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Transcriptional regulation, orchestrated by the interplay between transcription factors (TFs) and enhancers, governs gene expression dynamics crucial for cellular processes. While gross qualitative fluctuations in transcription factor-dependent gene expression patterning have a long history of characterization, the roles of these factors in the nuclei retaining expression in the presence or absence of these factors are now observable using modern techniques. Our study investigates the impact of Suppressor of Hairless (Su(H)), a broadly expressed transcription factor, on enhancer-driven transcriptional modulation using Drosophila early embryos as a model system. Building upon previous findings, we employ super-resolution microscopy to dissect Su(H)'s influence on sog-Distal (sogD) enhancer activity specifically in nuclei with preserved sogD-driven expression in the absence of Su(H) binding. We demonstrate that Su(H) occupancy perturbations alter expression levels and bursting dynamics. Notably, Su(H) absence during embryonic development exhibits region-specific effects, inhibiting expression dorsally and stabilizing expression ventrally, implying a nuanced role in enhancer regulation. Our findings shed light on the intricate mechanisms that govern transcriptional dynamics and suggest a critical patterning role for Notch/Hairless signaling in sog expression as embryos transition to gastrulation. [ABSTRACT FROM AUTHOR]

Many biological and medical questions can be modeled using time-to-event data in finite-state Markov chains, with the phase-type distribution describing intervals between events. We solve the inverse problem: given a phase-type distribution, can we identify the transition rate parameters of the underlying Markov chain? For a specific class of solvable Markov models, we show this problem has a unique solution up to finite symmetry transformations, and we outline a recursive method for computing symbolic solutions for these models across any number of states. Using the Thomas decomposition technique from computer algebra, we further provide symbolic solutions for any model. Interestingly, different models with the same state count but distinct transition graphs can yield identical phase-type distributions. To distinguish among these, we propose additional properties beyond just the time to the next event. We demonstrate the method’s applicability by inferring transcriptional regulation models from single-cell transcription imaging data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Genetically identical cell populations growing in the same environment show a large degree of heterogeneity in gene expression profiles, resulting in significant phenotypic consequences. One of the important sources of this heterogeneity is transcriptional bursting. In recent years, single cell transcriptome sequencing (scRNA-seq) data have been widely used to infer the kinetics of transcriptional bursting. Moreover, the increasing evidence showed that genes are jointly regulated by several competitive pathways when activated by external signals, and there is molecular memory in the process of gene state switching. Therefore, a gene expression model considering both gene activation pathway and molecular memory can better reflect the nature of transcriptional bursting. In this paper, we used an approximate Bayesian computation algorithm called ABC-PRC combined with a gene expression model that considered crosstalk and memory, to infer the kinetics of transcriptional bursting. We analyze the internal relationship between transcriptional bursting and gene expression using scRNA-seq data obtained from mouse embryonic stem cells and mouse embryonic fibroblasts. Both model analysis and data simulations demonstrate that the bimodal coefficient increases with the increase of burst size, and the noise intensity decreases with the increase of burst frequency. Additionally, we show that some previously proposed models can be simplified as special cases of the gene expression model and inference algorithm proposed here under certain circumstances. 1. Genome-wide transcriptional bursting kinetics are inferred by approximate Bayesian algorithm ABC-PRC both from scRNA-seq data and gene expression models. 2. This article uses a combination of data-driven and model-driven methods to study the intrinsic relationship between transcriptional bursting and gene expression. 3. The model and inference process used in this article can be simplified and widely applied to the classical telegraph model and the generalized telegraph model. [ABSTRACT FROM AUTHOR]

Transcriptional regulation, orchestrated by the interplay between transcription factors (TFs) and enhancers, governs gene expression dynamics crucial for cellular processes. While gross qualitative fluctuations in transcription factor-dependent gene expression patterning have a long history of characterization, the roles of these factors in the nuclei retaining expression in the presence or absence of these factors are now observable using modern techniques. Our study investigates the impact of Suppressor of Hairless (Su(H)), a broadly expressed transcription factor, on enhancer-driven transcriptional modulation using Drosophila early embryos as a model system. Building upon previous findings, we employ super-resolution microscopy to dissect Su(H)’s influence on sog-Distal (sogD) enhancer activity specifically in nuclei with preserved sogD-driven expression in the absence of Su(H) binding. We demonstrate that Su(H) occupancy perturbations alter expression levels and bursting dynamics. Notably, Su(H) absence during embryonic development exhibits region-specific effects, inhibiting expression dorsally and stabilizing expression ventrally, implying a nuanced role in enhancer regulation. Our findings shed light on the intricate mechanisms that govern transcriptional dynamics and suggest a critical patterning role for Notch/Hairless signaling in sog expression as embryos transition to gastrulation.

Recent quantitative measurements have revealed dynamics of transcription factor DNA binding, preinitiation complex assembly, chromatin remodeling, and transcriptional bursting in living cells. Timescale comparison between transcription initiation events (input) and transcriptional burst kinetics (output) reveals mechanisms of transcription regulation. Transcription dynamics are shaped by the complex interplay of transient DNA binding of regulatory factors and the local environment (chromatin, clustering of regulatory factors, and enhancer–promoter interactions). Emerging imaging technologies are beginning to facilitate the dissection of input–output relationships at single genes to understand how regulatory factors affect rate-limiting steps. Recent imaging studies have captured the dynamics of regulatory events of transcription inside living cells. These events include transcription factor (TF) DNA binding, chromatin remodeling and modification, enhancer–promoter (E–P) proximity, cluster formation, and preinitiation complex (PIC) assembly. Together, these molecular events culminate in stochastic bursts of RNA synthesis, but their kinetic relationship remains largely unclear. In this review, we compare the timescales of upstream regulatory steps (input) with the kinetics of transcriptional bursting (output) to generate mechanistic models of transcription dynamics in single cells. We highlight open questions and potential technical advances to guide future endeavors toward a quantitative and kinetic understanding of transcription regulation. [ABSTRACT FROM AUTHOR]

Cells must tightly regulate their gene expression programs and yet rapidly respond to acute biochemical and biophysical cues within their environment. This information is transmitted to the nucleus through various signaling cascades, culminating in the activation or repression of target genes. Transcription factors (TFs) are key mediators of these signals, binding to specific regulatory elements within chromatin. While live-cell imaging has conclusively proven that TF–chromatin interactions are highly dynamic, how such transient interactions can have long-term impacts on developmental trajectories and disease progression is still largely unclear. In this review, we summarize our current understanding of the dynamic nature of TF functions, starting with a historical overview of early live-cell experiments. We highlight key factors that govern TF dynamics and how TF dynamics, in turn, affect downstream transcriptional bursting. Finally, we conclude with open challenges and emerging technologies that will further our understanding of transcriptional regulation. [ABSTRACT FROM AUTHOR]

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.

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.

Predicting how interactions between transcription factors and regulatory DNA sequence dictate rates of transcription and, ultimately, drive developmental outcomes remains an open challenge in physical biology. Using stripe 2 of the even-skipped gene in Drosophila embryos as a case study, we dissect the regulatory forces underpinning a key step along the developmental decision-making cascade: the generation of cytoplasmic mRNA patterns via the control of transcription in individual cells. Using live imaging and computational approaches, we found that the transcriptional burst frequency is modulated across the stripe to control the mRNA production rate. However, we discovered that bursting alone cannot quantitatively recapitulate the formation of the stripe and that control of the window of time over which each nucleus transcribes even-skipped plays a critical role in stripe formation. Theoretical modeling revealed that these regulatory strategies (bursting and the time window) respond in different ways to input transcription factor concentrations, suggesting that the stripe is shaped by the interplay of 2 distinct underlying molecular processes.

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. [ABSTRACT FROM AUTHOR]

Information from developmental signaling pathways must be accurately decoded to generate transcriptional outcomes. In the case of Notch, the intracellular domain (NICD) transduces the signal directly to the nucleus. How enhancers decipher NICD in the real time of developmental decisions is not known. Using the MS2-MCP system to visualize nascent transcripts in single cells in Drosophila embryos, we reveal how two target enhancers read Notch activity to produce synchronized and sustained profiles of transcription. By manipulating the levels of NICD and altering specific motifs within the enhancers, we uncover two key principles. First, increased NICD levels alter transcription by increasing duration rather than frequency of transcriptional bursts. Second, priming of enhancers by tissue-specific transcription factors is required for NICD to confer synchronized and sustained activity; in their absence, transcription is stochastic and bursty. The dynamic response of an individual enhancer to NICD thus differs depending on the cellular context.

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.

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.

Chemotherapy, although effective in treating cancer, can induce various cellular responses, including senescence and drug resistance. Here, we investigate the transcriptomic alterations induced by doxorubicin (DOX), a commonly used chemotherapeutic agent, in human colon cancer cells. Using single-cell RNA sequencing, we identified distinct cell populations and their transcriptional profiles following subtoxic DOX treatment, revealing cell clusters characterized by differential expression of genes involved in cell cycle regulation and interferon (IFN) signaling. DOX-persisting proliferating cells exhibited upregulation of genes reported to be linked to the unphosphorylated form of ISGF3 (U‐ISGF3) transcription factor. Furthermore, we found that HSH2D , a poor prognostic marker, was highly upregulated in doxorubicin-surviving proliferative cells, and its expression was correlated with U-ISGF3-related genes. Analysis of transcription kinetics via mathematical modeling revealed that the number of mRNA molecules produced per transcriptional burst was increased for U-ISGF3-related genes. We also observed altered gene co-expression patterns of U-ISGF3-related genes and others upon DOX treatment, which potentially contributes to chemoresistance of DOX-surviving proliferative cells and may influence cancer cell fate after chemotherapy. Our findings highlight U-ISGF3-related genes and the JAK/STAT pathway as potential therapeutic targets for overcoming chemoresistance in colon cancer. [ABSTRACT FROM AUTHOR]

Abstract Background The temporal progression of many fundamental processes in cells and organisms, including homeostasis, differentiation and development, are governed by gene regulatory networks (GRNs). GRNs balance fluctuations in the output of their genes, which trace back to the stochasticity of molecular interactions. Although highly desirable to understand life processes, predicting the temporal progression of gene products within a GRN is challenging when considering stochastic events such as transcription factor–DNA interactions or protein production and degradation. Results We report a method to simulate and infer GRNs including genes and biochemical reactions at molecular detail. In our approach, we consider each network element to be isolated from other elements during small time intervals, after which we synchronize molecule numbers across all network elements. Thereby, the temporal behaviour of network elements is decoupled and can be treated by local stochastic or deterministic solutions. We demonstrate the working principle of this modular approach with a repressive gene cascade comprising four genes. By considering a deterministic time evolution within each time interval for all elements, our method approaches the solution of the system of deterministic differential equations associated with the GRN. By allowing genes to stochastically switch between on and off states or by considering stochastic production of gene outputs, we are able to include increasing levels of stochastic detail and approximate the solution of a Gillespie simulation. Thereby, CaiNet is able to reproduce noise-induced bi-stability and oscillations in dynamically complex GRNs. Notably, our modular approach further allows for a simple consideration of deterministic delays. We further infer relevant regulatory connections and steady-state parameters of a GRN of up to ten genes from steady-state measurements by identifying each gene of the network with a single perceptron in an artificial neuronal network and using a gradient decent method originally designed to train recurrent neural networks. To facilitate setting up GRNs and using our simulation and inference method, we provide a fast computer-aided interactive network simulation environment, CaiNet. Conclusion We developed a method to simulate GRNs at molecular detail and to infer the topology and steady-state parameters of GRNs. Our method and associated user-friendly framework CaiNet should prove helpful to analyze or predict the temporal progression of reaction networks or GRNs in cellular and organismic biology. CaiNet is freely available at https://gitlab.com/GebhardtLab/CaiNet .

Gene expression in mammalian cells is inherently stochastic and mRNAs are synthesized in discrete bursts. Single-cell transcriptomics provides an unprecedented opportunity to explore the transcriptome-wide kinetics of transcriptional bursting. However, current analysis methods provide limited accuracy in bursting inference due to substantial noise inherent to single-cell transcriptomic data. In this study, we developed BISC, a Bayesian method for inferring bursting parameters from single cell transcriptomic data. Based on a beta-gamma-Poisson model, BISC modeled the mean–variance dependency to achieve accurate estimation of bursting parameters from noisy data. Evaluation based on both simulation and real intron sequential RNA fluorescence in situ hybridization data showed improved accuracy and reliability of BISC over existing methods, especially for genes with low expression values. Further application of BISC found bursting frequency but not bursting size was strongly associated with gene expression regulation. Moreover, our analysis provided new mechanistic insights into the functional role of enhancer and superenhancer by modulating both bursting frequency and size. BISC also formulated a downstream framework to identify differential bursting (in frequency and size separately) genes in samples under different conditions. Applying to multiple datasets (a mouse embryonic cell and fibroblast dataset, a human immune cell dataset and a human pancreatic cell dataset), BISC identified known cell-type signature genes that were missed by differential expression analysis, providing additional insights in understanding the cell-specific stochastic gene transcription. Applying to datasets of human lung and colon cancers, BISC successfully detected tumor signature genes based on alterations in bursting kinetics, which illustrates its value in understanding disease development regarding transcriptional bursting. Collectively, BISC provides a new tool for accurately inferring bursting kinetics and detecting differential bursting genes. This study also produced new insights in the role of transcriptional bursting in regulating gene expression, cell identity and tumor progression. [ABSTRACT FROM AUTHOR]

Many biological and medical questions can be modeled using time-to-event data in finite-state Markov chains, with the phase-type distribution describing intervals between events. We solve the inverse problem: given a phase-type distribution, can we identify the transition rate parameters of the underlying Markov chain? For a specific class of solvable Markov models, we show this problem has a unique solution up to finite symmetry transformations, and we outline a recursive method for computing symbolic solutions for these models across any number of states. Using the Thomas decomposition technique from computer algebra, we further provide symbolic solutions for any model. Interestingly, different models with the same state count but distinct transition graphs can yield identical phase-type distributions. To distinguish among these, we propose additional properties beyond just the time to the next event. We demonstrate the method's applicability by inferring transcriptional regulation models from single-cell transcription imaging data., (© 2024. The Author(s), under exclusive licence to the Society for Mathematical Biology.)

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential. [ABSTRACT FROM AUTHOR]

Abstract Cell‐to‐cell heterogeneity is a feature of the tumor necrosis factor (TNF)‐stimulated inflammatory response mediated by the transcription factor NF‐κB, motivating an exploration of the underlying sources of this noise. Here, we combined single‐transcript measurements with computational models to study transcriptional noise at six NF‐κB‐regulated inflammatory genes. In the basal state, NF‐κB‐target genes displayed an inverse correlation between mean and noise characteristic of transcriptional bursting. By analyzing transcript distributions with a bursting model, we found that TNF primarily activated transcription by increasing burst size while maintaining burst frequency for gene promoters with relatively high basal histone 3 acetylation (AcH3) that marks open chromatin environments. For promoters with lower basal AcH3 or when AcH3 was decreased with a small molecule drug, the contribution of burst frequency to TNF activation increased. Finally, we used a mathematical model to show that TNF positive feedback amplified gene expression noise resulting from burst size–mediated transcription, leading to a subset of cells with high TNF protein expression. Our results reveal potential sources of noise underlying intercellular heterogeneity in the TNF‐mediated inflammatory response.

Heat Shock (HS) signaling is activated in response to various types of cellular stress. This activation serves to protect cells from immediate threats in the surrounding environment. However, activation of HS signaling occurs in a heterogeneous manner within each cell population and can alter the epigenetic state of the cell, ultimately leading to long-term abnormalities in body function. Here, we summarize recent research findings obtained using molecular and genetic tools to track cells where HS signaling is activated. We then discuss the potential further applications of these tools, their limitations, and the necessary caveats in interpreting data obtained with these tools. [ABSTRACT FROM AUTHOR]

Abstract 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.

Background: The temporal progression of many fundamental processes in cells and organisms, including homeostasis, differentiation and development, are governed by gene regulatory networks (GRNs). GRNs balance fluctuations in the output of their genes, which trace back to the stochasticity of molecular interactions. Although highly desirable to understand life processes, predicting the temporal progression of gene products within a GRN is challenging when considering stochastic events such as transcription factor–DNA interactions or protein production and degradation. Results: We report a method to simulate and infer GRNs including genes and biochemical reactions at molecular detail. In our approach, we consider each network element to be isolated from other elements during small time intervals, after which we synchronize molecule numbers across all network elements. Thereby, the temporal behaviour of network elements is decoupled and can be treated by local stochastic or deterministic solutions. We demonstrate the working principle of this modular approach with a repressive gene cascade comprising four genes. By considering a deterministic time evolution within each time interval for all elements, our method approaches the solution of the system of deterministic differential equations associated with the GRN. By allowing genes to stochastically switch between on and off states or by considering stochastic production of gene outputs, we are able to include increasing levels of stochastic detail and approximate the solution of a Gillespie simulation. Thereby, CaiNet is able to reproduce noise-induced bi-stability and oscillations in dynamically complex GRNs. Notably, our modular approach further allows for a simple consideration of deterministic delays. We further infer relevant regulatory connections and steady-state parameters of a GRN of up to ten genes from steady-state measurements by identifying each gene of the network with a single perceptron in an artificial neuronal network and using a gradient decent method originally designed to train recurrent neural networks. To facilitate setting up GRNs and using our simulation and inference method, we provide a fast computer-aided interactive network simulation environment, CaiNet. Conclusion: We developed a method to simulate GRNs at molecular detail and to infer the topology and steady-state parameters of GRNs. Our method and associated user-friendly framework CaiNet should prove helpful to analyze or predict the temporal progression of reaction networks or GRNs in cellular and organismic biology. CaiNet is freely available at https://gitlab.com/GebhardtLab/CaiNet. [ABSTRACT FROM AUTHOR]

Abstract 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.

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zhang, Cao, Wang, Yin, Sun, Tian, Hu, Tan and Zhang.)

Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Many eukaryotic genes are expressed in randomly initiated bursts that are punctuated by periods of quiescence. Here, we show that the intermittent access of the promoters to transcription factors through relatively impervious chromatin contributes to this "noisy" transcription. We tethered a nuclease-deficient Cas9 fused to a histone acetyl transferase at the promoters of two endogenous genes in HeLa cells. An assay for transposase-accessible chromatin using sequencing showed that the activity of the histone acetyl transferase altered the chromatin architecture locally without introducing global changes in the nucleus and rendered the targeted promoters constitutively accessible. We measured the gene expression variability from the gene loci by performing single-molecule fluorescence in situ hybridization against mature messenger RNAs (mRNAs) and by imaging nascent mRNA molecules present at active gene loci in single cells. Because of the increased accessibility of the promoter to transcription factors, the transcription from two genes became less noisy, even when the average levels of expression did not change. In addition to providing evidence for chromatin accessibility as a determinant of the noise in gene expression, our study offers a mechanism for controlling gene expression noise which is otherwise unavoidable. [ABSTRACT FROM AUTHOR]

Transcriptional activation of eukaryotic genes is accompanied, in general, by a change in the sensitivity of promoter chromatin to endonucleases. The structural basis of this alteration has remained elusive for decades; but the change has been viewed as a transformation of one structure into another, from "closed" to "open" chromatin. In contradistinction to this static and deterministic view of the problem, a dynamical and probabilistic theory of promoter chromatin has emerged as its solution. This theory, which we review here, explains observed variation in promoter chromatin structure at the level of single gene molecules and provides a molecular basis for random bursting in transcription-the conjecture that promoters stochastically transition between transcriptionally conducive and inconducive states. The mechanism of transcriptional regulation may be understood only in probabilistic terms.

Abstract The rapid development of technologies provides the potential to perform real‐time visualization of transcriptional bursting patterns, superenhancer formation and sensitivity to perturbation, and interactions between enhancers, promoters, and regulators during the burst. The transcriptional bursting‐induced fluctuation can modify cell capacities, cell–cell communications, cell responses to microenvironmental changes, and forms of cell death. A large number of clinical and translational studies describe the existence of heterogeneity among cells, tissues, and organs but mechanism‐based understanding of how and why the heterogeneity exists and how it is formed. The transcriptional bursting, fluctuation, and control determine the development of heterogeneity and optimize cell functions in the cell development and differentiation, contribute to the initiation of cell dysfunction and tumorigenesis in response to environments, and development/evolvement of hyper/hyposensitivity to drugs. Spatiotemporal monitoring of transcriptional bursting and control provides a new insight and deeper understanding of spatiotemporal molecular medicine by integrating the transcriptional positioning and function with cell phenotypes, cell–cell communication, and clinical phenomes.

The rapid development of technologies provides the potential to perform real‐time visualization of transcriptional bursting patterns, superenhancer formation and sensitivity to perturbation, and interactions between enhancers, promoters, and regulators during the burst. The transcriptional bursting‐induced fluctuation can modify cell capacities, cell–cell communications, cell responses to microenvironmental changes, and forms of cell death. A large number of clinical and translational studies describe the existence of heterogeneity among cells, tissues, and organs but mechanism‐based understanding of how and why the heterogeneity exists and how it is formed. The transcriptional bursting, fluctuation, and control determine the development of heterogeneity and optimize cell functions in the cell development and differentiation, contribute to the initiation of cell dysfunction and tumorigenesis in response to environments, and development/evolvement of hyper/hyposensitivity to drugs. Spatiotemporal monitoring of transcriptional bursting and control provides a new insight and deeper understanding of spatiotemporal molecular medicine by integrating the transcriptional positioning and function with cell phenotypes, cell–cell communication, and clinical phenomes. [ABSTRACT FROM AUTHOR]

Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging. In this article, we present CARDAMOM (Cell type Analysis from scRna-seq Data achieved from a Mixture MOdel), a new algorithm for inferring a GRN from timestamped scRNA-seq data, which crucially exploits these notions of metastability and transcriptional bursting. We show that such inference can be seen as the successive resolution of as many regression problem as timepoints, after a preliminary clustering of the whole set of cells with regards to their associated bursts frequency. We demonstrate the ability of CARDAMOM to infer a reliable GRN from in silico expression datasets, with good computational speed. To the best of our knowledge, this is the first description of a method which uses the concept of metastability for performing GRN inference. [ABSTRACT FROM AUTHOR]

Cell‐to‐cell heterogeneity is a feature of the tumor necrosis factor (TNF)‐stimulated inflammatory response mediated by the transcription factor NF‐κB, motivating an exploration of the underlying sources of this noise. Here, we combined single‐transcript measurements with computational models to study transcriptional noise at six NF‐κB‐regulated inflammatory genes. In the basal state, NF‐κB‐target genes displayed an inverse correlation between mean and noise characteristic of transcriptional bursting. By analyzing transcript distributions with a bursting model, we found that TNF primarily activated transcription by increasing burst size while maintaining burst frequency for gene promoters with relatively high basal histone 3 acetylation (AcH3) that marks open chromatin environments. For promoters with lower basal AcH3 or when AcH3 was decreased with a small molecule drug, the contribution of burst frequency to TNF activation increased. Finally, we used a mathematical model to show that TNF positive feedback amplified gene expression noise resulting from burst size–mediated transcription, leading to a subset of cells with high TNF protein expression. Our results reveal potential sources of noise underlying intercellular heterogeneity in the TNF‐mediated inflammatory response. SYNOPSIS: Single‐cell transcript measurements show that TNF stimulation primarily increases transcriptional burst size at NF‐κB target genes. Chromatin measurements and mathematical modeling are used to explore the sources and consequences of TNF‐mediated bursting. NF‐κB‐target genes display an inverse correlation between mean and noise characteristic of transcriptional bursting.TNF primarily activates transcription by increasing burst size while maintaining burst frequency for promoters with relatively high basal histone 3 acetylation.The contribution of burst frequency to TNF activation increases when basal AcH3 at target promoters is reduced.Mathematical modeling shows that TNF positive feedback amplification of gene expression noise can produce a subset of cells with high TNF protein expression. [ABSTRACT FROM AUTHOR]

Enhancers bind transcription factors, chromatin regulators, and non-coding transcripts to modulate the expression of target genes. Here, we report 3D genome structures of single mouse ES cells as they are induced to exit pluripotency and transition through a formative stage prior to undergoing neuroectodermal differentiation. We find that there is a remarkable reorganization of 3D genome structure where inter-chromosomal intermingling increases dramatically in the formative state. This intermingling is associated with the formation of a large number of multiway hubs that bring together enhancers and promoters with similar chromatin states from typically 5–8 distant chromosomal sites that are often separated by many Mb from each other. In the formative state, genes important for pluripotency exit establish contacts with emerging enhancers within these multiway hubs, suggesting that the structural changes we have observed may play an important role in modulating transcription and establishing new cell identities. [Display omitted] • 3D genome structure and H3K27me3 levels are reorganized in formative state pluripotency • Hundreds of multiway chromatin hubs form in each formative state cell • Enhancer-promoter interactions are reconfigured in multiway hubs • The structural reorganization in the formative state is regulated by DNMT3a/b and TET1 Lando, Ma, et al. report 3D genome structures of single mouse ES cells as they begin to differentiate and transition through a formative state. Multiway chromatin hubs form in the formative state, providing a place where genes that are going to be turned on establish long-range contacts with emerging enhancers. [ABSTRACT FROM AUTHOR]

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. SYNOPSIS: Single‐molecule imaging of transcripts is combined with mathematical modelling to investigate how the mRNA distributions of core‐clock circadian genes evolve over the circadian cycle while considering multiple sources of intrinsic and extrinsic variability. Single molecule fluorescence in‐situ hybridization (smFISH) of core‐clock genes at multiple time points over the circadian cycle shows that the mean mRNA level oscillates but there is significant variability in mRNA counts between cells.Bayesian model selection identifies a mixture model of negative binomials as the preferred model of the mRNA counts.Noise decomposition of the preferred model shows that a small percentage of measured variation in core clock transcript number is attributable to circadian time. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) stands as a cutting-edge technology widely used in biological and biomedical research. Existing scRNA-seq methods rely on reverse transcription (RT) and second-strand synthesis (SSS) to convert RNA to cDNA before amplification. However, these methods often suffer from limited RT/SSS efficiency, which compromises the sensitivity of RNA detection. Here, we develop a new method, linearly amplified single-stranded RNA-derived transcriptome sequencing (LAST-seq), which directly amplifies the original single-stranded RNA without prior RT and SSS and offers high-sensitivity RNA detection and a low level of technical noise in single-cell transcriptome analysis. LAST-seq has been applied to quantify transcriptional bursting kinetics in human cells, advancing our understanding of chromatin organization's role in regulating gene expression. Key features • An RNase H/DNA polymerase-based strategy to attach the T7 promoter to single-stranded RNA. • T7 promoter mediated IVT on single stranded RNA template at single cell level., Competing Interests: Competing interestsThe authors declare no competing interests., (©Copyright : © 2024 The Authors; This is an open access article under the CC BY-NC license.)

We used live imaging to visualize the transcriptional dynamics of the Drosophila melanogaster even-skipped gene at single-cell and high-temporal resolution as its seven stripe expression pattern forms, and developed tools to characterize and visualize how transcriptional bursting varies over time and space. We find that despite being created by the independent activity of five enhancers, even-skipped stripes are sculpted by the same kinetic phenomena: a coupled increase of burst frequency and amplitude. By tracking the position and activity of individual nuclei, we show that stripe movement is driven by the exchange of bursting nuclei from the posterior to anterior stripe flanks. Our work provides a conceptual, theoretical and computational framework for dissecting pattern formation in space and time, and reveals how the coordinated transcriptional activity of individual nuclei shapes complex developmental patterns.

RNA, the transcriptional output of genomes, not only templates protein synthesis or directly engages in catalytic functions, but can feed back to the genome and serve as regulatory input for gene expression. Transcripts affecting the RNA abundance of other genes act by mechanisms similar to and in concert with protein factors that control transcription. Through recruitment or blocking of activating and silencing complexes to specific genomic loci, RNA and protein factors can favor transcription or lower the local gene expression potential. Most regulatory proteins enter nuclei from all directions to start the search for increased affinity to specific DNA sequences or to other proteins nearby genuine gene targets. In contrast, RNAs emerge from spatial point sources within nuclei, their encoding genes. A transcriptional burst can result in the local appearance of multiple nascent RNA copies at once, in turn increasing local nucleic acid density and RNA motif abundance before diffusion into the nuclear neighborhood. The confined initial localization of regulatory RNAs causing accumulation of protein co-factors raises the intriguing possibility that target specificity of non-coding, and probably coding, RNAs is achieved through gene/RNA positioning and spatial proximity to regulated genomic regions. Here we review examples of positional cis conservation of regulatory RNAs with respect to target genes, spatial proximity of enhancer RNAs to promoters through DNA looping and RNA-mediated formation of membrane-less structures to control chromatin structure and expression. We speculate that linear and spatial proximity between regulatory RNA-encoding genes and gene targets could possibly ease the evolutionary pressure on maintaining regulatory RNA sequence conservation.

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.

Noisy gene expression generates diverse phenotypes, but little is known about mechanisms that modulate noise. Combining experiments and modeling, we studied how tumor necrosis factor (TNF) initiates noisy expression of latent HIV via the transcription factor nuclear factor κB (NF-κB) and how the HIV genomic integration site modulates noise to generate divergent (low-versus-high) phenotypes of viral activation. We show that TNF-induced transcriptional noise varies more than mean transcript number and that amplification of this noise explains low-versus-high viral activation. For a given integration site, live-cell imaging shows that NF-κB activation correlates with viral activation, but across integration sites, NF-κB activation cannot account for differences in transcriptional noise and phenotypes. Instead, differences in transcriptional noise are associated with differences in chromatin state and RNA polymerase II regulation. We conclude that, whereas NF-κB regulates transcript abundance in each cell, the chromatin environment modulates noise in the population to support diverse HIV activation in response to TNF.

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. [ABSTRACT FROM AUTHOR]

RNA, the transcriptional output of genomes, not only templates protein synthesis or directly engages in catalytic functions, but can feed back to the genome and serve as regulatory input for gene expression. Transcripts affecting the RNA abundance of other genes act by mechanisms similar to and in concert with protein factors that control transcription. Through recruitment or blocking of activating and silencing complexes to specific genomic loci, RNA and protein factors can favor transcription or lower the local gene expression potential. Most regulatory proteins enter nuclei from all directions to start the search for increased affinity to specific DNA sequences or to other proteins nearby genuine gene targets. In contrast, RNAs emerge from spatial point sources within nuclei, their encoding genes. A transcriptional burst can result in the local appearance of multiple nascent RNA copies at once, in turn increasing local nucleic acid density and RNA motif abundance before diffusion into the nuclear neighborhood. The confined initial localization of regulatory RNAs causing accumulation of protein co-factors raises the intriguing possibility that target specificity of non-coding, and probably coding, RNAs is achieved through gene/RNA positioning and spatial proximity to regulated genomic regions. Here we review examples of positional cis conservation of regulatory RNAs with respect to target genes, spatial proximity of enhancer RNAs to promoters through DNA looping and RNA-mediated formation of membrane-less structures to control chromatin structure and expression. We speculate that linear and spatial proximity between regulatory RNA-encoding genes and gene targets could possibly ease the evolutionary pressure on maintaining regulatory RNA sequence conservation. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing data have complex features such as dropout events, overdispersion, and high-magnitude outliers, resulting in complicated probability distributions of mRNA abundances that are statistically characterized in terms of a zero-inated negative binomial (ZINB) model. Here we provide a mesoscopic kinetic foundation for the widely used ZINB model based on the biochemical reaction kinetics underlying transcription. Using multiscale modeling and simplification techniques, we show that the ZINB distribution of mRNA abundance and the related phenomenon of transcriptional bursting naturally emerge from a three-state stochastic transcription model. We further reveal a nontrivial quantitative relationship between dropout events and transcriptional bursting, which provides novel insights into how the burst size and burst frequency affect the dropout rate. Two different biophysical origins of overdispersion are also clarified at the single-cell level. [ABSTRACT FROM AUTHOR]

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. Demystification of the term 'transcriptional bursting.' Models with one or two gene states are unable to accurately describe dynamic transcription for many genes. Many alternative multistate models have been proposed, but these may be highly context specific. Understanding the contributions of numerous different cellular features and processes to bursting is required to build more accurate and general models of transcription dynamics. Emerging imaging technologies are beginning to facilitate the monitoring of these diverse sources of regulation. [ABSTRACT FROM AUTHOR]

Single-molecule imaging inside living cells has revealed that transcription factors (TFs) bind to DNA transiently, but a long-standing question is how this transient binding is related to transcription activation. Here, we devised a microscopy method to simultaneously measure transient TF binding at a single locus and the effect of these binding events on transcription. We show that DNA binding of the yeast TF Gal4 activates transcription of a target gene within a few seconds, with at least ∼20% efficiency and with a high initiation rate of ∼1 RNA/s. Gal4 DNA dissociation decreases transcription rapidly. Moreover, at a gene with multiple binding sites, individual Gal4 molecules only rarely stay bound throughout the entire burst but instead frequently exchange during a burst to increase the transcriptional burst duration. Our results suggest a mechanism for enhancer regulation in more complex eukaryotes, where TF cooperativity and exchange enable robust and responsive transcription regulation. [Display omitted] • In vivo imaging of transcription factor Gal4 DNA binding and GAL10 transcription • Gal4 association and dissociation activates and deactivates GAL10 transcription rapidly • Gal4 binds DNA surprisingly long, but rarely throughout the entire burst • Gal4 molecules frequently exchange to increase the transcriptional burst duration Pomp et al. use simultaneous imaging of transcription factor DNA binding and transcriptional activity of a single target gene in living cells to understand their kinetic relationship. Transcription factor Gal4 binds DNA transiently but exchanges frequently to prolong transcriptionally active periods. Transcription factor cooperativity and exchange enable robust transcription regulation. [ABSTRACT FROM AUTHOR]

Abstract Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average expression across cells. Single-cell RNA sequencing allows the comparison of expression distribution between the two alleles of a diploid organism and the characterization of allele-specific bursting. Here, we propose SCALE to analyze genome-wide allele-specific bursting, with adjustment of technical variability. SCALE detects genes exhibiting allelic differences in bursting parameters and genes whose alleles burst non-independently. We apply SCALE to mouse blastocyst and human fibroblast cells and find that cis control in gene expression overwhelmingly manifests as differences in burst frequency.

Transcription is a highly dynamic process, which, for many genes, occurs in stochastic bursts. Studying what regulates these stochastic bursts requires visualization and quantification of transcription dynamics in single living cells. Such measurements of bursting can be accomplished by labeling nascent transcripts of single genes fluorescently with the MS2 and PP7 RNA labeling techniques. Live-cell single-molecule microscopy of transcription in real time allows for the extraction of transcriptional bursting kinetics inside single cells. This chapter describes how to set up the MS2 or PP7 RNA labeling system of endogenous genes in both budding yeast (Saccharomyces cerevisiae) and mammalian cells (mouse embryonic stem cells). We include how to genetically engineer the cells with the MS2 and PP7 system, describe how to perform the live-microscopy experiments and discuss how to extract transcriptional bursting parameters of the genes of interest., (© 2024. The Author(s).)