Transcriptional regulation of lineage commitment--a stochastic model of cell fate decisions

Artificial-intelligence-driven Innovations in Mechanistic Computational Modeling and Digital Twins for Biomedical Applications

Understanding of complex biological systems remains a significant challenge due to their high dimensionality, nonlinearity, and context-specific behavior. Artificial intelligence (AI) and mechanistic modeling are becoming essential tools for studying such complex systems. Mechanistic modeling can facilitate the construction of simulatable models that are interpretable but often struggle with scalability and parameters estimation. AI can integrate multi-omics data to create predictive models, but it lacks interpretability. The gap between these two modeling methods limits our ability to develop comprehensive and predictive models for biomedical applications. This article reviews the most recent advancements in the integration of AI and mechanistic modeling to fill this gap. Recently, with omics availability, AI has led to new discoveries in mechanistic computational modeling. The mechanistic models can also help in getting insight into the mechanism for prediction made by AI models. This integration is helpful in modeling complex systems, estimating the parameters that are hard to capture in experiments, and creating surrogate models to reduce computational costs because of expensive mechanistic model simulations. This article focuses on advancements in mechanistic computational models and AI models and their integration for scientific discoveries in biology, pharmacology, drug discovery and diseases. The mechanistic models with AI integration can facilitate biological discoveries to advance our understanding of disease mechanisms, drug development, and personalized medicine. The article also highlights the role of AI and mechanistic model integration in the development of more advanced models in the biomedical domain, such as medical digital twins and virtual patients for pharmacological discoveries.

Attractor Landscape Analysis Reveals a Reversion Switch in the Transition of Colorectal Tumorigenesis

A cell fate change such as tumorigenesis incurs critical transition. It remains a longstanding challenge whether the underlying mechanism can be unraveled and a molecular switch that can reverse such transition is found. Here a systems framework, REVERT, is presented with which can reconstruct the core molecular regulatory network model and a reversion switch based on single-cell transcriptome data over the transition process is identified. The usefulness of REVERT is demonstrated by applying it to single-cell transcriptome of patient-derived matched organoids of colon cancer and normal colon. REVERT is a generic framework that can be applied to investigate various cell fate transition phenomena.

Regulation of developmentally controlled enhancer activity by extrinsic signals in normal and malignant cells: AP-1 at the centre

The ability of cells to respond to external stimuli is one of the characteristics of life as we know it. Multicellular organisms have developed a huge machinery that interprets the cellular environment and instigates an appropriate cellular response by changing gene expression, metabolism, proliferation state and motility. Decades of research have studied the pathways transmitting the various signals within the cell. However, whilst we know most of the players, we know surprisingly little about the mechanistic details of how extrinsic signals are interpreted and integrated within the genome. In this article we revisit the long-standing debate of whether factors regulating cellular growth (cytokines) act in an instructive or permissive fashion on cell fate decisions. We touch upon this topic by highlighting the paradigm of AP-1 as one of the most important signaling-responsive transcription factor family and summarize our work and that of others to explain what is known about cytokine responsive cis-regulatory elements driving differential gene expression. We propose that cytokines and, by extension, multiple types of external signals are the main drivers of cell differentiation and act via inducible transcription factors that transmit signaling processes to the genome and are essential for changing gene expression to drive transitions between gene regulatory networks. Importantly, inducible transcription factors cooperate with cell type specific factors within a pre-existing chromatin landscape and integrate multiple signaling pathways at specific enhancer elements, to both maintain and alter cellular identities. We also propose that signaling processes and signaling responsive transcription factors are at the heart of tumor development.

Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study

Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Hematopoietic Stem Cells: Nature and Niche Nurture

Like all cells, hematopoietic stem cells (HSCs) and their offspring, the hematopoietic progenitor cells (HPCs), are highly sociable. Their capacity to interact with bone marrow niche cells and respond to environmental cytokines orchestrates the generation of the different types of blood and immune cells. The starting point for engineering hematopoiesis ex vivo is the nature of HSCs, and a longstanding premise is that they are a homogeneous population of cells. However, recent findings have shown that adult bone marrow HSCs are really a mixture of cells, with many having lineage affiliations. A second key consideration is: Do HSCs "choose" a lineage in a random and cell-intrinsic manner, or are they instructed by cytokines? Since their discovery, the hematopoietic cytokines have been viewed as survival and proliferation factors for lineage committed HPCs. Some are now known to also instruct cell lineage choice. These fundamental changes to our understanding of hematopoiesis are important for placing niche support in the right context and for fabricating an ex vivo environment to support HSC development.

FGF2 modulates simultaneously the mode, the rate of division and the growth fraction in cultures of radial glia

Radial glial progenitors in the mammalian developing neocortex have been shown to follow a deterministic differentiation program restricted to an asymmetric-only mode of division. This feature seems incompatible with their well-known ability to increase in number when cultured in vitro, driven by fibroblast growth factor 2 and other mitogenic signals. The changes in their differentiation dynamics that allow this transition from in vivo asymmetric-only division mode to an in vitro self-renewing culture have not been fully characterized. Here, we combine experiments of radial glia cultures with numerical models and a branching process theoretical formalism to show that fibroblast growth factor 2 has a triple effect by simultaneously increasing the growth fraction, promoting symmetric divisions and shortening the length of the cell cycle. These combined effects partner to establish and sustain a pool of rapidly proliferating radial glial progenitors in vitro. We also show that, in conditions of variable proliferation dynamics, the branching process tool outperforms other commonly used methods based on thymidine analogs, such as BrdU and EdU, in terms of accuracy and reliability.

Highlighted Article: When mode and/or rate of division are changing, a branching process, rather than a thymidine analog method, provides temporal resolution, it is more robust and does not interfere with cell homeostasis.

Modeling binary and graded cone cell fate patterning in the mouse retina

Nervous systems are incredibly diverse, with myriad neuronal subtypes defined by gene expression. How binary and graded fate characteristics are patterned across tissues is poorly understood. Expression of opsin photopigments in the cone photoreceptors of the mouse retina provides an excellent model to address this question. Individual cones express S-opsin only, M-opsin only, or both S-opsin and M-opsin. These cell populations are patterned along the dorsal-ventral axis, with greater M-opsin expression in the dorsal region and greater S-opsin expression in the ventral region. Thyroid hormone signaling plays a critical role in activating M-opsin and repressing S-opsin. Here, we developed an image analysis approach to identify individual cone cells and evaluate their opsin expression from immunofluorescence imaging tiles spanning roughly 6 mm along the D-V axis of the mouse retina. From analyzing the opsin expression of ~250,000 cells, we found that cones make a binary decision between S-opsin only and co-expression competent fates. Co-expression competent cells express graded levels of S- and M-opsins, depending nonlinearly on their position in the dorsal-ventral axis. M- and S-opsin expression display differential, inverse patterns. Using these single-cell data, we developed a quantitative, probabilistic model of cone cell decisions in the retinal tissue based on thyroid hormone signaling activity. The model recovers the probability distribution for cone fate patterning in the mouse retina and describes a minimal set of interactions that are necessary to reproduce the observed cell fates. Our study provides a paradigm describing how differential responses to regulatory inputs generate complex patterns of binary and graded cell fates.

The development of a cell in a mammalian tissue is governed by a complex regulatory network that responds to many input signals to give the cell a distinct identity, a process referred to as cell-fate specification. Some of these cell fates have binary on-or-off gene expression patterns, while others have graded gene expression that changes across the tissue. Differentiation of the photoreceptor cells that sense light in the mouse retina provides a good example of this process. Here, we explore how complex patterns of cell fates are specified in the mouse retina by building a computational model based on analysis of a large number of photoreceptor cells from microscopy images of whole retinas. We use the data and the model to study what exactly it means for a cell to have a binary or graded cell fate and how these cell fates can be distinguished from each other. Our study shows how tens-of-thousands of individual photoreceptor cells can be patterned across a complex tissue by a regulatory network, creating a different outcome depending upon the received inputs.

Bone marrow adipose cells - cellular interactions and changes with obesity

The bone marrow is a spatially restricted niche, housing cells of the hematopoietic and mesenchymal lineages in various hierarchical commitment states. Although highly localized, cells within this niche are also subject to regulation by environmental and/or circulatory changes through extensive vascularization. Bone marrow adipocytes, derived from mesenchymal stem cells and once known as marrow space fillers, are a heterogeneous population. These cells reside in distinct niches within the bone marrow and interact with proximal cells, such as hematopoietic precursors and lineage-committed cells. In this diverse cellular milieu, bone marrow adipocytes influence commitment decisions and cellular lineage selection by interacting with stem and progenitor cells. In addition, bone marrow adipocytes respond to environmental changes, such as obesity, by undergoing hypertrophy, hyperplasia or adoption of characteristics resembling those of peripheral brown, beige or white adipocytes. Here, we review recent findings and concepts on the influence of bone marrow adipocytes on hematopoietic and other cellular lineages within this niche. We discuss how changes in local, systemic, cellular and secreted signals impact on mesenchymal stem cell expansion, differentiation and lineage commitment. Furthermore, we highlight that bone marrow adipocytes may be intermediaries conveying environmental cues to influence hematopoietic cellular survival, proliferation and preferential differentiation.

Loss of Kat2a enhances transcriptional noise and depletes acute myeloid leukemia stem-like cells

Acute Myeloid Leukemia (AML) is an aggressive hematological malignancy with abnormal progenitor self-renewal and defective white blood cell differentiation. Its pathogenesis comprises subversion of transcriptional regulation, through mutation and by hijacking normal chromatin regulation. Kat2a is a histone acetyltransferase central to promoter activity, that we recently associated with stability of pluripotency networks, and identified as a genetic vulnerability in AML. Through combined chromatin profiling and single-cell transcriptomics of a conditional knockout mouse, we demonstrate that Kat2a contributes to leukemia propagation through preservation of leukemia stem-like cells. Kat2a loss impacts transcription factor binding and reduces transcriptional burst frequency in a subset of gene promoters, generating enhanced variability of transcript levels. Destabilization of target programs shifts leukemia cell fate out of self-renewal into differentiation. We propose that control of transcriptional variability is central to leukemia stem-like cell propagation, and establish a paradigm exploitable in different tumors and distinct stages of cancer evolution.

To be red or white: lineage commitment and maintenance of the hematopoietic system by the "inner myeloid"

Differentiation of hematopoietic stem and progenitor cells is tightly regulated depending on environmental changes in order to maintain homeostasis. Transcription factors direct the development of hematopoietic cells, such as GATA-1 for erythropoiesis and PU.1 for myelopoiesis. However, recent findings obtained from single-cell analyses raise the question of whether these transcription factors are "initiators" or just "executors" of differentiation, leaving the initiation of hematopoietic stem and progenitor cell differentiation (i.e. lineage commitment) unclear. While a stochastic process is likely involved in commitment, it cannot fully explain the homeostasis of hematopoiesis nor "on-demand" hematopoiesis in response to environmental changes. Transcription factors BACH1 and BACH2 may regulate both commitment and on-demand hematopoiesis because they control erythroid-myeloid and lymphoid-myeloid differentiation by repressing the myeloid program, and their activities are repressed in response to infectious and inflammatory conditions. We summarize possible mechanisms of lineage commitment of hematopoietic stem and progenitor cells suggested by recent findings and discuss the erythroid and lymphoid commitment of hematopoietic stem and progenitor cells, focusing on the gene regulatory network composed of genes encoding key transcription factors. Surprising similarity exists between commitment to erythroid and lymphoid lineages, including repression of the myeloid program by BACH factors. The suggested gene regulatory network of BACH factors sheds light on the myeloid-based model of hematopoiesis. This model will help to understand the tuning of hematopoiesis in higher eukaryotes in the steady-state condition as well as in emergency conditions, the evolutional history of the system, aging and hematopoietic disorders.

Limit-cycle oscillatory coexpression of cross-inhibitory transcription factors: a model mechanism for lineage promiscuity

Lineage switches are genetic regulatory motifs that govern and maintain the commitment of a developing cell to a particular cell fate. A canonical example of a lineage switch is the pair of transcription factors PU.1 and GATA-1, of which the former is affiliated with the myeloid and the latter with the erythroid lineage within the haematopoietic system. On a molecular level, PU.1 and GATA-1 positively regulate themselves and antagonize each other via direct protein-protein interactions. Here we use mathematical modelling to identify a novel type of dynamic behaviour that can be supported by such a regulatory architecture. Guided by the specifics of the PU.1-GATA-1 interaction, we formulate, using the law of mass action, a system of differential equations for the key molecular concentrations. After a series of systematic approximations, the system is reduced to a simpler one, which is tractable to phase-plane and linearization methods. The reduced system formally resembles, and generalizes, a well-known model for competitive species from mathematical ecology. However, in addition to the qualitative regimes exhibited by a pair of competitive species (exclusivity, bistable exclusivity, stable-node coexpression) it also allows for oscillatory limit-cycle coexpression. A key outcome of the model is that, in the context of cell-fate choice, such oscillations could be harnessed by a differentiating cell to prime alternately for opposite outcomes; a bifurcation-theory approach is adopted to characterize this possibility.

Haematopoietic stem cells: entropic landscapes of differentiation

The metaphor of a potential epigenetic differentiation landscape broadly suggests that during differentiation a stem cell approaches a stable equilibrium state from a higher free energy towards a stable equilibrium state which represents the final cell type. It has been conjectured that there is an analogy to the concept of entropy in statistical mechanics. In this context, in the undifferentiated state, the entropy would be large since fewer constraints exist on the gene expression programmes of the cell. As differentiation progresses, gene expression programmes become more and more constrained and thus the entropy would be expected to decrease. In order to assess these predictions, we compute the Shannon entropy for time-resolved single-cell gene expression data in two different experimental set-ups of haematopoietic differentiation. We find that the behaviour of this entropy measure is in contrast to these predictions. In particular, we find that the Shannon entropy is not a decreasing function of developmental pseudo-time but instead it increases towards the time point of commitment before decreasing again. This behaviour is consistent with an increase in gene expression disorder observed in populations sampled at the time point of commitment. Single cells in these populations exhibit different combinations of regulator activity that suggest the presence of multiple configurations of a potential differentiation network as a result of multiple entry points into the committed state.

Histone Acetyltransferase KAT2A Stabilizes Pluripotency with Control of Transcriptional Heterogeneity

Cell fate transitions in mammalian stem cell systems have often been associated with transcriptional heterogeneity; however, existing data have failed to establish a functional or mechanistic link between the two phenomena. Experiments in unicellular organisms support the notion that transcriptional heterogeneity can be used to facilitate adaptability to environmental changes and have identified conserved chromatin‐associated factors that modulate levels of transcriptional noise. Herein, we show destabilization of pluripotency‐associated gene regulatory networks through increased transcriptional heterogeneity of mouse embryonic stem cells in which paradigmatic histone acetyl‐transferase, and candidate noise modulator, Kat2a (yeast orthologue Gcn5), have been inhibited. Functionally, network destabilization associates with reduced pluripotency and accelerated mesendodermal differentiation, with increased probability of transitions into lineage commitment. Thus, we show evidence of a relationship between transcriptional heterogeneity and cell fate transitions through manipulation of the histone acetylation landscape of mouse embryonic stem cells, suggesting a general principle that could be exploited in other normal and malignant stem cell fate transitions. stem cells2018;36:1828–11

Kinetic models of hematopoietic differentiation

As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning "knobs" in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous-valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi-scale models integrating cell dynamical and transcriptional models. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Developmental Biology > Stem Cell Biology and Regeneration.

Inferring gene regulatory networks from single-cell data: a mechanistic approach

Background

The recent development of single-cell transcriptomics has enabled gene expression to be measured in individual cells instead of being population-averaged. Despite this considerable precision improvement, inferring regulatory networks remains challenging because stochasticity now proves to play a fundamental role in gene expression. In particular, mRNA synthesis is now acknowledged to occur in a highly bursty manner.

Results

We propose to view the inference problem as a fitting procedure for a mechanistic gene network model that is inherently stochastic and takes not only protein, but also mRNA levels into account. We first explain how to build and simulate this network model based upon the coupling of genes that are described as piecewise-deterministic Markov processes. Our model is modular and can be used to implement various biochemical hypotheses including causal interactions between genes. However, a naive fitting procedure would be intractable. By performing a relevant approximation of the stationary distribution, we derive a tractable procedure that corresponds to a statistical hidden Markov model with interpretable parameters. This approximation turns out to be extremely close to the theoretical distribution in the case of a simple toggle-switch, and we show that it can indeed fit real single-cell data. As a first step toward inference, our approach was applied to a number of simple two-gene networks simulated in silico from the mechanistic model and satisfactorily recovered the original networks.

Conclusions

Our results demonstrate that functional interactions between genes can be inferred from the distribution of a mechanistic, dynamical stochastic model that is able to describe gene expression in individual cells. This approach seems promising in relation to the current explosion of single-cell expression data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0487-0) contains supplementary material, which is available to authorized users.

SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

Motivation

Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.

Results

We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development.

Availability and implementation

MATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites.

Supplementary information

Supplementary data are available at Bioinformatics online.

A conceptual and computational framework for modelling and understanding the non-equilibrium gene regulatory networks of mouse embryonic stem cells

The capacity of pluripotent embryonic stem cells to differentiate into any cell type in the body makes them invaluable in the field of regenerative medicine. However, because of the complexity of both the core pluripotency network and the process of cell fate computation it is not yet possible to control the fate of stem cells. We present a theoretical model of stem cell fate computation that is based on Halley and Winkler’s Branching Process Theory (BPT) and on Greaves et al.’s agent-based computer simulation derived from that theoretical model. BPT abstracts the complex production and action of a Transcription Factor (TF) into a single critical branching process that may dissipate, maintain, or become supercritical. Here we take the single TF model and extend it to multiple interacting TFs, and build an agent-based simulation of multiple TFs to investigate the dynamics of such coupled systems. We have developed the simulation and the theoretical model together, in an iterative manner, with the aim of obtaining a deeper understanding of stem cell fate computation, in order to influence experimental efforts, which may in turn influence the outcome of cellular differentiation. The model used is an example of self-organization and could be more widely applicable to the modelling of other complex systems. The simulation based on this model, though currently limited in scope in terms of the biology it represents, supports the utility of the Halley and Winkler branching process model in describing the behaviour of stem cell gene regulatory networks. Our simulation demonstrates three key features: (i) the existence of a critical value of the branching process parameter, dependent on the details of the cistrome in question; (ii) the ability of an active cistrome to “ignite” an otherwise fully dissipated cistrome, and drive it to criticality; (iii) how coupling cistromes together can reduce their critical branching parameter values needed to drive them to criticality.

Pluripotent stem cells possess the capacity both to renew themselves indefinitely and to differentiate to any cell type in the body. Thus the ability to direct stem cell differentiation would have immense potential in regenerative medicine. There is a massive amount of biological data relevant to stem cells; here we exploit data relating to stem cell differentiation to help understand cell behaviour and complexity. These cells contain a dynamic, non-equilibrium network of genes regulated in part by transcription factors expressed by the network itself. Here we take an existing theoretical framework, Transcription Factor Branching Processes, which explains how these genetic networks can have critical behaviour, and can tip between low and full expression. We use this theory as the basis for the design and implementation of a computational simulation platform, which we then use to run a variety of simulation experiments, to gain a better understanding how these various transcription factors can combine, interact, and influence each other. The simulation parameters are derived from experimental data relating to the core factors in pluripotent stem cell differentiation. The simulation results determine the critical values of branching process parameters, and how these are modulated by the various interacting transcription factors.

Single cell transcriptomics reveals unanticipated features of early hematopoietic precursors

Molecular changes underlying stem cell differentiation are of fundamental interest. scRNA-seq on murine hematopoietic stem cells (HSC) and their progeny MPP1 separated the cells into 3 main clusters with distinct features: active, quiescent, and an un-characterized cluster. Induction of anemia resulted in mobilization of the quiescent to the active cluster and of the early to later stage of cell cycle, with marked increase in expression of certain transcription factors (TFs) while maintaining expression of interferon response genes. Cells with surface markers of long term HSC increased the expression of a group of TFs expressed highly in normal cycling MPP1 cells. However, at least Id1 and Hes1 were significantly activated in both HSC and MPP1 cells in anemic mice. Lineage-specific genes were differently expressed between cells, and correlated with the cell cycle stages with a specific augmentation of erythroid related genes in the G2/M phase. Most lineage specific TFs were stochastically expressed in the early precursor cells, but a few, such as Klf1, were detected only at very low levels in few precursor cells. The activation of these factors may correlate with stages of differentiation. This study reveals effects of cell cycle progression on the expression of lineage specific genes in precursor cells, and suggests that hematopoietic stress changes the balance of renewal and differentiation in these homeostatic cells.

Fluctuations of the transcription factor ATML1 generate the pattern of giant cells in the Arabidopsis sepal

Multicellular development produces patterns of specialized cell types. Yet, it is often unclear how individual cells within a field of identical cells initiate the patterning process. Using live imaging, quantitative image analyses and modeling, we show that during Arabidopsis thaliana sepal development, fluctuations in the concentration of the transcription factor ATML1 pattern a field of identical epidermal cells to differentiate into giant cells interspersed between smaller cells. We find that ATML1 is expressed in all epidermal cells. However, its level fluctuates in each of these cells. If ATML1 levels surpass a threshold during the G2 phase of the cell cycle, the cell will likely enter a state of endoreduplication and become giant. Otherwise, the cell divides. Our results demonstrate a fluctuation-driven patterning mechanism for how cell fate decisions can be initiated through a random yet tightly regulated process.

Dissecting stem cell differentiation using single cell expression profiling

Many assumptions about the way cells behave are based on analyses of populations. However, it is now widely recognized that even apparently pure populations can display a remarkable level of heterogeneity. This is particularly true in stem cell biology where it hinders our understanding of normal development and the development of strategies for regenerative medicine. Over the past decade technologies facilitating gene expression analysis at the single cell level have become widespread, providing access to rare cell populations and insights into population structure and function. Here we review the contributions of single cell biology to understanding stem cell differentiation so far, both as a new methodology for defining cell types and a tool for understanding the complexities of cellular decision-making.

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.

Determining Physical Mechanisms of Gene Expression Regulation from Single Cell Gene Expression Data

Many genes are expressed in bursts, which can contribute to cell-to-cell heterogeneity. It is now possible to measure this heterogeneity with high throughput single cell gene expression assays (single cell qPCR and RNA-seq). These experimental approaches generate gene expression distributions which can be used to estimate the kinetic parameters of gene expression bursting, namely the rate that genes turn on, the rate that genes turn off, and the rate of transcription. We construct a complete pipeline for the analysis of single cell qPCR data that uses the mathematics behind bursty expression to develop more accurate and robust algorithms for analyzing the origin of heterogeneity in experimental samples, specifically an algorithm for clustering cells by their bursting behavior (Simulated Annealing for Bursty Expression Clustering, SABEC) and a statistical tool for comparing the kinetic parameters of bursty expression across populations of cells (Estimation of Parameter changes in Kinetics, EPiK). We applied these methods to hematopoiesis, including a new single cell dataset in which transcription factors (TFs) involved in the earliest branchpoint of blood differentiation were individually up- and down-regulated. We could identify two unique sub-populations within a seemingly homogenous group of hematopoietic stem cells. In addition, we could predict regulatory mechanisms controlling the expression levels of eighteen key hematopoietic transcription factors throughout differentiation. Detailed information about gene regulatory mechanisms can therefore be obtained simply from high throughput single cell gene expression data, which should be widely applicable given the rapid expansion of single cell genomics.

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.

Topologically associated domains enriched for lineage-specific genes reveal expression-dependent nuclear topologies during myogenesis

The linear distribution of genes across chromosomes and the spatial localization of genes within the nucleus are related to their transcriptional regulation. The mechanistic consequences of linear gene order, and how it may relate to the functional output of genome organization, remain to be fully resolved, however. Here we tested the relationship between linear and 3D organization of gene regulation during myogenesis. Our analysis has identified a subset of topologically associated domains (TADs) that are significantly enriched for muscle-specific genes. These lineage-enriched TADs demonstrate an expression-dependent pattern of nuclear organization that influences the positioning of adjacent nonenriched TADs. Therefore, lineage-enriched TADs inform cell-specific genome organization during myogenesis. The reduction of allelic spatial distance of one of these domains, which contains Myogenin, correlates with reduced transcriptional variability, identifying a potential role for lineage-specific nuclear topology. Using a fusion-based strategy to decouple mitosis and myotube formation, we demonstrate that the cell-specific topology of syncytial nuclei is dependent on cell division. We propose that the effects of linear and spatial organization of gene loci on gene regulation are linked through TAD architecture, and that mitosis is critical for establishing nuclear topologies during cellular differentiation.

Mechanisms of fate decision and lineage commitment during haematopoiesis

Blood stem cells need to both perpetuate themselves (self-renew) and differentiate into all mature blood cells to maintain blood formation throughout life. However, it is unclear how the underlying gene regulatory network maintains this population of self-renewing and differentiating stem cells and how it accommodates the transition from a stem cell to a mature blood cell. Our current knowledge of transcriptomes of various blood cell types has mainly been advanced by population-level analysis. However, a population of seemingly homogenous blood cells may include many distinct cell types with substantially different transcriptomes and abilities to make diverse fate decisions. Therefore, understanding the cell-intrinsic differences between individual cells is necessary for a deeper understanding of the molecular basis of their behaviour. Here we review recent single-cell studies in the haematopoietic system and their contribution to our understanding of the mechanisms governing cell fate choices and lineage commitment.

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.

Variability of Gene Expression Identifies Transcriptional Regulators of Early Human Embryonic Development

An analysis of gene expression variability can provide an insightful window into how regulatory control is distributed across the transcriptome. In a single cell analysis, the inter-cellular variability of gene expression measures the consistency of transcript copy numbers observed between cells in the same population. Application of these ideas to the study of early human embryonic development may reveal important insights into the transcriptional programs controlling this process, based on which components are most tightly regulated. Using a published single cell RNA-seq data set of human embryos collected at four-cell, eight-cell, morula and blastocyst stages, we identified genes with the most stable, invariant expression across all four developmental stages. Stably-expressed genes were found to be enriched for those sharing indispensable features, including essentiality, haploinsufficiency, and ubiquitous expression. The stable genes were less likely to be associated with loss-of-function variant genes or human recessive disease genes affected by a DNA copy number variant deletion, suggesting that stable genes have a functional impact on the regulation of some of the basic cellular processes. Genes with low expression variability at early stages of development are involved in regulation of DNA methylation, responses to hypoxia and telomerase activity, whereas by the blastocyst stage, low-variability genes are enriched for metabolic processes as well as telomerase signaling. Based on changes in expression variability, we identified a putative set of gene expression markers of morulae and blastocyst stages. Experimental validation of a blastocyst-expressed variability marker demonstrated that HDDC2 plays a role in the maintenance of pluripotency in human ES and iPS cells. Collectively our analyses identified new regulators involved in human embryonic development that would have otherwise been missed using methods that focus on assessment of the average expression levels; in doing so, we highlight the value of studying expression variability for single cell RNA-seq data.

Patterning of the angiosperm female gametophyte through the prism of theoretical paradigms

The FG (female gametophyte) of flowering plants (angiosperms) is a simple highly polar structure composed of only a few cell types. The FG develops from a single cell through mitotic divisions to generate, depending on the species, four to 16 nuclei in a syncytium. These nuclei are then partitioned into three or four distinct cell types. The mechanisms underlying the specification of the nuclei in the FG has been a focus of research over the last decade. Nevertheless, we are far from understanding the patterning mechanisms that govern cell specification. Although some results were previously interpreted in terms of static positional information, several lines of evidence now show that local interactions are important. In the present article, we revisit the available data on developmental mutants and cell fate markers in the light of theoretical frameworks for biological patterning. We argue that a further dissection of the mechanisms may be impeded by the combinatorial and dynamical nature of developmental cues. However, accounting for these properties of developing systems is necessary to disentangle the diversity of the phenotypic manifestations of the underlying molecular interactions.

Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis

We explore cell heterogeneity during spontaneous and transcription-factor-driven commitment for network inference in hematopoiesis. Since individual genes display discrete OFF states or a distribution of ON levels, we compute and combine pairwise gene associations from binary and continuous components of gene expression in single cells. Ddit3 emerges as a regulatory node with positive linkage to erythroid regulators and negative association with myeloid determinants. Ddit3 loss impairs erythroid colony output from multipotent cells, while forcing Ddit3 in granulo-monocytic progenitors (GMPs) enhances self-renewal and impedes differentiation. Network analysis of Ddit3-transduced GMPs reveals uncoupling of myeloid networks and strengthening of erythroid linkages. RNA sequencing suggests that Ddit3 acts through development or stabilization of a precursor upstream of GMPs with inherent Meg-E potential. The enrichment of Gata2 target genes in Ddit3-dependent transcriptional responses suggests that Ddit3 functions in an erythroid transcriptional network nucleated by Gata2.

Single-cell PCR profiling of gene expression in hematopoiesis

Single-cell analysis of gene expression offers the possibility of exploring cellular and molecular heterogeneity in stem and developmental cell systems, including cancer, to infer routes of cellular specification and their respective gene regulatory modules. PCR-based technologies, although limited to the analysis of a predefined set of genes, afford a cost-effective balance of throughput and biological information and have become a method of choice in stem cell laboratories. Here we describe an experimental and analytical protocol based on the Fluidigm microfluidics platform for the simultaneous expression analysis of 48 or 96 genes in multiples of 48 or 96 cells. We detail wet laboratory procedures and describe clustering, principal component analysis, correlation, and classification tools for the inference of cellular pathways and gene networks.

Sca-1+Lin-CD117- mesenchymal stem/stromal cells induce the generation of novel IRF8-controlled regulatory dendritic cells through Notch-RBP-J signaling

Mesenchymal stem/stromal cells (MSCs) can influence the destiny of hematopoietic stem/progenitor cells (HSCs) and exert broadly immunomodulatory effects on immune cells. However, how MSCs regulate the differentiation of regulatory dendritic cells (regDCs) from HSCs remains incompletely understood. In this study, we show that mouse bone marrow-derived Sca-1(+)Lin(-)CD117(-) MSCs can drive HSCs to differentiate into a novel IFN regulatory factor (IRF)8-controlled regDC population (Sca(+) BM-MSC-driven DC [sBM-DCs]) when cocultured without exogenous cytokines. The Notch pathway plays a critical role in the generation of the sBM-DCs by controlling IRF8 expression in an RBP-J-dependent way. We observed a high level of H3K27me3 methylation and a low level of H3K4me3 methylation at the Irf8 promoter during sBM-DC induction. Importantly, infusion of sBM-DCs could alleviate colitis in mice with inflammatory bowel disease by inhibiting lymphocyte proliferation and increasing the numbers of CD4(+)CD25(+) regulatory T cells. Thus, these data infer a possible mechanism for the development of regDCs and further support the role of MSCs in treating immune disorders.

Cell dynamics and gene expression control in tissue homeostasis and development

During tissue and organ development and maintenance, the dynamic regulation of cellular proliferation and differentiation allows cells to build highly elaborate structures. The development of the vertebrate retina or the maintenance of adult intestinal crypts, for instance, involves the arrangement of newly created cells with different phenotypes, the proportions of which need to be tightly controlled. While some of the basic principles underlying these processes developing and maintaining these organs are known, much remains to be learnt from how cells encode the necessary information and use it to attain those complex but reproducible arrangements. Here, we review the current knowledge on the principles underlying cell population dynamics during tissue development and homeostasis. In particular, we discuss how stochastic fate assignment, cell division, feedback control and cellular transition states interact during organ and tissue development and maintenance in multicellular organisms. We propose a framework, involving the existence of a transition state in which cells are more susceptible to signals that can affect their gene expression state and influence their cell fate decisions. This framework, which also applies to systems much more amenable to quantitative analysis like differentiating embryonic stem cells, links gene expression programmes with cell population dynamics.

Obesity-driven disruption of haematopoiesis and the bone marrow niche

Obesity markedly increases susceptibility to a range of diseases and simultaneously undermines the viability and fate selection of haematopoietic stem cells (HSCs), and thus the kinetics of leukocyte production that is critical to innate and adaptive immunity. Considering that blood cell production and the differentiation of HSCs and their progeny is orchestrated, in part, by complex interacting signals emanating from the bone marrow microenvironment, it is not surprising that conditions that disturb bone marrow structure inevitably disrupt both the numbers and lineage-fates of these key blood cell progenitors. In addition to the increased adipose burden in visceral and subcutaneous compartments, obesity causes a marked increase in the size and number of adipocytes encroaching into the bone marrow space, almost certainly disturbing HSC interactions with neighbouring cells, which include osteoblasts, osteoclasts, mesenchymal cells and endothelial cells. As the global obesity pandemic grows, the short-term and long-term consequences of increased bone marrow adiposity on HSC lineage selection and immune function remain uncertain. This Review discusses the differentiation and function of haematopoietic cell populations, the principal physicochemical components of the bone marrow niche, and how this environment influences HSCs and haematopoiesis in general. The effect of adipocytes and adiposity on HSC and progenitor cell populations is also discussed, with the goal of understanding how obesity might compromise the core haematopoietic system.

Deterministic and stochastic approaches in the clinical application of mesenchymal stromal cells (MSCs)

Mesenchymal stromal cells (MSCs) have enormous intrinsic clinical value due to their multi-lineage differentiation capacity, support of hemopoiesis, immunoregulation and growth factors/cytokines secretion. MSCs have thus been the object of extensive research for decades. After completion of many pre-clinical and clinical trials, MSC-based therapy is now facing a challenging phase. Several clinical trials have reported moderate, non-durable benefits, which caused initial enthusiasm to wane, and indicated an urgent need to optimize the efficacy of therapeutic, platform-enhancing MSC-based treatment. Recent investigations suggest the presence of multiple in vivo MSC ancestors in a wide range of tissues, which contribute to the heterogeneity of the starting material for the expansion of MSCs. This variability in the MSC culture-initiating cell population, together with the different types of enrichment/isolation and cultivation protocols applied, are hampering progress in the definition of MSC-based therapies. International regulatory statements require a precise risk/benefit analysis, ensuring the safety and efficacy of treatments. GMP validation allows for quality certification, but the prediction of a clinical outcome after MSC-based therapy is correlated not only to the possible morbidity derived by cell production process, but also to the biology of the MSCs themselves, which is highly sensible to unpredictable fluctuation of isolating and culture conditions. Risk exposure and efficacy of MSC-based therapies should be evaluated by pre-clinical studies, but the batch-to-batch variability of the final medicinal product could significantly limit the predictability of these studies. The future success of MSC-based therapies could lie not only in rational optimization of therapeutic strategies, but also in a stochastic approach during the assessment of benefit and risk factors.

Stochastic NANOG fluctuations allow mouse embryonic stem cells to explore pluripotency

Heterogeneous expression of the transcription factor NANOG has been linked to the existence of various functional states in pluripotent stem cells. This heterogeneity seems to arise from fluctuations of Nanog expression in individual cells, but a thorough characterization of these fluctuations and their impact on the pluripotent state is still lacking. Here, we have used a novel fluorescent reporter to investigate the temporal dynamics of NANOG expression in mouse embryonic stem cells (mESCs), and to dissect the lineage potential of mESCs at different NANOG states. Our results show that stochastic NANOG fluctuations are widespread in mESCs, with essentially all expressing cells showing fluctuations in NANOG levels, even when cultured in ground-state conditions (2i media). We further show that fluctuations have similar kinetics when mESCs are cultured in standard conditions (serum plus leukemia inhibitory factor) or ground-state conditions, implying that NANOG fluctuations are inherent to the pluripotent state. We have then compared the developmental potential of low-NANOG and high-NANOG mESCs, grown in different conditions, and confirm that mESCs are more susceptible to enter differentiation at the low-NANOG state. Further analysis by gene expression profiling reveals that low-NANOG cells have marked expression of lineage-affiliated genes, with variable profiles according to the signalling environment. By contrast, high-NANOG cells show a more stable expression profile in different environments, with minimal expression of lineage markers. Altogether, our data support a model in which stochastic NANOG fluctuations provide opportunities for mESCs to explore multiple lineage options, modulating their probability to change functional state.

Stem cell differentiation as a many-body problem

Stem cell differentiation has been viewed as coming from transitions between attractors on an epigenetic landscape that governs the dynamics of a regulatory network involving many genes. Rigorous definition of such a landscape is made possible by the realization that gene regulation is stochastic, owing to the small copy number of the transcription factors that regulate gene expression and because of the single-molecule nature of the gene itself. We develop an approximation that allows the quantitative construction of the epigenetic landscape for large realistic model networks. Applying this approach to the network for embryonic stem cell development explains many experimental observations, including the heterogeneous distribution of the transcription factor Nanog and its role in safeguarding the stem cell pluripotency, which can be understood by finding stable steady-state attractors and the most probable transition paths between those attractors. We also demonstrate that the switching rate between attractors can be significantly influenced by the gene expression noise arising from the fluctuations of DNA occupancy when binding to a specific DNA site is slow.

Spurious transcription factor binding: non-functional or genetically redundant?

Transcription factor binding sites (TFBSs) on the DNA are generally accepted as the key nodes of gene control. However, the multitudes of TFBSs identified in genome-wide studies, some of them seemingly unconstrained in evolution, have prompted the view that in many cases TF binding may serve no biological function. Yet, insights from transcriptional biochemistry, population genetics and functional genomics suggest that rather than segregating into 'functional' or 'non-functional', TFBS inputs to their target genes may be generally cumulative, with varying degrees of potency and redundancy. As TFBS redundancy can be diminished by mutations and environmental stress, some of the apparently 'spurious' sites may turn out to be important for maintaining adequate transcriptional regulation under these conditions. This has significant implications for interpreting the phenotypic effects of TFBS mutations, particularly in the context of genome-wide association studies for complex traits.

Transcriptional mechanisms of cell fate decisions revealed by single cell expression profiling

Transcriptional networks regulate cell fate decisions, which occur at the level of individual cells. However, much of what we know about their structure and function comes from studies averaging measurements over large populations of cells, many of which are functionally heterogeneous. Such studies conceal the variability between cells and so prevent us from determining the nature of heterogeneity at the molecular level. In recent years, many protocols and platforms have been developed that allow the high throughput analysis of gene expression in single cells, opening the door to a new era of biology. Here, we discuss the need for single cell gene expression analysis to gain deeper insights into the transcriptional control of cell fate decisions, and consider the insights it has provided so far into transcriptional regulatory networks in development.