A single molecule view of gene expression

Development of Proximity-Activated Programmable Multicomponent Nucleic Acid Enzymes for Simultaneous Visualization of Multiple mRNA Splicing Variants in Living Cells

RNA splicing is a key regulatory process of gene expression that can increase the transcriptome complexity. Simultaneous monitoring of multiple splicing variants in living cells is critical for gaining new insight into cell development. Herein, we demonstrate the development of proximity-activated, programmable multicomponent nucleic acid enzymes (MNAzymes) for the simultaneous visualization of multiple RNA splicing variants (i.e., BRCA1 WT and BRCA1 Δ11q) in living cells. The presence of BRCA1 WT and BRCA1 Δ11q can specifically bring their corresponding partzymes into the proximity of each other to form two active MNAzyme motifs. Subsequently, the active sites of reporter probes 1 and 2 are cyclically cleaved by two activated MNAzyme motifs, respectively, to release abundant Cy3 and Cy5 fluorescent molecules, generating enhanced fluorescence signals for the simultaneous detection of BRCA1 WT and BRCA1 Δ11q in vitro and in vivo. Notably, this assay can be simply and isothermally conducted in a one-step format without the necessity for unstable protein enzymes, precise temperature control, and complex operation procedures. This method can sensitively detect 2.46 fM BRCA1 WT and 2.77 fM BRCA1 Δ11q and accurately distinguish breast cancer patients from healthy individuals by measuring target BRCA1 splicing variants from the tissue samples. Moreover, this method can real-time image BRCA1 splicing variants in living cells and can be extended to detect other cellular target RNAs (e.g., miRNAs, piRNAs, lncRNAs, and circRNAs) by simply changing the sequences of substrate arms, holding promising applications in clinical diagnosis and precise therapy.

Recent approaches lead to a deeper understanding of diverse segmentation mechanisms in insects, with a focus on the pair-rule genes

The division of the insect embryo into repeated units - segments - is a fundamental feature of the body plan. The genes controlling this process in Drosophila melanogaster were identified in genetic screens and characterized in that species in numerous studies in the 1980s and 1990s. These genes form a well-established hierarchy and have been leveraged to examine gene regulation, transcriptional machinery, chromatin structure, and more. Much of the genetic toolkit identified in Drosophila is highly conserved throughout the animal kingdom, spearheading the field of evolutionary developmental biology or Evo-Devo. Accordingly, a 'Drosophila-centric' approach has examined the evolutionary conservation of orthologs of Drosophila segmentation genes in closely and distantly related insects. Here, we report on progress in both Drosophila and emerging model insects in recent years (2022 to present), with much of the new research related to the pair-rule subset of segmentation genes. We highlight new findings on 'classic' Drosophila genes, revealing unexpected roles of genes and cis-regulatory elements in this species. We further report on the expanding knowledge about mechanisms regulating to segmentation in emerging model insects that are distantly related to Drosophila, including those that pattern segments sequentially. We also describe technical advances in both Drosophila and nonmodel species that are currently progressing research in this field.

What can we learn when fitting a simple telegraph model to a complex gene expression model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

Understanding Cell Differentiation Through Single-Cell Approaches: Conceptual Challenges of the Systemic Approach

The cells of a multicellular organism are derived from a single zygote and genetically almost identical. Yet, they are phenotypically very different. This difference is the result of a process commonly called cell differentiation. How the phenotypic diversity emerges during ontogenesis or regeneration is a central and intensely studied but still unresolved issue in biology. Cell biology is facing conceptual challenges that are frequently confused with methodological difficulties. How to define a cell type? What stability or change means in the context of cell differentiation and how to deal with the ubiquitous molecular variations seen in the living cells? What are the driving forces of the change? We propose to reframe the problem of cell differentiation in a systemic way by incorporating different theoretical approaches. The new conceptual framework is able to capture the insights made at different levels of cellular organization and considered previously as contradictory. It also provides a formal strategy for further experimental studies.

Multiplexed Immunofluorescence and Single-Molecule RNA Fluorescence In Situ Hybridization in Mouse Skeletal Myofibers

RNA fluorescence in situ hybridization (FISH) is a powerful method to determine the abundance and localization of mRNA molecules in cells. While modern RNA FISH techniques allow quantification at single molecule resolution, most methods are optimized for mammalian cell culture and are not easily applied to in vivo tissue settings. Single-molecule RNA detection in skeletal muscle cells has been particularly challenging due to the thickness and high autofluorescence of adult muscle tissue and a lack of in vitro models for mature muscle cells (myofibers). Here, we present a method for isolation of adult myofibers from mouse skeletal muscle and detection of single mRNA molecules and proteins using multiplexed RNA FISH and immunofluorescence.

Phase separation reduces cell-to-cell variability of transcriptional bursting

Gene expression is a stochastic and noisy process often occurring in "bursts". Experiments have shown that the compartmentalization of proteins by liquid-liquid phase separation is conducive to reducing the noise of gene expression. Therefore, an important goal is to explore the role of bursts in phase separation noise reduction processes. We propose a coupled model that includes phase separation and a two-state gene expression process. Using the timescale separation method, we obtain approximate solutions for the expectation, variance, and noise strength of the dilute phase. We find that a higher burst frequency weakens the ability of noise reduction by phase separation, but as the burst size increases, this ability first increases and then decreases. This study provides a deeper understanding of phase separation to reduce noise in the stochastic gene expression with burst kinetics.

A circadian-like gene network programs the timing and dosage of heterochronic miRNA transcription during C. elegans development

Development relies on the exquisite control of both the timing and the levels of gene expression to achieve robust developmental transitions. How cis- and trans-acting factors control both aspects simultaneously is unclear. We show that transcriptional pulses of the temporal patterning microRNA (miRNA) lin-4 are generated by two nuclear hormone receptors (NHRs) in C. elegans, NHR-85 and NHR-23, whose mammalian orthologs, Rev-Erb and ROR, function in the circadian clock. Although Rev-Erb and ROR antagonize each other to control once-daily transcription in mammals, NHR-85/NHR-23 heterodimers bind cooperatively to lin-4 regulatory elements to induce a single pulse of expression during each larval stage. Each pulse's timing, amplitude, and duration are dictated by the phased expression of these NHRs and the C. elegans Period ortholog, LIN-42, that binds to and represses NHR-85. Therefore, during nematode temporal patterning, an evolutionary rewiring of circadian clock components couples the timing of gene expression to the control of transcriptional dosage.

Allele pairing at Sun1-enriched domains at the nuclear periphery via T1A3 tandem DNA repeats

Spatiotemporal gene regulation is fundamental to the biology of diploid cells. Therefore, effective communication between two alleles and their geometry in the nucleus is important. However, the mechanism that fine-tunes the expression from each of the two alleles of an autosome is enigmatic. Establishing an allele-specific gene expression visualization system in living cells, we show that alleles of biallelically expressed Cth and Ttc4 genes are paired prior to acquiring monoallelic expression. We found that active alleles of monoallelic genes are preferentially localized at Sun1-enriched domains at the nuclear periphery. These peripherally localized active DNA loci are enriched with adenine-thymidine-rich tandem repeats that interact with Hnrnpd and reside in a Hi-C-defined A compartment within the B compartment. Our results demonstrate the biological significance of T ₁ A ₃ tandem repeat sequences in genome organization and how the regulation of gene expression, at the level of individual alleles, relates to their spatial arrangement.

The minimal intrinsic stochasticity of constitutively expressed eukaryotic genes is sub-Poissonian

Stochastic variation in gene products ("noise") is an inescapable by-product of gene expression. Noise must be minimized to allow for the reliable execution of cellular functions. However, noise cannot be suppressed beyond an intrinsic lower limit. For constitutively expressed genes, this limit is believed to be Poissonian, meaning that the variance in mRNA numbers cannot be lower than their mean. Here, we show that several cell division genes in fission yeast have mRNA variances significantly below this limit, which cannot be explained by the classical gene expression model for low-noise genes. Our analysis reveals that multiple steps in both transcription and mRNA degradation are essential to explain this sub-Poissonian variance. The sub-Poissonian regime differs qualitatively from previously characterized noise regimes, a hallmark being that cytoplasmic noise is reduced when the mRNA export rate increases. Our study re-defines the lower limit of eukaryotic gene expression noise and identifies molecular requirements for ultra-low noise which are expected to support essential cell functions.

Catalytic DNA-Assisted Mass Production of Arbitrary Single-Stranded DNA

Synthetic single-stranded (ss) DNA is a cornerstone for life and materials science, yet the purity, quantity, length, and customizability of synthetic DNA are still limiting in various applications. Here, we present PECAN, paired-end cutting assisted by DNAzymes (DNA enzymes or deoxyribozymes), which enables mass production of ssDNA of arbitrary sequence (up to 7000 nucleotides, or nt) with single-base precision. At the core of PECAN technique are two newly identified classes of DNAzymes, each robustly self-hydrolyzing with minimal sequence requirement up- or down-stream of its cleavage site. Flanking the target ssDNA with a pair of such DNAzymes generates a precursor ssDNA amplifiable by pseudogene-recombinant bacteriophage, which subsequently releases the target ssDNA in large quantities after efficient auto-processing. PECAN produces ssDNA of virtually any terminal bases and compositions with >98.5 % purity at the milligram-to-gram scale. We demonstrate the feasibility of using PECAN ssDNA for RNA in situ detection, homology-directed genome editing, and DNA-based data storage.

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.'

Formation of synthetic RNA protein granules using engineered phage-coat-protein -RNA complexes

Liquid-solid transition, also known as gelation, is a specific form of phase separation in which molecules cross-link to form a highly interconnected compartment with solid - like dynamical properties. Here, we utilize RNA hairpin coat-protein binding sites to form synthetic RNA based gel-like granules via liquid-solid phase transition. We show both in-vitro and in-vivo that hairpin containing synthetic long non-coding RNA (slncRNA) molecules granulate into bright localized puncta. We further demonstrate that upon introduction of the coat-proteins, less-condensed gel-like granules form with the RNA creating an outer shell with the proteins mostly present inside the granule. Moreover, by tracking puncta fluorescence signals over time, we detected addition or shedding events of slncRNA-CP nucleoprotein complexes. Consequently, our granules constitute a genetically encoded storage compartment for protein and RNA with a programmable controlled release profile that is determined by the number of hairpins encoded into the RNA. Our findings have important implications for the potential regulatory role of naturally occurring granules and for the broader biotechnology field.

Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy but in experiments, cells may have two gene copies as cells replicate their genome during the cell cycle. While it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.

Analysis of a non-Markov transcription model with nuclear RNA export and RNA nuclear retention

Transcription involves gene activation, nuclear RNA export (NRE) and RNA nuclear retention (RNR). All these processes are multistep and biochemical. A multistep reaction process can create memories between reaction events, leading to non-Markovian kinetics. This raises an unsolved issue: how does molecular memory affect stochastic transcription in the case that NRE and RNR are simultaneously considered? To address this issue, we analyze a non-Markov model, which considers multistep activation, multistep NRE and multistep RNR can interpret many experimental phenomena. In order to solve this model, we introduce an effective transition rate for each reaction. These effective transition rates, which explicitly decode the effect of molecular memory, can transform the original non-Markov issue into an equivalent Markov one. Based on this technique, we derive analytical results, showing that molecular memory can significantly affect the nuclear and cytoplasmic mRNA mean and noise. In addition to the results providing insights into the role of molecular memory in gene expression, our modeling and analysis provide a paradigm for studying more complex stochastic transcription processes.

CPEB3-dependent increase in GluA2 subunits impairs excitatory transmission onto inhibitory interneurons in a mouse model of fragile X

Fragile X syndrome (FXS) is a leading cause of inherited intellectual disability and autism. Whereas dysregulated RNA translation in Fmr1 knockout (KO) mice, a model of FXS, is well studied, little is known about aberrant transcription. Using single-molecule mRNA detection, we show that mRNA encoding the AMPAR subunit GluA2 (but not GluA1) is elevated in dendrites and at transcription sites of hippocampal neurons of Fmr1 KO mice, indicating elevated GluA2 transcription. We identify CPEB3, a protein implicated in memory consolidation, as an upstream effector critical to GluA2 mRNA expression in FXS. Increased GluA2 mRNA is translated into an increase in GluA2 subunits, a switch in synaptic AMPAR phenotype from GluA2-lacking, Ca²⁺-permeable to GluA2-containing, Ca²⁺-impermeable, reduced inhibitory synaptic transmission, and loss of NMDAR-independent LTP at glutamatergic synapses onto CA1 inhibitory interneurons. These factors could contribute to an excitatory/inhibitory imbalance-a common theme in FXS and other autism spectrum disorders.

Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning

Computational dynamic ODE models of cell function describing biochemical reactions have been created for decades, but on a small scale. Still, they have been highly effective in describing and predicting behaviors. For example, oscillatory phospho-ERK levels were predicted and confirmed in MAPK signaling encompassing both positive and negative feedback loops. These models typically were limited and not adapted to large datasets so commonly found today. But importantly, ODE models describe reaction networks in well-mixed systems representing the cell and can be simulated with ordinary differential equations that are solved deterministically. Stochastic solutions, which can account for noisy reaction networks, in some cases, also improve predictions. Today, dynamic ODE models rarely encompass an entire cell even though it might be expected that an upload of the large genomic, transcriptomic, and proteomic datasets may allow whole cell models. It is proposed here to combine output from simulated dynamic ODE models, completed with omics data, to discover both biomarkers in cancer a priori and molecular targets in the Machine Learning setting.

In Situ Imaging of mRNA Splicing Variants by SpliceRCA

We present a method for multiplexed in situ imaging of individual RNA splicing variants. This method enables quantifying RNA splicing variants with single-molecule resolution, discriminating splicing isoforms with single-base precision as well as analyzing the subcellular localization of transcripts. With this technology, it is possible to study cell heterogeneity of gene expression, potentially helping to decipher rich diversity in posttranscriptional function and assist clinical monitoring.

Image-Based Single-Molecule Analysis of Notch-Dependent Transcription in Its Natural Context

Notch signaling is crucial to animal development and homeostasis. Notch triggers the transcription of its target genes, which produce diverse outcomes depending on context. The high resolution and spatially precise assessment of Notch-dependent transcription is essential for understanding how Notch operates normally in its native context in vivo and how Notch defects lead to pathogenesis. Here we present biological and computational methods to assess Notch-dependent transcriptional activation in stem cells within their niche, focusing on germline stem cells in the nematode Caenorhabditis elegans. Specifically, we describe visualization of single RNAs in fixed gonads using single-molecule RNA fluorescence in situ hybridization (smFISH), live imaging of transcriptional bursting in the intact organism using the MS2 system, and custom-made MATLAB codes, implementing new image processing algorithms to capture the spatiotemporal patterns of Notch-dependent transcriptional activation. These methods allow a powerful analysis of in vivo transcriptional activation and its dynamics in a whole tissue. Our methods can be adapted to essentially any tissue or cell type for any transcript.

Quantitative imaging of transcription in living Drosophila embryos reveals the impact of core promoter motifs on promoter state dynamics

Genes are expressed in stochastic transcriptional bursts linked to alternating active and inactive promoter states. A major challenge in transcription is understanding how promoter composition dictates bursting, particularly in multicellular organisms. We investigate two key Drosophila developmental promoter motifs, the TATA box (TATA) and the Initiator (INR). Using live imaging in Drosophila embryos and new computational methods, we demonstrate that bursting occurs on multiple timescales ranging from seconds to minutes. TATA-containing promoters and INR-containing promoters exhibit distinct dynamics, with one or two separate rate-limiting steps respectively. A TATA box is associated with long active states, high rates of polymerase initiation, and short-lived, infrequent inactive states. In contrast, the INR motif leads to two inactive states, one of which relates to promoter-proximal polymerase pausing. Surprisingly, the model suggests pausing is not obligatory, but occurs stochastically for a subset of polymerases. Overall, our results provide a rationale for promoter switching during zygotic genome activation.

Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage

The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution.

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.

FISH and chips: a review of microfluidic platforms for FISH analysis

Fluorescence in situ hybridization (FISH) allows visualization of specific nucleic acid sequences within an intact cell or a tissue section. It is based on molecular recognition between a fluorescently labeled probe that penetrates the cell membrane of a fixed but intact sample and hybridizes to a nucleic acid sequence of interest within the cell, rendering a measurable signal. FISH has been applied to, for example, gene mapping, diagnosis of chromosomal aberrations and identification of pathogens in complex samples as well as detailed studies of cellular structure and function. However, FISH protocols are complex, they comprise of many fixation, incubation and washing steps involving a range of solvents and temperatures and are, thus, generally time consuming and labor intensive. The complexity of the process, the relatively high-priced fluorescent probes and the fairly high-end microscopy needed for readout render the whole process costly and have limited wider uptake of this powerful technique. In recent years, there have been attempts to transfer FISH assay protocols onto microfluidic lab-on-a-chip platforms, which reduces the required amount of sample and reagents, shortens incubation times and, thus, time to complete the protocol, and finally has the potential for automating the process. Here, we review the wide variety of approaches for lab-on-chip-based FISH that have been demonstrated at proof-of-concept stage, ranging from FISH analysis of immobilized cell layers, and cells trapped in arrays, to FISH on tissue slices. Some researchers have aimed to develop simple devices that interface with existing equipment and workflows, whilst others have aimed to integrate the entire FISH protocol into a fully autonomous FISH on-chip system. Whilst the technical possibilities for FISH on-chip are clearly demonstrated, only a small number of approaches have so far been converted into off-the-shelf products for wider use beyond the research laboratory.

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.

Decoding the Notch signal

Notch signalling controls many key cellular processes which differ according to the context where the pathway is deployed due to the transcriptional activation of specific sets of genes. The pathway is unusual in its lack of amplification, also raising the question of how it can efficiently activate transcription with limited amounts of nuclear activity. Here, we focus on mechanisms that enable Notch to produce appropriate transcriptional responses and speculate on models that could explain the current gaps in knowledge.

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.

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.

Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

Gene expression analysis of rare or heterogeneous cell populations such as disseminated cancer cells (DCCs) requires a sensitive method allowing reliable analysis of single cells. Therefore, we developed and explored the feasibility of a quantitative PCR (qPCR) assay to analyze single-cell cDNA pre-amplified using a previously established whole transcriptome amplification (WTA) protocol. We carefully selected and optimized multiple steps of the protocol, e.g. re-amplification of WTA products, quantification of amplified cDNA yields and final qPCR quantification, to identify the most reliable and accurate workflow for quantitation of gene expression of the ERBB2 gene in DCCs. We found that absolute quantification outperforms relative quantification. We then validated the performance of our method on single cells of established breast cancer cell lines displaying distinct levels of HER2 protein. The different protein levels were faithfully reflected by transcript expression across the tested cell lines thereby proving the accuracy of our approach. Finally, we applied our method to breast cancer DCCs of a patient undergoing anti-HER2-directed therapy. Here, we were able to measure ERBB2 expression levels in all HER2-protein-positive DCCs. In summary, we developed a reliable single-cell qPCR assay applicable to measure distinct levels of ERBB2 in DCCs.

Dynamics of Notch-Dependent Transcriptional Bursting in Its Native Context

Transcription is well known to be inherently stochastic and episodic, but the regulation of transcriptional dynamics is not well understood. Here, we analyze how Notch signaling modulates transcriptional bursting during animal development. Our focus is Notch regulation of transcription in germline stem cells of the nematode C. elegans. Using the MS2 system to visualize nascent transcripts and live imaging to record dynamics, we analyze bursting as a function of position within the intact animal. We find that Notch-dependent transcriptional activation is indeed "bursty"; that wild-type Notch modulates burst duration (ON-time) rather than duration of pauses between bursts (OFF-time) or mean burst intensity; and that a mutant Notch receptor, which is compromised for assembly into the Notch transcription factor complex, primarily modifies burst size (duration × intensity). These analyses thus visualize the effect of a canonical signaling pathway on metazoan transcriptional bursting in its native context.

Enhancer Priming Enables Fast and Sustained Transcriptional Responses to Notch Signaling

Information from developmental signaling pathways must be accurately decoded to generate transcriptional outcomes. In the case of Notch, the intracellular domain (NICD) transduces the signal directly to the nucleus. How enhancers decipher NICD in the real time of developmental decisions is not known. Using the MS2-MCP system to visualize nascent transcripts in single cells in Drosophila embryos, we reveal how two target enhancers read Notch activity to produce synchronized and sustained profiles of transcription. By manipulating the levels of NICD and altering specific motifs within the enhancers, we uncover two key principles. First, increased NICD levels alter transcription by increasing duration rather than frequency of transcriptional bursts. Second, priming of enhancers by tissue-specific transcription factors is required for NICD to confer synchronized and sustained activity; in their absence, transcription is stochastic and bursty. The dynamic response of an individual enhancer to NICD thus differs depending on the cellular context.

Illuminating Genomic Dark Matter with RNA Imaging

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

First-passage time statistics of stochastic transcription process for time-dependent reaction rates

Transcription in gene expression is an intrinsically noisy process which involves production and degradation of mRNAs. An important quantity to describe this stochastic process is the first-passage time (FPT), i.e., the time taken by mRNAs to reach a particular threshold. The process of transcription can be modelled as a simple birth-death process, assuming that the promoter is always in an active state and to encode the stochastic environment we consider the transcription rate to be time dependent. This generalization is suitable to capture bursty mRNA dynamics usually modelled as an ON-Off model and simplifies the calculation of FPT statistics for a cell population. We study the role of periodic modulation of the transcription rate on different moments of FPT distribution of a population of cells. Our calculation shows that for sinusoidal modulation there exists an extremal value of mean FPT as a function of the time period and phase of the transcription signal. However, for the square wave modulation of transcription rates simulation results show that the extremal value of the MFPT behaves monotonically with the variation of the phase.

Progress and Challenges for Live-cell Imaging of Genomic Loci Using CRISPR-based Platforms

Chromatin conformation, localization, and dynamics are crucial regulators of cellular behaviors. Although fluorescence in situ hybridization-based techniques have been widely utilized for investigating chromatin architectures in healthy and diseased states, the requirement for cell fixation precludes the comprehensive dynamic analysis necessary to fully understand chromatin activities. This has spurred the development and application of a variety of imaging methodologies for visualizing single chromosomal loci in the native cellular context. In this review, we describe currently-available approaches for imaging single genomic loci in cells, with special focus on clustered regularly interspaced short palindromic repeats (CRISPR)-based imaging approaches. In addition, we discuss some of the challenges that limit the application of CRISPR-based genomic imaging approaches, and potential solutions to address these challenges. We anticipate that, with continued refinement of CRISPR-based imaging techniques, significant understanding can be gained to help decipher chromatin activities and their relevance to cellular physiology and pathogenesis.

Using Cell Cultures for the Investigation of Treatments for Attention Deficit Hyperactivity Disorder: A Systematic Review

BACKGROUND

Advances in basic and molecular biology have promoted the use of cell cultures in a wide range of areas, including the evaluation of drug efficacy, safety and toxicity.

OBJECTIVE

This article aims to provide a general overview of the methodological parameters of cell cultures used to investigate therapeutic options for Attention Deficit Hyperactivity Disorder.

METHOD

A systematic search was performed in the electronic databases PubMed, Scopus, and DOAJ. In vitro experimental studies using cell cultures were included.

RESULTS

A total of 328 studies were initially identified, with 16 included for qualitative synthesis. Seven studies used neuronal cells (SH-SY5Y neuroblastoma and PC12 cell line) and nine used nonneuronal cells. All the studies described the culture conditions, but most studies were inconsistent with regard to reporting results and raw data. Only one-third of the studies performed cell viability assays, while a further 30% conducted gene expression analysis. Other additional tests included electrophysiological evaluation and transporter activity. More than 50% of the studies evaluated the effects of drugs such as methylphenidate and atomoxetine, while plant extracts were assessed in four studies and polyunsaturated fatty acids in one.

CONCLUSION

We suggested a flowchart to guide the planning and execution of studies, and a checklist to be completed by authors to allow the standardized reporting of results. This may guide the elaboration of laboratory protocols and further in vitro studies.

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

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

A System for Gene Expression Noise Control in Yeast

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

Nascent RNA kinetics: Transient and steady state behavior of models of transcription

Regulation of transcription is a vital process in cells, but mechanistic details of this regulation still remain elusive. The dominant approach to unravel the dynamics of transcriptional regulation is to first develop mathematical models of transcription and then experimentally test the predictions these models make for the distribution of mRNA and protein molecules at the individual cell level. However, these measurements are affected by a multitude of downstream processes which make it difficult to interpret the measurements. Recent experimental advancements allow for counting the nascent mRNA number of a gene as a function of time at the single-cell level. These measurements closely reflect the dynamics of transcription. In this paper, we consider a general mechanism of transcription with stochastic initiation and deterministic elongation and probe its impact on the temporal behavior of nascent RNA levels. Using techniques from queueing theory, we derive exact analytical expressions for the mean and variance of the nascent RNA distribution as functions of time. We apply these analytical results to obtain the mean and variance of nascent RNA distribution for specific models of transcription. These models of initiation exhibit qualitatively distinct transient behaviors for both the mean and variance which further allows us to discriminate between them. Stochastic simulations confirm these results. Overall the analytical results presented here provide the necessary tools to connect mechanisms of transcription initiation to single-cell measurements of nascent RNA.

Conceptual Challenges of the Systemic Approach in Understanding Cell Differentiation

The cells of a multicellular organism are derived from a single zygote and genetically identical. Yet, they are phenotypically very different. This difference is the result of a process commonly called cell differentiation. How the phenotypic diversity emerges during ontogenesis or regeneration is a central and intensely studied but still unresolved issue in biology. Cell biology is facing conceptual challenges that are frequently confused with methodological difficulties. How to define a cell type? What stability or change means in the context of cell differentiation and how to deal with the ubiquitous molecular variations seen in the living cells? What are the driving forces of the change? We propose to reframe the problem of cell differentiation in a systemic way by incorporating different theoretical approaches. The new conceptual framework is able to capture the insights made at different levels of cellular organization and considered previously as contradictory. It also provides a formal strategy for further experimental studies.

LlamaTags: A Versatile Tool to Image Transcription Factor Dynamics in Live Embryos

Embryonic cell fates are defined by transcription factors that are rapidly deployed, yet attempts to visualize these factors in vivo often fail because of slow fluorescent protein maturation. Here, we pioneer a protein tag, LlamaTag, which circumvents this maturation limit by binding mature fluorescent proteins, making it possible to visualize transcription factor concentration dynamics in live embryos. Implementing this approach in the fruit fly Drosophila melanogaster, we discovered stochastic bursts in the concentration of transcription factors that are correlated with bursts in transcription. We further used LlamaTags to show that the concentration of protein in a given nucleus heavily depends on transcription of that gene in neighboring nuclei; we speculate that this inter-nuclear signaling is an important mechanism for coordinating gene expression to delineate straight and sharp boundaries of gene expression. Thus, LlamaTags now make it possible to visualize the flow of information along the central dogma in live embryos.

Dosage-Dependent Expression Variation Suppressed on the Drosophila Male X Chromosome

DNA copy number variation is associated with many high phenotypic heterogeneity disorders. We systematically examined the impact of Drosophila melanogaster deletions on gene expression profiles to ask whether increased expression variability owing to reduced gene dose might underlie this phenotypic heterogeneity. Indeed, we found that one-dose genes have higher gene expression variability relative to two-dose genes. We then asked whether this increase in variability could be explained by intrinsic noise within cells due to stochastic biochemical events, or whether expression variability is due to extrinsic noise arising from more complex interactions. Our modeling showed that intrinsic gene expression noise averages at the organism level and thus cannot explain increased variation in one-dose gene expression. Interestingly, expression variability was related to the magnitude of expression compensation, suggesting that regulation, induced by gene dose reduction, is noisy. In a remarkable exception to this rule, the single X chromosome of males showed reduced expression variability, even compared with two-dose genes. Analysis of sex-transformed flies indicates that X expression variability is independent of the male differentiation program. Instead, we uncovered a correlation between occupancy of the chromatin-modifying protein encoded by males absent on the first (mof) and expression variability, linking noise suppression to the specialized X chromosome dosage compensation system. MOF occupancy on autosomes in both sexes also lowered transcriptional noise. Our results demonstrate that gene dose reduction can lead to heterogeneous responses, which are often noisy. This has implications for understanding gene network regulatory interactions and phenotypic heterogeneity. Additionally, chromatin modification appears to play a role in dampening transcriptional noise.

An Improved Method for Measuring Chromatin-binding Dynamics Using Time-dependent Formaldehyde Crosslinking

Formaldehyde crosslinking is widely used in combination with chromatin immunoprecipitation (ChIP) to measure the locations along DNA and relative levels of transcription factor (TF)-DNA interactions in vivo. However, the measurements that are typically made do not provide unambiguous information about the dynamic properties of these interactions. We have developed a method to estimate binding kinetic parameters from time-dependent formaldehyde crosslinking data, called crosslinking kinetics (CLK) analysis. Cultures of yeast cells are crosslinked with formaldehyde for various periods of time, yielding the relative ChIP signal at particular loci. We fit the data using the mass-action CLK model to extract kinetic parameters of the TF-chromatin interaction, including the on- and off-rates and crosslinking rate. From the on- and off-rate we obtain the occupancy and residence time. The following protocol is the second iteration of this method, CLKv2, updated with improved crosslinking and quenching conditions, more information about crosslinking rates, and systematic procedures for modeling the observed kinetic regimes. CLKv2 analysis has been applied to investigate the binding behavior of the TATA-binding protein (TBP), and a selected subset of other TFs. The protocol was developed using yeast cells, but may be applicable to cells from other organisms as well.

Quantitative, in situ analysis of mRNAs and proteins with subcellular resolution

We describe here a method, termed immunoFISH, for simultaneous in situ analysis of the composition and distribution of proteins and individual RNA transcripts in single cells. Individual RNA molecules are labeled by hybridization and target proteins are concurrently stained using immunofluorescence. Multicolor fluorescence images are acquired and analyzed to determine the abundance, composition, and distribution of hybridized probes and immunofluorescence. We assessed the ability of immunoFISH to simultaneous quantify protein and transcript levels and distribution in cultured HER2 positive breast cancer cells and human breast tumor samples. We demonstrated the utility of this assay in several applications including demonstration of the existence of a layer of normal myoepithelial KRT14 expressing cells that separate HER2+ cancer cells from the stromal and immune microenvironment in HER2+ invasive breast cancer. Our studies show that immunoFISH provides quantitative information about the spatial heterogeneity in transcriptional and proteomic features that exist between and within cells.

Second-generation method for analysis of chromatin binding with formaldehyde-cross-linking kinetics

Formaldehyde-cross-linking underpins many of the most commonly used experimental approaches in the chromatin field, especially in capturing site-specific protein-DNA interactions. Extending such assays to assess the stability and binding kinetics of protein-DNA interactions is more challenging, requiring absolute measurements with a relatively high degree of physical precision. We previously described an experimental framework called the cross-linking kinetics (CLK) assay, which uses time-dependent formaldehyde-cross-linking data to extract kinetic parameters of chromatin binding. Many aspects of formaldehyde behavior in cells are unknown or undocumented, however, and could potentially affect CLK data analyses. Here, we report biochemical results that better define the properties of formaldehyde-cross-linking in budding yeast cells. These results have the potential to inform interpretations of "standard" chromatin assays, including chromatin immunoprecipitation. Moreover, the chemical complexity we uncovered resulted in the development of an improved method for measuring binding kinetics with the CLK approach. Optimum conditions included an increased formaldehyde concentration and more robust glycine-quench conditions. Notably, we observed that formaldehyde-cross-linking rates can vary dramatically for different protein-DNA interactions in vivo Some interactions were cross-linked much faster than the in vivo macromolecular interactions, making them suitable for kinetic analysis. For other interactions, we found the cross-linking reaction occurred on the same time scale or slower than binding dynamics; for these interactions, it was sometimes possible to compute the in vivo equilibrium-binding constant but not binding on- and off-rates. This improved method yields more accurate in vivo binding kinetics estimates on the minute time scale.

Live-cell p53 single-molecule binding is modulated by C-terminal acetylation and correlates with transcriptional activity

Live-cell microscopy has highlighted that transcription factors bind transiently to chromatin but it is not clear if the duration of these binding interactions can be modulated in response to an activation stimulus, and if such modulation can be controlled by post-translational modifications of the transcription factor. We address this question for the tumor suppressor p53 by combining live-cell single-molecule microscopy and single cell in situ measurements of transcription and we show that p53-binding kinetics are modulated following genotoxic stress. The modulation of p53 residence times on chromatin requires C-terminal acetylation—a classical mark for transcriptionally active p53—and correlates with the induction of transcription of target genes such as CDKN1a. We propose a model in which the modification state of the transcription factor determines the coupling between transcription factor abundance and transcriptional activity by tuning the transcription factor residence time on target sites.

Both transcription binding kinetics and post-translational modifications of transcription factors are thought to play a role in the modulation of transcription. Here the authors use single-molecule tracking to directly demonstrate that p53 acetylation modulates promoter residence time and transcriptional activity.

Single-Cell and Single-Molecule Analysis of Gene Expression Regulation

Recent advancements in single-cell and single-molecule imaging technologies have resolved biological processes in time and space that are fundamental to understanding the regulation of gene expression. Observations of single-molecule events in their cellular context have revealed highly dynamic aspects of transcriptional and post-transcriptional control in eukaryotic cells. This approach can relate transcription with mRNA abundance and lifetimes. Another key aspect of single-cell analysis is the cell-to-cell variability among populations of cells. Definition of heterogeneity has revealed stochastic processes, determined characteristics of under-represented cell types or transitional states, and integrated cellular behaviors in the context of multicellular organisms. In this review, we discuss novel aspects of gene expression of eukaryotic cells and multicellular organisms revealed by the latest advances in single-cell and single-molecule imaging technology.