Measuring Transcription Dynamics of Individual Genes Inside Living Cells

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.

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.

STREAMING-Tag System: Technology to Enable Visualization of Transcriptional Activity and Subnuclear Localization of Specific Endogenous Genes

The Spliced TetO REpeAt, MS2 repeat, and INtein sandwiched reporter Gene tag (STREAMING-tag) system enables imaging of nuclear localization as well as the transcription activity of a specific endogenous gene at sub-100-nm resolution in living cells. The use of this system combined with imaging of epigenome states enables a detailed analysis of the impact of epigenome status on transcriptional dynamics. In this chapter, we describe a method for quantifying distances between Nanog gene and clusters of cofactor BRD4 using the STREAMING-tag system in mouse embryonic stem cells.

BISC: accurate inference of transcriptional bursting kinetics from single-cell transcriptomic data

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.

Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04541-6.

Image-Based Single-Molecule Analysis of Notch-Dependent Transcription in Its Natural Context

Notch signaling is crucial to animal development and homeostasis. Notch triggers the transcription of its target genes, which produce diverse outcomes depending on context. The high resolution and spatially precise assessment of Notch-dependent transcription is essential for understanding how Notch operates normally in its native context in vivo and how Notch defects lead to pathogenesis. Here we present biological and computational methods to assess Notch-dependent transcriptional activation in stem cells within their niche, focusing on germline stem cells in the nematode Caenorhabditis elegans. Specifically, we describe visualization of single RNAs in fixed gonads using single-molecule RNA fluorescence in situ hybridization (smFISH), live imaging of transcriptional bursting in the intact organism using the MS2 system, and custom-made MATLAB codes, implementing new image processing algorithms to capture the spatiotemporal patterns of Notch-dependent transcriptional activation. These methods allow a powerful analysis of in vivo transcriptional activation and its dynamics in a whole tissue. Our methods can be adapted to essentially any tissue or cell type for any transcript.

Crystallin gene expression: Insights from studies of transcriptional bursting

Cellular differentiation is marked by temporally and spatially regulated gene expression. The ocular lens is one of the most powerful mammalian model system since it is composed from only two cell subtypes, called lens epithelial and fiber cells. Lens epithelial cells differentiate into fiber cells through a series of spatially and temporally orchestrated processes, including massive production of crystallins, cellular elongation and the coordinated degradation of nuclei and other organelles. Studies of transcriptional and posttranscriptional gene regulatory mechanisms in lens provide a wide range of opportunities to understand global molecular mechanisms of gene expression as steady-state levels of crystallin mRNAs reach very high levels comparable to globin genes in erythrocytes. Importantly, dysregulation of crystallin gene expression results in lens structural abnormalities and cataracts. The mRNA life cycle is comprised of multiple stages, including transcription, splicing, nuclear export into cytoplasm, stabilization, localization, translation and ultimate decay. In recent years, development of modern mRNA detection methods with single molecule and single cell resolution enabled transformative studies to visualize the mRNA life cycle to generate novel insights into the sequential regulatory mechanisms of gene expression during embryogenesis. This review is focused on recent major advancements in studies of transcriptional bursting in differentiating lens fiber cells, analysis of nascent mRNA expression from bi-directional promoters, transient nuclear accumulation of specific mRNAs, condensation of chromatin prior lens fiber cell denucleation, and outlines future studies to probe the interactions of individual mRNAs with specific RNA-binding proteins (RBPs) in the cytoplasm and regulation of translation and mRNA decay.

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.

Single-Cell Allele-Specific Gene Expression Analysis

Allele-specific expression is traditionally studied by bulk RNA sequencing, which measures average gene expression across cells. Single-cell RNA sequencing (scRNA-seq) allows the comparison of expression distribution between the two alleles of a diploid organism, and characterization of allele-specific bursting. Here we describe SCALE, a bioinformatic and statistical framework for allele-specific gene expression analysis by scRNA-seq. SCALE estimates genome-wide bursting kinetics at the allelic level while accounting for technical bias and other complicating factors such as cell size. SCALE detects genes with significantly different bursting kinetics between the two alleles, as well as genes where the two alleles exhibit non-independent bursting processes. Here, we illustrate SCALE on a mouse blastocyst single-cell dataset with step-by-step demonstration from the upstream bioinformatic processing to the downstream biological interpretation of SCALE's output.

SCALE: modeling allele-specific gene expression by single-cell RNA sequencing

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.

Stochastic modeling and numerical simulation of gene regulatory networks with protein bursting

Gene expression is inherently stochastic. Advanced single-cell microscopy techniques together with mathematical models for single gene expression led to important insights in elucidating the sources of intrinsic noise in prokaryotic and eukaryotic cells. In addition to the finite size effects due to low copy numbers, translational bursting is a dominant source of stochasticity in cell scenarios involving few short lived mRNA transcripts with high translational efficiency (as is typically the case for prokaryotes), causing protein synthesis to occur in random bursts. In the context of gene regulation cascades, the Chemical Master Equation (CME) governing gene expression has in general no closed form solution, and the accurate stochastic simulation of the dynamics of complex gene regulatory networks is a major computational challenge. The CME associated to a single gene self regulatory motif has been previously approximated by a one dimensional time dependent partial integral differential equation (PIDE). However, to the best of our knowledge, multidimensional versions for such PIDE have not been developed yet. Here we propose a multidimensional PIDE model for regulatory networks involving multiple genes with self and cross regulations (in which genes can be regulated by different transcription factors) derived as the continuous counterpart of a CME with jump process. The model offers a reliable description of systems with translational bursting. In order to provide an efficient numerical solution, we develop a semilagrangian method to discretize the differential part of the PIDE, combined with a composed trapezoidal quadrature formula to approximate the integral term. We apply the model and numerical method to study sustained stochastic oscillations and the development of competence, a particular case of transient differentiation attained by certain bacterial cells under stress conditions. We found that the resulting probability distributions are distinguishable from those characteristic of other transient differentiation processes. In this way, they can be employed as markers or signatures that identify such phenomena from bacterial population experimental data, for instance. The computational efficiency of the semilagrangian method makes it suitable for purposes like model identification and parameter estimation from experimental data or, in combination with optimization routines, the design of gene regulatory networks under molecular noise.

Quantitative measurement of transcription dynamics in living cells

In a wide range of organisms the kinetics of transcription have been found to be noisy, with "bursts" or "pulses" of transcription interspersed with irregular periods of inactivity. The in vivo analysis of transcription dynamics can be most directly monitored using RNA stem loop motifs derived from MS2 and other bacteriophages. Here we describe the implementation of the MS2 RNA detection system and the steps required for precise measurement of transcription dynamics in highly motile cells. Automated image processing techniques are used to track large numbers of cells and measure transcription in a systematic and unbiased manner. We discuss popular methods for automatic image segmentation and frame-to-frame tracking of cells, and the considerations required to make measurements as quantitatively as possible.