Transition states and cell fate decisions in epigenetic landscapes

Metabolic and Transcriptional Changes across Osteogenic Differentiation of Mesenchymal Stromal Cells

Mesenchymal stromal cells (MSCs) are multipotent post-natal stem cells with applications in tissue engineering and regenerative medicine. MSCs can differentiate into osteoblasts, chondrocytes, or adipocytes, with functional differences in cells during osteogenesis accompanied by metabolic changes. The temporal dynamics of these metabolic shifts have not yet been fully characterized and are suspected to be important for therapeutic applications such as osteogenesis optimization. Here, our goal was to characterize the metabolic shifts that occur during osteogenesis. We profiled five key extracellular metabolites longitudinally (glucose, lactate, glutamine, glutamate, and ammonia) from MSCs from four donors to classify osteogenic differentiation into three metabolic stages, defined by changes in the uptake and secretion rates of the metabolites in cell culture media. We used a combination of untargeted metabolomic analysis, targeted analysis of ¹³C-glucose labelled intracellular data, and RNA-sequencing data to reconstruct a gene regulatory network and further characterize cellular metabolism. The metabolic stages identified in this proof-of-concept study provide a framework for more detailed investigations aimed at identifying biomarkers of osteogenic differentiation and small molecule interventions to optimize MSC differentiation for clinical applications.

Transcriptome Dynamics of Epidermal Reprogramming during Direct Shoot Regeneration in Torenia fournieri

Shoot regeneration involves reprogramming of somatic cells and de novo organization of shoot apical meristems (SAMs). In the best-studied model system of shoot regeneration using Arabidopsis, regeneration is mediated by the auxin-responsive pluripotent callus formation from pericycle or pericycle-like tissues according to the lateral root development pathway. In contrast, shoot regeneration can be induced directly from fully differentiated epidermal cells of stem explants of Torenia fournieri (Torenia), without intervening the callus mass formation in culture with cytokinin; yet, its molecular mechanisms remain unaddressed. Here, we characterized this direct shoot regeneration by cytological observation and transcriptome analyses. The results showed that the gene expression profile rapidly changes upon culture to acquire a mixed signature of multiple organs/tissues, possibly associated with epidermal reprogramming. Comparison of transcriptomes between three different callus-inducing cultures (callus induction by auxin, callus induction by wounding and protoplast culture) of Arabidopsis and the Torenia stem culture identified genes upregulated in all the four culture systems as candidates of common factors of cell reprogramming. These initial changes proceeded independently of cytokinin, followed by cytokinin-dependent, transcriptional activations of nucleolar development and cell cycle. Later, SAM regulatory genes became highly expressed, leading to SAM organization in the foci of proliferating cells in the epidermal layer. Our findings revealed three distinct phases with different transcriptomic and regulatory features during direct shoot regeneration from the epidermis in Torenia, which provides a basis for further investigation of shoot regeneration in this unique culture system.

Insights into Intra-Tumoral Heterogeneity: Transcriptional Profiling of Chemoresistant MPM Cell Subpopulations Reveals Involvement of NFkB and DNA Repair Pathways and Contributes a Prognostic Signature

Chemoresistance is a hallmark of malignant pleural mesothelioma (MPM) management and the expression of ALDH1A3 is responsible for the survival and activity of MPM chemoresistant cell subpopulations (ALDH^bright cells). We enriched mesothelioma ALDH^bright cells to near homogeneity by FACS sorting and an Aldefluor assay and performed unbiased Affymetrix gene expression profiling. Viability and ELISA assays were used to rule out significant apoptosis in the sorted cell subpopulations and to assess target engagement by butein. Statistical analysis of the results, pathway enrichment and promoter enrichment were employed for the generation of the data. Q-RTPCR was used to validate a subset of the identified, modulated mRNAs In this work, we started from the observation that the mRNA levels of the ALDH1A3 isoform could prognostically stratify MPM patients. Thus, we purified MPM ALDH^bright cells from NCI-H2595 cells and interrogated their gene expression (GES) profile. We analyzed the GES of the purified cells at both a steady state and upon treatment with butein (a multifunctional tetrahydroxy-chalcone), which abates the ALDH^bright cell number, thereby exerting chemo-sensitizing effects in vitro and in vivo. We identified 924 genes modulated in a statistically significant manner as a function of ALDH status and of the response to the inhibitor. Pathway and promoter enrichment identified the molecular determinant of high ALDH status and how butein treatment altered the molecular portrait of those chemoresistant cell subpopulations. Further, we unraveled an eighteen-gene signature with high prognostic significance for MPM patients, and showed that most of the identified prognostic contributors escaped the analysis of unfractionated samples. This work proves that digging into the unexplored field of intra-tumor heterogeneity (ITH) by working at the cell subpopulation level may provide findings of prognostic relevance, in addition to mechanistic insights into tumor resistance to therapy.

Reduction of a stochastic model of gene expression: Lagrangian dynamics gives access to basins of attraction as cell types and metastabilty

Differentiation is the process whereby a cell acquires a specific phenotype, by differential gene expression as a function of time. This is thought to result from the dynamical functioning of an underlying Gene Regulatory Network (GRN). The precise path from the stochastic GRN behavior to the resulting cell state is still an open question. In this work we propose to reduce a stochastic model of gene expression, where a cell is represented by a vector in a continuous space of gene expression, to a discrete coarse-grained model on a limited number of cell types. We develop analytical results and numerical tools to perform this reduction for a specific model characterizing the evolution of a cell by a system of piecewise deterministic Markov processes (PDMP). Solving a spectral problem, we find the explicit variational form of the rate function associated to a large deviations principle, for any number of genes. The resulting Lagrangian dynamics allows us to define a deterministic limit of which the basins of attraction can be identified to cellular types. In this context the quasipotential, describing the transitions between these basins in the weak noise limit, can be defined as the unique solution of an Hamilton-Jacobi equation under a particular constraint. We develop a numerical method for approximating the coarse-grained model parameters, and show its accuracy for a symmetric toggle-switch network. We deduce from the reduced model an approximation of the stationary distribution of the PDMP system, which appears as a Beta mixture. Altogether those results establish a rigorous frame for connecting GRN behavior to the resulting cellular behavior, including the calculation of the probability of jumps between cell types.

Single-cell multi-omics sequencing: application trends, COVID-19, data analysis issues and prospects

Single-cell sequencing is a biotechnology to sequence one layer of genomic information for individual cells in a tissue sample. For example, single-cell DNA sequencing is to sequence the DNA from every single cell. Increasing in complexity, single-cell multi-omics sequencing, or single-cell multimodal omics sequencing, is to profile in parallel multiple layers of omics information from a single cell. In practice, single-cell multi-omics sequencing actually detects multiple traits such as DNA, RNA, methylation information and/or protein profiles from the same cell for many individuals in a tissue sample. Multi-omics sequencing has been widely applied to systematically unravel interplay mechanisms of key components and pathways in cell. This survey overviews recent developments in single-cell multi-omics sequencing, and their applications to understand complex diseases in particular the COVID-19 pandemic. We also summarize machine learning and bioinformatics techniques used in the analysis of the intercorrelated multilayer heterogeneous data. We observed that variational inference and graph-based learning are popular approaches, and Seurat V3 is a commonly used tool to transfer the missing variables and labels. We also discussed two intensively studied issues relating to data consistency and diversity and commented on currently cared issues surrounding the error correction of data pairs and data imputation methods. The survey is concluded with some open questions and opportunities for this extraordinary field.

Disruption of a GATA2-TAL1-ERG regulatory circuit promotes erythroid transition in healthy and leukemic stem cells

Changes in gene regulation and expression govern orderly transitions from hematopoietic stem cells to terminally differentiated blood cell types. These transitions are disrupted during leukemic transformation, but knowledge of the gene regulatory changes underpinning this process is elusive. We hypothesized that identifying core gene regulatory networks in healthy hematopoietic and leukemic cells could provide insights into network alterations that perturb cell state transitions. A heptad of transcription factors (LYL1, TAL1, LMO2, FLI1, ERG, GATA2, and RUNX1) bind key hematopoietic genes in human CD34+ hematopoietic stem and progenitor cells (HSPCs) and have prognostic significance in acute myeloid leukemia (AML). These factors also form a densely interconnected circuit by binding combinatorially at their own, and each other's, regulatory elements. However, their mutual regulation during normal hematopoiesis and in AML cells, and how perturbation of their expression levels influences cell fate decisions remains unclear. In this study, we integrated bulk and single-cell data and found that the fully connected heptad circuit identified in healthy HSPCs persists, with only minor alterations in AML, and that chromatin accessibility at key heptad regulatory elements was predictive of cell identity in both healthy progenitors and leukemic cells. The heptad factors GATA2, TAL1, and ERG formed an integrated subcircuit that regulates stem cell-to-erythroid transition in both healthy and leukemic cells. Components of this triad could be manipulated to facilitate erythroid transition providing a proof of concept that such regulatory circuits can be harnessed to promote specific cell-type transitions and overcome dysregulated hematopoiesis.

Epigenome-Metabolome-Epigenome signaling cascade in cell biological processes

Cell fate determination as a fundamental question in cell biology has been extensively studied at different regulatory levels for many years. However, the mechanisms of multi-level regulation of cell fate determination remain unclear. Recently we have proposed an Epigenome-Metabolome-Epigenome (E-M-E) signaling cascade model to describe the crossover cooperation during mouse somatic cell reprogramming. In this review, we summarize the broad roles of E-M-E signaling cascade in different cell biological processes including cell differentiation and dedifferentiation, cell specialization, cell proliferation and cell pathological processes. Precise E-M-E signaling cascades are critical in these cell biological processes, and it is of worth to explore each step of E-M-E signaling cascade. E-M-E signaling cascade model sheds light on and may open a window to explore the mechanisms of multi-level regulation of cell biological processes.

A pan-metazoan concept for adult stem cells: the wobbling Penrose landscape

Adult stem cells (ASCs) in vertebrates and model invertebrates (e.g. Drosophila melanogaster) are typically long‐lived, lineage‐restricted, clonogenic and quiescent cells with somatic descendants and tissue/organ‐restricted activities. Such ASCs are mostly rare, morphologically undifferentiated, and undergo asymmetric cell division. Characterized by ‘stemness’ gene expression, they can regulate tissue/organ homeostasis, repair and regeneration. By contrast, analysis of other animal phyla shows that ASCs emerge at different life stages, present both differentiated and undifferentiated phenotypes, and may possess amoeboid movement. Usually pluri/totipotent, they may express germ‐cell markers, but often lack germ‐line sequestering, and typically do not reside in discrete niches. ASCs may constitute up to 40% of animal cells, and participate in a range of biological phenomena, from whole‐body regeneration, dormancy, and agametic asexual reproduction, to indeterminate growth. They are considered legitimate units of selection. Conceptualizing this divergence, we present an alternative stemness metaphor to the Waddington landscape: the ‘wobbling Penrose’ landscape. Here, totipotent ASCs adopt ascending/descending courses of an ‘Escherian stairwell’, in a lifelong totipotency pathway. ASCs may also travel along lower stemness echelons to reach fully differentiated states. However, from any starting state, cells can change their stemness status, underscoring their dynamic cellular potencies. Thus, vertebrate ASCs may reflect just one metazoan ASC archetype.

Noise distorts the epigenetic landscape and shapes cell-fate decisions

The Waddington epigenetic landscape has become an iconic representation of the cellular differentiation process. Recent single-cell transcriptomic data provide new opportunities for quantifying this originally conceptual tool, offering insight into the gene regulatory networks underlying cellular development. While many methods for constructing the landscape have been proposed, by far the most commonly employed approach is based on computing the landscape as the negative logarithm of the steady-state probability distribution. Here, we use simple models to highlight the complexities and limitations that arise when reconstructing the potential landscape in the presence of stochastic fluctuations. We consider how the landscape changes in accordance with different stochastic systems and show that it is the subtle interplay between the deterministic and stochastic components of the system that ultimately shapes the landscape. We further discuss how the presence of noise has important implications for the identifiability of the regulatory dynamics from experimental data. A record of this paper's transparent peer review process is included in the supplemental information.

Epigenetic dynamics shaping melanophore and iridophore cell fate in zebrafish

BACKGROUND

Zebrafish pigment cell differentiation provides an attractive model for studying cell fate progression as a neural crest progenitor engenders diverse cell types, including two morphologically distinct pigment cells: black melanophores and reflective iridophores. Nontrivial classical genetic and transcriptomic approaches have revealed essential molecular mechanisms and gene regulatory circuits that drive neural crest-derived cell fate decisions. However, how the epigenetic landscape contributes to pigment cell differentiation, especially in the context of iridophore cell fate, is poorly understood.

RESULTS

We chart the global changes in the epigenetic landscape, including DNA methylation and chromatin accessibility, during neural crest differentiation into melanophores and iridophores to identify epigenetic determinants shaping cell type-specific gene expression. Motif enrichment in the epigenetically dynamic regions reveals putative transcription factors that might be responsible for driving pigment cell identity. Through this effort, in the relatively uncharacterized iridophores, we validate alx4a as a necessary and sufficient transcription factor for iridophore differentiation and present evidence on alx4a's potential regulatory role in guanine synthesis pathway.

CONCLUSIONS

Pigment cell fate is marked by substantial DNA demethylation events coupled with dynamic chromatin accessibility to potentiate gene regulation through cis-regulatory control. Here, we provide a multi-omic resource for neural crest differentiation into melanophores and iridophores. This work led to the discovery and validation of iridophore-specific alx4a transcription factor.

Identification, visualization, statistical analysis and mathematical modeling of high-feedback loops in gene regulatory networks

Background

Feedback loops in gene regulatory networks play pivotal roles in governing functional dynamics of cells. Systems approaches demonstrated characteristic dynamical features, including multistability and oscillation, of positive and negative feedback loops. Recent experiments and theories have implicated highly interconnected feedback loops (high-feedback loops) in additional nonintuitive functions, such as controlling cell differentiation rate and multistep cell lineage progression. However, it remains challenging to identify and visualize high-feedback loops in complex gene regulatory networks due to the myriad of ways in which the loops can be combined. Furthermore, it is unclear whether the high-feedback loop structures with these potential functions are widespread in biological systems. Finally, it remains challenging to understand diverse dynamical features, such as high-order multistability and oscillation, generated by individual networks containing high-feedback loops. To address these problems, we developed HiLoop, a toolkit that enables discovery, visualization, and analysis of several types of high-feedback loops in large biological networks.

Results

HiLoop not only extracts high-feedback structures and visualize them in intuitive ways, but also quantifies the enrichment of overrepresented structures. Through random parameterization of mathematical models derived from target networks, HiLoop presents characteristic features of the underlying systems, including complex multistability and oscillations, in a unifying framework. Using HiLoop, we were able to analyze realistic gene regulatory networks containing dozens to hundreds of genes, and to identify many small high-feedback systems. We found more than a 100 human transcription factors involved in high-feedback loops that were not studied previously. In addition, HiLoop enabled the discovery of an enrichment of high feedback in pathways related to epithelial-mesenchymal transition.

Conclusions

HiLoop makes the study of complex networks accessible without significant computational demands. It can serve as a hypothesis generator through identification and modeling of high-feedback subnetworks, or as a quantification method for motif enrichment analysis. As an example of discovery, we found that multistep cell lineage progression may be driven by either specific instances of high-feedback loops with sparse appearances, or generally enriched topologies in gene regulatory networks. We expect HiLoop’s usefulness to increase as experimental data of regulatory networks accumulate. Code is freely available for use or extension at https://github.com/BenNordick/HiLoop.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04405-z.

Cellular origins and lineage relationships of the intestinal epithelium

Knowledge of the development and hierarchical organization of tissues is key to understanding how they are perturbed in injury and disease, as well as how they may be therapeutically manipulated to restore homeostasis. The rapidly regenerating intestinal epithelium harbors diverse cell types and their lineage relationships have been studied using numerous approaches, from classical label-retaining and genetic lineage tracing methods to novel transcriptome-based annotations. Here, we describe the developmental trajectories that dictate differentiation and lineage specification in the intestinal epithelium. We focus on the most recent single-cell RNA-sequencing (scRNA-seq)-based strategies for understanding intestinal epithelial cell lineage relationships, underscoring how they have refined our view of the development of this tissue and highlighting their advantages and limitations. We emphasize how these technologies have been applied to understand the dynamics of intestinal epithelial cells in homeostatic and injury-induced regeneration models.

Dissecting transition cells from single-cell transcriptome data through multiscale stochastic dynamics

Advances in single-cell technologies allow scrutinizing of heterogeneous cell states, however, detecting cell-state transitions from snap-shot single-cell transcriptome data remains challenging. To investigate cells with transient properties or mixed identities, we present MuTrans, a method based on multiscale reduction technique to identify the underlying stochastic dynamics that prescribes cell-fate transitions. By iteratively unifying transition dynamics across multiple scales, MuTrans constructs the cell-fate dynamical manifold that depicts progression of cell-state transitions, and distinguishes stable and transition cells. In addition, MuTrans quantifies the likelihood of all possible transition trajectories between cell states using coarse-grained transition path theory. Downstream analysis identifies distinct genes that mark the transient states or drive the transitions. The method is consistent with the well-established Langevin equation and transition rate theory. Applying MuTrans to datasets collected from five different single-cell experimental platforms, we show its capability and scalability to robustly unravel complex cell fate dynamics induced by transition cells in systems such as tumor EMT, iPSC differentiation and blood cell differentiation. Overall, our method bridges data-driven and model-based approaches on cell-fate transitions at single-cell resolution.

Application of Thermodynamics and Protein–Protein Interaction Network Topology for Discovery of Potential New Treatments for Temporal Lobe Epilepsy

In this paper, we propose a bioinformatics-based method, which introduces thermodynamic measures and topological characteristics aimed to identify potential drug targets for pharmaco-resistant epileptic patients. We apply the Gibbs homology analysis to the protein–protein interaction network characteristic of temporal lobe epilepsy. With the identification of key proteins involved in the disease, particularly a number of ribosomal proteins, an assessment of their inhibitors is the next logical step. The results of our work offer a direction for future development of prospective therapeutic solutions for epilepsy patients, especially those who are not responding to the current standard of care.

Single-cell analysis of cell fate bifurcation in the chordate Ciona

BACKGROUND

Inductive signaling interactions between different cell types are a major mechanism for the further diversification of embryonic cell fates. Most blastomeres in the model chordate Ciona robusta become restricted to a single predominant fate between the 64-cell and mid-gastrula stages. The deeply stereotyped and well-characterized Ciona embryonic cell lineages allow the transcriptomic analysis of newly established cell types very early in their divergence from sibling cell states without the pseudotime inference needed in the analysis of less synchronized cell populations. This is the first ascidian study to use droplet scRNAseq with large numbers of analyzed cells as early as the 64-cell stage when major lineages such as primary notochord first become fate restricted.

RESULTS AND CONCLUSIONS

We identify 59 distinct cell states, including new subregions of the b-line neural lineage and the early induction of the tail tip epidermis. We find that 34 of these cell states are directly or indirectly dependent on MAPK-mediated signaling critical to early Ciona patterning. Most of the MAPK-dependent bifurcations are canalized with the signal-induced cell fate lost upon MAPK inhibition, but the posterior endoderm is unique in being transformed into a novel state expressing some but not all markers of both endoderm and muscle. Divergent gene expression between newly bifurcated sibling cell types is dominated by upregulation in the induced cell type. The Ets family transcription factor Elk1/3/4 is uniquely upregulated in nearly all the putatively direct inductions. Elk1/3/4 upregulation together with Ets transcription factor binding site enrichment analysis enables inferences about which bifurcations are directly versus indirectly controlled by MAPK signaling. We examine notochord induction in detail and find that the transition between a Zic/Ets-mediated regulatory state and a Brachyury/FoxA-mediated regulatory state is unexpectedly late. This supports a "broad-hourglass" model of cell fate specification in which many early tissue-specific genes are induced in parallel to key tissue-specific transcriptional regulators via the same set of transcriptional inputs.

Epigenetic instability may alter cell state transitions and anticancer drug resistance

Drug resistance is a significant obstacle to successful and durable anti-cancer therapy. Targeted therapy is often effective during early phases of treatment; however, eventually cancer cells adapt and transition to drug-resistant cells states rendering the treatment ineffective. It is proposed that cell state can be a determinant of drug efficacy and manipulated to affect the development of anticancer drug resistance. In this work, we developed two stochastic cell state models and an integrated stochastic-deterministic model referenced to brain tumors. The stochastic cell state models included transcriptionally-permissive and -restrictive states based on the underlying hypothesis that epigenetic instability mitigates lock-in of drug-resistant states. When moderate epigenetic instability was implemented the drug-resistant cell populations were reduced, on average, by 60%, whereas a high level of epigenetic disruption reduced them by about 90%. The stochastic-deterministic model utilized the stochastic cell state model to drive the dynamics of the DNA repair enzyme, methylguanine-methyltransferase (MGMT), that repairs temozolomide (TMZ)-induced O6-methylguanine (O6mG) adducts. In the presence of epigenetic instability, the production of MGMT decreased that coincided with an increase of O6mG adducts following a multiple-dose regimen of TMZ. Generation of epigenetic instability via epigenetic modifier therapy could be a viable strategy to mitigate anticancer drug resistance.

Modeling the effects of EMT-immune dynamics on carcinoma disease progression

During progression from carcinoma in situ to an invasive tumor, the immune system is engaged in complex sets of interactions with various tumor cells. Tumor cell plasticity alters disease trajectories via epithelial-to-mesenchymal transition (EMT). Several of the same pathways that regulate EMT are involved in tumor-immune interactions, yet little is known about the mechanisms and consequences of crosstalk between these regulatory processes. Here we introduce a multiscale evolutionary model to describe tumor-immune-EMT interactions and their impact on epithelial cancer progression from in situ to invasive disease. Through simulation of patient cohorts in silico, the model predicts that a controllable region maximizes invasion-free survival. This controllable region depends on properties of the mesenchymal tumor cell phenotype: its growth rate and its immune-evasiveness. In light of the model predictions, we analyze EMT-inflammation-associated data from The Cancer Genome Atlas, and find that association with EMT worsens invasion-free survival probabilities. This result supports the predictions of the model, and leads to the identification of genes that influence outcomes in bladder and uterine cancer, including FGF pathway members. These results suggest new means to delay disease progression, and demonstrate the importance of studying cancer-immune interactions in light of EMT.

Assessment of epigenotoxic profiles of Dongjiang River: A comprehensive of chemical analysis, in vitro bioassay and in silico approach

This research explored the occurrence, epigenetic toxic profiling and main toxic pollutants of POPs in surface water of Dongjiang River, southern China. The concentrations of selected POPs including polycyclic aromatic hydrocarbons (PAHs), endocrine disrupting chemicals (EDCs), phthalate esters (PAEs) and polybrominated diphenyl ethers (PBDEs) of surface water from 18 sites were investigated. ∑₁₆PAHs and ∑₄EDCs were at a moderate level, while ∑₆PAEs and ∑₆PBDEs had low pollution levels. PAHs, EDCs and PAEs showed higher concentrations in dry season than those in wet season, and the loading of selected POPs in tributaries was higher than those in mainstream due to intensive manufactures and lower runoff volume. Moreover, activities of DNA methyltransferase (DNMT)1, histone deacetylase (HDAC2, HDAC8) were confirmed to be sensitive indicators for epigenetic toxicity. The DNMT1-mediated epigenetic equivalency toxicity of organic extracts in Dongjiang River were more serious than those of HDAC2 and HDAC8. Correlation analysis shown binding affinity between POPs and DNMT1, HDAC2 and HDAC8 could be regarded as toxic equivalency factors. Risk assessment suggested that 4-nonylphenol and bisphenol A were the largest contributors to epigenetic risk. This study is the first attempt to quantify epigenetic toxicity and epigenetic risk evaluation of river water.

The cell in the age of the genomic revolution: Cell Regulatory Networks

Over the last few years an intense activity in the areas of advanced microscopy and quantitative cell biology has put the focus on the morphogenetic events that shape embryos. The interest in these processes is taking place against the backdrop of genomic studies, particularly of global patterns of gene expression at the level of single cells, which cannot fully account for the way cells build tissues and organs. Here we discuss the need to integrate the activity of genes with that of cells and propose the need to develop a framework, based on cellular processes and cell interactions, that parallels that which has been created for gene activity in the form of Gene Regulatory Networks (GRNs). We begin to do this by suggesting elements for building Cell Regulatory Networks (CRNs). In the same manner that GRNs create schedules of gene expression that result in the emergence of cell fates over time, CRNs create tissues and organs i.e. space. We also suggest how GRNs and CRNs might interact in the building of embryos through feedback loops involving mechanics and tissue tectonics.

Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics

As the single cell field races to characterize each cell type, state, and behavior, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multi-omics analysis. Merged with mathematical models, these algorithms will be able to forecast future states of biological systems, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps analogous to weather systems. Thus, systems biology for forecasting biological system dynamics from multi-omic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.

From Bench to Keyboard and Back Again: A Brief History of Lambda Phage Modeling

Cellular decision making is the process whereby cells choose one developmental pathway from multiple possible ones, either spontaneously or due to environmental stimuli. Examples in various cell types suggest an almost inexhaustible plethora of underlying molecular mechanisms. In general, cellular decisions rely on the gene regulatory network, which integrates external signals to drive cell fate choice. The search for general principles of such a process benefits from appropriate biological model systems that reveal how and why certain gene regulatory mechanisms drive specific cellular decisions according to ecological context and evolutionary outcomes. In this article, we review the historical and ongoing development of the phage lambda lysis-lysogeny decision as a model system to investigate all aspects of cellular decision making. The unique generality, simplicity, and richness of phage lambda decision making render it a constant source ofmathematical modeling-aided inspiration across all of biology. We discuss the origins and progress of quantitative phage lambda modeling from the 1950s until today, as well as its possible future directions. We provide examples of how modeling enabled methods and theory development, leading to new biological insights by revealing gaps in the theory and pinpointing areas requiring further experimental investigation. Overall, we highlight the utility of theoretical approaches both as predictive tools, to forecast the outcome of novel experiments, and as explanatory tools, to elucidate the natural processes underlying experimental data.

A hydraulic instability drives the cell death decision in the nematode germline

Oocytes are large cells that develop into an embryo upon fertilization1. As interconnected germ cells mature into oocytes, some of them grow-typically at the expense of others that undergo cell death2-4. We present evidence that in the nematode Caenorhabditis elegans, this cell-fate decision is mechanical and related to tissue hydraulics. An analysis of germ cell volumes and material fluxes identifies a hydraulic instability that amplifies volume differences and causes some germ cells to grow and others to shrink, a phenomenon that is related to the two-balloon instability5. Shrinking germ cells are extruded and they die, as we demonstrate by artificially reducing germ cell volumes via thermoviscous pumping6. Our work reveals a hydraulic symmetry-breaking transition central to the decision between life and death in the nematode germline.

On the origins and conceptual frameworks of natural plasticity-Lessons from single-cell models in C. elegans

How flexible are cell identities? This problem has fascinated developmental biologists for several centuries and can be traced back to Abraham Trembley's pioneering manipulations of Hydra to test its regeneration abilities in the 1700s. Since the cell theory in the mid-19th century, developmental biology has been dominated by a single framework in which embryonic cells are committed to specific cell fates, progressively and irreversibly acquiring their differentiated identities. This hierarchical, unidirectional and irreversible view of cell identity has been challenged in the past decades through accumulative evidence that many cell types are more plastic than previously thought, even in intact organisms. The paradigm shift introduced by such plasticity calls into question several other key traditional concepts, such as how to define a differentiated cell or more generally cellular identity, and has brought new concepts, such as distinct cellular states. In this review, we want to contribute to this representation by attempting to clarify the conceptual and theoretical frameworks of cell plasticity and identity. In the context of these new frameworks we describe here an atlas of natural plasticity of cell identity in C. elegans, including our current understanding of the cellular and molecular mechanisms at play. The worm further provides interesting cases at the borderlines of cellular plasticity that highlight the conceptual challenges still ahead. We then discuss a set of future questions and perspectives arising from the studies of natural plasticity in the worm that are shared with other reprogramming and plasticity events across phyla.

Statistical mechanics meets single-cell biology

Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted.

c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network

The rapid advancement of single-cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. However, compared to bulk RNA sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq) suffers from higher noise and lower coverage, which brings new computational difficulties. Based on statistical independence, cell-specific network (CSN) is able to quantify the overall associations between genes for each cell, yet suffering from a problem of overestimation related to indirect effects. To overcome this problem, we propose the c-CSN method, which can construct the conditional cell-specific network (CCSN) for each cell. c-CSN method can measure the direct associations between genes by eliminating the indirect associations. c-CSN can be used for cell clustering and dimension reduction on a network basis of single cells. Intuitively, each CCSN can be viewed as the transformation from less “reliable” gene expression to more “reliable” gene–gene associations in a cell. Based on CCSN, we further design network flow entropy (NFE) to estimate the differentiation potency of a single cell. A number of scRNA-seq datasets were used to demonstrate the advantages of our approach. 1) One direct association network is generated for one cell. 2) Most existing scRNA-seq methods designed for gene expression matrices are also applicable to c-CSN-transformed degree matrices. 3) CCSN-based NFE helps resolving the direction of differentiation trajectories by quantifying the potency of each cell. c-CSN is publicly available at https://github.com/LinLi-0909/c-CSN.

Potential Landscapes, Bifurcations, and Robustness of Tristable Networks

Bistable switches that produce all-or-none responses have been found to regulate a number of natural cellular decision making processes, and subsequently synthetic switches were designed to exploit their potential. However, an increasing number of studies, particularly in the context of cellular differentiation, highlight the existence of a mixed state that can be explained by tristable switches. The criterion for designing robust tristable switches still remains to be understood from the perspective of network topology. To address such a question, we calculated the robustness of several 2- and 3-component network motifs, connected via only two positive feedback loops, in generating tristable signal response curves. By calculating the effective potential landscape and following its modifications with the bifurcation parameter, we constructed one-parameter bifurcation diagrams of these models in a high-throughput manner for a large combinations of parameters. We report here that introduction of a self-activatory positive feedback loop, directly or indirectly, into a mutual inhibition loop leads to generating the most robust tristable response. The high-throughput approach of our method further allowed us to determine the robustness of four types of tristable responses that originate from the relative locations of four bifurcation points. Using the method, we also analyzed the role of additional mutual inhibition loops in stabilizing the mixed state.

Phylodynamics for cell biologists

Multicellular organisms are composed of cells connected by ancestry and descent from progenitor cells. The dynamics of cell birth, death, and inheritance within an organism give rise to the fundamental processes of development, differentiation, and cancer. Technical advances in molecular biology now allow us to study cellular composition, ancestry, and evolution at the resolution of individual cells within an organism or tissue. Here, we take a phylogenetic and phylodynamic approach to single-cell biology. We explain how "tree thinking" is important to the interpretation of the growing body of cell-level data and how ecological null models can benefit statistical hypothesis testing. Experimental progress in cell biology should be accompanied by theoretical developments if we are to exploit fully the dynamical information in single-cell data.

SOX4 maintains the stemness of cancer cells via transcriptionally enhancing HDAC1 revealed by comparative proteomics study

Background

Cancer stem cells (CSCs) are the root of human cancer development and the major cause of treatment failure. Aberrant elevation of SOX4, a member of SOX (SRY-related HMG-box) family transcription factors, has been identified in many types of human cancer and promotes cancer development. However, the role of SOX4 in CSCs, especially at a proteome-wide level, has remained elusive. The aim of this study is to investigate the effect of SOX4 on the stemness of CSCs and reveal the underlying mechanisms by identification of SOX4-induced proteome changes through proteomics study.

Results

Overexpression of SOX4 promotes sphere formation and self-renewal of colorectal cancer cells in vitro and in vivo and elevates the expression levels of CSCs markers. Through iTRAQ-based quantitative proteomics analysis, 215 differentially expressed proteins (128 upregulated, 87 downregulated) in SOX4-overexpressing HCT-116 spheres were identified. The bioinformatic analysis highlighted the importance of HDAC1 as the fundamental roles of its impacted pathways in stem cell maintenance, including Wnt, Notch, cell cycle, and transcriptional misregulation in cancer. The mechanistic study showed that SOX4 directly binds to the promoter of HDAC1, promotes HDAC1 transcription, thereby supporting the stemness of colorectal cancer cells. HDAC1 hallmarks colorectal cancer stem cells and depletion of HDAC1 abolished the stimulatory effect of SOX4. Furthermore, SOX4-HDAC1 axis is conserved in multiple types of cancer.

Conclusions

The results of this study reveal SOX4-induced proteome changes in HCT-116 spheres and demonstrates that transcriptional activation of HDAC1 is the primary mechanism underlying SOX4 maintaining CSCs. This finding suggests that HDAC1 is a potential drug target for eradicating SOX4-driven human CSCs.

Regulatory Dynamics of Cell Differentiation Revealed by True Time Series From Multinucleate Single Cells

Dynamics of cell fate decisions are commonly investigated by inferring temporal sequences of gene expression states by assembling snapshots of individual cells where each cell is measured once. Ordering cells according to minimal differences in expression patterns and assuming that differentiation occurs by a sequence of irreversible steps, yields unidirectional, eventually branching Markov chains with a single source node. In an alternative approach, we used multi-nucleate cells to follow gene expression taking true time series. Assembling state machines, each made from single-cell trajectories, gives a network of highly structured Markov chains of states with different source and sink nodes including cycles, revealing essential information on the dynamics of regulatory events. We argue that the obtained networks depict aspects of the Waddington landscape of cell differentiation and characterize them as reachability graphs that provide the basis for the reconstruction of the underlying gene regulatory network.

Noise and the molecular processes underlying cell fate decision-making

Cell fate decision-making events involve the interplay of many molecular processes, ranging from signal transduction to genetic regulation, as well as a set of molecular and physiological feedback loops. Each aspect offers a rich field of investigation in its own right, but to understand the whole process, even in simple terms, we need to consider them together. Here we attempt to characterise this process by focussing on the roles of noise during cell fate decisions. We use a range of recent results to develop a view of the sequence of events by which a cell progresses from a pluripotent or multipotent to a differentiated state: chromatin organisation, transcription factor stoichiometry, and cellular signalling all change during this progression, and all shape cellular variability, which becomes maximal at the transition state.

Predicting Cancer Evolution Using Cell State Dynamics

One of the biggest challenges in cancer is predicting its initiation and course of progression. In this issue of Cancer Research, Rockne and colleagues use state transition theory to predict how peripheral mononuclear blood cells in mice transition from a healthy state to acute myeloid leukemia. They found that critical transcriptomic perturbations could predict initiation and progression of the disease. This is an important step toward accurately predicting cancer evolution, which may eventually facilitate early diagnosis of cancer and disease recurrence, and which could potentially inform on timing of therapeutic interventions.See related article by Rockne et al., 3157.

Tolerant/Persister Cancer Cells and the Path to Resistance to Targeted Therapy

The capacity of cancer to adapt to treatment and evolve is a major limitation for targeted therapies. While the role of new acquired mutations is well-established, recent findings indicate that resistance can also arise from subpopulations of tolerant/persister cells that survive in the presence of the treatment. Different processes contribute to the emergence of these cells, including pathway rebound through the release of negative feedback loops, transcriptional rewiring mediated by chromatin remodeling and autocrine/paracrine communication among tumor cells and within the tumor microenvironment. In this review, we discuss the non-genetic mechanisms that eventually result in cancer resistance to targeted therapies, with a special focus on those involving changes in gene expression.

DNA methylation in blood-Potential to provide new insights into cell biology

Epigenetics plays a fundamental role in cellular development and differentiation; epigenetic mechanisms, such as DNA methylation, are involved in gene regulation and the exquisite nuance of expression changes seen in the journey from pluripotency to final differentiation. Thus, DNA methylation as a marker of cell identify has the potential to reveal new insights into cell biology. We mined publicly available DNA methylation data with a machine-learning approach to identify differentially methylated loci between different white blood cell types. We then interrogated the DNA methylation and mRNA expression of candidate loci in CD4⁺, CD8⁺, CD14⁺, CD19⁺ and CD56⁺ fractions from 12 additional, independent healthy individuals (6 male, 6 female). ‘Classic’ immune cell markers such as CD8 and CD19 showed expected methylation/expression associations fitting with established dogma that hypermethylation is associated with the repression of gene expression. We also observed large differential methylation at loci which are not established immune cell markers; some of these loci showed inverse correlations between methylation and mRNA expression (such as PARK2, DCP2). Furthermore, we validated these observations further in publicly available DNA methylation and RNA sequencing datasets. Our results highlight the value of mining publicly available data, the utility of DNA methylation as a discriminatory marker and the potential value of DNA methylation to provide additional insights into cell biology and developmental processes.

RWRNET: A Gene Regulatory Network Inference Algorithm Using Random Walk With Restart

Inferring gene regulatory networks from expression data is essential in identifying complex regulatory relationships among genes and revealing the mechanism of certain diseases. Various computation methods have been developed for inferring gene regulatory networks. However, these methods focus on the local topology of the network rather than on the global topology. From network optimisation standpoint, emphasising the global topology of the network also reduces redundant regulatory relationships. In this study, we propose a novel network inference algorithm using Random Walk with Restart (RWRNET) that combines local and global topology relationships. The method first captures the local topology through three elements of random walk and then combines the local topology with the global topology by Random Walk with Restart. The Markov Blanket discovery algorithm is then used to deal with isolated genes. The proposed method is compared with several state-of-the-art methods on the basis of six benchmark datasets. Experimental results demonstrated the effectiveness of the proposed method.

Buffering noise: KAT2A modular contributions to stabilization of transcription and cell identity in cancer and development

KAT2A is a histone acetyltransferase recently identified as a vulnerability in at least some forms of Acute Myeloid Leukemia (AML). Its loss or inhibition prompts leukemia stem cells out of self-renewal and into differentiation with ultimate exhaustion of the leukemia pool. We have recently linked the Kat2a requirement in AML to control of transcriptional noise, reflecting an evolutionary-conserved role of Kat2a in promoting burst-like promoter activity and stabilizing gene expression. We suggest that through this role, Kat2a contributes to preservation of cell identity. KAT2A exerts its acetyltransferase activity in the context of two macromolecular complexes, Spt-Ada-Gcn5-Acetyltransferase (SAGA) and Ada-Two-A-Containing (ATAC), but the specific contribution of each complex to stabilization of gene expression is currently unknown. By reviewing specific gene targets and requirements of the two complexes in cancer and development, we suggest that SAGA regulates lineage-specific programs, and ATAC maintains biosynthetic activity through control of ribosomal protein and translation-associated genes, on which cells may be differentially dependent. While our data suggest that KAT2A-mediated regulation of transcriptional noise in AML may be exerted through ATAC, we discuss potential caveats and probe general vs. complex-specific contributions of KAT2A to transcriptional stability, with implications for control and perturbation of cell identity.

Unbelievable but True: Epigenetics and Chromatin in Fungi

Evolutionary innovations in chromatin biology have been recently discovered through the study of fungi. In Saccharomyces cerevisiae, a prion form of a deacetylase complex assembles over subtelomeric domains that produces a heritable gene expression state that enables resistance to stress. In Candida albicans, stress triggers adaptive chromosome destabilization via erasure a centromeric histone H3, CENP-A; a process that cooperates with a newly evolved H2A variant lacking a mitotic phosphorylation site. Finally, in Cryptococcus neoformans, the loss of a cytosine DNA methyltransferase at least 50 million years ago has enabled the Darwinian evolution of methylation patterns over geological timescales. These studies reveal a remarkable genetic and epigenetic evolutionary plasticity of the chromatin fiber, despite the highly conserved structure of the nucleosome.

Improved detection of tumor suppressor events in single-cell RNA-Seq data

Tissue-specific transcription factors are frequently inactivated in cancer. To fully dissect the heterogeneity of such tumor suppressor events requires single-cell resolution, yet this is challenging because of the high dropout rate. Here we propose a simple yet effective computational strategy called SCIRA to infer regulatory activity of tissue-specific transcription factors at single-cell resolution and use this tool to identify tumor suppressor events in single-cell RNA-Seq cancer studies. We demonstrate that tissue-specific transcription factors are preferentially inactivated in the corresponding cancer cells, suggesting that these are driver events. For many known or suspected tumor suppressors, SCIRA predicts inactivation in single cancer cells where differential expression does not, indicating that SCIRA improves the sensitivity to detect changes in regulatory activity. We identify NKX2-1 and TBX4 inactivation as early tumor suppressor events in normal non-ciliated lung epithelial cells from smokers. In summary, SCIRA can help chart the heterogeneity of tumor suppressor events at single-cell resolution.

Human Cell Atlas and cell-type authentication for regenerative medicine

In modern biology, the correct identification of cell types is required for the developmental study of tissues and organs and the production of functional cells for cell therapies and disease modeling. For decades, cell types have been defined on the basis of morphological and physiological markers and, more recently, immunological markers and molecular properties. Recent advances in single-cell RNA sequencing have opened new doors for the characterization of cells at the individual and spatiotemporal levels on the basis of their RNA profiles, vastly transforming our understanding of cell types. The objective of this review is to survey the current progress in the field of cell-type identification, starting with the Human Cell Atlas project, which aims to sequence every cell in the human body, to molecular marker databases for individual cell types and other sources that address cell-type identification for regenerative medicine based on cell data guidelines.

Enhancer Predictions and Genome-Wide Regulatory Circuits

Spatiotemporal control of gene expression during development requires orchestrated activities of numerous enhancers, which are cis-regulatory DNA sequences that, when bound by transcription factors, support selective activation or repression of associated genes. Proper activation of enhancers is critical during embryonic development, adult tissue homeostasis, and regeneration, and inappropriate enhancer activity is often associated with pathological conditions such as cancer. Multiple consortia [e.g., the Encyclopedia of DNA Elements (ENCODE) Consortium and National Institutes of Health Roadmap Epigenomics Mapping Consortium] and independent investigators have mapped putative regulatory regions in a large number of cell types and tissues, but the sequence determinants of cell-specific enhancers are not yet fully understood. Machine learning approaches trained on large sets of these regulatory regions can identify core transcription factor binding sites and generate quantitative predictions of enhancer activity and the impact of sequence variants on activity. Here, we review these computational methods in the context of enhancer prediction and gene regulatory network models specifying cell fate.

Sci-fate characterizes the dynamics of gene expression in single cells

Gene expression programs change over time, differentiation and development, and in response to stimuli. However, nearly all techniques for profiling gene expression in single cells do not directly capture transcriptional dynamics. In the present study, we present a method for combined single-cell combinatorial indexing and messenger RNA labeling (sci-fate), which uses combinatorial cell indexing and 4-thiouridine labeling of newly synthesized mRNA to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. We used sci-fate to study the cortisol response in >6,000 single cultured cells. From these data, we quantified the dynamics of the cell cycle and glucocorticoid receptor activation, and explored their intersection. Finally, we developed software to infer and analyze cell-state transitions. We anticipate that sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems.

Epigenetic cell fate in Candida albicans is controlled by transcription factor condensates acting at super-enhancer-like elements

Cell identity in eukaryotes is controlled by transcriptional regulatory networks (TRNs) that define cell type-specific gene expression. In the opportunistic fungal pathogen Candida albicans, TRNs regulate epigenetic switching between two alternative cell states, ‘white’ and ‘opaque’, that exhibit distinct host interactions. Here, we reveal that the transcription factors (TFs) regulating cell identity contain prion-like domains (PrLDs) that enable liquid-liquid demixing and the formation of phase-separated condensates. Multiple white-opaque TFs can co-assemble into complex condensates as observed on single DNA molecules. Moreover, heterotypic interactions between PrLDs supports the assembly of multifactorial condensates at a synthetic locus within live eukaryotic cells. Mutation of the Wor1 PrLD revealed that substitution of acidic residues abolished its ability to phase separate and to co-recruit other TFs in live cells, as well as its function in C. albicans cell fate determination. Together, these studies reveal that PrLDs support the assembly of TF complexes that control fungal cell identity and highlight parallels with the ‘super-enhancers’ that regulate mammalian cell fate.

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.