Single-molecule approaches to stochastic gene expression

Exploring the central dogma through the lens of gene expression noise

Over the past two decades, cell-to-cell heterogeneity has garnered increasing attention due to its critical role in both developmental and pathological processes. This growing interest has been driven, in part, by the advancements in live-cell and single-molecule imaging techniques. These techniques have provided mechanistic insights into how processes, transcription in particular, contribute to gene expression noise and, ultimately, cell-to-cell heterogeneity. More recently, however, research has expanded to explore how downstream steps in the central dogma influence gene expression noise. In this review, we mostly examine the impact of transcriptional processes on the generation of gene expression noise but also discuss how post-transcriptional mechanisms modulate noise and its propagation to the protein level. This evaluation emphasizes the need for further investigation into how processes beyond transcription shape gene expression noise, highlighting unanswered questions that remain in the field.

Quantification of subcellular RNA localization through direct detection of RNA oxidation

Across cell types and organisms, thousands of RNAs display asymmetric subcellular distributions. Studying this process requires quantifying abundances of specific RNAs at precise subcellular locations. To analyze subcellular transcriptomes, multiple proximity-based techniques have been developed in which RNAs near a localized bait protein are specifically labeled, facilitating their biotinylation and purification. However, these complex methods are often laborious and require expensive enrichment reagents. To streamline the analysis of localized RNA populations, we developed Oxidation-Induced Nucleotide Conversion sequencing (OINC-seq). In OINC-seq, RNAs near a genetically encoded, localized bait protein are specifically oxidized in a photo-controllable manner. These oxidation events are then directly detected and quantified using high-throughput sequencing and our software package, PIGPEN, without the need for biotin-mediated enrichment. We demonstrate that OINC-seq can induce and quantify RNA oxidation with high specificity in a dose- and light-dependent manner. We further show the spatial specificity of OINC-seq by using it to quantify subcellular transcriptomes associated with the cytoplasm, ER, nucleus, and the inner and outer membranes of mitochondria. Finally, using transgenic zebrafish, we demonstrate that OINC-seq allows proximity-mediated RNA labeling in live animals. In sum, OINC-seq together with PIGPEN provide an accessible workflow for analyzing localized RNAs across different biological systems.

Induction of a Müller Glial Cell-Specific Protective Pathway Safeguards the Retina From Diabetes-Induced Damage

Diabetes can lead to cell type-specific responses in the retina, including vascular lesions, glial dysfunction, and neurodegeneration, all of which contribute to retinopathy. However, the molecular mechanisms underlying these cell type-specific responses, and the cell types that are sensitive to diabetes have not been fully elucidated. Using single-cell transcriptomics, we profiled the transcriptional changes induced by diabetes in different retinal cell types in rat models as the disease progressed. Rod photoreceptors, a subtype of amacrine interneurons, and Müller glial cells (MGs) exhibited rapid responses to diabetes at the transcript levels. Genes associated with ion regulation were upregulated in all three cell types, suggesting a common response to diabetes. Furthermore, focused studies revealed that although MG initially increased the expression of genes playing protective roles, they cannot sustain this beneficial effect. We explored one of the candidate protective genes, Zinc finger protein 36 homolog (Zfp36), and observed that depleting Zfp36 in rat MGs in vivo using adeno-associated virus-based tools exacerbated diabetes-induced phenotypes, including glial reactivation, neurodegeneration, and vascular defects. Overexpression of Zfp36 slowed the development of these phenotypes. This work unveiled retinal cell types that are sensitive to diabetes and demonstrated that MGs can mount protective responses through Zfp36.

ARTICLE HIGHLIGHTS

Quantification of subcellular RNA localization through direct detection of RNA oxidation

Across cell types and organisms, thousands of RNAs display asymmetric subcellular distributions. The study of this process often requires quantifying abundances of specific RNAs at precise subcellular locations. To analyze subcellular transcriptomes, multiple proximity-based techniques have been developed in which RNAs near a localized bait protein are specifically labeled, facilitating their biotinylation and purification. However, these complex methods are often laborious and require expensive enrichment reagents. To streamline the analysis of localized RNA populations, we developed Oxidation-Induced Nucleotide Conversion sequencing (OINC-seq). In OINC-seq, RNAs near a genetically encoded, localized bait protein are specifically oxidized in a photo-controllable manner. These oxidation events are then directly detected and quantified using high-throughput sequencing and our software package, PIGPEN, without the need for biotin-mediated enrichment. We demonstrate that OINC-seq can induce and quantify RNA oxidation with high specificity in a dose- and light-dependent manner. We further show the spatial specificity of OINC-seq by using it to quantify subcellular transcriptomes associated with the cytoplasm, ER, nucleus, and the inner and outer membranes of mitochondria. Finally, using transgenic zebrafish, we demonstrate that OINC-seq allows proximity-mediated RNA labeling in live animals. In sum, OINC-seq together with PIGPEN provide an accessible workflow for the analysis of localized RNAs across different biological systems.

Growth phase-dependent ribonucleic acid production dynamics

Transcriptional events play a crucial role in major cellular processes that specify the activity of an individual cells and influences cell population behavior in response to environment. Active (ON) and an inactive (OFF) states controls the transcriptional burst. Yet, the mechanism and kinetics of ON/OFF-state across the different growth phases of Escherichia coli remains elusive. Here, we have used a single mRNA detection method in live-cells to comprehend the ON/OFF mechanism of the first transcriptional (TF) and consecutive events (TC) controlled by lactose promoters, Plac and Plac/ara1. We determined that the duration of TF ON/OFF has different modes, exhibiting a close to inverse behavior to that of TC ON/OFF. Dynamics of ON/OFF states in fast and slow-dividing cells were affected by the promoter region during the initiation of transcription. Period of TF ON-state defines the behavior of TC by altering the number and the frequency of mRNAs formed. Furthermore, we have shown that delayed OFF-time in TF affects the dynamics of TC in both states, which is mainly determined by the upstream promoter region. Furthermore, using elongation arrest experiments, we independently validate that mRNA noise in TC is governed by the delayed OFF-period in TF. We have identified the position of the regulatory regions that plays a crucial role in noise (Fano) modulation. Taken together, our results suggest that the dynamics of the first transcriptional event, TF, pre-defines the diversity of the population.

How do stochastic processes and genetic threshold effects explain incomplete penetrance and inform causal disease mechanisms?

Incomplete penetrance is the rule rather than the exception in Mendelian disease. In syndromic monogenic disorders, phenotypic variability can be viewed as the combination of incomplete penetrance for each of multiple independent clinical features. Within genetically identical individuals, such as isogenic model organisms, stochastic variation at molecular and cellular levels is the primary cause of incomplete penetrance according to a genetic threshold model. By defining specific probability distributions of causal biological readouts and genetic liability values, stochasticity and incomplete penetrance provide information about threshold values in biological systems. Ascertainment of threshold values has been achieved by simultaneous scoring of relatively simple phenotypes and quantitation of molecular readouts at the level of single cells. However, this is much more challenging for complex morphological phenotypes using experimental and reductionist approaches alone, where cause and effect are separated temporally and across multiple biological modes and scales. Here I consider how causal inference, which integrates observational data with high confidence causal models, might be used to quantify the relative contribution of different sources of stochastic variation to phenotypic diversity. Collectively, these approaches could inform disease mechanisms, improve predictions of clinical outcomes and prioritize gene therapy targets across modes and scales of gene function. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Single-molecule RNA-FISH analysis reveals stochasticity in reactivation of latent HIV-1 regulated by Nuclear Orphan Receptors NR4A and cMYC

HIV-1 eradication strategies require complete reactivation of HIV-1 latent cells by Latency Reversing Agents (LRA). Current methods lack effectiveness due to incomplete proviral reactivation. We employed a single-molecule RNA-FISH (smRNA-FISH) and FISH-Quant analysis and found that proviral reactivation is highly variable from cell-to-cell, stochastic, and occurs in bursts and waves, with different kinetics in response to diverse LRAs. Approximately 1-5% of latent cells exhibited stochastic reactivation without LRAs. Through single-cell RNA-seq analysis, we identified NR4A3 and cMYC as extrinsic factors associated with stochastic HIV-1 reactivation. Concomitant with HIV-1 reactivation cMYC was downregulated and NR4A3 was upregulated in both latent cell lines and primary CD4+ T-cells from aviremic patients. By inhibiting cMYC using SN-38, an active metabolite of irinotecan, we induced NR4A3 and HIV-1 expression. Our results suggest that inherent stochasticity in proviral reactivation contributes to cell-to-cell variability, which could potentially be modulated by drugs targeting cMYC and NR4A3.

miRNA circuit modules for precise, tunable control of gene expression

The ability to express transgenes at specified levels is critical for understanding cellular behaviors, and for applications in gene and cell therapy. Transfection, viral vectors, and other gene delivery methods produce varying protein expression levels, with limited quantitative control, while targeted knock-in and stable selection are inefficient and slow. Active compensation mechanisms can improve precision, but the need for additional proteins or lack of tunability have prevented their widespread use. Here, we introduce a toolkit of compact, synthetic miRNA-based circuit modules that provide precise, tunable control of transgenes across diverse cell types. These circuits, termed DIMMERs (Dosage-Invariant miRNA-Mediated Expression Regulators) use multivalent miRNA regulatory interactions within an incoherent feed-forward loop architecture to achieve nearly uniform protein expression over more than two orders of magnitude variation in underlying gene dosages or transcription rates. They also allow coarse and fine control of expression, and are portable, functioning across diverse cell types. In addition, a heuristic miRNA design algorithm enables the creation of orthogonal circuit variants that independently control multiple genes in the same cell. These circuits allowed dramatically improved CRISPR imaging, and super-resolution imaging of EGFR receptors with transient transfections. The toolbox provided here should allow precise, tunable, dosage-invariant expression for research, gene therapy, and other biotechnology applications.

On the ever-growing functional versatility of the CRISPR-Cas13 system

CRISPR-Cas systems evolved in prokaryotes to implement a powerful antiviral immune response as a result of sequence-specific targeting by ribonucleoproteins. One of such systems consists of an RNA-guided RNA endonuclease, known as CRISPR-Cas13. In very recent years, this system is being repurposed in different ways in order to decipher and engineer gene expression programmes. Here, we discuss the functional versatility of the CRISPR-Cas13 system, which includes the ability for RNA silencing, RNA editing, RNA tracking, nucleic acid detection and translation regulation. This functional palette makes the CRISPR-Cas13 system a relevant tool in the broad field of systems and synthetic biology.

Dynamics of Whole Transcriptome Analysis (WTA) and Surface markers expression (AbSeq) in Immune Cells of COVID-19 Patients and Recovered captured through Single Cell Genomics

Introduction

Single-cell multi-omics studies, such as multidimensional transcriptomics (whole transcriptomic analysis, WTA), and surface marker analysis (antibody sequencing, AbSeq), have turned out to be valuable techniques that offer inaccessible possibilities for single-cell profiling of mRNA, lncRNA, and proteins.

Methods

We used this technique to understand the dynamics of mRNA and protein-level differences in healthy, COVID-19-infected and recovered individuals using peripheral blood mononuclear cells (PBMCs). Our results demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state.

Results

We demonstrate that compared to mRNA expression, protein abundance is a better indicator of the disease state. We observed high levels of cell identity and regulatory markers, CD3E, CD4, CD8A, CD5, CD7, GITR, and KLRB1 in healthy individuals, whereas markers related to cell activation, CD38, CD28, CD69, CD62L, CD14, and CD16 elevated in the SARS-CoV-2 infected patients at both WTA and AbSeq levels. Curiously, in recovered individuals, there was a high expression of cytokine and chemokine receptors (CCR5, CCR7, CCR4, CXCR3, and PTGRD2). We also observed variations in the expression of markers within cell populations under different states.

Discussion

Furthermore, our study emphasizes the significance of employing an oligo-based method (AbSeq) that can help in diagnosis, prognosis, and protection from disease/s by identifying cell surface markers that are unique to different cell types or states. It also allows simultaneous study of a vast array of markers, surpassing the constraints of techniques like FACS to query the vast repertoire of proteins.

Aging disrupts spatiotemporal regulation of germline stem cells and niche integrity

A major factor driving stem cell decline is stem cell niche aging, but its molecular mechanism remains elusive. We use the Caenorhabditis elegans distal tip cell (DTC), the mesenchymal niche that employs Notch signaling to regulate germline stem cells (GSCs), as an in vivo niche aging model and delineate the molecular details of the DTC/niche aging process. Here, we demonstrate that a drastic decrease in C. elegans germline fecundity, which begins even in early adulthood, is mainly due to an age-induced disruption in spatial regulation of Notch-dependent transcription in the germline combined with a moderate reduction in Notch transcription at both tissue and cellular levels. Consequently, the Notch-responsive GSC pool shifts from the distal end of the gonad to a more proximal region, disrupting the distal-to-proximal germline polarity. We find that this GSC pool shift is due to a dislocation of the DTC/niche nucleus, which is associated with age-induced changes in the structure and morphology of the DTC/niche. Our findings reveal a critical link between physiological changes in the aging niche, their consequences in stem cell regulation, and germline tissue functions.

Assessing Markovian and Delay Models for Single-Nucleus RNA Sequencing

The serial nature of reactions involved in the RNA life-cycle motivates the incorporation of delays in models of transcriptional dynamics. The models couple a transcriptional process to a fairly general set of delayed monomolecular reactions with no feedback. We provide numerical strategies for calculating the RNA copy number distributions induced by these models, and solve several systems with splicing, degradation, and catalysis. An analysis of single-cell and single-nucleus RNA sequencing data using these models reveals that the kinetics of nuclear export do not appear to require invocation of a non-Markovian waiting time.

Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise

Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.

Enhanced Transcriptional Strength of HIV-1 Subtype C Minimizes Gene Expression Noise and Confers Stability to the Viral Latent State

Stochastic fluctuations in gene expression emanating from the HIV-1 long terminal repeat (LTR), amplified by the Tat positive feedback circuit, determine the choice between viral infection fates: active transcription (ON) or transcriptional silence (OFF). The emergence of several transcription factor binding site (TFBS) variant strains in HIV-1 subtype C (HIV-1C), especially those containing the duplication of the NF-κB motif, mandates the evaluation of the effect of enhanced transcriptional strength on gene expression noise and its influence on viral fate selection switch. Using a panel of subgenomic LTR-variant strains containing different copy numbers of the NF-κB motif (ranging from 0 to 4), we used flow cytometry, mRNA quantification, and pharmacological perturbations to demonstrate an inverse correlation between promoter strength and gene expression noise in Jurkat T cells and primary CD4⁺ T cells. The inverse correlation is consistent in clonal cell populations at constant intracellular concentrations of Tat and when NF-κB levels were regulated pharmacologically. Further, we show that strong LTRs containing at least two copies of the NF-κB motif in the enhancer establish a more stable latent state and demonstrate more rapid latency reversal than weak LTRs containing fewer motifs. We also demonstrate a cooperative binding of NF-κB to the motif cluster in HIV-1C LTRs containing two, three, or four NF-κB motifs (Hill coefficient [H] = 2.61, 3.56, and 3.75, respectively). The present work alludes to a possible evolution of the HIV-1C LTR toward gaining transcriptional strength associated with attenuated gene expression noise with implications for viral latency. IMPORTANCE Over the past two consecutive decades, HIV-1 subtype C (HIV-1C) has been undergoing directional evolution toward augmenting the transcriptional strength of the long terminal repeat (LTR) by adding more copies of the existing transcription factor binding site (TFBS) by sequence duplication. Additionally, the duplicated elements are genetically diverse, suggesting broader-range signal receptivity by variant LTRs. The HIV-1 promoter is inherently noisy, and the stochastic fluctuations in gene expression of variant LTRs may influence the active transcription (ON)/transcriptional silence (OFF) latency decisions. The evolving NF-κB motif variations of HIV-1C offer a powerful opportunity to examine how the transcriptional strength of the LTR might influence gene expression noise. Our work here shows that the augmented transcriptional strength of the HIV-1C LTR leads to concomitantly reduced gene expression noise, consequently leading to stabler latency maintenance and rapid latency reversal. The present work offers a novel lead toward appreciating the molecular mechanisms governing HIV-1 latency.

Localization of Noise in Biochemical Networks

Noise, or uncertainty in biochemical networks, has become an important aspect of many biological problems. Noise can arise and propagate from external factors and probabilistic chemical reactions occurring in small cellular compartments. For species survival, it is important to regulate such uncertainties in executing vital cell functions. Regulated noise can improve adaptability, whereas uncontrolled noise can cause diseases. Simulation can provide a detailed analysis of uncertainties, but parameters such as rate constants and initial conditions are usually unknown. A general understanding of noise dynamics from the perspective of network structure is highly desirable. In this study, we extended the previously developed law of localization for characterizing noise in terms of (co)variances and developed noise localization theory. With linear noise approximation, we can expand a biochemical network into an extended set of differential equations representing a fictitious network for pseudo-components consisting of variances and covariances, together with chemical species. Through localization analysis, perturbation responses at the steady state of pseudo-components can be summarized into a sensitivity matrix that only requires knowledge of network topology. Our work allows identification of buffering structures at the level of species, variances, and covariances and can provide insights into noise flow under non-steady-state conditions in the form of a pseudo-chemical reaction. We tested noise localization in various systems, and here we discuss its implications and potential applications. Results show that this theory is potentially applicable in discriminating models, scanning network topologies with interesting noise behavior, and designing and perturbing networks with the desired response.

Variable DNA topology is an epigenetic generator of physiological heterogeneity in bacterial populations

Transcription is a noisy and stochastic process that produces sibling-to-sibling variations in physiology across a population of genetically identical cells. This pattern of diversity reflects, in part, the burst-like nature of transcription. Transcription bursting has many causes and a failure to remove the supercoils that accumulate in DNA during transcription elongation is an important contributor. Positive supercoiling of the DNA ahead of the transcription elongation complex can result in RNA polymerase stalling if this DNA topological roadblock is not removed. The relaxation of these positive supercoils is performed by the ATP-dependent type II topoisomerases DNA gyrase and topoisomerase IV. Interference with the action of these topoisomerases involving, inter alia, topoisomerase poisons, fluctuations in the [ATP]/[ADP] ratio, and/or the intervention of nucleoid-associated proteins with GapR-like or YejK-like activities, may have consequences for the smooth operation of the transcriptional machinery. Antibiotic-tolerant (but not resistant) persister cells are among the phenotypic outliers that may emerge. However, interference with type II topoisomerase activity can have much broader consequences, making it an important epigenetic driver of physiological diversity in the bacterial population.

The missing link between genetic association and regulatory function

The genetic basis of most traits is highly polygenic and dominated by non-coding alleles. It is widely assumed that such alleles exert small regulatory effects on the expression of cis-linked genes. However, despite the availability of gene expression and epigenomic datasets, few variant-to-gene links have emerged. It is unclear whether these sparse results are due to limitations in available data and methods, or to deficiencies in the underlying assumed model. To better distinguish between these possibilities, we identified 220 gene-trait pairs in which protein-coding variants influence a complex trait or its Mendelian cognate. Despite the presence of expression quantitative trait loci near most GWAS associations, by applying a gene-based approach we found limited evidence that the baseline expression of trait-related genes explains GWAS associations, whether using colocalization methods (8% of genes implicated), transcription-wide association (2% of genes implicated), or a combination of regulatory annotations and distance (4% of genes implicated). These results contradict the hypothesis that most complex trait-associated variants coincide with homeostatic expression QTLs, suggesting that better models are needed. The field must confront this deficit and pursue this 'missing regulation.'

Assembly of Biologically Functional Structures by Nucleic Acid Templating: Implementation of a Strategy to Overcome Inhibition by Template Excess

Delivery of therapeutic molecules to pathogenic cells is often hampered by unintended toxicity to normal cells. In principle, this problem can be circumvented if the therapeutic effector molecule is split into two inactive components, and only assembled on or within the target cell itself. Such an in situ process can be realized by exploiting target-specific molecules as templates to direct proximity-enhanced assembly. Modified nucleic acids carrying inert precursor fragments can be designed to co-hybridize on a target-specific template nucleic acid, such that the enforced proximity accelerates assembly of a functional molecule for antibody recognition. We demonstrate the in vitro feasibility of this adaptation of nucleic acid-templated synthesis (NATS) using oligonucleotides bearing modified peptides (“haplomers”), for templated assembly of a mimotope recognized by the therapeutic antibody trastuzumab. Enforced proximity promotes mimotope assembly via traceless native chemical ligation. Nevertheless, titration of participating haplomers through template excess is a potential limitation of trimolecular NATS. In order to overcome this problem, we devised a strategy where haplomer hybridization can only occur in the presence of target, without being subject to titration effects. This generalizable NATS modification may find future applications in enabling directed targeting of pathological cells.

Stochastic gene transcription with non-competitive transcription regulatory architecture

The transcription factors, such as activators and repressors, can interact with the promoter of gene either in a competitive or non-competitive way. In this paper, we construct a stochastic model with non-competitive transcriptional regulatory architecture and develop an analytical theory that re-establishes the experimental results with an improved data fitting. The analytical expressions in the theory allow us to study the nature of the system corresponding to any of its parameters and hence, enable us to find out the factors that govern the regulation of gene expression for that architecture. We notice that, along with transcriptional reinitiation and repressors, there are other parameters that can control the noisiness of this network. We also observe that, the Fano factor (at mRNA level) varies from sub-Poissonian regime to super-Poissonian regime. In addition to the aforementioned properties, we observe some anomalous characteristics of the Fano factor (at mRNA level) and that of the variance of protein at lower activator concentrations in the presence of repressor molecules. This model is useful to understand the architecture of interactions which may buffer the stochasticity inherent to gene transcription.

Quantification of Microbial Robustness in Yeast

Stable cell performance in a fluctuating environment is essential for sustainable bioproduction and synthetic cell functionality; however, microbial robustness is rarely quantified. Here, we describe a high-throughput strategy for quantifying robustness of multiple cellular functions and strains in a perturbation space. We evaluated quantification theory on experimental data and concluded that the mean-normalized Fano factor allowed accurate, reliable, and standardized quantification. Our methodology applied to perturbations related to lignocellulosic bioethanol production showed that the industrial bioethanol producing strain Saccharomyces cerevisiae Ethanol Red exhibited both higher and more robust growth rates than the laboratory strain CEN.PK and industrial strain PE-2, while a more robust product yield traded off for lower mean levels. The methodology validated that robustness is function-specific and characterized by positive and negative function-specific trade-offs. Systematic quantification of robustness to end-use perturbations will be important to analyze and construct robust strains with more predictable functions.

Cell Networks in Endocrine/Neuroendocrine Gland Function

Reproduction, growth, stress, and metabolism are determined by endocrine/neuroendocrine systems that regulate circulating hormone concentrations. All these systems generate rhythms and changes in hormone pulsatility observed in a variety of pathophysiological states. Thus, the output of endocrine/neuroendocrine systems must be regulated within a narrow window of effective hormone concentrations but must also maintain a capacity for plasticity to respond to changing physiological demands. Remarkably most endocrinologists still have a "textbook" view of endocrine gland organization which has emanated from 20^th century histological studies on thin 2D tissue sections. However, 21^st -century technological advances, including in-depth 3D imaging of specific cell types have vastly changed our knowledge. We now know that various levels of multicellular organization can be found across different glands, that organizational motifs can vary between species and can be modified to enhance or decrease hormonal release. This article focuses on how the organization of cells regulates hormone output using three endocrine/neuroendocrine glands that present different levels of organization and complexity: the adrenal medulla, with a single neuroendocrine cell type; the anterior pituitary, with multiple intermingled cell types; and the pancreas with multiple intermingled cell types organized into distinct functional units. We give an overview of recent methodologies that allow the study of the different components within endocrine systems, particularly their temporal and spatial relationships. We believe the emerging findings about network organization, and its impact on hormone secretion, are crucial to understanding how homeostatic regulation of endocrine axes is carried out within endocrine organs themselves. © 2022 American Physiological Society. Compr Physiol 12:3371-3415, 2022.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

A Framework for Investigating Rules of Life by Establishing Zones of Influence

The incredible complexity of biological processes across temporal and spatial scales hampers defining common underlying mechanisms driving the patterns of life. However, recent advances in sequencing, big data analysis, machine learning, and molecular dynamics simulation have renewed the hope and urgency of finding potential hidden rules of life. There currently exists no framework to develop such synoptic investigations. Some efforts aim to identify unifying rules of life across hierarchical levels of time, space, and biological organization, but not all phenomena occur across all the levels of these hierarchies. Instead of identifying the same parameters and rules across levels, we posit that each level of a temporal and spatial scale and each level of biological organization has unique parameters and rules that may or may not predict outcomes in neighboring levels. We define this neighborhood, or the set of levels, across which a rule functions as the zone of influence. Here, we introduce the zone of influence framework and explain using three examples: (a) randomness in biology, where we use a Poisson process to describe processes from protein dynamics to DNA mutations to gene expressions, (b) island biogeography, and (c) animal coloration. The zone of influence framework may enable researchers to identify which levels are worth investigating for a particular phenomenon and reframe the narrative of searching for a unifying rule of life to the investigation of how, when, and where various rules of life operate.

Single-cell RNA profiling identifies diverse cellular responses to EWSR1/FLI1 downregulation in Ewing sarcoma cells

BACKGROUND

The EWSR1/FLI1 gene fusion is the most common rearrangement leading to cell transformation in Ewing sarcoma (ES). Previous studies have indicated that expression at the cellular level is heterogeneous, and that levels of expression may oscillate, conferring different cellular characteristics. In ES the role of EWSR1/FLI1 in regulating subpopulation dynamics is currently unknown.

METHODS

We used siRNA to transiently suppress EWSR1/FLI1 expression and followed population dynamics using both single cell expression profiling, CyTOF and functional assays to define characteristics of exponentially growing ES cells and of ES cells in which EWSR1/FLI1 had been downregulated. Novel transcriptional states with distinct features were assigned using random forest feature selection in combination with machine learning. Cells isolated from ES xenografts in immune-deficient mice were interrogated to determine whether characteristics of specific subpopulations of cells in vitro could be identified. Stem-like characteristics were assessed by primary and secondary spheroid formation in vitro, and invasion/motility was determined for each identified subpopulation. Autophagy was determined by expression profiling, cell sorting and immunohistochemical staining.

RESULTS

We defined a workflow to study EWSR1/FLI1 driven transcriptional states and phenotypes. We tracked EWSR1/FLI1 dependent proliferative activity over time to discover sources of intra-tumoral diversity. Single-cell RNA profiling was used to compare expression profiles in exponentially growing populations (si-Control) or in two dormant populations (D1, D2) in which EWSR1/FLI1 had been suppressed. Three distinct transcriptional states were uncovered contributing to ES intra-heterogeneity. Our predictive model identified ~1% cells in a dormant-like state and ~ 2-4% cells with stem-like and neural stem-like features in an exponentially proliferating ES cell line and in ES xenografts. Following EWSR1/FLI1 knockdown, cells re-entering the proliferative cycle exhibited greater stem-like properties, whereas for those cells remaining quiescent, FAM134B-dependent dormancy may provide a survival mechanism.

CONCLUSIONS

We show that time-dependent changes induced by suppression of oncogenic EWSR1/FLI1 expression induces dormancy, with different subpopulation dynamics. Cells re-entering the proliferative cycle show enhanced stem-like characteristics, whereas those remaining dormant for prolonged periods appear to survive through autophagy. Cells with these characteristics identified in exponentially growing cell populations and in tumor xenografts may confer drug resistance and could potentially contribute to metastasis.

Co-opting disorder into order: Intrinsically disordered proteins and the early evolution of complex multicellularity

Intrinsically disordered proteins (IDPs) are proteins that lack rigid structures yet play important roles in myriad biological phenomena. A distinguishing feature of IDPs is that they often mediate specific biological outcomes via multivalent weak cooperative interactions with multiple partners. Here, we show that several proteins specifically associated with processes that were key in the evolution of complex multicellularity in the lineage leading to the multicellular green alga Volvox carteri are IDPs. We suggest that, by rewiring cellular protein interaction networks, IDPs facilitated the co-option of ancestral pathways for specialized multicellular functions, underscoring the importance of IDPs in the early evolution of complex multicellularity.

Absolute quantitation of individual SARS-CoV-2 RNA molecules provides a new paradigm for infection dynamics and variant differences

Despite an unprecedented global research effort on SARS-CoV-2, early replication events remain poorly understood. Given the clinical importance of emergent viral variants with increased transmission, there is an urgent need to understand the early stages of viral replication and transcription. We used single-molecule fluorescence in situ hybridisation (smFISH) to quantify positive sense RNA genomes with 95% detection efficiency, while simultaneously visualising negative sense genomes, subgenomic RNAs, and viral proteins. Our absolute quantification of viral RNAs and replication factories revealed that SARS-CoV-2 genomic RNA is long-lived after entry, suggesting that it avoids degradation by cellular nucleases. Moreover, we observed that SARS-CoV-2 replication is highly variable between cells, with only a small cell population displaying high burden of viral RNA. Unexpectedly, the B.1.1.7 variant, first identified in the UK, exhibits significantly slower replication kinetics than the Victoria strain, suggesting a novel mechanism contributing to its higher transmissibility with important clinical implications.

Protein conformational dynamics and phenotypic switching

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure but exist as conformational ensembles. Because of their structural plasticity, they can interact with multiple partners. The protein interactions between IDPs and their partners form scale-free protein interaction networks (PINs) that facilitate information flow in the cell. Because of their plasticity, IDPs typically occupy hub positions in cellular PINs. Furthermore, their conformational dynamics and propensity for post-translational modifications contribute to "conformational" noise which is distinct from the well-recognized transcriptional noise. Therefore, upregulation of IDPs in response to a specific input, such as stress, contributes to increased noise and, hence, an increase in stochastic, "promiscuous" interactions. These interactions lead to activation of latent pathways or can induce "rewiring" of the PIN to yield an optimal output underscoring the critical role of IDPs in regulating information flow. We have used PAGE4, a highly intrinsically disordered stress-response protein as a paradigm. Employing a variety of experimental and computational techniques, we have elucidated the role of PAGE4 in phenotypic switching of prostate cancer cells at a systems level. These cumulative studies over the past decade provide a conceptual framework to better understand how IDP conformational dynamics and conformational noise might facilitate cellular decision-making.

RNA BioMolecular Electronics: towards new tools for biophysics and biomedicine

The last half-century has witnessed the birth and development of a new multidisciplinary field at the edge between materials science, nanoscience, engineering, and chemistry known as Molecular Electronics. This field deals with the electronic properties of individual molecules and their integration as active components in electronic circuits and has also been applied to biomolecules, leading to BioMolecular Electronics and opening new perspectives for single-molecule biophysics and biomedicine. Herein, we provide a brief introduction and overview of the BioMolecular electronics field, focusing on nucleic acids and potential applications for these measurements. In particular, we review the recent demonstration of the first single-molecule electrical detection of a biologically-relevant nucleic acid. We also show how this could be used to study biomolecular interactions and applications in liquid biopsy for early cancer detection, among others. Finally, we discuss future perspectives and challenges in the applications of this fascinating research field.

RNA imaging in bacteria

The spatiotemporal regulation of gene expression plays an essential role in many biological processes. Recently, several imaging-based RNA labeling and detection methods, both in fixed and live cells, were developed and now enable the study of transcript abundance, localization and dynamics. Here, we review the main single-cell techniques for RNA visualization with fluorescence microscopy and describe their applications in bacteria.

dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells

MOTIVATION

Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here, we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images.

RESULTS

The spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high-quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli. We conclude that dNEMO is an ideal user interface for rapid and accurate measurement of fluorescent molecular assemblies in biological imaging data.

AVAILABILITY AND IMPLEMENTATION

The data and software are freely available online at https://github.com/recleelab.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CloneSeq: A highly sensitive analysis platform for the characterization of 3D-cultured single-cell-derived clones

Single-cell assays have revealed the importance of heterogeneity in many biological systems. However, limited sensitivity is a major hurdle for uncovering cellular variation. To overcome it, we developed CloneSeq, combining clonal expansion inside 3D hydrogel spheres and droplet-based RNA sequencing (RNA-seq). We show that clonal cells maintain similar transcriptional profiles and cell states. CloneSeq of lung cancer cells revealed cancer-specific subpopulations, including cancer stem-like cells, that were not revealed by scRNA-seq. Clonal expansion within 3D soft microenvironments supported cellular stemness of embryonic stem cells (ESCs) even without pluripotent media, and it improved epigenetic reprogramming efficiency of mouse embryonic fibroblasts. CloneSeq of ESCs revealed that the differentiation decision is made early during Oct4 downregulation and is maintained during early clonal expansion. Together, we show CloneSeq can be adapted to different biological systems to discover rare subpopulations by leveraging the enhanced sensitivity within clones.

Quantitative measurements of early alphaviral replication dynamics in single cells reveals the basis for superinfection exclusion

While decades of research have elucidated many steps of the alphavirus lifecycle, the earliest replication dynamics have remained unclear. This missing time window has obscured early replicase strand-synthesis behavior and prevented elucidation of how the first events of infection might influence subsequent viral competition. Using quantitative live-cell and single-molecule imaging, we observed the initial replicase activity and its strand preferences in situ and measured the trajectory of replication over time. Under this quantitative framework, we investigated viral competition, where one alphavirus is able to exclude superinfection by a second homologous virus. We show that this appears as an indirect phenotypic consequence of a bidirectional competition between the two species, coupled with the rapid onset of viral replication and a limited total cellular carrying capacity. Together, these results emphasize the utility of analyzing viral kinetics within single cells.

Pluripotent stem cell-derived models of neurological diseases reveal early transcriptional heterogeneity

BACKGROUND

Many neurodegenerative diseases develop only later in life, when cells in the nervous system lose their structure or function. In many forms of neurodegenerative diseases, this late-onset phenomenon remains largely unexplained.

RESULTS

Analyzing single-cell RNA sequencing from Alzheimer's disease (AD) and Huntington's disease (HD) patients, we find increased transcriptional heterogeneity in disease-state neurons. We hypothesize that transcriptional heterogeneity precedes neurodegenerative disease pathologies. To test this idea experimentally, we use juvenile forms (72Q; 180Q) of HD iPSCs, differentiate them into committed neuronal progenitors, and obtain single-cell expression profiles. We show a global increase in gene expression variability in HD. Autophagy genes become more stable, while energy and actin-related genes become more variable in the mutant cells. Knocking down several differentially variable genes results in increased aggregate formation, a pathology associated with HD. We further validate the increased transcriptional heterogeneity in CHD8+/- cells, a model for autism spectrum disorder.

CONCLUSIONS

Overall, our results suggest that although neurodegenerative diseases develop over time, transcriptional regulation imbalance is present already at very early developmental stages. Therefore, an intervention aimed at this early phenotype may be of high diagnostic value.

Trajectory-based energy landscapes of gene regulatory networks

Multistability and natural biological variability can result in significant heterogeneity within a cell population, leading to challenges in understanding and modulating cell behavior. Energy landscapes can offer qualitatively intuitive visualizations of cell phenotype and facilitate a more quantitative understanding of cellular dynamics, but current methods for landscape generation are mathematically involved and often require specific system properties (e.g., ergodicity or independent gene/protein probability distributions) that do not always hold. Here, we present a simple kinetic Monte Carlo-based method for landscape generation from a system of ordinary differential equations using only simulation trajectories initialized throughout the phase space of interest. The resulting landscape produces three quantitative features relevant to understanding cell behavior: stability (reflected by the depth or potential of landscape valleys), velocity (representing average directional movement on the landscape), and variance in velocity (indicative of landscape positions with heterogeneous movements). We applied this method to a genetic toggle switch, a core decision-making network in binary cellular responses, to elucidate effects of biologically relevant intrinsic and extrinsic cues. Intrinsic noise, such as stochasticity in transcription-translation and differences in cell cycle position, manifests through changes in valley width and position, reflecting increased population heterogeneity and more probabilistic cell fate transitions. The landscapes also capture the effect of an external inducer, revealing a quantitative correlation between the rate of cell fate transition and the energy barrier above a threshold inducer concentration determined by the permissivity of the valley. Further, in tracking dynamically changing landscapes under time-varying external cues, we unexpectedly found that an oscillatory inducer input can modulate cell fate heterogeneity and lead to periodic cell fate transitions entrained to the input frequency, depending on the intrinsic degradation rate of the switch. The landscape generation approach outlined herein is generalizable to other network topologies and may provide new quantitative insights into their dynamics.

Using Models to (Re-)Design Synthetic Circuits

Mathematical models play an important role in the design of synthetic gene circuits, by guiding the choice of biological components and their assembly into novel gene networks. Here, we present a guide for biologists to build and utilize models of gene networks (synthetic or natural) to analyze dynamical properties of these networks while considering the low numbers of molecules inside cells that results in stochastic gene expression. We start by describing how to write down a model and discussing the level of details to include. We then briefly demonstrate how to simulate a network's dynamics using deterministic differential equations that assume high numbers of molecules. To consider the role of stochastic gene expression in single cells, we provide a detailed tutorial on running stochastic Gillespie simulations of a network, including instructions on coding the Gillespie algorithm with example code. Finally, we illustrate how using a combination of quantitative experimental characterization of a synthetic circuit and mathematical modeling can guide the iterative redesign of a synthetic circuit to achieve the desired properties. This is shown using a classic synthetic oscillator, the repressilator, which we recently redesigned into the most precise and robust synthetic oscillator to date. We thus provide a toolkit for synthetic biologists to build more precise and robust synthetic circuits, which should lead to a deeper understanding of the dynamics of gene regulatory networks.

Sensitivity analysis of the reaction occurrence and recurrence times in steady-state biochemical networks

Continuous-time stationary Markov jump processes among discrete sites are encountered in disparate biochemical ambits. Sites and connecting dynamical events form a 'network' in which the sites are the available system's states, and the events are site-to-site transitions, or even neutral processes in which the system does not change site but the event is however detectable. Examples include conformational transitions in single biomolecules, stochastic chemical kinetics in the space of the molecules copy numbers, and even macroscopic steady-state reactive mixtures if one adopts the viewpoint of a tagged molecule (or even of a molecular moiety) whose state may change when it is involved in a chemical reaction. Among the variety of dynamical descriptors, here we focus on the first occurrence times (starting from a given site) and on the recurrence times of an event of interest. We develop the sensitivity analysis for the lowest moments of the statistical distribution of such times with respect to the rate constants of the network. In particular, simple expressions and inequalities allow us to establish a direct relationship between selective variation of rate constants and effect on average times and variances. As illustrative cases we treat the substrate inhibition in enzymatic catalysis in which a tagged enzyme molecule jumps between three states, and the basic two-site model of stochastic gene expression in which the single gene switches between active and inactive forms.

A matter of time: Using dynamics and theory to uncover mechanisms of transcriptional bursting

Eukaryotic transcription generally occurs in bursts of activity lasting minutes to hours; however, state-of-the-art measurements have revealed that many of the molecular processes that underlie bursting, such as transcription factor binding to DNA, unfold on timescales of seconds. This temporal disconnect lies at the heart of a broader challenge in physical biology of predicting transcriptional outcomes and cellular decision-making from the dynamics of underlying molecular processes. Here, we review how new dynamical information about the processes underlying transcriptional control can be combined with theoretical models that predict not only averaged transcriptional dynamics, but also their variability, to formulate testable hypotheses about the molecular mechanisms underlying transcriptional bursting and control.

Recent Advances in the Molecular Beacon Technology for Live-Cell Single-Molecule Imaging

Nucleic acids, aside from being best known as the carrier of genetic information, are versatile biomaterials for constructing nanoscopic devices for biointerfacing, owing to their unique properties such as specific base pairing and predictable structure. For live-cell analysis of native RNA transcripts, the most widely used nucleic acid-based nanodevice has been the molecular beacon (MB), a class of stem-loop-forming probes that is activated to fluoresce upon hybridization with target RNA. Here, we overview efforts that have been made in developing MB-based bioassays for sensitive intracellular analysis, particularly at the single-molecule level. We also describe challenges that are currently limiting the widespread use of MBs and provide possible solutions. With continued refinement of MBs in terms of labeling specificity and detection accuracy, accompanied by new development in imaging platforms with unprecedented sensitivity, the application of MBs is envisioned to expand in various biological research fields.

From deterministic to fuzzy decision-making in artificial cells

Building autonomous artificial cells capable of homeostasis requires regulatory networks to gather information and make decisions that take time and cost energy. Decisions based on few molecules may be inaccurate but are cheap and fast. Realizing decision-making with a few molecules in artificial cells has remained a challenge. Here, we show decision-making by a bistable gene network in artificial cells with constant protein turnover. Reducing the number of gene copies from 10⁵ to about 10 per cell revealed a transition from deterministic and slow decision-making to a fuzzy and rapid regime dominated by small-number fluctuations. Gene regulation was observed at lower DNA and protein concentrations than necessary in equilibrium, suggesting rate enhancement by co-expressional localization. The high-copy regime was characterized by a sharp transition and hysteresis, whereas the low-copy limit showed strong fluctuations, state switching, and cellular individuality across the decision-making point. Our results demonstrate information processing with low-power consumption inside artificial cells.

Signaling pathways have an inherent need for noise to acquire information

BACKGROUND

All living systems acquire information about their environment. At the cellular level, they do so through signaling pathways. Such pathways rely on reversible binding interactions between molecules that detect and transmit the presence of an extracellular cue or signal to the cell's interior. These interactions are inherently stochastic and thus noisy. On the one hand, noise can cause a signaling pathway to produce the same response for different stimuli, which reduces the amount of information a pathway acquires. On the other hand, in processes such as stochastic resonance, noise can improve the detection of weak stimuli and thus the acquisition of information. It is not clear whether the kinetic parameters that determine a pathway's operation cause noise to reduce or increase the acquisition of information.

RESULTS

We analyze how the kinetic properties of the reversible binding interactions used by signaling pathways affect the relationship between noise, the response to a signal, and information acquisition. Our results show that, under a wide range of biologically sensible parameter values, a noisy dynamic of reversible binding interactions is necessary to produce distinct responses to different stimuli. As a consequence, noise is indispensable for the acquisition of information in signaling pathways.

CONCLUSIONS

Our observations go beyond previous work by showing that noise plays a positive role in signaling pathways, demonstrating that noise is essential when such pathways acquire information.

Methods for spatial and temporal imaging of the different steps involved in RNA processing at single-molecule resolution

RNA plays a quintessential role as a messenger of information from genotype (DNA) to phenotype (proteins), as well as acts as a regulatory molecule (noncoding RNAs). All steps in the journey of RNA from synthesis (transcription), splicing, transport, localization, translation, to its eventual degradation, comprise important steps in gene expression, thereby controlling the fate of the cell. This lifecycle refers to the majority of RNAs (primarily mRNAs), but not other RNAs such as tRNAs. Imaging these processes in fixed cells and in live cells has been an important tool in developing an understanding of the regulatory steps in RNAs journey. Single-cell and single-molecule imaging techniques enable a much deeper understanding of cellular biology, which is not possible with bulk studies involving RNA isolated from a large pool of cells. Classic techniques, such as fluorescence in situ hybridization (FISH), as well as more recent aptamer-based approaches, have provided detailed insights into RNA localization, and have helped to predict the functions carried out by many RNA species. However, there are still certain processing steps that await high-resolution imaging, which is an exciting and upcoming area of research. In this review, we will discuss the methods that have revolutionized single-molecule resolution imaging in general, the steps of RNA processing in which these methods have been used, and new emerging technologies. This article is categorized under: RNA Export and Localization > RNA Localization RNA Methods > RNA Analyses in Cells RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions.

Short Residence Times of DNA-Bound Transcription Factors Can Reduce Gene Expression Noise and Increase the Transmission of Information in a Gene Regulation System

Gene expression noise is not just ubiquitous but also variable, and we still do not understand some of the most elementary factors that affect it. Among them is the residence time of a transcription factor (TF) on DNA, the mean time that a DNA-bound TF remains bound. Here, we use a stochastic model of transcriptional regulation to study how residence time affects the gene expression noise that arises when a TF induces gene expression. We find that the effect of residence time on gene expression noise depends on the TF’s concentration and its affinity to DNA, which determine the level of induction of the gene. At high levels of induction, residence time has no effect on gene expression noise. However, as the level of induction decreases, short residence times reduce gene expression noise. The reason is that fast on-off TF binding dynamics prevent long periods where proteins are predominantly synthesized or degraded, which can cause excessive fluctuations in gene expression. As a consequence, short residence times can help a gene regulation system acquire information about the cellular environment it operates in. Our predictions are consistent with the observation that experimentally measured residence times are usually modest and lie between seconds to minutes.

Probing Mechanisms of Transcription Elongation Through Cell-to-Cell Variability of RNA Polymerase

The process of transcription initiation and elongation are primary points of control in the regulation of gene expression. Although biochemical studies have uncovered the mechanisms involved in controlling transcription at each step, how these mechanisms manifest in vivo at the level of individual genes is still unclear. Recent experimental advances have enabled single-cell measurements of RNA polymerase (RNAP) molecules engaged in the process of transcribing a gene of interest. In this article, we use Gillespie simulations to show that measurements of cell-to-cell variability of RNAP numbers and interpolymerase distances can reveal the prevailing mode of regulation of a given gene. Mechanisms of regulation at each step, from initiation to elongation dynamics, produce qualitatively distinct signatures, which can further be used to discern between them. Most intriguingly, depending on the initiation kinetics, stochastic elongation can either enhance or suppress cell-to-cell variability at the RNAP level. To demonstrate the value of this framework, we analyze RNAP number distribution data for ribosomal genes in Saccharomyces cerevisiae from three previously published studies and show that this approach provides crucial mechanistic insights into the transcriptional regulation of these genes.

A technical review and guide to RNA fluorescence in situ hybridization

RNA-fluorescence in situ hybridization (FISH) is a powerful tool to visualize target messenger RNA transcripts in cultured cells, tissue sections or whole-mount preparations. As the technique has been developed over time, an ever-increasing number of divergent protocols have been published. There is now a broad selection of options available to facilitate proper tissue preparation, hybridization, and post-hybridization background removal to achieve optimal results. Here we review the technical aspects of RNA-FISH, examining the most common methods associated with different sample types including cytological preparations and whole-mounts. We discuss the application of commonly used reagents for tissue preparation, hybridization, and post-hybridization washing and provide explanations of the functional roles for each reagent. We also discuss the available probe types and necessary controls to accurately visualize gene expression. Finally, we review the most recent advances in FISH technology that facilitate both highly multiplexed experiments and signal amplification for individual targets. Taken together, this information will guide the methods development process for investigators that seek to perform FISH in organisms that lack documented or optimized protocols.

The role of fluctuations in determining cellular network thermodynamics

The steady state distributions of phenotypic responses within an isogenic population of cells result from both deterministic and stochastic characteristics of biochemical networks. A biochemical network can be characterized by a multidimensional potential landscape based on the distribution of responses and a diffusion matrix of the correlated dynamic fluctuations between N-numbers of intracellular network variables. In this work, we develop a thermodynamic description of biological networks at the level of microscopic interactions between network variables. The Boltzmann H-function defines the rate of free energy dissipation of a network system and provides a framework for determining the heat associated with the nonequilibrium steady state and its network components. The magnitudes of the landscape gradients and the dynamic correlated fluctuations of network variables are experimentally accessible. We describe the use of Fokker-Planck dynamics to calculate housekeeping heat from the experimental data by a method that we refer to as Thermo-FP. The method provides insight into the composition of the network and the relative thermodynamic contributions from network components. We surmise that these thermodynamic quantities allow determination of the relative importance of network components to overall network control. We conjecture that there is an upper limit to the rate of dissipative heat produced by a biological system that is associated with system size or modularity, and we show that the dissipative heat has a lower bound.

Noise in the Vertebrate Segmentation Clock Is Boosted by Time Delays but Tamed by Notch Signaling

Taming cell-to-cell variability in gene expression is critical for precise pattern formation during embryonic development. To investigate the source and buffering mechanism of expression variability, we studied a biological clock, the vertebrate segmentation clock, controlling the precise spatiotemporal patterning of the vertebral column. By counting single transcripts of segmentation clock genes in zebrafish, we show that clock genes have low RNA amplitudes and expression variability is primarily driven by gene extrinsic sources, which is suppressed by Notch signaling. We further show that expression noise surprisingly increases from the posterior progenitor zone to the anterior segmentation and differentiation zone. Our computational model reproduces the spatial noise profile by incorporating spatially increasing time delays in gene expression. Our results, suggesting that expression variability is controlled by the balance of time delays and cell signaling in a vertebrate tissue, will shed light on the accuracy of natural clocks in multi-cellular systems and inspire engineering of robust synthetic oscillators.

Understanding biochemical processes in the presence of sub-diffusive behavior of biomolecules in solution and living cells

In order to maintain cellular function, biomolecules like protein, DNA, and RNAs have to diffuse to the target spaces within the cell. Changes in the cytosolic microenvironment or in the nucleus during the fulfillment of these cellular processes affect their mobility, folding, and stability thereby impacting the transient or stable interactions with their adjacent neighbors in the organized and dynamic cellular interior. Using classical Brownian motion to elucidate the diffusion behavior of these biomolecules is hard considering their complex nature. The understanding of biomolecular diffusion inside cells still remains elusive due to the lack of a proper model that can be extrapolated to these cases. In this review, we have comprehensively addressed the progresses in this field, laying emphasis on the different aspects of anomalous diffusion in the different biochemical reactions in cell interior. These experiment-based models help to explain the diffusion behavior of biomolecules in the cytosolic and nuclear microenvironment. Moreover, since understanding of biochemical reactions within living cellular system is our main focus, we coupled the experimental observations with the concept of sub-diffusion from in vitro to in vivo condition. We believe that the pairing between the understanding of complex behavior and structure-function paradigm of biological molecules would take us forward by one step in order to solve the puzzle around diseases caused by cellular dysfunction.

Estimating RNA numbers in single cells by RNA fluorescent tagging and flow cytometry

Estimating the statistics of single-cell RNA numbers has become a key source of information on gene expression dynamics. One of the most informative methods of in vivo single-RNA detection is MS2d-GFP tagging. So far, it requires microscopy and laborious semi-manual image analysis, which hampers the amount of collectable data. To overcome this limitation, we present a new methodology for quantifying the mean, standard deviation, and skewness of single-cell distributions of RNA numbers, from flow cytometry data on cells expressing RNA tagged with MS2d-GFP. The quantification method, based on scaling flow-cytometry data from microscopy single-cell data on integer-valued RNA numbers, is shown to readily produce precise, big data on in vivo single-cell distributions of RNA numbers and, thus, can assist in studies of transcription dynamics.

Decoding the grammar of transcriptional regulation from RNA polymerase measurements: models and their applications

The genomic revolution has indubitably brought about a paradigm shift in the field of molecular biology, wherein we can sequence, write and re-write genomes. In spite of achieving such feats, we still lack a quantitative understanding of how cells integrate environmental and intra-cellular signals at the promoter and accordingly regulate the production of messenger RNAs. This current state of affairs is being redressed by recent experimental breakthroughs which enable the counting of RNA polymerase molecules (or the corresponding nascent RNAs) engaged in the process of transcribing a gene at the single-cell level. Theorists, in conjunction, have sought to unravel the grammar of transcriptional regulation by harnessing the various statistical properties of these measurements. In this review, we focus on the recent progress in developing falsifiable models of transcription that aim to connect the molecular mechanisms of transcription to single-cell polymerase measurements. We discuss studies where the application of such models to the experimental data have led to novel mechanistic insights into the process of transcriptional regulation. Such interplay between theory and experiments will likely contribute towards the exciting journey of unfurling the governing principles of transcriptional regulation ranging from bacteria to higher organisms.