Single-molecule approaches to stochastic gene expression

Single-cell imaging reveals unexpected heterogeneity of telomerase reverse transcriptase expression across human cancer cell lines

Telomerase is pathologically reactivated in most human cancers, where it maintains chromosomal telomeres and allows immortalization. Because telomerase reverse transcriptase (TERT) is usually the limiting component for telomerase activation, numerous studies have measured TERT mRNA levels in populations of cells or in tissues. In comparison, little is known about TERT expression at the single-cell and single-molecule level. To address this, we analyzed TERT expression across 10 human cancer lines using single-molecule RNA fluorescent in situ hybridization (FISH) and made several unexpected findings. First, there was substantial cell-to-cell variation in number of transcription sites and ratio of transcription sites to gene copies. Second, previous classification of lines as having monoallelic or biallelic TERT expression was found to be inadequate for capturing the TERT gene expression patterns. Finally, spliced TERT mRNA had primarily nuclear localization in cancer cells and induced pluripotent stem cells (iPSCs), in stark contrast to the expectation that spliced mRNA should be predominantly cytoplasmic. These data reveal unappreciated heterogeneity, complexity, and unconventionality in TERT expression across human cancer cells.

Distribution of Initiation Times Reveals Mechanisms of Transcriptional Regulation in Single Cells

Transcription is the dominant point of control of gene expression. Biochemical studies have revealed key molecular components of transcription and their interactions, but the dynamics of transcription initiation in cells is still poorly understood. This state of affairs is being remedied with experiments that observe transcriptional dynamics in single cells using fluorescent reporters. Quantitative information about transcription initiation dynamics can also be extracted from experiments that use electron micrographs of RNA polymerases caught in the act of transcribing a gene (Miller spreads). Inspired by these data, we analyze a general stochastic model of transcription initiation and elongation and compute the distribution of transcription initiation times. We show that different mechanisms of initiation leave distinct signatures in the distribution of initiation times that can be compared to experiments. We analyze published data from micrographs of RNA polymerases transcribing ribosomal RNA genes in Escherichia coli and compare the observed distributions of interpolymerase distances with the predictions from previously hypothesized mechanisms for the regulation of these genes. Our analysis demonstrates the potential of measuring the distribution of time intervals between initiation events as a probe for dissecting mechanisms of transcription initiation in live cells.

Illuminating Genomic Dark Matter with RNA Imaging

In the postgenomic era, it is clear that the human genome encodes thousands of long noncoding RNAs (lncRNAs). Along the way, RNA imaging (e.g., RNA fluorescence in situ hybridization [RNA-FISH]) has been instrumental in identifying powerful roles for lncRNAs based on their subcellular localization patterns. Here, we explore how RNA imaging technologies have shed new light on how, when, and where lncRNAs may play functional roles. Specifically, we will synthesize the underlying principles of RNA imaging techniques by exploring several landmark lncRNA imaging studies that have illuminated key insights into lncRNA biology.

Single-cell correlations of mRNA and protein content in a human monocytic cell line after LPS stimulation

The heterogeneity of mRNA and protein expression at the single-cell level can reveal fundamental information about cellular response to external stimuli, including the sensitivity, timing, and regulatory interactions of genes. Here we describe a fully automated system to digitally count the intron, mRNA, and protein content of up to five genes of interest simultaneously in single-cells. Full system automation of 3D microscope scans and custom image analysis routines allows hundreds of individual cells to be automatically segmented and the mRNA-protein content to be digitally counted. Single-molecule intron and mRNA content is measured by single-molecule fluorescence in-situ hybridization (smFISH), while protein content is quantified though the use of antibody probes. To mimic immune response to bacterial infection, human monocytic leukemia cells (THP-1) were stimulated with lipopolysaccharide (LPS), and the expression of two inflammatory genes, IL1β (interleukin 1β) and TNF-α (tumor necrosis factor α), were simultaneously quantified by monitoring the intron, mRNA, and protein levels over time. The simultaneous labeling of cellular content allowed for a series of correlations at the single-cell level to be explored, both in the progressive maturation of a single gene (intron-mRNA-protein) and comparative analysis between the two immune response genes. In the absence of LPS stimulation, mRNA expression of IL1β and TNF-α were uncorrelated. Following LPS stimulation, mRNA expression of the two genes became more correlated, consistent with a model in which IL1β and TNF-α upregulation occurs in parallel through independent mechanistic pathways. This smFISH methodology can be applied to different complex biological systems to provide valuable insight into highly dynamic gene mechanisms that determine cell plasticity and heterogeneity of cellular response.

Intrinsically disordered proteins and phenotypic switching: Implications in cancer

It is now well established that intrinsically disordered proteins (IDPs) that constitute a large part of the proteome across the three kingdoms, play critical roles in several biological processes including phenotypic switching. However, dysregulated expression of IDPs that engage in promiscuous interactions can lead to pathological states. In this chapter, using cancer as a paradigm, we discuss how IDP conformational dynamics and the resultant conformational noise can modulate phenotypic switching. Thus, contrary to the prevailing wisdom that phenotypic switching is highly deterministic (has a genetic underpinning) in cancer, emerging evidence suggests that non-genetic mechanisms, at least in part due to the conformational noise, may also be a confounding factor in phenotypic switching.

Elucidating effects of reaction rates on dynamics of the lac circuit in Escherichia coli

Gene expression is regulated by a complex transcriptional network. It is of interest to quantify uncertainty of not knowing accurately reaction rates of underlying biochemical reactions, and to understand how they affect gene expression. Assuming a kinetic model of the lac circuit in Escherichia coli, regardless of how many reactions are involved in transcription regulation, transcription rate is shown to be the most important parameter affecting steady state production of mRNA and protein in the cell. In particular, doubling the transcription rate approximately doubles the number of mRNA synthesized at steady state for any rates of transcription inhibition and activation. On the other hand, increasing the rate of transcription inhibition by 10% reduces the average steady state count of mRNA by about 7%, whereas changes in the rate of transcription activation appear to have no such effect. Furthermore, for wide range of reaction rates in the kinetic model of the lac genetic switch considered, protein production was observed to always reach a maximum before the degradation reduces its count to zero, and this maximum was found to be always at least 27 protein molecules. Such value appears to be a fundamental structural property of genetic circuits making it very robust against changes in the internal and external conditions.

A System for Gene Expression Noise Control in Yeast

Gene expression noise arises from stochastic variation in the synthesis and degradation of mRNA and protein molecules and creates differences in protein numbers across populations of genetically identical cells. Such variability can lead to imprecision and reduced performance of both native and synthetic networks. In principle, gene expression noise can be controlled through the rates of transcription, translation and degradation, such that different combinations of those rates lead to the same protein concentrations but at different noise levels. Here, we present a "noise tuner" which allows orthogonal control over the transcription and the mRNA degradation rates by two different inducer molecules. Combining experiments with theoretical analysis, we show that in this system the noise is largely determined by the transcription rate, whereas the mean expression is determined by both the transcription rate and mRNA stability and can thus be decoupled from the noise. This noise tuner enables 2-fold changes in gene expression noise over a 5-fold range of mean protein levels. We demonstrated the efficacy of the noise tuner in a complex regulatory network by varying gene expression noise in the mating pathway of Saccharomyces cerevisiae, which allowed us to control the output noise and the mutual information transduced through the pathway. The noise tuner thus represents an effective tool of gene expression noise control, both to interrogate noise sensitivity of natural networks and enhance performance of synthetic circuits.

Synthetic STARR-seq reveals how DNA shape and sequence modulate transcriptional output and noise

The binding of transcription factors to short recognition sequences plays a pivotal role in controlling the expression of genes. The sequence and shape characteristics of binding sites influence DNA binding specificity and have also been implicated in modulating the activity of transcription factors downstream of binding. To quantitatively assess the transcriptional activity of tens of thousands of designed synthetic sites in parallel, we developed a synthetic version of STARR-seq (synSTARR-seq). We used the approach to systematically analyze how variations in the recognition sequence of the glucocorticoid receptor (GR) affect transcriptional regulation. Our approach resulted in the identification of a novel highly active functional GR binding sequence and revealed that sequence variation both within and flanking GR’s core binding site can modulate GR activity without apparent changes in DNA binding affinity. Notably, we found that the sequence composition of variants with similar activity profiles was highly diverse. In contrast, groups of variants with similar activity profiles showed specific DNA shape characteristics indicating that DNA shape may be a better predictor of activity than DNA sequence. Finally, using single cell experiments with individual enhancer variants, we obtained clues indicating that the architecture of the response element can independently tune expression mean and cell-to cell variability in gene expression (noise). Together, our studies establish synSTARR as a powerful method to systematically study how DNA sequence and shape modulate transcriptional output and noise.

The expression level of genes is controlled by transcription factors, which are proteins that bind to genomic response elements that contain their recognition DNA sequence. Importantly, genes are not simply turned on but need to be expressed at the right level. This is, at least in part, assured by the sequence composition of genomic response elements. Here, we studied how the recognition DNA sequence influences gene regulation by a transcription factor called the glucocorticoid receptor. Specifically, we developed a method to test the activity of variants in a highly parallelized setting where everything is kept identical except for the sequence of the binding site. The systematic analysis of tens of thousands of sequence variants facilitated the identification of a previously unknown sequence variant with high activity. Moreover, we report how sequence variation of the response element influences cell-to-cell variability in expression levels. Finally, we observe similar activity profiles for distinct sequence variants that share similar three-dimensional DNA shape characteristics arguing that the three-dimensional perception of DNA by the glucocorticoid receptor, modulates its activity towards individual target genes.

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.

Single transcript imaging to assay gene expression in wholemount Drosophila melanogaster tissues

Single molecule Fluorescence in situ Hybridization (smFISH) for mRNA provides a powerful quantitative handle on expression from endogenous gene loci. While the method has been widely applied in cells in culture, applications to primary tissue samples remain fewer, and often use involved cryosectioning. Even apart from quantitative access to absolute transcript counts in specific tissue volumes, many other advantages of smFISH can be envisaged in tissue samples. Primary among these are the ability to report on subtle differences in expression among different cell types within a tissue, and the ability to correlate the expression from different target genes. Here, we present a modified method of smFISH applicable on various primary wholemount tissues from the fruit fly Drosophila melanogaster, and show the efficacy of the method in a variety of larval and adult tissue, and embryos. We also combine smFISH in tissue with immunofluorescence to demonstrate the possibility of capturing transcriptional and translational aspects of gene expression in the same tissue. Given the widespread use of Drosophila melanogaster as a model system in Developmental Biology and Genetics, such methods are likely to be of wide interest and could yield rich information about gene expression in tissues from this organism.

Gene expression distribution deconvolution in single-cell RNA sequencing

We developed deconvolution of single-cell expression distribution (DESCEND), a method to recover cross-cell distribution of the true gene expression level from observed counts in single-cell RNA sequencing, allowing adjustment of known confounding cell-level factors. With the recovered distribution, DESCEND provides reliable estimates of distribution-based measurements, such as the dispersion of true gene expression and the probability that true gene expression is positive. This is important, as with better estimates of these measurements, DESCEND clarifies and improves many downstream analyses including finding differentially expressed genes, identifying cell types, and selecting differentiation markers. Another contribution is that we verified using nine public datasets a simple “Poisson-alpha” noise model for the technical noise of unique molecular identifier-based single-cell RNA-sequencing data, clarifying the current intense debate on this issue.

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND’s noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers.

Kinetic Modeling of Virus Growth in Cells

When a virus infects a host cell, it hijacks the biosynthetic capacity of the cell to produce virus progeny, a process that may take less than an hour or more than a week. The overall time required for a virus to reproduce depends collectively on the rates of multiple steps in the infection process, including initial binding of the virus particle to the surface of the cell, virus internalization and release of the viral genome within the cell, decoding of the genome to make viral proteins, replication of the genome, assembly of progeny virus particles, and release of these particles into the extracellular environment. For a large number of virus types, much has been learned about the molecular mechanisms and rates of the various steps. However, in only relatively few cases during the last 50 years has an attempt been made-using mathematical modeling-to account for how the different steps contribute to the overall timing and productivity of the infection cycle in a cell. Here we review the initial case studies, which include studies of the one-step growth behavior of viruses that infect bacteria (Qβ, T7, and M13), human immunodeficiency virus, influenza A virus, poliovirus, vesicular stomatitis virus, baculovirus, hepatitis B and C viruses, and herpes simplex virus. Further, we consider how such models enable one to explore how cellular resources are utilized and how antiviral strategies might be designed to resist escape. Finally, we highlight challenges and opportunities at the frontiers of cell-level modeling of virus infections.

Applications of the CRISPR/Cas system beyond gene editing

Since the discovery of the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated system (Cas) as a tool for gene editing a plethora of locus-specific as well as genome-wide approaches have been developed that allow efficient and reproducible manipulation of genomic sequences. However, the seemingly unbound potential of CRISPR/Cas does not stop with its utilization as a site-directed nuclease. Mutations in its catalytic centers render Cas9 (dCas9) a universal recruitment platform that can be utilized to control transcription, visualize DNA sequences, investigate in situ proteome compositions and manipulate epigenetic modifications at user-defined genomic loci. In this review, we give a comprehensive introduction and overview of the development, improvement and application of recent dCas9-based approaches.

Burst-Like Transcription of Mutant and Wildtype MYH7-Alleles as Possible Origin of Cell-to-Cell Contractile Imbalance in Hypertrophic Cardiomyopathy

Hypertrophic Cardiomyopathy (HCM) has been related to many different mutations in more than 20 different, mostly sarcomeric proteins. While development of the HCM-phenotype is thought to be triggered by the different mutations, a common mechanism remains elusive. Studying missense-mutations in the ventricular beta-myosin heavy chain (β-MyHC, MYH7) we hypothesized that significant contractile heterogeneity exists among individual cardiomyocytes of HCM-patients that results from cell-to-cell variation in relative expression of mutated vs. wildtype β-MyHC. To test this hypothesis, we measured force-calcium-relationships of cardiomyocytes isolated from myocardium of heterozygous HCM-patients with either β-MyHC-mutation Arg723Gly or Arg200Val, and from healthy controls. From the myocardial samples of the HCM-patients we also obtained cryo-sections, and laser-microdissected single cardiomyocytes for quantification of mutated vs. wildtype MYH7-mRNA using a single cell RT-qPCR and restriction digest approach. We characterized gene transcription by visualizing active transcription sites by fluorescence in situ hybridization of intronic and exonic sequences of MYH7-pre-mRNA. For both mutations, cardiomyocytes showed large cell-to-cell variation in Ca⁺⁺-sensitivity. Interestingly, some cardiomyocytes were essentially indistinguishable from controls what might indicate that they had no mutant β-MyHC while others had highly reduced Ca⁺⁺-sensitivity suggesting substantial fractions of mutant β-MyHC. Single-cell MYH7-mRNA-quantification in cardiomyocytes of the same patients revealed high cell-to-cell variability of mutated vs. wildtype mRNA, ranging from essentially pure mutant to essentially pure wildtype MYH7-mRNA. We found 27% of nuclei without active transcription sites which is inconsistent with continuous gene transcription but suggests burst-like transcription of MYH7. Model simulations indicated that burst-like, stochastic on/off-switching of MYH7 transcription, which is independent for mutant and wildtype alleles, could generate the observed cell-to-cell variation in the fraction of mutant vs. wildtype MYH7-mRNA, a similar variation in β-MyHC-protein, and highly heterogeneous Ca⁺⁺-sensitivity of individual cardiomyocytes. In the long run, such contractile imbalance in the myocardium may well induce progressive structural distortions like cellular and myofibrillar disarray and interstitial fibrosis, as they are typically observed in HCM.

The impact of non-genetic heterogeneity on cancer cell death

The goal of cancer chemotherapy is to induce homogeneous cell death within the population of targeted cancer cells. However, no two cells are exactly alike at the molecular level, and sensitivity to drug-induced cell death, therefore, varies within a population. Genetic alterations can contribute to this variability and lead to selection for drug resistant clones. However, there is a growing appreciation for the role of non-genetic variation in producing drug-tolerant cellular states that exhibit reduced sensitivity to cell death for extended periods of time, from hours to weeks. These cellular states may result from individual variation in epigenetics, gene expression, metabolism, and other processes that impact drug mechanism of action or the execution of cell death. Such population-level non-genetic heterogeneity may contribute to treatment failure and provide a cellular "substrate" for the emergence of genetic alterations that confer frank drug resistance.

Use it or average it: stochasticity in plant development

A process that is stochastic has a probabilistic or randomly determined outcome. At the molecular level, all processes are stochastic; but development is highly reproducible, suggesting that plants and other multicellular organisms have evolved mechanisms to ensure robustness (achieving correct development despite stochastic and environmental perturbations). Mechanisms of robustness can be discovered through isolating mutants with increased variability in phenotype; such mutations do not necessarily change the average phenotype. Surprisingly, some developmental robustness mechanisms actually exploit stochasticity as a useful source of variation. For example, gene expression is stochastic and can be utilized to create subtle differences between identical cells that can initiate the patterning of specialized cell types. Stochasticity can also be used to promote robustness through spatiotemporal averaging-stochasticity can be averaged out across space and over time. Thus, organisms often harness stochasticity to ensure robust development.

Influence of multiplicative stochastic variation on translational elongation rates

Experimental data indicate that stochastic effects exerted at the level of translation contribute substantially to the variation in abundance of proteins expressed at moderate to high levels. This study analyzes the theoretical consequences of fluctuations in residue-specific elongation rates during translation. A simple analytical framework shows that rate variation during elongation gives rise to protein production rates that consist of sums of products of random variables. Simulations show that because the contribution to total variation of products of random variables greatly exceeds that of sums of random variables, the overall distribution exhibits approximately log-normal behavior. Empirical fits of the data can be satisfied by either sums of log-normal distributions, or sums of log-normal and log-logistic distributions. Elongation rate stochastic variation offers an accounting for a major component of biological variation. The analysis provided here highlights a probability distribution that is a natural extension of the Poisson and has broad applicability to many types of multiplicative noise processes.

Likelihood for transcriptions in a genetic regulatory system under asymmetric stable Lévy noise

This work is devoted to investigating the evolution of concentration in a genetic regulation system, when the synthesis reaction rate is under additive and multiplicative asymmetric stable Lévy fluctuations. By focusing on the impact of skewness (i.e., non-symmetry) in the probability distributions of noise, we find that via examining the mean first exit time (MFET) and the first escape probability (FEP), the asymmetric fluctuations, interacting with nonlinearity in the system, lead to peculiar likelihood for transcription. This includes, in the additive noise case, realizing higher likelihood of transcription for larger positive skewness (i.e., asymmetry) index β, causing a stochastic bifurcation at the non-Gaussianity index value α = 1 (i.e., it is a separating point or line for the likelihood for transcription), and achieving a turning point at the threshold value β≈-0.5 (i.e., beyond which the likelihood for transcription suddenly reversed for α values). The stochastic bifurcation and turning point phenomena do not occur in the symmetric noise case (β = 0). While in the multiplicative noise case, non-Gaussianity index value α = 1 is a separating point or line for both the MFET and the FEP. We also investigate the noise enhanced stability phenomenon. Additionally, we are able to specify the regions in the whole parameter space for the asymmetric noise, in which we attain desired likelihood for transcription. We have conducted a series of numerical experiments in "regulating" the likelihood of gene transcription by tuning asymmetric stable Lévy noise indexes. This work offers insights for possible ways of achieving gene regulation in experimental research.

Acceleration of bursty multiprotein target search kinetics on DNA by colocalisation

Proteins are capable of locating specific targets on DNA by employing a facilitated diffusion process with intermittent 1D and 3D search steps. We here uncover the implications of colocalisation of protein production and DNA binding sites via computer simulations.

Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq

Cellular functions are strongly dependent on surrounding cells and environmental factors. Current technologies are limited in their ability to characterize the spatial location and gene programs of cells in poorly structured and dynamic niches. We developed a method, NICHE-seq, that combines photoactivatable fluorescent reporters, two-photon microscopy, and single-cell RNA sequencing (scRNA-seq) to infer the cellular and molecular composition of niches. We applied NICHE-seq to examine the high-order assembly of immune cell networks. NICHE-seq is highly reproducible in spatial tissue reconstruction, enabling identification of rare niche-specific immune subpopulations and gene programs, including natural killer cells within infected B cell follicles and distinct myeloid states in the spleen and tumor. This study establishes NICHE-seq as a broadly applicable method for elucidating high-order spatial organization of cell types and their molecular pathways.

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.

What's Luck Got to Do with It: Single Cells, Multiple Fates, and Biological Nondeterminism

The field of single-cell biology has morphed from a philosophical digression at its inception, to a playground for quantitative biologists, to a major area of biomedical research. The last several years have witnessed an explosion of new technologies, allowing us to apply even more of the modern molecular biology toolkit to single cells. Conceptual progress, however, has been comparatively slow. Here, we provide a framework for classifying both the origins of the differences between individual cells and the consequences of those differences. We discuss how the concept of "random" differences is context dependent, and propose that rigorous definitions of inputs and outputs may bring clarity to the discussion. We also categorize ways in which probabilistic behavior may influence cellular function, highlighting studies that point to exciting future directions in the field.

Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression

Polycomb repressive complexes (PRCs) are important histone modifiers, which silence gene expression; yet, there exists a subset of PRC-bound genes actively transcribed by RNA polymerase II (RNAPII). It is likely that the role of Polycomb repressive complex is to dampen expression of these PRC-active genes. However, it is unclear how this flipping between chromatin states alters the kinetics of transcription. Here, we integrate histone modifications and RNAPII states derived from bulk ChIP-seq data with single-cell RNA-sequencing data. We find that Polycomb repressive complex-active genes have greater cell-to-cell variation in expression than active genes, and these results are validated by knockout experiments. We also show that PRC-active genes are clustered on chromosomes in both two and three dimensions, and interactions with active enhancers promote a stabilization of gene expression noise. These findings provide new insights into how chromatin regulation modulates stochastic gene expression and transcriptional bursting, with implications for regulation of pluripotency and development.Polycomb repressive complexes modify histones but it is unclear how changes in chromatin states alter kinetics of transcription. Here, the authors use single-cell RNAseq and ChIPseq to find that actively transcribed genes with Polycomb marks have greater cell-to-cell variation in expression.

Illuminating Messengers: An Update and Outlook on RNA Visualization in Bacteria

To be able to visualize the abundance and spatiotemporal features of RNAs in bacterial cells would permit obtaining a pivotal understanding of many mechanisms underlying bacterial cell biology. The first methods that allowed observing single mRNA molecules in individual cells were introduced by Bertrand et al. (1998) and Femino et al. (1998). Since then, a plethora of techniques to image RNA molecules with the aid of fluorescence microscopy has emerged. Many of these approaches are useful for the large eukaryotic cells but their adaptation to study RNA, specifically mRNA molecules, in bacterial cells progressed relatively slow. Here, an overview will be given of fluorescent techniques that can be used to reveal specific RNA molecules inside fixed and living single bacterial cells. It includes a critical evaluation of their caveats as well as potential solutions.

Photoreceptor Fate Determination in the Vertebrate Retina

Photoreceptors are highly specialized primary sensory neurons that sense light and initiate vision. This critical role is well demonstrated by the fact that visual impairment accompanies photoreceptor loss or dysfunction in many human diseases. With the remarkable advances in stem cell research, one therapeutic approach is to use stem cells to generate photoreceptors and then engraft them into diseased eyes. Knowledge of the molecular mechanisms that control photoreceptor genesis during normal development can greatly aid in the production of photoreceptor cells for this approach. This article will discuss advances in our understanding of the molecular mechanisms that regulate photoreceptor fate determination during development. Recent lineage studies have shown that there are distinct retinal progenitor cells (RPCs) that produce specific combinations of daughter cell types, including photoreceptors and other types of retinal cells. Gene regulatory networks, in which transcription factors interact via cis-regulatory DNA elements, have been discovered that operate within distinct RPCs, and/or newly postmitotic cells, to direct the choice of photoreceptor fate.

cgCorrect: a method to correct for confounding cell-cell variation due to cell growth in single-cell transcriptomics

Accessing gene expression at a single-cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remains obscured when using traditional population-based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and modeling the data. One such issue is differences in cell size, which introduce additional variability into the data and for which appropriate normalization techniques are needed. Otherwise, these differences in cell size may obscure genuine heterogeneities among cell populations and lead to overdispersed steady-state distributions of mRNA transcript numbers. We present cgCorrect, a statistical framework to correct for differences in cell size that are due to cell growth in single-cell transcriptomics data. We derive the probability for the cell-growth-corrected mRNA transcript number given the measured, cell size-dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportionally to the cell's volume during the cell cycle. cgCorrect can be used for both data normalization and to analyze the steady-state distributions used to infer the gene expression mechanism. We demonstrate its applicability on both simulated data and single-cell quantitative real-time polymerase chain reaction (PCR) data from mouse blood stem and progenitor cells (and to quantitative single-cell RNA-sequencing data obtained from mouse embryonic stem cells). We show that correcting for differences in cell size affects the interpretation of the data obtained by typically performed computational analysis.

Determining the Limitations and Benefits of Noise in Gene Regulation and Signal Transduction through Single Cell, Microscopy-Based Analysis

Stochastic fluctuations, termed "noise," in the level of biological molecules can greatly impact cellular functions. While biological noise can sometimes be detrimental, recent studies have provided an increasing number of examples in which biological noise can be functionally beneficial. Rather than provide an exhaustive review of the growing literature in this field, in this review, we focus on single-cell studies based on quantitative microscopy that have generated a deeper understanding of the sources, characteristics, limitations, and benefits of biological noise. Specifically, we highlight studies showing how noise can help coordinate the expression of multiple downstream target genes, impact the channel capacity of signaling networks, and interact synergistically with oscillatory dynamics to enhance the sensitivity of signal processing. We conclude with a discussion of current challenges and future opportunities.

On the Spatial Organization of mRNA, Plasmids, and Ribosomes in a Bacterial Host Overexpressing Membrane Proteins

By using fluorescence imaging, we provide a time-resolved single-cell view on coupled defects in transcription, translation, and growth during expression of heterologous membrane proteins in Lactococcus lactis. Transcripts encoding poorly produced membrane proteins accumulate in mRNA-dense bodies at the cell poles, whereas transcripts of a well-expressed homologous membrane protein show membrane-proximal localization in a translation-dependent fashion. The presence of the aberrant polar mRNA foci correlates with cessation of cell division, which is restored once these bodies are cleared. In addition, activation of the heat-shock response and a loss of nucleoid-occluded ribosomes are observed. We show that the presence of a native-like N-terminal domain is key to SRP-dependent membrane localization and successful production of membrane proteins. The work presented gives new insights and detailed understanding of aberrant membrane protein biogenesis, which can be used for strategies to optimize membrane protein production.

Single-molecule fluorescence resonance energy transfer in molecular biology

Single-molecule fluorescence resonance energy transfer (smFRET) is a powerful technique for studying the conformation dynamics and interactions of individual biomolecules. In this review, we describe the concept and principle of smFRET, illustrate general instrumentation and microscopy settings for experiments, and discuss the methods and algorithms for data analysis. Subsequently, we review applications of smFRET in protein conformational changes, ion channel open-close properties, receptor-ligand interactions, nucleic acid structure regulation, vesicle fusion, and force induced conformational dynamics. Finally, we discuss the main limitations of smFRET in molecular biology.

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.

Design of a bistable switch to control cellular uptake

Bistable switches are widely used in synthetic biology to trigger cellular functions in response to environmental signals. All bistable switches developed so far, however, control the expression of target genes without access to other layers of the cellular machinery. Here, we propose a bistable switch to control the rate at which cells take up a metabolite from the environment. An uptake switch provides a new interface to command metabolic activity from the extracellular space and has great potential as a building block in more complex circuits that coordinate pathway activity across cell cultures, allocate metabolic tasks among different strains or require cell-to-cell communication with metabolic signals. Inspired by uptake systems found in nature, we propose to couple metabolite import and utilization with a genetic circuit under feedback regulation. Using mathematical models and analysis, we determined the circuit architectures that produce bistability and obtained their design space for bistability in terms of experimentally tuneable parameters. We found an activation-repression architecture to be the most robust switch because it displays bistability for the largest range of design parameters and requires little fine-tuning of the promoters' response curves. Our analytic results are based on on-off approximations of promoter activity and are in excellent qualitative agreement with simulations of more realistic models. With further analysis and simulation, we established conditions to maximize the parameter design space and to produce bimodal phenotypes via hysteresis and cell-to-cell variability. Our results highlight how mathematical analysis can drive the discovery of new circuits for synthetic biology, as the proposed circuit has all the hallmarks of a toggle switch and stands as a promising design to control metabolic phenotypes across cell cultures.

Experimental Design for Stochastic Models of Nonlinear Signaling Pathways Using an Interval-Wise Linear Noise Approximation and State Estimation

BACKGROUND

Computational modeling is a key technique for analyzing models in systems biology. There are well established methods for the estimation of the kinetic parameters in models of ordinary differential equations (ODE). Experimental design techniques aim at devising experiments that maximize the information encoded in the data. For ODE models there are well established approaches for experimental design and even software tools. However, data from single cell experiments on signaling pathways in systems biology often shows intrinsic stochastic effects prompting the development of specialized methods. While simulation methods have been developed for decades and parameter estimation has been targeted for the last years, only very few articles focus on experimental design for stochastic models.

METHODS

The Fisher information matrix is the central measure for experimental design as it evaluates the information an experiment provides for parameter estimation. This article suggest an approach to calculate a Fisher information matrix for models containing intrinsic stochasticity and high nonlinearity. The approach makes use of a recently suggested multiple shooting for stochastic systems (MSS) objective function. The Fisher information matrix is calculated by evaluating pseudo data with the MSS technique.

RESULTS

The performance of the approach is evaluated with simulation studies on an Immigration-Death, a Lotka-Volterra, and a Calcium oscillation model. The Calcium oscillation model is a particularly appropriate case study as it contains the challenges inherent to signaling pathways: high nonlinearity, intrinsic stochasticity, a qualitatively different behavior from an ODE solution, and partial observability. The computational speed of the MSS approach for the Fisher information matrix allows for an application in realistic size models.

Stochastic modeling of biochemical systems with multistep reactions using state-dependent time delay

To deal with the growing scale of molecular systems, sophisticated modelling techniques have been designed in recent years to reduce the complexity of mathematical models. Among them, a widely used approach is delayed reaction for simplifying multistep reactions. However, recent research results suggest that a delayed reaction with constant time delay is unable to describe multistep reactions accurately. To address this issue, we propose a novel approach using state-dependent time delay to approximate multistep reactions. We first use stochastic simulations to calculate time delay arising from multistep reactions exactly. Then we design algorithms to calculate time delay based on system dynamics precisely. To demonstrate the power of proposed method, two processes of mRNA degradation are used to investigate the function of time delay in determining system dynamics. In addition, a multistep pathway of metabolic synthesis is used to explore the potential of the proposed method to simplify multistep reactions with nonlinear reaction rates. Simulation results suggest that the state-dependent time delay is a promising and accurate approach to reduce model complexity and decrease the number of unknown parameters in the models.

Transitions in a genetic transcriptional regulatory system under Lévy motion

Based on a stochastic differential equation model for a single genetic regulatory system, we examine the dynamical effects of noisy fluctuations, arising in the synthesis reaction, on the evolution of the transcription factor activator in terms of its concentration. The fluctuations are modeled by Brownian motion and α-stable Lévy motion. Two deterministic quantities, the mean first exit time (MFET) and the first escape probability (FEP), are used to analyse the transitions from the low to high concentration states. A shorter MFET or higher FEP in the low concentration region facilitates such a transition. We have observed that higher noise intensities and larger jumps of the Lévy motion shortens the MFET and thus benefits transitions. The Lévy motion activates a transition from the low concentration region to the non-adjacent high concentration region, while Brownian motion can not induce this phenomenon. There are optimal proportions of Gaussian and non-Gaussian noises, which maximise the quantities MFET and FEP for each concentration, when the total sum of noise intensities are kept constant. Because a weaker stability indicates a higher transition probability, a new geometric concept is introduced to quantify the basin stability of the low concentration region, characterised by the escaping behaviour.

Cell type-specific transcriptome profiling in mammalian brains

A mammalian brain contains numerous types of cells. Advances in neuroscience in the past decade allow us to identify and isolate neural cells of interest from mammalian brains. Recent developments in high-throughput technologies, such as microarrays and next-generation sequencing (NGS), provide detailed information on gene expression in pooled cells on a genomic scale. As a result, many novel genes have been found critical in cell type-specific transcriptional regulation. These differentially expressed genes can be used as molecular signatures, unique to a particular class of neural cells. Use of this gene expression-based approach can further differentiate neural cell types into subtypes, potentially linking some of them with neurological diseases. In this article, experimental techniques used to purify neural cells are described, followed by a review on recent microarray- or NGS-based transcriptomic studies of common neural cell types. The future prospects of cell type-specific research are also discussed.

Nutrient Control of Yeast Gametogenesis Is Mediated by TORC1, PKA and Energy Availability

Cell fate choices are tightly controlled by the interplay between intrinsic and extrinsic signals, and gene regulatory networks. In Saccharomyces cerevisiae, the decision to enter into gametogenesis or sporulation is dictated by mating type and nutrient availability. These signals regulate the expression of the master regulator of gametogenesis, IME1. Here we describe how nutrients control IME1 expression. We find that protein kinase A (PKA) and target of rapamycin complex I (TORC1) signalling mediate nutrient regulation of IME1 expression. Inhibiting both pathways is sufficient to induce IME1 expression and complete sporulation in nutrient-rich conditions. Our ability to induce sporulation under nutrient rich conditions allowed us to show that respiration and fermentation are interchangeable energy sources for IME1 transcription. Furthermore, we find that TORC1 can both promote and inhibit gametogenesis. Down-regulation of TORC1 is required to activate IME1. However, complete inactivation of TORC1 inhibits IME1 induction, indicating that an intermediate level of TORC1 signalling is required for entry into sporulation. Finally, we show that the transcriptional repressor Tup1 binds and represses the IME1 promoter when nutrients are ample, but is released from the IME1 promoter when both PKA and TORC1 are inhibited. Collectively our data demonstrate that nutrient control of entry into sporulation is mediated by a combination of energy availability, TORC1 and PKA activities that converge on the IME1 promoter.

Mechanical slowing-down of cytoplasmic diffusion allows in vivo counting of proteins in individual cells

Many key regulatory proteins in bacteria are present in too low numbers to be detected with conventional methods, which poses a particular challenge for single-cell analyses because such proteins can contribute greatly to phenotypic heterogeneity. Here we develop a microfluidics-based platform that enables single-molecule counting of low-abundance proteins by mechanically slowing-down their diffusion within the cytoplasm of live Escherichia coli (E. coli) cells. Our technique also allows for automated microscopy at high throughput with minimal perturbation to native physiology, as well as viable enrichment/retrieval. We illustrate the method by analysing the control of the master regulator of the E. coli stress response, RpoS, by its adapter protein, SprE (RssB). Quantification of SprE numbers shows that though SprE is necessary for RpoS degradation, it is expressed at levels as low as 3–4 molecules per average cell cycle, and fluctuations in SprE are approximately Poisson distributed during exponential phase with no sign of bursting.

Several proteins are expressed at too low abundance in the Escherichia coli (E. coli) proteome to be detected by standard methods. Here, the authors create a microfluidics-based platform enabling single-molecule counting of low-abundance proteins by mechanically slowing-down their diffusion in live E. coli.

Viral genome imaging of hepatitis C virus to probe heterogeneous viral infection and responses to antiviral therapies

Hepatitis C virus (HCV) is a positive single-stranded RNA virus of enormous global health importance, with direct-acting antiviral therapies replacing an immunostimulatory interferon-based regimen. The dynamics of HCV positive and negative-strand viral RNAs (vRNAs) under antiviral perturbations have not been studied at the single-cell level, leaving a gap in our understanding of antiviral kinetics and host-virus interactions. Here, we demonstrate quantitative imaging of HCV genomes in multiple infection models, and multiplexing of positive and negative strand vRNAs and host antiviral RNAs. We capture the varying kinetics with which antiviral drugs with different mechanisms of action clear HCV infection, finding the NS5A inhibitor daclatasvir to induce a rapid decline in negative-strand viral RNAs. We also find that the induction of host antiviral genes upon interferon treatment is positively correlated with viral load in single cells. This study adds smFISH to the toolbox available for analyzing the treatment of RNA virus infections.

Exploiting Natural Fluctuations to Identify Kinetic Mechanisms in Sparsely Characterized Systems

From biochemistry to ecology, many biological systems are stochastic, complex, and sparsely characterized. In such systems, each component may respond to changes in any directly or indirectly connected components, thus requiring knowledge of the whole to predict the dynamics of the parts. Here, we address this challenge by deriving relations between properties of fluctuations that only reflect local interactions between a subset of components but are invariant to all indirectly connected dynamics. This greatly reduces the number of assumptions when evaluating dynamic models experimentally. We illustrate the approach by revisiting systematic single-cell gene expression data, and we show that the observed fluctuations contradict the assumptions made in most published models of stochastic gene expression, even when accounting for the possibility of systematic experimental artifacts.

Fluctuations and synchrony of RNA synthesis in nucleoli

Ribosomal RNA (rRNA) sequences are synthesized at exceptionally high rates and, together with ribosomal proteins (r-proteins), are utilized as building blocks for the assembly of pre-ribosomal particles. Although it is widely acknowledged that tight regulation and coordination of rRNA and r-protein production are fundamentally important for the maintenance of cellular homeostasis, still little is known about the real-time kinetics of the ribosome component synthesis in individual cells. In this communication we introduce a label-free MicroRaman spectrometric approach for monitoring rRNA synthesis in live cultured cells. Remarkably high and rapid fluctuations of rRNA production rates were revealed by this technique. Strikingly, the changes in the rRNA output were synchronous for ribosomal genes located in separate nucleoli of the same cell. Our findings call for the development of new concepts to elucidate the coordination of ribosomal components production. In this regard, numerical modeling further demonstrated that the production of rRNA and r-proteins can be coordinated, regardless of the fluctuations in rRNA synthesis. Overall, our quantitative data reveal a spectacular interplay of inherently stochastic rates of RNA synthesis and the coordination of gene expression.

Enhancer Regulation of Transcriptional Bursting Parameters Revealed by Forced Chromatin Looping

Mammalian genes transcribe RNA not continuously, but in bursts. Transcriptional output can be modulated by altering burst fraction or burst size, but how regulatory elements control bursting parameters remains unclear. Single-molecule RNA FISH experiments revealed that the β-globin enhancer (LCR) predominantly augments transcriptional burst fraction of the β-globin gene with modest stimulation of burst size. To specifically measure the impact of long-range chromatin contacts on transcriptional bursting, we forced an LCR-β-globin promoter chromatin loop. We observed that raising contact frequencies increases burst fraction but not burst size. In cells in which two developmentally distinct LCR-regulated globin genes are cotranscribed in cis, burst sizes of both genes are comparable. However, allelic co-transcription of both genes is statistically disfavored, suggesting mutually exclusive LCR-gene contacts. These results are consistent with competition between the β-type globin genes for LCR contacts and suggest that LCR-promoter loops are formed and released with rapid kinetics.

The developmental genetics of biological robustness

BACKGROUND

Living organisms are continuously confronted with perturbations, such as environmental changes that include fluctuations in temperature and nutrient availability, or genetic changes such as mutations. While some developmental systems are affected by such challenges and display variation in phenotypic traits, others continue consistently to produce invariable phenotypes despite perturbation. This ability of a living system to maintain an invariable phenotype in the face of perturbations is termed developmental robustness. Biological robustness is a phenomenon observed across phyla, and studying its mechanisms is central to deciphering the genotype-phenotype relationship. Recent work in yeast, animals and plants has shown that robustness is genetically controlled and has started to reveal the underlying mechinisms behind it.

SCOPE AND CONCLUSIONS

Studying biological robustness involves focusing on an important property of developmental traits, which is the phenotypic distribution within a population. This is often neglected because the vast majority of developmental biology studies instead focus on population aggregates, such as trait averages. By drawing on findings in animals and yeast, this Viewpoint considers how studies on plant developmental robustness may benefit from strict definitions of what is the developmental system of choice and what is the relevant perturbation, and also from clear distinctions between gene effects on the trait mean and the trait variance. Recent advances in quantitative developmental biology and high-throughput phenotyping now allow the design of targeted genetic screens to identify genes that amplify or restrict developmental trait variance and to study how variation propagates across different phenotypic levels in biological systems. The molecular characterization of more quantitative trait loci affecting trait variance will provide further insights into the evolution of genes modulating developmental robustness. The study of robustness mechanisms in closely related species will address whether mechanisms of robustness are evolutionarily conserved.