Cellular network entropy as the energy potential in Waddington's differentiation landscape

Endogenous retroviruses in multiple sclerosis: A network-based etiopathogenic model

The present perspective article proposes an etiopathological model for multiple sclerosis pathogenesis and progression associated with the activation of human endogenous retroviruses. We reviewed preclinical, clinical, epidemiological, and evolutionary evidence indicating how the complex, multi-level interplay of genetic traits and environmental factors contributes to multiple sclerosis. We propose that endogenous retroviruses transactivation acts as a critical node in disease development. We also discuss the rationale for combined anti-retroviral therapy in multiple sclerosis as a disease-modifying therapeutic strategy. Finally, we propose that the immuno-pathogenic process triggered by endogenous retrovirus activation can be extended to aging and aging-related neurodegeneration. In this regard, endogenous retroviruses can be envisioned to act as epigenetic noise, favoring the proliferation of disorganized cellular subpopulations and accelerating system-specific "aging". Since inflammation and aging are two sides of the same coin (plastic dis-adaptation to external stimuli with system-specific degree of freedom), the two conditions may be epiphenomenal products of increased epigenomic entropy. Inflammation accelerates organ-specific aging, disrupting communication throughout critical systems of the body and producing symptoms. Overlapping neurological symptoms and syndromes may emerge from the activity of shared molecular networks that respond to endogenous retroviruses' reactivation.

Computational single-cell methods for predicting cancer risk

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.

Topological and geometric analysis of cell states in single-cell transcriptomic data

Single-cell RNA sequencing (scRNA-seq) enables dissecting cellular heterogeneity in tissues, resulting in numerous biological discoveries. Various computational methods have been devised to delineate cell types by clustering scRNA-seq data, where clusters are often annotated using prior knowledge of marker genes. In addition to identifying pure cell types, several methods have been developed to identify cells undergoing state transitions, which often rely on prior clustering results. The present computational approaches predominantly investigate the local and first-order structures of scRNA-seq data using graph representations, while scRNA-seq data frequently display complex high-dimensional structures. Here, we introduce scGeom, a tool that exploits the multiscale and multidimensional structures in scRNA-seq data by analyzing the geometry and topology through curvature and persistent homology of both cell and gene networks. We demonstrate the utility of these structural features to reflect biological properties and functions in several applications, where we show that curvatures and topological signatures of cell and gene networks can help indicate transition cells and the differentiation potential of cells. We also illustrate that structural characteristics can improve the classification of cell types.

Transcriptomic and proteomic profiles of fetal versus adult mesenchymal stromal cells and mesenchymal stromal cell-derived extracellular vesicles

BACKGROUND

Mesenchymal stem/stromal cells (MSCs) can regenerate tissues through engraftment and differentiation but also via paracrine signalling via extracellular vesicles (EVs). Fetal-derived MSCs (fMSCs) have been shown, both in vitro and in animal studies, to be more efficient than adult MSC (aMSCs) in generating bone and muscle but the underlying reason for this difference has not yet been clearly elucidated. In this study, we aimed to systematically investigate the differences between fetal and adult MSCs and MSC-derived EVs at the phenotypic, RNA, and protein levels.

METHODS

We carried out a detailed and comparative characterization of culture-expanded fetal liver derived MSCs (fMSCs) and adult bone marrow derived MSCs (aMSCs) phenotypically, and the MSCs and MSC-derived EVs were analysed using transcriptomics and proteomics approaches with RNA Sequencing and Mass Spectrometry.

RESULTS

Fetal MSCs were smaller, exhibited increased proliferation and colony-forming capacity, delayed onset of senescence, and demonstrated superior osteoblast differentiation capability compared to their adult counterparts. Gene Ontology analysis revealed that fMSCs displayed upregulated gene sets such as "Positive regulation of stem cell populations", "Maintenance of stemness" and "Muscle cell development/contraction/Myogenesis" in comparison to aMSCs. Conversely, aMSCs displayed upregulated gene sets such as "Complement cascade", "Adipogenesis", "Extracellular matrix glycoproteins" and "Cellular metabolism", and on the protein level, "Epithelial cell differentiation" pathways. Signalling entropy analysis suggested that fMSCs exhibit higher signalling promiscuity and hence, higher potency than aMSCs. Gene ontology comparisons revealed that fetal MSC-derived EVs (fEVs) were enriched for "Collagen fibril organization", "Protein folding", and "Response to transforming growth factor beta" compared to adult MSC-derived EVs (aEVs), whereas no significant difference in protein expression in aEVs compared to fEVs could be detected.

CONCLUSIONS

This study provides detailed and systematic insight into the differences between fMSCs and aMSCs, and MSC-derived EVs. The key finding across phenotypic, transcriptomic and proteomic levels is that fMSCs exhibit higher potency than aMSCs, meaning they are in a more undifferentiated state. Additionally, fMSCs and fMSC-derived EVs may possess greater bone forming capacity compared to aMSCs. Therefore, using fMSCs may lead to better treatment efficacy, especially in musculoskeletal diseases.

Multi-scale geometric network analysis identifies melanoma immunotherapy response gene modules

Melanoma response to immune-modulating therapy remains incompletely characterized at the molecular level. In this study, we assess melanoma immunotherapy response using a multi-scale network approach to identify gene modules with coordinated gene expression in response to treatment. Using gene expression data of melanoma before and after treatment with nivolumab, we modeled gene expression changes in a correlation network and measured a key network geometric property, dynamic Ollivier-Ricci curvature, to distinguish critical edges within the network and reveal multi-scale treatment-response gene communities. Analysis identified six distinct gene modules corresponding to sets of genes interacting in response to immunotherapy. One module alone, overlapping with the nuclear factor kappa-B pathway (NFkB), was associated with improved patient survival and a positive clinical response to immunotherapy. This analysis demonstrates the usefulness of dynamic Ollivier-Ricci curvature as a general method for identifying information-sharing gene modules in cancer.

Charting cellular differentiation trajectories with Ricci flow

Complex biological processes, such as cellular differentiation, require intricate rewiring of intra-cellular signalling networks. Previous characterisations revealed a raised network entropy underlies less differentiated and malignant cell states. A connection between entropy and Ricci curvature led to applications of discrete curvatures to biological networks. However, predicting dynamic biological network rewiring remains an open problem. Here we apply Ricci curvature and Ricci flow to biological network rewiring. By investigating the relationship between network entropy and Forman-Ricci curvature, theoretically and empirically on single-cell RNA-sequencing data, we demonstrate that the two measures do not always positively correlate, as previously suggested, and provide complementary rather than interchangeable information. We next employ Ricci flow to derive network rewiring trajectories from stem cells to differentiated cells, accurately predicting true intermediate time points in gene expression time courses. In summary, we present a differential geometry toolkit for understanding dynamic network rewiring during cellular differentiation and cancer.

Pathway level subtyping identifies a slow-cycling biological phenotype associated with poor clinical outcomes in colorectal cancer

Molecular stratification using gene-level transcriptional data has identified subtypes with distinctive genotypic and phenotypic traits, as exemplified by the consensus molecular subtypes (CMS) in colorectal cancer (CRC). Here, rather than gene-level data, we make use of gene ontology and biological activation state information for initial molecular class discovery. In doing so, we defined three pathway-derived subtypes (PDS) in CRC: PDS1 tumors, which are canonical/LGR5+ stem-rich, highly proliferative and display good prognosis; PDS2 tumors, which are regenerative/ANXA1+ stem-rich, with elevated stromal and immune tumor microenvironmental lineages; and PDS3 tumors, which represent a previously overlooked slow-cycling subset of tumors within CMS2 with reduced stem populations and increased differentiated lineages, particularly enterocytes and enteroendocrine cells, yet display the worst prognosis in locally advanced disease. These PDS3 phenotypic traits are evident across numerous bulk and single-cell datasets, and demark a series of subtle biological states that are currently under-represented in pre-clinical models and are not identified using existing subtyping classifiers.

Warming during embryogenesis induces a lasting transcriptomic signature in fishes

Exposure to elevated temperatures during embryogenesis can influence the plasticity of tissues in later life. Despite these long-term changes in plasticity, few differentially expressed genes are ever identified, suggesting that the developmental programming of later life plasticity may occur through the modulation of other aspects of transcriptomic architecture, such as gene network organisation. Here, we use network modelling approaches to demonstrate that warm temperatures during embryonic development (developmental warming) have consistent effects in later life on the organisation of transcriptomic networks across four diverse species of fishes: Scyliorhinus canicula, Danio rerio, Dicentrarchus labrax, and Gasterosteus aculeatus. The transcriptomes of developmentally warmed fishes are characterised by an increased entropy of their pairwise gene interaction networks, implying a less structured, more 'random' set of gene interactions. We also show that, in zebrafish subject to developmental warming, the entropy of an individual gene within a network is associated with that gene's probability of expression change during temperature acclimation in later life. However, this association is absent in animals reared under 'control' conditions. Thus, the thermal environment experienced during embryogenesis can alter transcriptomic organisation in later life, and these changes may influence an individual's responsiveness to future temperature challenges.

Multi-Scale Geometric Network Analysis Identifies Melanoma Immunotherapy Response Gene Modules

Melanoma response to immune-modulating therapy remains incompletely characterized at the molecular level. In this study, we assess melanoma immunotherapy response using a multi-scale network approach to identify gene modules with coordinated gene expression in response to treatment. Using gene expression data of melanoma before and after treatment with nivolumab, we modeled gene expression changes in a correlation network and measured a key network geometric property, dynamic Ollivier-Ricci curvature, to distinguish critical edges within the network and reveal multi-scale treatment-response gene communities. Analysis identified six distinct gene modules corresponding to sets of genes interacting in response to immunotherapy. One module alone, overlapping with the nuclear factor kappa-B pathway (NFKB), was associated with improved patient survival and a positive clinical response to immunotherapy. This analysis demonstrates the usefulness of dynamic Ollivier-Ricci curvature as a general method for identifying information-sharing gene modules in cancer.

Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator

The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.

Cell Adaptive Fitness and Cancer Evolutionary Dynamics

Genome instability of cancer cells translates into increased entropy and lower information processing capacity, leading to metabolic reprograming toward higher energy states, presumed to be aligned with a cancer growth imperative. Dubbed as the cell adaptive fitness, the proposition postulates that the coupling between cell signaling and metabolism constrains cancer evolutionary dynamics along trajectories privileged by the maintenance of metabolic sufficiency for survival. In particular, the conjecture postulates that clonal expansion becomes restricted when genetic alterations induce a sufficiently high level of disorder, that is, high entropy, in the regulatory signaling network, abrogating as a result the ability of cancer cells to successfully replicate, leading to a stage of clonal stagnation. The proposition is analyzed in the context of an in-silico model of tumor evolutionary dynamics to illustrate how cell-inherent adaptive fitness may predictably constrain clonal evolution of tumors, which would have significant implications for the design of adaptive cancer therapies.

Accurate Estimation of Single-Cell Differentiation Potency Based on Network Topology and Gene Ontology Information

One important task in single-cell analysis is to quantify the differentiation potential of single cells. Though various single-cell potency measures have been proposed, they are based on individual biological sources, thus not robust and reliable. It is still a challenge to combine multiple sources to generate a relatively reliable and robust measure to estimate differentiation. In this paper, we propose a New Centrality measure with Gene ontology information (NCG) to estimate single-cell potency. NCG is designed by combining network topology property with edge clustering coefficient, and gene function information using gene ontology function similarity scores. NCG distinguishes pluripotent cells from non-pluripotent cells with high accuracy, correctly ranks different cell types by their differentiation potency, tracks changes during the differentiation process, and constructs the lineage trajectory from human myoblasts into skeletal muscle cells. These indicate that NCG is a reliable and robust measure to estimate single-cell potency. NCG is anticipated to be a useful tool for identifying novel stem or progenitor cell phenotypes from single-cell RNA-Seq data. The source codes and datasets are available at https://github.com/Xinzhe-Ni/NCG.

Characterizing the variation in chromosome structure ensembles in the context of the nuclear microenvironment

Inside the nucleus, chromosomes are subjected to direct physical interaction between different components, active forces, and thermal noise, leading to the formation of an ensemble of three-dimensional structures. However, it is still not well understood to what extent and how the structural ensemble varies from one chromosome region or cell-type to another. We designed a statistical analysis technique and applied it to single-cell chromosome imaging data to reveal the heterogeneity of individual chromosome structures. By analyzing the resulting structural landscape, we find that the largest dynamic variation is the overall radius of gyration of the chromatin region, followed by domain reorganization within the region. By comparing different human cell-lines and experimental perturbation data using this statistical analysis technique and a network-based similarity quantification approach, we identify both cell-type and condition-specific features of the structural landscapes. We identify a relationship between epigenetic state and the properties of chromosome structure fluctuation and validate this relationship through polymer simulations. Overall, our study suggests that the types of variation in a chromosome structure ensemble are cell-type as well as region-specific and can be attributed to constraints placed on the structure by factors such as variation in epigenetic state.

Recent work has revealed principles of how chromosomes are folded and structured inside the human nucleus. It is now even possible to microscopically trace the path of chromosomes in 3D in individual cells. With this data, we can start to examine how much variation exists in chromosome structure and what biological factors may restrict or enhance this variation. Are chromosomes stuck in just a few possible positions or do they move around more freely, sampling many configurations? Here, we use a mathematical approach to compare chromosome structure variation in different cell types, at different locations along the genome, and when key structural proteins are removed. Through these comparisons and dynamic simulations of chromosome behavior, we identify factors that may constrain or promote variation in chromosome structure.

ORIGINS: a protein network-based approach to quantify cell pluripotency from scRNA-seq data

Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT (Chen et al., 2019) and CytoTRACE (Gulati et al., 2020), for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set.•

ORIGINS is a methodology to quantify pluripotency from scRNA-seq data implemented as a freely available R package.

•

ORIGINS uses the protein-protein interaction network associated with differentiation and the data set expression matrix to calculate a score (differentiation activity) that quantifies pluripotency for each cell.

Systematic identification of cell-fate regulatory programs using a single-cell atlas of mouse development

Waddington's epigenetic landscape is a metaphor frequently used to illustrate cell differentiation. Recent advances in single-cell genomics are altering our understanding of the Waddington landscape, yet the molecular mechanisms of cell-fate decisions remain poorly understood. We constructed a cell landscape of mouse lineage differentiation during development at the single-cell level and described both lineage-common and lineage-specific regulatory programs during cell-type maturation. We also found lineage-common regulatory programs that are broadly active during the development of invertebrates and vertebrates. In particular, we identified Xbp1 as an evolutionarily conserved regulator of cell-fate determinations across different species. We demonstrated that Xbp1 transcriptional regulation is important for the stabilization of the gene-regulatory networks for a wide range of mouse cell types. Our results offer genetic and molecular insights into cellular gene-regulatory programs and will serve as a basis for further advancing the understanding of cell-fate decisions.

Computational identification of preneoplastic cells displaying high stemness and risk of cancer progression

Evidence points towards the differentiation state of cells as a marker of cancer risk and progression. Measuring the differentiation state of single cells in a preneoplastic population could thus enable novel strategies for early detection and risk prediction. Recent maps of somatic mutagenesis in normal tissues from young healthy individuals have revealed cancer driver mutations, indicating that these do not correlate well with differentiation state and that other molecular events also contribute to cancer development. We hypothesized that the differentiation state of single cells can be measured by estimating the regulatory activity of the transcription factors (TFs) that control differentiation within that cell lineage. To this end, we present a novel computational method called CancerStemID that estimates a stemness index of cells from single-cell RNA-Seq data. CancerStemID is validated in two human esophageal squamous cell carcinoma (ESCC) cohorts, demonstrating how it can identify undifferentiated preneoplastic cells whose transcriptomic state is overrepresented in invasive cancer. Spatial transcriptomics and whole-genome bisulfite sequencing demonstrated that differentiation activity of tissue-specific TFs was decreased in cancer cells compared to the basal cell-of-origin layer and established that differentiation state correlated with differential DNA methylation at the promoters of these TFs, independently of underlying NOTCH1 and TP53 mutations. The findings were replicated in a mouse model of ESCC development, and the broad applicability of CancerStemID to other cancer-types was demonstrated. In summary, these data support an epigenetic stem-cell model of oncogenesis and highlight a novel computational strategy to identify stem-like preneoplastic cells that undergo positive selection.

Trophectoderm differentiation to invasive syncytiotrophoblast is promoted by endometrial epithelial cells during human embryo implantation

STUDY QUESTION

How does the human embryo breach the endometrial epithelium at implantation?

SUMMARY ANSWER

Embryo attachment to the endometrial epithelium promotes the formation of multinuclear syncytiotrophoblast from trophectoderm, which goes on to breach the epithelial layer.

WHAT IS KNOWN ALREADY

A significant proportion of natural conceptions and assisted reproduction treatments fail due to unsuccessful implantation. The trophectoderm lineage of the embryo attaches to the endometrial epithelium before breaching this barrier to implant into the endometrium. Trophectoderm-derived syncytiotrophoblast has been observed in recent in vitro cultures of peri-implantation embryos, and historical histology has shown invasive syncytiotrophoblast in embryos that have invaded beyond the epithelium, but the cell type mediating invasion of the epithelial layer at implantation is unknown.

STUDY DESIGN, SIZE, DURATION

Fresh and frozen human blastocyst-stage embryos (n = 46) or human trophoblast stem cell (TSC) spheroids were co-cultured with confluent monolayers of the Ishikawa endometrial epithelial cell line to model the epithelial phase of implantation in vitro. Systems biology approaches with published transcriptomic datasets were used to model the epithelial phase of implantation in silico.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Human embryos surplus to treatment requirements were consented for research. Day 6 blastocysts were co-cultured with Ishikawa cell layers until Day 8, and human TSC spheroids modelling blastocyst trophectoderm were co-cultured with Ishikawa cell layers for 48 h. Embryo and TSC morphology was assessed by immunofluorescence microscopy, and TSC differentiation by real-time quantitative PCR (RT-qPCR) and ELISA. Single-cell human blastocyst transcriptomes, and bulk transcriptomes of TSC and primary human endometrial epithelium were used to model the trophectoderm-epithelium interaction in silico. Hypernetworks, pathway analysis, random forest machine learning and RNA velocity were employed to identify gene networks associated with implantation.

MAIN RESULTS AND THE ROLE OF CHANCE

The majority of embryos co-cultured with Ishikawa cell layers from Day 6 to 8 breached the epithelial layer (37/46), and syncytiotrophoblast was seen in all of these. Syncytiotrophoblast was observed at the embryo-epithelium interface before breaching, and syncytiotrophoblast mediated all pioneering breaching events observed (7/7 events). Multiple independent syncytiotrophoblast regions were seen in 26/46 embryos, suggesting derivation from different regions of trophectoderm. Human TSC spheroids co-cultured with Ishikawa layers also exhibited syncytiotrophoblast formation upon invasion into the epithelium. RT-qPCR comparison of TSC spheroids in isolated culture and co-culture demonstrated epithelium-induced upregulation of syncytiotrophoblast genes CGB (P = 0.03) and SDC1 (P = 0.008), and ELISA revealed the induction of hCGβ secretion (P = 0.03). Secretory-phase primary endometrial epithelium surface transcriptomes were used to identify trophectoderm surface binding partners to model the embryo-epithelium interface. Hypernetwork analysis established a group of 25 epithelium-interacting trophectoderm genes that were highly connected to the rest of the trophectoderm transcriptome, and epithelium-coupled gene networks in cells of the polar region of the trophectoderm exhibited greater connectivity (P < 0.001) and more organized connections (P < 0.0001) than those in the mural region. Pathway analysis revealed a striking similarity with syncytiotrophoblast differentiation, as 4/6 most highly activated pathways upon TSC-syncytiotrophoblast differentiation (false discovery rate (FDR < 0.026)) were represented in the most enriched pathways of epithelium-coupled gene networks in both polar and mural trophectoderm (FDR < 0.001). Random forest machine learning also showed that 80% of the endometrial epithelium-interacting trophectoderm genes identified in the hypernetwork could be quantified as classifiers of TSC-syncytiotrophoblast differentiation. This multi-model approach suggests that invasive syncytiotrophoblast formation from both polar and mural trophectoderm is promoted by attachment to the endometrial epithelium to enable embryonic invasion.

LARGE SCALE DATA

No omics datasets were generated in this study, and those used from previously published studies are cited.

LIMITATIONS, REASONS FOR CAUTION

In vitro and in silico models may not recapitulate the dynamic embryo-endometrial interactions that occur in vivo. The influence of other cellular compartments in the endometrium, including decidual stromal cells and leukocytes, was not represented in these models.

WIDER IMPLICATIONS OF THE FINDINGS

Understanding the mechanism of human embryo breaching of the epithelium and the gene networks involved is crucial to improve implantation success rates after assisted reproduction. Moreover, early trophoblast lineages arising at the epithelial phase of implantation form the blueprint for the placenta and thus underpin foetal growth trajectories, pregnancy health and offspring health.

STUDY FUNDING/COMPETING INTEREST(S)

This work was funded by grants from Wellbeing of Women, Diabetes UK, the NIHR Local Comprehensive Research Network and Manchester Clinical Research Facility, and the Department of Health Scientist Practitioner Training Scheme. None of the authors has any conflict of interest to declare.

Phylotranscriptomic patterns of network stochasticity and pathway dynamics during embryogenesis

MOTIVATION

The hourglass model is a popular evo-devo model depicting that the developmental constraints in the middle of a developmental process are higher, and hence the phenotypes are evolutionarily more conserved, than those that occur in early and late ontogeny stages. Although this model has been supported by studies analyzing developmental gene expression data, the evolutionary explanation and molecular mechanism behind this phenomenon are not fully understood yet. To approach this problem, Raff proposed a hypothesis and claimed that higher interconnectivity among elements in an organism during organogenesis resulted in the larger constraints at the mid-developmental stage. By employing stochastic network analysis and gene-set pathway analysis, we aim to demonstrate such changes of interconnectivity claimed in Raff's hypothesis.

RESULTS

We first compared the changes of network randomness among developmental processes in different species by measuring the stochasticity within the biological network in each developmental stage. By tracking the network entropy along each developmental process, we found that the network stochasticity follows an anti-hourglass trajectory, and such a pattern supports Raff's hypothesis in dynamic changes of interconnections among biological modules during development. To understand which biological functions change during the transition of network stochasticity, we sketched out the pathway dynamics along the developmental stages and found that species may activate similar groups of biological processes across different stages. Moreover, higher interspecies correlations are found at the mid-developmental stages.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors

Network analysis methods can potentially quantify cancer aberrations in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high-grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs) and to rank key genes associated with prognosis. Copy number alterations (CNAs) from targeted and whole-exome sequencing data were extracted for HGSOC patients (n = 45) treated with ICIs. CNAs at a gene level were represented on a protein–protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier–Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan–Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups. The network curvature analysis stratified patients at high risk of mortality with p = 0.00047 in Kaplan–Meier analysis in HGSOC patients receiving ICI. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. Network curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.

Prrx1b restricts fibrosis and promotes Nrg1-dependent cardiomyocyte proliferation during zebrafish heart regeneration

Fibroblasts are activated to repair the heart following injury. Fibroblast activation in the mammalian heart leads to a permanent fibrotic scar that impairs cardiac function. In other organisms, such as zebrafish, cardiac injury is followed by transient fibrosis and scar-free regeneration. The mechanisms that drive scarring versus scar-free regeneration are not well understood. Here, we show that the homeobox-containing transcription factor Prrx1b is required for scar-free regeneration of the zebrafish heart as the loss of Prrx1b results in excessive fibrosis and impaired cardiomyocyte proliferation. Through lineage tracing and single-cell RNA sequencing, we find that Prrx1b is activated in epicardial-derived cells where it restricts TGFβ ligand expression and collagen production. Furthermore, through combined in vitro experiments in human fetal epicardial-derived cells and in vivo rescue experiments in zebrafish, we conclude that Prrx1 stimulates Nrg1 expression and promotes cardiomyocyte proliferation. Collectively, these results indicate that Prrx1 is a key transcription factor that balances fibrosis and regeneration in the injured zebrafish heart. This article has an associated 'The people behind the papers' interview.

Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data

Motivation

An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells.

Results

Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts.

Availability and implementation

CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT.

Supplementary information

Supplementary data are available at Bioinformatics online.

Myonuclear transcriptional dynamics in response to exercise following satellite cell depletion

Skeletal muscle is composed of post-mitotic myofibers that form a syncytium containing hundreds of myonuclei. Using a progressive exercise training model in the mouse and single nucleus RNA-sequencing (snRNA-seq) for high-resolution characterization of myonuclear transcription, we show myonuclear functional specialization in muscle. After 4 weeks of exercise training, snRNA-seq reveals that resident muscle stem cells, or satellite cells, are activated with acute exercise but demonstrate limited lineage progression while contributing to muscle adaptation. In the absence of satellite cells, a portion of nuclei demonstrates divergent transcriptional dynamics associated with mixed-fate identities compared with satellite cell replete muscles. These data provide a compendium of information about how satellite cells influence myonuclear transcription in response to exercise.

scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy

During early embryonic development, cell fate commitment represents a critical transition or "tipping point" of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene-gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the "dark genes" that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.

A CD44/Brg1 nuclear complex confers mesenchymal progenitor cells with enhanced fibrogenicity in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive fibrotic lung disease. We previously identified fibrogenic mesenchymal progenitor cells (MPCs) in the lungs of patients with IPF who serve as drivers of progressive fibrosis. Recent single-cell RNA sequencing work revealed that IPF MPCs with the highest transcriptomic network entropy differ the most from control MPCs and that increased CD44 was a marker of these IPF MPCs. We hypothesize that IPF MPCs with high CD44 (CD44^hi) expression will display enhanced fibrogenicity. We demonstrate that CD44-expressing MPCs are present at the periphery of the IPF fibroblastic focus, placing them in regions of active fibrogenesis. In a humanized mouse xenograft model, CD44^hi IPF MPCs are more fibrogenic than CD44^lo IPF MPCs, and knockdown of CD44 diminishes their fibrogenicity. CD44^hi IPF MPCs display increased expression of pluripotency markers and enhanced self-renewal compared with CD44^lo IPF MPCs, properties potentiated by IL-8. The mechanism involves the accumulation of CD44 within the nucleus, where it associates with the chromatin modulator protein Brahma-related gene 1 (Brg1) and the zinc finger E-box binding homeobox 1 (Zeb1) transcription factor. This CD44/Brg1/Zeb1 nuclear protein complex targets the Sox2 gene, promoting its upregulation and self-renewal. Our data implicate CD44 interaction with the epigenetic modulator protein Brg1 in conveying IPF MPCs with cell-autonomous fibrogenicity.

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.

Cellular reprogramming: Mathematics meets medicine

Generating needed cell types using cellular reprogramming is a promising strategy for restoring tissue function in injury or disease. A common method for reprogramming is addition of one or more transcription factors that confer a new function or identity. Advancements in transcription factor selection and delivery have culminated in successful grafting of autologous reprogrammed cells, an early demonstration of their clinical utility. Though cellular reprogramming has been successful in a number of settings, identification of appropriate transcription factors for a particular transformation has been challenging. Computational methods enable more sophisticated prediction of relevant transcription factors for reprogramming by leveraging gene expression data of initial and target cell types, and are built on mathematical frameworks ranging from information theory to control theory. This review highlights the utility and impact of these mathematical frameworks in the field of cellular reprogramming. This article is categorized under: Reproductive System Diseases > Reproductive System Diseases>Genetics/Genomics/Epigenetics Reproductive System Diseases > Reproductive System Diseases>Stem Cells and Development Reproductive System Diseases > Reproductive System Diseases>Computational Models.

Probing pluripotency gene regulatory networks with quantitative live cell imaging

Live cell imaging uniquely enables the measurement of dynamic events in single cells, but it has not been used often in the study of gene regulatory networks. Network components can be examined in relation to one another by quantitative live cell imaging of fluorescent protein reporter cell lines that simultaneously report on more than one network component. A series of dual-reporter cell lines would allow different combinations of network components to be examined in individual cells. Dynamical information about interacting network components in individual cells is critical to predictive modeling of gene regulatory networks, and such information is not accessible through omics and other end point techniques. Achieving this requires that gene-edited cell lines are appropriately designed and adequately characterized to assure the validity of the biological conclusions derived from the expression of the reporters. In this brief review we discuss what is known about the importance of dynamics to network modeling and review some recent advances in optical microscopy methods and image analysis approaches that are making the use of quantitative live cell imaging for network analysis possible. We also discuss how strategies for genetic engineering of reporter cell lines can influence the biological relevance of the data.

Identification of Differential Gene Groups From Single-Cell Transcriptomes Using Network Entropy

A complex tissue contains a variety of cells with distinct molecular signatures. Single-cell RNA sequencing has characterized the transcriptomes of different cell types and enables researchers to discover the underlying mechanisms of cellular heterogeneity. A critical task in single-cell transcriptome studies is to uncover transcriptional differences among specific cell types. However, the intercellular transcriptional variation is usually confounded with high level of technical noise, which masks the important biological signals. Here, we propose a new computational method DiffGE for differential analysis, adopting network entropy to measure the expression dynamics of gene groups among different cell types and to identify the highly differential gene groups. To evaluate the effectiveness of our proposed method, DiffGE is applied to three independent single-cell RNA-seq datasets and to identify the highly dynamic gene groups that exhibit distinctive expression patterns in different cell types. We compare the results of our method with those of three widely applied algorithms. Further, the gene function analysis indicates that these detected differential gene groups are significantly related to cellular regulation processes. The results demonstrate the power of our method in evaluating the transcriptional dynamics and identifying highly differential gene groups among different cell types.

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.

Single-cell RNA sequencing reveals that lung mesenchymal progenitor cells in IPF exhibit pathological features early in their differentiation trajectory

In Idiopathic Pulmonary Fibrosis (IPF), there is unrelenting scarring of the lung mediated by pathological mesenchymal progenitor cells (MPCs) that manifest autonomous fibrogenicity in xenograft models. To determine where along their differentiation trajectory IPF MPCs acquire fibrogenic properties, we analyzed the transcriptome of 335 MPCs isolated from the lungs of 3 control and 3 IPF patients at the single-cell level. Using transcriptional entropy as a metric for differentiated state, we found that the least differentiated IPF MPCs displayed the largest differences in their transcriptional profile compared to control MPCs. To validate entropy as a surrogate for differentiated state functionally, we identified increased CD44 as a characteristic of the most entropic IPF MPCs. Using FACS to stratify IPF MPCs based on CD44 expression, we determined that CD44^hi IPF MPCs manifested an increased capacity for anchorage-independent colony formation compared to CD44^lo IPF MPCs. To validate our analysis morphologically, we used two differentially expressed genes distinguishing IPF MPCs from control (CD44, cell surface; and MARCKS, intracellular). In IPF lung tissue, pathological MPCs resided in the highly cellular perimeter region of the fibroblastic focus. Our data support the concept that IPF fibroblasts acquire a cell-autonomous pathological phenotype early in their differentiation trajectory.

Perturbation-Driven Entropy as a Source of Cancer Cell Heterogeneity

Intratumor heterogeneity is a key hallmark of cancer that contributes to progression and therapeutic resistance. Phenotypic heterogeneity is in part caused by Darwinian selection of subclones that arise by random (epi)genetic aberrations. In addition, cancer cells are endowed with increased cellular plasticity compared with their normal counterparts, further adding to their heterogeneous behavior. However, the molecular mechanisms underpinning cancer cell plasticity are incompletely understood. Here, I outline the hypothesis that cancer-associated perturbations collectively disrupt normal gene regulatory networks (GRNs) by increasing their entropy. Importantly, in this model both somatic driver and passenger alterations contribute to 'perturbation-driven entropy', thereby increasing phenotypic heterogeneity and evolvability. This additional layer of heterogeneity may contribute to our understanding of cancer evolution and therapeutic resistance.

Glioblastoma Stem Cells: Driving Resilience through Chaos

Glioblastoma is an aggressive and heterogeneous tumor in which glioblastoma stem cells (GSCs) are at the apex of an entropic hierarchy and impart devastating therapy resistance. The high entropy of GSCs is driven by a permissive epigenetic landscape and a mutational landscape that revokes crucial cellular checkpoints. The GSC population encompasses a complex array of diverse microstates that are defined and maintained by a wide variety of attractors including the complex tumor ecosystem and therapeutic intervention. Constant dynamic transcriptional fluctuations result in a highly adaptable and heterogeneous entity primed for therapy evasion and survival. Analyzing the transcriptional, epigenetic, and metabolic landscapes of GSC dynamics in the context of a stochastically fluctuating tumor network will provide novel strategies to target resistant populations of GSCs in glioblastoma.

Entropy as a Robustness Marker in Genetic Regulatory Networks

Genetic regulatory networks have evolved by complexifying their control systems with numerous effectors (inhibitors and activators). That is, for example, the case for the double inhibition by microRNAs and circular RNAs, which introduce a ubiquitous double brake control reducing in general the number of attractors of the complex genetic networks (e.g., by destroying positive regulation circuits), in which complexity indices are the number of nodes, their connectivity, the number of strong connected components and the size of their interaction graph. The stability and robustness of the networks correspond to their ability to respectively recover from dynamical and structural disturbances the same asymptotic trajectories, and hence the same number and nature of their attractors. The complexity of the dynamics is quantified here using the notion of attractor entropy: it describes the way the invariant measure of the dynamics is spread over the state space. The stability (robustness) is characterized by the rate at which the system returns to its equilibrium trajectories (invariant measure) after a dynamical (structural) perturbation. The mathematical relationships between the indices of complexity, stability and robustness are presented in case of Markov chains related to threshold Boolean random regulatory networks updated with a Hopfield-like rule. The entropy of the invariant measure of a network as well as the Kolmogorov-Sinaï entropy of the Markov transition matrix ruling its random dynamics can be considered complexity, stability and robustness indices; and it is possible to exploit the links between these notions to characterize the resilience of a biological system with respect to endogenous or exogenous perturbations. The example of the genetic network controlling the kinin-kallikrein system involved in a pathology called angioedema shows the practical interest of the present approach of the complexity and robustness in two cases, its physiological normal and pathological, abnormal, dynamical behaviors.

Bioinformatics analysis and identification of potential genes related to pathogenesis of cervical intraepithelial neoplasia

The aim of this study was to explore and identify the key genes and signal pathways contributing to cervical intraepithelial neoplasia (CIN). The gene expression profiles of GSE63514 were downloaded from Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened performing with packages in software R. After Gene ontology terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyzing, and Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA) was used to analyze these genes. Then sub-modules were subsequently analyzed base CIN grade, and protein-protein interaction (PPI) network of DEGs were constructed. 537 DEGs were screened in total, consisting 331 up-regulated genes and 206 down-regulated genes in CIN samples compared to normal samples. The most DEGs were enriched in chromosomal region in cellular component (CC), organelle fission inbiological process (BP) and ATPase activity in molecular function (MF). KEGG pathway enrichment analyzing found the DEGs were mainly concentrated in 10 pathways. The results of GSEA mainly enriched in 4 functional sets: E2F-Targets, G2M-Checkpoint, Mitotic-Spindle and Spermatogenesis. A total of 6 modules were identified by WCGNA. Subsequently, grey module was the highest correlation (Cor=0.78, P=5e-22) and 31 genes were taken as candidate hub genes for CIN high grade risk (weighted correlation coefficients >0.80). Finally, diagnostic analysis showed that in addition to CCDC7, the expression levels of the remaining 13 DEGs have a high diagnostic value (AUC>0.8 and P<0.05). These findings provided a new sight into the understanding of molecular functions for CIN.

Single-cell analysis uncovers that metabolic reprogramming by ErbB2 signaling is essential for cardiomyocyte proliferation in the regenerating heart

While the heart regenerates poorly in mammals, efficient heart regeneration occurs in zebrafish. Studies in zebrafish have resulted in a model in which preexisting cardiomyocytes dedifferentiate and reinitiate proliferation to replace the lost myocardium. To identify which processes occur in proliferating cardiomyocytes we have used a single-cell RNA-sequencing approach. We uncovered that proliferating border zone cardiomyocytes have very distinct transcriptomes compared to the nonproliferating remote cardiomyocytes and that they resemble embryonic cardiomyocytes. Moreover, these cells have reduced expression of mitochondrial genes and reduced mitochondrial activity, while glycolysis gene expression and glucose uptake are increased, indicative for metabolic reprogramming. Furthermore, we find that the metabolic reprogramming of border zone cardiomyocytes is induced by Nrg1/ErbB2 signaling and is important for their proliferation. This mechanism is conserved in murine hearts in which cardiomyocyte proliferation is induced by activating ErbB2 signaling. Together these results demonstrate that glycolysis regulates cardiomyocyte proliferation during heart regeneration.

Heart attacks are a common cause of death in the Western world. During a heart attack, oxygen levels in the affected part of the heart decrease, which causes heart muscle cells to die. In humans the dead cells are replaced by a permanent scar that stabilizes the injury but does not completely heal it. As a result, individuals have a lower quality of life after a heart attack and are more likely to die from a subsequent attack.

Unlike humans, zebrafish are able to regenerate their hearts after injury: heart muscle cells close to a wound divide to produce new cells that slowly replace the scar tissue and restore normal function to the area. It remains unclear, however, what stimulates the heart muscle cells of zebrafish to start dividing. To address this question, Honkoop, de Bakker et al. used a technique called single-cell sequencing to study heart muscle cells in wounded zebrafish hearts.

The experiments identified a group of heart muscle cells close to the site of the wound that multiplied to repair the damage. This group of cells had altered their metabolism compared to other heart muscle cells so that they relied on a pathway called glycolysis to produce the energy and building blocks they needed to proliferate. Blocking glycolysis impaired the ability of the heart muscle cells to divide, indicating that this switch is necessary for the heart to regenerate. Further experiments showed that a signaling cascade, which includes the molecules Nrg1 and ErbB2, induces heart muscle cells in both zebrafish and mouse hearts to switch to glycolysis and undergo division.

These findings indicate that activating glycolysis in heart muscle cells may help to stimulate the heart to regenerate after a heart attack or other injury. The next step following on from this work is to develop methods to activate glycolysis and promote cell division in injured hearts.

Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine

Traditional approaches to cancer therapy seek common molecular targets in tumors from different patients. However, molecular profiles differ between patients, and most tumors exhibit inherent heterogeneity. Hence, imprecise targeting commonly results in side effects, reduced efficacy, and drug resistance. By contrast, personalized medicine aims to establish a molecular diagnosis specific to each patient, which is currently feasible due to the progress achieved with high-throughput technologies. In this report, we explored data from human RNA-seq and protein–protein interaction (PPI) networks using bioinformatics to investigate the relationship between tumor entropy and aggressiveness. To compare PPI subnetworks of different sizes, we calculated the Shannon entropy associated with vertex connections of differentially expressed genes comparing tumor samples with their paired control tissues. We found that the inhibition of up-regulated connectivity hubs led to a higher reduction of subnetwork entropy compared to that obtained with the inhibition of targets selected at random. Furthermore, these hubs were described to be participating in tumor processes. We also found a significant negative correlation between subnetwork entropies of tumors and the respective 5-year survival rates of the corresponding cancer types. This correlation was also observed considering patients with lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) based on the clinical data from The Cancer Genome Atlas database (TCGA). Thus, network entropy increases in parallel with tumor aggressiveness but does not correlate with PPI subnetwork size. This correlation is consistent with previous reports and allowed us to assess the number of hubs to be inhibited for therapy to be effective, in the context of precision medicine, by reference to the 100% patient survival rate 5 years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterize tumor heterogeneity.

Single-Cell RNA Sequencing Reveals a Developmental Hierarchy in Langerhans Cell Histiocytosis

In this issue of Cancer Discovery, Halbritter and colleagues utilize single-cell RNA sequencing to dissect the cellular hierarchy in Langerhans cell histiocytosis. They identified a remarkably consistent composition of 14 cellular subsets across all patients with a range of clinical spectrums consistent with a shared developmental hierarchy driven by key transcriptional regulators.See related article by Halbritter et al., p. 1406.

Single-cell landscape in mammary epithelium reveals bipotent-like cells associated with breast cancer risk and outcome

Adult stem-cells may serve as the cell-of-origin for cancer, yet their unbiased identification in single cell RNA sequencing data is challenging due to the high dropout rate. In the case of breast, the existence of a bipotent stem-like state is also controversial. Here we apply a marker-free algorithm to scRNA-Seq data from the human mammary epithelium, revealing a high-potency cell-state enriched for an independent mammary stem-cell expression module. We validate this stem-like state in independent scRNA-Seq data. Our algorithm further predicts that the stem-like state is bipotent, a prediction we are able to validate using FACS sorted bulk expression data. The bipotent stem-like state correlates with clinical outcome in basal breast cancer and is characterized by overexpression of YBX1 and ENO1, two modulators of basal breast cancer risk. This study illustrates the power of a marker-free computational framework to identify a novel bipotent stem-like state in the mammary epithelium.

Epigenomics and Single-Cell Sequencing Define a Developmental Hierarchy in Langerhans Cell Histiocytosis

Langerhans cell histiocytosis (LCH) is a rare neoplasm predominantly affecting children. It occupies a hybrid position between cancers and inflammatory diseases, which makes it an attractive model for studying cancer development. To explore the molecular mechanisms underlying the pathophysiology of LCH and its characteristic clinical heterogeneity, we investigated the transcriptomic and epigenomic diversity in primary LCH lesions. Using single-cell RNA sequencing, we identified multiple recurrent types of LCH cells within these biopsies, including putative LCH progenitor cells and several subsets of differentiated LCH cells. We confirmed the presence of proliferative LCH cells in all analyzed biopsies using IHC, and we defined an epigenomic and gene-regulatory basis of the different LCH-cell subsets by chromatin-accessibility profiling. In summary, our single-cell analysis of LCH uncovered an unexpected degree of cellular, transcriptomic, and epigenomic heterogeneity among LCH cells, indicative of complex developmental hierarchies in LCH lesions. SIGNIFICANCE: This study sketches a molecular portrait of LCH lesions by combining single-cell transcriptomics with epigenome profiling. We uncovered extensive cellular heterogeneity, explained in part by an intrinsic developmental hierarchy of LCH cells. Our findings provide new insights and hypotheses for advancing LCH research and a starting point for personalizing therapy.See related commentary by Gruber et al., p. 1343.This article is highlighted in the In This Issue feature, p. 1325.

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 .

Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology

Nowadays, networks are pervasively used as examples of models suitable to mathematically represent and visualize the complexity of systems associated with many diseases, including cancer. In the cancer context, the concept of network entropy has guided many studies focused on comparing equilibrium to disequilibrium (i.e., perturbed) conditions. Since these conditions reflect both structural and dynamic properties of network interaction maps, the derived topological characterizations offer precious support to conduct cancer inference. Recent innovative directions have emerged in network medicine addressing especially experimental omics approaches integrated with a variety of other data, from molecular to clinical and also electronic records, bioimaging etc. This work considers a few theoretically relevant concepts likely to impact the future of applications in personalized/precision/translational oncology. The focus goes to specific properties of networks that are still not commonly utilized or studied in the oncological domain, and they are: controllability, synchronization and symmetry. The examples here provided take inspiration from the consideration of metastatic processes, especially their progression through stages and their hallmark characteristics. Casting these processes into computational frameworks and identifying network states with specific modular configurations may be extremely useful to interpret or even understand dysregulation patterns underlying cancer, and associated events (onset, progression) and disease phenotypes.

Multi-Omic Data Interpretation to Repurpose Subtype Specific Drug Candidates for Breast Cancer

Triple-negative breast cancer (TNBC), which is largely synonymous with the basal-like molecular subtype, is the 5th leading cause of cancer deaths for women in the United States. The overall prognosis for TNBC patients remains poor given that few treatment options exist; including targeted therapies (not FDA approved), and multi-agent chemotherapy as standard-of-care treatment. TNBC like other complex diseases is governed by the perturbations of the complex interaction networks thereby elucidating the underlying molecular mechanisms of this disease in the context of network principles, which have the potential to identify targets for drug development. Here, we present an integrated "omics" approach based on the use of transcriptome and interactome data to identify dynamic/active protein-protein interaction networks (PPINs) in TNBC patients. We have identified three highly connected modules, EED, DHX9, and AURKA, which are extremely activated in TNBC tumors compared to both normal tissues and other breast cancer subtypes. Based on the functional analyses, we propose that these modules are potential drivers of proliferation and, as such, should be considered candidate molecular targets for drug development or drug repositioning in TNBC. Consistent with this argument, we repurposed steroids, anti-inflammatory agents, anti-infective agents, cardiovascular agents for patients with basal-like breast cancer. Finally, we have performed essential metabolite analysis on personalized genome-scale metabolic models and found that metabolites such as sphingosine-1-phosphate and cholesterol-sulfate have utmost importance in TNBC tumor growth.

Towards a Quantitative Understanding of Cell Identity

Cells have traditionally been characterized using expression levels of a few proteins that are thought to specify phenotype. This requires a priori selection of proteins, which can introduce descriptor bias, and neglects the wealth of additional molecular information nested within each cell in a population, which often makes these sparse descriptors qualitative. Recently, more unbiased and quantitative cell characterization has been made possible by new high-throughput, information-dense experimental approaches and data-driven computational methods. This review discusses such quantitative descriptors in the context of three central concepts of cell identity: definition, creation, and stability. Collectively, these concepts are essential for constructing quantitative phenotypic landscapes, which will enhance our understanding of cell biology and facilitate cell engineering for research and clinical applications.

Quantifying Waddington's epigenetic landscape: a comparison of single-cell potency measures

MOTIVATION

Estimating differentiation potency of single cells is a task of great biological and clinical significance, as it may allow identification of normal and cancer stem cell phenotypes. However, very few single-cell potency models have been proposed, and their robustness and reliability across independent studies have not yet been fully assessed.

RESULTS

Using nine independent single-cell RNA-Seq experiments, we here compare four different single-cell potency models to each other, in their ability to discriminate cells that ought to differ in terms of differentiation potency. Two of the potency models approximate potency via network entropy measures that integrate the single-cell RNA-Seq profile of a cell with a protein interaction network. The comparison between the four models reveals that integration of RNA-Seq data with a protein interaction network dramatically improves the robustness and reliability of single-cell potency estimates. We demonstrate that underlying this robustness is a correlation relationship, according to which high differentiation potency is positively associated with overexpression of network hubs. We further show that overexpressed network hubs are strongly enriched for ribosomal mitochondrial proteins, suggesting that their mRNA levels may provide a universal marker of a cell's potency. Thus, this study provides novel systems-biological insight into cellular potency and may provide a foundation for improved models of differentiation potency with far-reaching implications for the discovery of novel stem cell or progenitor cell phenotypes.

Structural robustness of mammalian transcription factor networks reveals plasticity across development

Network biology aims to understand cell behavior through the analysis of underlying complex biomolecular networks. Inference of condition-specific interaction networks from epigenomic data enables the characterization of the structural plasticity that regulatory networks can acquire in different tissues of the same organism. From this perspective, uncovering specific patterns of variation by comparing network structure among tissues could provide insights into systems-level mechanisms underlying cell behavior. Following this idea, here we propose an empirical framework to analyze mammalian tissue-specific networks, focusing on characterizing and contrasting their structure and behavior in response to perturbations. We structurally represent the state of the cell/tissue by condition specific transcription factor networks generated using DNase-seq chromatin accessibility data, and we profile their systems behavior in terms of the structural robustness against random and directed perturbations. Using this framework, we unveil the structural heterogeneity existing among tissues at different levels of differentiation. We uncover a novel and conserved systems property of regulatory networks underlying embryonic stem cells (ESCs): in contrast to terminally differentiated tissues, the promiscuous regulatory connectivity of ESCs produces a globally homogeneous network resulting in increased structural robustness. We show that this property is associated with a more permissive, less restrictive chromatin accesibility state in ESCs. Possible biological consequences of this property are discussed.

Hierarchical closeness-based properties reveal cancer survivability and biomarker genes in molecular signaling networks

Specific molecular signaling networks underlie different cancer types and quantitative analyses on those cancer networks can provide useful information about cancer treatments. Their structural metrics can reveal survivability of cancer patients and be used to identify biomarker genes for early cancer detection. In this study, we devised a novel structural metric called hierarchical closeness (HC) entropy and found that it was negatively correlated with 5-year survival rates. We also made an interesting observation that a network of higher HC entropy was likely to be more robust against mutations. This finding suggested that cancers of high HC entropy tend to be incurable because their signaling networks are robust to perturbations caused by treatment. We also proposed a novel core identification method based on the reachability factor in the HC measure. The cores were permitted to decompose such that the negative relationship between HC entropy and cancer survival rate was consistently conserved in every core level. Interestingly, we observed that many promising biomarker genes for early cancer detection reside in the innermost core of a signaling network. Taken together, the proposed analyses of the hierarchical structure of cancer signaling networks may be useful in developing future novel cancer treatments.

scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

Motivation

Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data.

Results

Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using 'single-cell energy' and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are-in combination-more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates.

Availability and implementation

A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath.

Supplementary information

Supplementary data are available at Bioinformatics online.

DNA methylome analysis reveals distinct epigenetic patterns of ascending aortic dissection and bicuspid aortic valve

Aims

Epigenetics may mediate the effects of environmental risk factors on disease, including heart disease. Thus, measuring the DNA methylome offers the opportunity to identify novel disease biomarkers and novel insights into disease mechanisms. The DNA methylation landscape of ascending aortic dissection (AD) and bicuspid aortic valve (BAV) with aortic aneurysmal dilatation remain uncharacterized. The present study aimed to explore the genome-wide DNA methylation landscape underpinning these two diseases.

Methods and results

We used Illumina 450k DNA methylation beadarrays to analyse 21 ascending aorta samples, including 10 cases with AD, 5 with BAV and 6 healthy controls. We adjusted for intra-sample cellular heterogeneity, providing the first unbiased genome-wide exploration of the DNA methylation landscape underpinning these two diseases. We discover that both diseases are characterized by loss of DNA methylation at non-CpG sites. We validate this non-CpG hypomethylation signature with pyrosequencing. In contrast to non-CpGs, AD and BAV exhibit distinct DNA methylation landscapes at CpG sites, with BAV characterized mainly by hypermethylation of EZH2 targets. In the case of AD, integrative DNA methylation gene expression analysis reveals that AD is characterized by a dedifferentiated smooth muscle cell phenotype. Our integrative analysis further reveals hypomethylation associated overexpression of RARA in AD, a pattern which is also seen in cells exposed to smoke toxins.

Conclusion

Our data supports a model in which increased cellular proliferation in AD and BAV underpins loss of methylation at non-CpG sites. Our data further supports a model, in which AD is associated with an inflammatory vascular remodeling process, possibly mediated by the epigenome and linked to environmental risk factors such as smoking.

NetLand: quantitative modeling and visualization of Waddington's epigenetic landscape using probabilistic potential

Summary

Waddington’s epigenetic landscape is a powerful metaphor for cellular dynamics driven by gene regulatory networks (GRNs). Its quantitative modeling and visualization, however, remains a challenge, especially when there are more than two genes in the network. A software tool for Waddington’s landscape has not been available in the literature. We present NetLand, an open-source software tool for modeling and simulating the kinetic dynamics of GRNs, and visualizing the corresponding Waddington’s epigenetic landscape in three dimensions without restriction on the number of genes in a GRN. With an interactive and graphical user interface, NetLand can facilitate the knowledge discovery and experimental design in the study of cell fate regulation (e.g. stem cell differentiation and reprogramming).

Availability and Implementation

NetLand can run under operating systems including Windows, Linux and OS X. The executive files and source code of NetLand as well as a user manual, example models etc. can be downloaded from http://netland-ntu.github.io/NetLand/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

Networks used in biological applications at different scales (molecule, cell and population) are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system) as well as in their discrete Boolean versions (e.g., non-linear Hopfield system); in both cases, the notion of interaction graph G(J) associated to its Jacobian matrix J, and also the concepts of frustrated nodes, positive or negative circuits of G(J), kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i) attractor entropy, (ii) isochronal entropy and (iii) entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.