Dynamic heterogeneity and DNA methylation in embryonic stem cells

The influence of cell source on the senescence of human mesenchymal stem/stromal cells

While mesenchymal stem/stromal cells (MSCs) exhibit the ability to self-renew, they are not immortal; they eventually reach a point of irreversible growth cessation and functional deterioration following a limited series of population doublings, referred to as replicative senescence. When evaluated according to the criteria set by the International Society for Cell Therapy (ISCT), MSCs show significant differences in their senescence patterns and other characteristics related to their phenotype and function. These differences are attributed to the source of the MSCs and the conditions in which they are grown. MSCs derived from fetal or adult sources have variations in their genome stability, as well as in the expression and epigenetic profile of the cells, which in turn affects their secretome. Understanding the key factors of MSC senescence based on cell source can help to develop effective strategies for regulating senescence and improving the therapeutic potential.

Deterministic patterns in single-cell transcriptomic data

We report the existence of deterministic patterns in statistical plots of single-cell transcriptomic data. We develop a theory showing that the patterns are neither artifacts introduced by the measurement process nor due to underlying biological mechanisms. Rather they naturally emerge from finite sample size effects. The theory precisely predicts the patterns in data from multiplexed error-robust fluorescence in situ hybridization and five different types of single-cell sequencing platforms.

Single-cell RNA sequencing algorithms underestimate changes in transcriptional noise compared to single-molecule RNA imaging

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise remains unclear. Here, we utilize a small-molecule perturbation (5'-iodo-2'-deoxyuridine [IdU]) to amplify noise and assess noise quantification from numerous single-cell RNA sequencing (scRNA-seq) algorithms on human and mouse datasets and then compare it to noise quantification from single-molecule RNA fluorescence in situ hybridization (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise-without altered mean expression levels-for ∼90% of genes and that smFISH analysis verifies noise amplification for the vast majority of tested genes. Collectively, the analyses suggest that most scRNA-seq algorithms (including a simple normalization approach) are appropriate for quantifying noise, although all algorithms appear to systematically underestimate noise changes compared to smFISH. For practical purposes, this analysis further argues that IdU noise enhancement is globally penetrant-i.e., homeostatically increasing noise without altering mean expression levels-and could enable investigations of the physiological impacts of transcriptional noise.

Propagating pluripotency - The conundrum of self-renewal

The discovery of mouse embryonic stem cells in 1981 transformed research in mammalian developmental biology and functional genomics. The subsequent generation of human pluripotent stem cells (PSCs) and the development of molecular reprogramming have opened unheralded avenues for drug discovery and cell replacement therapy. Here, I review the history of PSCs from the perspective that long-term self-renewal is a product of the in vitro signaling environment, rather than an intrinsic feature of embryos. I discuss the relationship between pluripotent states captured in vitro to stages of epiblast in the embryo and suggest key considerations for evaluation of PSCs. A remaining fundamental challenge is to determine whether naïve pluripotency can be propagated from the broad range of mammals by exploiting common principles in gene regulatory architecture.

An expanded view of cell competition

Cell competition arises in heterogeneous tissues when neighbouring cells sense their relative fitness and undergo selection. It has been a challenge to define contexts in which cell competition is a physiologically relevant phenomenon and to understand the cellular features that underlie fitness and fitness sensing. Drawing on examples across a range of contexts and length scales, we illuminate molecular and cellular features that could underlie fitness in diverse tissue types and processes to promote and reinforce long-term maintenance of tissue function. We propose that by broadening the scope of how fitness is defined and the circumstances in which cell competition can occur, the field can unlock the potential of cell competition as a lens through which heterogeneity and its role in the fundamental principles of complex tissue organisation can be understood.

Transcriptional bursting dynamics in gene expression

Gene transcription is a stochastic process that occurs in all organisms. Transcriptional bursting, a critical molecular dynamics mechanism, creates significant heterogeneity in mRNA and protein levels. This heterogeneity drives cellular phenotypic diversity. Currently, the lack of a comprehensive quantitative model limits the research on transcriptional bursting. This review examines various gene expression models and compares their strengths and weaknesses to guide researchers in selecting the most suitable model for their research context. We also provide a detailed summary of the key metrics related to transcriptional bursting. We compared the temporal dynamics of transcriptional bursting across species and the molecular mechanisms influencing these bursts, and highlighted the spatiotemporal patterns of gene expression differences by utilizing metrics such as burst size and burst frequency. We summarized the strategies for modeling gene expression from both biostatistical and biochemical reaction network perspectives. Single-cell sequencing data and integrated multiomics approaches drive our exploration of cutting-edge trends in transcriptional bursting mechanisms. Moreover, we examined classical methods for parameter estimation that help capture dynamic parameters in gene expression data, assessing their merits and limitations to facilitate optimal parameter estimation. Our comprehensive summary and review of the current transcriptional burst dynamics theories provide deeper insights for promoting research on the nature of cell processes, cell fate determination, and cancer diagnosis.

The proteomic landscape and temporal dynamics of mammalian gastruloid development

Gastrulation is the highly coordinated process by which the early embryo breaks symmetry, establishes germ layers and a body plan, and sets the stage for organogenesis. As early mammalian development is challenging to study in vivo, stem cell-derived models have emerged as powerful surrogates, e.g. human and mouse gastruloids. However, although single cell RNA-seq (scRNA-seq) and high-resolution imaging have been extensively applied to characterize such in vitro embryo models, a paucity of measurements of protein dynamics and regulation leaves a major gap in our understanding. Here, we sought to address this by applying quantitative proteomics to human and mouse gastruloids at four key stages of their differentiation (naïve ESCs, primed ESCs, early gastruloids, late gastruloids). To the resulting data, we perform network analysis to map the dynamics of expression of macromolecular protein complexes and biochemical pathways, including identifying cooperative proteins that associate with them. With matched RNA-seq and phosphosite data from these same stages, we investigate pathway-, stage- and species-specific aspects of translational and post-translational regulation, e.g. finding peri-gastrulation stages of human and mice to be discordant with respect to the mitochondrial transcriptome vs. proteome, and nominating novel kinase-substrate relationships based on phosphosite dynamics. Finally, we leverage correlated dynamics to identify conserved protein networks centered around congenital disease genes. Altogether, our data (https://gastruloid.brotmanbaty.org/) and analyses showcase the potential of intersecting in vitro embryo models and proteomics to advance our understanding of early mammalian development in ways not possible through transcriptomics alone.

Redox heterogeneity in mouse embryonic stem cells individualizes cell fate decisions

Pluripotent embryonic stem cells (ESCs) can develop into any cell type in the body. Yet, the regulatory mechanisms that govern cell fate decisions during embryogenesis remain largely unknown. We now demonstrate that mouse ESCs (mESCs) display large natural variations in mitochondrial reactive oxygen species (mitoROS) levels that individualize their nuclear redox state, H3K4me3 landscape, and cell fate. While mESCs with high mitoROS levels (mitoROSHIGH) differentiate toward mesendoderm and form the primitive streak during gastrulation, mESCs, which generate less ROS, choose the alternative neuroectodermal fate. Temporal studies demonstrated that mesendodermal (ME) specification of mitoROSHIGH mESCs is mediated by a Nrf2-controlled switch in the nuclear redox state, triggered by the accumulation of redox-sensitive H3K4me3 marks, and executed by a hitherto unknown ROS-dependent activation process of the Wnt signaling pathway. In summary, our study explains how ESC heterogeneity is generated and used by individual cells to decide between distinct cellular fates.

Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise, remains unclear. Here we utilize a small-molecule perturbation (IdU) to amplify noise and assess noise quantification from numerous scRNA-seq algorithms on human and mouse datasets, and then compare to noise quantification from single-molecule RNA FISH (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise, without altered mean-expression levels, for ~90% of genes and that smFISH analysis verifies noise amplification for the vast majority of genes tested. Collectively, the analyses suggest that most scRNA-seq algorithms are appropriate for quantifying noise including a simple normalization approach, although all of these systematically underestimate noise compared to smFISH. From a practical standpoint, this analysis argues that IdU is a globally penetrant noise-enhancer molecule-amplifying noise without altering mean-expression levels-which could enable investigations of the physiological impacts of transcriptional noise.

Combinatorial quantification of 5mC and 5hmC at individual CpG dyads and the transcriptome in single cells reveals modulators of DNA methylation maintenance fidelity

Inheritance of 5-methylcytosine from one cell generation to the next by DNA methyltransferase 1 (DNMT1) plays a key role in regulating cellular identity. While recent work has shown that the activity of DNMT1 is imprecise, it remains unclear how the fidelity of DNMT1 is tuned in different genomic and cell state contexts. Here we describe Dyad-seq, a method to quantify the genome-wide methylation status of cytosines at the resolution of individual CpG dinucleotides to find that the fidelity of DNMT1-mediated maintenance methylation is related to the local density of DNA methylation and the landscape of histone modifications. To gain deeper insights into methylation/demethylation turnover dynamics, we first extended Dyad-seq to quantify all combinations of 5-methylcytosine and 5-hydroxymethylcytosine at individual CpG dyads. Next, to understand how cell state transitions impact maintenance methylation, we scaled the method down to jointly profile genome-wide methylation levels, maintenance methylation fidelity and the transcriptome from single cells (scDyad&T-seq). Using scDyad&T-seq, we demonstrate that, while distinct cell states can substantially impact the activity of the maintenance methylation machinery, locally there exists an intrinsic relationship between DNA methylation density, histone modifications and DNMT1-mediated maintenance methylation fidelity that is independent of cell state.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

Analysis of a detailed multi-stage model of stochastic gene expression using queueing theory and model reduction

We introduce a biologically detailed, stochastic model of gene expression describing the multiple rate-limiting steps of transcription, nuclear pre-mRNA processing, nuclear mRNA export, cytoplasmic mRNA degradation and translation of mRNA into protein. The processes in sub-cellular compartments are described by an arbitrary number of processing stages, thus accounting for a significantly finer molecular description of gene expression than conventional models such as the telegraph, two-stage and three-stage models of gene expression. We use two distinct tools, queueing theory and model reduction using the slow-scale linear-noise approximation, to derive exact or approximate analytic expressions for the moments or distributions of nuclear mRNA, cytoplasmic mRNA and protein fluctuations, as well as lower bounds for their Fano factors in steady-state conditions. We use these to study the phase diagram of the stochastic model; in particular we derive parametric conditions determining three types of transitions in the properties of mRNA fluctuations: from sub-Poissonian to super-Poissonian noise, from high noise in the nucleus to high noise in the cytoplasm, and from a monotonic increase to a monotonic decrease of the Fano factor with the number of processing stages. In contrast, protein fluctuations are always super-Poissonian and show weak dependence on the number of mRNA processing stages. Our results delineate the region of parameter space where conventional models give qualitatively incorrect results and provide insight into how the number of processing stages, e.g. the number of rate-limiting steps in initiation, splicing and mRNA degradation, shape stochastic gene expression by modulation of molecular memory.

Unexplored power of CRISPR-Cas9 in neuroscience, a multi-OMICs review

The neuroscience and neurobiology of gene editing to enhance learning and memory is of paramount interest to the scientific community. The advancements of CRISPR system have created avenues to treat neurological disorders by means of versatile modalities varying from expression to suppression of genes and proteins. Neurodegenerative disorders have also been attributed to non-canonical DNA secondary structures by affecting neuron activity through controlling gene expression, nucleosome shape, transcription, translation, replication, and recombination. Changing DNA regulatory elements which could contribute to the fate and function of neurons are thoroughly discussed in this review. This study presents the ability of CRISPR system to boost learning power and memory, treat or cure genetically-based neurological disorders, and alleviate psychiatric diseases by altering the activity and the irritability of the neurons at the synaptic cleft through DNA manipulation, and also, epigenetic modifications using Cas9. We explore and examine how each different OMIC techniques can come useful when altering DNA sequences. Such insight into the underlying relationship between OMICs and cellular behaviors leads us to better neurological and psychiatric therapeutics by intelligently designing and utilizing the CRISPR/Cas9 technology.

miRNA circuit modules for precise, tunable control of gene expression

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

Ancestors' Gift: Parental Early Exposure to the Environmentally Realistic Pesticide Mixture Drives Offspring Phenotype in a Larger Extent Than Direct Exposure in the Pacific Oyster, Crassostrea gigas

Marine organisms are threatened by the presence of pesticides in coastal waters. Among them, the Pacific oyster is one of the most studied invertebrates in marine ecotoxicology where numerous studies highlighted the multiscale impacts of pesticides. In the past few years, a growing body of literature has reported the epigenetic outcomes of xenobiotics. Because DNA methylation is an epigenetic mark implicated in organism development and is meiotically heritable, it raises the question of the multigenerational implications of xenobiotic-induced epigenetic alterations. Therefore, we performed a multigenerational exposure to an environmentally relevant mixture of 18 pesticides (nominal sum concentration: 2.85 μg·L-1) during embryo-larval stages (0-48 hpf) of a second generation (F1) for which parents where already exposed or not in F0. Gene expression, DNA methylation, and physiological end points were assessed throughout the life cycle of individuals. Overall, the multigenerational effect has a greater influence on the phenotype than the exposure itself. Thus, multigenerational phenotypic effects were observed: individuals descending from exposed parents exhibited lower epinephrine-induced metamorphosis and field survival rates. At the molecular level, RNA-seq and Methyl-seq data analyses performed in gastrula embryos and metamorphosis-competent pediveliger (MCP) larvae revealed a clear F0 treatment-dependent discrimination. Some genes implicated into shell secretion and immunity exhibited F1:F0 treatment interaction patterns (e.g., Calm and Myd88). Those results suggest that low chronic environmental pesticide contamination can alter organisms beyond the individual scale level and have long-term adaptive implications.

Quantifying and correcting bias in transcriptional parameter inference from single-cell data

The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: mRNA synthesis rate, the switching on rate (the on state being the active transcriptional state), and the switching off rate. This model assumes no extrinsic noise, i.e., parameters do not vary between cells, and thus estimated parameters are to be understood as approximating the average values in a population. The accuracy of this approximation is currently unclear. Here, we develop a theory that explains the size and sign of estimation bias when inferring parameters from single-cell data using the standard telegraph model. We find specific bias signatures depending on the source of extrinsic noise (which parameter is most variable across cells) and the mode of transcriptional activity. If gene expression is not bursty then the population averages of all three parameters are overestimated if extrinsic noise is in the synthesis rate; underestimation occurs if extrinsic noise is in the switching on rate; both underestimation and overestimation can occur if extrinsic noise is in the switching off rate. We find that some estimated parameters tend to infinity as the size of extrinsic noise approaches a critical threshold. In contrast when gene expression is bursty, we find that in all cases the mean burst size (ratio of the synthesis rate to the switching off rate) is overestimated while the mean burst frequency (the switching on rate) is underestimated. We estimate the size of extrinsic noise from the covariance matrix of sequencing data and use this together with our theory to correct published estimates of transcriptional parameters for mammalian genes.

Transcriptional Factors Mediated Reprogramming to Pluripotency

A unique kind of pluripotent cell, i.e., Induced pluripotent stem cells (iPSCs), now being targeted for iPSC synthesis, are produced by reprogramming animal and human differentiated cells (with no change in genetic makeup for the sake of high efficacy iPSCs formation). The conversion of specific cells to iPSCs has revolutionized stem cell research by making pluripotent cells more controllable for regenerative therapy. For the past 15 years, somatic cell reprogramming to pluripotency with force expression of specified factors has been a fascinating field of biomedical study. For that technological primary viewpoint reprogramming method, a cocktail of four transcription factors (TF) has required: Kruppel-like factor 4 (KLF4), four-octamer binding protein 34 (OCT3/4), MYC and SOX2 (together referred to as OSKM) and host cells. IPS cells have great potential for future tissue replacement treatments because of their ability to self-renew and specialize in all adult cell types, although factor-mediated reprogramming mechanisms are still poorly understood medically. This technique has dramatically improved performance and efficiency, making it more useful in drug discovery, disease remodeling, and regenerative medicine. Moreover, in these four TF cocktails, more than 30 reprogramming combinations were proposed, but for reprogramming effectiveness, only a few numbers have been demonstrated for the somatic cells of humans and mice. Stoichiometry, a combination of reprogramming agents and chromatin remodeling compounds, impacts kinetics, quality, and efficiency in stem cell research.

Exact solution of a three-stage model of stochastic gene expression including cell-cycle dynamics

The classical three-stage model of stochastic gene expression predicts the statistics of single cell mRNA and protein number fluctuations as a function of the rates of promoter switching, transcription, translation, degradation and dilution. While this model is easily simulated, its analytical solution remains an unsolved problem. Here we modify this model to explicitly include cell-cycle dynamics and then derive an exact solution for the time-dependent joint distribution of mRNA and protein numbers. We show large differences between this model and the classical model which captures cell-cycle effects implicitly via effective first-order dilution reactions. In particular we find that the Fano factor of protein numbers calculated from a population snapshot measurement are underestimated by the classical model whereas the correlation between mRNA and protein can be either over- or underestimated, depending on the timescales of mRNA degradation and promoter switching relative to the mean cell-cycle duration time.

Live-cell imaging uncovers the relationship between histone acetylation, transcription initiation, and nucleosome mobility

Histone acetylation and RNA polymerase II phosphorylation are associated with transcriptionally active chromatin, but their spatiotemporal relationship in live cells remains poorly understood. To address this problem, we combine Fab-based labeling of endogenous protein modifications with single-molecule tracking to quantify the dynamics of chromatin enriched with histone H3 lysine-27 acetylation (H3K27ac) and RNA polymerase II serine-5 phosphorylation (RNAP2-Ser5ph). Our analysis reveals that chromatin enriched with these two modifications is generally separate. In these separated sites, we show that the two modifications are inversely correlated with one another on the minutes time scale and that single nucleosomes within each region display distinct and opposing dynamics on the subsecond time scale. While nucleosomes diffuse ~15% faster in chromatin enriched with H3K27ac, they diffuse ~15% slower in chromatin enriched with RNAP2-Ser5ph. These results argue that high levels of H3K27ac and RNAP2-Ser5ph are not often present together at the same place and time, but rather each marks distinct transcriptionally poised or active sites, respectively.

Tuning Methylation-Dependent Silencing Dynamics by Synthetic Modulation of CpG Density

Methylation of cytosines in CG dinucleotides (CpGs) within promoters has been shown to lead to gene silencing in mammals in natural contexts. Recently, engineered recruitment of methyltransferases (DNMTs) at specific loci was shown to be sufficient to silence synthetic and endogenous gene expression through this mechanism. A critical parameter for DNA methylation-based silencing is the distribution of CpGs within the target promoter. However, how the number or density of CpGs in the target promoter affects the dynamics of silencing by DNMT recruitment has remained unclear. Here, we constructed a library of promoters with systematically varying CpG content, and analyzed the rate of silencing in response to recruitment of DNMT. We observed a tight correlation between silencing rate and CpG content. Further, methylation-specific analysis revealed a constant accumulation rate of methylation at the promoter after DNMT recruitment. We identified a single CpG site between TATA box and transcription start site (TSS) that accounted for a substantial part of the difference in silencing rates between promoters with differing CpG content, indicating that certain residues play disproportionate roles in controlling silencing. Together, these results provide a library of promoters for synthetic epigenetic and gene regulation applications, as well as insights into the regulatory link between CpG content and silencing rate.

Quantitative biochemical phenotypic heterogeneity of macrophages after myelin debris phagocytosis at a single cell level by synchrotron radiation fourier transform infrared microspectroscopy

During the immuno-inflammatory pathophysiological process of spinal cord injury, traumatic brain injury, and ischemic stroke, macrophages play an important role in phagocytizing and clearing degenerated myelin debris. After phagocytizing myelin debris, the biochemical phenotypes related to the biological function of macrophages show vast heterogeneity; however, it is not fully understood. Detecting biochemical changes after myelin debris phagocytosis by macrophages at a single-cell level is helpful to characterize phenotypic and functional heterogeneity. In this study, based on the cell model of myelin debris phagocytosis by macrophages in vitro, the biochemical changes in macrophages were investigated using Synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy. Infrared spectrum fluctuations, principal component analysis, and cell-to-cell Euclidean distance statistical analysis of specific spectrum regions revealed dynamic and significant changes in proteins and lipids within macrophages after myelin debris phagocytosis. Thus, SR-FTIR microspectroscopy is a powerful identification toolkit for exploring biochemical phenotype heterogeneity transformation that may be of great importance to providing an evaluation strategy for studying cell functions related to cellular substance distribution and metabolism.

Tuning methylation-dependent silencing dynamics by synthetic modulation of CpG density

Methylation of cytosines in CG dinucleotides (CpGs) within promoters has been shown to lead to gene silencing in mammals in natural contexts. Recently, engineered recruitment of methyltransferases (DNMTs) at specific loci was shown to be sufficient to silence synthetic and endogenous gene expression through this mechanism. A critical parameter for DNA methylation-based silencing is the distribution of CpGs within the target promoter. However, how the number or density of CpGs in the target promoter affects the dynamics of silencing by DNMT recruitment has remained unclear. Here we constructed a library of promoters with systematically varying CpG content, and analyzed the rate of silencing in response to recruitment of DNMT. We observed a tight correlation between silencing rate and CpG content. Further, methylation-specific analysis revealed a constant accumulation rate of methylation at the promoter after DNMT recruitment. We identified a single CpG site between TATA box and transcription start site (TSS) that accounted for a substantial part of the difference in silencing rates between promoters with differing CpG content, indicating that certain residues play disproportionate roles in controlling silencing. Together, these results provide a library of promoters for synthetic epigenetic and gene regulation applications, as well as insights into the regulatory link between CpG content and silencing rate.

Combinatorial quantification of 5mC and 5hmC at individual CpG dyads and the transcriptome in single cells reveals modulators of DNA methylation maintenance fidelity

Transmission of 5-methylcytosine (5mC) from one cell generation to the next plays a key role in regulating cellular identity in mammalian development and diseases. While recent work has shown that the activity of DNMT1, the protein responsible for the stable inheritance of 5mC from mother to daughter cells, is imprecise; it remains unclear how the fidelity of DNMT1 is tuned in different genomic and cell state contexts. Here we describe Dyad-seq, a method that combines enzymatic detection of modified cytosines with nucleobase conversion techniques to quantify the genome-wide methylation status of cytosines at the resolution of individual CpG dinucleotides. We find that the fidelity of DNMT1-mediated maintenance methylation is directly related to the local density of DNA methylation, and for genomic regions that are lowly methylated, histone modifications can dramatically alter the maintenance methylation activity. Further, to gain deeper insights into the methylation and demethylation turnover dynamics, we extended Dyad-seq to quantify all combinations of 5mC and 5-hydroxymethylcytosine (5hmC) at individual CpG dyads to show that TET proteins preferentially hydroxymethylate only one of the two 5mC sites in a symmetrically methylated CpG dyad rather than sequentially convert both 5mC to 5hmC. To understand how cell state transitions impact DNMT1-mediated maintenance methylation, we scaled the method down and combined it with the measurement of mRNA to simultaneously quantify genome-wide methylation levels, maintenance methylation fidelity and the transcriptome from the same cell (scDyad&T-seq). Applying scDyad&T-seq to mouse embryonic stem cells transitioning from serum to 2i conditions, we observe dramatic and heterogenous demethylation and the emergence of transcriptionally distinct subpopulations that are closely linked to the cell-to-cell variability in loss of DNMT1-mediated maintenance methylation activity, with regions of the genome that escape 5mC reprogramming retaining high levels of maintenance methylation fidelity. Overall, our results demonstrate that while distinct cell states can substantially impact the genome-wide activity of the DNA methylation maintenance machinery, locally there exists an intrinsic relationship between DNA methylation density, histone modifications and DNMT1-mediated maintenance methylation fidelity that is independent of cell state.

One model fits all: Combining inference and simulation of gene regulatory networks

The rise of single-cell data highlights the need for a nondeterministic view of gene expression, while offering new opportunities regarding gene regulatory network inference. We recently introduced two strategies that specifically exploit time-course data, where single-cell profiling is performed after a stimulus: HARISSA, a mechanistic network model with a highly efficient simulation procedure, and CARDAMOM, a scalable inference method seen as model calibration. Here, we combine the two approaches and show that the same model driven by transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically relevant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene interactions. We verify that CARDAMOM quantitatively reconstructs causal links when the data is simulated from HARISSA, and demonstrate its performance on experimental data collected on in vitro differentiating mouse embryonic stem cells. Overall, this integrated strategy largely overcomes the limitations of disconnected inference and simulation.

Epigenetics as a mediator of plasticity in cancer

The concept of an epigenetic landscape describing potential cellular fates arising from pluripotent cells, first advanced by Conrad Waddington, has evolved in light of experiments showing nondeterministic outcomes of regulatory processes and mathematical methods for quantifying stochasticity. In this Review, we discuss modern approaches to epigenetic and gene regulation landscapes and the associated ideas of entropy and attractor states, illustrating how their definitions are both more precise and relevant to understanding cancer etiology and the plasticity of cancerous states. We address the interplay between different types of regulatory landscapes and how their changes underlie cancer progression. We also consider the roles of cellular aging and intrinsic and extrinsic stimuli in modulating cellular states and how landscape alterations can be quantitatively mapped onto phenotypic outcomes and thereby used in therapy development.

Coupling gene expression dynamics to cell size dynamics and cell cycle events: Exact and approximate solutions of the extended telegraph model

The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by the cell cycle phase, cellular growth and division, and other crucial aspects of cellular biology. Here, we derive the analytical time-dependent solution of an extended telegraph model that explicitly considers the doubling of gene copy numbers upon DNA replication, dependence of the mRNA synthesis rate on cellular volume, gene dosage compensation, partitioning of molecules during cell division, cell-cycle duration variability, and cell-size control strategies. Based on the time-dependent solution, we obtain the analytical distributions of transcript numbers for lineage and population measurements in steady-state growth and also find a linear relation between the Fano factor of mRNA fluctuations and cell volume fluctuations. We show that generally the lineage and population distributions in steady-state growth cannot be accurately approximated by the steady-state solution of extrinsic noise models, i.e. a telegraph model with parameters drawn from probability distributions. This is because the mRNA lifetime is often not small enough compared to the cell cycle duration to erase the memory of division and replication. Accurate approximations are possible when this memory is weak, e.g. for genes with bursty expression and for which there is sufficient gene dosage compensation when replication occurs.

Transcription factor antagonism regulates heterogeneity in embryonic stem cell states

Gene expression heterogeneity underlies cell states and contributes to developmental robustness. While heterogeneity can arise from stochastic transcriptional processes, the extent to which it is regulated is unclear. Here, we characterize the regulatory program underlying heterogeneity in murine embryonic stem cell (mESC) states. We identify differentially active and transcribed enhancers (DATEs) across states. DATEs regulate differentially expressed genes and are distinguished by co-binding of transcription factors Klf4 and Zfp281. In contrast to other factors that interact in a positive feedback network stabilizing mESC cell-type identity, Klf4 and Zfp281 drive opposing transcriptional and chromatin programs. Abrogation of factor binding to DATEs dampens variation in gene expression, and factor loss alters kinetics of switching between states. These results show antagonism between factors at enhancers results in gene expression heterogeneity and formation of cell states, with implications for the generation of diverse cell types during development.

Cost-efficient boundary-free surface patterning achieves high effective-throughput of time-lapse microscopy experiments

Time-lapse microscopy plays critical roles in the studies of cellular dynamics. However, setting up a time-lapse movie experiments is not only laborious but also with low output, mainly due to the cell-losing problem (i.e., cells moving out of limited field of view), especially in a long-time recording. To overcome this issue, we have designed a cost-efficient way that enables cell patterning on the imaging surfaces without any physical boundaries. Using mouse embryonic stem cells as an example system, we have demonstrated that our boundary-free patterned surface solves the cell-losing problem without disturbing their cellular phenotype. Statistically, the presented system increases the effective-throughput of time-lapse microscopy experiments by an order of magnitude.

Approximating solutions of the Chemical Master equation using neural networks

The Chemical Master Equation (CME) provides an accurate description of stochastic biochemical reaction networks in well-mixed conditions, but it cannot be solved analytically for most systems of practical interest. Although Monte Carlo methods provide a principled means to probe system dynamics, the large number of simulations typically required can render the estimation of molecule number distributions and other quantities infeasible. In this article, we aim to leverage the representational power of neural networks to approximate the solutions of the CME and propose a framework for the Neural Estimation of Stochastic Simulations for Inference and Exploration (Nessie). Our approach is based on training neural networks to learn the distributions predicted by the CME from relatively few stochastic simulations. We show on biologically relevant examples that simple neural networks with one hidden layer can capture highly complex distributions across parameter space, thereby accelerating computationally intensive tasks such as parameter exploration and inference.

RNA velocity unraveled

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.

Transcriptional heterogeneity and cell cycle regulation as central determinants of primitive endoderm priming

During embryonic development cells acquire identity as they proliferate, implying that an intrinsic facet of cell fate choice requires coupling lineage decisions to cell division. How is the cell cycle regulated to promote or suppress heterogeneity and differentiation? We explore this question combining time lapse imaging with single-cell RNA-seq in the contexts of self-renewal, priming, and differentiation of mouse embryonic stem cells (ESCs) towards the Primitive Endoderm (PrE) lineage. Since ESCs are derived from the inner cell mass (ICM) of the mammalian blastocyst, ESCs in standard culture conditions are transcriptionally heterogeneous containing dynamically interconverting subfractions primed for either of the two ICM lineages, Epiblast and PrE. Here, we find that differential regulation of cell cycle can tip the balance between these primed populations, such that naïve ESC culture promotes Epiblast-like expansion and PrE differentiation stimulates the selective survival and proliferation of PrE-primed cells. In endoderm differentiation, this change is accompanied by a counter-intuitive increase in G1 length, also observed in vivo. While fibroblast growth factor/extracellular signal-regulated kinase (FGF/ERK) signalling is a key regulator of ESC differentiation and PrE specification, we find it is not just responsible for ESCs heterogeneity, but also the inheritance of similar cell cycles between sisters and cousins. Taken together, our results indicate a tight relationship between transcriptional heterogeneity and cell cycle regulation in lineage specification, with primed cell populations providing a pool of flexible cell types that can be expanded in a lineage-specific fashion while allowing plasticity during early determination.

Species-Specific Enhancer Activity of OCT4 in Porcine Pluripotency: The Porcine OCT4 Reporter System Could Monitor Pluripotency in Porcine Embryo Development and Embryonic Stem Cells

The present study examined the activity and function of the pig OCT4 enhancer in the porcine early embryonic development stage and porcine authentic embryonic stem cells. OCT4 is known as a pluripotent regulator, and its upstream regulatory region-based dual-fluorescence protein reporter system controlled by distal and proximal enhancers is broadly used in studies examining the states and mechanism of pluripotency. We analyzed how this reporter system functions during early embryo development and in stem cells using a previously established porcine-specific reporter system. We demonstrated that the porcine OCT4 distal enhancer and proximal enhancer were activated with different expression patterns simultaneously as the expression of pluripotent marker genes changed during the development of in vitro pathenotes and the establishment of porcine embryonic stem cells (ESCs). This work demonstrates the applicability of the porcine OCT4 upstream region-derived dual-fluorescence reporter system, which may be applied to investigations of species-specific pluripotency in porcine-origin cells. These reporter systems may be useful tools for studies of porcine-specific pluripotency, early embryo development, and embryonic stem cells.

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.

Inference and uncertainty quantification of stochastic gene expression via synthetic models

Estimating uncertainty in model predictions is a central task in quantitative biology. Biological models at the single-cell level are intrinsically stochastic and nonlinear, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations, a synthetic model, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.

Dynamics and functional roles of splicing factor autoregulation

Non-core spliceosome components are essential, conserved regulators of alternative splicing. They provide concentration-dependent control of diverse pre-mRNAs. Many splicing factors direct unproductive splicing of their own pre-mRNAs through negative autoregulation. However, the impact of such feedback loops on splicing dynamics at the single-cell level remains unclear. Here, we developed a system to quantitatively analyze negative autoregulatory splicing dynamics by splicing factor SRSF1 in response to perturbations in single HEK293 cells. We show that negative autoregulatory splicing provides critical functions for gene regulation, establishing a ceiling of SRSF1 protein concentration, reducing cell-cell heterogeneity in SRSF1 levels, and buffering variation in transcription. Most important, it adapts SRSF1 splicing activity to variations in demand from other pre-mRNA substrates. A minimal mathematical model of autoregulatory splicing explains these experimentally observed features and provides values for effective biochemical parameters. These results reveal the unique functional roles that splicing negative autoregulation plays in homeostatically regulating transcriptional programs.

Covariation of Pluripotency Markers and Biomechanical Properties in Mouse Embryonic Stem Cells

Pluripotent cells are subject to much interest as a source of differentiated cellular material for research models, regenerative medical therapies and novel applications such as lab-cultured meat. Greater understanding of the pluripotent state and control over its differentiation is therefore desirable. The role of biomechanical properties in directing cell fate and cell behavior has been increasingly well described in recent years. However, many of the mechanisms which control cell morphology and mechanical properties in somatic cells are absent from pluripotent cells. We leveraged naturally occurring variation in biomechanical properties and expression of pluripotency genes in murine ESCs to investigate the relationship between these parameters. We observed considerable variation in a Rex1-GFP expression reporter line and found that this variation showed no apparent correlation to cell spreading morphology as determined by circularity, Feret ratio, phase contrast brightness or cell spread area, either on a parameter-by-parameter basis, or when evaluated using a combined metric derived by principal component analysis from the four individual criteria. We further confirmed that cell volume does not co-vary with Rex1-GFP expression. Interestingly, we did find that a subpopulation of cells that were readily detached by gentle agitation collectively exhibited higher expression of Nanog, and reduced LmnA expression, suggesting that elevated pluripotency gene expression may correlate with reduced adhesion to the substrate. Furthermore, atomic force microscopy and quantitative fluorescent imaging revealed a connection between cell stiffness and Rex1-GFP reporter expression. Cells expressing high levels of Rex1-GFP are consistently of a relatively low stiffness, while cells with low levels of Rex1-GFP tend toward higher stiffness values. These observations indicate some interaction between pluripotency gene expression and biomechanical properties, but also support a strong role for other interactions between the cell culture regime and cellular biomechanical properties, occurring independently of the core transcriptional network that supports pluripotency.

Nanotechnology Tools Enabling Biological Discovery

Over the years, the engineering aspect of nanotechnology has been significantly exploited. Medical intervention strategies have been developed by leveraging existing molecular biology knowledge and combining it with nanotechnology tools to improve outcomes. However, little attention has been paid to harnessing the strengths of nanotechnology as a biological discovery tool. Fundamental understanding of controlling dynamic biological processes at the subcellular level is key to developing personalized therapeutic and diagnostic interventions. Single-cell analyses using intravital microscopy, expansion microscopy, and microfluidic-based platforms have been helping to better understand cell heterogeneity in healthy and diseased cells, a major challenge in oncology. Also, single-cell analysis has revealed critical signaling pathways and biological intracellular components with key biological functions. The physical manipulation enabled by nanotools can allow real-time monitoring of biological changes at a single-cell level by sampling intracellular fluid from the same cell. The formation of intercellular highways by nanotube-like structures has important clinical implications such as metastasis development. The integration of nanomaterials into optical and molecular imaging techniques has rendered valuable morphological, structural, and biological information. Nanoscale imaging unravels mechanisms of temporality by enabling the visualization of nanoscale dynamics never observed or measured between individual cells with standard biological techniques. The exceptional sensitivity of nanozymes, artificial enzymes, make them perfect components of the next-generation mobile diagnostics devices. Here, we highlight these impactful cancer-related biological discoveries enabled by nanotechnology and producing a paradigm shift in cancer research and oncology.

Modeling bursty transcription and splicing with the chemical master equation

Splicing cascades that alter gene products posttranscriptionally also affect expression dynamics. We study a class of processes and associated distributions that emerge from models of bursty promoters coupled to directed acyclic graphs of splicing. These solutions provide full time-dependent joint distributions for an arbitrary number of species with general noise behaviors and transient phenomena, offering qualitative and quantitative insights about how splicing can regulate expression dynamics. Finally, we derive a set of quantitative constraints on the minimum complexity necessary to reproduce gene coexpression patterns using synchronized burst models. We validate these findings by analyzing long-read sequencing data, where we find evidence of expression patterns largely consistent with these constraints.

Principles for the design of multicellular engineered living systems

Remarkable progress in bioengineering over the past two decades has enabled the formulation of fundamental design principles for a variety of medical and non-medical applications. These advancements have laid the foundation for building multicellular engineered living systems (M-CELS) from biological parts, forming functional modules integrated into living machines. These cognizant design principles for living systems encompass novel genetic circuit manipulation, self-assembly, cell-cell/matrix communication, and artificial tissues/organs enabled through systems biology, bioinformatics, computational biology, genetic engineering, and microfluidics. Here, we introduce design principles and a blueprint for forward production of robust and standardized M-CELS, which may undergo variable reiterations through the classic design-build-test-debug cycle. This Review provides practical and theoretical frameworks to forward-design, control, and optimize novel M-CELS. Potential applications include biopharmaceuticals, bioreactor factories, biofuels, environmental bioremediation, cellular computing, biohybrid digital technology, and experimental investigations into mechanisms of multicellular organisms normally hidden inside the "black box" of living cells.

Ascorbic acid and all-trans retinoic acid promote proliferation of chicken blastoderm cells (cBCs) by mediating DNA demethylation

Chicken blastoderm cells (cBCs) obtained from stage X (EG&K) embryos are easily available materials for the study of cell development. However, cBCs are not widely used because they are hard to maintain in long-term culture in vitro. To solve this problem, ascorbic acid (AA; also known as vitamin C (VC)) and all-trans retinoic acid (ATRA) were added into basic culture medium to promote cell growth. Results suggested that cultured cBCs possessed strongly proliferative activity and maintained their pluripotency on the support of chicken embryonic fibroblast (CEF) feeder. Moreover, when VC or/and ATRA was added, the number and area of cBC colonies increased significantly compared with the control group. The expression of pluripotency genes (Sox2 and Nanog) and cell cycle-regulated genes (CCND1 and CDK6) was upregulated obviously. Furthermore, results showed that 5hmC levels in VC and RA groups increased significantly by DNA dot blot and immunofluorescence staining. These results provide strong evidence that VC and ATRA induced DNA demethylation and enhanced 5hmC level. The level of H3K27me3 was raised, while the level of H3K9me2 was reduced by addition of VC and ATRA. Finally, the expression of Tet1 and Dnmt3b was upregulated remarkably. Therefore, these results indicated that VC and ATRA enhanced DNA demethylation and then promoted cBC survival and proliferation in vitro.

Epigenetic reorganization during early embryonic lineage specification

BACKGROUND

Dynamic chromatin reorganization occurs during two waves of cell lineage specification process, blastocyst formation and gastrulation, to generate distinct cell types. Epigenetic defects have been associated with severe developmental defects and diseases. How epigenetic remodeling coordinates the two lineage specification waves is becoming uncovered, benefiting from the development and application of new technologies including low-input or single-cell epigenome analysis approached in the past few years.

OBJECTIVE

In this review, we aim to highlight the most recent findings on epigenetic remodeling in cell lineage specification during blastocyst formation and gastrulation.

METHODS

First, we introduce how DNA methylation dynamically changes in blastocyst formation and gastrulation and its function in transcriptional regulation lineage-specific genes. Then, we discuss widespread remodeling of histone modification at promoters and enhancers in orchestrating the trajectory of cell lineage specification. Finally, we review dynamics of chromatin accessibility and 3D structure regulating developmental gene expression and associating with specific transcription factor binding events at stage specific manner. We also highlight the key questions that remain to be answered to fully understand chromatin regulation and reorganization in lineage specification.

CONCLUSION

Here, we summarize the recent advances and discoveries on epigenetic reorganization and its roles in blastocyst formation and gastrulation, and how it cooperates with the lineage specification, painting from global sequencing data from mouse in vivo tissues.

Mean-field theory accurately captures the variation of copy number distributions across the mRNA life cycle

We consider a stochastic model where a gene switches between two states, an mRNA transcript is released in the active state, and subsequently it undergoes an arbitrary number of sequential unimolecular steps before being degraded. The reactions effectively describe various stages of the mRNA life cycle such as initiation, elongation, termination, splicing, export, and degradation. We construct a mean-field approach that leads to closed-form steady-state distributions for the number of transcript molecules at each stage of the mRNA life cycle. By comparison with stochastic simulations, we show that the approximation is highly accurate over all the parameter space, independent of the type of expression (constitutive or bursty) and of the shape of the distribution (unimodal, bimodal, and nearly bimodal). The theory predicts that in a population of identical cells, any bimodality is gradually washed away as the mRNA progresses through its life cycle.

The primitive streak and cellular principles of building an amniote body through gastrulation

[Figure: see text].

A mathematical model which examines age-related stochastic fluctuations in DNA maintenance methylation

Due to its complexity and its ubiquitous nature the ageing process remains an enduring biological puzzle. Many molecular mechanisms and biochemical process have become synonymous with ageing. However, recent findings have pinpointed epigenetics as having a key role in ageing and healthspan. In particular age related changes to DNA methylation offer the possibility of monitoring the trajectory of biological ageing and could even be used to predict the onset of diseases such as cancer, Alzheimer's disease and cardiovascular disease. At the molecular level emerging evidence strongly suggests the regulatory processes which govern DNA methylation are subject to intracellular stochasticity. It is challenging to fully understand the impact of stochasticity on DNA methylation levels at the molecular level experimentally. An ideal solution is to use mathematical models to capture the essence of the stochasticity and its outcomes. In this paper we present a novel stochastic model which accounts for specific methylation levels within a gene promoter. Uncertainty of the eventual site-specific methylation levels for different values of methylation age, depending on the initial methylation levels were analysed. Our model predicts the observed bistable levels in CpG islands. In addition, simulations with various levels of noise indicate that uncertainty predominantly spreads through the hypermethylated region of stability, especially for large values of input noise. A key outcome of the model is that CpG islands with high to intermediate methylation levels tend to be more susceptible to dramatic DNA methylation changes due to increasing methylation age.

PRC1 drives Polycomb-mediated gene repression by controlling transcription initiation and burst frequency

The Polycomb repressive system plays a fundamental role in controlling gene expression during mammalian development. To achieve this, Polycomb repressive complexes 1 and 2 (PRC1 and PRC2) bind target genes and use histone modification-dependent feedback mechanisms to form Polycomb chromatin domains and repress transcription. The inter-relatedness of PRC1 and PRC2 activity at these sites has made it difficult to discover the specific components of Polycomb chromatin domains that drive gene repression and to understand mechanistically how this is achieved. Here, by exploiting rapid degron-based approaches and time-resolved genomics, we kinetically dissect Polycomb-mediated repression and discover that PRC1 functions independently of PRC2 to counteract RNA polymerase II binding and transcription initiation. Using single-cell gene expression analysis, we reveal that PRC1 acts uniformly within the cell population and that repression is achieved by controlling transcriptional burst frequency. These important new discoveries provide a mechanistic and conceptual framework for Polycomb-dependent transcriptional control.

A narrative review of tumor heterogeneity and challenges to tumor drug therapy

Objective

To accurately evaluate tumor heterogeneity, make multidimensional diagnosis according to the causes and phenotypes of tumor heterogeneity, and assist in the individualized treatment of tumors.

Background

Tumor heterogeneity is one of the most essential characteristics of malignant tumors. In tumor recurrence, development, and evolution, tumor heterogeneity can lead to the formation of different cell groups with other molecular characteristics. Tumor heterogeneity can be characterized by the uneven distribution of tumor cell subsets of other genes between and within the disease site (spatial heterogeneity) or the time change of cancer cell molecular composition (temporal heterogeneity). The discovery of tumor targeting drugs has dramatically promoted tumor therapy. However, the existence of heterogeneity seriously affects the effect of tumor treatment and the prognosis of patients.

Methods

The literature discussing tumor heterogeneity and its resistance to tumor therapy was broadly searched to analyze tumor heterogeneity as well as the challenges and solutions for gene detection and tumor drug therapy.

Conclusions

Tumor heterogeneity is affected by many factors consist of internal cell factors and cell microenvironment. Tumor heterogeneity greatly hinders effective and individualized tumor treatment. Understanding the fickle of tumors in multiple dimensions and flexibly using a variety of detection methods to capture the changes of tumors can help to improve the design of diagnosis and treatment plans for cancer and benefit millions of patients.

Gene expression at a single-molecule level: implications for myelodysplastic syndromes and acute myeloid leukemia

Nongenetic heterogeneity, or gene expression stochasticity, is an important source of variability in biological systems. With the advent and improvement of single molecule resolution technologies, it has been shown that transcription dynamics and resultant transcript number fluctuations generate significant cell-to-cell variability that has important biological effects and may contribute substantially to both tissue homeostasis and disease. In this respect, the pathophysiology of stem cell-derived malignancies such as acute myeloid leukemia and myelodysplastic syndromes, which has historically been studied at the ensemble level, may require reevaluation. To that end, it is our aim in this review to highlight the results of recent single-molecule, biophysical, and systems studies of gene expression dynamics, with the explicit purpose of demonstrating how the insights from these basic science studies may help inform and progress the field of leukemia biology and, ultimately, research into novel therapies.

Lineages of embryonic stem cells show non-Markovian state transitions

Pluripotent embryonic stem cells (ESCs) constitute the cell types of the adult vertebrate through a series of developmental state transitions. These states can be defined by expression levels of marker genes, such as Nanog and Sox2. In culture, ESCs reversibly transition between states. However, whether ESCs retain memory of their previous states or transition in a memoryless (Markovian) process remains relatively unknown. Here, we show some highly dynamic lineages of ESCs do not exhibit the Markovian property: their previous states and kin relations influence future choices. Unexpectedly, the distribution of lineages across their composition between states is constant over time, contrasting with the predictions of a Markov model. Additionally, highly dynamic ESC lineages show skewed cell fate distributions after retinoic acid differentiation. Together, these data suggest ESC lineage is an important variable governing future cell states, with implications for stem cell function and development.

Quantitative control of noise in mammalian gene expression by dynamic histone regulation

Fluctuation ('noise') in gene expression is critical for mammalian cellular processes. Numerous mechanisms contribute to its origins, yet the mechanisms behind large fluctuations that are induced by single transcriptional activators remain elusive. Here, we probed putative mechanisms by studying the dynamic regulation of transcriptional activator binding, histone regulator inhibitors, chromatin accessibility, and levels of mRNAs and proteins in single cells. Using a light-induced expression system, we showed that the transcriptional activator could form an interplay with dual functional co-activator/histone acetyltransferases CBP/p300. This interplay resulted in substantial heterogeneity in H3K27ac, chromatin accessibility, and transcription. Simultaneous attenuation of CBP/p300 and HDAC4/5 reduced heterogeneity in the expression of endogenous genes, suggesting that this mechanism is universal. We further found that the noise was reduced by pulse-wide modulation of transcriptional activator binding possibly as a result of alternating the epigenetic states. Our findings suggest a mechanism for the modulation of noise in synthetic and endogenous gene expression systems.