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.

POT1a deficiency in mesenchymal niches perturbs B-lymphopoiesis.

Protection of telomeres 1a (POT1a) is a telomere binding protein. A decrease of POT1a is related to myeloid-skewed haematopoiesis with ageing, suggesting that protection of telomeres is essential to sustain multi-potency. Since mesenchymal stem cells (MSCs) are a constituent of the hematopoietic niche in bone marrow, their dysfunction is associated with haematopoietic failure. However, the importance of telomere protection in MSCs has yet to be elucidated. Here, we show that genetic deletion of POT1a in MSCs leads to intracellular accumulation of fatty acids and excessive ROS and DNA damage, resulting in impaired osteogenic-differentiation. Furthermore, MSC-specific POT1a deficient mice exhibited skeletal retardation due to reduction of IL-7 producing bone lining osteoblasts. Single-cell gene expression profiling of bone marrow from POT1a deficient mice revealed that B-lymphopoiesis was selectively impaired. These results demonstrate that bone marrow microenvironments composed of POT1a deficient MSCs fail to support B-lymphopoiesis, which may underpin age-related myeloid-bias in haematopoiesis.

An information-theoretic approach to single cell sequencing analysis.

BACKGROUND: Single-cell sequencing (sc-Seq) experiments are producing increasingly large data sets. However, large data sets do not necessarily contain large amounts of information. RESULTS: Here, we formally quantify the information obtained from a sc-Seq experiment and show that it corresponds to an intuitive notion of gene expression heterogeneity. We demonstrate a natural relation between our notion of heterogeneity and that of cell type, decomposing heterogeneity into that component attributable to differential expression between cell types (inter-cluster heterogeneity) and that remaining (intra-cluster heterogeneity). We test our definition of heterogeneity as the objective function of a clustering algorithm, and show that it is a useful descriptor for gene expression patterns associated with different cell types. CONCLUSIONS: Thus, our definition of gene heterogeneity leads to a biologically meaningful notion of cell type, as groups of cells that are statistically equivalent with respect to their patterns of gene expression. Our measure of heterogeneity, and its decomposition into inter- and intra-cluster, is non-parametric, intrinsic, unbiased, and requires no additional assumptions about expression patterns. Based on this theory, we develop an efficient method for the automatic unsupervised clustering of cells from sc-Seq data, and provide an R package implementation.

Single-cell RNA-sequence analysis of human bone marrow reveals new targets for isolation of skeletal stem cells using spherical nucleic acids.

There is a wealth of data indicating human bone marrow contains skeletal stem cells (SSC) with the capacity for osteogenic, chondrogenic and adipogenic differentiation. However, current methods to isolate SSCs are restricted by the lack of a defined marker, limiting understanding of SSC fate, immunophenotype, function and clinical application. The current study applied single-cell RNA-sequencing to profile human adult bone marrow populations from 11 donors and identified novel targets for SSC enrichment. Spherical nucleic acids were used to detect these mRNA targets in SSCs. This methodology was able to rapidly isolate potential SSCs found at a frequency of <1 in 1,000,000 in human bone marrow, with the capacity for tri-lineage differentiation in vitro and ectopic bone formation in vivo. The current studies detail the development of a platform to advance SSC enrichment from human bone marrow, offering an invaluable resource for further SSC characterisation, with significant therapeutic impact therein.

Stem cell biology needs a theory.

Stem cell biologists are increasingly making use of computational models to decipher their data. However, there is sometimes uncertainty about what makes a "good" model. The purpose of this commentary is to argue for closer integration of experiment and theory in stem cell research and propose guidelines for good theory.

Transcriptional programming of immunoregulatory responses in human Langerhans cells.

Human epidermal Langerhans cells (LCs) maintain immune homeostasis in the skin. To examine transcriptional programming of human primary LCs during homeostasis, we performed scRNA-seq analysis of LCs before and after migration from the epidermis, coupled with functional assessment of their regulatory T cell priming capabilities. The analysis revealed that steady-state LCs exist in a continuum of maturation states and upregulate antigen presentation genes along with an immunoregulatory module including the genes IDO1, LGALS1, LAMTOR1, IL4I, upon their migration. The migration-induced transition in genomic state is accompanied by the ability of LCs to more efficiently prime regulatory T cell responses in co-culture assays. Computational analyses of the scRNAseq datasets using SCENIC and Partial Information Decomposition in Context identified a set of migration-induced transcription factors including IRF4, KLF6 and RelB as key nodes within a immunoregulatory gene regulatory network. These findings support a model in which efficient priming of immunoregulatory responses by LCs is dependent on coordinated upregulation of a migration-coupled maturation program with a immunoregulation-promoting genomic module.

Fluctuations in T cell receptor and pMHC interactions regulate T cell activation.

Adaptive immune responses depend on interactions between T cell receptors (TCRs) and peptide major histocompatibility complex (pMHC) ligands located on the surface of T cells and antigen presenting cells (APCs), respectively. As TCRs and pMHCs are often only present at low copy numbers their interactions are inherently stochastic, yet the role of stochastic fluctuations on T cell function is unclear. Here, we introduce a minimal stochastic model of T cell activation that accounts for serial TCR-pMHC engagement, reversible TCR conformational change and TCR aggregation. Analysis of this model indicates that it is not the strength of binding between the T cell and the APC cell per se that elicits an immune response, but rather the information imparted to the T cell from the encounter, as assessed by the entropy rate of the TCR-pMHC binding dynamics. This view provides an information-theoretic interpretation of T cell activation that explains a range of experimental observations. Based on this analysis, we propose that effective T cell therapeutics may be enhanced by optimizing the inherent stochasticity of TCR-pMHC binding dynamics.

The geometry of cell fate.

Waddington's epigenetic landscape is a powerful metaphor for development. Recent work shows how molecular "noise" can shape the architecture of this landscape and how universal properties of cell fate commitment can be distilled from careful consideration of its geometry.

Geometry and symmetry in biochemical reaction systems.

Complex systems of intracellular biochemical reactions have a central role in regulating cell identities and functions. Biochemical reaction systems are typically studied using the language and tools of graph theory. However, graph representations only describe pairwise interactions between molecular species and so are not well suited to modelling complex sets of reactions that may involve numerous reactants and/or products. Here, we make use of a recently developed hypergraph theory of chemical reactions that naturally allows for higher-order interactions to explore the geometry and quantify functional redundancy in biochemical reactions systems. Our results constitute a general theory of automorphisms for oriented hypergraphs and describe the effect of automorphism group structure on hypergraph Laplacian spectra.

Dual dean entrainment with volume ratio modulation for efficient droplet co-encapsulation: extreme single-cell indexing.

The future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular system. Current approaches are limited by the Poisson statistic, requiring dilute cell suspensions and associated losses in throughput. In this contribution, we apply Dean entrainment to both cell and bead inputs, defining different volume packets to effect efficient co-encapsulation. Volume ratio scaling was explored to identify optimal conditions. This enabled the co-encapsulation of single cells with reporter beads at rates of ∼1 million cells per hour, while increasing assay signal-to-noise with cell multiplet rates of ∼2.5% and capturing ∼70% of cells. The method, called Pirouette coupling, extends our capacity to investigate biological systems.

Universal principles of lineage architecture and stem cell identity in renewing tissues.

Adult tissues in multicellular organisms typically contain a variety of stem, progenitor and differentiated cell types arranged in a lineage hierarchy that regulates healthy tissue turnover. Lineage hierarchies in disparate tissues often exhibit common features, yet the general principles regulating their architecture are not known. Here, we provide a formal framework for understanding the relationship between cell molecular 'states' and cell 'types', based on the topology of admissible cell state trajectories. We show that a self-renewing cell type - if defined as suggested by this framework - must reside at the top of any homeostatic renewing lineage hierarchy, and only there. This architecture arises as a natural consequence of homeostasis, and indeed is the only possible way that lineage architectures can be constructed to support homeostasis in renewing tissues. Furthermore, under suitable feedback regulation, for example from the stem cell niche, we show that the property of 'stemness' is entirely determined by the cell environment, in accordance with the notion that stem cell identities are contextual and not determined by hard-wired, cell-intrinsic characteristics. This article has an associated 'The people behind the papers' interview.

An Esrrb and Nanog Cell Fate Regulatory Module Controlled by Feed Forward Loop Interactions.

Cell fate decisions during development are governed by multi-factorial regulatory mechanisms including chromatin remodeling, DNA methylation, binding of transcription factors to specific loci, RNA transcription and protein synthesis. However, the mechanisms by which such regulatory "dimensions" coordinate cell fate decisions are currently poorly understood. Here we quantified the multi-dimensional molecular changes that occur in mouse embryonic stem cells (mESCs) upon depletion of Estrogen related receptor beta (Esrrb), a key pluripotency regulator. Comparative analyses of expression changes subsequent to depletion of Esrrb or Nanog, indicated that a system of interlocked feed-forward loops involving both factors, plays a central part in regulating the timing of mESC fate decisions. Taken together, our meta-analyses support a hierarchical model in which pluripotency is maintained by an Oct4-Sox2 regulatory module, while the timing of differentiation is regulated by a Nanog-Esrrb module.

Modeling Stem Cell Fates using Non-Markov Processes.

Epigenetic memories play an important part in regulating stem cell identities. Tools from the theory of non-Markov processes may help us understand these memories better and develop a more integrated view of stem cell fate and function.

Micro RNA Targets in HIV Latency: Insights into Novel Layers of Latency Control.

Despite the considerable progress that has been made in identifying cellular factors and pathways that contribute to establishment and maintenance of the latent HIV reservoir, it remains the major obstacle to eradicating this virus. Most recently, noncoding genes have been implicated in regulation of HIV expression. In this study, small RNA sequencing was used to profile expression of microRNAs (miRNAs) in a primary CD4+ T cell in vitro model of HIV latency. Previously, we have shown that protein-coding genes dysregulated in this model were enriched for the p53 signaling pathway, which was confirmed experimentally. We further found a link between p53 signaling and dysregulated long noncoding RNAs. In this study, we hypothesized that miRNAs may provide an additional level of regulation of the p53 signaling pathway during HIV latency. Twenty-six miRNAs were identified to be dysregulated in our latency model. A subset of these miRNAs was validated by real-time quantitative polymerase chain reaction. Predicted messenger RNA (mRNA) targets and cellular pathways enriched for mRNA targets were identified using several analytical methods. Our analyses showed that many protein-coding genes and pathways targeted by dysregulated miRNAs have relevance to regulation of HIV expression or establishment of HIV latency. The p53 signaling pathway was found among pathways that were targeted by dysregulated miRNAs at a greater level than expected by chance. This study provides a mechanistic insight into regulation of the p53 pathway through miRNAs that may contribute to the establishment of latency.

Machine Learning of Hematopoietic Stem Cell Divisions from Paired Daughter Cell Expression Profiles Reveals Effects of Aging on Self-Renewal.

Changes in stem cell activity may underpin aging. However, these changes are not completely understood. Here, we combined single-cell profiling with machine learning and in vivo functional studies to explore how hematopoietic stem cell (HSC) divisions patterns evolve with age. We first trained an artificial neural network (ANN) to accurately identify cell types in the hematopoietic hierarchy and predict their age from single-cell gene-expression patterns. We then used this ANN to compare identities of daughter cells immediately after HSC divisions and found that the self-renewal ability of individual HSCs declines with age. Furthermore, while HSC cell divisions are deterministic and intrinsically regulated in young and old age, they are variable and niche sensitive in mid-life. These results indicate that the balance between intrinsic and extrinsic regulation of stem cell activity alters substantially with age and help explain why stem cell numbers increase through life, yet regenerative potency declines.

Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing.

Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning-the branch of machine learning that concerns passing information from one domain to another-can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

Heterogeneity and 'memory' in stem cell populations.

Modern single cell experiments have revealed unexpected heterogeneity in apparently functionally 'pure' cell populations. However, we are still lacking a conceptual framework to understand this heterogeneity. Here, we propose that cellular memories-changes in the molecular status of a cell in response to a stimulus, that modify the ability of the cell to respond to future stimuli-are an essential ingredient in any such theory. We illustrate this idea by considering a simple age-structured model of stem cell proliferation that takes account of mitotic memories. Using this model we argue that asynchronous mitosis generates heterogeneity that is central to stem cell population function. This model naturally explains why stem cell numbers increase through life, yet regenerative potency simultaneously declines.

Single platelet variability governs population sensitivity and initiates intrinsic heterotypic responses.

Investigations into the nature of platelet functional variety and consequences for homeostasis require new methods for resolving single platelet phenotypes. Here we combine droplet microfluidics with flow cytometry for high throughput single platelet function analysis. A large-scale sensitivity continuum was shown to be a general feature of human platelets from individual donors, with hypersensitive platelets coordinating significant sensitivity gains in bulk platelet populations and shown to direct aggregation in droplet-confined minimal platelet systems. Sensitivity gains scaled with agonist potency (convulxin > TRAP-14>ADP) and reduced the collagen and thrombin activation threshold required for platelet population polarization into pro-aggregatory and pro-coagulant states. The heterotypic platelet response results from an intrinsic behavioural program. The method and findings invite future discoveries into the nature of hypersensitive platelets and how community effects produce population level responses in health and disease.