters have lower background expression when inactive. We propose that tissue-specific genes have evolved to use two different strategies for their differential expression across tissues.

Promoters adopt distinct dynamic manifestations depending on transcription factor context

Cells respond to external signals and stresses by activating transcription factors (TF), which induce gene expression changes. Prior work suggests that signal-specific gene expression changes are partly achieved because different gene promoters exhibit distinct induction dynamics in response to the same TF input signal. Here, using high-throughput quantitative single-cell measurements and a novel statistical method, we systematically analyzed transcriptional responses to a large number of dynamic TF inputs. In particular, we quantified the scaling behavior among different transcriptional features extracted from the measured trajectories such as the gene activation delay or duration of promoter activity. Surprisingly, we found that even the same gene promoter can exhibit qualitatively distinct induction and scaling behaviors when exposed to different dynamic TF contexts. While it was previously known that promoters fall into distinct classes, here we show that the same promoter can switch between different classes depending on context. Thus, promoters can adopt context-dependent "manifestations". Our analysis suggests that the full complexity of signal processing by genetic circuits may be significantly underestimated when studied in only specific contexts.

Nonequilibrium models of optimal enhancer function

Simple biophysical models successfully describe bacterial regulatory code, by predicting gene expression from DNA sequences that bind specialized regulatory proteins. Analogous simple models fail in multicellular organisms, where regulatory proteins bind DNA very transiently, yet, nevertheless, effect precise control over gene expression. To date, the more general, “nonequilibrium” models have proven difficult to analyze and connect to data. Here, we reduce this complexity theoretically, by constructing simple nonequilibrium models which perform optimal gene regulation within known experimental constraints.

In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate,” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics—an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future.

A matter of time: Using dynamics and theory to uncover mechanisms of transcriptional bursting

Eukaryotic transcription generally occurs in bursts of activity lasting minutes to hours; however, state-of-the-art measurements have revealed that many of the molecular processes that underlie bursting, such as transcription factor binding to DNA, unfold on timescales of seconds. This temporal disconnect lies at the heart of a broader challenge in physical biology of predicting transcriptional outcomes and cellular decision-making from the dynamics of underlying molecular processes. Here, we review how new dynamical information about the processes underlying transcriptional control can be combined with theoretical models that predict not only averaged transcriptional dynamics, but also their variability, to formulate testable hypotheses about the molecular mechanisms underlying transcriptional bursting and control.

Identifying chromatin features that regulate gene expression distribution

Gene expression variability, differences in the number of mRNA per cell across a population of cells, is ubiquitous across diverse organisms with broad impacts on cellular phenotypes. The role of chromatin in regulating average gene expression has been extensively studied. However, what aspects of the chromatin contribute to gene expression variability is still underexplored. Here we addressed this problem by leveraging chromatin diversity and using a systematic investigation of randomly integrated expression reporters to identify what aspects of chromatin microenvironment contribute to gene expression variability. Using DNA barcoding and split-pool decoding, we created a large library of isogenic reporter clones and identified reporter integration sites in a massive and parallel manner. By mapping our measurements of reporter expression at different genomic loci with multiple epigenetic profiles including the enrichment of transcription factors and the distance to different chromatin states, we identified new factors that impact the regulation of gene expression distributions.

Exactly solvable models of stochastic gene expression

Stochastic models are key to understanding the intricate dynamics of gene expression. However, the simplest models that only account for active and inactive states of a gene fail to capture common observations in both prokaryotic and eukaryotic organisms. Here, we consider multistate models of gene expression that generalize the canonical Telegraph process and are capable of capturing the joint effects of transcription factors, heterochromatin state, and DNA accessibility (or, in prokaryotes, sigma-factor activity) on transcript abundance. We propose two approaches for solving classes of these generalized systems. The first approach offers a fresh perspective on a general class of multistate models and allows us to "decompose" more complicated systems into simpler processes, each of which can be solved analytically. This enables us to obtain a solution of any model from this class. Next, we develop an approximation method based on a power series expansion of the stationary distribution for an even broader class of multistate models of gene transcription. We further show that models from both classes cannot have a heavy-tailed distribution in the absence of extrinsic noise. The combination of analytical and computational solutions for these realistic gene expression models also holds the potential to design synthetic systems and control the behavior of naturally evolved gene expression systems in guiding cell-fate decisions.

Buffering noise: KAT2A modular contributions to stabilization of transcription and cell identity in cancer and development

KAT2A is a histone acetyltransferase recently identified as a vulnerability in at least some forms of Acute Myeloid Leukemia (AML). Its loss or inhibition prompts leukemia stem cells out of self-renewal and into differentiation with ultimate exhaustion of the leukemia pool. We have recently linked the Kat2a requirement in AML to control of transcriptional noise, reflecting an evolutionary-conserved role of Kat2a in promoting burst-like promoter activity and stabilizing gene expression. We suggest that through this role, Kat2a contributes to preservation of cell identity. KAT2A exerts its acetyltransferase activity in the context of two macromolecular complexes, Spt-Ada-Gcn5-Acetyltransferase (SAGA) and Ada-Two-A-Containing (ATAC), but the specific contribution of each complex to stabilization of gene expression is currently unknown. By reviewing specific gene targets and requirements of the two complexes in cancer and development, we suggest that SAGA regulates lineage-specific programs, and ATAC maintains biosynthetic activity through control of ribosomal protein and translation-associated genes, on which cells may be differentially dependent. While our data suggest that KAT2A-mediated regulation of transcriptional noise in AML may be exerted through ATAC, we discuss potential caveats and probe general vs. complex-specific contributions of KAT2A to transcriptional stability, with implications for control and perturbation of cell identity.

Gene-Specific Linear Trends Constrain Transcriptional Variability of the Toll-like Receptor Signaling

Single-cell gene expression is inherently variable, but how this variability is controlled in response to stimulation remains unclear. Here, we use single-cell RNA-seq and single-molecule mRNA counting (smFISH) to study inducible gene expression in the immune toll-like receptor system. We show that mRNA counts of tumor necrosis factor α conform to a standard stochastic switch model, while transcription of interleukin-1β involves an additional regulatory step resulting in increased heterogeneity. Despite different modes of regulation, systematic analysis of single-cell data for a range of genes demonstrates that the variability in transcript count is linearly constrained by the mean response over a range of conditions. Mathematical modeling of smFISH counts and experimental perturbation of chromatin state demonstrates that linear constraints emerge through modulation of transcriptional bursting along with gene-specific relationships. Overall, our analyses demonstrate that the variability of the inducible single-cell mRNA response is constrained by transcriptional bursting.

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Single-cell TNF-α and IL-1β mRNA responses are differentially controlled

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Variability of TLR-induced responses scale linearly with mean mRNA counts

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Gene-specific constraints emerge via modulation of transcriptional bursting

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Chromatin state regulates transcriptional bursting of IL-1β

SIR2 Expression Noise Can Generate Heterogeneity in Viability but Does Not Affect Cell-to-Cell Epigenetic Silencing of Subtelomeric URA3 in Yeast

Chromatin structure clearly modulates gene expression noise, but the reverse influence has never been investigated, namely how the cell-to-cell expression heterogeneity of chromatin modifiers may generate variable rates of epigenetic modification. Sir2 is a well-characterized histone deacetylase of the Sirtuin family. It strongly influences chromatin silencing, especially at telomeres, subtelomeres and rDNA. This ability to influence epigenetic landscapes makes it a good model to study the largely unexplored interplay between gene expression noise and other epigenetic processes leading to phenotypic diversification. Here, we addressed this question by investigating whether noise in the expression of SIR2 was associated with cell-to-cell heterogeneity in the frequency of epigenetic silencing at subtelomeres in Saccharomyces cerevisiae Using cell sorting to isolate subpopulations with various expression levels, we found that heterogeneity in the cellular concentration of Sir2 does not lead to heterogeneity in the epigenetic silencing of subtelomeric URA3 between these subpopulations. We also noticed that SIR2 expression noise can generate cell-to-cell variability in viability, with lower levels being associated with better viability. This work shows that SIR2 expression fluctuations are not sufficient to generate cell-to-cell heterogeneity in the epigenetic silencing of URA3 at subtelomeres in Saccharomyces cerevisiae but can strongly affect cellular viability.

Genome-wide kinetic properties of transcriptional bursting in mouse embryonic stem cells

Transcriptional bursting is the stochastic activation and inactivation of promoters, contributing to cell-to-cell heterogeneity in gene expression. However, the mechanism underlying the regulation of transcriptional bursting kinetics (burst size and frequency) in mammalian cells remains elusive. In this study, we performed single-cell RNA sequencing to analyze the intrinsic noise and mRNA levels for elucidating the transcriptional bursting kinetics in mouse embryonic stem cells. Informatics analyses and functional assays revealed that transcriptional bursting kinetics was regulated by a combination of promoter- and gene body-binding proteins, including the polycomb repressive complex 2 and transcription elongation factors. Furthermore, large-scale CRISPR-Cas9-based screening identified that the Akt/MAPK signaling pathway regulated bursting kinetics by modulating transcription elongation efficiency. These results uncovered the key molecular mechanisms underlying transcriptional bursting and cell-to-cell gene expression noise in mammalian cells.

Allele-specific single-cell RNA sequencing reveals different architectures of intrinsic and extrinsic gene expression noises

Gene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the low throughput of the existing method for measuring the two noise components, the architectures of intrinsic and extrinsic expression noises remain elusive. Using allele-specific single-cell RNA sequencing, we here estimate the two noise components of 3975 genes in mouse fibroblast cells. Our analyses verify predicted influences of several factors such as the TATA-box and microRNA targeting on intrinsic or extrinsic noises and reveal gene function-associated noise trends implicating the action of natural selection. These findings unravel differential regulations, optimizations, and biological consequences of intrinsic and extrinsic noises and can aid the construction of desired synthetic circuits.

What Is a Transcriptional Burst?

The idea that gene activity can be discontinuous will not surprise many biologists - many genes are restricted in when and where they can be expressed. Yet during the past 15 years, a collection of observations compiled under the umbrella term 'transcriptional bursting' has received considerable interest. Direct visualization of the dynamics of discontinuous transcription has expanded our understanding of basic transcriptional mechanisms and their regulation and provides a real-time readout of gene activity during the life of a cell. In this review, we try to reconcile the different views of the transcriptional process emerging from studies of bursting, and how this work contextualizes the relative importance of different regulatory inputs to normal dynamic ranges of gene activity.

Mammalian gene expression variability is explained by underlying cell state

Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.

Loss of Kat2a enhances transcriptional noise and depletes acute myeloid leukemia stem-like cells

Acute Myeloid Leukemia (AML) is an aggressive hematological malignancy with abnormal progenitor self-renewal and defective white blood cell differentiation. Its pathogenesis comprises subversion of transcriptional regulation, through mutation and by hijacking normal chromatin regulation. Kat2a is a histone acetyltransferase central to promoter activity, that we recently associated with stability of pluripotency networks, and identified as a genetic vulnerability in AML. Through combined chromatin profiling and single-cell transcriptomics of a conditional knockout mouse, we demonstrate that Kat2a contributes to leukemia propagation through preservation of leukemia stem-like cells. Kat2a loss impacts transcription factor binding and reduces transcriptional burst frequency in a subset of gene promoters, generating enhanced variability of transcript levels. Destabilization of target programs shifts leukemia cell fate out of self-renewal into differentiation. We propose that control of transcriptional variability is central to leukemia stem-like cell propagation, and establish a paradigm exploitable in different tumors and distinct stages of cancer evolution.