Direct measurement of transcription factor dissociation excludes a simple operator occupancy model for gene regulation

How to scan naked DNA using promiscuous recognition and no clamping: a model for pioneer transcription factors

Most DNA scanning proteins uniquely recognize their cognate sequence motif and slide on DNA assisted by some sort of clamping interface. The pioneer transcription factors that control cell fate in eukaryotes must forgo both elements to gain access to DNA in naked and chromatin forms; thus, whether or how these factors scan naked DNA is unknown. Here, we use single-molecule techniques to investigate naked DNA scanning by the Engrailed homeodomain (enHD) as paradigm of highly promiscuous recognition and open DNA binding interface. We find that enHD scans naked DNA quite effectively, and about 200000-fold faster than expected for a continuous promiscuous slide. To do so, enHD scans about 675 bp of DNA in 100 ms and then redeploys stochastically to another location 530 bp afar in just 10 ms. During the scanning phase enHD alternates between slow- and medium-paced modes every 3 and 40 ms, respectively. We also find that enHD binds nucleosomes and does so with enhanced affinity relative to naked DNA. Our results demonstrate that pioneer-like transcription factors can in principle do both, target nucleosomes and scan active DNA efficiently. The hybrid scanning mechanism used by enHD appears particularly well suited for the highly complex genomic signals of eukaryotic cells.

Analysis of Fluorescent Proteins for Observing Single Gene Locus in a Live and Fixed Escherichia coli Cell

Fluorescent proteins (FPs) are essential tools for advanced microscopy techniques such as super-resolution imaging, single-particle tracking, and quantitative single-molecule counting. Various FPs fused to DNA-binding proteins have been used to observe the subcellular location and movement of specific gene loci in living and fixed bacterial cells. However, quantitative assessments of the properties of FPs for gene locus measurements are still lacking. Here, we assessed various FPs to observe specific gene loci in live and fixed Escherichia coli cells using a fluorescent repressor-operator binding system (FROS), tet operator-Tet repressor proteins (TetR). Tsr-fused FPs were used to assess the intensity and photostability of various FPs (five red FPs: mCherry2, FusionRed, mRFP, mCrimson3, and dKatushka; and seven yellow FPs: SYFP2, Venus, mCitrine, YPet, mClover3, mTopaz, and EYFP) at the single-molecule level in living cells. These FPs were then used for gene locus measurements using FROS. Our results indicate that TetR-mCrimson3 (red) and TetR-EYFP (yellow) had better properties for visualizing gene loci than the other TetR-FPs. Furthermore, fixation procedures affected the clustering of diffusing TetR-FPs and altered the locations of the TetR-FP foci. Fixation with formaldehyde consistently disrupted proper DNA locus observations using TetR-FPs. Notably, the foci measured using TetR-mCrimson3 remained close to their original positions in live cells after glyoxal fixation. This in vivo study provides a cell-imaging guide for the use of FPs for gene-locus observation in E. coli and a scheme for evaluating the use of FPs for other cell-imaging purposes.

The unreasonable effectiveness of equilibrium gene regulation through the cell cycle

Systems like the prototypical lac operon can reliably hold repression of transcription upon DNA replication across cell cycles with just 10 repressor molecules per cell and behave as if they were at equilibrium. The origin of this phenomenology is still an unresolved question. Here, we develop a general theory to analyze strong perturbations in quasi-equilibrium systems and use it to quantify the effects of DNA replication in gene regulation. We find a scaling law linking actual with predicted equilibrium transcription via a single kinetic parameter. We show that even the lac operon functions beyond the physical limits of naive regulation through compensatory mechanisms that suppress non-equilibrium effects. Synthetic systems without adjuvant activators, such as the cAMP receptor protein (CRP), lack this reliability. Our results provide a rationale for the function of CRP, beyond just being a tunable activator, as a mitigator of cell cycle perturbations.

Governing principles of transcriptional logic out of equilibrium

To survive, adapt, and develop, cells respond to external and internal stimuli by tightly regulating transcription. Transcriptional regulation involves the combinatorial binding of a repertoire of transcription factors to DNA, which often results in switch-like binary outputs akin to Boolean logic gates. Recent experimental studies have demonstrated that in eukaryotes, transcription factor binding to DNA often involves energy expenditure, thereby driving the system out of equilibrium. The governing principles of transcriptional logic operations out of equilibrium remain unexplored. Here, we employ a simple two-input, single-locus model of transcription that can accommodate both equilibrium and nonequilibrium mechanisms. Using this model, we find that nonequilibrium regimes can give rise to all the logic operations accessible in equilibrium. Strikingly, energy expenditure alters the regulatory function of the two transcription factors in a mutually exclusive manner. This allows for the emergence of new logic operations that are inaccessible in equilibrium. Overall, our results show that energy expenditure can expand the range of cellular decision-making without the need for more complex promoter architectures.

Dynamics of cell wall-binding proteins at a single molecule level: B. subtilis autolysins show different kinds of motion

The bacterial cell wall is a meshwork of crosslinked peptidoglycan strands, with a thickness of up to 50 nm in Firmicutes. Little is known about how proteins move through the cell wall to find sites of enzymatic activity. Cell wall synthesis for cell elongation involves the integration of new peptidoglycan strands by integral membrane proteins, as well as the degradation of existing strands by so-called autolysins, soluble proteins that are secreted through the cell membrane. Autolysins comprise different classes of proteases and glucanases and mostly contain cell-wall binding domains in addition to their catalytic domain. We have studied dynamics of Bacillus subtilis autolysins LytC, a major endopeptidase required for lateral cell wall growth, and LytF, a peptidase acting at the newly formed division site in order to achieve separation of daughter cells. We show that both proteins, fused to moxVenus are present as three distinct populations of different diffusion constants. The fastest population is compatible with free diffusion in a crowded liquid environment, that is similar to that of cytosolic enzymes, likely reflecting autolysins diffusing through the periplasm. The medium mobile fraction can be explained by constrained motion through a polymeric substance, indicating mobility of autolysins through the wall similar to that of DNA-binding proteins within the nucleoid. The slow-mobile fraction are most likely autolysins bound to their specific substrate sites. We show that LytF is more static during exponential phase, while LytC appears to be more active during the transition to stationary phase. Both autolysins became more static in backgrounds lacking redundant other autolysins, suggesting stochastic competition for binding sites. On the other hand, lack of inhibitor IseA or autolysin CwlS lead to an altered preference for polar localization of LytF within the cell wall, revealing that inhibitors and autolysins also affect each other's pattern of localization, in addition to their activity.

Transcription-replication interactions reveal principles of bacterial genome regulation

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome1. These modes interact with a changing cellular environment to yield highly dynamic expression patterns2. In bacteria, the relationship between a gene's regulatory architecture and its expression is well understood for individual model gene circuits3,4. However, a broader perspective of these dynamics at the genome-scale is lacking, in part because bacterial transcriptomics have hitherto captured only a static snapshot of expression averaged across millions of cells5. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of regulatory modes based on each gene's transcriptional response to its own replication, which we term the Transcription-Replication Interaction Profile (TRIP). We found that the response to the universal perturbation of chromosomal replication integrates biological regulatory factors with biophysical molecular events on the chromosome to reveal a gene's local regulatory context. While the TRIPs of many genes conform to a gene dosage-dependent pattern, others diverge in distinct ways, including altered timing or amplitude of expression, and this is shaped by factors such as intra-operon position, repression state, or presence on mobile genetic elements. Our transcriptome analysis also simultaneously captures global properties, such as the rates of replication and transcription, as well as the nestedness of replication patterns. This work challenges previous notions of the drivers of expression heterogeneity within a population of cells, and unearths a previously unseen world of gene transcription dynamics.

Competing constraints shape the nonequilibrium limits of cellular decision-making

Gene regulation is central to cellular function. Yet, despite decades of work, we lack quantitative models that can predict how transcriptional control emerges from molecular interactions at the gene locus. Thermodynamic models of transcription, which assume that gene circuits operate at equilibrium, have previously been employed with considerable success in the context of bacterial systems. However, the presence of ATP-dependent processes within the eukaryotic transcriptional cycle suggests that equilibrium models may be insufficient to capture how eukaryotic gene circuits sense and respond to input transcription factor concentrations. Here, we employ simple kinetic models of transcription to investigate how energy dissipation within the transcriptional cycle impacts the rate at which genes transmit information and drive cellular decisions. We find that biologically plausible levels of energy input can lead to significant gains in how rapidly gene loci transmit information but discover that the regulatory mechanisms underlying these gains change depending on the level of interference from noncognate activator binding. When interference is low, information is maximized by harnessing energy to push the sensitivity of the transcriptional response to input transcription factors beyond its equilibrium limits. Conversely, when interference is high, conditions favor genes that harness energy to increase transcriptional specificity by proofreading activator identity. Our analysis further reveals that equilibrium gene regulatory mechanisms break down as transcriptional interference increases, suggesting that energy dissipation may be indispensable in systems where noncognate factor interference is sufficiently large.

Gene expression model inference from snapshot RNA data using Bayesian non-parametrics

Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional dynamics. Although RNA expression data encode information on gene expression models, existing computational frameworks do not perform simultaneous Bayesian inference of gene expression models and parameters from such data. Rather, gene expression models-composed of gene states, their connectivities and associated parameters-are currently deduced by pre-specifying gene state numbers and connectivity before learning associated rate parameters. Here we propose a method to learn full distributions over gene states, state connectivities and associated rate parameters, simultaneously and self-consistently from single-molecule RNA counts. We propagate noise from fluctuating RNA counts over models by treating models themselves as random variables. We achieve this within a Bayesian non-parametric paradigm. We demonstrate our method on the Escherichia coli lacZ pathway and the Saccharomyces cerevisiae STL1 pathway, and verify its robustness on synthetic data.

Versatile Strategy for the Construction of a Transcription Factor-Based Orthogonal Gene Expression Toolbox in Monascus spp

Gene expression is needed to be conducted in an orthogonal manner and controllable independently from the host's native regulatory system. However, there is a shortage of gene expression regulatory toolboxes that function orthogonally from each other and toward the host. Herein, we developed a strategy based on the mutant library to generate orthogonal gene expression toolboxes. A transcription factor, MaR, located in the Monascus azaphilone biosynthetic gene cluster, was taken as a typical example. Nine DNA-binding residues of MaR were identified by molecular simulation and site-directed mutagenesis. We created five MaR multi-site saturation mutagenesis libraries consisting of 10743 MaR variants on the basis of five cognate promoters. A functional analysis revealed that all five tested promoters were orthogonally regulated by five different MaR variants, respectively. Furthermore, fine gene expression tunability and high signal sensitivity of this toolbox are demonstrated by introducing chemically inducible expression modules, designing synthetic promoter elements, and creating protein-protein interaction between MaRs. This study paves the way for a bottom-up approach to build orthogonal gene expression toolboxes.

Molecular basis for lethal cross-talk between two unrelated bacterial transcription factors - the regulatory protein of a restriction-modification system and the repressor of a defective prophage

Bacterial gene expression depends on the efficient functioning of global transcriptional networks, however their interconnectivity and orchestration rely mainly on the action of individual DNA binding proteins called transcription factors (TFs). TFs interact not only with their specific target sites, but also with secondary (off-target) sites, and vary in their promiscuity. It is not clear yet what mechanisms govern the interactions with secondary sites, and how such rewiring affects the overall regulatory network, but this could clearly constrain horizontal gene transfer. Here, we show the molecular mechanism of one such off-target interaction between two unrelated TFs in Escherichia coli: the C regulatory protein of a Type II restriction-modification system, and the RacR repressor of a defective prophage. We reveal that the C protein interferes with RacR repressor expression, resulting in derepression of the toxic YdaT protein. These results also provide novel insights into regulation of the racR-ydaST operon. We mapped the C regulator interaction to a specific off-target site, and also visualized C protein dynamics, revealing intriguing differences in single molecule dynamics in different genetic contexts. Our results demonstrate an apparent example of horizontal gene transfer leading to adventitious TF cross-talk with negative effects on the recipient's viability. More broadly, this study represents an experimentally-accessible model of a regulatory constraint on horizontal gene transfer.

Heterogeneity of Subcellular Diffusion in Bacteria Based on Spatial Segregation of Ribosomes and Nucleoids

It has long become clear that in spite of generally lacking internal membrane systems, bacteria contain well-structured subcellular structures of usually filamentous proteins, and a preferred 3D arrangement of their chromosome(s). Some of these systems are set up by so-called cytoskeletal elements, or by polar landmark proteins, but the mechanism of specific localization is still unclear in most cases. Intriguingly, apart from such spatially organizing systems, the bacterial cytoplasm has unusual properties in terms of the diffusion of molecules, which varies between different sites within the cell. In many bacteria, chromosomes are compacted into centrally located nucleoids, being orderly folded as opposed to consisting of random coils of DNA. In these bacteria, there is a separation of transcription and translation, such that transcription by RNA polymerase occurs on the nucleoids, and translation takes place mostly at the cell poles and directly underneath the cell membrane, because 70S ribosomes accumulate at sites surrounding the nucleoids. Interestingly, accumulation of ribosomes appears to slow down diffusion of enzymes, noticeable for larger enzyme complexes, while nucleoids provide areas of confined motion for DNA-binding proteins, yet acceleration zones for non-DNA-binding proteins. Crowded regions at the cell poles set up zones of higher concentration of the translation machinery, shortening diffusion distances for rate-limiting translation factor/ribosome interactions, and of metabolic enzymes, possibly speeding up pathways containing low concentrations of metabolites. Thus, heterogeneous diffusion adds another layer of subcellular organization on top of cytoskeletal elements.

Eukaryotic gene regulation at equilibrium, or non?

Models of transcriptional regulation that assume equilibrium binding of transcription factors have been less successful at predicting gene expression from sequence in eukaryotes than in bacteria. This could be due to the non-equilibrium nature of eukaryotic regulation. Unfortunately, the space of possible non-equilibrium mechanisms is vast and predominantly uninteresting. The key question is therefore how this space can be navigated efficiently, to focus on mechanisms and models that are biologically relevant. In this review, we advocate for the normative role of theory-theory that prescribes rather than just describes-in providing such a focus. Theory should expand its remit beyond inferring mechanistic models from data, towards identifying non-equilibrium gene regulatory schemes that may have been evolutionarily selected, despite their energy consumption, because they are precise, reliable, fast, or otherwise outperform regulation at equilibrium. We illustrate our reasoning by toy examples for which we provide simulation code.

Robustness of DNA looping across multiple cell divisions in individual bacteria

DNA looping has emerged as a central paradigm of transcriptional regulation, as it is shared across many living systems. One core property of DNA looping-based regulation is its ability to greatly enhance repression or activation of genes with only a few copies of transcriptional regulators. However, this property based on a small number of proteins raises the question of the robustness of such a mechanism with respect to the large intracellular perturbations taking place during growth and division of the cell. Here we address the issue of sensitivity to variations of intracellular parameters of gene regulation by DNA looping. We use the lac system as a prototype to experimentally identify the key features of the robustness of DNA looping in growing Escherichia coli cells. Surprisingly, we observe time intervals of tight repression spanning across division events, which can sometimes exceed 10 generations. Remarkably, the distribution of such long time intervals exhibits memoryless statistics that is mostly insensitive to repressor concentration, cell division events, and the number of distinct loops accessible to the system. By contrast, gene regulation becomes highly sensitive to these perturbations when DNA looping is absent. Using stochastic simulations, we propose that the observed robustness to division emerges from the competition between fast, multiple rebinding events of repressors and slow initiation rate of the RNA polymerase. We argue that fast rebinding events are a direct consequence of DNA looping that ensures robust gene repression across a range of intracellular perturbations.

Positive supercoiling favors transcription elongation through lac repressor-mediated DNA loops

Some proteins, like the lac repressor (LacI), mediate long-range loops that alter DNA topology and create torsional barriers. During transcription, RNA polymerase generates supercoiling that may facilitate passage through such barriers. We monitored E. coli RNA polymerase progress along templates in conditions that prevented, or favored, 400 bp LacI-mediated DNA looping. Tethered particle motion measurements revealed that RNA polymerase paused longer at unlooped LacI obstacles or those barring entry to a loop than those barring exit from the loop. Enhanced dissociation of a LacI roadblock by the positive supercoiling generated ahead of a transcribing RNA polymerase within a torsion-constrained DNA loop may be responsible for this reduction in pause time. In support of this idea, RNA polymerase transcribed 6-fold more slowly through looped DNA and paused at LacI obstacles for 66% less time on positively supercoiled compared to relaxed templates, especially under increased tension (torque). Positive supercoiling propagating ahead of polymerase facilitated elongation along topologically complex, protein-coated templates.

Sequence specificity in DNA binding is mainly governed by association

Sequence-specific binding of proteins to DNA is essential for accessing genetic information. We derive a model that predicts an anticorrelation between the macroscopic association and dissociation rates of DNA binding proteins. We tested the model for thousands of different lac operator sequences with a protein binding microarray and by observing kinetics for individual lac repressor molecules in single-molecule experiments. We found that sequence specificity is mainly governed by the efficiency with which the protein recognizes different targets. The variation in probability of recognizing different targets is at least 1.7 times as large as the variation in microscopic dissociation rates. Modulating the rate of binding instead of the rate of dissociation effectively reduces the risk of the protein being retained on nontarget sequences while searching.

Dynamic transcription regulation at the single-molecule level

Cell fate changes during development, differentiation, and reprogramming are largely controlled at the transcription level. The DNA-binding transcription factors (TFs) often act in a combinatorial fashion to alter chromatin states and drive cell type-specific gene expression. Recent advances in fluorescent microscopy technologies have enabled direct visualization of biomolecules involved in the process of transcription and its regulatory events at the single-molecule level in living cells. Remarkably, imaging and tracking individual TF molecules at high temporal and spatial resolution revealed that they are highly dynamic in searching and binding cognate targets, rather than static and binding constantly. In combination with investigation using techniques from biochemistry, structure biology, genetics, and genomics, a more well-rounded view of transcription regulation is emerging. In this review, we briefly cover the technical aspects of live-cell single-molecule imaging and focus on the biological relevance and interpretation of the single-molecule dynamic features of transcription regulatory events observed in the native chromatin environment of living eukaryotic cells. We also discuss how these dynamic features might shed light on mechanistic understanding of transcription regulation.

Quantifying the regulatory role of individual transcription factors in Escherichia coli

Gene regulation often results from the action of multiple transcription factors (TFs) acting at a promoter, obscuring the individual regulatory effect of each TF on RNA polymerase (RNAP). Here we measure the fundamental regulatory interactions of TFs in E. coli by designing synthetic target genes that isolate individual TFs' regulatory effects. Using a thermodynamic model, each TF's regulatory interactions are decoupled from TF occupancy and interpreted as acting through (de)stabilization of RNAP and (de)acceleration of transcription initiation. We find that the contribution of each mechanism depends on TF identity and binding location; regulation immediately downstream of the promoter is insensitive to TF identity, but the same TFs regulate by distinct mechanisms upstream of the promoter. These two mechanisms are uncoupled and can act coherently, to reinforce the observed regulatory role (activation/repression), or incoherently, wherein the TF regulates two distinct steps with opposing effects.

Transcription Factor RBPJL Is Able to Repress Notch Target Gene Expression but Is Non-Responsive to Notch Activation

Simple Summary

The transcription factor RBPJ is an integral part of the Notch signaling cascade. RBPJ can function as a coactivator when Notch signaling is activated but acts as a repressor in the absence of a Notch stimulus. Here, we characterized the function of RBPJL, a pancreas-specific paralog of RBPJ. Upon depletion of RBPJ using CRISPR/Cas9, we observed specific upregulation of Notch target gene expression. Reconstitution with RBPJL can compensate for the lack of RBPJ function in the repression of Notch target genes but is not able to mediate the Notch-dependent activation of gene expression. On the molecular level, we identified a limited capacity of RBPJL to interact with activated Notch1–4.

Abstract

The Notch signaling pathway is an evolutionary conserved signal transduction cascade present in almost all tissues and is required for embryonic and postnatal development, as well as for stem cell maintenance, but it is also implicated in tumorigenesis including pancreatic cancer and leukemia. The transcription factor RBPJ forms a coactivator complex in the presence of a Notch signal, whereas it represses Notch target genes in the absence of a Notch stimulus. In the pancreas, a specific paralog of RBPJ, called RBPJL, is expressed and found as part of the heterotrimeric PTF1-complex. However, the function of RBPJL in Notch signaling remains elusive. Using molecular modeling, biochemical and functional assays, as well as single-molecule time-lapse imaging, we show that RBPJL and RBPJ, despite limited sequence homology, possess a high degree of structural similarity. RBPJL is specifically expressed in the exocrine pancreas, whereas it is mostly undetectable in pancreatic tumour cell lines. Importantly, RBPJL is not able to interact with Notch−1 to −4 and it does not support Notch-mediated transactivation. However, RBPJL can bind to canonical RBPJ DNA elements and shows migration dynamics comparable to that of RBPJ in the nuclei of living cells. Importantly, RBPJL is able to interact with SHARP/SPEN, the central corepressor of the Notch pathway. In line with this, RBPJL is able to fully reconstitute transcriptional repression at Notch target genes in cells lacking RBPJ. Together, RBPJL can act as an antagonist of RBPJ, which renders cells unresponsive to the activation of Notch.

Nonequilibrium model of short-range repression in gene transcription regulation

Transcription factors are proteins that regulate gene activity by activating or repressing gene transcription. A special class of transcriptional repressors operates via a short-range mechanism, making local DNA regions inaccessible to binding by activators, and thus providing an indirect repressive action on the target gene. This mechanism is commonly modeled assuming that repressors interact with DNA under thermodynamic equilibrium and neglecting some configurations of the gene regulatory region. We elaborate on a more general nonequilibrium model of short-range repression using the graph formalism for transitions between gene states, and we apply analytical calculations to compare it with the equilibrium model in terms of the repression strength and expression noise. In contrast to the equilibrium approach, the new model allows us to separate two basic mechanisms of short-range repression. The first mechanism is associated with the recruiting of factors that mediate chromatin condensation, and the second one concerns the blocking of factors that mediate chromatin loosening. The nonequilibrium model demonstrates better performance on previously published gene expression data obtained for transcription factors controlling Drosophila development, and furthermore it predicts that the first repression mechanism is the most favorable in this system. The presented approach can be scaled to larger gene networks and can be used to infer specific modes and parameters of transcriptional regulation from gene expression data.

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

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

Imaging-based screens of pool-synthesized cell libraries

Mapping a genetic perturbation to a change in phenotype is at the core of biological research. Advances in microscopy have transformed these studies, but they have largely been confined to examining a few strains or cell lines at a time. In parallel, there has been a revolution in creating synthetic libraries of genetically altered cells with relative ease. Here we describe methods that combine these powerful tools to perform live-cell imaging of pool-generated strain libraries for improved biological discovery.

Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments

DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful information on their stability within protein complexes and insight into the understanding of biological processes. Single-particle tracking allows for direct visualization of protein-DNA kinetics, however, identifying whether a molecule is bound to DNA can be non-trivial. Further complications arise when tracking molecules for extended durations in processes with slow kinetics. We developed a machine learning approach, termed Bound2Learn, using output from a widely used tracking software, to robustly classify tracks in order to accurately estimate residence times. We validated our approach in silico, and in live-cell data from Escherichia coli and Saccharomyces cerevisiae. Our method has the potential for broad utility and is applicable to other organisms.

Reconciling kinetic and thermodynamic models of bacterial transcription

The study of transcription remains one of the centerpieces of modern biology with implications in settings from development to metabolism to evolution to disease. Precision measurements using a host of different techniques including fluorescence and sequencing readouts have raised the bar for what it means to quantitatively understand transcriptional regulation. In particular our understanding of the simplest genetic circuit is sufficiently refined both experimentally and theoretically that it has become possible to carefully discriminate between different conceptual pictures of how this regulatory system works. This regulatory motif, originally posited by Jacob and Monod in the 1960s, consists of a single transcriptional repressor binding to a promoter site and inhibiting transcription. In this paper, we show how seven distinct models of this so-called simple-repression motif, based both on thermodynamic and kinetic thinking, can be used to derive the predicted levels of gene expression and shed light on the often surprising past success of the thermodynamic models. These different models are then invoked to confront a variety of different data on mean, variance and full gene expression distributions, illustrating the extent to which such models can and cannot be distinguished, and suggesting a two-state model with a distribution of burst sizes as the most potent of the seven for describing the simple-repression motif.

Gene Regulation in and out of Equilibrium

Determining whether and how a gene is transcribed are two of the central processes of life. The conceptual basis for understanding such gene regulation arose from pioneering biophysical studies in eubacteria. However, eukaryotic genomes exhibit vastly greater complexity, which raises questions not addressed by this bacterial paradigm. First, how is information integrated from many widely separated binding sites to determine how a gene is transcribed? Second, does the presence of multiple energy-expending mechanisms, which are absent from eubacterial genomes, indicate that eukaryotes are capable of improved forms of genetic information processing? An updated biophysical foundation is needed to answer such questions. We describe the linear framework, a graph-based approach to Markov processes, and show that it can accommodate many previous studies in the field. Under the assumption of thermodynamic equilibrium, we introduce a language of higher-order cooperativities and show how it can rigorously quantify gene regulatory properties suggested by experiment. We point out that fundamental limits to information processing arise at thermodynamic equilibrium and can only be bypassed through energy expenditure. Finally, we outline some of the mathematical challenges that must be overcome to construct an improved biophysical understanding of gene regulation.

Nonequilibrium models of optimal enhancer function

Simple biophysical models successfully describe bacterial regulatory code, by predicting gene expression from DNA sequences that bind specialized regulatory proteins. Analogous simple models fail in multicellular organisms, where regulatory proteins bind DNA very transiently, yet, nevertheless, effect precise control over gene expression. To date, the more general, “nonequilibrium” models have proven difficult to analyze and connect to data. Here, we reduce this complexity theoretically, by constructing simple nonequilibrium models which perform optimal gene regulation within known experimental constraints.

In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate,” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics—an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future.

A lattice model on the rate of in vivo site-specific DNA-protein interactions

We develop a lattice model of site-specific DNA-protein interactions under in vivo conditions where DNA is modelled as a self-avoiding random walk that is embedded in a cubic lattice box resembling the living cell. The protein molecule searches for its cognate site on DNA via a combination of three dimensional (3D) and one dimensional (1D) random walks. Hopping and intersegmental transfers occur depending on the conformational state of DNA. Results show that the search acceleration ratio (= search time in pure 3D route/search time in 3D and 1D routes) asymptotically increases towards a limiting value as the dilution factor of DNA (= volume of the cell/the volume of DNA) tends towards infinity. When the dilution ratio is low, then hopping and intersegmental transfers significantly enhance the search efficiency over pure sliding. At high dilution ratio, hopping does not enhance the search efficiency much since under such situation DNA will be in a relaxed conformation that favors only sliding. In the absence of hopping and intersegmental transfers, there exists an optimum sliding time at which the search acceleration ratio attains a maximum in line with the current theoretical results. However, existence of such optimum sliding length disappears in the presence of hopping. When the DNA is confined in a small volume inside the cell resembling a natural cell system, then there exists an optimum dilution and compression ratios (= total cell volume/volume in which DNA is confined) at which the search acceleration factor attains a maximum especially in the presence of hopping and intersegmental transfers. These optimum values are consistent with the values observed in the Escherichia coli cell system. In the absence of confinement of DNA, position of the specific binding site on the genomic DNA significantly influences the search acceleration. However, such position dependent changes in the search acceleration ratio will be nullified in the presence of hopping and intersegmental transfers especially when the DNA is confined in a small volume that is embedded in an outer cell.

Bacterial Vivisection: How Fluorescence-Based Imaging Techniques Shed a Light on the Inner Workings of Bacteria

The rise in fluorescence-based imaging techniques over the past 3 decades has improved the ability of researchers to scrutinize live cell biology at increased spatial and temporal resolution. In microbiology, these real-time vivisections structurally changed the view on the bacterial cell away from the "watery bag of enzymes" paradigm toward the perspective that these organisms are as complex as their eukaryotic counterparts. Capitalizing on the enormous potential of (time-lapse) fluorescence microscopy and the ever-extending pallet of corresponding probes, initial breakthroughs were made in unraveling the localization of proteins and monitoring real-time gene expression. However, later it became clear that the potential of this technique extends much further, paving the way for a focus-shift from observing single events within bacterial cells or populations to obtaining a more global picture at the intra- and intercellular level. In this review, we outline the current state of the art in fluorescence-based vivisection of bacteria and provide an overview of important case studies to exemplify how to use or combine different strategies to gain detailed information on the cell's physiology. The manuscript therefore consists of two separate (but interconnected) parts that can be read and consulted individually. The first part focuses on the fluorescent probe pallet and provides a perspective on modern methodologies for microscopy using these tools. The second section of the review takes the reader on a tour through the bacterial cell from cytoplasm to outer shell, describing strategies and methods to highlight architectural features and overall dynamics within cells.

Quantitative dissection of transcription in development yields evidence for transcription-factor-driven chromatin accessibility

Thermodynamic models of gene regulation can predict transcriptional regulation in bacteria, but in eukaryotes, chromatin accessibility and energy expenditure may call for a different framework. Here, we systematically tested the predictive power of models of DNA accessibility based on the Monod-Wyman-Changeux (MWC) model of allostery, which posits that chromatin fluctuates between accessible and inaccessible states. We dissected the regulatory dynamics of hunchback by the activator Bicoid and the pioneer-like transcription factor Zelda in living Drosophila embryos and showed that no thermodynamic or non-equilibrium MWC model can recapitulate hunchback transcription. Therefore, we explored a model where DNA accessibility is not the result of thermal fluctuations but is catalyzed by Bicoid and Zelda, possibly through histone acetylation, and found that this model can predict hunchback dynamics. Thus, our theory-experiment dialogue uncovered potential molecular mechanisms of transcriptional regulatory dynamics, a key step toward reaching a predictive understanding of developmental decision-making.

Genome-wide off-rates reveal how DNA binding dynamics shape transcription factor function

Protein-DNA interactions are dynamic, and these dynamics are an important aspect of chromatin-associated processes such as transcription or replication. Due to a lack of methods to study on- and off-rates across entire genomes, protein-DNA interaction dynamics have not been studied extensively. Here, we determine in vivo off-rates for the Saccharomyces cerevisiae chromatin organizing factor Abf1, at 191 sites simultaneously across the yeast genome. Average Abf1 residence times span a wide range, varying between 4.2 and 33 min. Sites with different off-rates are associated with different functional characteristics. This includes their transcriptional dependency on Abf1, nucleosome positioning and the size of the nucleosome-free region, as well as the ability to roadblock RNA polymerase II for termination. The results show how off-rates contribute to transcription factor function and that DIVORSEQ (Determining In Vivo Off-Rates by SEQuencing) is a meaningful way of investigating protein-DNA binding dynamics genome-wide.

Toward a translationally independent RNA-based synthetic oscillator using deactivated CRISPR-Cas

In synthetic circuits, CRISPR-Cas systems have been used effectively for endpoint changes from an initial state to a final state, such as in logic gates. Here, we use deactivated Cas9 (dCas9) and deactivated Cas12a (dCas12a) to construct dynamic RNA ring oscillators that cycle continuously between states over time in bacterial cells. While our dCas9 circuits using 103-nt guide RNAs showed irregular fluctuations with a wide distribution of peak-to-peak period lengths averaging approximately nine generations, a dCas12a oscillator design with 40-nt CRISPR RNAs performed much better, having a strongly repressed off-state, distinct autocorrelation function peaks, and an average peak-to-peak period length of ∼7.5 generations. Along with free-running oscillator circuits, we measure repression response times in open-loop systems with inducible RNA steps to compare with oscillator period times. We track thousands of cells for 24+ h at the single-cell level using a microfluidic device. In creating a circuit with nearly translationally independent behavior, as the RNAs control each others' transcription, we present the possibility for a synthetic oscillator generalizable across many organisms and readily linkable for transcriptional control.

Inherent regulatory asymmetry emanating from network architecture in a prevalent autoregulatory motif

Predicting gene expression from DNA sequence remains a major goal in the field of gene regulation. A challenge to this goal is the connectivity of the network, whose role in altering gene expression remains unclear. Here, we study a common autoregulatory network motif, the negative single-input module, to explore the regulatory properties inherited from the motif. Using stochastic simulations and a synthetic biology approach in E. coli, we find that the TF gene and its target genes have inherent asymmetry in regulation, even when their promoters are identical; the TF gene being more repressed than its targets. The magnitude of asymmetry depends on network features such as network size and TF-binding affinities. Intriguingly, asymmetry disappears when the growth rate is too fast or too slow and is most significant for typical growth conditions. These results highlight the importance of accounting for network architecture in quantitative models of gene expression.

Long-term association of a transcription factor with its chromatin binding site can stabilize gene expression and cell fate commitment

Some kinds of transcription factor proteins are very important in initiating and guiding cell fate differentiation. Overexpression of these factors can force many other kinds of cells to become muscle or nerve. Examples are MyoD for muscle and Ascl1 for nerve. It is not known how long such a factor must remain bound to its binding site for it to have its function; this could be seconds, minutes, hours, or days. We have developed a procedure to determine the required residence time for the Ascl1 nerve factor to have its function. This factor remains closely associated with its chromatin binding site for hours or days. This may be a general characteristic of such factors in nondividing (adult) cells.

Some lineage-determining transcription factors are overwhelmingly important in directing embryonic cells to a particular differentiation pathway, such as Ascl1 for nerve. They also have an exceptionally strong ability to force cells to change from an unrelated pathway to one preferred by their action. Transcription factors are believed to have a very short residence time of only a few seconds on their specific DNA or chromatin-binding sites. We have developed a procedure in which DNA containing one copy of the binding site for the neural-inducing factor Ascl1 is injected directly into a Xenopus oocyte nucleus which has been preloaded with a limiting amount of the Ascl1 transcription factor protein. This is followed by a further injection of DNA as a competitor, either in a plasmid or in chromosomal DNA, containing the same binding site but with a different reporter. Importantly, expression of the reporter provides a measure of the function of the transcription factor in addition to its residence time. The same long residence time and resistance to competition are seen with the estrogen receptor and its DNA response elements. We find that in this nondividing oocyte, the nerve-inducing factor Ascl1 can remain bound to a specific chromatin site for hours or days and thereby help to stabilize gene expression. This stability of transcription factor binding to chromatin is a necessary part of its action because removal of this factor causes discontinuation of its effect on gene expression. Stable transcription factor binding may be a characteristic of nondividing cells.

Figure 1 Theory Meets Figure 2 Experiments in the Study of Gene Expression

It is tempting to believe that we now own the genome. The ability to read and rewrite it at will has ushered in a stunning period in the history of science. Nonetheless, there is an Achilles' heel exposed by all of the genomic data that has accrued: We still do not know how to interpret them. Many genes are subject to sophisticated programs of transcriptional regulation, mediated by DNA sequences that harbor binding sites for transcription factors, which can up- or down-regulate gene expression depending upon environmental conditions. This gives rise to an input-output function describing how the level of expression depends upon the parameters of the regulated gene-for instance, on the number and type of binding sites in its regulatory sequence. In recent years, the ability to make precision measurements of expression, coupled with the ability to make increasingly sophisticated theoretical predictions, has enabled an explicit dialogue between theory and experiment that holds the promise of covering this genomic Achilles' heel. The goal is to reach a predictive understanding of transcriptional regulation that makes it possible to calculate gene expression levels from DNA regulatory sequence. This review focuses on the canonical simple repression motif to ask how well the models that have been used to characterize it actually work. We consider a hierarchy of increasingly sophisticated experiments in which the minimal parameter set learned at one level is applied to make quantitative predictions at the next. We show that these careful quantitative dissections provide a template for a predictive understanding of the many more complex regulatory arrangements found across all domains of life.

Inferring quantity and qualities of superimposed reaction rates from single molecule survival time distributions

Actions of molecular species, for example binding of transcription factors to chromatin, may comprise several superimposed reaction pathways. The number and the rate constants of such superimposed reactions can in principle be resolved by inverse Laplace transformation of the corresponding distribution of reaction lifetimes. However, current approaches to solve this transformation are challenged by photobleaching-prone fluorescence measurements of lifetime distributions. Here, we present a genuine rate identification method (GRID), which infers the quantity, rates and amplitudes of dissociation processes from fluorescence lifetime distributions using a dense grid of possible decay rates. In contrast to common multi-exponential analysis of lifetime distributions, GRID is able to distinguish between broad and narrow clusters of decay rates. We validate GRID by simulations and apply it to CDX2-chromatin interactions measured by live cell single molecule fluorescence microscopy. GRID reveals well-separated narrow decay rate clusters of CDX2, in part overlooked by multi-exponential analysis. We discuss the amplitudes of the decay rate spectrum in terms of frequency of observed events and occupation probability of reaction states. We further demonstrate that a narrow decay rate cluster is compatible with a common model of TF sliding on DNA.

Stochastic Gene Expression Influences the Selection of Antibiotic Resistance Mutations

Bacteria can resist antibiotics by expressing enzymes that remove or deactivate drug molecules. Here, we study the effects of gene expression stochasticity on efflux and enzymatic resistance. We construct an agent-based model that stochastically simulates multiple biochemical processes in the cell and we observe the growth and survival dynamics of the cell population. Resistance-enhancing mutations are introduced by varying parameters that control the enzyme expression or efficacy. We find that stochastic gene expression can cause complex dynamics in terms of survival and extinction for these mutants. Regulatory mutations, which augment the frequency and duration of resistance gene transcription, can provide limited resistance by increasing mean expression. Structural mutations, which modify the enzyme or efflux efficacy, provide most resistance by improving the binding affinity of the resistance protein to the antibiotic; increasing the enzyme's catalytic rate alone may contribute to resistance if drug binding is not rate limiting. Overall, we identify conditions where regulatory mutations are selected over structural mutations, and vice versa. Our findings show that stochastic gene expression is a key factor underlying efflux and enzymatic resistances and should be taken into consideration in future antibiotic research.

Enhancement of LacI binding in vivo

Transcription factors (TFs) bind to specific sequences in DNA to regulate transcription. Despite extensive measurements of TFs’ dissociation constant (K_d) in vitro, their apparent K_din vivo are usually unknown. LacI, a bacterial TF, is often used to artificially recruit proteins onto eukaryotic genomes. As LacI binds tightly to its recognition site (LacO) in vitro with a K_d about 10 picomolar (pM), it is often assumed that LacI also has high affinity to LacO in vivo. In this work, we measured LacI binding in living yeast cells using a fluorescent repressor operator system and found an apparent K_d of ∼0.6 μM, four orders of magnitude higher than that in vitro. By genetically altering (i) GFP-LacI structure, (ii) GFP-LacI stability, (iii) chromosome accessibility and (iv) LacO sequence, we reduced the apparent K_d to <10 nM. It turns out that the GFP tagging location and the fusion protein stability have a large effect on LacI binding, but surprisingly, chromosome accessibility only plays a mild role. These findings contribute to our quantitative understanding of the features that affect the apparent K_d of TF in cells. They also provide guidance for future design of more specific chromosomal recruitment through high-affinity TFs.

Lighting up the central dogma for predictive developmental biology

Although the last 30years have witnessed the mapping of the wiring diagrams of the gene regulatory networks that dictate cell fate and animal body plans, specific understanding building on such network diagrams that shows how DNA regulatory regions control gene expression lags far behind. These networks have yet to yield the predictive power necessary to, for example, calculate how the concentration dynamics of input transcription factors and DNA regulatory sequence prescribes output patterns of gene expression that, in turn, determine body plans themselves. Here, we argue that reaching a predictive understanding of developmental decision-making calls for an interplay between theory and experiment aimed at revealing how the regulation of the processes of the central dogma dictate network connections and how network topology guides cells toward their ultimate developmental fate. To make this possible, it is crucial to break free from the snapshot-based understanding of embryonic development facilitated by fixed-tissue approaches and embrace new technologies that capture the dynamics of developmental decision-making at the single cell level, in living embryos.

A Model for the Spatiotemporal Design of Gene Regulatory Circuits †

Mathematical modeling assists the design of synthetic regulatory networks by providing a detailed mechanistic understanding of biological systems. Models that can predict the performance of a design are fundamental for synthetic biology since they minimize iterations along the design-build-test lifecycle. Such predictability depends crucially on what assumptions (i.e., biological simplifications) the model considers. Here, we challenge a common assumption when it comes to the modeling of bacterial-based gene regulation: considering negligible the effects of intracellular physical space. It is commonly assumed that molecules, such as transcription factors (TF), are homogeneously distributed inside a cell, so there is no need to model their diffusion. We describe a mathematical model that accounts for molecular diffusion and show how simulations of network performance are decisively affected by the distance between its components. Specifically, the model focuses on the search by a TF for its target promoter. The combination of local searches, via one-dimensional sliding along the chromosome, and global searches, via three-dimensional diffusion through the cytoplasm, determine TF-promoter interplay. Previous experimental results with engineered bacteria in which the distance between TF source and target was minimized or enlarged were successfully reproduced by the spatially resolved model we introduce here. This suggests that the spatial specification of the circuit alone can be exploited as a design parameter in synthetic biology to select programmable output levels.

Measuring transcription at a single gene copy reveals hidden drivers of bacterial individuality

Single-cell measurements of mRNA copy-number inform our understanding of stochastic gene expression [1-3], but these measurements coarse-grain over the individual copies of the gene, where transcription and its regulation stochastically take plasce [4, 5]. Here we combine single-molecule quantification of mRNA and gene loci to measure the transcriptional activity of an endogenous gene in individual Escherichia coli bacteria. Interpreted using a theoretical model for mRNA dynamics, the single-cell data allows us to obtain the probabilistic rates of promoter switching, transcription initiation and elongation, mRNA release and degradation. Unexpectedly, we find that gene activity can be strongly coupled to the transcriptional state of another copy of the same gene present in the cell, and to the event of gene replication during the bacterial cell cycle. These gene-copy and cell-cycle correlations demonstrate the limits of mapping whole-cell mRNA numbers to the underlying stochastic gene activity, and instead highlight the contribution of previously hidden variables to the observed population heterogeneity.

From single bacterial cell imaging towards in vivo single-molecule biochemistry studies

Bacteria as single-cell organisms are important model systems to study cellular mechanisms and functions. In recent years and with the help of advanced fluorescence microscopy techniques, immense progress has been made in characterizing and quantifying the behavior of single bacterial cells on the basis of molecular interactions and assemblies in the complex environment of live cultures. Importantly, single-molecule imaging enables the in vivo determination of the stoichiometry and molecular architecture of subcellular structures, yielding detailed, quantitative, spatiotemporally resolved molecular maps and unraveling dynamic heterogeneities and subpopulations on the subcellular level. Nevertheless, open challenges remain. Here, we review the past and current status of the field, discuss example applications and give insights into future trends.

Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes

Single-cell experiments show that gene expression is stochastic and bursty, a feature that can emerge from slow switching between promoter states with different activities. In addition to slow chromatin and/or DNA looping dynamics, one source of long-lived promoter states is the slow binding and unbinding kinetics of transcription factors to promoters, i.e. the non-adiabatic binding regime. Here, we introduce a simple analytical framework, known as a piecewise deterministic Markov process (PDMP), that accurately describes the stochastic dynamics of gene expression in the non-adiabatic regime. We illustrate the utility of the PDMP on a non-trivial dynamical system by analysing the properties of a titration-based oscillator in the non-adiabatic limit. We first show how to transform the underlying chemical master equation into a PDMP where the slow transitions between promoter states are stochastic, but whose rates depend upon the faster deterministic dynamics of the transcription factors regulated by these promoters. We show that the PDMP accurately describes the observed periods of stochastic cycles in activator and repressor-based titration oscillators. We then generalize our PDMP analysis to more complicated versions of titration-based oscillators to explain how multiple binding sites lengthen the period and improve coherence. Last, we show how noise-induced oscillation previously observed in a titration-based oscillator arises from non-adiabatic and discrete binding events at the promoter site.

Theory of Site-Specific DNA-Protein Interactions in the Presence of Nucleosome Roadblocks

We show that nucleosomes exert a maximal amount of hindrance to the one-dimensional diffusion of transcription factors (TFs) when they are present between TFs and their cognate sites on DNA. The effective one-dimensional diffusion coefficient of TFs (χ_TF) decreases with a rise in the free-energy barrier (μ_NU) of the sliding of nucleosomes as χ_TF∝exp(-μ_NU). The average time (η_L) required by TFs to slide over L sites on DNA increases with μ_NU as η_L∝exp(μ_NU). When TFs move close to nucleosomes, then they exhibit typical subdiffusion. Nucleosomes can enhance the search dynamics of TFs when TFs are present between nucleosomes and TF binding sites. These results suggest that nucleosome-depleted regions around the cognate sites of TFs are mandatory for efficient site-specific binding of TFs. Remarkably, the genome-wide in vivo positioning pattern of TFs shows a maximum at their specific binding sites where the occupancy of nucleosomes shows a minimum. This could be a consequence of an increasing level of breathing dynamics of nucleosome cores and decreasing levels of fluctuations in the DNA binding domains of TFs as they move across TF binding sites. The dynamics of TFs becomes slow as they approach their cognate sites so that TFs form a tight site-specific complex, whereas the dynamics of nucleosomes becomes rapid so that they quickly pass through the cognate sites of TFs. Several in vivo data sets on the genome-wide positioning pattern of nucleosomes and TFs agree well with our arguments. The retarding effects of nucleosomes can be minimized when the degree of condensation of DNA is such that it can permit a jump size associated with the dynamics of TFs beyond ∼160-180 bp.

Price of disorder in the lac repressor hinge helix

The Lac system of genes has been pivotal in understanding gene regulation. When the lac repressor protein binds to the correct DNA sequence, the hinge region of the protein goes through a disorder to order transition. The structure of this region of the protein is well understood when it is in this bound conformation, but less so when it is not. Structural studies show that this region is flexible. Our simulations show this region is extremely flexible in solution; however, a high concentration of salt can help kinetically trap the hinge helix. Thermodynamically, disorder is more favorable without the DNA present.

Single-molecule imaging correlates decreasing nuclear volume with increasing TF-chromatin associations during zebrafish development

Zygotic genome activation (ZGA), the onset of transcription after initial quiescence, is a major developmental step in many species, which occurs after ten cell divisions in zebrafish embryos. How transcription factor (TF)-chromatin interactions evolve during early development to support ZGA is largely unknown. We establish single molecule tracking in live developing zebrafish embryos using reflected light-sheet microscopy to visualize two fluorescently labeled TF species, mEos2-TBP and mEos2-Sox19b. We further develop a data acquisition and analysis scheme to extract quantitative information on binding kinetics and bound fractions during fast cell cycles. The chromatin-bound fraction of both TFs increases during early development, as expected from a physical model of TF-chromatin interactions including a decreasing nuclear volume and increasing DNA accessibility. For Sox19b, data suggests the increase is mainly due to the shrinking nucleus. Our single molecule approach provides quantitative insight into changes of TF-chromatin associations during the developmental period embracing ZGA.

Single-Molecule Kinetics in Living Cells

In the past decades, advances in microscopy have made it possible to study the dynamics of individual biomolecules in vitro and resolve intramolecular kinetics that would otherwise be hidden in ensemble averages. More recently, single-molecule methods have been used to image, localize, and track individually labeled macromolecules in the cytoplasm of living cells, allowing investigations of intermolecular kinetics under physiologically relevant conditions. In this review, we illuminate the particular advantages of single-molecule techniques when studying kinetics in living cells and discuss solutions to specific challenges associated with these methods.