Dynamic heterogeneity and DNA methylation in embryonic stem cells

Heterogeneous pattern of DNA methylation in developmentally important genes correlates with its chromatin conformation

Background

DNA methylation is a major epigenetic modification, playing a crucial role in the development and differentiation of higher organisms. DNA methylation is also known to regulate transcription by gene repression. Various developmental genes such as c-mos, HoxB5, Sox11, and Sry show tissue-specific gene expression that was shown to be regulated by promoter DNA methylation. The aim of the present study is to investigate the establishment of chromatin marks (active or repressive) in relation to heterogeneous methylation in the promoter regions of these developmentally important genes.

Results

Chromatin-immunoprecipitation (ChIP) assays were performed to immuno-precipitate chromatin by antibodies against both active (H3K4me3) and repressive (H3K9me3) chromatin regions. The analysis of ChIP results showed that both the percentage input and fold enrichment of activated chromatin was higher in tissues expressing the respective genes as compared to the tissues not expressing the same set of genes. This was true for all the genes selected for the study (c-mos, HoxB5, Sox11, and Sry). These findings illustrate that inconsistent DNA methylation patterns (sporadic, mosaic and heterogeneous) may also influence gene regulation, thereby resulting in the modulation of chromatin conformation.

Conclusions

These findings illustrate that various patterns of DNA methylation (asynchronous, mosaic and heterogeneous) correlates with chromatin modification, resulting in the gene regulation.

Synthetic recording and in situ readout of lineage information in single cells

Reconstructing the lineage relationships and dynamic event histories of individual cells within their native spatial context is a long-standing challenge in biology. Many biological processes of interest occur in optically opaque or physically inaccessible contexts, necessitating approaches other than direct imaging. Here we describe a synthetic system that enables cells to record lineage information and event histories in the genome in a format that can be subsequently read out of single cells in situ. This system, termed memory by engineered mutagenesis with optical in situ readout (MEMOIR), is based on a set of barcoded recording elements termed scratchpads. The state of a given scratchpad can be irreversibly altered by CRISPR/Cas9-based targeted mutagenesis, and later read out in single cells through multiplexed single-molecule RNA fluorescence hybridization (smFISH). Using MEMOIR as a proof of principle, we engineered mouse embryonic stem cells to contain multiple scratchpads and other recording components. In these cells, scratchpads were altered in a progressive and stochastic fashion as the cells proliferated. Analysis of the final states of scratchpads in single cells in situ enabled reconstruction of lineage information from cell colonies. Combining analysis of endogenous gene expression with lineage reconstruction in the same cells further allowed inference of the dynamic rates at which embryonic stem cells switch between two gene expression states. Finally, using simulations, we show how parallel MEMOIR systems operating in the same cell could enable recording and readout of dynamic cellular event histories. MEMOIR thus provides a versatile platform for information recording and in situ, single-cell readout across diverse biological systems.

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process

In some recent studies, a view emerged that stochastic dynamics governing the switching of cells from one differentiation state to another could be characterized by a peak in gene expression variability at the point of fate commitment. We have tested this hypothesis at the single-cell level by analyzing primary chicken erythroid progenitors through their differentiation process and measuring the expression of selected genes at six sequential time-points after induction of differentiation. In contrast to population-based expression data, single-cell gene expression data revealed a high cell-to-cell variability, which was masked by averaging. We were able to show that the correlation network was a very dynamical entity and that a subgroup of genes tend to follow the predictions from the dynamical network biomarker (DNB) theory. In addition, we also identified a small group of functionally related genes encoding proteins involved in sterol synthesis that could act as the initial drivers of the differentiation. In order to assess quantitatively the cell-to-cell variability in gene expression and its evolution in time, we used Shannon entropy as a measure of the heterogeneity. Entropy values showed a significant increase in the first 8 h of the differentiation process, reaching a peak between 8 and 24 h, before decreasing to significantly lower values. Moreover, we observed that the previous point of maximum entropy precedes two paramount key points: an irreversible commitment to differentiation between 24 and 48 h followed by a significant increase in cell size variability at 48 h. In conclusion, when analyzed at the single cell level, the differentiation process looks very different from its classical population average view. New observables (like entropy) can be computed, the behavior of which is fully compatible with the idea that differentiation is not a “simple” program that all cells execute identically but results from the dynamical behavior of the underlying molecular network.

A single-cell transcriptomics analysis offers a new dynamical view of the differentiation process, involving an increase in between-cell variability prior to commitment.

The differentiation process has classically been seen as a stereotyped program leading from one progenitor toward a functional cell. This vision was based upon cell population-based analyses averaged over millions of cells. However, new methods have recently emerged that allow interrogation of the molecular content at the single-cell level, challenging this view with a new model suggesting that cell-to-cell gene expression stochasticity could play a key role in differentiation. We took advantage of a physiologically relevant avian cellular model to analyze the expression level of 92 genes in individual cells collected at several time-points during differentiation. We first observed that the process analyzed at the single-cell level is very different and much less well ordered than the population-based average view. Furthermore, we showed that cell-to-cell variability in gene expression peaks transiently before strongly decreasing. This rise in variability precedes two key events: an irreversible commitment to differentiation, followed by a significant increase in cell size variability. Altogether, our results support the idea that differentiation is not a “simple” series of well-ordered molecular events executed identically by all cells in a population but likely results from dynamical behavior of the underlying molecular network.

Quantitative imaging of mammalian transcriptional dynamics: from single cells to whole embryos

Probing dynamic processes occurring within the cell nucleus at the quantitative level has long been a challenge in mammalian biology. Advances in bio-imaging techniques over the past decade have enabled us to directly visualize nuclear processes in situ with unprecedented spatial and temporal resolution and single-molecule sensitivity. Here, using transcription as our primary focus, we survey recent imaging studies that specifically emphasize the quantitative understanding of nuclear dynamics in both time and space. These analyses not only inform on previously hidden physical parameters and mechanistic details, but also reveal a hierarchical organizational landscape for coordinating a wide range of transcriptional processes shared by mammalian systems of varying complexity, from single cells to whole embryos.

Cell Lineage Trees Bear Fruit

Two inference approaches harness the information present in cell lineage trees to better understand the dynamic transitions between cell states.

Dissecting mechanisms of mouse embryonic stem cells heterogeneity through a model-based analysis of transcription factor dynamics

Pluripotent mouse embryonic stem cells (mESCs) show heterogeneous expression levels of transcription factors (TFs) involved in pluripotency regulation, among them Nanog and Rex1. The expression of both TFs can change dynamically between states of high and low activity, correlating with the cells' capacity for self-renewal. Stochastic fluctuations as well as sustained oscillations in gene expression are possible mechanisms to explain this behaviour, but the lack of suitable data hampered their clear distinction. Here, we present a systems biology approach in which novel experimental data on TF heterogeneity is complemented by an agent-based model of mESC self-renewal. Because the model accounts for intracellular interactions, cell divisions and heredity structures, it allows for evaluating the consistency of the proposed mechanisms with data on population growth and on TF dynamics after cell sorting. Our model-based analysis revealed that a bistable, noise-driven network model fulfils the minimal requirements to consistently explain Nanog and Rex1 expression dynamics in heterogeneous and sorted mESC populations. Moreover, we studied the impact of TF-related proliferation capacities on the frequency of state transitions and demonstrate that cellular genealogies can provide insights into the heredity structures of mESCs.

Epigenetics of cell fate reprogramming and its implications for neurological disorders modelling

The reprogramming of human induced pluripotent stem cells (hiPSCs) proceeds in a stepwise manner with reprogramming factors binding and epigenetic composition changes during transition to maintain the epigenetic landscape, important for pluripotency. There arises a question as to whether the aberrant epigenetic state after reprogramming leads to epigenetic defects in induced stem cells causing unpredictable long term effects in differentiated cells. In this review, we present a comprehensive view of epigenetic alterations accompanying reprogramming, cell maintenance and differentiation as factors that influence applications of hiPSCs in stem cell based technologies. We conclude that sample heterogeneity masks DNA methylation signatures in subpopulations of cells and thus believe that beside a genetic evaluation, extensive epigenomic screening should become a standard procedure to ensure hiPSCs state before they are used for genome editing and differentiation into neurons of interest. In particular, we suggest that exploitation of the single-cell composition of the epigenome will provide important insights into heterogeneity within hiPSCs subpopulations to fast forward development of reliable hiPSC-based analytical platforms in neurological disorders modelling and before completed hiPSC technology will be implemented in clinical approaches.

Bistable Epigenetic States Explain Age-Dependent Decline in Mesenchymal Stem Cell Heterogeneity

The molecular mechanisms by which heterogeneity, a major characteristic of stem cells, is achieved are yet unclear. We here study the expression of the membrane stem cell antigen-1 (Sca-1) in mouse bone marrow mesenchymal stem cell (MSC) clones. We show that subpopulations with varying Sca-1 expression profiles regenerate the Sca-1 profile of the mother population within a few days. However, after extensive replication in vitro, the expression profiles shift to lower values and the regeneration time increases. Study of the promoter of Ly6a unravels that the expression level of Sca-1 is related to the promoter occupancy by the activating histone mark H3K4me3. We demonstrate that these findings can be consistently explained by a computational model that considers positive feedback between promoter H3K4me3 modification and gene transcription. This feedback implicates bistable epigenetic states which the cells occupy with an age-dependent frequency due to persistent histone (de-)modification. Our results provide evidence that MSC heterogeneity, and presumably that of other stem cells, is associated with bistable epigenetic states and suggest that MSCs are subject to permanent state fluctuations. Stem Cells 2017;35:694-704.

Linkage disequilibrium analysis of allelic heterogeneity in DNA methylation

Heterogeneity of DNA methylation status among alleles is observed in various cell types and is involved in epigenetic gene regulation and cancer biology. However, the individual methylation profile within each allele has not yet been examined at the whole-genome level. In the present study, we applied linkage disequilibrium analysis to the DNA methylation data obtained from whole-genome bisulfite sequencing studies in mouse germline and other types of cells. We found that the methylation status of 2 consecutive CpG sites showed deviation from equilibrium frequency toward concordant linkage (both methylated or both unmethylated) in germline cells. In the imprinting loci where methylation of constituent alleles is known, our analysis detected the deviation toward the concordant linkage as expected. In addition, we applied this analysis to the transitional zone between methylated and unmethylated regions and to the cells undergoing epigenetic reprogramming. In both cases, deviation to the concordant-linked alleles was conspicuous, indicating that the methylation pattern is not random but rather concordant within each allele. These results will provide the key to understanding the mechanism underlying allelic heterogeneity.

A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to characterize differences in expression in the presence of distinct expression states within and among biological conditions. We demonstrate that this framework can detect differential expression patterns under a wide range of settings. Compared to existing approaches, this method has higher power to detect subtle differences in gene expression distributions that are more complex than a mean shift, and can characterize those differences. The freely available R package scDD implements the approach.

Transition states and cell fate decisions in epigenetic landscapes

Waddington's epigenetic landscape is an abstract metaphor frequently used to represent the relationship between gene activity and cell fates during development. Over the past few years, it has become a useful framework for interpreting results from single-cell transcriptomics experiments. It has led to the proposal that, during fate transitions, cells experience smooth, continuous progressions of global transcriptional activity, which can be captured by (pseudo)temporal dynamics. Here, focusing strictly on the fate decision events, we suggest an alternative view: that fate transitions occur in a discontinuous, stochastic manner whereby signals modulate the probability of the transition events.

Pluripotent stem cells as a model for embryonic patterning: From signaling dynamics to spatial organization in a dish

In vivo studies have identified the signaling pathways and transcription factors involved in patterning the vertebrate embryo, but much remains unknown about how these are organized in space and time to orchestrate embryogenesis. Recently, embryonic stem cells have been established as a platform for studying spatial pattern formation and differentiation dynamics in the early mammalian embryo. The ease of observing and manipulating stem cell systems promises to fill gaps in our understanding of developmental dynamics and identify aspects that are uniquely human. Developmental Dynamics 245:976-990, 2016. © 2016 Wiley Periodicals, Inc.

Single-cell pluripotency regulatory networks

Pluripotent stem cells (PSCs) are a popular model system for investigating development, tissue regeneration, and repair. Although much is known about the molecular mechanisms that regulate the balance between self-renewal and lineage commitment in PSCs, the spatiotemporal integration of responsive signaling pathways with core transcriptional regulatory networks are complex and only partially understood. Moreover, measurements made on populations of cells reveal only average properties of the underlying regulatory networks, obscuring their fine detail. Here, we discuss the reconstruction of regulatory networks in individual cells using novel single-cell transcriptomics and proteomics, in order to expand our understanding of the molecular basis of pluripotency, including the role of cell-cell variability within PSC populations, and ways in which networks may be controlled in order to reliably manipulate cell behavior.

Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects

Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self-renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation-specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments

An important experimental design question for high-throughput time series studies is the number of replicates required for accurate reconstruction of the profiles. Due to budget and sample availability constraints, more replicates imply fewer time points and vice versa. We analyze the performance of dense and replicate sampling by developing a theoretical framework that focuses on a restricted yet expressive set of possible curves over a wide range of noise levels and by analyzing real expression data. For both the theoretical analysis and experimental data, we observe that, under reasonable noise levels, autocorrelations in the time series data allow dense sampling to better determine the correct levels of non-sampled points when compared to replicate sampling. A Java implementation of our framework can be used to determine the best replicate strategy given the expected noise. These results provide theoretical support to the large number of high-throughput time series experiments that do not use replicates.

Visualizing allele-specific expression in single cells reveals epigenetic mosaicism in an H19 loss-of-imprinting mutant

Imprinting defects can lead to inappropriate expression from the normally silenced allele, but it remains unclear whether every cell in a mutant organism follows the population average. Ginart et al. applied a new fluorescence in situ hybridization method that measures allele-specific expression in single cells to address this question in mutants exhibiting aberrant H19/Igf2 imprinting.

Imprinting is a classic mammalian epigenetic phenomenon that results in expression from a single parental allele. Imprinting defects can lead to inappropriate expression from the normally silenced allele, but it remains unclear whether every cell in a mutant organism follows the population average, which would have profound implications for human imprinting disorders. Here, we apply a new fluorescence in situ hybridization method that measures allele-specific expression in single cells to address this question in mutants exhibiting aberrant H19/Igf2 (insulin-like growth factor 2) imprinting. We show that mutant primary embryonic mouse fibroblasts are comprised of two subpopulations: one expressing both H19 alleles and another expressing only the maternal copy. Only in the latter cell population is Igf2 expression detected. Furthermore, the two subpopulations are stable in that cells do not interconvert between the two expression patterns. Combined small input methylation analysis and transcriptional imaging revealed that these two mutant subpopulations exhibit distinct methylation patterns at their imprinting control regions. Consistently, pharmacological inhibition of DNA methylation reduced the proportion of monoallelic cells. Importantly, we observed that the same two subpopulations are also present in vivo within murine cardiac tissue. Our results establish that imprinting disorders can display striking single-cell heterogeneity in their molecular phenotypes and suggest that such heterogeneity may underlie epigenetic mosaicism in human imprinting disorders.

The Mendelian disorders of the epigenetic machinery

The Mendelian disorders of the epigenetic machinery are genetic disorders that involve disruption of the various components of the epigenetic machinery (writers, erasers, readers, and remodelers) and are thus expected to have widespread downstream epigenetic consequences. Studying this group may offer a unique opportunity to learn about the role of epigenetics in health and disease. Among these patients, neurological dysfunction and, in particular, intellectual disability appears to be a common phenotype; however, this is often seen in association with other more specific features in respective disorders. The specificity of some of the clinical features raises the question whether specific cell types are particularly sensitive to the loss of these factors. Most of these disorders demonstrate dosage sensitivity as loss of a single allele appears to be sufficient to cause the observed phenotypes. Although the pathogenic sequence is unknown for most of these disorders, there are several examples where disrupted expression of downstream target genes accounts for a substantial portion of the phenotype; hence, it may be useful to systematically map such disease-relevant target genes. Finally, two of these disorders (Rubinstein-Taybi and Kabuki syndromes) have shown post-natal rescue of markers of the neurological dysfunction with drugs that lead to histone deacetylase inhibition, indicating that some of these disorders may be treatable causes of intellectual disability.

Signal transduction controls heterogeneous NF-κB dynamics and target gene expression through cytokine-specific refractory states

Cells respond dynamically to pulsatile cytokine stimulation. Here we report that single, or well-spaced pulses of TNFα (>100 min apart) give a high probability of NF-κB activation. However, fewer cells respond to shorter pulse intervals (<100 min) suggesting a heterogeneous refractory state. This refractory state is established in the signal transduction network downstream of TNFR and upstream of IKK, and depends on the level of the NF-κB system negative feedback protein A20. If a second pulse within the refractory phase is IL-1β instead of TNFα, all of the cells respond. This suggests a mechanism by which two cytokines can synergistically activate an inflammatory response. Gene expression analyses show strong correlation between the cellular dynamic response and NF-κB-dependent target gene activation. These data suggest that refractory states in the NF-κB system constitute an inherent design motif of the inflammatory response and we suggest that this may avoid harmful homogenous cellular activation.

Single Cell Analysis Reveals Concomitant Transcription of Pluripotent and Lineage Markers During the Early Steps of Differentiation of Embryonic Stem Cells

The differentiation of embryonic stem cells is associated with extensive changes in gene expression. It is not yet clear whether these changes are the result of binary switch-like mechanisms or that of continuous and progressive variation. Here, I have used immunostaining and single molecule RNA fluorescence in situ hybridization (FISH) to assess changes in the expression of the well-known pluripotency-associated gene Pou5f1 (also known as Oct4) and early differentiation markers Sox1 and T-brachyury in single cells during the early steps of differentiation of mouse embryonic stem cells. I found extensive overlap between the expression of Pou5f1/Sox1 or Pou5f1/T-brachyury shortly after the initiation of differentiation towards either the neuronal or the mesendodermal lineage, but no evidence of correlation between their respective expression levels. Quantitative analysis of transcriptional output at the sites of nascent transcription revealed that Pou5f1 and Sox1 were transcribed in pulses and that embryonic stem cell differentiation was accompanied by changes in pulsing frequencies. The progressive induction of Sox1 was further associated with an increase in the average size of individual transcriptional bursts. Surprisingly, single cells that actively and simultaneously transcribe both the pluripotency- and the lineage-associated genes could easily be found in the differentiating population. The results presented here show for the first time that lineage priming can occur in cells that are actively transcribing a pluripotent marker. Furthermore, they suggest that this process is associated with changes in transcriptional dynamics.

Comparative Epigenomic Profiling of the DNA Methylome in Mouse and Zebrafish Uncovers High Interspecies Divergence

The DNA methylation landscape is dynamically patterned during development and distinct methylation patterns distinguish healthy from diseased cells. However, whether tissue-specific methylation patterns are conserved across species is not known. We used comparative methylome analysis of base-resolution DNA methylation profiles from the liver and brain of mouse and zebrafish generated by reduced representation bisulfite sequencing to identify the conserved and divergent aspects of the methylome in these commonly used vertebrate model organisms. On average, 24% of CpGs are methylated in mouse livers and the pattern of methylation was highly concordant among four male mice from two different strains. The same level of methylation (24.2%) was identified in mouse brain. In striking contrast, zebrafish had 63 and 70% of CpG methylation in the liver and brain, respectively. This is attributed, in part, to the higher percentage of the zebrafish genome occupied by transposable elements (52% vs. 45% in mice). Thus, the species identity was more significant in determining methylome patterning than was the similarity in organ function. Conserved features of the methylome across tissues and species was the exclusion of methylation from promoters and from CpG islands near transcription start sites, and the clustering of methylated CpGs in gene bodies and intragenic regions. These data suggest that DNA methylation reflects species-specific genome structure, and supports the notion that DNA methylation in non-promoter regions may contribute to genome evolution.

Single-cell gene expression profiling and cell state dynamics: collecting data, correlating data points and connecting the dots

Single-cell analyses of transcript and protein expression profiles-more precisely, single-cell resolution analysis of molecular profiles of cell populations-have now entered the center stage with widespread applications of single-cell qPCR, single-cell RNA-Seq and CyTOF. These high-dimensional population snapshot techniques are complemented by low-dimensional time-resolved, microscopy-based monitoring methods. Both fronts of advance have exposed a rich heterogeneity of cell states within uniform cell populations in many biological contexts, producing a new kind of data that has triggered computational analysis methods for data visualization, dimensionality reduction, and cluster (subpopulation) identification. The next step is now to go beyond collecting data and correlating data points: to connect the dots, that is, to understand what actually underlies the identified data patterns. This entails interpreting the 'clouds of points' in state space as a manifestation of the underlying molecular regulatory network. In that way control of cell state dynamics can be formalized as a quasi-potential landscape, as first proposed by Waddington. We summarize key methods of data acquisition and computational analysis and explain the principles that link the single-cell resolution measurements to dynamical systems theory.

Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies

Identification and quantification of the characteristics of stem cell preparations is critical for understanding stem cell biology and for the development and manufacturing of stem cell based therapies. We have developed image analysis and visualization software that allows effective use of time-lapse microscopy to provide spatial and dynamic information from large numbers of human embryonic stem cell colonies. To achieve statistically relevant sampling, we examined >680 colonies from 3 different preparations of cells over 5 days each, generating a total experimental dataset of 0.9 terabyte (TB). The 0.5 Giga-pixel images at each time point were represented by multi-resolution pyramids and visualized using the Deep Zoom Javascript library extended to support viewing Giga-pixel images over time and extracting data on individual colonies. We present a methodology that enables quantification of variations in nominally-identical preparations and between colonies, correlation of colony characteristics with Oct4 expression, and identification of rare events.

Nanog, Oct4 and Tet1 interplay in establishing pluripotency

A few central transcription factors inside mouse embryonic stem (ES) cells and induced pluripotent stem (iPS) cells are believed to control the cells’ pluripotency. Characterizations of pluripotent state were put forward on both transcription factor and epigenetic levels. Whereas core players have been identified, it is desirable to map out gene regulatory networks which govern the reprogramming of somatic cells as well as the early developmental decisions. Here we propose a multiple level model where the regulatory network of Oct4, Nanog and Tet1 includes positive feedback loops involving DNA-demethylation around the promoters of Oct4 and Tet1. We put forward a mechanistic understanding of the regulatory dynamics which account for i) Oct4 overexpression is sufficient to induce pluripotency in somatic cell types expressing the other Yamanaka reprogramming factors endogenously; ii) Tet1 can replace Oct4 in reprogramming cocktail; iii) Nanog is not necessary for reprogramming however its over-expression leads to enhanced self-renewal; iv) DNA methylation is the key to the regulation of pluripotency genes; v) Lif withdrawal leads to loss of pluripotency. Overall, our paper proposes a novel framework combining transcription regulation with DNA methylation modifications which, takes into account the multi-layer nature of regulatory mechanisms governing pluripotency acquisition through reprogramming.

Highly variable cancer subpopulations that exhibit enhanced transcriptome variability and metastatic fitness

Individual cells within a tumour can exhibit distinct genetic and molecular features. The impact of such diversification on metastatic potential is unknown. Here we identify clonal human breast cancer subpopulations that display different levels of morphological and molecular diversity. Highly variable subpopulations are more proficient at metastatic colonization and chemotherapeutic survival. Through single-cell RNA-sequencing, inter-cell transcript expression variability is identified as a defining feature of the highly variable subpopulations that leads to protein-level variation. Furthermore, we identify high variability in the spliceosomal machinery gene set. Engineered variable expression of the spliceosomal gene SNRNP40 promotes metastasis, attributable to cells with low expression. Clinically, low SNRNP40 expression is associated with metastatic relapse. Our findings reveal transcriptomic variability generation as a mechanism by which cancer subpopulations can diversify gene expression states, which may allow for enhanced fitness under changing environmental pressures encountered during cancer progression.

Phenotypic and genetic intra-tumor heterogeneity have an important role in cancer progression and therapeutic resistance. Here, the authors show that phenotypically variable tumor subpopulations exhibit higher metastatic potential and display enhanced intra-clonal transcriptomic variability, likely promoted by deregulated spliceosome activity.

Transcription Dynamics in Living Cells

The transcription cycle can be roughly divided into three stages: initiation, elongation, and termination. Understanding the molecular events that regulate all these stages requires a dynamic view of the underlying processes. The development of techniques to visualize and quantify transcription in single living cells has been essential in revealing the transcription kinetics. They have revealed that (a) transcription is heterogeneous between cells and (b) transcription can be discontinuous within a cell. In this review, we discuss the progress in our quantitative understanding of transcription dynamics in living cells, focusing on all parts of the transcription cycle. We present the techniques allowing for single-cell transcription measurements, review evidence from different organisms, and discuss how these experiments have broadened our mechanistic understanding of transcription regulation.

Epigenetic modulators, modifiers and mediators in cancer aetiology and progression

This year is the tenth anniversary of the publication in this journal of a model suggesting the existence of 'tumour progenitor genes'. These genes are epigenetically disrupted at the earliest stages of malignancies, even before mutations, and thus cause altered differentiation throughout tumour evolution. The past decade of discovery in cancer epigenetics has revealed a number of similarities between cancer genes and stem cell reprogramming genes, widespread mutations in epigenetic regulators, and the part played by chromatin structure in cellular plasticity in both development and cancer. In the light of these discoveries, we suggest here a framework for cancer epigenetics involving three types of genes: 'epigenetic mediators', corresponding to the tumour progenitor genes suggested earlier; 'epigenetic modifiers' of the mediators, which are frequently mutated in cancer; and 'epigenetic modulators' upstream of the modifiers, which are responsive to changes in the cellular environment and often linked to the nuclear architecture. We suggest that this classification is helpful in framing new diagnostic and therapeutic approaches to cancer.

Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity

We report scM&T-seq, a method for parallel single-cell genome-wide methylome and transcriptome sequencing that allows for the discovery of associations between transcriptional and epigenetic variation. Profiling of 61 mouse embryonic stem cells confirmed known links between DNA methylation and transcription. Notably, the method revealed previously unrecognized associations between heterogeneously methylated distal regulatory elements and transcription of key pluripotency genes.

A continuum model of transcriptional bursting

Transcription occurs in stochastic bursts. Early models based upon RNA hybridisation studies suggest bursting dynamics arise from alternating inactive and permissive states. Here we investigate bursting mechanism in live cells by quantitative imaging of actin gene transcription, combined with molecular genetics, stochastic simulation and probabilistic modelling. In contrast to early models, our data indicate a continuum of transcriptional states, with a slowly fluctuating initiation rate converting the gene between different levels of activity, interspersed with extended periods of inactivity. We place an upper limit of 40 s on the lifetime of fluctuations in elongation rate, with initiation rate variations persisting an order of magnitude longer. TATA mutations reduce the accessibility of high activity states, leaving the lifetime of on- and off-states unchanged. A continuum or spectrum of gene states potentially enables a wide dynamic range for cell responses to stimuli.

DOI:http://dx.doi.org/10.7554/eLife.13051.001

Understanding how gene activity is regulated relies on accurate measurements of the output of genes. Proteins are generated from genes via a multi-step process. In the first step, called transcription, the DNA of a gene is copied by complex cell machinery to create molecules of mRNA. Subsequently, these mRNA molecules are ‘translated’ into proteins.

Previous studies have assayed gene transcription by measuring mRNA production in millions of cells at the same time. The resulting measurements give the impression that transcription occurs as a continuous, smooth process. However, when individual gene transcription is measured in single cells, mRNA production between cells is unexpectedly variable. This challenged the view that transcription is a continuous process.

One idea that explains this variability – the "two-state" or "bursting" model – proposes that genes switch between "on" and "off" states with a certain probability. Thus, at any one time, a gene will be off in many cells and on in others. However, the methods used in these experiments measure mRNA in dead cells, and so the dynamic switching of genes between on and off states was presumed, but not accurately measured.

Corrigan et al. have now imaged the transcription of a single gene – a gene for a protein called actin – in living cells of an amoeba called Dictyostelium. Genetic techniques and computational modeling were then used to explore what affects the variability in this gene’s activity. These approaches revealed that transcription occurs across a spectrum of activity, rather than in rigid on or off states. The transcription process itself may also contribute to where a gene’s activity sits on this spectrum. Furthermore, Corrigan et al. found that a specific DNA sequence found at the start of the actin gene, that is also found in many genes in complex life-forms, is required for the gene to reach the highest levels of activity on the spectrum.

This spectrum of activity states could allow cells to finely tune their responses to the signals they receive. A future challenge will be to assess how the activity of other genes compare to the actin gene and to discover what underlies the variation in the timing of transcription’s different stages.

DOI:http://dx.doi.org/10.7554/eLife.13051.002

Molecular features of cellular reprogramming and development

Differentiating somatic cells are progressively restricted to specialized functions during ontogeny, but they can be experimentally directed to form other cell types, including those with complete embryonic potential. Early nuclear reprogramming methods, such as somatic cell nuclear transfer (SCNT) and cell fusion, posed significant technical hurdles to precise dissection of the regulatory programmes governing cell identity. However, the discovery of reprogramming by ectopic expression of a defined set of transcription factors, known as direct reprogramming, provided a tractable platform to uncover molecular characteristics of cellular specification and differentiation, cell type stability and pluripotency. We discuss the control and maintenance of cellular identity during developmental transitions as they have been studied using direct reprogramming, with an emphasis on transcriptional and epigenetic regulation.

Emerging Modeling Concepts and Solutions in Stem Cell Research

Modern stem cell research, as well as other fields of contemporary biology involves quantitative sciences in many ways. Identifying candidates for key differentiation or reprogramming factors, tracing global transcriptome changes, or finding drugs is now broadly involves bioinformatics and biostatistics. However, the next key step, understanding the underlying reasons and establishing causal links leading to differentiation or reprogramming requires qualitative and quantitative biological models describing complex biological systems. Currently, quantitative modeling is a challenging science, capable to deliver rather modest results or predictions. What model types are the most popular and what features of stem cell behavior they are capturing? What new insights do we expect from the computational modeling of stem cells in the foreseeable future? Current review attempts to approach these essential questions by considering published quantitative models and solutions emerging in the area of stem cell research.

Chicken embryonic stem cells and primordial germ cells display different heterochromatic histone marks than their mammalian counterparts

Background

Chromatin epigenetics participate in control of gene expression during metazoan development. DNA methylation and post-translational modifications (PTMs) of histones have been extensively characterised in cell types present in, or derived from, mouse embryos. In embryonic stem cells (ESCs) derived from blastocysts, factors involved in deposition of epigenetic marks regulate properties related to self-renewal and pluripotency. In the germ lineage, changes in histone PTMs and DNA demethylation occur during formation of the primordial germ cells (PGCs) to reset the epigenome of the future gametes. Trimethylation of histone H3 on lysine 27 (H3K27me3) by Polycomb group proteins is involved in several epigenome-remodelling steps, but it remains unclear whether these epigenetic features are conserved in non-mammalian vertebrates. To investigate this question, we compared the abundance and nuclear distribution of the main histone PTMs, 5-methylcytosine (5mC) and 5-hydroxymethylcytosine (5hmC) in chicken ESCs, PGCs and blastodermal cells (BCs) with differentiated chicken ESCs and embryonic fibroblasts. In addition, we analysed the expression of chromatin modifier genes to better understand the establishment and dynamics of chromatin epigenetic profiles.

Results

The nuclear distributions of most PTMs and 5hmC in chicken stem cells were similar to what has been described for mammalian cells. However, unlike mouse pericentric heterochromatin (PCH), chicken ESC PCH contained high levels of trimethylated histone H3 on lysine 27 (H3K27me3). In differentiated chicken cells, PCH was less enriched in H3K27me3 relative to chromatin overall. In PGCs, the H3K27me3 global level was greatly reduced, whereas the H3K9me3 level was elevated. Most chromatin modifier genes known in mammals were expressed in chicken ESCs, PGCs and BCs. Genes presumably involved in de novo DNA methylation were very highly expressed. DNMT3B and HELLS/SMARCA6 were highly expressed in chicken ESCs, PGCs and BCs compared to differentiated chicken ESCs and embryonic fibroblasts, and DNMT3A was strongly expressed in ESCs, differentiated ESCs and BCs.

Conclusions

Chicken ESCs and PGCs differ from their mammalian counterparts with respect to H3K27 methylation. High enrichment of H3K27me3 at PCH is specific to pluripotent cells in chicken. Our results demonstrate that the dynamics in chromatin constitution described during mouse development is not universal to all vertebrate species.

Electronic supplementary material

The online version of this article (doi:10.1186/s13072-016-0056-6) contains supplementary material, which is available to authorized users.

Single-cell analysis of transcription kinetics across the cell cycle

Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation.

The Pluripotency Factor NANOG Binds to GLI Proteins and Represses Hedgehog-mediated Transcription

The Hedgehog (HH) signaling pathway is essential for the maintenance and response of several types of stem cells. To study the transcriptional response of stem cells to HH signaling, we searched for proteins binding to GLI proteins, the transcriptional effectors of the HH pathway in mouse embryonic stem (ES) cells. We found that both GLI3 and GLI1 bind to the pluripotency factor NANOG. The ectopic expression of NANOG inhibits GLI1-mediated transcriptional responses in a dose-dependent fashion. In differentiating ES cells, the presence of NANOG reduces the transcriptional response of cells to HH. Finally, we found thatGli1andNanogare co-expressed in ES cells at high levels. We propose that NANOG acts as a negative feedback component that provides stem cell-specific regulation of the HH pathway.

Imaging Transcription: Past, Present, and Future

Transcription, the first step of gene expression, is exquisitely regulated in higher eukaryotes to ensure correct development and homeostasis. Traditional biochemical, genetic, and genomic approaches have proved successful at identifying factors, regulatory sequences, and potential pathways that modulate transcription. However, they typically only provide snapshots or population averages of the highly dynamic, stochastic biochemical processes involved in transcriptional regulation. Single-molecule live-cell imaging has, therefore, emerged as a complementary approach capable of circumventing these limitations. By observing sequences of molecular events in real time as they occur in their native context, imaging has the power to derive cause-and-effect relationships and quantitative kinetics to build predictive models of transcription. Ongoing progress in fluorescence imaging technology has brought new microscopes and labeling technologies that now make it possible to visualize and quantify the transcription process with single-molecule resolution in living cells and animals. Here we provide an overview of the evolution and current state of transcription imaging technologies. We discuss some of the important concepts they uncovered and present possible future developments that might solve long-standing questions in transcriptional regulation.

A role of stochastic phenotype switching in generating mosaic endothelial cell heterogeneity

Previous studies have shown that biological noise may drive dynamic phenotypic mosaicism in isogenic unicellular organisms. However, there is no evidence for a similar mechanism operating in metazoans. Here we show that the endothelial-restricted gene, von Willebrand factor (VWF), is expressed in a mosaic pattern in the capillaries of many vascular beds and in the aorta. In capillaries, the mosaicism is dynamically regulated, with VWF switching between ON and OFF states during the lifetime of the animal. Clonal analysis of cultured endothelial cells reveals that dynamic mosaic heterogeneity is controlled by a low-barrier, noise-sensitive bistable switch that involves random transitions in the DNA methylation status of the VWF promoter. Finally, the hearts of VWF-null mice demonstrate an abnormal endothelial phenotype as well as cardiac dysfunction. Together, these findings suggest a novel stochastic phenotype switching strategy for adaptive homoeostasis in the adult vasculature.

Gene expression variability in clonal populations: Causes and consequences

During the last decade it has been shown that among cell variation in gene expression plays an important role within clonal populations. Here, we provide an overview of the different mechanisms contributing to gene expression variability in clonal populations. These are ranging from inherent variations in the biochemical process of gene expression itself, such as intrinsic noise, extrinsic noise and bistability to individual responses to variations in the local micro-environment, a phenomenon called phenotypic plasticity. Also genotypic variations caused by clonal evolution and phase variation can contribute to gene expression variability. Consequently, gene expression studies need to take these fluctuations in expression into account. However, frequently used techniques for expression quantification, such as microarrays, RNA sequencing, quantitative PCR and gene reporter fusions classically determine the population average of gene expression. Here, we discuss how these techniques can be adapted towards single cell analysis by integration with single cell isolation, RNA amplification and microscopy. Alternatively more qualitative selection-based techniques, such as mutant screenings, in vivo expression technology (IVET) and recombination-based IVET (RIVET) can be applied for detection of genes expressed only within a subpopulation. Finally, differential fluorescence induction (DFI), a protocol specially designed for single cell expression is discussed.

Dissecting Transcriptional Heterogeneity in Pluripotency: Single Cell Analysis of Mouse Embryonic Stem Cells

Mouse Embryonic Stem cells (mESCs) show heterogeneous and dynamic expression of important pluripotency regulatory factors. Single-cell analysis has revealed the existence of cell-to-cell variability in the expression of individual genes in mESCs. Understanding how these heterogeneities are regulated and what their functional consequences are is crucial to obtain a more comprehensive view of the pluripotent state.In this chapter we describe how to analyze transcriptional heterogeneity by monitoring gene expression of Nanog, Oct4, and Sox2, using single-molecule RNA FISH in single mESCs grown in different cell culture medium. We describe in detail all the steps involved in the protocol, from RNA detection to image acquisition and processing, as well as exploratory data analysis.

The bimodally expressed microRNA miR-142 gates exit from pluripotency

A stem cell's decision to self‐renew or differentiate is thought to critically depend on signaling cues provided by its environment. It is unclear whether stem cells have the intrinsic capacity to control their responsiveness to environmental signals that can be fluctuating and noisy. Using a novel single‐cell microRNA activity reporter, we show that miR‐142 is bimodally expressed in embryonic stem cells, creating two states indistinguishable by pluripotency markers. A combination of modeling and quantitative experimental data revealed that mESCs switch stochastically between the two miR‐142 states. We find that cells with high miR‐142 expression are irresponsive to differentiation signals while cells with low miR‐142 expression can respond to differentiation cues. We elucidate the molecular mechanism underpinning the bimodal regulation of miR‐142 as a double‐negative feedback loop between miR‐142 and KRAS/ERK signaling and derive a quantitative description of this bistable system. miR‐142 switches the activation status of key intracellular signaling pathways thereby locking cells in an undifferentiated state. This reveals a novel mechanism to maintain a stem cell reservoir buffered against fluctuating signaling environments.

Integration of Signaling Pathways with the Epigenetic Machinery in the Maintenance of Stem Cells

Stem cells balance their self-renewal and differentiation potential by integrating environmental signals with the transcriptional regulatory network. The maintenance of cell identity and/or cell lineage commitment relies on the interplay of multiple factors including signaling pathways, transcription factors, and the epigenetic machinery. These regulatory modules are strongly interconnected and they influence the pattern of gene expression of stem cells, thus guiding their cellular fate. Embryonic stem cells (ESCs) represent an invaluable tool to study this interplay, being able to indefinitely self-renew and to differentiate towards all three embryonic germ layers in response to developmental cues. In this review, we highlight those mechanisms of signaling to chromatin, which regulate chromatin modifying enzymes, histone modifications, and nucleosome occupancy. In addition, we report the molecular mechanisms through which signaling pathways affect both the epigenetic and the transcriptional state of ESCs, thereby influencing their cell identity. We propose that the dynamic nature of oscillating signaling and the different regulatory network topologies through which those signals are encoded determine specific gene expression programs, leading to the fluctuation of ESCs among multiple pluripotent states or to the establishment of the necessary conditions to exit pluripotency.

Variation is function: Are single cell differences functionally important?: Testing the hypothesis that single cell variation is required for aggregate function

There is a growing appreciation of the extent of transcriptome variation across individual cells of the same cell type. While expression variation may be a byproduct of, for example, dynamic or homeostatic processes, here we consider whether single-cell molecular variation per se might be crucial for population-level function. Under this hypothesis, molecular variation indicates a diversity of hidden functional capacities within an ensemble of identical cells, and this functional diversity facilitates collective behavior that would be inaccessible to a homogenous population. In reviewing this topic, we explore possible functions that might be carried by a heterogeneous ensemble of cells; however, this question has proven difficult to test, both because methods to manipulate molecular variation are limited and because it is complicated to define, and measure, population-level function. We consider several possible methods to further pursue the hypothesis that variation is function through the use of comparative analysis and novel experimental techniques.

Differential variability and correlation of gene expression identifies key genes involved in neuronal differentiation

Background

Understanding the dynamics of stem cell differentiation processes at the molecular level is a central challenge in developmental biology and regenerative medicine. Although the dynamic behaviors of differentiation regulators have been partially characterized, the architecture regulating the underlying molecular systems remains unclear.

Result

System-level analysis of transcriptional data was performed to characterize the dynamics of molecular networks in neural differentiation of stem cells. Expression of a network module of genes tightly co-expressed in mouse embryonic stem (ES) cells fluctuated greatly among cell populations before differentiation, but became stable following neural differentiation. During the neural differentiation process, genes exhibiting both differential variance and differential correlation between undifferentiated and differentiating states were related to developmental functions such as body axis development, neuronal movement, and transcriptional regulation. Furthermore, these genes were genetically associated with neuronal differentiation, providing support for the idea they are not only differentiation markers but could also play important roles in neural differentiation. Comparisons with transcriptional data from human induced pluripotent stem (iPS) cells revealed that the system of genes dynamically regulated during neural differentiation is conserved between mouse and human.

Conclusions

The results of this study provide a systematic analytical framework for identifying key genes involved in neural differentiation by detecting their dynamical behaviors, as well as a basis for understanding the dynamic molecular mechanisms underlying the processes of neural differentiation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0231-6) contains supplementary material, which is available to authorized users.

Quantitative gene expression analysis in Caenorhabditis elegans using single molecule RNA FISH

Advances in fluorescent probe design and synthesis have allowed the uniform in situ labeling of individual RNA molecules. In a technique referred to as single molecule RNA FISH (smRNA FISH), the labeled RNA molecules can be imaged as diffraction-limited spots and counted using image analysis algorithms. Single RNA counting has provided valuable insights into the process of gene regulation. This microscopy-based method has often revealed a high cell-to-cell variability in expression levels, which has in turn led to a growing interest in investigating the biological significance of gene expression noise. Here we describe the application of the smRNA FISH technique to samples of Caenorhabditis elegans, a well-characterized model organism.

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BACKGROUND

The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.

METHODS

We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories.

RESULTS

The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue.

CONCLUSIONS

The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state

Chromatin profiling provides a versatile means to investigate functional genomic elements and their regulation. However, current methods yield ensemble profiles that are insensitive to cell-to-cell variation. Here we combine microfluidics, DNA barcoding and sequencing to collect chromatin data at single-cell resolution. We demonstrate the utility of the technology by assaying thousands of individual cells, and using the data to deconvolute a mixture of ES cells, fibroblasts and hematopoietic progenitors into high-quality chromatin state maps for each cell type. The data from each single cell is sparse, comprising on the order of 1000 unique reads. However, by assaying thousands of ES cells, we identify a spectrum of sub-populations defined by differences in chromatin signatures of pluripotency and differentiation priming. We corroborate these findings by comparison to orthogonal single-cell gene expression data. Our method for single-cell analysis reveals aspects of epigenetic heterogeneity not captured by transcriptional analysis alone.

Network plasticity of pluripotency transcription factors in embryonic stem cells

Transcription factor (TF) networks are thought to regulate embryonic stem cell (ESC) pluripotency. However, TF expression dynamics and regulatory mechanisms are poorly understood. We use reporter mouse ESC lines allowing non-invasive quantification of Nanog or Oct4 protein levels and continuous long-term single-cell tracking and quantification over many generations to reveal diverse TF protein expression dynamics. For cells with low Nanog expression, we identified two distinct colony types: one re-expressed Nanog in a mosaic pattern, and the other did not re-express Nanog over many generations. Although both expressed pluripotency markers, they exhibited differences in their TF protein correlation networks and differentiation propensities. Sister cell analysis revealed that differences in Nanog levels are not necessarily accompanied by differences in the expression of other pluripotency factors. Thus, regulatory interactions of pluripotency TFs are less stringently implemented in individual self-renewing ESCs than assumed at present.

Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment

Recent studies have revealed extensive genetic and non-genetic variation across different geographical regions of a tumor or throughout different stages of tumor progression, which is referred to as intra-tumor heterogeneity. Several causes contribute to this phenomenon, including genomic instability, epigenetic alteration, plastic gene expression, signal transduction, and microenvironmental differences. These variables may affect key signaling pathways that regulate cancer cell growth, drive phenotypic diversity, and pose challenges to cancer treatment. Understanding the mechanisms underlying this heterogeneity will support the development of effective therapeutic strategies.

Shaping protein distributions in stochastic self-regulated gene expression networks

In this work, we study connections between dynamic behavior and network parameters, for self-regulatory networks. To that aim, a method to compute the regions in the space of parameters that sustain bimodal or binary protein distributions has been developed. Such regions are indicative of stochastic dynamics manifested either as transitions between absence and presence of protein or between two positive protein levels. The method is based on the continuous approximation of the chemical master equation, unlike other approaches that make use of a deterministic description, which as will be shown can be misleading. We find that bimodal behavior is a ubiquitous phenomenon in cooperative gene expression networks under positive feedback. It appears for any range of transcription and translation rate constants whenever leakage remains below a critical threshold. Above such a threshold, the region in the parameters space which sustains bimodality persists, although restricted to low transcription and high translation rate constants. Remarkably, such a threshold is independent of the transcription or translation rates or the proportion of an active or inactive promoter and depends only on the level of cooperativity. The proposed method can be employed to identify bimodal or binary distributions leading to stochastic dynamics with specific switching properties, by searching inside the parameter regions that sustain such behavior.