Control of Transcript Variability in Single Mammalian Cells

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.

Stem Cell Signaling Pathways in the Small Intestine

The ability of stem cells to divide and differentiate is necessary for tissue repair and homeostasis. Appropriate spatial and temporal mechanisms are needed. Local intercellular signaling increases expression of specific genes that mediate and maintain differentiation. Diffusible signaling molecules provide concentration-dependent induction of specific patterns of cell types or regions. Differentiation of adjacent cells, on the other hand, requires cell–cell contact and subsequent signaling. These two types of signals work together to allow stem cells to provide what organisms require. The ability to grow organoids has increased our understanding of the cellular and molecular features of small “niches” that modulate stem cell function in various organs, including the small intestine.

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.

microRNA-mediated noise processing in cells: A fight or a game?

In the past decades, microRNAs (miRNA) have much attracted the attention of researchers at the interface between life and theoretical sciences for their involvement in post-transcriptional regulation and related diseases. Thanks to the always more sophisticated experimental techniques, the role of miRNAs as "noise processing units" has been further elucidated and two main ways of miRNA noise-control have emerged by combinations of theoretical and experimental studies. While on one side miRNAs were thought to buffer gene expression noise, it has recently been suggested that miRNAs could also increase the cell-to-cell variability of their targets. In this Mini Review, we focus on the role of miRNAs in molecular noise processing and on the advantages as well as current limitations of theoretical modelling.

Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels

Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysis. For the majority of proteins whose variability in expression has a heritable component, we find that genetic variants act in trans, with notably fewer variants acting in cis. Furthermore, we highlight using Mendelian Randomization that these variability-Quantitative Trait Loci might be driven by the cis regulation of upstream genes. This indicates that natural selection may balance the impact of gene regulation in cis with downstream impacts on expression variability in trans.

Estrogen-induced transcription at individual alleles is independent of receptor level and active conformation but can be modulated by coactivators activity

Steroid hormones are pivotal modulators of pathophysiological processes in many organs, where they interact with nuclear receptors to regulate gene transcription. However, our understanding of hormone action at the single cell level remains incomplete. Here, we focused on estrogen stimulation of the well-characterized GREB1 and MYC target genes that revealed large differences in cell-by-cell responses, and, more interestingly, between alleles within the same cell, both over time and hormone concentration. We specifically analyzed the role of receptor level and activity state during allele-by-allele regulation and found that neither receptor level nor activation status are the determinant of maximal hormonal response, indicating that additional pathways are potentially in place to modulate cell- and allele-specific responses. Interestingly, we found that a small molecule inhibitor of the arginine methyltransferases CARM1 and PRMT6 was able to increase, in a gene specific manner, the number of active alleles/cell before and after hormonal stimulation, suggesting that mechanisms do indeed exist to modulate hormone receptor responses at the single cell and allele level.

Keeping the Proportions of Protein Complex Components in Check

How do cells maintain relative proportions of protein complex components? Advances in quantitative, genome-wide measurements have begun to shed light onto the roles of protein synthesis and degradation in establishing the precise proportions in living cells: on the one hand, ribosome profiling studies indicate that proteins are already produced in the correct relative proportions. On the other hand, proteomic studies found that many complexes contain subunits that are made in excess and subsequently degraded. Here, we discuss these seemingly contradictory findings, emerging principles, and remaining open questions. We conclude that establishing precise protein levels involves both coordinated synthesis and post-translational fine-tuning via protein degradation.

Coupled membrane transporters reduce noise

Molecular systems are inherently probabilistic and operate in a noisy environment, yet, despite all these uncertainties, molecular functions are surprisingly reliable and robust. The principles used by natural systems to deal with noise are still not well understood, especially in a nonhomogeneous environment where molecules can diffuse across different compartments. In this paper we show that membrane transport mechanisms have very effective properties of noise reduction. In particular, we show that active transport mechanisms (those that can transport against a gradient of concentration by using energy or by means of the concentration gradient of other substances), such as symporters and antiporters, have surprising efficiency in noise reduction, which outperforms passive diffusion mechanisms and are well below Poisson levels. We link our results to the coupled transport of potassium, sodium, and glucose to show that the noise in internal glucose level can be greatly reduced. Our results show that compartmentalization can be a highly effective mechanism of noise reduction and suggests that membrane transport could give this extra benefit, contributing to the emergence of complex compartmentalization in eukaryotes.

Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells

The stochasticity of gene expression presents significant challenges to the modeling of genetic networks. A two-state model describing promoter switching, transcription, and messenger RNA (mRNA) decay is the standard model of stochastic mRNA dynamics in eukaryotic cells. Here, we extend this model to include mRNA maturation, cell division, gene replication, dosage compensation, and growth-dependent transcription. We derive expressions for the time-dependent distributions of nascent mRNA and mature mRNA numbers, provided two assumptions hold: 1) nascent mRNA dynamics are much faster than those of mature mRNA; and 2) gene-inactivation events occur far more frequently than gene-activation events. We confirm that thousands of eukaryotic genes satisfy these assumptions by using data from yeast, mouse, and human cells. We use the expressions to perform a sensitivity analysis of the coefficient of variation of mRNA fluctuations averaged over the cell cycle, for a large number of genes in mouse embryonic stem cells, identifying degradation and gene-activation rates as the most sensitive parameters. Furthermore, it is shown that, despite the model's complexity, the time-dependent distributions predicted by our model are generally well approximated by the negative binomial distribution. Finally, we extend our model to include translation, protein decay, and auto-regulatory feedback, and derive expressions for the approximate time-dependent protein-number distributions, assuming slow protein decay. Our expressions enable us to study how complex biological processes contribute to the fluctuations of gene products in eukaryotic cells, as well as allowing a detailed quantitative comparison with experimental data via maximum-likelihood methods.

Eleven grand challenges in single-cell data science

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Mammalian gene expression variability is explained by underlying cell state

Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here, we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH), we measured the multivariate gene expression distribution of 150 genes related to Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.

Phase separation provides a mechanism to reduce noise in cells

Expression of proteins inside cells is noisy, causing variability in protein concentration among identical cells. A central problem in cellular control is how cells cope with this inherent noise. Compartmentalization of proteins through phase separation has been suggested as a potential mechanism to reduce noise, but systematic studies to support this idea have been missing. In this study, we used a physical model that links noise in protein concentration to theory of phase separation to show that liquid droplets can effectively reduce noise. We provide experimental support for noise reduction by phase separation using engineered proteins that form liquid-like compartments in mammalian cells. Thus, phase separation can play an important role in biological signal processing and control.

MLL-AF9 initiates transformation from fast-proliferating myeloid progenitors

Cancer is a hyper-proliferative disease. Whether the proliferative state originates from the cell-of-origin or emerges later remains difficult to resolve. By tracking de novo transformation from normal hematopoietic progenitors expressing an acute myeloid leukemia (AML) oncogene MLL-AF9, we reveal that the cell cycle rate heterogeneity among granulocyte-macrophage progenitors (GMPs) determines their probability of transformation. A fast cell cycle intrinsic to these progenitors provide permissiveness for transformation, with the fastest cycling 3% GMPs acquiring malignancy with near certainty. Molecularly, we propose that MLL-AF9 preserves gene expression of the cellular states in which it is expressed. As such, when expressed in the naturally-existing, rapidly-cycling immature myeloid progenitors, this cell state becomes perpetuated, yielding malignancy. In humans, high CCND1 expression predicts worse prognosis for MLL fusion AMLs. Our work elucidates one of the earliest steps toward malignancy and suggests that modifying the cycling state of the cell-of-origin could be a preventative approach against malignancy.

Single-Cell Transcriptome Analysis Reveals Estrogen Signaling Coordinately Augments One-Carbon, Polyamine, and Purine Synthesis in Breast Cancer

Estrogen drives breast cancer (BCa) progression by directly activating estrogen receptor α (ERα). However, because of the stochastic nature of gene transcription, it is important to study the estrogen signaling pathway at the single-cell level to fully understand how ERα regulates transcription. Here, we performed single-cell transcriptome analysis on ERα-positive BCa cells following 17β-estradiol stimulation and reconstructed the dynamic estrogen-responsive transcriptional network from discrete time points into a pseudotemporal continuum. Notably, differentially expressed genes show an estrogen-stimulated metabolic switch that favors biosynthesis but reduces estrogen degradation. Moreover, folate-mediated one-carbon metabolism is reprogrammed through the mitochondrial folate pathway and polyamine and purine synthesis are upregulated coordinately. Finally, we show AZIN1 and PPAT are direct ERα targets that are essential for BCa cell survival and growth. In summary, our study highlights the dynamic transcriptional heterogeneity in ERα-positive BCa cells upon estrogen stimulation and uncovers a mechanism of estrogen-mediated metabolic switch.

Stochastic transcription in the p53-mediated response to DNA damage is modulated by burst frequency

Discontinuous transcription has been described for different mammalian cell lines and numerous promoters. However, our knowledge of how the activity of individual promoters is adjusted by dynamic signaling inputs from transcription factors is limited. To address this question, we characterized the activity of selected target genes that are regulated by pulsatile accumulation of the tumor suppressor p53 in response to ionizing radiation. We performed time-resolved measurements of gene expression at the single-cell level by smFISH and used the resulting data to inform a mathematical model of promoter activity. We found that p53 target promoters are regulated by frequency modulation of stochastic bursting and can be grouped along three archetypes of gene expression. The occurrence of these archetypes cannot solely be explained by nuclear p53 abundance or promoter binding of total p53. Instead, we provide evidence that the time-varying acetylation state of p53's C-terminal lysine residues is critical for gene-specific regulation of stochastic bursting.

Mass Cytometry Imaging for the Study of Human Diseases-Applications and Data Analysis Strategies

High parameter imaging is an important tool in the life sciences for both discovery and healthcare applications. Imaging Mass Cytometry (IMC) and Multiplexed Ion Beam Imaging (MIBI) are two relatively recent technologies which enable clinical samples to be simultaneously analyzed for up to 40 parameters at subcellular resolution. Importantly, these "Mass Cytometry Imaging" (MCI) modalities are being rapidly adopted for studies of the immune system in both health and disease. In this review we discuss, first, the various applications of MCI to date. Second, due to the inherent challenge of analyzing high parameter spatial data, we discuss the various approaches that have been employed for the processing and analysis of data from MCI experiments.

Constraining the complexity of promoter dynamics using fluctuations in gene expression

Gene expression is an inherently stochastic process with transcription of mRNAs often occurring in bursts: short periods of activity followed by typically longer periods of inactivity. While a simple model involving switching between two promoter states has been widely used to analyze transcription dynamics, recent experimental observations have provided evidence for more complex kinetic schemes underlying bursting. Specifically, experiments provide evidence for complexity in promoter dynamics during the switch from the transcriptionally inactive to the transcriptionally active state. An open question in the field is: what is the minimal complexity needed to model promoter dynamics and how can we determine this? Here, we show that measurements of mRNA fluctuations can be used to set fundamental bounds on the complexity of promoter dynamics. We study models wherein the switching time distribution from transcriptionally inactive to active states is described by a general waiting-time distribution. Using approaches from renewal theory and queueing theory, we derive analytical expressions which connect the Fano factor of mRNA distributions to the waiting-time distribution for promoter switching between inactive and active states. The results derived lead to bounds on the minimal number of promoter states and thus allow us to derive bounds on the minimal complexity of promoter dynamics based on single-cell measurements of mRNA levels.

Experimental evolution reveals a general role for the methyltransferase Hmt1 in noise buffering

Cell-to-cell heterogeneity within an isogenic population has been observed in prokaryotic and eukaryotic cells. Such heterogeneity often manifests at the level of individual protein abundance and may have evolutionary benefits, especially for organisms in fluctuating environments. Although general features and the origins of cellular noise have been revealed, details of the molecular pathways underlying noise regulation remain elusive. Here, we used experimental evolution of Saccharomyces cerevisiae to select for mutations that increase reporter protein noise. By combining bulk segregant analysis and CRISPR/Cas9-based reconstitution, we identified the methyltransferase Hmt1 as a general regulator of noise buffering. Hmt1 methylation activity is critical for the evolved phenotype, and we also show that two of the Hmt1 methylation targets can suppress noise. Hmt1 functions as an environmental sensor to adjust noise levels in response to environmental cues. Moreover, Hmt1-mediated noise buffering is conserved in an evolutionarily distant yeast species, suggesting broad significance of noise regulation.

Experimental evolution in yeast reveals that the methyltransferase Hmt1 functions as a mediator connecting environmental stimuli to cellular noise; Hmt1-mediated noise buffering is conserved in an evolutionarily distant yeast.

MIGA2 Links Mitochondria, the ER, and Lipid Droplets and Promotes De Novo Lipogenesis in Adipocytes

Physical contact between organelles is vital to the function of eukaryotic cells. Lipid droplets (LDs) are dynamic organelles specialized in lipid storage that interact physically with mitochondria in several cell types. The mechanisms coupling these organelles are, however, poorly understood, and the cell-biological function of their interaction remains largely unknown. Here, we discover in adipocytes that the outer mitochondrial membrane protein MIGA2 links mitochondria to LDs. We identify an amphipathic LD-targeting motif and reveal that MIGA2 binds to the membrane proteins VAP-A or VAP-B in the endoplasmic reticulum (ER). We find that in adipocytes MIGA2 is involved in promoting triglyceride (TAG) synthesis from non-lipid precursors. Our data indicate that MIGA2 links reactions of de novo lipogenesis in mitochondria to TAG production in the ER, thereby facilitating efficient lipid storage in LDs. Based on its presence in many tissues, MIGA2 is likely critical for lipid and energy homeostasis in a wide spectrum of cell types.

Stable integrant-specific differences in bimodal HIV-1 expression patterns revealed by high-throughput analysis

HIV-1 gene expression is regulated by host and viral factors that interact with viral motifs and is influenced by proviral integration sites. Here, expression variation among integrants was followed for hundreds of individual proviral clones within polyclonal populations throughout successive rounds of virus and cultured cell replication, with limited findings using CD4+ cells from donor blood consistent with observations in immortalized cells. Tracking clonal behavior by proviral “zip codes” indicated that mutational inactivation during reverse transcription was rare, while clonal expansion and proviral expression states varied widely. By sorting for provirus expression using a GFP reporter in the nef open reading frame, distinct clone-specific variation in on/off proportions were observed that spanned three orders of magnitude. Tracking GFP phenotypes over time revealed that as cells divided, their progeny alternated between HIV transcriptional activity and non-activity. Despite these phenotypic oscillations, the overall GFP+ population within each clone was remarkably stable, with clones maintaining clone-specific equilibrium mixtures of GFP+ and GFP- cells. Integration sites were analyzed for correlations between genomic features and the epigenetic phenomena described here. Integrants inserted in the sense orientation of genes were more frequently found to be GFP negative than those in the antisense orientation, and clones with high GFP+ proportions were more distal to repressive H3K9me3 peaks than low GFP+ clones. Clones with low frequencies of GFP positivity appeared to expand more rapidly than clones for which most cells were GFP+, even though the tested proviruses were Vpr-. Thus, much of the increase in the GFP- population in these polyclonal pools over time reflected differential clonal expansion. Together, these results underscore the temporal and quantitative variability in HIV-1 gene expression among proviral clones that are conferred in the absence of metabolic or cell-type dependent variability, and shed light on cell-intrinsic layers of regulation that affect HIV-1 population dynamics.

Very few HIV-1 infected cells persist in patients for more than a couple days, but those that do pose life-long health risks. Strategies designed to eliminate these cells have been based on assumptions about what viral properties allow infected cell survival. However, such approaches for HIV-1 eradication have not yet shown therapeutic promise, possibly because many assumptions about virus persistence are based on studies involving a limited number of infected cell types, the averaged behavior of cells in diverse populations, or snapshot views. Here, we developed a high-throughput approach to study hundreds of distinct HIV-1 infected cells and their progeny over time in an unbiased way. This revealed that each virus established its own pattern of gene expression that, upon infected cell division, was stably transmitted to all progeny cells. Expression patterns consisted of alternating waves of activity and inactivity, with the extent of activity differing among infected cell families over a 1000-fold range. The dynamics and variability among infected cells and within complex populations that the work here revealed has not previously been evident, and may help establish more accurate correlates of persistent HIV-1 infection.

Visualizing RNA dynamics in live cells with bright and stable fluorescent RNAs

Fluorescent RNAs (FRs), aptamers that bind and activate fluorescent dyes, have been used to image abundant cellular RNA species. However, limitations such as low brightness and limited availability of dye/aptamer combinations with different spectral characteristics have limited use of these tools in live mammalian cells and in vivo. Here, we develop Peppers, a series of monomeric, bright and stable FRs with a broad range of emission maxima spanning from cyan to red. Peppers allow simple and robust imaging of diverse RNA species in live cells with minimal perturbation of the target RNA's transcription, localization and translation. Quantification of the levels of proteins and their messenger RNAs in single cells suggests that translation is governed by normal enzyme kinetics but with marked heterogeneity. We further show that Peppers can be used for imaging genomic loci with CRISPR display, for real-time tracking of protein-RNA tethering, and for super-resolution imaging. We believe these FRs will be useful tools for live imaging of cellular RNAs.

Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression

The spatial organization of RNAs within cells and spatial patterning of cells within tissues play crucial roles in many biological processes. Here, we demonstrate that multiplexed error-robust FISH (MERFISH) can achieve near-genome-wide, spatially resolved RNA profiling of individual cells with high accuracy and high detection efficiency. Using this approach, we identified RNA species enriched in different subcellular compartments, observed transcriptionally distinct cell states corresponding to different cell-cycle phases, and revealed spatial patterning of transcriptionally distinct cells. Spatially resolved transcriptome quantification within cells further enabled RNA velocity and pseudotime analysis, which revealed numerous genes with cell cycle-dependent expression. We anticipate that spatially resolved transcriptome analysis will advance our understanding of the interplay between gene regulation and spatial context in biological systems.

The expression profiles and spatial distributions of RNAs regulate many cellular functions. Image-based transcriptomic approaches provide powerful means to measure both expression and spatial information of RNAs in individual cells within their native environment. Among these approaches, multiplexed error-robust fluorescence in situ hybridization (MERFISH) has achieved spatially resolved RNA quantification at transcriptome scale by massively multiplexing single-molecule FISH measurements. Here, we increased the gene throughput of MERFISH and demonstrated simultaneous measurements of RNA transcripts from ∼10,000 genes in individual cells with ∼80% detection efficiency and ∼4% misidentification rate. We combined MERFISH with cellular structure imaging to determine subcellular compartmentalization of RNAs. We validated this approach by showing enrichment of secretome transcripts at the endoplasmic reticulum, and further revealed enrichment of long noncoding RNAs, RNAs with retained introns, and a subgroup of protein-coding mRNAs in the cell nucleus. Leveraging spatially resolved RNA profiling, we developed an approach to determine RNA velocity in situ using the balance of nuclear versus cytoplasmic RNA counts. We applied this approach to infer pseudotime ordering of cells and identified cells at different cell-cycle states, revealing ∼1,600 genes with putative cell cycle-dependent expression and a gradual transcription profile change as cells progress through cell-cycle stages. Our analysis further revealed cell cycle-dependent and cell cycle-independent spatial heterogeneity of transcriptionally distinct cells. We envision that the ability to perform spatially resolved, genome-wide RNA profiling with high detection efficiency and accuracy by MERFISH could help address a wide array of questions ranging from the regulation of gene expression in cells to the development of cell fate and organization in tissues.

Chromosome-wide co-fluctuation of stochastic gene expression in mammalian cells

Gene expression is subject to stochastic noise, but to what extent and by which means such stochastic variations are coordinated among different genes are unclear. We hypothesize that neighboring genes on the same chromosome co-fluctuate in expression because of their common chromatin dynamics, and verify it at the genomic scale using allele-specific single-cell RNA-sequencing data of mouse cells. Unexpectedly, the co-fluctuation extends to genes that are over 60 million bases apart. We provide evidence that this long-range effect arises in part from chromatin co-accessibilities of linked loci attributable to three-dimensional proximity, which is much closer intra-chromosomally than inter-chromosomally. We further show that genes encoding components of the same protein complex tend to be chromosomally linked, likely resulting from natural selection for intracellular among-component dosage balance. These findings have implications for both the evolution of genome organization and optimal design of synthetic genomes in the face of gene expression noise.

Challenges in measuring and understanding biological noise

Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.

Deciphering human ribonucleoprotein regulatory networks

RNA-binding proteins (RBPs) control and coordinate each stage in the life cycle of RNAs. Although in vivo binding sites of RBPs can now be determined genome-wide, most studies typically focused on individual RBPs. Here, we examined a large compendium of 114 high-quality transcriptome-wide in vivo RBP-RNA cross-linking interaction datasets generated by the same protocol in the same cell line and representing 64 distinct RBPs. Comparative analysis of categories of target RNA binding preference, sequence preference, and transcript region specificity was performed, and identified potential posttranscriptional regulatory modules, i.e. specific combinations of RBPs that bind to specific sets of RNAs and targeted regions. These regulatory modules represented functionally related proteins and exhibited distinct differences in RNA metabolism, expression variance, as well as subcellular localization. This integrative investigation of experimental RBP-RNA interaction evidence and RBP regulatory function in a human cell line will be a valuable resource for understanding the complexity of post-transcriptional regulation.

Atlas of Subcellular RNA Localization Revealed by APEX-Seq

We introduce APEX-seq, a method for RNA sequencing based on direct proximity labeling of RNA using the peroxidase enzyme APEX2. APEX-seq in nine distinct subcellular locales produced a nanometer-resolution spatial map of the human transcriptome as a resource, revealing extensive patterns of localization for diverse RNA classes and transcript isoforms. We uncover a radial organization of the nuclear transcriptome, which is gated at the inner surface of the nuclear pore for cytoplasmic export of processed transcripts. We identify two distinct pathways of messenger RNA localization to mitochondria, each associated with specific sets of transcripts for building complementary macromolecular machines within the organelle. APEX-seq should be widely applicable to many systems, enabling comprehensive investigations of the spatial transcriptome.

Post-Transcriptional Noise Control

Recent evidence indicates that transcriptional bursts are intrinsically amplified by messenger RNA cytoplasmic processing to generate large stochastic fluctuations in protein levels. These fluctuations can be exploited by cells to enable probabilistic bet-hedging decisions. But large fluctuations in gene expression can also destabilize cell-fate commitment. Thus, it is unclear if cells temporally switch from high to low noise, and what mechanisms enable this switch. Here, the discovery of a post-transcriptional mechanism that attenuates noise in HIV is reviewed. Early in its life cycle, HIV amplifies transcriptional fluctuations to probabilistically select alternate fates, whereas at late times, HIV utilizes a post-transcriptional feedback mechanism to commit to a specific fate. Reanalyzing various reported post-transcriptional negative feedback architectures reveals that they attenuate noise more efficiently than classic transcriptional autorepression, leading to the derivation of an assay to detect post-transcriptional motifs. It is hypothesized that coupling transcriptional and post-transcriptional autoregulation enables efficient temporal noise control to benefit developmental bet-hedging decisions.

Exploring single cells in space and time during tissue development, homeostasis and regeneration

Complex 3D tissues arise during development following tightly organized events in space and time. In particular, gene regulatory networks and local interactions between single cells lead to emergent properties at the tissue and organism levels. To understand the design principles of tissue organization, we need to characterize individual cells at given times, but we also need to consider the collective behavior of multiple cells across different spatial and temporal scales. In recent years, powerful single cell methods have been developed to characterize cells in tissues and to address the challenging questions of how different tissues are formed throughout development, maintained in homeostasis, and repaired after injury and disease. These approaches have led to a massive increase in data pertaining to both mRNA and protein abundances in single cells. As we review here, these new technologies, in combination with in toto live imaging, now allow us to bridge spatial and temporal information quantitatively at the single cell level and generate a mechanistic understanding of tissue development.

Multidimensional analysis of Gammaherpesvirus RNA expression reveals unexpected heterogeneity of gene expression

Virus-host interactions are frequently studied in bulk cell populations, obscuring cell-to-cell variation. Here we investigate endogenous herpesvirus gene expression at the single-cell level, combining a sensitive and robust fluorescent in situ hybridization platform with multiparameter flow cytometry, to study the expression of gammaherpesvirus non-coding RNAs (ncRNAs) during lytic replication, latent infection and reactivation in vitro. This method allowed robust detection of viral ncRNAs of murine gammaherpesvirus 68 (γHV68), Kaposi’s sarcoma associated herpesvirus and Epstein-Barr virus, revealing variable expression at the single-cell level. By quantifying the inter-relationship of viral ncRNA, viral mRNA, viral protein and host mRNA regulation during γHV68 infection, we find heterogeneous and asynchronous gene expression during latency and reactivation, with reactivation from latency identified by a distinct gene expression profile within rare cells. Further, during lytic replication with γHV68, we find many cells have limited viral gene expression, with only a fraction of cells showing robust gene expression, dynamic RNA localization, and progressive infection. Lytic viral gene expression was enhanced in primary fibroblasts and by conditions associated with enhanced viral replication, with multiple subpopulations of cells present in even highly permissive infection conditions. These findings, powered by single-cell analysis integrated with automated clustering algorithms, suggest inefficient or abortive γHV infection in many cells, and identify substantial heterogeneity in viral gene expression at the single-cell level.

The gammaherpesviruses are a group of DNA tumor viruses that establish lifelong infection. How these viruses infect and manipulate cells has frequently been studied in bulk populations of cells. While these studies have been incredibly insightful, there is limited understanding of how virus infection proceeds within a single cell. Here we present a new approach to quantify gammaherpesvirus gene expression at the single-cell level. This method allows us to detect cell-to-cell variation in the expression of virus non-coding RNAs, an important and understudied class of RNAs which do not encode for proteins. By examining multiple features of virus gene expression, this method further reveals significant variation in infection between cells across multiple stages of infection, even in conditions generally thought to be highly uniform. These studies emphasize that gammaherpesvirus infection can be surprisingly heterogeneous when viewed at the level of the individual cell. Because this approach can be broadly applied across diverse viruses, this study affords new opportunities to understand the complexity of virus infection within single cells.

Predictive models of subcellular localization of long RNAs

Export to the cytoplasm is a key regulatory junction for both protein-coding mRNAs and long noncoding RNAs (lncRNAs), and cytoplasmic enrichment varies dramatically both within and between those groups. We used a new computational approach and RNA-seq data from human and mouse cells to quantify the genome-wide association between cytoplasmic/nuclear ratios of both gene groups and various factors, including expression levels, splicing efficiency, gene architecture, chromatin marks, and sequence elements. Splicing efficiency emerged as the main predictive factor, explaining up to a third of the variability in localization. Combination with other features allowed predictive models that could explain up to 45% of the variance for protein-coding genes and up to 34% for lncRNAs. Factors associated with localization were similar between lncRNAs and mRNAs with some important differences. Readily accessible features can thus be used to predict RNA localization.

Self-organization and symmetry breaking in intestinal organoid development

Intestinal organoids are complex three-dimensional structures that mimic the cell type composition and tissue organization of the intestine by recapitulating the self-organizing ability of cell populations derived from a single intestinal stem cell. Crucial in this process is a first symmetry-breaking event, in which only a fraction of identical cells in a symmetrical sphere differentiate into Paneth cells, which generate the stem cell niche and lead to asymmetric structures such as crypts and villi. We here combine single-cell quantitative genomic and imaging approaches to characterize the development of intestinal organoids from single cells. We show that their development follows a regeneration process driven by transient Yap1 activation. Cell-to-cell variability in Yap1, emerging in symmetrical spheres, initiates a Notch/Dll1 activation driving the symmetry-breaking event and the formation of the first Paneth cell. Our findings reveal how single cells exposed to a uniform growth-promoting environment have the intrinsic ability to generate emergent, self-organized behavior resulting in the formation of complex multicellular asymmetric structures.

Techniques for Studying Decoding of Single Cell Dynamics

Cells must be able to interpret signals they encounter and reliably generate an appropriate response. It has long been known that the dynamics of transcription factor and kinase activation can play a crucial role in selecting an individual cell's response. The study of cellular dynamics has expanded dramatically in the last few years, with dynamics being discovered in novel pathways, new insights being revealed about the importance of dynamics, and technological improvements increasing the throughput and capabilities of single cell measurements. In this review, we highlight the important developments in this field, with a focus on the methods used to make new discoveries. We also include a discussion on improvements in methods for engineering and measuring single cell dynamics and responses. Finally, we will briefly highlight some of the many challenges and avenues of research that are still open.

Computational Modeling to Quantify the Contributions of VEGFR1, VEGFR2, and Lateral Inhibition in Sprouting Angiogenesis

Sprouting angiogenesis is a necessary process in regeneration and development as well as in tumorigenesis. VEGF-A is the main pro-angiogenic chemoattractant and it can bind to the decoy receptor VEGFR1 or to VEGFR2 to induce sprouting. Active sprout cells express Dll4, which binds to Notch1 on neighboring cells, in turn inhibiting VEGFR2 expression. It is known that the balance between VEGFR2 and VEGFR1 determines tip selection and network architecture, however the quantitative interrelationship of the receptors and their interrelated balances, also with relation to Dll4-Notch1 signaling, remains yet largely unknown. Here, we present an agent-based computer model of sprouting angiogenesis, integrating VEGFR1 and VEGFR2 in a detailed model of cellular signaling. Our model reproduces experimental data on VEGFR1 knockout. We show that soluble VEGFR1 improves the efficiency of angiogenesis by directing sprouts away from existing cells over a wide range of parameters. Our analysis unravels the relevance of the stability of the active notch intracellular domain as a dominating hub in this regulatory network. Our analysis quantitatively dissects the regulatory interactions in sprouting angiogenesis. Because we use a detailed model of intracellular signaling, the results of our analysis are directly linked to biological entities. We provide our computational model and simulation engine for integration in complementary modeling approaches.

The dynamic lifecycle of mRNA in the nucleus

The mRNA molecule roams through the nucleus on its way out to the cytoplasm. mRNA encounters and is bound by many protein factors, from the moment it begins to emerge from RNA polymerase II and during its travel in the nucleoplasm, where it will come upon chromatin and nuclear bodies. Some of the protein factors that engage with the mRNA can process it, until finally reaching a mature state fit for export through the nuclear pore complex (NPC). Examining the lifecycle of mRNAs in living cells using mRNA tagging techniques opens a window into our understanding of the rules that drive the dynamics of gene expression from transcription to mRNA export.

Memory and relatedness of transcriptional activity in mammalian cell lineages

Phenotypically identical mammalian cells often display considerable variability in transcript levels of individual genes. How transcriptional activity propagates in cell lineages, and how this varies across genes is poorly understood. Here we combine live-cell imaging of short-lived transcriptional reporters in mouse embryonic stem cells with mathematical modelling to quantify the propagation of transcriptional activity over time and across cell generations in phenotypically homogenous cells. In sister cells we find mean transcriptional activity to be strongly correlated and transcriptional dynamics tend to be synchronous; both features control how quickly transcriptional levels in sister cells diverge in a gene-specific manner. Moreover, mean transcriptional activity is transmitted from mother to daughter cells, leading to multi-generational transcriptional memory and causing inter-family heterogeneity in gene expression.

Multiplexed profiling of RNA and protein expression signatures in individual cells using flow or mass cytometry

Advances in single-cell analysis technologies are providing novel insights into phenotypic and functional heterogeneity within seemingly identical cell populations. RNA within single cells can be analyzed using unbiased sequencing protocols or through more targeted approaches using in situ hybridization (ISH). The proximity ligation assay for RNA (PLAYR) approach is a sensitive and high-throughput technique that relies on in situ and proximal ligation to measure at least 27 specific RNAs by flow or mass cytometry. We provide detailed instructions for combining this technique with antibody-based detection of surface/internal protein, allowing simultaneous highly multiplexed profiling of RNA and protein expression at single-cell resolution. PLAYR overcomes limitations on multiplexing seen in previous branching DNA-based RNA detection techniques by integration of a transcript-specific oligonucleotide sequence within a rolling-circle amplification (RCA). This unique transcript-associated sequence can then be detected by heavy metal (for mass cytometry)- or fluorophore (for flow cytometry)-conjugated complementary detection oligonucleotides. Included in this protocol is methodology to label oligonucleotides with lanthanide metals for use in mass cytometry. When analyzed by mass cytometry, up to 40 variables (with scope for future expansion) can be measured simultaneously. We used the described protocol to demonstrate intraclonal heterogeneity within primary cells from chronic lymphocytic leukemia patients, but it can be adapted to other primary cells or cell lines in suspension. This robust, reliable and reproducible protocol can be completed in 2-3 d and can be paused at several stages for convenience.

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

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

New Insights into the Interplay between Non-Coding RNAs and RNA-Binding Protein HnRNPK in Regulating Cellular Functions

The emerging data indicates that non-coding RNAs (ncRNAs) epresent more than the “junk sequences” of the genome. Both miRNAs and long non-coding RNAs (lncRNAs) are involved in fundamental biological processes, and their deregulation may lead to oncogenesis and other diseases. As an important RNA-binding protein (RBP), heterogeneous nuclear ribonucleoprotein K (hnRNPK) is known to regulate gene expression through the RNA-binding domain involved in various pathways, such as transcription, splicing, and translation. HnRNPK is a highly conserved gene that is abundantly expressed in mammalian cells. The interaction of hnRNPK and ncRNAs defines the novel way through which ncRNAs affect the expression of protein-coding genes and form autoregulatory feedback loops. This review summarizes the interactions of hnRNPK and ncRNAs in regulating gene expression at transcriptional and post-transcriptional levels or by changing the genomic structure, highlighting their involvement in carcinogenesis, glucose metabolism, stem cell differentiation, virus infection and other cellular functions. Drawing connections between such discoveries might provide novel targets to control the biological outputs of cells in response to different stimuli.

Single cell functional genomics reveals the importance of mitochondria in cell-to-cell phenotypic variation

Mutations frequently have outcomes that differ across individuals, even when these individuals are genetically identical and share a common environment. Moreover, individual microbial and mammalian cells can vary substantially in their proliferation rates, stress tolerance, and drug resistance, with important implications for the treatment of infections and cancer. To investigate the causes of cell-to-cell variation in proliferation, we used a high-throughput automated microscopy assay to quantify the impact of deleting >1500 genes in yeast. Mutations affecting mitochondria were particularly variable in their outcome. In both mutant and wild-type cells mitochondrial membrane potential - but not amount - varied substantially across individual cells and predicted cell-to-cell variation in proliferation, mutation outcome, stress tolerance, and resistance to a clinically used anti-fungal drug. These results suggest an important role for cell-to-cell variation in the state of an organelle in single cell phenotypic variation.

The Ultimate qPCR Experiment: Producing Publication Quality, Reproducible Data the First Time

Quantitative PCR (qPCR) is one of the most common techniques for quantification of nucleic acid molecules in biological and environmental samples. Although the methodology is perceived to be relatively simple, there are a number of steps and reagents that require optimization and validation to ensure reproducible data that accurately reflect the biological question(s) being posed. This review article describes and illustrates the critical pitfalls and sources of error in qPCR experiments, along with a rigorous, stepwise process to minimize variability, time, and cost in generating reproducible, publication quality data every time. Finally, an approach to make an informed choice between qPCR and digital PCR technologies is described.

Intrinsic Dynamics of a Human Gene Reveal the Basis of Expression Heterogeneity

Transcriptional regulation in metazoans occurs through long-range genomic contacts between enhancers and promoters, and most genes are transcribed in episodic "bursts" of RNA synthesis. To understand the relationship between these two phenomena and the dynamic regulation of genes in response to upstream signals, we describe the use of live-cell RNA imaging coupled with Hi-C measurements and dissect the endogenous regulation of the estrogen-responsive TFF1 gene. Although TFF1 is highly induced, we observe short active periods and variable inactive periods ranging from minutes to days. The heterogeneity in inactive times gives rise to the widely observed "noise" in human gene expression and explains the distribution of protein levels in human tissue. We derive a mathematical model of regulation that relates transcription, chromosome structure, and the cell's ability to sense changes in estrogen and predicts that hypervariability is largely dynamic and does not reflect a stable biological state.

Buffering and Amplifying Transcriptional Noise During Cell Fate Specification

The molecular processes that drive gene transcription are inherently noisy. This noise often manifests in the form of transcriptional bursts, producing fluctuations in gene activity over time. During cell fate specification, this noise is often buffered to ensure reproducible developmental outcomes. However, sometimes noise is utilized as a “bet-hedging” mechanism to diversify functional roles across a population of cells. Studies of bacteria, yeast, and cultured cells have provided insights into the nature and roles of noise in transcription, yet we are only beginning to understand the mechanisms by which noise influences the development of multicellular organisms. Here we discuss the sources of transcriptional noise and the mechanisms that either buffer noise to drive reproducible fate choices or amplify noise to randomly specify fates.

Machine learning based classification of cells into chronological stages using single-cell transcriptomics

Age-associated deterioration of cellular physiology leads to pathological conditions. The ability to detect premature aging could provide a window for preventive therapies against age-related diseases. However, the techniques for determining cellular age are limited, as they rely on a limited set of histological markers and lack predictive power. Here, we implement GERAS (GEnetic Reference for Age of Single-cell), a machine learning based framework capable of assigning individual cells to chronological stages based on their transcriptomes. GERAS displays greater than 90% accuracy in classifying the chronological stage of zebrafish and human pancreatic cells. The framework demonstrates robustness against biological and technical noise, as evaluated by its performance on independent samplings of single-cells. Additionally, GERAS determines the impact of differences in calorie intake and BMI on the aging of zebrafish and human pancreatic cells, respectively. We further harness the classification ability of GERAS to identify molecular factors that are potentially associated with the aging of beta-cells. We show that one of these factors, junba, is necessary to maintain the proliferative state of juvenile beta-cells. Our results showcase the applicability of a machine learning framework to classify the chronological stage of heterogeneous cell populations, while enabling detection of candidate genes associated with aging.

From single cells to tissue self-organization

Self-organization is a process by which interacting cells organize and arrange themselves in higher order structures and patterns. To achieve this, cells must have molecular mechanisms to sense their complex local environment and interpret it to respond accordingly. A combination of cell-intrinsic and cell-extrinsic cues are decoded by the single cells dictating their behaviour, their differentiation and symmetry-breaking potential driving development, tissue remodeling and regenerative processes. A unifying property of these self-organized pattern-forming systems is the importance of fluctuations, cell-to-cell variability, or noise. Cell-to-cell variability is an inherent and emergent property of populations of cells that maximize the population performance instead of the individual cell, providing tissues the flexibility to develop and maintain homeostasis in diverse environments. In this review, we will explore the role of self-organization and cell-to-cell variability as fundamental properties of multicellularity-and the requisite of single-cell resolution for its understanding. Moreover, we will analyze how single cells generate emergent multicellular dynamics observed at the tissue level 'travelling' across different scales: spatial, temporal and functional.

A computational framework to study sub-cellular RNA localization

RNA localization is a crucial process for cellular function and can be quantitatively studied by single molecule FISH (smFISH). Here, we present an integrated analysis framework to analyze sub-cellular RNA localization. Using simulated images, we design and validate a set of features describing different RNA localization patterns including polarized distribution, accumulation in cell extensions or foci, at the cell membrane or nuclear envelope. These features are largely invariant to RNA levels, work in multiple cell lines, and can measure localization strength in perturbation experiments. Most importantly, they allow classification by supervised and unsupervised learning at unprecedented accuracy. We successfully validate our approach on representative experimental data. This analysis reveals a surprisingly high degree of localization heterogeneity at the single cell level, indicating a dynamic and plastic nature of RNA localization.

Automated analysis of RNA localisation in smFISH data has been elusive. Here, the authors simulate and use a large dataset of images to design and validate a framework for highly accurate classification of sub-cellular RNA localisation patterns from smFISH experiments.