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.

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.

FOXG1 targets BMP repressors and cell cycle inhibitors in human neural progenitor cells.

FOXG1 is a critical transcription factor in human brain where loss-of-function mutations cause a severe neurodevelopmental disorder, while increased FOXG1 expression is frequently observed in glioblastoma. FOXG1 is an inhibitor of cell patterning and an activator of cell proliferation in chordate model organisms but different mechanisms have been proposed as to how this occurs. To identify genomic targets of FOXG1 in human neural progenitor cells (NPCs), we engineered a cleavable reporter construct in endogenous FOXG1 and performed chromatin immunoprecipitation (ChIP) sequencing. We also performed deep RNA sequencing of NPCs from two females with loss-of-function mutations in FOXG1 and their healthy biological mothers. Integrative analyses of RNA and ChIP sequencing data showed that cell cycle regulation and Bone Morphogenic Protein (BMP) repression gene ontology categories were over-represented as FOXG1 targets. Using engineered brain cell lines, we show that FOXG1 specifically activates SMAD7 and represses CDKN1B. Activation of SMAD7 which inhibits BMP signaling may be one way that FOXG1 patterns the forebrain, while repression of cell cycle regulators such as CDKN1B may be one way that FOXG1 expands the NPC pool to ensure proper brain size. Our data reveal novel mechanisms on how FOXG1 may control forebrain patterning and cell proliferation in human brain development.

Kabuki syndrome stem cell models reveal locus specificity of histone methyltransferase 2D (KMT2D/MLL4).

Kabuki syndrome is frequently caused by loss-of-function mutations in one allele of histone 3 lysine 4 (H3K4) methyltransferase KMT2D and is associated with problems in neurological, immunological and skeletal system development. We generated heterozygous KMT2D knockout and Kabuki patient-derived cell models to investigate the role of reduced dosage of KMT2D in stem cells. We discovered chromosomal locus-specific alterations in gene expression, specifically a 110 Kb region containing Synaptotagmin 3 (SYT3), C-Type Lectin Domain Containing 11A (CLEC11A), Chromosome 19 Open Reading Frame 81 (C19ORF81) and SH3 And Multiple Ankyrin Repeat Domains 1 (SHANK1), suggesting locus-specific targeting of KMT2D. Using whole genome histone methylation mapping, we confirmed locus-specific changes in H3K4 methylation patterning coincident with regional decreases in gene expression in Kabuki cell models. Significantly reduced H3K4 peaks aligned with regions of stem cell maps of H3K27 and H3K4 methylation suggesting KMT2D haploinsufficiency impact bivalent enhancers in stem cells. Preparing the genome for subsequent differentiation cues may be of significant importance for Kabuki-related genes. This work provides a new insight into the mechanism of action of an important gene in bone and brain development and may increase our understanding of a specific function of a human disease-relevant H3K4 methyltransferase family member.

Lesch-Nyhan disease causes impaired energy metabolism and reduced developmental potential in midbrain dopaminergic cells.

Mutations in HPRT1, a gene encoding a rate-limiting enzyme for purine salvage, cause Lesch-Nyhan disease which is characterized by self-injury and motor impairments. We leveraged stem cell and genetic engineering technologies to model the disease in isogenic and patient-derived forebrain and midbrain cell types. Dopaminergic progenitor cells deficient in HPRT showed decreased intensity of all developmental cell-fate markers measured. Metabolic analyses revealed significant loss of all purine derivatives, except hypoxanthine, and impaired glycolysis and oxidative phosphorylation. real-time glucose tracing demonstrated increased shunting to the pentose phosphate pathway for de novo purine synthesis at the expense of ATP production. Purine depletion in dopaminergic progenitor cells resulted in loss of RHEB, impairing mTORC1 activation. These data demonstrate dopaminergic-specific effects of purine salvage deficiency and unexpectedly reveal that dopaminergic progenitor cells are programmed to a high-energy state prior to higher energy demands of terminally differentiated cells.

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.

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.

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.

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.

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-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs.

Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, it has proven difficult to generate functional hematopoietic stem cells (HSCs). To define the dynamics and heterogeneity of HSPCs that can be generated in vitro from hPSCs, we explored single-cell RNA sequencing (scRNAseq) in combination with single-cell protein expression analysis. Bioinformatics analyses and functional validation defined the transcriptomes of naïve progenitors and erythroid-, megakaryocyte-, and leukocyte-committed progenitors, and we identified CD44, CD326, ICAM2/CD9, and CD18, respectively, as markers of these progenitors. Using an artificial neural network that we trained on scRNAseq derived from human fetal liver, we identified a wide range of hPSC-derived HSPCs phenotypes, including a small group classified as HSCs. This transient HSC-like population decreased as differentiation proceeded, and was completely missing in the data set that had been generated using cells selected on the basis of CD43 expression. By comparing the single-cell transcriptome of in vitro-generated HSC-like cells with those generated within the fetal liver, we identified transcription factors and molecular pathways that can be explored in the future to improve the in vitro production of HSCs.

Theory of cell fate.

Cell fate decisions are controlled by complex intracellular molecular regulatory networks. Studies increasingly reveal the scale of this complexity: not only do cell fate regulatory networks contain numerous positive and negative feedback loops, they also involve a range of different kinds of nonlinear protein-protein and protein-DNA interactions. This inherent complexity and nonlinearity makes cell fate decisions hard to understand using experiment and intuition alone. In this primer, we will outline how tools from mathematics can be used to understand cell fate dynamics. We will briefly introduce some notions from dynamical systems theory, and discuss how they offer a framework within which to build a rigorous understanding of what we mean by a cell "fate", and how cells change fate. We will also outline how modern experiments, particularly high-throughput single-cell experiments, are enabling us to test and explore the limits of these ideas, and build a better understanding of cellular identities. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Biological Mechanisms > Cell Fates Models of Systems Properties and Processes > Cellular Models.

Machine Learning of Stem Cell Identities From Single-Cell Expression Data via Regulatory Network Archetypes.

The molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the "average" pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells-corresponding to naïve and formative pluripotent states and an early primitive endoderm state-and associate these configurations with particular combinations of regulatory network activity archetypes that govern different aspects of the cell's response to environmental stimuli, cell cycle status and core information processing circuitry. These results show how variability in cell identities arise naturally from alterations in underlying regulatory network dynamics and demonstrate how methods from machine learning may be used to better understand single cell biology, and the collective dynamics of cell communities.

Thrombopoietin Metabolically Primes Hematopoietic Stem Cells to Megakaryocyte-Lineage Differentiation.

During acute myelosuppression or thrombocytopenia, bone marrow (BM) hematopoietic cells respond rapidly to replenish peripheral blood platelets. While the cytokine thrombopoietin (Thpo) both regulates platelet production and maintains HSC potential, whether Thpo controls megakaryocyte (Mk)-lineage differentiation of HSCs is unclear. Here, we show that Thpo rapidly upregulates mitochondrial activity in HSCs, an activity accompanied by differentiation to an Mk lineage. Moreover, in unperturbed hematopoiesis, HSCs with high mitochondrial activity exhibit Mk-lineage differentiation in vitro and myeloid lineage-biased reconstitution in vivo. Furthermore, Thpo skewed HSCs to express the tetraspanin CD9, a pattern correlated with mitochondrial activity. Mitochondria-active HSCs are resistant to apoptosis and oxidative stress upon Thpo stimulation. Thpo-regulated mitochondrial activity associated with mitochondrial translocation of STAT3 phosphorylated at serine 727. Overall, we report an important role for Thpo in regulating rapid Mk-lineage commitment. Thpo-dependent changes in mitochondrial metabolism prime HSCs to undergo direct differentiation to an Mk lineage.

Stem Cell Differentiation as a Non-Markov Stochastic Process.

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

Nanog Fluctuations in Embryonic Stem Cells Highlight the Problem of Measurement in Cell Biology.

A number of important pluripotency regulators, including the transcription factor Nanog, are observed to fluctuate stochastically in individual embryonic stem cells. By transiently priming cells for commitment to different lineages, these fluctuations are thought to be important to the maintenance of, and exit from, pluripotency. However, because temporal changes in intracellular protein abundances cannot be measured directly in live cells, fluctuations are typically assessed using genetically engineered reporter cell lines that produce a fluorescent signal as a proxy for protein expression. Here, using a combination of mathematical modeling and experiment, we show that there are unforeseen ways in which widely used reporter strategies can systematically disturb the dynamics they are intended to monitor, sometimes giving profoundly misleading results. In the case of Nanog, we show how genetic reporters can compromise the behavior of important pluripotency-sustaining positive feedback loops, and induce a bifurcation in the underlying dynamics that gives rise to heterogeneous Nanog expression patterns in reporter cell lines that are not representative of the wild-type. These findings help explain the range of published observations of Nanog variability and highlight the problem of measurement in live cells.

Quantification of intracellular payload release from polymersome nanoparticles.

Polymersome nanoparticles (PMs) are attractive candidates for spatio-temporal controlled delivery of therapeutic agents. Although many studies have addressed cellular uptake of solid nanoparticles, there is very little data available on intracellular release of molecules encapsulated in membranous carriers, such as polymersomes. Here, we addressed this by developing a quantitative assay based on the hydrophilic dye, fluorescein. Fluorescein was encapsulated stably in PMs of mean diameter 85 nm, with minimal leakage after sustained dialysis. No fluorescence was detectable from fluorescein PMs, indicating quenching. Following incubation of L929 cells with fluorescein PMs, there was a gradual increase in intracellular fluorescence, indicating PM disruption and cytosolic release of fluorescein. By combining absorbance measurements with flow cytometry, we quantified the real-time intracellular release of a fluorescein at a single-cell resolution. We found that 173 ± 38 polymersomes released their payload per cell, with significant heterogeneity in uptake, despite controlled synchronisation of cell cycle. This novel method for quantification of the release of compounds from nanoparticles provides fundamental information on cellular uptake of nanoparticle-encapsulated compounds. It also illustrates the stochastic nature of population distribution in homogeneous cell populations, a factor that must be taken into account in clinical use of this technology.

Single-cell pluripotency regulatory networks.

Pluripotent stem cells (PSCs) are a popular model system for investigating development, tissue regeneration, and repair. Although much is known about the molecular mechanisms that regulate the balance between self-renewal and lineage commitment in PSCs, the spatiotemporal integration of responsive signaling pathways with core transcriptional regulatory networks are complex and only partially understood. Moreover, measurements made on populations of cells reveal only average properties of the underlying regulatory networks, obscuring their fine detail. Here, we discuss the reconstruction of regulatory networks in individual cells using novel single-cell transcriptomics and proteomics, in order to expand our understanding of the molecular basis of pluripotency, including the role of cell-cell variability within PSC populations, and ways in which networks may be controlled in order to reliably manipulate cell behavior.

MicroRNA-146a regulates human foetal femur derived skeletal stem cell differentiation by down-regulating SMAD2 and SMAD3.

MicroRNAs (miRs) play a pivotal role in a variety of biological processes including stem cell differentiation and function. Human foetal femur derived skeletal stem cells (SSCs) display enhanced proliferation and multipotential capacity indicating excellent potential as candidates for tissue engineering applications. This study has examined the expression and role of miRs in human foetal femur derived SSC differentiation along chondrogenic and osteogenic lineages. Cells isolated from the epiphyseal region of the foetal femur expressed higher levels of genes associated with chondrogenesis while cells from the foetal femur diaphyseal region expressed higher levels of genes associated with osteogenic differentiation. In addition to the difference in osteogenic and chondrogenic gene expression, epiphyseal and diaphyseal cells displayed distinct miRs expression profiles. miR-146a was found to be expressed by human foetal femur diaphyseal cells at a significantly enhanced level compared to epiphyseal populations and was predicted to target various components of the TGF-ß pathway. Examination of miR-146a function in foetal femur cells confirmed regulation of protein translation of SMAD2 and SMAD3, important TGF-ß and activin ligands signal transducers following transient overexpression in epiphyseal cells. The down-regulation of SMAD2 and SMAD3 following overexpression of miR-146a resulted in an up-regulation of the osteogenesis related gene RUNX2 and down-regulation of the chondrogenesis related gene SOX9. The current findings indicate miR-146a plays an important role in skeletogenesis through attenuation of SMAD2 and SMAD3 function and provide further insight into the role of miRs in human skeletal stem cell differentiation modulation with implications therein for bone reparation.

Visualization and clustering of high-dimensional transcriptome data using GATE.

The potential gains from advances in high-throughput experimental molecular biology techniques are commonly not fully realized since these techniques often produce more data than can be easily organized and visualized. To address these problems, GATE (Grid-Analysis of Time-Series Expression) was developed. GATE is an integrated software platform for the analysis and visualization of high-dimensional time-series datasets, which allows flexible interrogation of time-series data against a wide range of databases of prior knowledge, thus linking observed molecular dynamics to potential genetic, epigenetic, and signaling mechanisms responsible for observed dynamics. This article provides a brief guide to using GATE effectively.