Real-time observation of transcription initiation and elongation on an endogenous yeast gene

The Drosophila Pioneer Factor Zelda Modulates the Nuclear Microenvironment of a Dorsal Target Enhancer to Potentiate Transcriptional Output

Connecting the developmental patterning of tissues to the mechanistic control of RNA polymerase II remains a long-term goal of developmental biology. Many key elements have been identified in the establishment of spatial-temporal control of transcription in the early Drosophila embryo, a model system for transcriptional regulation. The dorsal-ventral axis of the Drosophila embryo is determined by the graded distribution of Dorsal (Dl), a homolog of the nuclear factor κB (NF-κB) family of transcriptional activators found in humans [1, 2]. A second maternally deposited factor, Zelda (Zld), is uniformly distributed in the embryo and is thought to act as a pioneer factor, increasing enhancer accessibility for transcription factors, such as Dl [3-9]. Here, we utilized the MS2 live imaging system to evaluate the expression of the Dl target gene short gastrulation (sog) to better understand how a pioneer factor affects the kinetic parameters of transcription. Our experiments indicate that Zld modifies probability of activation, the timing of this activation, and the rate at which transcription occurs. Our results further show that this effective rate increase is due to an increased accumulation of Dl at the site of transcription, suggesting that transcription factor "hubs" induced by Zld [10] functionally regulate transcription.

Physical principles and functional consequences of nuclear compartmentalization in budding yeast

One striking feature of eukaryotic nuclei is the existence of discrete regions, in which specific factors concentrate while others are excluded, thus forming microenvironments with different molecular compositions and biological functions. These domains are often referred to as subcompartments even though they are not membrane enclosed. Despite their functional importance the physical nature of these structures remains largely unknown. Here, we describe how the Saccharomyces cerevisiae nucleus is compartmentalized and discuss possible physical models underlying the formation and maintenance of chromatin associated subcompartments. Focusing on three particular examples, the nucleolus, silencing foci, and repair foci, we discuss the biological implications of these different models as well as possible approaches to challenge them in living cells.

Quantitative relationships between SMAD dynamics and target gene activation kinetics in single live cells

The transduction of extracellular signals through signaling pathways that culminate in a transcriptional response is central to many biological processes. However, quantitative relationships between activities of signaling pathway components and transcriptional output of target genes remain poorly explored. Here we developed a dual bioluminescence imaging strategy allowing simultaneous monitoring of nuclear translocation of the SMAD4 and SMAD2 transcriptional activators upon TGF-β stimulation, and the transcriptional response of the endogenous connective tissue growth factor (ctgf) gene. Using cell lines allowing to vary exogenous SMAD4/2 expression levels, we performed quantitative measurements of the temporal profiles of SMAD4/2 translocation and ctgf transcription kinetics in hundreds of individual cells at high temporal resolution. We found that while nuclear translocation efficiency had little impact on initial ctgf transcriptional activation, high total cellular SMAD4 but not SMAD2 levels increased the probability of cells to exhibit a sustained ctgf transcriptional response. The approach we present here allows time-resolved single cell quantification of transcription factor dynamics and transcriptional responses and thereby sheds light on the quantitative relationship between SMADs and target gene responses.

Advances in CRISPR-Cas systems for RNA targeting, tracking and editing

Clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein (Cas) systems, especially type II (Cas9) systems, have been widely used in gene/genome targeting. Modifications of Cas9 enable these systems to become platforms for precise DNA manipulations. However, the utilization of CRISPR-Cas systems in RNA targeting remains preliminary. The discovery of type VI CRISPR-Cas systems (Cas13) shed light on RNA-guided RNA targeting. Cas13d, the smallest Cas13 protein, with a length of only ~930 amino acids, is a promising platform for RNA targeting compatible with viral delivery systems. Much effort has also been made to develop Cas9, Cas13a and Cas13b applications for RNA-guided RNA targeting. The discovery of new RNA-targeting CRISPR-Cas systems as well as the development of RNA-targeting platforms with Cas9 and Cas13 will promote RNA-targeting technology substantially. Here, we review new advances in RNA-targeting CRISPR-Cas systems as well as advances in applications of these systems in RNA targeting, tracking and editing. We also compare these Cas protein-based technologies with traditional technologies for RNA targeting, tracking and editing. Finally, we discuss remaining questions and prospects for the future.

Writing a wrong: Coupled RNA polymerase II transcription and RNA quality control

Processing and maturation of precursor RNA species is coupled to RNA polymerase II transcription. Co-transcriptional RNA processing helps to ensure efficient and proper capping, splicing, and 3' end processing of different RNA species to help ensure quality control of the transcriptome. Many improperly processed transcripts are not exported from the nucleus, are restricted to the site of transcription, and are in some cases degraded, which helps to limit any possibility of aberrant RNA causing harm to cellular health. These critical quality control pathways are regulated by the highly dynamic protein-protein interaction network at the site of transcription. Recent work has further revealed the extent to which the processes of transcription and RNA processing and quality control are integrated, and how critically their coupling relies upon the dynamic protein interactions that take place co-transcriptionally. This review focuses specifically on the intricate balance between 3' end processing and RNA decay during transcription termination. This article is categorized under: RNA Turnover and Surveillance > Turnover/Surveillance Mechanisms RNA Processing > 3' End Processing RNA Processing > Splicing Mechanisms RNA Processing > Capping and 5' End Modifications.

Thirty years of neuroendocrinology: Technological advances pave the way for molecular discovery

Since the 1950s, the systems level interactions between the hypothalamus, pituitary and end organs such as the adrenal, thyroid and gonads have been well known; however, it is only over the last three decades that advances in molecular biology and information technology have provided a tremendous expansion of knowledge at the molecular level. Neuroendocrinology has benefitted from developments in molecular genetics, epigenetics and epigenomics, and most recently optogenetics and pharmacogenetics. This has enabled a new understanding of gene regulation, transcription, translation and post-translational regulation, which should help direct the development of drugs to treat neuroendocrine-related diseases.

Dynamics of transcriptional enhancers and chromosome topology in gene regulation

Transcriptional enhancers are regulatory DNAs that instruct when and where genes should be transcribed in response to a variety of intrinsic and external signals. They contain a cluster of binding sites for sequence-specific transcription factors and co-activators to determine the spatiotemporal specificity of gene activities during development. Enhancers are often positioned in distal locations from their target promoters. In some cases, they work over a million base pairs or more. In the traditional view, enhancers have been thought to stably interact with promoters in a targeted manner. However, quantitative imaging studies provide a far more dynamic picture of enhancer action. Moreover, recent Hi-C methods suggest that regulatory interactions are dynamically regulated by the higher-order chromosome topology. In this review, we summarize the emerging findings in the field and propose that assembly of "transcription hubs" in the context of 3D genome structure plays an important role in transcriptional regulation.

An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation

Transcription is a highly regulated and inherently stochastic process. The complexity of signal transduction and gene regulation makes it challenging to analyze how the dynamic activity of transcriptional regulators affects stochastic transcription. By combining a fast-acting, photo-regulatable transcription factor with nascent RNA quantification in live cells and an experimental setup for precise spatiotemporal delivery of light inputs, we constructed a platform for the real-time, single-cell interrogation of transcription in Saccharomyces cerevisiae. We show that transcriptional activation and deactivation are fast and memoryless. By analyzing the temporal activity of individual cells, we found that transcription occurs in bursts, whose duration and timing are modulated by transcription factor activity. Using our platform, we regulated transcription via light-driven feedback loops at the single-cell level. Feedback markedly reduced cell-to-cell variability and led to qualitative differences in cellular transcriptional dynamics. Our platform establishes a flexible method for studying transcriptional dynamics in single cells.

Recent advancement of light-based single-molecule approaches for studying biomolecules

Recent advances in single-molecule techniques have led to new discoveries in analytical chemistry, biophysics, and medicine. Understanding the structure and behavior of single biomolecules provides a wealth of information compared to studying large ensembles. However, developing single-molecule techniques is challenging and requires advances in optics, engineering, biology, and chemistry. In this paper, we will review the state of the art in single-molecule applications with a focus over the last few years of development. The advancements covered will mainly include light-based in vitro methods, and we will discuss the fundamentals of each with a focus on the platforms themselves. We will also summarize their limitations and current and future applications to the wider biological and chemical fields. This article is categorized under: Laboratory Methods and Technologies > Imaging Laboratory Methods and Technologies > Macromolecular Interactions, Methods Analytical and Computational Methods > Analytical Methods.

Progress and Challenges for Live-cell Imaging of Genomic Loci Using CRISPR-based Platforms

Chromatin conformation, localization, and dynamics are crucial regulators of cellular behaviors. Although fluorescence in situ hybridization-based techniques have been widely utilized for investigating chromatin architectures in healthy and diseased states, the requirement for cell fixation precludes the comprehensive dynamic analysis necessary to fully understand chromatin activities. This has spurred the development and application of a variety of imaging methodologies for visualizing single chromosomal loci in the native cellular context. In this review, we describe currently-available approaches for imaging single genomic loci in cells, with special focus on clustered regularly interspaced short palindromic repeats (CRISPR)-based imaging approaches. In addition, we discuss some of the challenges that limit the application of CRISPR-based genomic imaging approaches, and potential solutions to address these challenges. We anticipate that, with continued refinement of CRISPR-based imaging techniques, significant understanding can be gained to help decipher chromatin activities and their relevance to cellular physiology and pathogenesis.

Enhancer Histone Acetylation Modulates Transcriptional Bursting Dynamics of Neuronal Activity-Inducible Genes

Neuronal activity-inducible gene transcription correlates with rapid and transient increases in histone acetylation at promoters and enhancers of activity-regulated genes. Exactly how histone acetylation modulates transcription of these genes has remained unknown. We used single-cell in situ transcriptional analysis to show that Fos and Npas4 are transcribed in stochastic bursts in mouse neurons and that membrane depolarization increases mRNA expression by increasing burst frequency. We then expressed dCas9-p300 or dCas9-HDAC8 fusion proteins to mimic or block activity-induced histone acetylation locally at enhancers. Adding histone acetylation increased Fos transcription by prolonging burst duration and resulted in higher Fos protein levels and an elevation of resting membrane potential. Inhibiting histone acetylation reduced Fos transcription by reducing burst frequency and impaired experience-dependent Fos protein induction in the hippocampus in vivo. Thus, activity-inducible histone acetylation tunes the transcriptional dynamics of experience-regulated genes to affect selective changes in neuronal gene expression and cellular function.

Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells

Many cellular effectors of pluripotency are dynamically regulated. In principle, regulatory mechanisms can be inferred from single-cell observations of effector activity across time. However, rigorous inference techniques suitable for noisy, incomplete, and heterogeneous data are lacking. Here, we introduce stochastic inference on lineage trees (STILT), an algorithm capable of identifying stochastic models that accurately describe the quantitative behavior of cell fate markers observed using time-lapse microscopy data collected from proliferating cell populations. STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm. We use STILT to investigate the autoregulation of Nanog, a heterogeneously expressed core pluripotency factor, in mouse embryonic stem cells. STILT rejects the possibility of positive Nanog autoregulation with high confidence; instead, model predictions indicate weak negative feedback. We use STILT for rational experimental design and validate model predictions using novel experimental data. STILT is available for download as an open source framework from http://www.imsb.ethz.ch/research/claassen/Software/stilt---stochastic-inference-on-lineage-trees.html.

Lighting Up Gene Activation in Living Drosophila Embryos

With its rapid development, ease of collection, and the presence of a unique layer of nuclei located close to the surface, the Drosophila syncytial embryo is ideally suited to study the establishment of gene expression patterns during development. Recent improvements in RNA labeling technologies and confocal microscopy allow for visualizing gene activation and quantifying transcriptional dynamics in living Drosophila embryos. Here we review the available tools for mRNA fluorescent labeling and detection in live embryos and precisely describe the overall procedure, from design to mounting and confocal imaging.

Single-molecule imaging of transcription at damaged chromatin

How DNA double-strand breaks (DSBs) affect ongoing transcription remains elusive due to the lack of single-molecule resolution tools directly measuring transcription dynamics upon DNA damage. Here, we established new reporter systems that allow the visualization of individual nascent RNAs with high temporal and spatial resolution upon the controlled induction of a single DSB at two distinct chromatin locations: a promoter-proximal (PROP) region downstream the transcription start site and a region within an internal exon (EX2). Induction of a DSB resulted in a rapid suppression of preexisting transcription initiation regardless of the genomic location. However, while transcription was irreversibly suppressed upon a PROP DSB, damage at the EX2 region drove the formation of promoter-like nucleosome-depleted regions and transcription recovery. Two-color labeling of transcripts at sequences flanking the EX2 lesion revealed bidirectional break-induced transcription initiation. Transcriptome analysis further showed pervasive bidirectional transcription at endogenous intragenic DSBs. Our data provide a novel framework for interpreting the reciprocal interactions between transcription and DNA damage at distinct chromatin regions.

Live-Cell Imaging of mRNP-NPC Interactions in Budding Yeast

Single-molecule resolution imaging has become an important tool in the study of cell biology. Aptamer-based approaches (e.g., MS2 and PP7) allow for detection of single RNA molecules in living cells and have been used to study various aspects of mRNA metabolism, including mRNP nuclear export. Here we outline an imaging protocol for the study of interactions between mRNPs and nuclear pore complexes (NPCs) in the yeast S. cerevisiae, including mRNP export. We describe in detail the steps that allow for high-resolution live-cell mRNP imaging and measurement of mRNP interactions with NPCs using simultaneous two-color imaging. Our protocol discusses yeast strain construction, choice of marker proteins to label the nuclear pore complex, as well as imaging conditions that allow high signal-to-noise data acquisition. Moreover, we describe various aspects of postacquisition image analysis for single molecule tracking and image registration allowing for the characterization of mRNP-NPC interactions.

Intrinsic Dynamics of a Human Gene Reveal the Basis of Expression Heterogeneity

Transcriptional regulation in metazoans occurs through long-range genomic contacts between enhancers and promoters, and most genes are transcribed in episodic "bursts" of RNA synthesis. To understand the relationship between these two phenomena and the dynamic regulation of genes in response to upstream signals, we describe the use of live-cell RNA imaging coupled with Hi-C measurements and dissect the endogenous regulation of the estrogen-responsive TFF1 gene. Although TFF1 is highly induced, we observe short active periods and variable inactive periods ranging from minutes to days. The heterogeneity in inactive times gives rise to the widely observed "noise" in human gene expression and explains the distribution of protein levels in human tissue. We derive a mathematical model of regulation that relates transcription, chromosome structure, and the cell's ability to sense changes in estrogen and predicts that hypervariability is largely dynamic and does not reflect a stable biological state.

Imaging the Life and Death of mRNAs in Single Cells

RNA plays a central role in gene expression from its transcription in the nucleus through translation and degradation in the cytoplasm. Technological advances in fluorescent microscopy and labeling methodologies have made it possible to detect single molecules of RNA in both fixed and living cells. Here, we focus on the recent developments in RNA imaging that have allowed quantitatively measuring the lives of individual transcripts from birth to death and all the events in between in single cells and tissues. Direct observation of RNAs within their native cellular environment has revealed a complex layer of spatial and temporal regulation that has profoundly impacted our understanding of RNA biology.

Single-Molecule Tracking Approaches to Protein Synthesis Kinetics in Living Cells

Decades of traditional biochemistry, structural approaches, and, more recently, single-molecule-based in vitro techniques have provided us with an astonishingly detailed understanding of the molecular mechanism of ribosome-catalyzed protein synthesis. However, in order to understand these details in the context of cell physiology and population biology, new techniques to probe the dynamics of molecular processes inside the cell are needed. Recent years' development in super-resolved fluorescence microscopy has revolutionized imaging of intracellular processes, and we now have the possibility to directly peek into the microcosm of biomolecules in their native environment. In this Perspective, we discuss how these methods are currently being applied and further developed to study the kinetics of protein synthesis directly inside living cells.

Quantifying Single mRNA Translation Kinetics in Living Cells

One of the last hurdles to quantifying the full central dogma of molecular biology in living cells with single-molecule resolution has been the imaging of single messenger RNA (mRNA) translation. Here we describe how recent advances in protein tagging and imaging technologies are being used to precisely visualize and quantify the synthesis of nascent polypeptide chains from single mRNA in living cells. We focus on recent applications of repeat-epitope tags and describe how they enable quantification of single mRNA ribosomal densities, translation initiation and elongation rates, and translation site mobility and higher-order structure. Together with complementary live-cell assays to monitor translation using fast-maturing fluorophores and mRNA-binding protein knockoff, single-molecule studies are beginning to uncover striking and unexpected heterogeneity in gene expression at the level of translation.

Single-Molecule Kinetics in Living Cells

In the past decades, advances in microscopy have made it possible to study the dynamics of individual biomolecules in vitro and resolve intramolecular kinetics that would otherwise be hidden in ensemble averages. More recently, single-molecule methods have been used to image, localize, and track individually labeled macromolecules in the cytoplasm of living cells, allowing investigations of intermolecular kinetics under physiologically relevant conditions. In this review, we illuminate the particular advantages of single-molecule techniques when studying kinetics in living cells and discuss solutions to specific challenges associated with these methods.

Nascent RNA kinetics: Transient and steady state behavior of models of transcription

Regulation of transcription is a vital process in cells, but mechanistic details of this regulation still remain elusive. The dominant approach to unravel the dynamics of transcriptional regulation is to first develop mathematical models of transcription and then experimentally test the predictions these models make for the distribution of mRNA and protein molecules at the individual cell level. However, these measurements are affected by a multitude of downstream processes which make it difficult to interpret the measurements. Recent experimental advancements allow for counting the nascent mRNA number of a gene as a function of time at the single-cell level. These measurements closely reflect the dynamics of transcription. In this paper, we consider a general mechanism of transcription with stochastic initiation and deterministic elongation and probe its impact on the temporal behavior of nascent RNA levels. Using techniques from queueing theory, we derive exact analytical expressions for the mean and variance of the nascent RNA distribution as functions of time. We apply these analytical results to obtain the mean and variance of nascent RNA distribution for specific models of transcription. These models of initiation exhibit qualitatively distinct transient behaviors for both the mean and variance which further allows us to discriminate between them. Stochastic simulations confirm these results. Overall the analytical results presented here provide the necessary tools to connect mechanisms of transcription initiation to single-cell measurements of nascent RNA.

Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation

Many natural transcription factors are regulated in a pulsatile fashion, but it remains unknown whether synthetic gene expression systems can benefit from such dynamic regulation. Here we find, using a fast-acting, optogenetic transcription factor in Saccharomyces cerevisiae, that dynamic pulsatile signals reduce cell-to-cell variability in gene expression. We then show that by encoding such signals into a single input, expression mean and variability can be independently tuned. Further, we construct a light-responsive promoter library and demonstrate how pulsatile signaling also enables graded multi-gene regulation at fixed expression ratios, despite differences in promoter dose-response characteristics. Pulsatile regulation can thus lead to beneficial functional behaviors in synthetic biological systems, which previously required laborious optimization of genetic parts or the construction of synthetic gene networks.

Dynamic interplay between enhancer-promoter topology and gene activity

A long-standing question in gene regulation is how remote enhancers communicate with their target promoters, and specifically how chromatin topology dynamically relates to gene activation. Here, we combine genome editing and multi-color live imaging to simultaneously visualize physical enhancer-promoter interaction and transcription at the single-cell level in Drosophila embryos. By examining transcriptional activation of a reporter by the endogenous even-skipped enhancers, which are located 150 kb away, we identify three distinct topological conformation states and measure their transition kinetics. We show that sustained proximity of the enhancer to its target is required for activation. Transcription in turn affects the three-dimensional topology as it enhances the temporal stability of the proximal conformation and is associated with further spatial compaction. Furthermore, the facilitated long-range activation results in transcriptional competition at the locus, causing corresponding developmental defects. Our approach offers quantitative insight into the spatial and temporal determinants of long-range gene regulation and their implications for cellular fates.

Distribution shapes govern the discovery of predictive models for gene regulation

Systems biology seeks to combine experiments with computation to predict biological behaviors. However, despite tremendous data and knowledge, biological models make less-accurate predictions compared with other fields. By analyzing single-cell, single-molecule measurements of mRNA during yeast stress response, we explore why and how the shapes of experimental distributions control prediction accuracy. We show how asymmetric data distributions with long tails cause standard modeling approaches to yield excellent fits but make meaningless predictions. We show how these biases arise from the violation of fundamental assumptions in standard modeling approaches. We demonstrate how advanced computational tools solve this dilemma and achieve predictive understanding of spatiotemporal mechanisms of transcription control including RNA polymerase initiation and elongation and mRNA accumulation, transport, and decay.

Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. One might argue that the randomness and complexity of biological systems are the main barriers to predictive understanding, but these issues are not unique to biology. Instead, we hypothesize that the specific shapes of rare single-molecule event distributions produce substantial yet overlooked challenges for biological models. We demonstrate why modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, do not apply to the discrete, positive, and nonsymmetric distributions that characterize mRNA fluctuations in single cells. As an example, we integrate single-molecule measurements and advanced computational analyses to explore mitogen-activated protein kinase induction of multiple stress response genes. Through systematic analyses of different metrics to compare the same model to the same data, we elucidate why standard modeling approaches yield nonpredictive models for single-cell gene regulation. We further explain how advanced tools recover precise, reproducible, and predictive understanding of transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and decay.

Kaposi's Sarcoma-Associated Herpesvirus mRNA Accumulation in Nuclear Foci Is Influenced by Viral DNA Replication and Viral Noncoding Polyadenylated Nuclear RNA

Kaposi's sarcoma-associated herpesvirus (KSHV), like other herpesviruses, replicates within the nuclei of its human cell host and hijacks host machinery for expression of its genes. The activities that culminate in viral DNA synthesis and assembly of viral proteins into capsids physically concentrate in nuclear areas termed viral replication compartments. We sought to better understand the spatiotemporal regulation of viral RNAs during the KSHV lytic phase by examining and quantifying the subcellular localization of select viral transcripts. We found that viral mRNAs, as expected, localized to the cytoplasm throughout the lytic phase. However, dependent on active viral DNA replication, viral transcripts also accumulated in the nucleus, often in foci in and around replication compartments, independent of the host shutoff effect. Our data point to involvement of the viral long noncoding polyadenylated nuclear (PAN) RNA in the localization of an early, intronless viral mRNA encoding ORF59-58 to nuclear foci that are associated with replication compartments.IMPORTANCE Late in the lytic phase, mRNAs from Kaposi's sarcoma-associated herpesvirus accumulate in the host cell nucleus near viral replication compartments, centers of viral DNA synthesis and virion production. This work contributes spatiotemporal data on herpesviral mRNAs within the lytic host cell and suggests a mechanism for viral RNA accumulation. Our findings indicate that the mechanism is independent of the host shutoff effect and splicing but dependent on active viral DNA synthesis and in part on the viral noncoding RNA, PAN RNA. PAN RNA is essential for the viral life cycle, and its contribution to the nuclear accumulation of viral messages may facilitate propagation of the virus.

Role of Kip2 during early mitosis - impact on spindle pole body separation and chromosome capture

Mitotic spindle dynamics are regulated during the cell cycle by microtubule motor proteins. In Saccharomyces cerevisiae, one such protein is Kip2p, a plus-end motor that regulates the polymerization and stability of cytoplasmic microtubules (cMTs). Kip2p levels are regulated during the cell cycle, and its overexpression leads to the formation of hyper-elongated cMTs. To investigate the significance of varying Kip2p levels during the cell cycle and the hyper-elongated cMTs, we overexpressed KIP2 in the G1 phase and examined the effects on the separation of spindle pole bodies (SPBs) and chromosome segregation. Our results show that failure to regulate the cMT lengths during G1-S phase prevents the separation of SPBs. This, in turn, affects chromosome capture and leads to the activation of spindle assembly checkpoint (SAC) and causes mitotic arrest. These defects could be rescued by either the inactivation of checkpoint components or by co-overexpression of CIN8, which encodes a motor protein that elongates inter-polar microtubules (ipMTs). Hence, we propose that the maintenance of Kip2p level and cMT lengths during early cell division is important to ensure coordination between SPB separation and chromosome capture by kinetochore microtubules (kMTs).

Imaging mRNA In Vivo, from Birth to Death

RNA is the fundamental information transfer system in the cell. The ability to follow single messenger RNAs (mRNAs) from transcription to degradation with fluorescent probes gives quantitative information about how the information is transferred from DNA to proteins. This review focuses on the latest technological developments in the field of single-mRNA detection and their usage to study gene expression in both fixed and live cells. By describing the application of these imaging tools, we follow the journey of mRNA from transcription to decay in single cells, with single-molecule resolution. We review current theoretical models for describing transcription and translation that were generated by single-molecule and single-cell studies. These methods provide a basis to study how single-molecule interactions generate phenotypes, fundamentally changing our understating of gene expression regulation.

Cell-Cycle Modulation of Transcription Termination Factor Sen1

Many non-coding transcripts (ncRNA) generated by RNA polymerase II in S. cerevisiae are terminated by the Nrd1-Nab3-Sen1 complex. However, Sen1 helicase levels are surprisingly low compared with Nrd1 and Nab3, raising questions regarding how ncRNA can be terminated in an efficient and timely manner. We show that Sen1 levels increase during the S and G2 phases of the cell cycle, leading to increased termination activity of NNS. Overexpression of Sen1 or failure to modulate its abundance by ubiquitin-proteasome-mediated degradation greatly decreases cell fitness. Sen1 toxicity is suppressed by mutations in other termination factors, and NET-seq analysis shows that its overexpression leads to a decrease in ncRNA production and altered mRNA termination. We conclude that Sen1 levels are carefully regulated to prevent aberrant termination. We suggest that ncRNA levels and coding gene transcription termination are modulated by Sen1 to fulfill critical cell cycle-specific functions.

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Transcription termination factor Sen1 levels fluctuate throughout the cell cycle

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APC targets Sen1 for degradation during G1

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Reduced Sen1 levels lower efficiency of Sen1-mediated termination

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Sen1 overexpression reduces cell viability because of excessive termination

What shapes eukaryotic transcriptional bursting?

Isogenic cells in a common environment present a large degree of heterogeneity in gene expression. Part of this variability is attributed to transcriptional bursting: the stochastic activation and inactivation of promoters that leads to the discontinuous production of mRNA. The diversity in bursting patterns displayed by different genes suggests the existence of a connection between bursting and gene regulation. Experimental strategies such as single-molecule RNA FISH, MS2-GFP or short-lived protein reporters allow the quantification of transcriptional bursting and the comparison of bursting kinetics between conditions, allowing therefore the identification of molecular mechanisms modulating transcriptional bursting. In this review we recapitulate the impact on transcriptional bursting of different molecular aspects of transcription such as the chromatin environment, nucleosome occupancy, histone modifications, the number and affinity of regulatory elements, DNA looping and transcription factor availability. More specifically, we examine their role in tuning the burst size or the burst frequency. While some molecular mechanisms involved in transcription such as histone marks can affect every aspect of bursting, others predominantly influence the burst size (e.g. the number and affinity of cis-regulatory elements) or frequency (e.g. transcription factor availability).

Ras hyperactivation versus overexpression: Lessons from Ras dynamics in Candida albicans

Ras signaling in response to environmental cues is critical for cellular morphogenesis in eukaryotes. This signaling is tightly regulated and its activation involves multiple players. Sometimes Ras signaling may be hyperactivated. In C. albicans, a human pathogenic fungus, we demonstrate that dynamics of hyperactivated Ras1 (Ras1G13V or Ras1 in Hsp90 deficient strains) can be reliably differentiated from that of normal Ras1 at (near) single molecule level using fluorescence correlation spectroscopy (FCS). Ras1 hyperactivation results in significantly slower dynamics due to actin polymerization. Activating actin polymerization by jasplakinolide can produce hyperactivated Ras1 dynamics. In a sterol-deficient hyperfilamentous GPI mutant of C. albicans too, Ras1 hyperactivation results from Hsp90 downregulation and causes actin polymerization. Hyperactivated Ras1 co-localizes with G-actin at the plasma membrane rather than with F-actin. Depolymerizing actin with cytochalasin D results in faster Ras1 dynamics in these and other strains that show Ras1 hyperactivation. Further, ergosterol does not influence Ras1 dynamics.

Imaging transcriptional dynamics

Recent advances in imaging techniques have enabled visualizations of nascent transcripts or individual protein molecules at high spatiotemporal resolution, revealing the complex nature of transcriptional regulation. Here, we highlight recent studies that have provided comprehensive insights to transcriptional dynamics using such quantitative imaging techniques. Specifically, they demonstrated that transcriptional activity is stochastic, and such transcriptional bursting is modulated by multiple components like chromatin environments, concentration of transcription factors, and enhancer-promoter interactions. Moreover, recent studies suggested that regulation of transcriptional activity is more complex than previously thought, by showing that transcription factors and RNA polymerases also move within the cell with distinct kinetics and sometimes form dynamic clusters to mediate transcriptional initiation.

Assessing sufficiency and necessity of enhancer activities for gene expression and the mechanisms of transcription activation

Enhancers are important genomic regulatory elements directing cell type-specific transcription. They assume a key role during development and disease, and their identification and functional characterization have long been the focus of scientific interest. The advent of next-generation sequencing and clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9-based genome editing has revolutionized the means by which we study enhancer biology. In this review, we cover recent developments in the prediction of enhancers based on chromatin characteristics and their identification by functional reporter assays and endogenous DNA perturbations. We discuss that the two latter approaches provide different and complementary insights, especially in assessing enhancer sufficiency and necessity for transcription activation. Furthermore, we discuss recent insights into mechanistic aspects of enhancer function, including findings about cofactor requirements and the role of post-translational histone modifications such as monomethylation of histone H3 Lys4 (H3K4me1). Finally, we survey how these approaches advance our understanding of transcription regulation with respect to promoter specificity and transcriptional bursting and provide an outlook covering open questions and promising developments.

Regulatory Architecture of the LβT2 Gonadotrope Cell Underlying the Response to Gonadotropin-Releasing Hormone

The LβT2 mouse pituitary cell line has many characteristics of a mature gonadotrope and is a widely used model system for studying the developmental processes and the response to gonadotropin-releasing hormone (GnRH). The global epigenetic landscape, which contributes to cell-specific gene regulatory mechanisms, and the single-cell transcriptome response variation of LβT2 cells have not been previously investigated. Here, we integrate the transcriptome and genome-wide chromatin accessibility state of LβT2 cells during GnRH stimulation. In addition, we examine cell-to-cell variability in the transcriptional response to GnRH using Gel bead-in-Emulsion Drop-seq technology. Analysis of a bulk RNA-seq data set obtained 45 min after exposure to either GnRH or vehicle identified 112 transcripts that were regulated >4-fold by GnRH (FDR < 0.05). The top regulated transcripts constitute, as determined by Bayesian massive public data integration analysis, a human pituitary-relevant coordinated gene program. Chromatin accessibility [assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq)] data sets generated from GnRH-treated LβT2 cells identified more than 58,000 open chromatin regions, some containing notches consistent with bound transcription factor footprints. The study of the most prominent open regions showed that 75% were in transcriptionally active promoters or introns, supporting their involvement in active transcription. Lhb, Cga, and Egr1 showed significantly open chromatin over their promoters. While Fshb was closed over its promoter, several discrete significantly open regions were found at -40 to -90 kb, which may represent novel upstream enhancers. Chromatin accessibility determined by ATAC-seq was associated with high levels of gene expression determined by RNA-seq. We obtained high-quality single-cell Gel bead-in-Emulsion Drop-seq transcriptome data, with an average of >4,000 expressed genes/cell, from 1,992 vehicle- and 1,889 GnRH-treated cells. While the individual cell expression patterns showed high cell-to-cell variation, representing both biological and measurement variation, the average expression patterns correlated well with bulk RNA-seq data. Computational assignment of each cell to its precise cell cycle phase showed that the response to GnRH was unaffected by cell cycle. To our knowledge, this study represents the first genome-wide epigenetic and single-cell transcriptomic characterization of this important gonadotrope model. The data have been deposited publicly and should provide a resource for hypothesis generation and further study.

Live Imaging of mRNA Synthesis in Drosophila

mRNA synthesis is one of the earliest readouts of the activity of a transcribed gene, which is of particular interest in the context of metazoan cell fate specification. These processes are intrinsically dynamic and stochastic, which makes in vivo single-cell measurements inevitable. Here, we present the application of a technology that has been widely used in single celled organisms to measure transcriptional activity in developing embryos of the fruit fly Drosophila melanogaster. The method allows for quantification of instantaneous polymerase occupancy of active gene loci and thereby enables the development and testing of models of gene regulation in development.

Measuring mRNA Decay in Budding Yeast Using Single Molecule FISH

Cellular mRNA levels are determined by the rates of mRNA synthesis and mRNA decay. Typically, mRNA degradation kinetics are measured on a population of cells that are either chemically treated or genetically engineered to inhibit transcription. However, these manipulations can affect the mRNA decay process itself by inhibiting regulatory mechanisms that govern mRNA degradation, especially if they occur on short time-scales. Recently, single molecule fluorescent in situ hybridization (smFISH) approaches have been implemented to quantify mRNA decay rates in single, unperturbed cells. Here, we provide a step-by-step protocol that allows quantification of mRNA decay in single Saccharomyces cerevisiae using smFISH. Our approach relies on fluorescent labeling of single cytoplasmic mRNAs and nascent mRNAs found at active sites of transcription, coupled with mathematical modeling to derive mRNA half-lives. Commercially available, single-stranded smFISH DNA oligonucleotides (smFISH probes) are used to fluorescently label mRNAs followed by the quantification of cellular and nascent mRNAs using freely available spot detection algorithms. Our method enables quantification of mRNA decay of any mRNA in single, unperturbed yeast cells and can be implemented to quantify mRNA turnover in a variety of cell types as well as tissues.

Detection of the First Round of Translation: The TRICK Assay

Quantitative fluorescence microscopy techniques are frequently applied to answer fundamental biological questions. Single-molecule RNA imaging methods have enabled the direct observation of the initial steps of the mRNA life cycle in living cells, however, the dynamic mechanisms that regulate mRNA translation are still poorly understood. We have developed an RNA biosensor that can assess the translational state of individual mRNA transcripts with spatiotemporal resolution in living cells. In this chapter, we describe how to perform a TRICK (translating RNA imaging by coat protein knock-off) experiment and specifically focus on a detailed description of our image processing and data analysis procedure.

The Secret Life of RNA: Lessons from Emerging Methodologies

The last past decade has witnessed a revolution in our appreciation of transcriptome complexity and regulation. This remarkable expansion in our knowledge largely originates from the advent of high-throughput methodologies, and the consecutive discovery that up to 90% of eukaryotic genomes are transcribed, thus generating an unanticipated large range of noncoding RNAs (Hangauer et al., 15(4):112, 2014). Besides leading to the identification of new noncoding RNA species, transcriptome-wide studies have uncovered novel layers of posttranscriptional regulatory mechanisms controlling RNA processing, maturation or translation, and each contributing to the precise and dynamic regulation of gene expression. Remarkably, the development of systems-level studies has been accompanied by tremendous progress in the visualization of individual RNA molecules in single cells, such that it is now possible to image RNA species with a single-molecule resolution from birth to translation or decay. Monitoring quantitatively, with unprecedented spatiotemporal resolution, the fate of individual molecules has been key to understanding the molecular mechanisms underlying the different steps of RNA regulation. This has also revealed biologically relevant, intracellular and intercellular heterogeneities in RNA distribution or regulation. More recently, the convergence of imaging and high-throughput technologies has led to the emergence of spatially resolved transcriptomic techniques that provide a means to perform large-scale analyses while preserving spatial information. By generating transcriptome-wide data on single-cell RNA content, or even subcellular RNA distribution, these methodologies are opening avenues to a wide range of network-level studies at the cell and organ-level, and promise to strongly improve disease diagnostic and treatment.In this introductory chapter, we highlight how recently developed technologies aiming at detecting and visualizing RNA molecules have contributed to the emergence of entirely new research fields, and to dramatic progress in our understanding of gene expression regulation.

Time-dependent propagators for stochastic models of gene expression: an analytical method

The inherent stochasticity of gene expression in the context of regulatory networks profoundly influences the dynamics of the involved species. Mathematically speaking, the propagators which describe the evolution of such networks in time are typically defined as solutions of the corresponding chemical master equation (CME). However, it is not possible in general to obtain exact solutions to the CME in closed form, which is due largely to its high dimensionality. In the present article, we propose an analytical method for the efficient approximation of these propagators. We illustrate our method on the basis of two categories of stochastic models for gene expression that have been discussed in the literature. The requisite procedure consists of three steps: a probability-generating function is introduced which transforms the CME into (a system of) partial differential equations (PDEs); application of the method of characteristics then yields (a system of) ordinary differential equations (ODEs) which can be solved using dynamical systems techniques, giving closed-form expressions for the generating function; finally, propagator probabilities can be reconstructed numerically from these expressions via the Cauchy integral formula. The resulting ‘library’ of propagators lends itself naturally to implementation in a Bayesian parameter inference scheme, and can be generalised systematically to related categories of stochastic models beyond the ones considered here.

Single-cell systems biology: probing the basic unit of information flow

Gene expression varies across cells in a population or a tissue. This heterogeneity has come into sharp focus in recent years through developments in new imaging and sequencing technologies. However, our ability to measure variation has outpaced our ability to interpret it. Much of the variability may arise from random effects occurring in the processes of gene expression (transcription, RNA processing and decay, translation). The molecular basis of these effects is largely unknown. Likewise, a functional role of this variability in growth, differentiation and disease has only been elucidated in a few cases. In this review, we highlight recent experimental and theoretical advances for measuring and analyzing stochastic variation.

SpotLearn: Convolutional Neural Network for Detection of Fluorescence In Situ Hybridization (FISH) Signals in High-Throughput Imaging Approaches

DNA fluorescence in situ hybridization (FISH) is the technique of choice to map the position of genomic loci in three-dimensional (3D) space at the single allele level in the cell nucleus. High-throughput DNA FISH methods have recently been developed using complex libraries of fluorescently labeled synthetic oligonucleotides and automated fluorescence microscopy, enabling large-scale interrogation of genomic organization. Although the FISH signals generated by high-throughput methods can, in principle, be analyzed by traditional spot-detection algorithms, these approaches require user intervention to optimize each interrogated genomic locus, making analysis of tens or hundreds of genomic loci in a single experiment prohibitive. We report here the design and testing of two separate machine learning-based workflows for FISH signal detection in a high-throughput format. The two methods rely on random forest (RF) classification or convolutional neural networks (CNNs), respectively. Both workflows detect DNA FISH signals with high accuracy in three separate fluorescence microscopy channels for tens of independent genomic loci, without the need for manual parameter value setting on a per locus basis. In particular, the CNN workflow, which we named SpotLearn, is highly efficient and accurate in the detection of DNA FISH signals with low signal-to-noise ratio (SNR). We suggest that SpotLearn will be useful to accurately and robustly detect diverse DNA FISH signals in a high-throughput fashion, enabling the visualization and positioning of hundreds of genomic loci in a single experiment.

An efficient and assumption-free method to approximate protein level distribution in the two-states gene expression model

Stochastic fluctuations at each step of gene expression might influence protein levels distributions across cell populations. However, current methods to model protein distribution of intrinsic gene expression dynamics are either computationally inefficient or rely on ad hoc assumptions, e.g., that the gene is always active. Taking advantage of the simple form of lower-order moments of distribution, we developed an efficient and assumption-free protein distribution approximation method (EFPD), for the two state gene expression model to accurately approximate the distribution. By EFPD, we computed nearly identical intensity of gene expression regulation at mRNA and protein level, implying a profound link between transcription and translation. Finally, by extending EFPD to approximate the distribution of protein level at any arbitrary temporal state, we proposed an explanation for the role of stochastic noise in gene expression in the context of a continuously changing environment. EFPD can be a powerful tool for modeling the particular molecular mechanisms of targeted gene expression pattern.

Transcriptional precision and accuracy in development: from measurements to models and mechanisms

During development, genes are transcribed at specific times, locations and levels. In recent years, the emergence of quantitative tools has significantly advanced our ability to measure transcription with high spatiotemporal resolution in vivo. Here, we highlight recent studies that have used these tools to characterize transcription during development, and discuss the mechanisms that contribute to the precision and accuracy of the timing, location and level of transcription. We attempt to disentangle the discrepancies in how physicists and biologists use the term ‘precision' to facilitate interactions using a common language. We also highlight selected examples in which the coupling of mathematical modeling with experimental approaches has provided important mechanistic insights, and call for a more expansive use of mathematical modeling to exploit the wealth of quantitative data and advance our understanding of animal transcription.

Summary: This Review highlights how high-resolution quantitative tools and theoretical models have formed our current view of the mechanisms determining precision and accuracy in the timing, location and level of transcription in the Drosophila embryo.

Asymmetry between Activation and Deactivation during a Transcriptional Pulse

Transcription in eukaryotic cells occurs in gene-specific bursts or pulses of activity. Recent studies identified a spectrum of transcriptionally active “on-states,” interspersed with periods of inactivity, but these “off-states” and the process of transcriptional deactivation are poorly understood. To examine what occurs during deactivation, we investigate the dynamics of switching between variable rates. We measured live single-cell expression of luciferase reporters from human growth hormone or human prolactin promoters in a pituitary cell line. Subsequently, we applied a statistical variable-rate model of transcription, validated by single-molecule FISH, to estimate switching between transcriptional rates. Under the assumption that transcription can switch to any rate at any time, we found that transcriptional activation occurs predominantly as a single switch, whereas deactivation occurs with graded, stepwise decreases in transcription rate. Experimentally altering cAMP signalling with forskolin or chromatin remodelling with histone deacetylase inhibitor modifies the duration of defined transcriptional states. Our findings reveal transcriptional activation and deactivation as mechanistically independent, asymmetrical processes.

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Gene transcription switches between variable rates

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Single-cell microscopy and mathematical modeling quantifies switch dynamics

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We observe an asymmetry in the activation/deactivation of transcriptional bursts

An improved MS2 system for accurate reporting of the mRNA life cycle

The MS2-MCP system enables researchers to image multiple steps of the mRNA life cycle with high temporal and spatial resolution. However, for short-lived mRNAs, the tight binding of the MS2 coat protein (MCP) to the MS2 binding sites (MBS) protects the RNA from being efficiently degraded, and this confounds the study of mRNA regulation. Here, we describe a reporter system (MBSV6) with reduced affinity for the MCP, which allows mRNA degradation while preserving single-molecule detection determined by single-molecule FISH (smFISH) or live imaging. Constitutive mRNAs (MDN1 and DOA1) and highly-regulated mRNAs (GAL1 and ASH1) endogenously tagged with MBSV6 in Saccharomyces cerevisiae degrade normally. As a result, short-lived mRNAs were imaged throughout their complete life cycle. The MBSV6 reporter revealed that, in contrast to previous findings, coordinated recruitment of mRNAs at specialized structures such as P-bodies during stress did not occur, and mRNA degradation was heterogeneously distributed in the cytoplasm.

Upregulation of SPS100 gene expression by an antisense RNA via a switch of mRNA isoforms with different stabilities

Pervasive transcription of genomes generates multiple classes of non-coding RNAs. One of these classes are stable long non-coding RNAs which overlap coding genes in antisense direction (asRNAs). The function of such asRNAs is not fully understood but several cases of antisense-dependent gene expression regulation affecting the overlapping genes have been demonstrated. Using high-throughput yeast genetics and a limited set of four growth conditions we previously reported a regulatory function for ∼25% of asRNAs, most of which repress the expression of the sense gene. To further explore the roles of asRNAs we tested more conditions and identified 15 conditionally antisense-regulated genes, 6 of which exhibited antisense-dependent enhancement of gene expression. We focused on the sporulation-specific gene SPS100, which becomes upregulated upon entry into starvation or sporulation as a function of the antisense transcript SUT169. We demonstrate that the antisense effect is mediated by its 3' intergenic region (3'-IGR) and that this regulation can be transferred to other genes. Genetic analysis revealed that SUT169 functions by changing the relative expression of SPS100 mRNA isoforms from a short and unstable transcript to a long and stable species. These results suggest a novel mechanism of antisense-dependent gene regulation via mRNA isoform switching.