Single-molecule approaches to stochastic gene expression

Epigenetic control of immunity

Immunity relies on the heterogeneity of immune cells and their ability to respond to pathogen challenges. In the adaptive immune system, lymphocytes display a highly diverse antigen receptor repertoire that matches the vast diversity of pathogens. In the innate immune system, the cell's heterogeneity and phenotypic plasticity enable flexible responses to changes in tissue homeostasis caused by infection or damage. The immune responses are calibrated by the graded activity of immune cells that can vary from yeast-like proliferation to lifetime dormancy. This article describes key epigenetic processes that contribute to the function of immune cells during health and disease.

Imaging RNA Polymerase II transcription sites in living cells

Over the past twenty years, exciting developments in optical and molecular imaging approaches have allowed researchers to examine with unprecedented resolution the spatial organization of transcription sites in the nucleus. An attractive model that has developed from these studies is that active genes cluster to preformed transcription factories that contain multiple active RNA polymerases and transcription factor proteins required for efficient mRNA biogenesis. However, this model has been extensively debated in part due to the fact transcription factories and their features have only been documented in fixed cells. In this review, we will focus on recent live-cell imaging studies that are changing our understanding of transcription factories.

Heterogeneity in immune responses: from populations to single cells

The mammalian immune system is tasked with protecting the host against a broad range of threats. Understanding how immune populations leverage cellular diversity to achieve this breadth and flexibility, particularly during dynamic processes such as differentiation and antigenic response, is a core challenge that is well suited for single cell analysis. Recent years have witnessed transformative and intersecting advances in nanofabrication and genomics that enable deep profiling of individual cells, affording exciting opportunities to study heterogeneity in the immune response at an unprecedented scope. In light of these advances, here we review recent work exploring how immune populations generate and leverage cellular heterogeneity at multiple molecular and phenotypic levels. Additionally, we highlight opportunities for single cell technologies to shed light on the causes and consequences of heterogeneity in the immune system.

Stochastic genome-nuclear lamina interactions: modulating roles of Lamin A and BAF

The nuclear lamina (NL) is thought to aid in the spatial organization of interphase chromosomes by providing an anchoring platform for hundreds of large genomic regions named lamina associated domains (LADs). Recently, a new live-cell imaging approach demonstrated directly that LAD-NL interactions are dynamic and in part stochastic. Here we discuss implications of these new findings and introduce Lamin A and BAF as potential modulators of stochastic LAD positioning.

Discriminating cellular heterogeneity using microwell-based RNA cytometry

Discriminating cellular heterogeneity is important for understanding cellular physiology. However, it is limited by the technical difficulties of single-cell measurements. Here we develop a two-stage system to determine cellular heterogeneity. In the first stage, we perform multiplex single-cell RNA cytometry in a microwell array containing over 60,000 reaction chambers. In the second stage, we use the RNA cytometry data to determine cellular heterogeneity by providing a heterogeneity likelihood score (HLS). Moreover, we use Monte-Carlo simulation and RNA cytometry data to calculate the minimum number of cells required for detecting heterogeneity. We apply this system to characterize the RNA distributions of ageing-related genes in a highly purified mouse haematopoietic stem cell population. We identify genes that reveal novel heterogeneity of these cells. We also show that changes in expression of genes such as Birc6 during ageing can be attributed to the shift of relative portions of cells in the high-expressing subgroup versus low-expressing subgroup.

Single-molecule tracking of the transcription cycle by sub-second RNA detection

Transcription is an inherently stochastic, noisy, and multi-step process, in which fluctuations at every step can cause variations in RNA synthesis, and affect physiology and differentiation decisions in otherwise identical cells. However, it has been an experimental challenge to directly link the stochastic events at the promoter to transcript production. Here we established a fast fluorescence in situ hybridization (fastFISH) method that takes advantage of intrinsically unstructured nucleic acid sequences to achieve exceptionally fast rates of specific hybridization (∼10e7 M⁻¹s⁻¹), and allows deterministic detection of single nascent transcripts. Using a prototypical RNA polymerase, we demonstrated the use of fastFISH to measure the kinetic rates of promoter escape, elongation, and termination in one assay at the single-molecule level, at sub-second temporal resolution. The principles of fastFISH design can be used to study stochasticity in gene regulation, to select targets for gene silencing, and to design nucleic acid nanostructures.

DOI:http://dx.doi.org/10.7554/eLife.01775.001

The body produces proteins by transcribing DNA (genes) to make messenger RNA, which is then translated to make a protein. Transcription begins when an enzyme called RNA polymerase binds to the DNA and catalyzes the process by which genetic information from the double helix is copied to a complementary RNA transcript, which subsequently becomes the messenger RNA.

Because a living cell usually contains only one or a few copies (alleles) of a given gene, molecular fluctuations play a crucial role in cellular transcription. Therefore, studying transcription kinetics at the level of single molecules may provide critical insights into how cells deal with—or even take advantage of—molecular fluctuations. A number of different single-molecule techniques can be used to follow transcription, but these techniques are often relatively slow compared to transcription in living cells, or they suffer from other problems such as only being able to study one step in the transcription process.

Now, Zhang, Revyakin et al. have systematically devised a technique called ‘fastFISH’ that is fast enough to track the production of single RNA molecules directly and instantaneously. FastFISH builds on an existing technique called FISH—short for fluorescence in situ hybridization—in which fluorescent molecules are attached to single strands of DNA or RNA. These single strands pair with specific regions of complementary DNA or RNA molecules, and they can be visualized with a fluorescence microscope. However, conventional FISH is a ‘snap-shot’ technique that is not suitable for making real-time observations under physiological conditions.

FastFISH relies on single strands of fluorescently labeled DNA and RNA that bind to complementary strands of DNA or RNA extremely quickly, even under physiological conditions, because they contain only three of the four ‘regular’ nucleotides that make up DNA or RNA. As a proof of principle, Zhang, Revyakin et al. used fastFISH to study the kinetics of transcription by the bacteriophage T7 RNA polymerase and were able to measure multiple stages of the transcription cycle in a single-molecule experimental setup.

By allowing each stage of transcription to be tracked in real-time at the level of single-molecules, fastFISH will permit a more in-depth analysis of the factors that regulate how genes are expressed as proteins in our cells. Moreover, the ability to design single-strand probes that bind rapidly to DNA and RNA targets could have many additional applications, including new strategies for more efficient gene silencing.

DOI:http://dx.doi.org/10.7554/eLife.01775.002

A framework for scalable parameter estimation of gene circuit models using structural information

Motivation: Systematic and scalable parameter estimation is a key to construct complex gene regulatory models and to ultimately facilitate an integrative systems biology approach to quantitatively understand the molecular mechanisms underpinning gene regulation.

Results: Here, we report a novel framework for efficient and scalable parameter estimation that focuses specifically on modeling of gene circuits. Exploiting the structure commonly found in gene circuit models, this framework decomposes a system of coupled rate equations into individual ones and efficiently integrates them separately to reconstruct the mean time evolution of the gene products. The accuracy of the parameter estimates is refined by iteratively increasing the accuracy of numerical integration using the model structure. As a case study, we applied our framework to four gene circuit models with complex dynamics based on three synthetic datasets and one time series microarray data set. We compared our framework to three state-of-the-art parameter estimation methods and found that our approach consistently generated higher quality parameter solutions efficiently. Although many general-purpose parameter estimation methods have been applied for modeling of gene circuits, our results suggest that the use of more tailored approaches to use domain-specific information may be a key to reverse engineering of complex biological systems.

Availability:http://sfb.kaust.edu.sa/Pages/Software.aspx

Contact:xin.gao@kaust.edu.sa

Supplementary information:Supplementary data are available at Bioinformatics online.

Optimal performance of the tryptophan operon of E. coli: a stochastic, dynamical, mathematical-modeling approach

In this work, we develop a detailed, stochastic, dynamical model for the tryptophan operon of E. coli, and estimate all of the model parameters from reported experimental data. We further employ the model to study the system performance, considering the amount of biochemical noise in the trp level, the system rise time after a nutritional shift, and the amount of repressor molecules necessary to maintain an adequate level of repression, as indicators of the system performance regime. We demonstrate that the level of cooperativity between repressor molecules bound to the first two operators in the trp promoter affects all of the above enlisted performance characteristics. Moreover, the cooperativity level found in the wild-type bacterial strain optimizes a cost-benefit function involving low biochemical noise in the tryptophan level, short rise time after a nutritional shift, and low number of regulatory molecules.

Engineered nanopore of Phi29 DNA-packaging motor for real-time detection of single colon cancer specific antibody in serum

The ingenious design of the bacterial virus phi29 DNA packaging nanomotor with an elegant and elaborate channel has inspired its application for single molecule detection of antigen/antibody interactions. The hub of this bacterial virus nanomotor is a truncated cone-shaped connector consisting of 12 protein subunits. These subunits form a ring with a central 3.6-nm channel acting as a path for dsDNA to enter during packaging and to exit during infection. The connector has been inserted into a lipid bilayer. Herein, we reengineered an Epithelial Cell Adhesion Molecule (EpCAM) peptide into the C-terminal of nanopore as a probe to specifically detect EpCAM antibody (Ab) in nanomolar concentration at the single molecule level. The binding of Abs sequentially to each peptide probe induced stepwise blocks in current. The distinctive current signatures enabled us to analyze the docking and undocking kinetics of Ab-probe interactions and determine the Kd. The signal of EpCAM antibody can be discriminated from the background events in the presence of nonspecific antibody or serum. Our results demonstrate the feasibility of generating a highly sensitive platform for detecting antibodies at extremely low concentrations in the presence of contaminants.

Using variability in gene expression as a tool for studying gene regulation

With the advent of quantitative tools for measuring gene expression in single cells, researchers have made the discovery that in many contexts, messenger RNA and protein levels can vary widely from cell to cell, often because of inherently stochastic events associated with gene expression. The study of this cellular individuality has become a field of study in its own right, characterized by a blend of technological development, theoretical analysis, and, more recently, applications to biological phenomena. In this review, we focus on the use of the variability inherent to gene expression as a tool to understand gene regulation. We discuss the use of variability as a natural systems-level perturbation, its use in quantitatively characterizing the biological processes underlying transcription, and its application to the discovery of new gene regulatory interactions. We believe that use of variability can provide new biological insights into different aspects of transcriptional control and can provide a powerful complementary approach to that of existing techniques.

Message control in developmental transitions; deciphering chromatin's role using zebrafish genomics

Now that the sequencing of genomes has become routine, understanding how a given genome is used in different ways to obtain cell type diversity in an organism is the next frontier. How specific transcription programs are established during vertebrate embryogenesis, however, remains poorly understood. Transcription is influenced by chromatin structure, which determines the accessibility of DNA-binding proteins to the genome. Although large-scale genomics approaches have uncovered specific features of chromatin structure that are diagnostic for different cell types and developmental stages, our functional understanding of chromatin in transcriptional regulation during development is very limited. In recent years, zebrafish embryogenesis has emerged as an excellent vertebrate model system to investigate the functional relationship between chromatin organization, gene regulation and development in a dynamic environment. Here, we review how studies in zebrafish have started to improve our understanding of the role of chromatin structure in genome activation and pluripotency and in the potential inheritance of transcriptional states from parent to progeny.

A genome-scale integration and analysis of Lactococcus lactis translation data

Protein synthesis is a template polymerization process composed by three main steps: initiation, elongation, and termination. During translation, ribosomes are engaged into polysomes whose size is used for the quantitative characterization of translatome. However, simultaneous transcription and translation in the bacterial cytosol complicates the analysis of translatome data. We established a procedure for robust estimation of the ribosomal density in hundreds of genes from Lactococcus lactis polysome size measurements. We used a mechanistic model of translation to integrate the information about the ribosomal density and for the first time we estimated the protein synthesis rate for each gene and identified the rate limiting steps. Contrary to conventional considerations, we find significant number of genes to be elongation limited. This number increases during stress conditions compared to optimal growth and proteins synthesized at maximum rate are predominantly elongation limited. Consistent with bacterial physiology, we found proteins with similar rate and control characteristics belonging to the same functional categories. Under stress conditions, we found that synthesis rate of regulatory proteins is becoming comparable to proteins favored under optimal growth. These findings suggest that the coupling of metabolic states and protein synthesis is more important than previously thought.

Single-cell analysis reveals early manifestation of cancerous phenotype in pre-malignant esophageal cells

Cellular heterogeneity plays a pivotal role in a variety of functional processes in vivo including carcinogenesis. However, our knowledge about cell-to-cell diversity and how differences in individual cells manifest in alterations at the population level remains very limited mainly due to the lack of appropriate tools enabling studies at the single-cell level. We present a study on changes in cellular heterogeneity in the context of pre-malignant progression in response to hypoxic stress. Utilizing pre-malignant progression of Barrett's esophagus (BE) as a disease model system we studied molecular mechanisms underlying the progression from metaplastic to dysplastic (pre-cancerous) stage. We used newly developed methods enabling measurements of cell-to-cell differences in copy numbers of mitochondrial DNA, expression levels of a set of mitochondrial and nuclear genes involved in hypoxia response pathways, and mitochondrial membrane potential. In contrast to bulk cell studies reported earlier, our study shows significant differences between metaplastic and dysplastic BE cells in both average values and single-cell parameter distributions of mtDNA copy numbers, mitochondrial function, and mRNA expression levels of studied genes. Based on single-cell data analysis, we propose that mitochondria may be one of the key factors in pre-malignant progression in BE.

Efficient stochastic simulation of chemical kinetics networks using a weighted ensemble of trajectories

We apply the "weighted ensemble" (WE) simulation strategy, previously employed in the context of molecular dynamics simulations, to a series of systems-biology models that range in complexity from a one-dimensional system to a system with 354 species and 3680 reactions. WE is relatively easy to implement, does not require extensive hand-tuning of parameters, does not depend on the details of the simulation algorithm, and can facilitate the simulation of extremely rare events. For the coupled stochastic reaction systems we study, WE is able to produce accurate and efficient approximations of the joint probability distribution for all chemical species for all time t. WE is also able to efficiently extract mean first passage times for the systems, via the construction of a steady-state condition with feedback. In all cases studied here, WE results agree with independent "brute-force" calculations, but significantly enhance the precision with which rare or slow processes can be characterized. Speedups over "brute-force" in sampling rare events via the Gillespie direct Stochastic Simulation Algorithm range from ~10(12) to ~10(18) for characterizing rare states in a distribution, and ~10(2) to ~10(4) for finding mean first passage times.

High-throughput RNA FISH analysis by imaging flow cytometry reveals that pioneer factor Foxa1 reduces transcriptional stochasticity

Genes are regulated at the single-cell level. Here, we performed RNA FISH of thousands of cells by flow cytometry (flow-RNA FISH) to gain insight into transcriptional variability between individual cells. These experiments utilized the murine adenocarcinoma 3134 cell line with 200 copies of the MMTV-Ras reporter integrated at a single genomic locus. The MMTV array contains approximately 800-1200 binding sites for the glucocorticoid receptor (GR) and 600 binding sites for the pioneer factor Foxa1. Hormone activation of endogenous GR by dexamethasone treatment resulted in highly variable changes in the RNA FISH intensity (25-300 pixel intensity units) and size (1.25-15 µm), indicative of probabilistic or stochastic mechanisms governing GR and cofactor activation of the MMTV promoter. Exogenous expression of the pioneer factor Foxa1 increased the FISH signal intensity and size as expected for a chromatin remodeler that enhances transcriptional competence through increased chromatin accessibility. In addition, specific analysis of Foxa1-enriched cell sub-populations showed that low and high Foxa1 levels substantially lowered the cell-to-cell variability in the FISH intensity as determined by a noise calculation termed the % coefficient of variation. These results suggest that an additional function of the pioneer factor Foxa1 may be to decrease transcriptional noise.

Turbo FISH: a method for rapid single molecule RNA FISH

Advances in RNA fluorescence in situ hybridization (RNA FISH) have allowed practitioners to detect individual RNA molecules in single cells via fluorescence microscopy, enabling highly accurate and sensitive quantification of gene expression. However, current methods typically employ hybridization times on the order of 2–16 hours, limiting its potential in applications like rapid diagnostics. We present here a set of conditions for RNA FISH (dubbed Turbo RNA FISH) that allow us to make accurate measurements with no more than 5 minutes of hybridization time and 3 minutes of washing, and show that hybridization times can go as low as 30 seconds while still producing quantifiable images. We further show that rapid hybridization is compatible with our recently developed iceFISH and SNP FISH variants of RNA FISH that enable chromosome and single base discrimination, respectively. Our method is simple and cost effective, and has the potential to dramatically increase the throughput and realm of applicability of RNA FISH.

Stochastic model simulation using Kronecker product analysis and Zassenhaus formula approximation

Probabilistic Models are regularly applied in Genetic Regulatory Network modeling to capture the stochastic behavior observed in the generation of biological entities such as mRNA or proteins. Several approaches including Stochastic Master Equations and Probabilistic Boolean Networks have been proposed to model the stochastic behavior in genetic regulatory networks. It is generally accepted that Stochastic Master Equation is a fundamental model that can describe the system being investigated in fine detail, but the application of this model is computationally enormously expensive. On the other hand, Probabilistic Boolean Network captures only the coarse-scale stochastic properties of the system without modeling the detailed interactions. We propose a new approximation of the stochastic master equation model that is able to capture the finer details of the modeled system including bistabilities and oscillatory behavior, and yet has a significantly lower computational complexity. In this new method, we represent the system using tensors and derive an identity to exploit the sparse connectivity of regulatory targets for complexity reduction. The algorithm involves an approximation based on Zassenhaus formula to represent the exponential of a sum of matrices as product of matrices. We derive upper bounds on the expected error of the proposed model distribution as compared to the stochastic master equation model distribution. Simulation results of the application of the model to four different biological benchmark systems illustrate performance comparable to detailed stochastic master equation models but with considerably lower computational complexity. The results also demonstrate the reduced complexity of the new approach as compared to commonly used Stochastic Simulation Algorithm for equivalent accuracy.

Transcriptional regulation of lineage commitment--a stochastic model of cell fate decisions

Molecular mechanisms employed by individual multipotent cells at the point of lineage commitment remain largely uncharacterized. Current paradigms span from instructive to noise-driven mechanisms. Of considerable interest is also whether commitment involves a limited set of genes or the entire transcriptional program, and to what extent gene expression configures multiple trajectories into commitment. Importantly, the transient nature of the commitment transition confounds the experimental capture of committing cells. We develop a computational framework that simulates stochastic commitment events, and affords mechanistic exploration of the fate transition. We use a combined modeling approach guided by gene expression classifier methods that infers a time-series of stochastic commitment events from experimental growth characteristics and gene expression profiling of individual hematopoietic cells captured immediately before and after commitment. We define putative regulators of commitment and probabilistic rules of transition through machine learning methods, and employ clustering and correlation analyses to interrogate gene regulatory interactions in multipotent cells. Against this background, we develop a Monte Carlo time-series stochastic model of transcription where the parameters governing promoter status, mRNA production and mRNA decay in multipotent cells are fitted to experimental static gene expression distributions. Monte Carlo time is converted to physical time using cell culture kinetic data. Probability of commitment in time is a function of gene expression as defined by a logistic regression model obtained from experimental single-cell expression data. Our approach should be applicable to similar differentiating systems where single cell data is available. Within our system, we identify robust model solutions for the multipotent population within physiologically reasonable values and explore model predictions with regard to molecular scenarios of entry into commitment. The model suggests distinct dependencies of different commitment-associated genes on mRNA dynamics and promoter activity, which globally influence the probability of lineage commitment.

Transcriptional regulation by nicotine in dopaminergic neurons

Dopaminergic neurons in the substantia nigra pars compacta (SNc) degenerate in Parkinson's disease. These neurons robustly express several nicotinic acetylcholine receptor (nAChR) subtypes. Smoking appears to be neuroprotective for Parkinson's disease but the mechanism is unknown. To determine whether chronic nicotine-induced changes in gene expression contribute to the neuroprotective effects of smoking, we develop methods to measure the effect of prolonged nicotine exposure on the SNc neuronal transcriptome in an unbiased manner. Twenty neurons were collected using laser-capture microscopy and transcriptional changes were assessed using RNA deep sequencing. These results are the first whole-transcriptome analyses of chronic nicotine treatment in SNc neurons. Overall, 129 genes were significantly regulated: 67 upregulated, 62 downregulated. Nicotine-induced relief of endoplasmic reticulum (ER) stress has been postulated as a potential mechanism for the neuroprotective effects of smoking. Chronic nicotine did not significantly affect the expression of ER stress-related genes, nor of dopamine-related or nAChR genes, but it did modulate expression of 129 genes that could be relevant to the neuroprotective effects of smoking, including genes involved in (1) the ubiquitin-proteasome pathway, (2) cell cycle regulation, (3) chromatin modification, and (4) DNA binding and RNA regulation. We also report preliminary transcriptome data for single-cell dopaminergic and GABAergic neurons isolated from midbrain cultures. These novel techniques will facilitate advances in understanding the mechanisms taking place at the cellular level and may have applications elsewhere in the fields of neuroscience and molecular biology. The results give an emerging picture of the role of nicotine on the SNc and on dopaminergic neurons.

Heterogeneity of astrocytes: from development to injury - single cell gene expression

Astrocytes perform control and regulatory functions in the central nervous system; heterogeneity among them is still a matter of debate due to limited knowledge of their gene expression profiles and functional diversity. To unravel astrocyte heterogeneity during postnatal development and after focal cerebral ischemia, we employed single-cell gene expression profiling in acutely isolated cortical GFAP/EGFP-positive cells. Using a microfluidic qPCR platform, we profiled 47 genes encoding glial markers and ion channels/transporters/receptors participating in maintaining K⁺ and glutamate homeostasis per cell. Self-organizing maps and principal component analyses revealed three subpopulations within 10–50 days of postnatal development (P10–P50). The first subpopulation, mainly immature glia from P10, was characterized by high transcriptional activity of all studied genes, including polydendrocytic markers. The second subpopulation (mostly from P20) was characterized by low gene transcript levels, while the third subpopulation encompassed mature astrocytes (mainly from P30, P50). Within 14 days after ischemia (D3, D7, D14), additional astrocytic subpopulations were identified: resting glia (mostly from P50 and D3), transcriptionally active early reactive glia (mainly from D7) and permanent reactive glia (solely from D14). Following focal cerebral ischemia, reactive astrocytes underwent pronounced changes in the expression of aquaporins, nonspecific cationic and potassium channels, glutamate receptors and reactive astrocyte markers.

Stochastic effects as a force to increase the complexity of signaling networks

Cellular signaling networks are complex and appear to include many nonfunctional elements. Recently, it was suggested that nonfunctional interactions of proteins cause signaling noise, which, perhaps, shapes the signal transduction mechanism. However, the conditions under which molecular noise influences cellular information processing remain unclear. Here, we explore a large number of simple biological models of varying network sizes to understand the architectural conditions under which the interactions of signaling proteins can exhibit specific stochastic effects—called deviant effects—in which the average behavior of a biological system is substantially altered in the presence of molecular noise. We find that a small fraction of these networks does exhibit deviant effects and shares a common architectural feature whereas most of the networks show only insignificant levels of deviations. Interestingly, addition of seemingly unimportant interactions into protein networks gives rise to deviant effects.

Cell-cycle dependence of transcription dominates noise in gene expression

The large variability in mRNA and protein levels found from both static and dynamic measurements in single cells has been largely attributed to random periods of transcription, often occurring in bursts. The cell cycle has a pronounced global role in affecting transcriptional and translational output, but how this influences transcriptional statistics from noisy promoters is unknown and generally ignored by current stochastic models. Here we show that variable transcription from the synthetic tetO promoter in S. cerevisiae is dominated by its dependence on the cell cycle. Real-time measurements of fluorescent protein at high expression levels indicate tetO promoters increase transcription rate ∼2-fold in S/G2/M similar to constitutive genes. At low expression levels, where tetO promoters are thought to generate infrequent bursts of transcription, we observe random pulses of expression restricted to S/G2/M, which are correlated between homologous promoters present in the same cell. The analysis of static, single-cell mRNA measurements at different points along the cell cycle corroborates these findings. Our results demonstrate that highly variable mRNA distributions in yeast are not solely the result of randomly switching between periods of active and inactive gene expression, but instead largely driven by differences in transcriptional activity between G1 and S/G2/M.

There is an astonishing amount of variation in the number of mRNA and protein molecules generated from particular genes between genetically identical single cells grown in the same environment. Particularly for mRNA, the large variation seen from these “noisy” genes is consistent with the idea of transcriptional bursting where transcription occurs in random, intermittent periods of high activity. There is considerable experimental support for transcriptional bursting, and it is a primary feature of stochastic models of gene expression that account for variation. Still, it has long been recognized that variation, especially in protein levels, can occur because of global differences between genetically identical cells. We show that in budding yeast, mRNA variation is driven to a large extent by differences in the transcriptional activity of a noisy gene between different phases of the cell cycle. These differences are not because of specific cell-cycle regulation, and in some cases transcription appears restricted to certain phases, leading to pulses of mRNA production. These results raise new questions about the origins of transcriptional bursting and how the statistics of gene expression are regulated in a global way by the cell cycle.

Quantitative assessment of ratiometric bimolecular beacons as a tool for imaging single engineered RNA transcripts and measuring gene expression in living cells

Recently, we developed an oligonucleotide-based probe, ratiometric bimolecular beacon (RBMB), which generates a detectable fluorescent signal in living cells that express the target RNA. Here, we show that RBMBs can also be used to image single RNA transcripts in living cells, when the target RNA is engineered to contain as few as four hybridization sites. Moreover, comparison with single-molecule fluorescence in situ hybridization confirmed that RBMBs could be used to accurately quantify the number of RNA transcripts within individual cells. Measurements of gene expression could be acquired within 30 min and using a wide range of RBMB concentrations. The ability to acquire accurate measurements of RNA copy number in both HT-1080 cells and CHO cells also suggests that RBMBs can be used to image and quantify single RNA transcripts in a wide range of cell lines. Overall, these findings highlight the robustness and versatility of RBMBs as a tool for imaging RNA in live cells. We envision that the unique capabilities of RBMBs will open up new avenues for RNA research.

Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output, with important functional consequences. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs or proteins simultaneously, because genomic profiling methods could not be applied to single cells until very recently. Here we use single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.

Single-cell dynamics of genome-nuclear lamina interactions

The nuclear lamina (NL) interacts with hundreds of large genomic regions termed lamina associated domains (LADs). The dynamics of these interactions and the relation to epigenetic modifications are poorly understood. We visualized the fate of LADs in single cells using a "molecular contact memory" approach. In each nucleus, only ~30% of LADs are positioned at the periphery; these LADs are in intermittent molecular contact with the NL but remain constrained to the periphery. Upon mitosis, LAD positioning is not detectably inherited but instead is stochastically reshuffled. Contact of individual LADs with the NL is linked to transcriptional repression and H3K9 dimethylation in single cells. Furthermore, we identify the H3K9 methyltransferase G9a as a regulator of NL contacts. Collectively, these results highlight principles of the dynamic spatial architecture of chromosomes in relation to gene regulation.

Adiabatic reduction of a model of stochastic gene expression with jump Markov process

This paper considers adiabatic reduction in a model of stochastic gene expression with bursting transcription considered as a jump Markov process. In this model, the process of gene expression with auto-regulation is described by fast/slow dynamics. The production of mRNA is assumed to follow a compound Poisson process occurring at a rate depending on protein levels (the phenomena called bursting in molecular biology) and the production of protein is a linear function of mRNA numbers. When the dynamics of mRNA is assumed to be a fast process (due to faster mRNA degradation than that of protein) we prove that, with appropriate scalings in the burst rate, jump size or translational rate, the bursting phenomena can be transmitted to the slow variable. We show that, depending on the scaling, the reduced equation is either a stochastic differential equation with a jump Poisson process or a deterministic ordinary differential equation. These results are significant because adiabatic reduction techniques seem to have not been rigorously justified for a stochastic differential system containing a jump Markov process. We expect that the results can be generalized to adiabatic methods in more general stochastic hybrid systems.

Two DNA-encoded strategies for increasing expression with opposing effects on promoter dynamics and transcriptional noise

Individual cells from a genetically identical population exhibit substantial variation in gene expression. A significant part of this variation is due to noise in the process of transcription that is intrinsic to each gene, and is determined by factors such as the rate with which the promoter transitions between transcriptionally active and inactive states, and the number of transcripts produced during the active state. However, we have a limited understanding of how the DNA sequence affects such promoter dynamics. Here, we used single-cell time-lapse microscopy to compare the effect on transcriptional dynamics of two distinct types of sequence changes in the promoter that can each increase the mean expression of a cell population by similar amounts but through different mechanisms. We show that increasing expression by strengthening a transcription factor binding site results in slower promoter dynamics and higher noise as compared with increasing expression by adding nucleosome-disfavoring sequences. Our results suggest that when achieving the same mean expression, the strategy of using stronger binding sites results in a larger number of transcripts produced from the active state, whereas the strategy of adding nucleosome-disfavoring sequences results in a higher frequency of promoter transitions between active and inactive states. In the latter strategy, this increased sampling of the active state likely reduces the expression variability of the cell population. Our study thus demonstrates the effect of cis-regulatory elements on expression variability and points to concrete types of sequence changes that may allow partial decoupling of expression level and noise.

Solid-State and Biological Nanopore for Real-Time Sensing of Single Chemical and Sequencing of DNA

Sensitivity and specificity are two most important factors to take into account for molecule sensing, chemical detection and disease diagnosis. A perfect sensitivity is to reach the level where a single molecule can be detected. An ideal specificity is to reach the level where the substance can be detected in the presence of many contaminants. The rapidly progressing nanopore technology is approaching this threshold. A wide assortment of biomotors and cellular pores in living organisms perform diverse biological functions. The elegant design of these transportation machineries has inspired the development of single molecule detection based on modulations of the individual current blockage events. The dynamic growth of nanotechnology and nanobiotechnology has stimulated rapid advances in the study of nanopore based instrumentation over the last decade, and inspired great interest in sensing of single molecules including ions, nucleotides, enantiomers, drugs, and polymers such as PEG, RNA, DNA, and polypeptides. This sensing technology has been extended to medical diagnostics and third generation high throughput DNA sequencing. This review covers current nanopore detection platforms including both biological pores and solid state counterparts. Several biological nanopores have been studied over the years, but this review will focus on the three best characterized systems including α-hemolysin and MspA, both containing a smaller channel for the detection of single-strand DNA, as well as bacteriophage phi29 DNA packaging motor connector that contains a larger channel for the passing of double stranded DNA. The advantage and disadvantage of each system are compared; their current and potential applications in nanomedicine, biotechnology, and nanotechnology are discussed.

Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells

Single-cell variations in gene and protein expression are important during development and disease. Such cell-to-cell heterogeneities can be directly inspected one cell at a time, but global methods are usually not sensitive enough to work with the starting material of a single cell. Here we provide a detailed protocol for stochastic profiling, a method that infers single-cell regulatory heterogeneities by repeatedly sampling small collections of cells selected at random. Repeated stochastic sampling is performed by laser-capture microdissection or limiting dilution, followed by careful exponential cDNA amplification, hybridization to microarrays and statistical analysis. Stochastic profiling surveys the transcriptome for programs that are heterogeneously regulated among cellular subpopulations in their native tissue context. The protocol is readily optimized for specific biological applications and takes about 1 week to complete.

Logic of the inflammation-associated transcriptional response

Immune response to pathogens depends on coordinated regulation of numerous genes that contribute collectively to pathogen elimination and restoration of the integrity of the affected tissue. The pathogen-induced gene expression is governed largely by the signal-induced posttranslational histone modifications that facilitate assembly of the functionally distinct chromatin complexes. In this review, we describe the principles of chromatin-based gene regulation during innate immune responses. We discuss the ability of pathogens to hijack the host response by interfering with various arms of transcriptional machinery involved in the responses. In particular, we discuss the phenomenon of the histone mimicry where interaction between histones and transcriptional regulators is targeted by pathogens that carry the histone-like sequences (histone mimics). We show how the principle of isotone mimicry as an efficient way to control host gene expression has been sued for the development of novel anti-inflammatory pharmacological approaches.

Dynamic response diversity of NFAT isoforms in individual living cells

Processing of external information by mammalian cells often involves seemingly redundant isoforms of signaling molecules and transcription factors. Understanding the functional relevance of coexpressed isoforms that respond to the same signal and control a shared set of genes is still limited. Here we show, using imaging of individual living mammalian cells, that the closely related transcription factors NFAT1 and NFAT4 possess distinct nuclear localization dynamics in response to cell stimulation. NFAT4 shows a fast response, with rapid stochastic bursts of nuclear localization. Burst frequency grows with signal level, while response amplitude is fixed. In contrast, NFAT1 has a slow, continuous response, and its amplitude increases with signal level. These diverse dynamical features observed for single cells are translated into different impulse response strategies at the cell population level. We suggest that dynamic response diversity of seemingly redundant genes can provide cells with enhanced capabilities of temporal information processing.

Single-cell analysis of ribonucleotide reductase transcriptional and translational response to DNA damage

The ribonucleotide reductase (RNR) enzyme catalyzes an essential step in the production of deoxyribonucleotide triphosphates (dNTPs) in cells. Bulk biochemical measurements in synchronized Saccharomyces cerevisiae cells suggest that RNR mRNA production is maximal in late G(1) and S phases; however, damaged DNA induces RNR transcription throughout the cell cycle. But such en masse measurements reveal neither cell-to-cell heterogeneity in responses nor direct correlations between transcript and protein expression or localization in single cells which may be central to function. We overcame these limitations by simultaneous detection of single RNR transcripts and also Rnr proteins in the same individual asynchronous S. cerevisiae cells, with and without DNA damage by methyl methanesulfonate (MMS). Surprisingly, RNR subunit mRNA levels were comparably low in both damaged and undamaged G(1) cells and highly induced in damaged S/G(2) cells. Transcript numbers became correlated with both protein levels and localization only upon DNA damage in a cell cycle-dependent manner. Further, we showed that the differential RNR response to DNA damage correlated with variable Mec1 kinase activity in the cell cycle in single cells. The transcription of RNR genes was found to be noisy and non-Poissonian in nature. Our results provide vital insight into cell cycle-dependent RNR regulation under conditions of genotoxic stress.

Is central dogma a global property of cellular information flow?

The central dogma of molecular biology has come under scrutiny in recent years. Here, we reviewed high-throughput mRNA and protein expression data of Escherichia coli, Saccharomyces cerevisiae, and several mammalian cells. At both single cell and population scales, the statistical comparisons between the entire transcriptomes and proteomes show clear correlation structures. In contrast, the pair-wise correlations of single transcripts to proteins show nullity. These data suggest that the organizing structure guiding cellular processes is observed at omics-wide scale, and not at single molecule level. The central dogma, thus, globally emerges as an average integrated flow of cellular information.

Effects of gene length on the dynamics of gene expression

In Escherichia coli, the nucleotide length of a gene is bound to affect its expression dynamics. From simulations of a stochastic model of gene expression at the nucleotide and codon levels we show that, within realistic parameter values, the nucleotide length affects RNA and protein mean levels, as well as the expected transient time for RNA and protein numbers to change, following a signal. Fluctuations in RNA and protein numbers are found to be minimized for a small range of lengths, which matches the means of the distributions of lengths found in E. coli of both essential and non-essential genes. The variance of the length distribution for essential genes is found to be smaller than for non-essential genes, implying that these distributions are far from random. Finally, gene lengths are shown to affect the kinetics of a genetic switch, namely, the correlation between temporal proteins numbers, the stability of the two noisy attractors of the switch, and how biased is the choice of noisy attractor. The stability increases with gene length due to increased 'memory' about the previous states of the switch. We argue that, by affecting the dynamics of gene expression and of genetic circuits, gene lengths are subject to selection.

Type of noise defines global attractors in bistable molecular regulatory systems

The aim of this study is to demonstrate that in molecular dynamical systems with the underlying bi- or multistability, the type of noise determines the most strongly attracting steady state or stochastic attractor. As an example we consider a simple stochastic model of autoregulatory gene with a nonlinear positive feedback, which in the deterministic approximation has two stable steady state solutions. Three types of noise are considered: transcriptional and translational - due to the small number of gene product molecules and the gene switching noise - due to gene activation and inactivation transitions. We demonstrate that the type of noise in addition to the noise magnitude dictates the allocation of probability mass between the two stable steady states. In particular, we found that when the gene switching noise dominates over the transcriptional and translational noise (which is characteristic of eukaryotes), the gene preferentially activates, while in the opposite case, when the transcriptional noise dominates (which is characteristic of prokaryotes) the gene preferentially remains inactive. Moreover, even in the zero-noise limit, when the probability mass generically concentrates in the vicinity of one of two steady states, the choice of the most strongly attracting steady state is noise type-dependent. Although the epigenetic attractors are defined with the aid of the deterministic approximation of the stochastic regulatory process, their relative attractivity is controlled by the type of noise, in addition to noise magnitude. Since noise characteristics vary during the cell cycle and development, such mode of regulation can be potentially employed by cells to switch between alternative epigenetic attractors.

Optimality principles in the regulation of metabolic networks

One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular "task" of the network-its function-should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide.

Strand-specific RNA-seq reveals ordered patterns of sense and antisense transcription in Bacillus anthracis

BACKGROUND

Although genome-wide transcriptional analysis has been used for many years to study bacterial gene expression, many aspects of the bacterial transcriptome remain undefined. One example is antisense transcription, which has been observed in a number of bacteria, though the function of antisense transcripts, and their distribution across the bacterial genome, is still unclear.

METHODOLOGY/PRINCIPAL FINDINGS

Single-stranded RNA-seq results revealed a widespread and non-random pattern of antisense transcription covering more than two thirds of the B. anthracis genome. Our analysis revealed a variety of antisense structural patterns, suggesting multiple mechanisms of antisense transcription. The data revealed several instances of sense and antisense expression changes in different growth conditions, suggesting that antisense transcription may play a role in the ways in which B. anthracis responds to its environment. Significantly, genome-wide antisense expression occurred at consistently higher levels on the lagging strand, while the leading strand showed very little antisense activity. Intrasample gene expression comparisons revealed a gene dosage effect in all growth conditions, where genes farthest from the origin showed the lowest overall range of expression for both sense and antisense directed transcription. Additionally, transcription from both strands was verified using a novel strand-specific assay. The variety of structural patterns we observed in antisense transcription suggests multiple mechanisms for this phenomenon, suggesting that some antisense transcription may play a role in regulating the expression of key genes, while some may be due to chromosome replication dynamics and transcriptional noise.

CONCLUSIONS/SIGNIFICANCE

Although the variety of structural patterns we observed in antisense transcription suggest multiple mechanisms for antisense expression, our data also clearly indicate that antisense transcription may play a genome-wide role in regulating the expression of key genes in Bacillus species. This study illustrates the surprising complexity of prokaryotic RNA abundance for both strands of a bacterial chromosome.

The prolactin gene: a paradigm of tissue-specific gene regulation with complex temporal transcription dynamics

Transcription of numerous mammalian genes is highly pulsatile, with bursts of expression occurring with variable duration and frequency. The presence of this stochastic or 'noisy' expression pattern has been relatively unexplored in tissue systems. The prolactin gene provides a model of tissue-specific gene regulation resulting in pulsatile transcription dynamics in both cell lines and endocrine tissues. In most cell culture models, prolactin transcription appears to be highly variable between cells, with differences in transcription pulse duration and frequency. This apparently stochastic transcription is constrained by a transcriptional refractory period, which may be related to cycles of chromatin remodelling. We propose that prolactin transcription dynamics result from the summation of oscillatory cellular inputs and by regulation through chromatin remodelling cycles. Observations of transcription dynamics in cells within pituitary tissue show reduced transcriptional heterogeneity and can be grouped into a small number of distinct patterns. Thus, it appears that the tissue environment is able to reduce transcriptional noise to enable coordinated tissue responses to environmental change. We review the current knowledge on the complex tissue-specific regulation of the prolactin gene in pituitary and extra-pituitary sites, highlighting differences between humans and rodent experimental animal models. Within this context, we describe the transcription dynamics of prolactin gene expression and how this may relate to specific processes occurring within the cell.

Noise-induced metastability in biochemical networks

Intracellular biochemical reactions exhibit a rich dynamical phenomenology which cannot be explained within the framework of mean-field rate equations and additive noise. Here, we show that the presence of metastable states and radically different time scales are general features of a broad class of autocatalytic reaction networks, and that this fact may be exploited to gain analytical results. The latter point is demonstrated by a treatment of the paradigmatic Togashi-Kaneko reaction, which has resisted theoretical analysis for the last decade.

Multipurpose instantaneous microarray detection of acute encephalitis causing viruses and their expression profiles

Detection of multiple viruses is important for global analysis of gene or protein content and expression, opening up new prospects in terms of molecular and physiological systems for pathogenic diagnosis. Early diagnosis is crucial for disease treatment and control as it reduces inappropriate use of antiviral therapy and focuses surveillance activity. This requires the ability to detect and accurately diagnose infection at or close to the source/outbreak with minimum delay and the need for specific, accessible point-of-care diagnosis able to distinguish causative viruses and their subtypes. None of the available viral diagnostic assays combine a point-of-care format with the complex capability to identify a large range of human and animal viruses. Microarray detection provides a useful, labor-saving tool for detection of multiple viruses with several advantages, such as convenience and prevention of cross-contamination of polymerase chain reaction (PCR) products, which is of foremost importance in such applications. Recently, real-time PCR assays with the ability to confirm the amplification product and quantitate the target concentration have been developed. Furthermore, nucleotide sequence analysis of amplification products has facilitated epidemiological studies of infectious disease outbreaks and monitoring of treatment outcomes for infections, in particular for viruses that mutate at high frequency. This review discusses applications of microarray technology as a potential new tool for detection and identification of acute encephalitis-causing viruses in human serum, plasma, and cell cultures.

Single-cell analysis of population context advances RNAi screening at multiple levels

A large set of high-content RNAi screens investigating mammalian virus infection and multiple cellular activities is analysed to reveal the impact of population context on phenotypic variability and to identify indirect RNAi effects.

Cell population context determines phenotypes in RNAi screens of multiple cellular activities (including virus infection, cell size regulation, endocytosis, and lipid homeostasis), which can be accounted for by a combination of novel image analysis and multivariate statistical methods.

Accounting for cell population context-mediated effects strongly changes the reproducibility and consistency of RNAi screens across cell lines as well as of siRNAs targeting the same gene.

Such analyses can identify the perturbed regulation of population context dependent cell-to-cell variability, a novel perturbation phenotype.

Overall, these methods advance the use of large-scale RNAi screening for a systems-level understanding of cellular processes.

Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image-based RNAi screens. We developed a comprehensive computational approach that employs Bayesian and multivariate methods at the single-cell level. We applied these methods to 45 RNA interference screens of various sizes, including 7 druggable genome and 2 genome-wide screens, analysing 17 different mammalian virus infections and four related cell physiological processes. Analysing cell-based screens at this depth reveals widespread RNAi-induced changes in the population context of individual cells leading to indirect RNAi effects, as well as perturbations of cell-to-cell variability regulators. We find that accounting for indirect effects improves the consistency between siRNAs targeted against the same gene, and between replicate RNAi screens performed in different cell lines, in different labs, and with different siRNA libraries. In an era where large-scale RNAi screens are increasingly performed to reach a systems-level understanding of cellular processes, we show that this is often improved by analyses that account for and incorporate the single-cell microenvironment.

Real-time sensing and discrimination of single chemicals using the channel of phi29 DNA packaging nanomotor

A highly sensitive and reliable method to sense and identify a single chemical at extremely low concentrations and high contamination is important for environmental surveillance, homeland security, athlete drug monitoring, toxin/drug screening, and earlier disease diagnosis. This article reports a method for precise detection of single chemicals. The hub of the bacteriophage phi29 DNA packaging motor is a connector consisting of 12 protein subunits encircled into a 3.6 nm channel as a path for dsDNA to enter during packaging and to exit during infection. The connector has previously been inserted into a lipid bilayer to serve as a membrane-embedded channel. Herein we report the modification of the phi29 channel to develop a class of sensors to detect single chemicals. The lysine-234 of each protein subunit was mutated to cysteine, generating 12-SH ring lining the channel wall. Chemicals passing through this robust channel and interactions with the SH group generated extremely reliable, precise, and sensitive current signatures as revealed by single channel conductance assays. Ethane (57 Da), thymine (167 Da), and benzene (105 Da) with reactive thioester moieties were clearly discriminated upon interaction with the available set of cysteine residues. The covalent attachment of each analyte induced discrete stepwise blockage in current signature with a corresponding decrease in conductance due to the physical blocking of the channel. Transient binding of the chemicals also produced characteristic fingerprints that were deduced from the unique blockage amplitude and pattern of the signals. This study shows that the phi29 connector can be used to sense chemicals with reactive thioesters or maleimide using single channel conduction assays based on their distinct fingerprints. The results demonstrated that this channel system could be further developed into very sensitive sensing devices.

Duel of the fates: the role of transcriptional circuits and noise in CD4+ cells

CD4+ T cells play key roles in orchestrating adaptive immune responses, and are a popular model for mammalian cell differentiation. While immune regulation would seem to require exactly adjusted mRNA and protein expression levels of key factors, there is little evidence that this is strictly the case. Stochastic gene expression and plasticity of cell types contrast the apparent need for precision. Recent work has provided insight into the magnitude of molecular noise, as well as the relationship between noise, transcriptional circuits and epigenetic modifications in a variety of cell types. These processes and their interplay will also govern gene expression patterns in the different CD4+ cell types, and the determination of their cellular fates.

Stochastic modeling for the expression of a gene regulated by competing transcription factors

It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. Our stochastic simulation in the presence of both activators and repressors predicted a Hill-coefficient of the dose-response curve closer to the experimentally observed value than the calculated value based on the simple additive effects of activators alone and repressors alone. The simulation also reproduced the heterogeneity of gene expression levels among individual cells observed by Fluorescence Activated Cell Sorting analysis. Therefore, our approach may help to apply stochastic simulations to broader experimental data.

Using graph-based assessments within socratic tutorials to reveal and refine students' analytical thinking about molecular networks

Biological systems, from the molecular to the ecological, involve dynamic interaction networks. To examine student thinking about networks we used graphical responses, since they are easier to evaluate for implied, but unarticulated assumptions. Senior college level molecular biology students were presented with simple molecular level scenarios; surprisingly, most students failed to articulate the basic assumptions needed to generate reasonable graphical representations; their graphs often contradicted their explicit assumptions. We then developed a tiered Socratic tutorial based on leading questions designed to provoke metacognitive reflection. The activity is characterized by leading questions (prompts) designed to provoke meta-cognitive reflection. When applied in a group or individual setting, there was clear improvement in targeted areas. Our results highlight the promise of using graphical responses and Socratic prompts in a tutorial context as both a formative assessment for students and an informative feedback system for instructors, in part because graphical responses are relatively easy to evaluate for implied, but unarticulated assumptions.

Interplay between gene expression noise and regulatory network architecture

Complex regulatory networks orchestrate most cellular processes in biological systems. Genes in such networks are subject to expression noise, resulting in isogenic cell populations exhibiting cell-to-cell variation in protein levels. Increasing evidence suggests that cells have evolved regulatory strategies to limit, tolerate or amplify expression noise. In this context, fundamental questions arise: how can the architecture of gene regulatory networks generate, make use of or be constrained by expression noise? Here, we discuss the interplay between expression noise and gene regulatory network at different levels of organization, ranging from a single regulatory interaction to entire regulatory networks. We then consider how this interplay impacts a variety of phenomena, such as pathogenicity, disease, adaptation to changing environments, differential cell-fate outcome and incomplete or partial penetrance effects. Finally, we highlight recent technological developments that permit measurements at the single-cell level, and discuss directions for future research.

Recent advances in single-cell studies of gene regulation

A mechanistic understanding of gene regulatory network dynamics requires quantitative single-cell data of multiple network components in response to well-defined perturbations. Recent advances in the development of fluorescent biomarkers for proteins, detection of RNA and interactions, microfluidic technology, and high-resolution imaging have set the stage for a host of new studies that elucidate the important roles of stochasticity and cell-cell variability in response to external perturbations. In this review, we briefly describe methods for high-resolution visualization and the control of gene expression, along with application of these novel methods to recent studies involving gene networks.