Regulation of transcriptional bursting by a naturally oscillating signal

Optogenetic control of cAMP oscillations reveals frequency-selective transcription factor dynamics in Dictyostelium

Oscillatory dynamics and their modulation are crucial for cellular decision-making; however, analysing these dynamics remains challenging. Here, we present a tool that combines the light-activated adenylate cyclase mPAC with the cAMP biosensor Pink Flamindo, enabling precise manipulation and real-time monitoring of cAMP oscillation frequencies in Dictyostelium. High-frequency modulation of cAMP oscillations induced cell aggregation and multicellular formation, even at low cell densities, such as a few dozen cells. At the population level, chemotactic aggregation is driven by modulated frequency signals. Additionally, modulation of cAMP frequency significantly reduced the amplitude of the shuttling behaviour of the transcription factor GtaC, demonstrating low-pass filter characteristics capable of converting subtle oscillation changes, such as from 6 min to 4 min, into gene expression. These findings enhance our understanding of frequency-selective cellular decoding and its role in cellular signalling and development.

Multi-color fluorescence live-cell imaging in Dictyostelium discoideum

The cellular slime mold Dictyostelium discoideum, a member of the Amoebozoa, has been extensively studied in cell and developmental biology. D. discoideum is unique in that they are genetically tractable, with a wealth of data accumulated over half a century of research. Fluorescence live-cell imaging of D. discoideum has greatly facilitated studies on fundamental topics, including cytokinesis, phagocytosis, and cell migration. Additionally, its unique life cycle places Dictyostelium at the forefront of understanding aggregative multicellularity, a recurring evolutionary trait found across the Opisthokonta and Amoebozoa clades. The use of multiple fluorescent proteins (FP) and labels with separable spectral properties is critical for tracking cells in aggregates and identifying co-occurring biomolecular events and factors that underlie the dynamics of the cytoskeleton, membrane lipids, second messengers, and gene expression. However, in D. discoideum, the number of frequently used FP species is limited to two or three. In this study, we explored the use of new-generation FP for practical 4- to 5-color fluorescence imaging of D. discoideum. We showed that the yellow fluorescent protein Achilles and the red fluorescent protein mScarlet-I both yield high signals and allow sensitive detection of rapid gene induction. The color palette was further expanded to include blue (mTagBFP2 and mTurquosie2), large Stoke-shift LSSmGFP, and near-infrared (miRFP670nano3) FPs, in addition to the HaloTag ligand SaraFluor 650T. Thus, we demonstrated the feasibility of deploying 4- and 5- color imaging of D. discoideum using conventional confocal microscopy.Key words: fluorescence imaging, organelle, cytoskeleton, small GTPase, Dictyostelium.

Biphasic amplitude oscillator characterized by distinct dynamics of trough and crest

Biphasic amplitude dynamics (BAD) of oscillation have been observed in many biological systems. However, the specific topology structure and regulatory mechanisms underlying these biphasic amplitude dynamics remain elusive. Here, we searched all possible two-node circuit topologies and identified the core oscillator that enables robust oscillation. This core oscillator consists of a negative feedback loop between two nodes and a self-positive feedback loop of the input node, which result in the fast and slow dynamics of the two nodes, thereby achieving relaxation oscillation. Landscape theory was employed to study the stochastic dynamics and global stability of the system, allowing us to quantitatively describe the diverse positions and sizes of the Mexican hat. With increasing input strength, the size of the Mexican hat exhibits a gradual increase followed by a subsequent decrease. The self-activation of input node and the negative feedback on input node, which dominate the fast dynamics of the input node, were observed to regulate BAD in a bell-shaped manner. Both deterministic and statistical analysis results reveal that BAD is characterized by the linear and nonlinear dependence of the oscillation trough and crest on the input strength. In addition, combining with computational and theoretical analysis, we addressed that the linear response of trough to input is predominantly governed by the negative feedback, while the nonlinear response of crest is jointly regulated by the negative feedback loop and the self-positive feedback loop within the oscillator. Overall, this study provides a natural and physical basis for comprehending the occurrence of BAD in oscillatory systems, yielding guidance for the design of BAD in synthetic biology applications.

Collective signalling drives rapid jumping between cell states

Development can proceed in 'fits and starts', with rapid transitions between cell states involving concerted transcriptome-wide changes in gene expression. However, it is not clear how these transitions are regulated in complex cell populations, in which cells receive multiple inputs. We address this issue using Dictyostelium cells undergoing development in their physiological niche. A continuous single cell transcriptomics time series identifies a sharp 'jump' in global gene expression marking functionally different cell states. By simultaneously imaging the physiological dynamics of transcription and signalling, we show the jump coincides with the onset of collective oscillations of cAMP. Optogenetic control of cAMP pulses shows that different jump genes respond to distinct dynamic features of signalling. Late jump gene expression changes are almost completely dependent on cAMP, whereas transcript changes at the onset of the jump require additional input. The coupling of collective signalling with gene expression is a potentially powerful strategy to drive robust cell state transitions in heterogeneous signalling environments. Based on the context of the jump, we also conclude that sharp gene expression transitions may not be sufficient for commitment.

Controlling periodic long-range signalling to drive a morphogenetic transition

Cells use signal relay to transmit information across tissue scales. However, the production of information carried by signal relay remains poorly characterised. To determine how the coding features of signal relay are generated, we used the classic system for long-range signalling: the periodic cAMP waves that drive Dictyostelium collective migration. Combining imaging and optogenetic perturbation of cell signalling states, we find that migration is triggered by an increase in wave frequency generated at the signalling centre. Wave frequency is regulated by cAMP wave circulation, which organises the long-range signal. To determine the mechanisms modulating wave circulation, we combined mathematical modelling, the general theory of excitable media, and mechanical perturbations to test competing models. Models in which cell density and spatial patterning modulate the wave frequency cannot explain the temporal evolution of signalling waves. Instead, our evidence leads to a model where wave circulation increases the ability for cells to relay the signal, causing further increase in the circulation rate. This positive feedback between cell state and signalling pattern regulates the long-range signal coding that drives morphogenesis.

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

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

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

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.

Targeting the Transcriptome Through Globally Acting Components

Transcription is a step in gene expression that defines the identity of cells and its dysregulation is associated with diseases. With advancing technologies revealing molecular underpinnings of the cell with ever-higher precision, our ability to view the transcriptomes may have surpassed our knowledge of the principles behind their organization. The human RNA polymerase II (Pol II) machinery comprises thousands of components that, in conjunction with epigenetic and other mechanisms, drive specialized programs of development, differentiation, and responses to the environment. Parts of these programs are repurposed in oncogenic transformation. Targeting of cancers is commonly done by inhibiting general or broadly acting components of the cellular machinery. The critical unanswered question is how globally acting or general factors exert cell type specific effects on transcription. One solution, which is discussed here, may be among the events that take place at genes during early Pol II transcription elongation. This essay turns the spotlight on the well-known phenomenon of promoter-proximal Pol II pausing as a step that separates signals that establish pausing genome-wide from those that release the paused Pol II into the gene. Concepts generated in this rapidly developing field will enhance our understanding of basic principles behind transcriptome organization and hopefully translate into better therapies at the bedside.

Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage

The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution.

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.

Perspective: Controlling Epidermal Terminal Differentiation with Transcriptional Bursting and RNA Bodies

The outer layer of the skin, the epidermis, is the principal barrier to the external environment: post-mitotic cells terminally differentiate to form a tough outer cornified layer of enucleate and flattened cells that confer the majority of skin barrier function. Nuclear degradation is required for correct cornified envelope formation. This process requires mRNA translation during the process of nuclear destruction. In this review and perspective, we address the biology of transcriptional bursting and the formation of ribonuclear particles in model organisms including mammals, and then examine the evidence that these phenomena occur as part of epidermal terminal differentiation.

Signaling Mechanism of Transcriptional Bursting: A Technical Resolution-Independent Study

Simple Summary

Following changing cellular signals, various genes adjust their activities and initiate transcripts with the right rates. The precision of such a transcriptional response has a fundamental role in the survival and development of lives. Quite unexpectedly, gene transcription has been uncovered to occur in sporadic bursts, rather than in a continuous manner. This has raised a provoking issue of how the bursting transmits regulatory signals, and it remains controversial whether the burst size, frequency, or both, take the role of signal transmission. Here, this study showed that only the burst frequency was subject to modulation by activators that carry the regulatory signals. A higher activator concentration led to a larger frequency, whereas the size remains unchanged. When very high, the burst cluster emerged, which may be mistaken as a large burst. This work thus supports the conclusion that transcription regulation is in a “digital” way.

Abstract

Gene transcription has been uncovered to occur in sporadic bursts. However, due to technical difficulties in differentiating individual transcription initiation events, it remains debated as to whether the burst size, frequency, or both are subject to modulation by transcriptional activators. Here, to bypass technical constraints, we addressed this issue by introducing two independent theoretical methods including analytical research based on the classic two-model and information entropy research based on the architecture of transcription apparatus. Both methods connect the signaling mechanism of transcriptional bursting to the characteristics of transcriptional uncertainty (i.e., the differences in transcriptional levels of the same genes that are equally activated). By comparing the theoretical predictions with abundant experimental data collected from published papers, the results exclusively support frequency modulation. To further validate this conclusion, we showed that the data that appeared to support size modulation essentially supported frequency modulation taking into account the existence of burst clusters. This work provides a unified scheme that reconciles the debate on burst signaling.

Single-particle imaging of stress-promoters induction reveals the interplay between MAPK signaling, chromatin and transcription factors

Precise regulation of gene expression in response to environmental changes is crucial for cell survival, adaptation and proliferation. In eukaryotic cells, extracellular signal integration is often carried out by Mitogen-Activated Protein Kinases (MAPK). Despite a robust MAPK signaling activity, downstream gene expression can display a great variability between single cells. Using a live mRNA reporter, here we monitor the dynamics of transcription in Saccharomyces cerevisiae upon hyper-osmotic shock. We find that the transient activity of the MAPK Hog1 opens a temporal window where stress-response genes can be activated. We show that the first minutes of Hog1 activity are essential to control the activation of a promoter. Chromatin repression on a locus slows down this transition and contributes to the variability in gene expression, while binding of transcription factors increases the level of transcription. However, soon after Hog1 activity peaks, negative regulators promote chromatin closure of the locus and transcription progressively stops.

Cell and molecular transitions during efficient dedifferentiation

Dedifferentiation is a critical response to tissue damage, yet is not well understood, even at a basic phenomenological level. Developing Dictyostelium cells undergo highly efficient dedifferentiation, completed by most cells within 24 hr. We use this rapid response to investigate the control features of dedifferentiation, combining single cell imaging with high temporal resolution transcriptomics. Gene expression during dedifferentiation was predominantly a simple reversal of developmental changes, with expression changes not following this pattern primarily associated with ribosome biogenesis. Mutation of genes induced early in dedifferentiation did not strongly perturb the reversal of development. This apparent robustness may arise from adaptability of cells: the relative temporal ordering of cell and molecular events was not absolute, suggesting cell programmes reach the same end using different mechanisms. In addition, although cells start from different fates, they rapidly converged on a single expression trajectory. These regulatory features may contribute to dedifferentiation responses during regeneration.

What Is a Transcriptional Burst?

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

Transcriptional Bursting and Co-bursting Regulation by Steroid Hormone Release Pattern and Transcription Factor Mobility

Genes are transcribed in a discontinuous pattern referred to as RNA bursting, but the mechanisms regulating this process are unclear. Although many physiological signals, including glucocorticoid hormones, are pulsatile, the effects of transient stimulation on bursting are unknown. Here we characterize RNA synthesis from single-copy glucocorticoid receptor (GR)-regulated transcription sites (TSs) under pulsed (ultradian) and constant hormone stimulation. In contrast to constant stimulation, pulsed stimulation induces restricted bursting centered around the hormonal pulse. Moreover, we demonstrate that transcription factor (TF) nuclear mobility determines burst duration, whereas its bound fraction determines burst frequency. Using 3D tracking of TSs, we directly correlate TF binding and RNA synthesis at a specific promoter. Finally, we uncover a striking co-bursting pattern between TSs located at proximal and distal positions in the nucleus. Together, our data reveal a dynamic interplay between TF mobility and RNA bursting that is responsive to stimuli strength, type, modality, and duration.

Bursting the Notch Bubble: New Insights into In Vivo Transcriptional Dynamics

In this issue of Developmental Cell,Lee et al. (2019) and Falo-Sanjuan et al. (2019) examine the transcriptional dynamics of Notch signaling in vivo. Both groups show that Notch signaling produces "bursty" transcriptional responses and that variance in these responses stems from changes in duration, not frequency, of transcriptional bursts.

Dynamics of Notch-Dependent Transcriptional Bursting in Its Native Context

Transcription is well known to be inherently stochastic and episodic, but the regulation of transcriptional dynamics is not well understood. Here, we analyze how Notch signaling modulates transcriptional bursting during animal development. Our focus is Notch regulation of transcription in germline stem cells of the nematode C. elegans. Using the MS2 system to visualize nascent transcripts and live imaging to record dynamics, we analyze bursting as a function of position within the intact animal. We find that Notch-dependent transcriptional activation is indeed "bursty"; that wild-type Notch modulates burst duration (ON-time) rather than duration of pauses between bursts (OFF-time) or mean burst intensity; and that a mutant Notch receptor, which is compromised for assembly into the Notch transcription factor complex, primarily modifies burst size (duration × intensity). These analyses thus visualize the effect of a canonical signaling pathway on metazoan transcriptional bursting in its native context.

The fate of cells undergoing spontaneous DNA damage during development

Embryonic development involves extensive and often rapid cell proliferation. An unavoidable side effect of cell proliferation is DNA damage. The consequences of spontaneous DNA damage during development are not clear. Here, we define an approach to determine the effects of DNA damage on cell fate choice. Using single cell transcriptomics, we identified a subpopulation of Dictyostelium cells experiencing spontaneous DNA damage. Damaged cells displayed high expression of rad51, with the gene induced by multiple types of genotoxic stress. Using live imaging, we tracked high Rad51 cells from differentiation onset until cell fate assignment. High Rad51 cells were shed from multicellular structures, excluding damaged cells from the spore population. Cell shedding resulted from impaired cell motility and defective cell-cell adhesion, with damaged cells additionally defective in activation of spore gene expression. These data indicate DNA damage is not insulated from other aspects of cell physiology during development and multiple features of damaged cells prevent propagation of genetic error. Our approach is generally applicable for monitoring rare subpopulations during development, and permits analysis of developmental perturbations occurring within a physiological dynamic range.

Transition state dynamics during a stochastic fate choice

The generation of multiple fates from a uniform cell population via self-organisation is a recurring feature in development and regeneration. However, for most self-organising systems, we have little understanding of the processes that allow cells to become different. One of the clearest examples of developmental self-organisation is shown by Dictyostelium, with cells segregating into two major fates, stalk and spore, within multicellular aggregates. To characterise the gene expression decisions that underlie this cell fate bifurcation, we carried out single cell transcriptomics on Dictyostelium aggregates. Our data show the transition of progenitors into prespore and prestalk cells occurs via distinct developmental intermediates. Few cells were captured switching between states, with minimal overlap in fate marker expression between cell types, suggesting states are discrete and transitions rapid. Surprisingly, fate-specific transcript dynamics were a small proportion of overall gene expression changes, with transcript divergence coinciding precisely with large-scale remodelling of the transcriptome shared by prestalk and prespore cells. These observations suggest the stepwise separation of cell identity is temporally coupled to global expression transitions common to both fates.

Rhythmicity and waves in the cortex of single cells

Emergence of dynamic patterns in the form of oscillations and waves on the cortex of single cells is a fascinating and enigmatic phenomenon. Here we outline various theoretical frameworks used to model pattern formation with the goal of reducing complex, heterogeneous patterns into key parameters that are biologically tractable. We also review progress made in recent years on the quantitative and molecular definitions of these terms, which we believe have begun to transform single-cell dynamic patterns from a purely observational and descriptive subject to more mechanistic studies. Specifically, we focus on the nature of local excitable and oscillation events, their spatial couplings leading to propagating waves and the role of active membrane. Instead of arguing for their functional importance, we prefer to consider such patterns as basic properties of dynamic systems. We discuss how knowledge of these patterns could be used to dissect the structure of cellular organization and how the network-centric view could help define cellular functions as transitions between different dynamical states. Last, we speculate on how these patterns could encode temporal and spatial information.

This article is part of the theme issue ‘Self-organization in cell biology’.

First-passage time statistics of stochastic transcription process for time-dependent reaction rates

Transcription in gene expression is an intrinsically noisy process which involves production and degradation of mRNAs. An important quantity to describe this stochastic process is the first-passage time (FPT), i.e., the time taken by mRNAs to reach a particular threshold. The process of transcription can be modelled as a simple birth-death process, assuming that the promoter is always in an active state and to encode the stochastic environment we consider the transcription rate to be time dependent. This generalization is suitable to capture bursty mRNA dynamics usually modelled as an ON-Off model and simplifies the calculation of FPT statistics for a cell population. We study the role of periodic modulation of the transcription rate on different moments of FPT distribution of a population of cells. Our calculation shows that for sinusoidal modulation there exists an extremal value of mean FPT as a function of the time period and phase of the transcription signal. However, for the square wave modulation of transcription rates simulation results show that the extremal value of the MFPT behaves monotonically with the variation of the phase.

Gene transcription in bursting: a unified mode for realizing accuracy and stochasticity

There is accumulating evidence that, from bacteria to mammalian cells, messenger RNAs (mRNAs) are produced in intermittent bursts - a much 'noisier' process than traditionally thought. Based on quantitative measurements at individual promoters, diverse phenomenological models have been proposed for transcriptional bursting. Nevertheless, the underlying molecular mechanisms and significance for cellular signalling remain elusive. Here, we review recent progress, address the above issues and illuminate our viewpoints with simulation results. Despite being widely used in modelling and in interpreting experimental data, the traditional two-state model is far from adequate to describe or infer the molecular basis and stochastic principles of transcription. In bacteria, DNA supercoiling contributes to the bursting of those genes that express at high levels and are topologically constrained in short loops; moreover, low-affinity cis-regulatory elements and unstable protein complexes can play a key role in transcriptional regulation. Integrating data on the architecture, kinetics, and transcriptional input-output function is a promising approach to uncovering the underlying dynamic mechanism. For eukaryotes, distinct bursting features described by the multi-scale and continuum models coincide with those predicted by four theoretically derived principles that govern how the transcription apparatus operates dynamically. This consistency suggests a unified framework for comprehending bursting dynamics at the level of the structural and kinetic basis of transcription. Moreover, the existing models can be unified by a generic model. Remarkably, transcriptional bursting enables regulatory information to be transmitted in a digital manner, with the burst frequency representing the strength of regulatory signals. Such a mode guarantees high fidelity for precise transcriptional regulation and also provides sufficient randomness for realizing cellular heterogeneity.

The "mechanostat" principle in cell differentiation. The osteochondroprogenitor paradigm

The "mechanostat" principle may be depicted as an oscillating signal of a signaling molecule, in which the amplitude, frequency, cumulative level, delay, and duration of the curve encode the information for concrete cellular responses and biological activities. When the oscillating signal is kept sustained (present delay), cell exit may be performed, whereas when the oscillating signal remains robust, cell proliferation may take place. B-catenin-Wnt signaling pathway has a key role in the differentiation of osteochondroprogenitor cells. Sustained downregulation of the β-catenin-Wnt pathway forces osteochondroprogenitors to a chondrogenic fate instead of an osteoblastic one. Other signaling, for example, bone morphogenetic protein and Notch signaling pathways interact with the Wnt pathway. The crosstalk between biochemical and mechanical stimuli produces the final information that leads to the final cell fate decisions, through the "mechanostat" principle.

Rapid and efficient genetic engineering of both wild type and axenic strains of Dictyostelium discoideum

Dictyostelium has a mature technology for molecular-genetic manipulation based around transfection using several different selectable markers, marker re-cycling, homologous recombination and insertional mutagenesis, all supported by a well-annotated genome. However this technology is optimized for mutant, axenic cells that, unlike non-axenic wild type, can grow in liquid medium. There is a pressing need for methods to manipulate wild type cells and ones with defects in macropinocytosis, neither of which can grow in liquid media. Here we present a panel of molecular genetic techniques based on the selection of Dictyostelium transfectants by growth on bacteria rather than liquid media. As well as extending the range of strains that can be manipulated, these techniques are faster than conventional methods, often giving usable numbers of transfected cells within a few days. The methods and plasmids described here allow efficient transfection with extrachromosomal vectors, as well as chromosomal integration at a 'safe haven' for relatively uniform cell-to-cell expression, efficient gene knock-in and knock-out and an inducible expression system. We have thus created a complete new system for the genetic manipulation of Dictyostelium cells that no longer requires cell feeding on liquid media.

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

Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization

Gene transcription is a highly stochastic and dynamic process. As a result, the mRNA copy number of a given gene is heterogeneous both between cells and across time. We present a framework to model gene transcription in populations of cells with time-varying (stochastic or deterministic) transcription and degradation rates. Such rates can be understood as upstream cellular drives representing the effect of different aspects of the cellular environment. We show that the full solution of the master equation contains two components: a model-specific, upstream effective drive, which encapsulates the effect of cellular drives (e.g. entrainment, periodicity or promoter randomness) and a downstream transcriptional Poissonian part, which is common to all models. Our analytical framework treats cell-to-cell and dynamic variability consistently, unifying several approaches in the literature. We apply the obtained solution to characterize different models of experimental relevance, and to explain the influence on gene transcription of synchrony, stationarity, ergodicity, as well as the effect of time scales and other dynamic characteristics of drives. We also show how the solution can be applied to the analysis of noise sources in single-cell data, and to reduce the computational cost of stochastic simulations.

Transcriptional Output Transiently Spikes Upon Mitotic Exit

The pulsatile nature of gene activity has recently emerged as a general property of the transcriptional process. It has been shown that the frequency and amplitude of transcriptional bursts can be subjected to extrinsic regulation. Here we have investigated if these parameters were constant throughout the cell cycle using the single molecule RNA FISH technique. We found evidence of transcriptional spikes upon mitotic exit in three different human cell lines. Recording of cell growth prior to hybridization and immuno-RNA FISH analysis revealed that these spikes were short-lived and subsided before completion of cytokinesis. The transient post-mitotic increase in transcriptional output was found to be the result of cells displaying a higher number of active alleles and/or an increased number of nascent transcripts per active allele, indicating that both the burst fraction and the amplitude of individual bursts can be increased upon mitotic exit. Our results further suggest that distinct regulatory mechanisms are at work shortly after mitotic exit and during the rest of interphase. We speculate that transcriptional spikes are associated with chromatin decondensation, a hallmark of post-mitotic cells that might alter the dynamics of transcriptional regulators and effectors.

Generation of Single-Cell Transcript Variability by Repression

Gene expression levels vary greatly within similar cells, even within clonal cell populations [1]. These spontaneous expression differences underlie cell fate diversity in both differentiation and disease [2]. The mechanisms responsible for generating expression variability are poorly understood. Using single-cell transcriptomics, we show that transcript variability emerging during Dictyostelium differentiation is driven predominantly by repression rather than activation. The increased variability of repressed genes was observed over a broad range of expression levels, indicating that variability is actively imposed and not a passive statistical effect of the reduced numbers of molecules accompanying repression. These findings can be explained by a simple model of transcript production, with expression controlled by the frequency, rather than the magnitude, of transcriptional firing events. Our study reveals that the generation of differences between cells can be a direct consequence of the basic mechanisms of transcriptional regulation.

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Transcript variability during development is generated predominantly by repression

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Repression-derived variability can arise from frequency modulation of transcription

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Sub-populations of undifferentiated cells are primed to express developmental genes

Regulation of nucleosome positioning by a CHD Type III chromatin remodeler and its relationship to developmental gene expression in Dictyostelium

Nucleosome placement and repositioning can direct transcription of individual genes; however, the precise interactions of these events are complex and largely unresolved at the whole-genome level. The Chromodomain-Helicase-DNA binding (CHD) Type III proteins are a subfamily of SWI2/SNF2 proteins that control nucleosome positioning and are associated with several complex human disorders, including CHARGE syndrome and autism. Type III CHDs are required for multicellular development of animals and Dictyostelium but are absent in plants and yeast. These CHDs can mediate nucleosome translocation in vitro, but their in vivo mechanism is unknown. Here, we use genome-wide analysis of nucleosome positioning and transcription profiling to investigate the in vivo relationship between nucleosome positioning and gene expression during development of wild-type (WT) Dictyostelium and mutant cells lacking ChdC, a Type III CHD protein ortholog. We demonstrate major nucleosome positional changes associated with developmental gene regulation in WT. Loss of chdC caused an increase of intragenic nucleosome spacing and misregulation of gene expression, affecting ∼50% of the genes that are repositioned during WT development. These analyses demonstrate active nucleosome repositioning during Dictyostelium multicellular development, establish an in vivo function of CHD Type III chromatin remodeling proteins in this process, and reveal the detailed relationship between nucleosome positioning and gene regulation, as cells transition between developmental states.

C. elegans GLP-1/Notch activates transcription in a probability gradient across the germline stem cell pool

C. elegans Notch signaling maintains a pool of germline stem cells within their single-celled mesenchymal niche. Here we investigate the Notch transcriptional response in germline stem cells using single-molecule fluorescence in situ hybridization coupled with automated, high-throughput quantitation. This approach allows us to distinguish Notch-dependent nascent transcripts in the nucleus from mature mRNAs in the cytoplasm. We find that Notch-dependent active transcription sites occur in a probabilistic fashion and, unexpectedly, do so in a steep gradient across the stem cell pool. Yet these graded nuclear sites create a nearly uniform field of mRNAs that extends beyond the region of transcriptional activation. Therefore, active transcription sites provide a precise view of where the Notch-dependent transcriptional complex is productively engaged. Our findings offer a new window into the Notch transcriptional response and demonstrate the importance of assaying nascent transcripts at active transcription sites as a readout for canonical signaling.

Intercellular Variability in Protein Levels from Stochastic Expression and Noisy Cell Cycle Processes

Inside individual cells, expression of genes is inherently stochastic and manifests as cell-to-cell variability or noise in protein copy numbers. Since proteins half-lives can be comparable to the cell-cycle length, randomness in cell-division times generates additional intercellular variability in protein levels. Moreover, as many mRNA/protein species are expressed at low-copy numbers, errors incurred in partitioning of molecules between two daughter cells are significant. We derive analytical formulas for the total noise in protein levels when the cell-cycle duration follows a general class of probability distributions. Using a novel hybrid approach the total noise is decomposed into components arising from i) stochastic expression; ii) partitioning errors at the time of cell division and iii) random cell-division events. These formulas reveal that random cell-division times not only generate additional extrinsic noise, but also critically affect the mean protein copy numbers and intrinsic noise components. Counter intuitively, in some parameter regimes, noise in protein levels can decrease as cell-division times become more stochastic. Computations are extended to consider genome duplication, where transcription rate is increased at a random point in the cell cycle. We systematically investigate how the timing of genome duplication influences different protein noise components. Intriguingly, results show that noise contribution from stochastic expression is minimized at an optimal genome-duplication time. Our theoretical results motivate new experimental methods for decomposing protein noise levels from synchronized and asynchronized single-cell expression data. Characterizing the contributions of individual noise mechanisms will lead to precise estimates of gene expression parameters and techniques for altering stochasticity to change phenotype of individual cells.

Inside individual cells, gene products often occur at low molecular counts and are subject to considerable stochastic fluctuations (noise) in copy numbers over time. An important consequence of noisy expression is that the level of a protein can vary considerably even among genetically identical cells exposed to the same environment. Such non-genetic phenotypic heterogeneity is physiologically relevant and critically influences diverse cellular processes. In addition to noise sources inherent in gene product synthesis, recent experimental studies have uncovered additional noise mechanisms that critically effect expression. For example, the time within the cell cycle when a gene duplicates, and the time taken to complete cell cycle are governed by random processes. The key contribution of this work is development of novel mathematical results quantifying how cell cycle-related noise sources combine with stochastic expression to drive intercellular variability in protein molecular counts. Derived formulas lead to many counterintuitive results, such as increasing randomness in the timing of cell division can lower noise in the level of a protein. Finally, these results inform experimental strategies to systematically dissect the contributions of different noise sources in the expression of a gene of interest.

Noise processing by microRNA-mediated circuits: The Incoherent Feed-Forward Loop, revisited

The intrinsic stochasticity of gene expression is usually mitigated in higher eukaryotes by post-transcriptional regulation channels that stabilise the output layer, most notably protein levels. The discovery of small non-coding RNAs (miRNAs) in specific motifs of the genetic regulatory network has led to identifying noise buffering as the possible key function they exert in regulation. Recent in vitro and in silico studies have corroborated this hypothesis. It is however also known that miRNA-mediated noise reduction is hampered by transcriptional bursting in simple topologies. Here, using stochastic simulations validated by analytical calculations based on van Kampen's expansion, we revisit the noise-buffering capacity of the miRNA-mediated Incoherent Feed Forward Loop (IFFL), a small module that is widespread in the gene regulatory networks of higher eukaryotes, in order to account for the effects of intermittency in the transcriptional activity of the modulator gene. We show that bursting considerably alters the circuit's ability to control static protein noise. By comparing with other regulatory architectures, we find that direct transcriptional regulation significantly outperforms the IFFL in a broad range of kinetic parameters. This suggests that, under pulsatile inputs, static noise reduction may be less important than dynamical aspects of noise and information processing in characterising the performance of regulatory elements.

A continuum model of transcriptional bursting

Transcription occurs in stochastic bursts. Early models based upon RNA hybridisation studies suggest bursting dynamics arise from alternating inactive and permissive states. Here we investigate bursting mechanism in live cells by quantitative imaging of actin gene transcription, combined with molecular genetics, stochastic simulation and probabilistic modelling. In contrast to early models, our data indicate a continuum of transcriptional states, with a slowly fluctuating initiation rate converting the gene between different levels of activity, interspersed with extended periods of inactivity. We place an upper limit of 40 s on the lifetime of fluctuations in elongation rate, with initiation rate variations persisting an order of magnitude longer. TATA mutations reduce the accessibility of high activity states, leaving the lifetime of on- and off-states unchanged. A continuum or spectrum of gene states potentially enables a wide dynamic range for cell responses to stimuli.

DOI:http://dx.doi.org/10.7554/eLife.13051.001

Understanding how gene activity is regulated relies on accurate measurements of the output of genes. Proteins are generated from genes via a multi-step process. In the first step, called transcription, the DNA of a gene is copied by complex cell machinery to create molecules of mRNA. Subsequently, these mRNA molecules are ‘translated’ into proteins.

Previous studies have assayed gene transcription by measuring mRNA production in millions of cells at the same time. The resulting measurements give the impression that transcription occurs as a continuous, smooth process. However, when individual gene transcription is measured in single cells, mRNA production between cells is unexpectedly variable. This challenged the view that transcription is a continuous process.

One idea that explains this variability – the "two-state" or "bursting" model – proposes that genes switch between "on" and "off" states with a certain probability. Thus, at any one time, a gene will be off in many cells and on in others. However, the methods used in these experiments measure mRNA in dead cells, and so the dynamic switching of genes between on and off states was presumed, but not accurately measured.

Corrigan et al. have now imaged the transcription of a single gene – a gene for a protein called actin – in living cells of an amoeba called Dictyostelium. Genetic techniques and computational modeling were then used to explore what affects the variability in this gene’s activity. These approaches revealed that transcription occurs across a spectrum of activity, rather than in rigid on or off states. The transcription process itself may also contribute to where a gene’s activity sits on this spectrum. Furthermore, Corrigan et al. found that a specific DNA sequence found at the start of the actin gene, that is also found in many genes in complex life-forms, is required for the gene to reach the highest levels of activity on the spectrum.

This spectrum of activity states could allow cells to finely tune their responses to the signals they receive. A future challenge will be to assess how the activity of other genes compare to the actin gene and to discover what underlies the variation in the timing of transcription’s different stages.

DOI:http://dx.doi.org/10.7554/eLife.13051.002

Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation

Recent studies suggest that cells make stochastic choices with respect to differentiation or division. However, the molecular mechanism underlying such stochasticity is unknown. We previously proposed that the timing of vertebrate neuronal differentiation is regulated by molecular oscillations of a transcriptional repressor, HES1, tuned by a post-transcriptional repressor, miR-9. Here, we computationally model the effects of intrinsic noise on the Hes1/miR-9 oscillator as a consequence of low molecular numbers of interacting species, determined experimentally. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted.

Multiple cell and population-level interactions with mouse embryonic stem cell heterogeneity

Much of development and disease concerns the generation of gene expression differences between related cells sharing similar niches. However, most analyses of gene expression only assess population and time-averaged levels of steady-state transcription. The mechanisms driving differentiation are buried within snapshots of the average cell, lacking dynamic information and the diverse regulatory history experienced by individual cells. Here, we use a quantitative imaging platform with large time series data sets to determine the regulation of developmental gene expression by cell cycle, lineage, motility and environment. We apply this technology to the regulation of the pluripotency gene Nanog in mouse embryonic stem cells. Our data reveal the diversity of cell and population-level interactions with Nanog dynamics and heterogeneity, and how this regulation responds to triggers of pluripotency. Cell cycles are highly heterogeneous and cycle time increases with Nanog reporter expression, with longer, more variable cycle times as cells approach ground-state pluripotency. Nanog reporter expression is highly stable over multiple cell generations, with fluctuations within cycles confined by an attractor state. Modelling reveals an environmental component to expression stability, in addition to any cell-autonomous behaviour, and we identify interactions of cell density with both cycle behaviour and Nanog. Rex1 expression dynamics showed shared and distinct regulatory effects. Overall, our observations of multiple partially overlapping dynamic heterogeneities imply complex cell and environmental regulation of pluripotent cell behaviour, and suggest simple deterministic views of stem cell states are inappropriate.

Transient changes in intercellular protein variability identify sources of noise in gene expression

Protein levels differ considerably between otherwise identical cells, and these differences significantly affect biological function and phenotype. Previous work implicated various noise mechanisms that drive variability in protein copy numbers across an isogenic cell population. For example, transcriptional bursting of mRNAs has been shown to be a major source of noise in the expression of many genes. Additional expression variability, referred to as extrinsic noise, arises from intercellular variations in mRNA transcription and protein translation rates attributed to cell-to-cell differences in cell size, abundance of ribosomes, etc. We propose a method to determine the magnitude of different noise sources in a given gene of interest. The method relies on blocking transcription and measuring changes in protein copy number variability over time. Our results show that this signal has sufficient information to quantify both the extent of extrinsic noise and transcription bursting in gene expression. Moreover, if the mean mRNA count is known, then the relative contributions of transcription versus translation rate fluctuations to extrinsic noise can also be determined. In summary, our study provides an easy-to-implement method for characterizing noisy protein expression that complements existing techniques for studying stochastic dynamics of genetic circuits.

The GATA transcription factor GtaC regulates early developmental gene expression dynamics in Dictyostelium

In many systems, including the social amoeba Dictyostelium discoideum, development is often marked by dynamic morphological and transcriptional changes orchestrated by key transcription factors. However, efforts to examine sequential genome-wide changes of gene regulation in developmental processes have been fairly limited. Here we report the developmental regulatory dynamics of GtaC, a GATA-type zinc-finger transcription factor, through the analyses of serial ChIP- and RNA-sequencing data. GtaC is essential for developmental progression, decoding extracellular cAMP pulses during early development and may play a role in mediating cell-type differentiation at later stages. We find that GtaC exhibits temporally distinctive DNA-binding patterns concordant with each developmental stage. We identify direct GtaC targets and observe cotemporaneous GtaC-binding and developmental expression regulation. Our results suggest that GtaC regulates multiple physiological processes as Dictyostelium transitions from a group of unicellular amoebae to an integrated multicellular organism.

Development involves dynamic transcriptional changes. By serial ChIP- and RNA-sequencing, here, the authors show that GtaC, a GATA type transcription factor, exhibits temporally distinctive DNA binding and regulation of gene expression concordant with the development in the social amoeba Dictyostelium discoideum.

CAST: An automated segmentation and tracking tool for the analysis of transcriptional kinetics from single-cell time-lapse recordings

Fluorescence and bioluminescence time-lapse imaging allows to investigate a vast range of cellular processes at single-cell or even subcellular resolution. In particular, time-lapse imaging can provide uniquely detailed information on the fine kinetics of transcription, as well as on biological oscillations such as the circadian and cell cycles. However, we face a paucity of automated methods to quantify time-lapse imaging data with single-cell precision, notably throughout multiple cell cycles. We developed CAST (Cell Automated Segmentation and Tracking platform) to automatically and robustly detect the position and size of cells or nuclei, quantify the corresponding light signals, while taking into account both cell divisions (lineage tracking) and migration events. We present here how CAST analyzes bioluminescence data from a short-lived transcriptional luciferase reporter. However, our flexible and modular implementation makes it easily adaptable to a wide variety of time-lapse recordings. We exemplify how CAST efficiently quantifies single-cell gene expression over multiple cell cycles using mouse NIH3T3 culture cells with a luminescence expression driven by the Bmal1 promoter, a central gene of the circadian oscillator. We further illustrate how such data can be used to quantify transcriptional bursting in conditions of lengthened circadian period, revealing thereby remarkably similar bursting signature compared to the endogenous circadian condition despite marked period lengthening. In summary, we establish CAST as novel tool for the efficient segmentation, signal quantification, and tracking of time-lapse images from mammalian cell culture.

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.