Transition states and cell fate decisions in epigenetic landscapes

A Darwinian and Physical Look at Stem Cell Biology Helps Understanding the Role of Stochasticity in Development

Single-cell analysis allows biologists to gain huge insight into cell differentiation and tissue structuration. Randomness of differentiation, both in vitro and in vivo, of pluripotent (multipotent) stem cells is now demonstrated to be mainly based on stochastic gene expression. Nevertheless, it remains necessary to incorporate this inherent stochasticity of developmental processes within a coherent scheme. We argue here that the theory called ontophylogenesis is more relevant and better fits with experimental data than alternative theories which have been suggested based on the notions of self-organization and attractor states. The ontophylogenesis theory considers the generation of a differentiated state as a constrained random process: randomness is provided by the stochastic dynamics of biochemical reactions while the environmental constraints, including cell inner structures and cell-cell interactions, drive the system toward a stabilized state of equilibrium. In this conception, biological organization during development can be seen as the result of multiscale constraints produced by the dynamical organization of the biological system which retroacts on the stochastic dynamics at lower scales. This scheme makes it possible to really understand how the generation of reproducible structures at higher organization levels can be fully compatible with probabilistic behavior at the lower levels. It is compatible with the second law of thermodynamics but allows the overtaking of the limitations exhibited by models only based on entropy exchanges which cannot cope with the description nor the dynamics of the mesoscopic and macroscopic organization of biological systems.

Stem cell lineage survival as a noisy competition for niche access

Understanding to what extent stem cell potential is a cell-intrinsic property or an emergent behavior coming from global tissue dynamics and geometry is a key outstanding question of systems and stem cell biology. Here, we propose a theory of stem cell dynamics as a stochastic competition for access to a spatially localized niche, giving rise to a stochastic conveyor-belt model. Cell divisions produce a steady cellular stream which advects cells away from the niche, while random rearrangements enable cells away from the niche to be favorably repositioned. Importantly, even when assuming that all cells in a tissue are molecularly equivalent, we predict a common ("universal") functional dependence of the long-term clonal survival probability on distance from the niche, as well as the emergence of a well-defined number of functional stem cells, dependent only on the rate of random movements vs. mitosis-driven advection. We test the predictions of this theory on datasets of pubertal mammary gland tips and embryonic kidney tips, as well as homeostatic intestinal crypts. Importantly, we find good agreement for the predicted functional dependency of the competition as a function of position, and thus functional stem cell number in each organ. This argues for a key role of positional fluctuations in dictating stem cell number and dynamics, and we discuss the applicability of this theory to other settings.

Information Theory in Computational Biology: Where We Stand Today

"A Mathematical Theory of Communication" was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon's work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology-gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.

Network inference in systems biology: recent developments, challenges, and applications

One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses.

Epigenetics in Inflammatory Breast Cancer: Biological Features and Therapeutic Perspectives

Evidence has emerged implicating epigenetic alterations in inflammatory breast cancer (IBC) origin and progression. IBC is a rare and rapidly progressing disease, considered the most aggressive type of breast cancer (BC). At clinical presentation, IBC is characterized by diffuse erythema, skin ridging, dermal lymphatic invasion, and peau d'orange aspect. The widespread distribution of the tumor as emboli throughout the breast and intra- and intertumor heterogeneity is associated with its poor prognosis. In this review, we highlighted studies documenting the essential roles of epigenetic mechanisms in remodeling chromatin and modulating gene expression during mammary gland differentiation and the development of IBC. Compiling evidence has emerged implicating epigenetic changes as a common denominator linking the main risk factors (socioeconomic status, environmental exposure to endocrine disruptors, racial disparities, and obesity) with IBC development. DNA methylation changes and their impact on the diagnosis, prognosis, and treatment of IBC are also described. Recent studies are focusing on the use of histone deacetylase inhibitors as promising epigenetic drugs for treating IBC. All efforts must be undertaken to unravel the epigenetic marks that drive this disease and how this knowledge could impact strategies to reduce the risk of IBC development and progression.

scRCMF: Identification of Cell Subpopulations and Transition States From Single-Cell Transcriptomes

Single cell technologies provide an unprecedented opportunity to explore the heterogeneity in a biological process at the level of single cells. One major challenge in analyzing single cell data is to identify cell subpopulations, stable cell states, and cells in transition between states. To elucidate the transition mechanisms in cell fate dynamics, it is highly desirable to quantitatively characterize cellular states and intermediate states. Here, we present scRCMF, an unsupervised method that identifies stable cell states and transition cells by adopting a nonlinear optimization model that infers the latent substructures from a gene-cell matrix. We incorporate a random coefficient matrix-based regularization into the standard nonnegative matrix decomposition model to improve the reliability and stability of estimating latent substructures. To quantify the transition capability of each cell, we propose two new measures: single-cell transition entropy (scEntropy) and transition probability (scTP). When applied to two simulated and three published scRNA-seq datasets, scRCMF not only successfully captures multiple subpopulations and transition processes in large-scale data, but also identifies transition states and some known marker genes associated with cell state transitions and subpopulations. Furthermore, the quantity scEntropy is found to be significantly higher for transition cells than other cellular states during the global differentiation, and the scTP predicts the "fate decisions" of transition cells within the transition. The present study provides new insights into transition events during differentiation and development.

Personalized therapy design for systemic lupus erythematosus based on the analysis of protein-protein interaction networks

We analyzed protein expression data for Lupus patients, which have been obtained from publicly available databases. A combination of systems biology and statistical thermodynamics approaches was used to extract topological properties of the associated protein-protein interaction networks for each of the 291 patients whose samples were used to provide the molecular data. We have concluded that among the many proteins that appear to play critical roles in this pathology, most of them are either ribosomal proteins, ubiquitination pathway proteins or heat shock proteins. We propose some of the proteins identified in this study to be considered for drug targeting.

A system-level mechanistic explanation for asymmetric stem cell fates: Arabidopsis thaliana root niche as a study system

Asymmetric divisions maintain long-term stem cell populations while producing new cells that proliferate and then differentiate. Recent reports in animal systems show that divisions of stem cells can be uncoupled from their progeny differentiation, and the outcome of a division could be influenced by microenvironmental signals. But the underlying system-level mechanisms, and whether this dynamics also occur in plant stem cell niches (SCN), remain elusive. This article presents a cell fate regulatory network model that contributes to understanding such mechanism and identify critical cues for cell fate transitions in the root SCN. Novel computational and experimental results show that the transcriptional regulator SHR is critical for the most frequent asymmetric division previously described for quiescent centre stem cells. A multi-scale model of the root tip that simulated each cell's intracellular regulatory network, and the dynamics of SHR intercellular transport as a cell-cell coupling mechanism, was developed. It revealed that quiescent centre cell divisions produce two identical cells, that may acquire different fates depending on the feedback between SHR's availability and the state of the regulatory network. Novel experimental data presented here validates our model, which in turn, constitutes the first proposed systemic mechanism for uncoupled SCN cell division and differentiation.

The Basal Level of Gene Expression Associated with Chromatin Loosening Shapes Waddington Landscapes and Controls Cell Differentiation

The baseline level of transcription, which is variable and difficult to quantify, seriously complicates the normalization of comparative transcriptomic data, but its biological importance remains unappreciated. We show that this currently neglected ingredient is essential for controlling gene network multistability and therefore cellular differentiation. Basal expression is correlated to the degree of chromatin loosening measured by DNA accessibility and systematically leads to cellular dedifferentiation as assessed by transcriptomic signatures, irrespective of the molecular and cellular tools used. Modeling gene network motifs formally involved in developmental bifurcations reveals that the epigenetic landscapes of Waddington are restructured by the level of nonspecific expression, such that the attractors of progenitor and differentiated cells can be mutually exclusive. This mechanism is universal and holds beyond the particular nature of the genes involved, provided the multistable circuits are correctly described with autonomous basal expression. These results explain the relationships long established between gene expression noise, chromatin decondensation and cellular dedifferentiation, and highlight how heterochromatin maintenance is essential for preventing pathological cellular reprogramming, age-related diseases, and cancer.

Using the Gibbs Function as a Measure of Human Brain Development Trends from Fetal Stage to Advanced Age

We propose to use a Gibbs free energy function as a measure of the human brain development. We adopt this approach to the development of the human brain over the human lifespan: from a prenatal stage to advanced age. We used proteomic expression data with the Gibbs free energy to quantify human brain’s protein–protein interaction networks. The data, obtained from BioGRID, comprised tissue samples from the 16 main brain areas, at different ages, of 57 post-mortem human brains. We found a consistent functional dependence of the Gibbs free energies on age for most of the areas and both sexes. A significant upward trend in the Gibbs function was found during the fetal stages, which is followed by a sharp drop at birth with a subsequent period of relative stability and a final upward trend toward advanced age. We interpret these data in terms of structure formation followed by its stabilization and eventual deterioration. Furthermore, gender data analysis has uncovered the existence of functional differences, showing male Gibbs function values lower than female at prenatal and neonatal ages, which become higher at ages 8 to 40 and finally converging at late adulthood with the corresponding female Gibbs functions.

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.

Single-Cell Transcriptional Profiling of the Intestinal Epithelium

Emerging single-cell technologies, like single-cell RNA sequencing (scRNA-seq), enable the study of heterogeneous biological systems at cellular resolution. By profiling the set of expressed transcripts in each cell, single-cell transcriptomics has allowed for the cataloging of the cellular constituents of multiple organs and tissues, both in health and disease. In addition, these technologies have provided mechanistic insights into cellular function, cell state transitions, developmental trajectories and lineage relationships, as well as helped to dissect complex, population-level responses to environmental perturbations. scRNA-seq is particularly useful for characterizing the intestinal epithelium because it is a dynamic, rapidly self-renewing tissue comprised of more than a dozen specialized cell types. Here we discuss the fundamentals of single-cell transcriptomics of the murine small intestinal epithelium. We review the principles of proper experimental design and provide methods for the dissociation of the small intestinal epithelium into single cells followed by fluorescence-activated cell sorting (FACS) and for scRNA-seq using the 10× Genomics Chromium platform.

Revealing Dynamic Mechanisms of Cell Fate Decisions From Single-Cell Transcriptomic Data

Cell fate decisions play a pivotal role in development, but technologies for dissecting them are limited. We developed a multifunction new method, Topographer, to construct a "quantitative" Waddington's landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types, but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (~97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic), and gene expression (microscopic).

Stochastic Gene Choice during Cellular Differentiation

Genes in higher eukaryotes are regulated by long-range interactions, which can determine what combination of genes is expressed in a chromosomal segment. The choice of the genes can display exclusivity, independence, or co-occurrence. We introduced a simple measure to quantify this interdependence in gene expression and differentiated mouse embryonic stem cells to neurons to measure the single-cell expression of the gene isoforms in the protocadherin (Pcdh) cluster, a key component of neuronal diversity. As the neuronal progenitors mature into neurons, expression of the gene isoforms in the Pcdh array is initially concurrent. Even though the number of the expressed genes is increasing during differentiation, the expression shifts toward exclusivity. The expression frequency correlates highly with CTCF binding to the promoters and follows dynamically the changes in the binding during the differentiation. These findings aid in understanding the interplay between cellular differentiation and stochastic gene choice.

Cell lineage and communication network inference via optimization for single-cell transcriptomics

The use of single-cell transcriptomics has become a major approach to delineate cell subpopulations and the transitions between them. While various computational tools using different mathematical methods have been developed to infer clusters, marker genes, and cell lineage, none yet integrate these within a mathematical framework to perform multiple tasks coherently. Such coherence is critical for the inference of cell–cell communication, a major remaining challenge. Here, we present similarity matrix-based optimization for single-cell data analysis (SoptSC), in which unsupervised clustering, pseudotemporal ordering, lineage inference, and marker gene identification are inferred via a structured cell-to-cell similarity matrix. SoptSC then predicts cell–cell communication networks, enabling reconstruction of complex cell lineages that include feedback or feedforward interactions. Application of SoptSC to early embryonic development, epidermal regeneration, and hematopoiesis demonstrates robust identification of subpopulations, lineage relationships, and pseudotime, and prediction of pathway-specific cell communication patterns regulating processes of development and differentiation.

Drugs modulating stochastic gene expression affect the erythroid differentiation process

To better understand the mechanisms behind cells decision-making to differentiate, we assessed the influence of stochastic gene expression (SGE) modulation on the erythroid differentiation process. It has been suggested that stochastic gene expression has a role in cell fate decision-making which is revealed by single-cell analyses but studies dedicated to demonstrate the consistency of this link are still lacking. Recent observations showed that SGE significantly increased during differentiation and a few showed that an increase of the level of SGE is accompanied by an increase in the differentiation process. However, a consistent relation in both increasing and decreasing directions has never been shown in the same cellular system. Such demonstration would require to be able to experimentally manipulate simultaneously the level of SGE and cell differentiation in order to observe if cell behavior matches with the current theory. We identified three drugs that modulate SGE in primary erythroid progenitor cells. Both Artemisinin and Indomethacin decreased SGE and reduced the amount of differentiated cells. On the contrary, a third component called MB-3 simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment. Using single-cell analysis and modeling tools, we provide experimental evidence that, in a physiologically relevant cellular system, SGE is linked to differentiation.

Deciphering the Gene Regulatory Landscape Encoded in DNA Biophysical Features

Gene regulation in higher organisms involves a sophisticated interplay between genetic and epigenetic mechanisms. Despite advances, the logic in selective usage of certain genomic regions as regulatory elements remains unclear. Here we show that the inherent biophysical properties of the DNA encode epigenetic state and the underlying regulatory potential. We find that the propeller twist (ProT) level is indicative of genomic location of the regulatory elements, their strength, the affinity landscape of transcription factors, and distribution in the nuclear 3D space. We experimentally show that ProT levels confer increased DNA flexibility and surface accessibility, and thus potentially primes usage of high ProT regions as regulatory elements. ProT levels also correlate with occurrence and phenotypic consequences of mutations. Interestingly, cell-fate switches involve a transient usage of low ProT regulatory elements. Altogether, our work provides unprecedented insights into the gene regulatory landscape encoded in the DNA biophysical features.

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DNA shape features encode genomic surface accessibility and flexibility

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High ProT is a deterministic feature of enhancers

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ProT levels correlate with nuclear organization of epigenetic states

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Cell-fate switches involve a transient usage of low ProT regulatory elements

The Bright and Dark Side of DNA Methylation: A Matter of Balance

DNA methylation controls several cellular processes, from early development to old age, including biological responses to endogenous or exogenous stimuli contributing to disease transition. As a result, minimal DNA methylation changes during developmental stages drive severe phenotypes, as observed in germ-line imprinting disorders, while genome-wide alterations occurring in somatic cells are linked to cancer onset and progression. By summarizing the molecular events governing DNA methylation, we focus on the methods that have facilitated mapping and understanding of this epigenetic mark in healthy conditions and diseases. Overall, we review the bright (health-related) and dark (disease-related) side of DNA methylation changes, outlining how bulk and single-cell genomic analyses are moving toward the identification of new molecular targets and driving the development of more specific and less toxic demethylating agents.

Parameter inference in dynamical systems with co-dimension 1 bifurcations

Dynamical systems with intricate behaviour are all-pervasive in biology. Many of the most interesting biological processes indicate the presence of bifurcations, i.e. phenomena where a small change in a system parameter causes qualitatively different behaviour. Bifurcation theory has become a rich field of research in its own right and evaluating the bifurcation behaviour of a given dynamical system can be challenging. An even greater challenge, however, is to learn the bifurcation structure of dynamical systems from data, where the precise model structure is not known. Here, we study one aspects of this problem: the practical implications that the presence of bifurcations has on our ability to infer model parameters and initial conditions from empirical data; we focus on the canonical co-dimension 1 bifurcations and provide a comprehensive analysis of how dynamics, and our ability to infer kinetic parameters are linked. The picture thus emerging is surprisingly nuanced and suggests that identification of the qualitative dynamics-the bifurcation diagram-should precede any attempt at inferring kinetic parameters.

Roadmap to Local Tumour Growth: Insights from Cervical Cancer

Wide tumour excision is currently the standard approach to surgical treatment of solid cancers including carcinomas of the lower genital tract. This strategy is based on the premise that tumours exhibit isotropic growth potential. We reviewed and analysed local tumour spreading patterns in 518 patients with cancer of the uterine cervix who underwent surgical tumour resection. Based on data obtained from pathological examination of the surgical specimen, we applied computational modelling techniques to simulate local tumour spread in order to identify parameters influencing preferred infiltration patterns and used area-proportional Euler diagrams to detect and confirm ordered patterns of tumour spread. Some anatomical structures, e.g. tissues of the urinary bladder, were significantly more likely to be infiltrated than other structures, e.g. the ureter and the rectum. Computational models assuming isotropic growth could not explain these infiltration patterns. Introducing ontogenetic distance of a tissue relative to the uterine cervix as a parameter led to accurate predictions of the clinically observed infiltration likelihoods. The clinical data indicates that successive infiltration likelihoods of ontogenetically distant tissues are nearly perfect subsets of ontogenetically closer tissues. The prevailing assumption of isotropic tumour extension has significant shortcomings in the case of cervical cancer. Rather, cervical cancer spread seems to follow ontogenetically defined trajectories.

Co-emergence and Collapse: The Mesoscopic Approach for Conceptualizing and Investigating the Functional Integration of Organisms

The fall of reductionist approaches to explanation leaves biology with an unescapable challenge: how to decipher complex systems. This entails a number of very critical questions, the most basic ones being: "What do we mean by 'complex'?" and "What is the system we should look for?" In complex systems, constraints belong to a higher level that the molecular one and their effect reduces and constrains the manifold of the accessible internal states of the system itself. Function is related but not deterministically imposed by the underlying structure. It is quite unlikely that such kind of complexity could be grasped by current approaches focusing on a single organization scale. The natural co-emergence of systems, parts and properties can be adopted as a hypothesis-free conceptual framework to understand functional integration of organisms, including their hierarchical or multilevel patterns, and including the way scientific practice proceeds in approaching such complexity. External, "driving" factors - order parameters and control parameters provided by the surrounding microenvironment - are always required to "push" the components' fate into well-defined developmental directions. In the negative, we see that in pathological processes such as cancer, organizational fluidity, collapse of levels and dynamic heterogeneity make it hard to even find a level of observation for a stable explanandum to persist in scientific practice. Parts and the system both lose their properties once the system is destabilized. The mesoscopic approach is our proposal to conceptualizing, investigating and explaining in biology. "Mesoscopic way of thinking" is increasingly popular in the epistemology of biology and corresponds to looking for an explanation (and possibly a prediction) where "non-trivial determinism is maximal": the "most microscopic" level of organization is not necessarily the place where "the most relevant facts do happen." A fundamental re-thinking of the concept of causality is also due for order parameters to be carefully and correctly identified. In the biological realm, entities have relational properties only, as they depend ontologically on the context they happen to be in. The basic idea of a relational ontology is that, in our inventory of the world, relations are somehow prior to the relata (i.e., entities).

Transcriptional networks in acute myeloid leukemia

Acute myeloid leukemia (AML) is a complex disease characterized by a diverse range of recurrent molecular aberrations that occur in many different combinations. Components of transcriptional networks are a common target of these aberrations, leading to network-wide changes and deployment of novel or developmentally inappropriate transcriptional programs. Genome-wide techniques are beginning to reveal the full complexity of normal hematopoietic stem cell transcriptional networks and the extent to which they are deregulated in AML, and new understandings of the mechanisms by which AML cells maintain self-renewal and block differentiation are starting to emerge. The hope is that increased understanding of the network architecture in AML will lead to identification of key oncogenic dependencies that are downstream of multiple network aberrations, and that this knowledge will be translated into new therapies that target these dependencies. Here, we review the current state of knowledge of network perturbation in AML with a focus on major mechanisms of transcription factor dysregulation, including mutation, translocation, and transcriptional dysregulation, and discuss how these perturbations propagate across transcriptional networks. We will also review emerging mechanisms of network disruption, and briefly discuss how increased knowledge of network disruption is already being used to develop new therapies.

Sialylation is involved in cell fate decision during development, reprogramming and cancer progression

Sialylation, or the covalent addition of sialic acid to the terminal end of glycoproteins, is a biologically important modification that is involved in embryonic development, neurodevelopment, reprogramming, oncogenesis and immune responses. In this review, we have given a comprehensive overview of the current literature on the involvement of sialylation in cell fate decision during development, reprogramming and cancer progression. Sialylation is essential for early embryonic development and the deletion of UDP-GlcNAc 2-epimerase, a rate-limiting enzyme in sialic acid biosynthesis, is embryonically lethal. Furthermore, the sialyltransferase ST6GAL1 is required for somatic cell reprogramming, and its downregulation is associated with decreased reprogramming efficiency. In addition, sialylation levels and patterns are altered during cancer progression, indicating the potential of sialylated molecules as cancer biomarkers. Taken together, the current evidences demonstrate that sialylation is involved in crucial cell fate decision.

Dynamic BAF chromatin remodeling complex subunit inclusion promotes temporally distinct gene expression programs in cardiogenesis

Chromatin remodeling complexes instruct cellular differentiation and lineage specific transcription. The BRG1/BRM-associated factor (BAF) complexes are important for several aspects of differentiation. We show that the catalytic subunit gene Brg1 has a specific role in cardiac precursors (CPs) to initiate cardiac gene expression programs and repress non-cardiac expression. Using immunopurification with mass spectrometry, we have determined the dynamic composition of BAF complexes during mammalian cardiac differentiation, identifying several cell-type specific subunits. We focused on the CP- and cardiomyocyte (CM)-enriched subunits BAF60c (SMARCD3) and BAF170 (SMARCC2). Baf60c and Baf170 co-regulate gene expression with Brg1 in CPs, and in CMs their loss results in broadly deregulated cardiac gene expression. BRG1, BAF60c and BAF170 modulate chromatin accessibility, to promote accessibility at activated genes while closing chromatin at repressed genes. BAF60c and BAF170 are required for proper BAF complex composition, and BAF170 loss leads to retention of BRG1 at CP-specific sites. Thus, dynamic interdependent BAF complex subunit assembly modulates chromatin states and thereby participates in directing temporal gene expression programs in cardiogenesis.

Stem Cell Differentiation as a Non-Markov Stochastic Process

Pluripotent stem cells can self-renew in culture and differentiate along all somatic lineages in vivo. While much is known about the molecular basis of pluripotency, the mechanisms of differentiation remain unclear. Here, we profile individual mouse embryonic stem cells as they progress along the neuronal lineage. We observe that cells pass from the pluripotent state to the neuronal state via an intermediate epiblast-like state. However, analysis of the rate at which cells enter and exit these observed cell states using a hidden Markov model indicates the presence of a chain of unobserved molecular states that each cell transits through stochastically in sequence. This chain of hidden states allows individual cells to record their position on the differentiation trajectory, thereby encoding a simple form of cellular memory. We suggest a statistical mechanics interpretation of these results that distinguishes between functionally distinct cellular “macrostates” and functionally similar molecular “microstates” and propose a model of stem cell differentiation as a non-Markov stochastic process.

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We profile individual stem cells as they differentiate along the neural lineage

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Regulatory network changes and increased cell variability accompany differentiation

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Analysis of dynamics with a hidden Markov model reveals unobserved molecular states

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We propose a model of stem cell differentiation as a non-Markov stochastic process

Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures

While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher-order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell datasets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.

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PIDC infers gene regulatory networks from single-cell transcriptomic data

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Multivariate information measures and context in PIDC improve network inference

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Heterogeneity in single-cell data carries information about gene-gene interactions

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Fast, efficient, open-source software is made freely available

Estimating Differentiation Potency of Single Cells Using Single-Cell Entropy (SCENT)

The ability to measure molecular properties (e.g., mRNA expression) at the single-cell level is revolutionizing our understanding of cellular developmental processes and how these are altered in diseases like cancer. The need for computational methods aimed at extracting biological knowledge from such single-cell data has never been greater. Here, we present a detailed protocol for estimating differentiation potency of single cells, based on our Single-Cell ENTropy (SCENT) algorithm. The estimation of differentiation potency is based on an explicit biophysical model that integrates the RNA-Seq profile of a single cell with an interaction network to approximate potency as the entropy of a diffusion process on the network. We here focus on the implementation, providing a step-by-step introduction to the method and illustrating it on a real scRNA-Seq dataset profiling human embryonic stem cells and multipotent progenitors representing the 3 main germ layers. SCENT is aimed particularly at single-cell studies trying to identify novel stem-or-progenitor like phenotypes, and may be particularly valuable for the unbiased identification of cancer stem cells. SCENT is implemented in R, licensed under the GNU General Public Licence v3, and freely available from https://github.com/aet21/SCENT .

Morphological State Transition Dynamics in EGF-Induced Epithelial to Mesenchymal Transition

Epithelial to Mesenchymal Transition (EMT) is a multi-state process. Here, we investigated phenotypic state transition dynamics of Epidermal Growth Factor (EGF)-induced EMT in a breast cancer cell line MDA-MB-468. We have defined phenotypic states of these cells in terms of their morphologies and have shown that these cells have three distinct morphological states-cobble, spindle, and circular. The spindle and circular states are the migratory phenotypes. Using quantitative image analysis and mathematical modeling, we have deciphered state transition trajectories in different experimental conditions. This analysis shows that the phenotypic state transition during EGF-induced EMT in these cells is reversible, and depends upon the dose of EGF and level of phosphorylation of the EGF receptor (EGFR). The dominant reversible state transition trajectory in this system was cobble to circular to spindle to cobble. We have observed that there exists an ultrasensitive on/off switch involving phospho-EGFR that decides the transition of cells in and out of the circular state. In general, our observations can be explained by the conventional quasi-potential landscape model for phenotypic state transition. As an alternative to this model, we have proposed a simpler discretized energy-level model to explain the observed state transition dynamics.

Neuro-mesodermal progenitors (NMPs): a comparative study between pluripotent stem cells and embryo-derived populations

The mammalian embryo's caudal lateral epiblast (CLE) harbours bipotent progenitors, called neural mesodermal progenitors (NMPs), that contribute to the spinal cord and the paraxial mesoderm throughout axial elongation. Here, we performed a single cell analysis of different in vitro NMP populations produced either from embryonic stem cells (ESCs) or epiblast stem cells (EpiSCs) and compared them with E8.25 CLE mouse embryos. In our analysis of this region, our findings challenge the notion that NMPs can be defined by the exclusive co-expression of Sox2 and T at mRNA level. We analyse the in vitro NMP-like populations using a purpose-built support vector machine (SVM) based on the embryo CLE and use it as a classification model to compare the in vivo and in vitro populations. Our results show that NMP differentiation from ESCs leads to heterogeneous progenitor populations with few NMP-like cells, as defined by the SVM algorithm, whereas starting with EpiSCs yields a high proportion of cells with the embryo NMP signature. We find that the population from which the Epi-NMPs are derived in culture contains a node-like population, which suggests that this population probably maintains the expression of T in vitro and thereby a source of NMPs. In conclusion, differentiation of EpiSCs into NMPs reproduces events in vivo and suggests a sequence of events for the emergence of the NMP population.

A single-cell transcriptional roadmap for cardiopharyngeal fate diversification

In vertebrates, multipotent progenitors located in the pharyngeal mesoderm form cardiomyocytes and branchiomeric head muscles, but the dynamic gene expression programmes and mechanisms underlying cardiopharyngeal multipotency and heart versus head muscle fate choices remain elusive. Here, we used single-cell genomics in the simple chordate model Ciona to reconstruct developmental trajectories forming first and second heart lineages and pharyngeal muscle precursors and characterize the molecular underpinnings of cardiopharyngeal fate choices. We show that FGF-MAPK signalling maintains multipotency and promotes the pharyngeal muscle fate, whereas signal termination permits the deployment of a pan-cardiac programme, shared by the first and second heart lineages, to define heart identity. In the second heart lineage, a Tbx1/10-Dach pathway actively suppresses the first heart lineage programme, conditioning later cell diversity in the beating heart. Finally, cross-species comparisons between Ciona and the mouse evoke the deep evolutionary origins of cardiopharyngeal networks in chordates.

A single-cell transcriptomic atlas of the developing chicken limb

BACKGROUND

Through precise implementation of distinct cell type specification programs, differentially regulated in both space and time, complex patterns emerge during organogenesis. Thanks to its easy experimental accessibility, the developing chicken limb has long served as a paradigm to study vertebrate pattern formation. Through decades' worth of research, we now have a firm grasp on the molecular mechanisms driving limb formation at the tissue-level. However, to elucidate the dynamic interplay between transcriptional cell type specification programs and pattern formation at its relevant cellular scale, we lack appropriately resolved molecular data at the genome-wide level. Here, making use of droplet-based single-cell RNA-sequencing, we catalogue the developmental emergence of distinct tissue types and their transcriptome dynamics in the distal chicken limb, the so-called autopod, at cellular resolution.

RESULTS

Using single-cell RNA-sequencing technology, we sequenced a total of 17,628 cells coming from three key developmental stages of chicken autopod patterning. Overall, we identified 23 cell populations with distinct transcriptional profiles. Amongst them were small, albeit essential populations like the apical ectodermal ridge, demonstrating the ability to detect even rare cell types. Moreover, we uncovered the existence of molecularly distinct sub-populations within previously defined compartments of the developing limb, some of which have important signaling functions during autopod pattern formation. Finally, we inferred gene co-expression modules that coincide with distinct tissue types across developmental time, and used them to track patterning-relevant cell populations of the forming digits.

CONCLUSIONS

We provide a comprehensive functional genomics resource to study the molecular effectors of chicken limb patterning at cellular resolution. Our single-cell transcriptomic atlas captures all major cell populations of the developing autopod, and highlights the transcriptional complexity in many of its components. Finally, integrating our data-set with other single-cell transcriptomics resources will enable researchers to assess molecular similarities in orthologous cell types across the major tetrapod clades, and provide an extensive candidate gene list to functionally test cell-type-specific drivers of limb morphological diversification.

Genome-scale screens identify JNK-JUN signaling as a barrier for pluripotency exit and endoderm differentiation

Human embryonic and induced pluripotent stem cells (hESCs/hiPSCs) hold great promise for cell-based therapies and drug discovery. However, homogeneous differentiation remains a major challenge, highlighting the need for understanding developmental mechanisms. We performed genome-scale CRISPR screens to uncover regulators of definitive endoderm (DE) differentiation, which unexpectedly uncovered five JNK/JUN family genes as key barriers of DE differentiation. The JNK/JUN pathway does not act through directly inhibiting the DE enhancers. Instead JUN co-occupies ESC enhancers with OCT4, NANOG and SMAD2/3, and specifically inhibits the exit from the pluripotent state by impeding the decommissioning of ESC enhancers and inhibiting the reconfiguration of SMAD2/3 chromatin binding from ESC to DE enhancers. Therefore, the JNK/JUN pathway safeguards pluripotency from precocious DE differentiation. Direct pharmacological inhibition of JNK significantly improves the efficiencies of generating DE and DE-derived pancreatic and lung progenitor cells, highlighting the potential of harnessing the knowledge from developmental studies for regenerative medicine.

The Hidden Memory of Differentiating Cells

An integrated approach unifies experimental observations and mathematical modeling to represent differentiation dynamics as discrete transition events underpinned by stochastic transitions between hidden states.

Stochasticity, individuality and behavior

No two individuals are exactly alike. More than a simple platitude, this observation reflects the fundamentally stochastic nature of biological systems. The term 'stochastic' describes features that cannot be predicted a priori from readily measurable variables. In the dichotomous framework in which biological variation arises from genetic or environmental effects, stochastic effects are classified as environmental because they are not passed on to offspring - any non-heritable cause is, by definition, environmental. But non-heritable effects can be subdivided into those which can be predicted from measurable variables, and those that cannot. These latter effects are stochastic.

WASABI: a dynamic iterative framework for gene regulatory network inference

BACKGROUND

Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations.

RESULTS

In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus.

CONCLUSIONS

Together, these results demonstrate WASABI versatility and ability to tackle some general gene regulatory networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data.

Single-cell transcriptome profiling of the Ciona larval brain

The tadpole-type larva of Ciona has emerged as an intriguing model system for the study of neurodevelopment. The Ciona intestinalis connectome has been recently mapped, revealing the smallest central nervous system (CNS) known in any chordate, with only 177 neurons. This minimal CNS is highly reminiscent of larger CNS of vertebrates, sharing many conserved developmental processes, anatomical compartments, neuron subtypes, and even specific neural circuits. Thus, the Ciona tadpole offers a unique opportunity to understand the development and wiring of a chordate CNS at single-cell resolution. Here we report the use of single-cell RNAseq to profile the transcriptomes of single cells isolated by fluorescence-activated cell sorting (FACS) from the whole brain of Ciona robusta (formerly intestinalis Type A) larvae. We have also compared these profiles to bulk RNAseq data from specific subsets of brain cells isolated by FACS using cell type-specific reporter plasmid expression. Taken together, these datasets have begun to reveal the compartment- and cell-specific gene expression patterns that define the organization of the Ciona larval brain.

Transition state dynamics during a stochastic fate choice

The generation of multiple fates from a uniform cell population via self-organisation is a recurring feature in development and regeneration. However, for most self-organising systems, we have little understanding of the processes that allow cells to become different. One of the clearest examples of developmental self-organisation is shown by Dictyostelium, with cells segregating into two major fates, stalk and spore, within multicellular aggregates. To characterise the gene expression decisions that underlie this cell fate bifurcation, we carried out single cell transcriptomics on Dictyostelium aggregates. Our data show the transition of progenitors into prespore and prestalk cells occurs via distinct developmental intermediates. Few cells were captured switching between states, with minimal overlap in fate marker expression between cell types, suggesting states are discrete and transitions rapid. Surprisingly, fate-specific transcript dynamics were a small proportion of overall gene expression changes, with transcript divergence coinciding precisely with large-scale remodelling of the transcriptome shared by prestalk and prespore cells. These observations suggest the stepwise separation of cell identity is temporally coupled to global expression transitions common to both fates.

Inference of Developmental Gene Regulatory Networks Beyond Classical Model Systems: New Approaches in the Post-genomic Era

The advent of high-throughput sequencing (HTS) technologies has revolutionized the way we understand the transformation of genetic information into morphological traits. Elucidating the network of interactions between genes that govern cell differentiation through development is one of the core challenges in genome research. These networks are known as developmental gene regulatory networks (dGRNs) and consist largely of the functional linkage between developmental control genes, cis-regulatory modules, and differentiation genes, which generate spatially and temporally refined patterns of gene expression. Over the last 20 years, great advances have been made in determining these gene interactions mainly in classical model systems, including human, mouse, sea urchin, fruit fly, and worm. This has brought about a radical transformation in the fields of developmental biology and evolutionary biology, allowing the generation of high-resolution gene regulatory maps to analyze cell differentiation during animal development. Such maps have enabled the identification of gene regulatory circuits and have led to the development of network inference methods that can recapitulate the differentiation of specific cell-types or developmental stages. In contrast, dGRN research in non-classical model systems has been limited to the identification of developmental control genes via the candidate gene approach and the characterization of their spatiotemporal expression patterns, as well as to the discovery of cis-regulatory modules via patterns of sequence conservation and/or predicted transcription-factor binding sites. However, thanks to the continuous advances in HTS technologies, this scenario is rapidly changing. Here, we give a historical overview on the architecture and elucidation of the dGRNs. Subsequently, we summarize the approaches available to unravel these regulatory networks, highlighting the vast range of possibilities of integrating multiple technical advances and theoretical approaches to expand our understanding on the global gene regulation during animal development in non-classical model systems. Such new knowledge will not only lead to greater insights into the evolution of molecular mechanisms underlying cell identity and animal body plans, but also into the evolution of morphological key innovations in animals.

A time-resolved proteomic analysis of transcription factors regulating adipogenesis of human adipose derived stem cells

Adipogenesis is one of the key processes during obesity development. Better understanding of this process could advance our knowledge on obesity and its treatment. Transcription factors (TFs) are master regulators during adipogenesis, however, a system-wide analysis of TFs dynamic proteome during adipogenesis is lacking. Here, we profiled 472 TFs and systematically elucidated their roles during the first 7 days of adipogenesis of human adipose-derived stem cells (hADSCs) on proteome scale. We identified two main and four sub-phases during adipogenesis. The commitment phase (0 h-8 h) mainly mediated stem cell proliferation, differentiation and chromatin remodeling, in which proteins of SWI/SNF family are the key centroid nodes. The determination phase (1D-7D) predominately regulated fat cell differentiation and response to lipid and oxygen, which could be associated with terminal differentiation of adipocyte and responsible for maturation. PPARγ, CREB1 and MYC are the centroid nodes of this phase. Remarkably, we identified and verified three TFs (BATF3, MAFF and MXD4) as novel regulators of adipogenesis, whose over-expression could inhibit adipogenesis of hADSCs in vitro. Overall, our study provided a valuable TFs resource to understand the complex process of adipogenesis.

[Dissecting early development of the kidney by single cell transcriptomics]

Each of the billions of the cells in our body exhibits their identity with unique gene expression profile. Recent advances in single cell transcriptomics enable to conduct cell taxonomy identifying new cell types and to re-arrange cells in order of pseudo-time course describing differentiation status of each cell. Even though the cost is still high, the single cell transcriptomics now becomes one of the conventional assays. We have applied the single cell gene expression analysis to dissect human development. In this article, we show our recent progress on a study describing early development of the kidney using human iPS cells by the single cell transcriptomics.

Starvation and Pseudo-Starvation as Drivers of Cancer Metastasis through Translation Reprogramming

Considerable progress has been made in identifying microenvironmental signals that effect the reversible phenotypic transitions underpinning the early steps in the metastatic cascade. However, although the general principles underlying metastatic dissemination have been broadly outlined, a common theme that unifies many of the triggers of invasive behavior in tumors has yet to emerge. Here we discuss how many diverse signals that induce invasion converge on the reprogramming of protein translation via phosphorylation of eIF2α, a hallmark of the starvation response. These include starvation as a consequence of nutrient or oxygen limitation, or pseudo-starvation imposed by cell-extrinsic microenvironmental signals or by cell-intrinsic events, including oncogene activation. Since in response to resource limitation single-cell organisms undergo phenotypic transitions remarkably similar to those observed within tumors, we propose that a starvation/pseudo-starvation model to explain cancer progression provides an integrated and evolutionarily conserved conceptual framework to understand the progression of this complex disease.

Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Integrative analysis of multi-omics layers at single cell level is critical for accurate dissection of cell-to-cell variation within certain cell populations. Here we report scCAT-seq, a technique for simultaneously assaying chromatin accessibility and the transcriptome within the same single cell. We show that the combined single cell signatures enable accurate construction of regulatory relationships between cis-regulatory elements and the target genes at single-cell resolution, providing a new dimension of features that helps direct discovery of regulatory patterns specific to distinct cell identities. Moreover, we generate the first single cell integrated map of chromatin accessibility and transcriptome in early embryos and demonstrate the robustness of scCAT-seq in the precise dissection of master transcription factors in cells of distinct states. The ability to obtain these two layers of omics data will help provide more accurate definitions of “single cell state” and enable the deconvolution of regulatory heterogeneity from complex cell populations.

Heterogeneity in gene expression and epigenetic states exists across individual cells. Here, the authors develop scCAT-seq, a technique for simultaneously performing ATAC-seq and RNA-seq within the same single cell.

Parametric and non-parametric gradient matching for network inference: a comparison

Background

Reverse engineering of gene regulatory networks from time series gene-expression data is a challenging problem, not only because of the vast sets of candidate interactions but also due to the stochastic nature of gene expression. We limit our analysis to nonlinear differential equation based inference methods. In order to avoid the computational cost of large-scale simulations, a two-step Gaussian process interpolation based gradient matching approach has been proposed to solve differential equations approximately.

Results

We apply a gradient matching inference approach to a large number of candidate models, including parametric differential equations or their corresponding non-parametric representations, we evaluate the network inference performance under various settings for different inference objectives. We use model averaging, based on the Bayesian Information Criterion (BIC), to combine the different inferences. The performance of different inference approaches is evaluated using area under the precision-recall curves.

Conclusions

We found that parametric methods can provide comparable, and often improved inference compared to non-parametric methods; the latter, however, require no kinetic information and are computationally more efficient.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2590-7) contains supplementary material, which is available to authorized users.

Quantitative Modelling of the Waddington Epigenetic Landscape

C.H. Waddington introduced the epigenetic landscape as a metaphor to represent cellular decision-making during development. Like a population of balls rolling down a rough hillside, developing cells follow specific trajectories (valleys) and eventually come to rest in one or another low-energy state that represents a mature cell type. Waddington depicted the topography of this landscape as determined by interactions among gene products, thereby connecting genotype to phenotype. In modern terms, each point on the landscape represents a state of the underlying genetic regulatory network, which in turn is described by a gene expression profile. In this chapter we demonstrate how the mathematical formalism of Hopfield networks can be used to model this epigenetic landscape. Hopfield networks are auto-associative artificial neural networks; input patterns are stored as attractors of the network and can be recalled from noisy or incomplete inputs. The resulting models capture the temporal dynamics of a gene regulatory network, yielding quantitative insight into cellular development and phenotype.

Control of neural crest multipotency by Wnt signaling and the Lin28/let-7 axis

A crucial step in cell differentiation is the silencing of developmental programs underlying multipotency. While much is known about how lineage-specific genes are activated to generate distinct cell types, the mechanisms driving suppression of stemness are far less understood. To address this, we examined the regulation of the transcriptional network that maintains progenitor identity in avian neural crest cells. Our results show that a regulatory circuit formed by Wnt, Lin28a and let-7 miRNAs controls the deployment and the subsequent silencing of the multipotency program in a position-dependent manner. Transition from multipotency to differentiation is determined by the topological relationship between the migratory cells and the dorsal neural tube, which acts as a Wnt-producing stem cell niche. Our findings highlight a mechanism that rapidly silences complex regulatory programs, and elucidate how transcriptional networks respond to positional information during cell differentiation.

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.

Mitochondrial Metabolism in Major Neurological Diseases

Mitochondria are bilayer sub-cellular organelles that are an integral part of normal cellular physiology. They are responsible for producing the majority of a cell's ATP, thus supplying energy for a variety of key cellular processes, especially in the brain. Although energy production is a key aspect of mitochondrial metabolism, its role extends far beyond energy production to cell signaling and epigenetic regulation⁻functions that contribute to cellular proliferation, differentiation, apoptosis, migration, and autophagy. Recent research on neurological disorders suggest a major metabolic component in disease pathophysiology, and mitochondria have been shown to be in the center of metabolic dysregulation and possibly disease manifestation. This review will discuss the basic functions of mitochondria and how alterations in mitochondrial activity lead to neurological disease progression.