In this chapter, we describe techniques for the isolation and characterisation of skeletal stem cells from human bone marrow. The methods for enrichment of STRO-1+ and STRO-4+ cells using magnetic activated cell sorting are described and we also detail techniques for establishing and characterizing osteogenic, adipogenic, and chondrogenic cultures from these cells. Finally, we present methods for studying the ability of these cells to produce bone in vivo using diffusion chambers which have been implanted subcutaneously into mice.

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.

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.

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.

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.

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.

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.

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.

Langerhans cells (LCs) reside in the epidermis as a dense network of immune system sentinels, coordinating both immunogenic and tolerogenic immune responses. To determine molecular switches directing induction of LC immune activation, we performed mathematical modelling of gene regulatory networks identified by single cell RNA sequencing of LCs exposed to TNF-alpha, a key pro-inflammatory signal produced by the skin. Our approach delineated three programmes of LC phenotypic activation (immunogenic, tolerogenic or ambivalent), and confirmed that TNF-alpha enhanced LC immunogenic programming. Through regulon analysis followed by mutual information modelling, we identified IRF1 as the key transcription factor for the regulation of immunogenicity in LCs. Application of a mathematical toggle switch model, coupling IRF1 with tolerance-inducing transcription factors, determined the key set of transcription factors regulating the switch between tolerance and immunogenicity, and correctly predicted LC behaviour in LCs derived from different body sites. Our findings provide a mechanistic explanation of how combinatorial interactions between different transcription factors can coordinate specific transcriptional programmes in human LCs, interpreting the microenvironmental context of the local tissue microenvironments.

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.

ABSTRACTLangerhans cells (LCs) reside in the epidermis as a dense network of immune system sentinels, coordinating both immunogenic and tolerogenic immune responses. To determine molecular switches directing induction of LC immune activation, we performed mathematical modelling of gene regulatory networks identified by single cell RNA sequencing of LCs exposed to TNF, a key pro-inflammatory signal produced by the skin. Our approach delineated three programmes of LC phenotypic activation (immunogenic, tolerogenic or ambivalent), and confirmed that TNF enhanced LC immunogenic programming. Through regulon analysis followed by mutual information modelling, we identifiedIRF1as the key transcription factor for the regulation of immunogenicity in LCs. Application of a mathematical toggle switch model, couplingIRF1with tolerance-inducing transcription factors, determined the key set of transcription factors regulating the switch between tolerance and immunogenicity, and correctly predicted LC behaviour in LCs derived from different body sites. Our findings provide a mechanistic explanation of how combinatorial interactions between different transcription factors can coordinate specific transcriptional programmes in human LCs, interpreting the microenvironmental context of the local tissue microenvironments.

During embryonic development, haematopoietic stem and progenitor cells (HSPCs) develop in different waves at various anatomical sites. This process can be recapitulated in vitro by cytokine-mediated differentiation of human pluripotent stem cells (hPSCs) although the differentiation of haematopoietic stem cells (HSCs) remains a major challenge in the field. To depict the developmental dynamics and to study the heterogeneity of HSPCs that can be generated in vitro from hPSCs, we employed single cell RNA sequencing (scRNAseq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq). We identified the transcriptome and functionally validated naïve progenitors as well as erythroid, megakaryocyte and leukocyte-committed progenitors and we associated to these the expression of CD44, CD326, ICAM2/CD9 and CD18, respectively. To identify and compare the in vitro-generated cell types to those developed in vivo, we employed a machine learning approach. We trained an artificial neural network (ANN) using a human fetal liver scRNAseq dataset of hematopoietic cells from HSCs to mature cell types. We identified a wide range of hPSCs-derived phenotypes, including a small group classified as HSC/MPP. This transient HSC-like population decreased with the progression of the differentiation protocol and was not identified by the ANN in the datasets that we generated from sorted CD43+ cells. By comparing the transcriptome of these in vitro-generated HSC/MPP-like cells with those within the fetal liver we identified transcription factors and molecular pathways that can be exploited to improve the in vitro production of HSCs in the future. Finally, when these HSC-like cells were compared to yolk sac and AGM cells we observed that in vitro derived progenitors displayed signatures of high oxidative respiration, suggesting an overall divergent metabolic profile. During embryonic development, haematopoietic stem and progenitor cells (HSPCs) develop in different waves at various anatomical sites. This process can be recapitulated in vitro by cytokine-mediated differentiation of human pluripotent stem cells (hPSCs) although the differentiation of haematopoietic stem cells (HSCs) remains a major challenge in the field. To depict the developmental dynamics and to study the heterogeneity of HSPCs that can be generated in vitro from hPSCs, we employed single cell RNA sequencing (scRNAseq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq). We identified the transcriptome and functionally validated naïve progenitors as well as erythroid, megakaryocyte and leukocyte-committed progenitors and we associated to these the expression of CD44, CD326, ICAM2/CD9 and CD18, respectively. To identify and compare the in vitro-generated cell types to those developed in vivo, we employed a machine learning approach. We trained an artificial neural network (ANN) using a human fetal liver scRNAseq dataset of hematopoietic cells from HSCs to mature cell types. We identified a wide range of hPSCs-derived phenotypes, including a small group classified as HSC/MPP. This transient HSC-like population decreased with the progression of the differentiation protocol and was not identified by the ANN in the datasets that we generated from sorted CD43+ cells. By comparing the transcriptome of these in vitro-generated HSC/MPP-like cells with those within the fetal liver we identified transcription factors and molecular pathways that can be exploited to improve the in vitro production of HSCs in the future. Finally, when these HSC-like cells were compared to yolk sac and AGM cells we observed that in vitro derived progenitors displayed signatures of high oxidative respiration, suggesting an overall divergent metabolic profile.

Mathematical models are routinely used in the physical and engineering sciences to help understand complex systems and optimize industrial processes. There are numerous examples of the fruitful application of mathematical principles to problems in cell and molecular biology, and recent years have seen increasing interest in applying quantitative techniques to problems in biotechnology. This chapter reviews some ways, in which mathematical models and machine learning may be used to advance our understanding of the complex biological processes involved in cellular differentiation and tissue growth and development, for applications in tissue engineering and regenerative medicine. We will discuss how mathematical models can advance our understanding of stem cell differentiation; growth and development of stem and progenitor cell colonies; and the mechanisms that underpin spatial organization of structure in three-dimensional developing tissues. We will also discuss how recent developments in machine leaning are able to extract biological knowledge from the most complex experimental datasets. We will conclude by considering how computational techniques can be further applied to the design and optimization of effective tissue engineering strategies for clinical application. Although this field is in its infancy, the appropriate use of mathematical methods has considerable potential to transform tissue engineering from experimental concept to clinical reality.

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.

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

There is a wealth of data indicating human bone marrow derived stromal cells (HBMSCs) contain the skeletal stem cell (SSC) with the potential to differentiate along the stromal osteogenic, adipogenic and chondrogenic lineages. However, despite these advances, current methods to isolate skeletal stem cells (SSCs) from human tissues have proved challenging as no single specific marker has been identified limiting understanding of SSC fate, immunophenotype and the widespread clinical application of these cells. While a number of cell surface markers can enrich for SSCs, none of the proposed markers, alone, provide a platform to isolate single cells with the ability to form bone, cartilage, and adipose tissue in humans. The current study details the application of oligonucleotide-coated nanoparticles, spherical nucleic acids (SNAs), to rapidly isolate human cells using mRNAs signatures detected in SSCs in real time, to identify stem and progenitor skeletal populations using single cell RNA sequencing. Based on scRNA-seq of samples from 11 patients, this method was able to identify novel targets for SSC enrichment, which were assessed in a total of 80 patients. This methodology was able to isolate potential SSCs found at a frequency of <1 in 1,000,000 in human bone marrow, with a capacity for tri-lineage differentiation in vitro . The current approach provides new targets and a platform to advance SSC isolation, enrichment with significant therapeutic impact therein.

Machine learning is commonly employed to extract meaningful information from large and complex data. In situations where only scant data is available, algorithms can leverage abundant data from a separate (unrelated) context to address the learning problem. Here, we present two recently developed biomedical applications that take advantage of transfer learning to bridge the gap from model systems to human: single-cell label transfer and drug response prediction in patients.

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 naive 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.

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.