The dose dependence of glucocorticoid-inducible gene expression results from changes in the number of transcriptionally active templates

Transcription dynamics and genome organization in the mammalian nucleus: Recent advances

Single-molecule tracking (SMT) has emerged as the dominant technology to investigate the dynamics of chromatin-transcription factor (TF) interactions. How long a TF needs to bind to a regulatory site to elicit a transcriptional response is a fundamentally important question. However, highly divergent estimates of TF binding have been presented in the literature, stemming from differences in photobleaching correction and data analysis. TF movement is often interpreted as specific or non-specific association with chromatin, yet the dynamic nature of the chromatin polymer is often overlooked. In this perspective, we highlight how recent SMT studies have reshaped our understanding of TF dynamics, chromatin mobility, and genome organization in the mammalian nucleus, focusing on the technical details and biological implications of these approaches. In a remarkable convergence of fixed and live-cell imaging, we show how super-resolution and SMT studies of chromatin have dovetailed to provide a convincing nanoscale view of genome organization.

A more holistic view of the logarithmic dose-response curve offers greater insights into insulin responses

The stimulus-response curve is usually modeled by the Hill function due to its simplicity and clear molecular mechanisms (Michaelis-Menten type of kinetics). Unfortunately, the mechanisms do not explain why the stimulus is ubiquitously measured by logarithmic dose rather than the dose itself and why the log(dose)-response curve possesses such fine properties as symmetry and wide adjustability. Here, the dose-response is considered from a holistic perspective spanning multiple biological levels from molecules to the whole organism, which reveals that an appropriate model for log(dose) response is the cumulative normal distribution (CND) function, which had only statistical implication previously but now possess mechanistic-statistical duality. The present CND model establishes a connection between single-cell all-or-none responses and the graded response at the tissue/organism level, reveals the raison d'être of the logarithmic transformation, explains why log(dose)-response curve possesses many fine properties, and reveals new mechanisms of tissue/organism dose-response, including homogeneity-induced sensitivity. It also provides new insights into vital biological processes, such as the insulin dose-response.

Enhancing Single-Cell Western Blotting Sensitivity Using Diffusive Analyte Blotting and Antibody Conjugate Amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein content often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity Western blots on single cells (scWesterns) are attractive because they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by the affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address the sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in the limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates, which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 1000 molecules, a 120-fold improvement. This enables us to detect 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies compared to 84.5% of cells using in-gel detection. These results suggest the compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents─not previously accessible for in-gel use─for further signal amplification and detection of low-abundance targets.

Enhancing single-cell western blotting sensitivity using diffusive analyte blotting and antibody conjugate amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein contents often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity western blots on single cells (scWesterns) are attractive since they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 103 molecules, a 520-fold improvement. This enables us to detect 85% and 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies respectively, compared to 47% of cells using in-gel detection. These results suggest compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents - not previously accessible for in-gel use - for further signal amplification and detection of low abundance targets.

Immune heterogeneity in cardiovascular diseases from a single-cell perspective

A variety of immune cell subsets occupy different niches in the cardiovascular system, causing changes in the structure and function of the heart and vascular system, and driving the progress of cardiovascular diseases (CVDs). The immune cells infiltrating the injury site are highly diverse and integrate into a broad dynamic immune network that controls the dynamic changes of CVDs. Due to technical limitations, the effects and molecular mechanisms of these dynamic immune networks on CVDs have not been fully revealed. With recent advances in single-cell technologies such as single-cell RNA sequencing, systematic interrogation of the immune cell subsets is feasible and will provide insights into the way we understand the integrative behavior of immune populations. We no longer lightly ignore the role of individual cells, especially certain highly heterogeneous or rare subpopulations. We summarize the phenotypic diversity of immune cell subsets and their significance in three CVDs of atherosclerosis, myocardial ischemia and heart failure. We believe that such a review could enhance our understanding of how immune heterogeneity drives the progression of CVDs, help to elucidate the regulatory roles of immune cell subsets in disease, and thus guide the development of new immunotherapies.

Epigenetic fluctuations underlie gene expression timescales and variability

Isogenic populations of mammalian cells exhibit significant gene expression variability. This variability can be separated into two sources, cis, or allele-specific sources, and trans and global processes. Furthermore, each source of variability has its own timescale. Fast timescales will result in rapid fluctuation of gene expression whereas slow timescales will result in longer persistence of gene expression levels over time. Here we investigated sources of gene expression that are intrinsic, i.e. coming from cis-regulatory factors and follow slow timescales. To do so, we developed a reporter system that isolates allele-specific variability and measures its persistence in imaging and long-term fluctuation analysis experiments. Our results identify a new source of gene expression variability that is allele-specific but that is fluctuating on timescales of days. We hypothesized that allele-specific fluctuations of epigenetic regulatory factors are responsible for the newly discovered allele-specific and slow source of gene expression variability. Using mathematical modeling we showed that the addition of this effect to the two-state model is sufficient to account for all empirical observation. Furthermore, using direct assays of chromatin markers we find fluctuation in H3K4me3 levels that match the observed changes in gene expression levels providing direct experimental support of our model. Collectively, our work shows that slow fluctuations of regulatory chromatin modifications contribute to the variability in gene expression.

Fractional response analysis reveals logarithmic cytokine responses in cellular populations

Although we can now measure single-cell signaling responses with multivariate, high-throughput techniques our ability to interpret such measurements is still limited. Even interpretation of dose–response based on single-cell data is not straightforward: signaling responses can differ significantly between cells, encompass multiple signaling effectors, and have dynamic character. Here, we use probabilistic modeling and information-theory to introduce fractional response analysis (FRA), which quantifies changes in fractions of cells with given response levels. FRA can be universally performed for heterogeneous, multivariate, and dynamic measurements and, as we demonstrate, quantifies otherwise hidden patterns in single-cell data. In particular, we show that fractional responses to type I interferon in human peripheral blood mononuclear cells are very similar across different cell types, despite significant differences in mean or median responses and degrees of cell-to-cell heterogeneity. Further, we demonstrate that fractional responses to cytokines scale linearly with the log of the cytokine dose, which uncovers that heterogeneous cellular populations are sensitive to fold-changes in the dose, as opposed to additive changes.

Our ability to interpret single-cell multivariate signaling responses is still limited. Here the authors introduce fractional response analysis (FRA), involving fractional cell counting, capable of deconvoluting heterogeneous multivariate responses of cellular populations.

Altering transcription factor binding reveals comprehensive transcriptional kinetics of a basic gene

Transcription is a vital process activated by transcription factor (TF) binding. The active gene releases a burst of transcripts before turning inactive again. While the basic course of transcription is well understood, it is unclear how binding of a TF affects the frequency, duration and size of a transcriptional burst. We systematically varied the residence time and concentration of a synthetic TF and characterized the transcription of a synthetic reporter gene by combining single molecule imaging, single molecule RNA-FISH, live transcript visualisation and analysis with a novel algorithm, Burst Inference from mRNA Distributions (BIRD). For this well-defined system, we found that TF binding solely affected burst frequency and variations in TF residence time had a stronger influence than variations in concentration. This enabled us to device a model of gene transcription, in which TF binding triggers multiple successive steps before the gene transits to the active state and actual mRNA synthesis is decoupled from TF presence. We quantified all transition times of the TF and the gene, including the TF search time and the delay between TF binding and the onset of transcription. Our quantitative measurements and analysis revealed detailed kinetic insight, which may serve as basis for a bottom-up understanding of gene regulation.

Microfluidic Based Physical Approaches towards Single-Cell Intracellular Delivery and Analysis

The ability to deliver foreign molecules into a single living cell with high transfection efficiency and high cell viability is of great interest in cell biology for applications in therapeutic development, diagnostics, and drug delivery towards personalized medicine. Various physical delivery methods have long demonstrated the ability to deliver cargo molecules directly to the cytoplasm or nucleus and the mechanisms underlying most of the approaches have been extensively investigated. However, most of these techniques are bulk approaches that are cell-specific and have low throughput delivery. In comparison to bulk measurements, single-cell measurement technologies can provide a better understanding of the interactions among molecules, organelles, cells, and the microenvironment, which can aid in the development of therapeutics and diagnostic tools. To elucidate distinct responses during cell genetic modification, methods to achieve transfection at the single-cell level are of great interest. In recent years, single-cell technologies have become increasingly robust and accessible, although limitations exist. This review article aims to cover various microfluidic-based physical methods for single-cell intracellular delivery such as electroporation, mechanoporation, microinjection, sonoporation, optoporation, magnetoporation, and thermoporation and their analysis. The mechanisms of various physical methods, their applications, limitations, and prospects are also elaborated.

Insights into gene regulation: From regulatory genomic elements to DNA-protein and protein-protein interactions

Transcription is orchestrated by non-coding regulatory elements embedded in chromatin, which exist within the larger context of chromosome topology. Here, we review recent insights into the functions of non-coding regulatory elements and their protein interactors during transcription control. A picture emerges in which the topological environment constraints enhancer-promoter interactions and specific enhancer-bound proteins with distinct promoter-compatibilities refine target promoter choice. Such compatibilities are encoded within the sequences of enhancers and promoters and realized by diverse transcription factors and cofactors with distinct biochemical activities. An emerging property of transcription factors and cofactors is the formation of nuclear microenvironments or membraneless compartments that can have properties of phase-separated liquids. These environments are able to selectively enrich certain proteins and small molecules over others. Further investigation into the interaction of transcriptional regulators with themselves and regulatory DNA elements will help reveal the complexities of gene regulation within the context of the nucleus.

Measuring and Modeling Single-Cell Heterogeneity and Fate Decision in Mouse Embryos

Cellular heterogeneity is a property of any living system; however, its relationship with cellular fate decision remains an open question. Recent technological advances have enabled valuable insights, especially in complex systems such as the mouse embryo. In this review, we discuss recent studies that characterize cellular heterogeneity at different levels during mouse development, from the two-cell stage up to gastrulation. In addition to key experimental findings, we review mathematical modeling approaches that help researchers interpret these findings. Disentangling the role of heterogeneity in cell fate decision will likely rely on the refined integration of experiments, large-scale omics data, and mathematical modeling, complemented by the use of synthetic embryos and gastruloids as promising in vitro models.

First Responders Shape a Prompt and Sharp NF-κB-Mediated Transcriptional Response to TNF-α

Nuclear factor (NF)-κB controls the transcriptional response to inflammatory signals by translocating into the nucleus, but we lack a single-cell characterization of the resulting transcription dynamics. Here we show that upon tumor necrosis factor (TNF)-α transcription of NF-κB target genes is heterogeneous in individual cells but results in an average nascent transcription profile that is prompt (i.e., occurs almost immediately) and sharp (i.e., increases and decreases rapidly) compared with NF-κB nuclear localization. Using an NF-κB-controlled MS2 reporter we show that the single-cell nascent transcription is more heterogeneous than NF-κB translocation dynamics, with a fraction of synchronized “first responders” that shape the average transcriptional profile and are more prone to respond to multiple TNF-α stimulations. A mathematical model combining NF-κB-mediated gene activation and a gene refractory state is able to reproduce these features. Our work shows how the expression of target genes induced by transcriptional activators can be heterogeneous across single cells and yet time resolved on average.

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Nascent transcription upon TNF-α is heterogeneous, with a subset of “first responders”

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The average nascent transcription is prompt and sharper than NF-κB response

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First responders do not depend on NF-κB dynamics and respond more to pulsed stimuli

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A model including NF-κB and a gene refractory state reproduces these observations

Transcription in Living Cells: Molecular Mechanisms of Bursting

Transcription in several organisms from certain bacteria to humans has been observed to be stochastic in nature: toggling between active and inactive states. Periods of active nascent RNA synthesis known as bursts represent individual gene activation events in which multiple polymerases are initiated. Therefore, bursting is the single locus illustration of both gene activation and repression. Although transcriptional bursting was originally observed decades ago, only recently have technological advances enabled the field to begin elucidating gene regulation at the single-locus level. In this review, we focus on how biochemical, genomic, and single-cell data describe the regulatory steps of transcriptional bursts.

Stochastic models coupling gene expression and partitioning in cell division in Escherichia coli

Regulation of future RNA and protein numbers is a key process by which cells continuously best fit the environment. In bacteria, RNA and proteins exist in small numbers and their regulatory processes are stochastic. Consequently, there is cell-to-cell variability in these numbers, even between sister cells. Traditionally, the two most studied sources of this variability are gene expression and RNA and protein degradation, with evidence suggesting that the latter is subject to little regulation, when compared to the former. However, time-lapse microscopy and single molecule fluorescent tagging have produced evidence that cell division can also be a significant source of variability due to asymmetries in the partitioning of RNA and proteins. Relevantly, the impact of this noise differs from noise in production and degradation since, unlike these, it is not continuous. Rather, it occurs at specific time points, at which moment it can introduce major fluctuations. Several models have now been proposed that integrate noise from cell division, in addition to noise in gene expression, to mimic the dynamics of RNA and protein numbers of cell lineages. This is expected to be particularly relevant in genetic circuits, where significant fluctuations in one component protein, at specific time moments, are expected to perturb near-equilibrium states of the circuits, which can have long-lasting consequences. Here we review stochastic models coupling these processes in Escherichia coli, from single genes to small circuits.

Single-cell RNA sequencing reveals a heterogeneous response to Glucocorticoids in breast cancer cells

Steroid hormone receptors such as the Glucocorticoid Receptor (GR) mediate transcriptional responses to hormones and are frequently targeted in the treatment of human diseases. Experiments using bulk populations of cells have provided a detailed picture of the global transcriptional hormone response but are unable to interrogate cell-to-cell transcriptional heterogeneity. To examine the glucocorticoid response in individual cells, we performed single cell RNA sequencing (scRNAseq) in a human breast cancer cell line. The transcriptional response to hormone was robustly detected in individual cells and scRNAseq provided additional statistical power to identify over 100 GR-regulated genes that were not detected in bulk RNAseq. scRNAseq revealed striking cell-to-cell variability in the hormone response. On average, individual hormone-treated cells showed a response at only 30% of the total set of GR target genes. Understanding the basis of this heterogeneity will be critical for the development of more precise models of steroid hormone signaling.

Detecting Protein-Protein Interaction Based on Protein Fragment Complementation Assay

Proteins are the most critical executive molecules by responding to the instructions stored in the genetic materials in any form of life. More frequently, proteins do their jobs by acting as a roleplayer that interacts with other protein(s), which is more evident when the function of a protein is examined in the real context of a cell. Identifying the interactions between (or amongst) proteins is very crucial for the biochemistry investigation of an individual protein and for the attempts aiming to draw a holo-picture for the interacting members at the scale of proteomics (or protein-protein interactions mapping). Here, we introduced the currently available reporting systems that can be used to probe the interaction between candidate protein pairs based on the fragment complementation of some particular proteins. Emphasis was put on the principles and details of experimental design. These systems are dihydrofolate reductase (DHFR), β-lactamase, tobacco etch virus (TEV) protease, luciferase, β- galactosidase, GAL4, horseradish peroxidase (HRP), focal adhesion kinase (FAK), green fluorescent protein (GFP), and ubiquitin.

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

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

Intratumoral Heterogeneity: More Than Just Mutations

Most human tumors are composed of genetically and phenotypically heterogeneous cancer cell populations, which poses a major challenge for the clinical management of cancer patients. Advances of single-cell technologies have allowed the profiling of tumors at unprecedented depth, which, in combination with newly developed computational tools, enable the dissection of tumor evolution with increasing precision. However, our understanding of mechanisms that regulate intratumoral heterogeneity and our ability to modulate it has been lagging behind. Recent data demonstrate that epigenetic regulators, including histone demethylases, may control the cell-to-cell variability of transcriptomes and chromatin profiles and they may modulate therapeutic responses via this function. Thus, the therapeutic targeting of epigenetic enzymes may be used to decrease intratumoral cellular heterogeneity and treatment resistance, when used in combination with other types of agents.

Transcriptional noise in intact and TGF-beta treated human kidney cells; the importance of time-series designs

The transforming growth factor (TGF)-β signaling pathway plays a key role in various cellular processes. However, insufficient knowledge about the complex and sometimes paradoxical functions of this pathway hinders its therapeutic targeting. In this study, the transcriptional profile of seven mediators and downstream elements of the TGF-β pathway were assessed in TGF-β treated and untreated human kidney derived cells for 2 weeks in a time course manner. As expected the up-regulation of ACTA2 and COL1A2 was evident in the treated cells. However, we observed remarkable fluctuations in gene expression, even in the supposedly steady states. The magnitude of noise was diverse in the examined genes. Our findings underscore the significance of time-course designs for gene expression analyses and clearly show that misleading data can be obtained in single point measurements. Furthermore, we propose specific considerations in the interpretation of time-course data in the context of noisy gene expression.

Dynamics of gene regulatory networks with stochastic propensities

Gene regulatory networks (GRNs) control the production of proteins in cells. It is well-known that this process is not deterministic. Numerous studies employed a non-deterministic transition structure to model these networks. However, it is not realistic to expect state-to-state transition probabilities to remain constant throughout an organism’s lifetime. In this work, we focus on modeling GRN state transition (edge) variability using an ever-changing set of propensities. We suspect that the source of this variation is due to internal noise at the molecular level and can be modeled by introducing additional stochasticity into GRN models. We employ a beta distribution, whose parameters are estimated to capture the pattern inherent in edge behavior with minimum error. Additionally, we develop a method for obtaining propensities from a pre-determined network.

Inferring gene regulatory networks from single-cell data: a mechanistic approach

Background

The recent development of single-cell transcriptomics has enabled gene expression to be measured in individual cells instead of being population-averaged. Despite this considerable precision improvement, inferring regulatory networks remains challenging because stochasticity now proves to play a fundamental role in gene expression. In particular, mRNA synthesis is now acknowledged to occur in a highly bursty manner.

Results

We propose to view the inference problem as a fitting procedure for a mechanistic gene network model that is inherently stochastic and takes not only protein, but also mRNA levels into account. We first explain how to build and simulate this network model based upon the coupling of genes that are described as piecewise-deterministic Markov processes. Our model is modular and can be used to implement various biochemical hypotheses including causal interactions between genes. However, a naive fitting procedure would be intractable. By performing a relevant approximation of the stationary distribution, we derive a tractable procedure that corresponds to a statistical hidden Markov model with interpretable parameters. This approximation turns out to be extremely close to the theoretical distribution in the case of a simple toggle-switch, and we show that it can indeed fit real single-cell data. As a first step toward inference, our approach was applied to a number of simple two-gene networks simulated in silico from the mechanistic model and satisfactorily recovered the original networks.

Conclusions

Our results demonstrate that functional interactions between genes can be inferred from the distribution of a mechanistic, dynamical stochastic model that is able to describe gene expression in individual cells. This approach seems promising in relation to the current explosion of single-cell expression data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0487-0) contains supplementary material, which is available to authorized users.

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.

Contribution of clonally distinct subpopulations to heterogeneous production of inducible nitric oxide synthase by LPS-stimulated mouse macrophages

Expression of the inducible nitric oxide synthase (iNOS) gene in mouse macrophages can be induced by bacterial lipopolysaccharide (LPS). iNOS (EC 1.14.13.39) catalyzes production of the reactive nitrogen intermediate, nitric oxide (NO), which is very important to macrophage-mediated host defense in species such as the mouse and rat. We have investigated production of iNOS at the level of single cells through immunocytochemical analysis of LPS-stimulated macrophages. Both bone marrow culture-derived macrophages and those of the cell line, RAW 264.7, were examined. Heterogeneous production of iNOS within macrophage populations was explained in part by the existence of clones that were high producers of iNOS and, therefore, NO, while other clones were reproducibly low producers of each. All clonally derived populations continued to demonstrate heterogeneous iNOS production, suggesting that at least one additional mechanism must be responsible for the phenomenon of heterogeneity. In contrast to iNOS, LPS-stimulated macrophages synthesized interferonβ (IFNβ) uniformly throughout the population.

Population dynamics of inducible nitric oxide synthase production by LPSand LPS/IFNγ-stimulated mouse macrophages

The reactive nitrogen intermediate, nitric oxide (NO) is important in host defense against both NO-sensitive microorganisms and tumor cells. Macrophages are one of the chief inflammatory sources, especially when stimulated with the combination of LPS and interferonγ (IFNγ). It is not known, however, whether IFNγ-mediated augmentation of LPS-induced production of NO is the result of greater production by all cells or to the recruitment of more producer macrophages within a given population. This question was addressed, first, by stimulating mouse macrophages (either bone marrow culture-derived, inflammatory peritoneal or those of the cell line, RAW 264.7) with up to 10 U/ml IFNγ for as long as 24 h. Under these conditions, there was little or no production of NO and rare or no cells were immunocytochemically positive for the inducible form of nitric oxide synthase (iNOS), which catalyzes the production of NO. Populations similarly exposed to 1 ng/ml LPS were low producers of NO and contained somewhat more, but still only a few (< 15%), iNOS-positive cells. In contrast, as the concentration of IFNγ was increased (≥ 1 U/ml) in the presence of a constant amount of LPS (1 ng/ml), the principal effect was to increase both the production of NO and the number of iNOS-positive macrophages. The amount of iNOS expressed by some cells also appeared to be increased. Two important conclusions can be drawn from these findings: (1) there is heterogeneity in mouse macrophage populations with respect to the production of iNOS; and (2) increasing concentrations of IFNγ appear to augment LPS-induced secretion of NO by recruiting increasingly greater numbers of macrophages into the production of iNOS. Such results potentially provide important clues as to how IFNγ may be acting at the subcellular level to enhance iNOS synthesis.

Improved transgene expression in doxycycline-inducible embryonic stem cells by repeated chemical selection or cell sorting

Transgene-mediated programming is a preeminent strategy to direct cellular identity. To facilitate cell fate switching, lineage regulating genes must be efficiently and uniformly induced. However, gene expression is often heterogeneous in transgenic systems. Consistent with this notion, a non-uniform reporter gene expression was detected in our doxycycline (DOX)-regulated, murine embryonic stem (ES) cell clones. Interestingly, a significant fraction of cells within each clone failed to produce any reporter signals upon DOX treatment. We found that the majority of these non-responsive cells neither carry reporter transgene nor geneticin/G418 resistance. This observation suggested that our ES cell clones contained non-recombined cells that survived the G418 selection which was carried out during the establishment of these clones. We successfully eliminated most of these corrupted cells with repeated chemical (G418) selection, however, even after prolonged G418 treatments, a few cells remained non-responsive due to epigenetic silencing. We found that cell sorting has been the most efficient approach to select those cells which can uniformly and stably induce the integrated transgene in this ES cell based platform. Together, our data revealed that post-cloning chemical re-selection or cell sorting strongly facilitate the production of ES cell lines with a uniform transgene induction capacity.

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data

Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics.

What have single-molecule studies taught us about gene expression?

The production of a single mRNA is the result of many sequential steps, from docking of transcription factors to polymerase initiation, elongation, splicing, and, finally, termination. Much of our knowledge about the fundamentals of RNA synthesis and processing come from ensemble in vitro biochemical measurements. Single-molecule approaches are very much in this same reductionist tradition but offer exquisite sensitivity in space and time along with the ability to observe heterogeneous behavior and actually manipulate macromolecules. These techniques can also be applied in vivo, allowing one to address questions in living cells that were previously restricted to reconstituted systems. In this review, we examine the unique insights that single-molecule techniques have yielded on the mechanisms of gene expression.

Chromatin remodeling effects on enhancer activity

During organism development, a diversity of cell types emerges with disparate, yet stable profiles of gene expression with distinctive cellular functions. In addition to gene promoters, the genome contains enhancer regulatory sequences, which are implicated in cellular specialization by facilitating cell-type and tissue-specific gene expression. Enhancers are DNA binding elements characterized by highly sophisticated and various mechanisms of action allowing for the specific interaction of general and tissue-specific transcription factors (TFs). However, eukaryotic organisms package their genetic material into chromatin, generating a physical barrier for TFs to interact with their cognate sequences. The ability of TFs to bind DNA regulatory elements is also modulated by changes in the chromatin structure, including histone modifications, histone variants, ATP-dependent chromatin remodeling, and the methylation status of DNA. Furthermore, it has recently been revealed that enhancer sequences are also transcribed into a set of enhancer RNAs with regulatory potential. These interdependent processes act in the context of a complex network of chromatin interactions, which together contributes to a renewed vision of how gene activation is coordinated in a cell-type-dependent manner. In this review, we describe the interplay between genetic and epigenetic aspects associated with enhancers and discuss their possible roles on enhancer function.

Transcription Dynamics in Living Cells

The transcription cycle can be roughly divided into three stages: initiation, elongation, and termination. Understanding the molecular events that regulate all these stages requires a dynamic view of the underlying processes. The development of techniques to visualize and quantify transcription in single living cells has been essential in revealing the transcription kinetics. They have revealed that (a) transcription is heterogeneous between cells and (b) transcription can be discontinuous within a cell. In this review, we discuss the progress in our quantitative understanding of transcription dynamics in living cells, focusing on all parts of the transcription cycle. We present the techniques allowing for single-cell transcription measurements, review evidence from different organisms, and discuss how these experiments have broadened our mechanistic understanding of transcription regulation.

Applications of Imaging Flow Cytometry for Microalgae

The ability to image large numbers of cells at high resolution enhances flow cytometric analysis of cells and cell populations. In particular, the ability to image intracellular features adds a unique aspect to analyses, and can enable correlation between molecular phenomena resulting in alterations in cellular phenotype. Unicellular microalgae are amenable to high-throughput analysis to capture the diversity of cell types in natural samples, or diverse cellular responses in clonal populations, especially using imaging cytometry. Using examples from our laboratory, we review applications of imaging cytometry, specifically using an Amnis(®) ImageStream(®)X instrument, to characterize photosynthetic microalgae. Some of these examples highlight advantages of imaging flow cytometry for certain research objectives, but we also include examples that would not necessarily require imaging and could be performed on a conventional cytometer to demonstrate other concepts in cytometric evaluation of microalgae. We demonstrate the value of these approaches for (1) analysis of populations, (2) documentation of cellular features, and (3) analysis of gene expression.

Single-Cell Isolation and Gene Analysis: Pitfalls and Possibilities

During the last two decades single-cell analysis (SCA) has revealed extensive phenotypic differences within homogenous cell populations. These phenotypic differences are reflected in the stochastic nature of gene regulation, which is often masked by qualitatively and quantitatively averaging in whole tissue analyses. The ability to isolate transcripts and investigate how genes are regulated at the single cell level requires highly sensitive and refined methods. This paper reviews different strategies currently used for SCA, including harvesting, reverse transcription, and amplification of the RNA, followed by methods for transcript quantification. The review provides the historical background to SCA, discusses limitations, and current and future possibilities in this exciting field of research.

Roles of factorial noise in inducing bimodal gene expression

Some gene regulatory systems can exhibit bimodal distributions of mRNA or protein although the deterministic counterparts are monostable. This noise-induced bimodality is an interesting phenomenon and has important biological implications, but it is unclear how different sources of expression noise (each source creates so-called factorial noise that is defined as a component of the total noise) contribute separately to this stochastic bimodality. Here we consider a minimal model of gene regulation, which is monostable in the deterministic case. Although simple, this system contains factorial noise of two main kinds: promoter noise due to switching between gene states and transcriptional (or translational) noise due to synthesis and degradation of mRNA (or protein). To better trace the roles of factorial noise in inducing bimodality, we also analyze two limit models, continuous and adiabatic approximations, apart from the exact model. We show that in the case of slow gene switching, the continuous model where only promoter noise is considered can exhibit bimodality; in the case of fast switching, the adiabatic model where only transcriptional or translational noise is considered can also exhibit bimodality but the exact model cannot; and in other cases, both promoter noise and transcriptional or translational noise can cooperatively induce bimodality. Since slow gene switching and large protein copy numbers are characteristics of eukaryotic cells, whereas fast gene switching and small protein copy numbers are characteristics of prokaryotic cells, we infer that eukaryotic stochastic bimodality is induced mainly by promoter noise, whereas prokaryotic stochastic bimodality is induced primarily by transcriptional or translational noise.

Affinity proteomics to study endogenous protein complexes: pointers, pitfalls, preferences and perspectives

Dissecting and studying cellular systems requires the ability to specifically isolate distinct proteins along with the co-assembled constituents of their associated complexes. Affinity capture techniques leverage high affinity, high specificity reagents to target and capture proteins of interest along with specifically associated proteins from cell extracts. Affinity capture coupled to mass spectrometry (MS)-based proteomic analyses has enabled the isolation and characterization of a wide range of endogenous protein complexes. Here, we outline effective procedures for the affinity capture of protein complexes, highlighting best practices and common pitfalls.

Analytic approaches to stochastic gene expression in multicellular systems

Deterministic thermodynamic models of the complex systems, which control gene expression in metazoa, are helping researchers identify fundamental themes in the regulation of transcription. However, quantitative single cell studies are increasingly identifying regulatory mechanisms that control variability in expression. Such behaviors cannot be captured by deterministic models and are poorly suited to contemporary stochastic approaches that rely on continuum approximations, such as Langevin methods. Fortunately, theoretical advances in the modeling of transcription have assembled some general results that can be readily applied to systems being explored only through a deterministic approach. Here, I review some of the recent experimental evidence for the importance of genetically regulating stochastic effects during embryonic development and discuss key results from Markov theory that can be used to model this regulation. I then discuss several pairs of regulatory mechanisms recently investigated through a Markov approach. In each case, a deterministic treatment predicts no difference between the mechanisms, but the statistical treatment reveals the potential for substantially different distributions of transcriptional activity. In this light, features of gene regulation that seemed needlessly complex evolutionary baggage may be appreciated for their key contributions to reliability and precision of gene expression.

Dynamic regulation of transcriptional states by chromatin and transcription factors

The interaction of regulatory proteins with the complex nucleoprotein structures that are found in mammalian cells involves chromatin reorganization at multiple levels. Mechanisms that support these transitions are complex on many timescales, which range from milliseconds to minutes or hours. In this Review, we discuss emerging concepts regarding the function of regulatory elements in living cells. We also explore the involvement of these dynamic and stochastic processes in the evolution of fluctuating transcriptional activity states that are now commonly reported in eukaryotic systems.

Stimulus-induced modulation of transcriptional bursting in a single mammalian gene

Mammalian genes are often transcribed discontinuously as short bursts of RNA synthesis followed by longer silent periods. However, how these "on" and "off" transitions, together with the burst sizes, are modulated in single cells to increase gene expression upon stimulation is poorly characterized. By combining single-cell time-lapse luminescence imaging with stochastic modeling of the time traces, we quantified the transcriptional responses of the endogenous connective tissue growth factor gene to different physiological stimuli: serum and TGF-β1. Both stimuli caused a rapid and acute increase in burst sizes. Whereas TGF-β1 showed prolonged transcriptional activation mediated by an increase of transcription rate, serum stimulation resulted in a large and temporally tight first transcriptional burst, followed by a refractory period in the range of hours. Our study thus reveals how different physiological stimuli can trigger kinetically distinct transcriptional responses of the same gene.

Direct observation of frequency modulated transcription in single cells using light activation

Single-cell analysis has revealed that transcription is dynamic and stochastic, but tools are lacking that can determine the mechanism operating at a single gene. Here we utilize single-molecule observations of RNA in fixed and living cells to develop a single-cell model of steroid-receptor mediated gene activation. We determine that steroids drive mRNA synthesis by frequency modulation of transcription. This digital behavior in single cells gives rise to the well-known analog dose response across the population. To test this model, we developed a light-activation technology to turn on a single steroid-responsive gene and follow dynamic synthesis of RNA from the activated locus.

DOI:http://dx.doi.org/10.7554/eLife.00750.001

The process by which a gene is expressed as a protein consists of two stages: transcription, which involves the DNA of the gene being copied into messenger RNA (mRNA); and translation, in which the mRNA is used as a template to assemble amino acids into a protein. Transcription and translation are controlled by many interlinked pathways, which ensures that genes are expressed when and where required.

One of these regulatory pathways involves steroid receptors. The binding of a steroid molecule to its receptor causes the receptor to move into the nucleus and interact with a specific gene, triggering transcription of that gene. When measured at the level of the whole organism, this transcriptional response is dose-dependent—the more steroid molecules that are present, the greater the amount of transcription. However, this is not the case in single cells, in which transcription is either activated or not. This ‘on/off’ behaviour is also seen over time: steroid-activated transcription occurs in bursts, separated by periods of inactivity.

To unravel the molecular mechanism behind this phenomenon, Larson et al. created a light-activated form of the ligand that activates a specific steroid receptor. Using this molecule, they were able to switch transcription of the gene controlled by that receptor on and off. They then used fluorescent proteins to label the mRNA and protein molecules that were produced as a result.

They found that activating the steroid receptor increases the likelihood of transcription occurring inside a cell, but not the duration of individual bursts of transcriptional activity, nor the amount of mRNA produced during each burst. Activation of a steroid receptor seems to control transcription by reducing the length of time each cell spends in the ‘off’ state between bursts.

Larson et al. incorporated their findings into a model that also takes into account the natural variability in levels of transcription between cells, and found that this could explain how the digital (on/off) control of transcription at the cellular level leads to analogue, dose-dependent control at the level of a whole organism. These findings should lead to further insights into how transcription is controlled at the molecular level.

DOI:http://dx.doi.org/10.7554/eLife.00750.002

Unliganded progesterone receptor-mediated targeting of an RNA-containing repressive complex silences a subset of hormone-inducible genes

A close chromatin conformation precludes gene expression in eukaryotic cells. Genes activated by external cues have to overcome this repressive state by locally changing chromatin structure to a more open state. Although much is known about hormonal gene activation, how basal repression of regulated genes is targeted to the correct sites throughout the genome is not well understood. Here we report that in breast cancer cells, the unliganded progesterone receptor (PR) binds genomic sites and targets a repressive complex containing HP1γ (heterochromatin protein 1γ), LSD1 (lysine-specific demethylase 1), HDAC1/2, CoREST (corepressor for REST [RE1 {neuronal repressor element 1} silencing transcription factor]), KDM5B, and the RNA SRA (steroid receptor RNA activator) to 20% of hormone-inducible genes, keeping these genes silenced prior to hormone treatment. The complex is anchored via binding of HP1γ to H3K9me3 (histone H3 tails trimethylated on Lys 9). SRA interacts with PR, HP1γ, and LSD1, and its depletion compromises the loading of the repressive complex to target chromatin-promoting aberrant gene derepression. Upon hormonal treatment, the HP1γ-LSD1 complex is displaced from these constitutively poorly expressed genes as a result of rapid phosphorylation of histone H3 at Ser 10 mediated by MSK1, which is recruited to the target sites by the activated PR. Displacement of the repressive complex enables the loading of coactivators needed for chromatin remodeling and activation of this set of genes, including genes involved in apoptosis and cell proliferation. These results highlight the importance of the unliganded PR in hormonal regulation of breast cancer cells.

Eukaryotic transcriptional dynamics: from single molecules to cell populations

Transcriptional regulation is achieved through combinatorial interactions between regulatory elements in the human genome and a vast range of factors that modulate the recruitment and activity of RNA polymerase. Experimental approaches for studying transcription in vivo now extend from single-molecule techniques to genome-wide measurements. Parallel to these developments is the need for testable quantitative and predictive models for understanding gene regulation. These conceptual models must also provide insight into the dynamics of transcription and the variability that is observed at the single-cell level. In this Review, we discuss recent results on transcriptional regulation and also the models those results engender. We show how a non-equilibrium description informs our view of transcription by explicitly considering time- and energy-dependence at the molecular level.

Systematic identification of signal-activated stochastic gene regulation

Although much has been done to elucidate the biochemistry of signal transduction and gene regulatory pathways, it remains difficult to understand or predict quantitative responses. We integrate single-cell experiments with stochastic analyses, to identify predictive models of transcriptional dynamics for the osmotic stress response pathway in Saccharomyces cerevisiae. We generate models with varying complexity and use parameter estimation and cross-validation analyses to select the most predictive model. This model yields insight into several dynamical features, including multistep regulation and switchlike activation for several osmosensitive genes. Furthermore, the model correctly predicts the transcriptional dynamics of cells in response to different environmental and genetic perturbations. Because our approach is general, it should facilitate a predictive understanding for signal-activated transcription of other genes in other pathways or organisms.

On-chip cellomics assay enabling algebraic and geometric understanding of epigenetic information in cellular networks of living systems. 1. Temporal aspects of epigenetic information in bacteria

A series of studies aimed at developing methods and systems of analyzing epigenetic information in cells and in cell networks, as well as that of genetic information, was examined to expand our understanding of how living systems are determined. Because cells are minimum units reflecting epigenetic information, which is considered to map the history of a parallel-processing recurrent network of biochemical reactions, their behaviors cannot be explained by considering only conventional DNA information-processing events. The role of epigenetic information on cells, which complements their genetic information, was inferred by comparing predictions from genetic information with cell behaviour observed under conditions chosen to reveal adaptation processes, population effects and community effects. A system of analyzing epigenetic information was developed starting from the twin complementary viewpoints of cell regulation as an “algebraic” system (emphasis on temporal aspects) and as a “geometric” system (emphasis on spatial aspects). Exploiting the combination of latest microfabrication technology and measurement technologies, which we call on-chip cellomics assay, we can control and re-construct the environments and interaction of cells from “algebraic” and “geometric” viewpoints. In this review, temporal viewpoint of epigenetic information, a part of the series of single-cell-based “algebraic” and “geometric” studies of celluler systems in our research groups, are summerized and reported. The knowlege acquired from this study may lead to the use of cells that fully control practical applications like cell-based drug screening and the regeneration of organs.

Complex dynamics of transcription regulation

Transcription is a tightly regulated cellular function which can be triggered by endogenous (intrinsic) or exogenous (extrinsic) signals. The development of novel techniques to examine the dynamic behavior of transcription factors and the analysis of transcriptional activity at the single cell level with increased temporal resolution has revealed unexpected elements of stochasticity and dynamics of this process. Emerging research reveals a complex picture, wherein a wide range of time scales and temporal transcription patterns overlap to generate transcriptional programs. The challenge now is to develop a perspective that can guide us to common underlying mechanisms, and consolidate these findings. Here we review the recent literature on temporal dynamics and stochastic gene regulation patterns governed by intrinsic or extrinsic signals, utilizing the glucocorticoid receptor (GR)-mediated transcriptional model to illustrate commonality of these emerging concepts. This article is part of a Special Issue entitled: Chromatin in time and space.

Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant

A new experimental approach reveals a bet-hedging strategy in unstressed, clonal yeast cells, whereby they adopt a range of growth states that correlate with expression of a trehalose-synthesis regulator and predict resistance to future stress.

Genetically identical cells grown in the same culture display striking cell-to-cell heterogeneity in gene expression and other traits. A crucial challenge is to understand how much of this heterogeneity reflects the noise tolerance of a robust system and how much serves a biological function. In bacteria, stochastic gene expression results in cell-to-cell heterogeneity that might serve as a bet-hedging mechanism, allowing a few cells to survive through an antimicrobial treatment while others perish. Despite its clinical importance, the molecular mechanisms underlying bet hedging remain unclear. Here, we investigate the mechanisms of bet hedging in Saccharomyces cerevisiae using a new high-throughput microscopy assay that monitors variable protein expression, morphology, growth rate, and survival outcomes of tens of thousands of yeast microcolonies simultaneously. We find that clonal populations display broad distributions of growth rates and that slow growth predicts resistance to heat killing in a probabalistic manner. We identify several gene products that are likely to play a role in bet hedging and confirm that Tsl1, a trehalose-synthesis regulator, is an important component of this resistance. Tsl1 abundance correlates with growth rate and replicative age and predicts survival. Our results suggest that yeast bet hedging results from multiple epigenetic growth states determined by a combination of stochastic and deterministic factors.

Genetically identical cells grown in the same environment can display heterogeneity in their morphology, behavior, and composition of their cellular components. In some microorganisms, such cellular heterogeneity can underlie a phenomenon known as bet hedging because it enables some cells to survive in harsh environments, hence increasing the overall population fitness when environmental shifts are unpredictable. Bet hedging is likely to be an important strategy by which microbes infect humans and evade antimicrobial treatments, yet little is known of how cellular heterogeneity contributes to microbial survival. Here, we study the mechanisms underlying bet hedging in yeast. We find that populations of genetically identical yeast contain a broad distribution of growth rates and that slow growth predicts resistance to heat killing in a graded fashion. We identify several gene products that are likely to play a role in this bet-hedging strategy and confirm that Tsl1, a regulator of the production of the disaccharide trehalose, is an important component of acute stress resistance. Finally, we find that old age in cells correlates with a Tsl1-abundant, stress-resistant cell state. Our results suggest that trehalose synthesis is part of a complex and multifactorial mechanism that underlies bet hedging in yeast.

Live imaging of nascent RNA dynamics reveals distinct types of transcriptional pulse regulation

Transcription of genes can be discontinuous, occurring in pulses or bursts. It is not clear how properties of transcriptional pulses vary between different genes. We compared the pulsing of five housekeeping and five developmentally induced genes by direct imaging of single gene transcriptional events in individual living Dictyostelium cells. Each gene displayed its own transcriptional signature, differing in probability of firing and pulse duration, frequency, and intensity. In contrast to the prevailing view from both prokaryotes and eukaryotes that transcription displays binary behavior, strongly expressed housekeeping genes altered the magnitude of their transcriptional pulses during development. These nonbinary "tunable" responses may be better suited than stochastic switch behavior for housekeeping functions. Analysis of RNA synthesis kinetics using fluorescence recovery after photobleaching implied modulation of housekeeping-gene pulse strength occurs at the level of transcription initiation rather than elongation. In addition, disparities between single cell and population measures of transcript production suggested differences in RNA stability between gene classes. Analysis of stability using RNAseq revealed no major global differences in stability between developmental and housekeeping transcripts, although strongly induced RNAs showed unusually rapid decay, indicating tight regulation of expression.

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.

What do expression dynamics tell us about the mechanism of transcription?

Single-cell microscopy studies have the potential to provide an unprecedented view of gene expression with exquisite spatial and temporal sensitivity. However, there is a challenge to connect the holistic cellular view with a reductionist biochemical view. In particular, experimental efforts to characterize the in vivo regulation of transcription have focused primarily on measurements of the dynamics of transcription factors and chromatin modifying factors. Such measurements have elucidated the transient nature of many nuclear interactions. In the past few years, experimental approaches have emerged that allow for interrogation of the output of transcription at the single-molecule, single-cell level. Here, I summarize the experimental results and models that aim to provide an integrated view of transcriptional regulation.

Shrink-induced single-cell plastic microwell array

The ability to interrogate and track single cells over time in a high-throughput format would provide critical information for fundamental biological understanding of processes and for various applications, including drug screening and toxicology. We have developed an ultrarapid and simple method to create single-cell wells of controllable diameter and depth with commodity shrink-wrap film and tape. Using a programmable CO(2) laser, we cut hole arrays into the tape. The tape then serves as a shadow mask to selectively etch wells into commodity shrink-wrap film by O(2) plasma. When the shrink-wrap film retracts upon briefly heating, high-aspect plastic microwell arrays with diameters down to 20 μm are readily achieved. We calibrated the loading procedure with fluorescent microbeads. Finally, we demonstrate the utility of the wells by loading fluorescently labeled single human embryonic stem cells into the wells.