Diffusion maps for high-dimensional single-cell analysis of differentiation data

Single Cell Gene Expression to Understand the Dynamic Architecture of the Heart

The recent development of single cell gene expression technologies, and especially single cell transcriptomics, have revolutionized the way biologists and clinicians investigate organs and organisms, allowing an unprecedented level of resolution to the description of cell demographics in both healthy and diseased states. Single cell transcriptomics provide information on prevalence, heterogeneity, and gene co-expression at the individual cell level. This enables a cell-centric outlook to define intracellular gene regulatory networks and to bridge toward the definition of intercellular pathways otherwise masked in bulk analysis. The technologies have developed at a fast pace producing a multitude of different approaches, with several alternatives to choose from at any step, including single cell isolation and capturing, lysis, RNA reverse transcription and cDNA amplification, library preparation, sequencing, and computational analyses. Here, we provide guidelines for the experimental design of single cell RNA sequencing experiments, exploring the current options for the crucial steps. Furthermore, we provide a complete overview of the typical data analysis workflow, from handling the raw sequencing data to making biological inferences. Significantly, advancements in single cell transcriptomics have already contributed to outstanding exploratory and functional studies of cardiac development and disease models, as summarized in this review. In conclusion, we discuss achievable outcomes of single cell transcriptomics' applications in addressing unanswered questions and influencing future cardiac clinical applications.

T Helper Cell Cytokines Modulate Intestinal Stem Cell Renewal and Differentiation

In the small intestine, a niche of accessory cell types supports the generation of mature epithelial cell types from intestinal stem cells (ISCs). It is unclear, however, if and how immune cells in the niche affect ISC fate or the balance between self-renewal and differentiation. Here, we use single-cell RNA sequencing (scRNA-seq) to identify MHC class II (MHCII) machinery enrichment in two subsets of Lgr5+ ISCs. We show that MHCII+ Lgr5+ ISCs are non-conventional antigen-presenting cells in co-cultures with CD4+ T helper (Th) cells. Stimulation of intestinal organoids with key Th cytokines affects Lgr5+ ISC renewal and differentiation in opposing ways: pro-inflammatory signals promote differentiation, while regulatory cells and cytokines reduce it. In vivo genetic perturbation of Th cells or MHCII expression on Lgr5+ ISCs impacts epithelial cell differentiation and IEC fate during infection. These interactions between Th cells and Lgr5+ ISCs, thus, orchestrate tissue-wide responses to external signals.

t-Distributed Stochastic Neighbor Embedding Method with the Least Information Loss for Macromolecular Simulations

Dimensionality reduction methods are usually applied on molecular dynamics simulations of macromolecules for analysis and visualization purposes. It is normally desired that suitable dimensionality reduction methods could clearly distinguish functionally important states with different conformations for the systems of interest. However, common dimensionality reduction methods for macromolecules simulations, including predefined order parameters and collective variables (CVs), principal component analysis (PCA), and time-structure based independent component analysis (t-ICA), only have limited success due to significant key structural information loss. Here, we introduced the t-distributed stochastic neighbor embedding (t-SNE) method as a dimensionality reduction method with minimum structural information loss widely used in bioinformatics for analyses of macromolecules, especially biomacromolecules simulations. It is demonstrated that both one-dimensional (1D) and two-dimensional (2D) models of the t-SNE method are superior to distinguish important functional states of a model allosteric protein system for free energy and mechanistic analysis. Projections of the model protein simulations onto 1D and 2D t-SNE surfaces provide both clear visual cues and quantitative information, which is not readily available using other methods, regarding the transition mechanism between two important functional states of this protein.

CD49b defines functionally mature Treg cells that survey skin and vascular tissues

Regulatory T (Treg) cells prevent autoimmunity by limiting immune responses and inflammation in the secondary lymphoid organs and nonlymphoid tissues. While unique subsets of Treg cells have been described in some nonlymphoid tissues, their relationship to Treg cells in secondary lymphoid organs and circulation remains unclear. Furthermore, it is possible that Treg cells from similar tissue types share largely similar properties. We have identified a short-lived effector Treg cell subset that expresses the α2 integrin, CD49b, and exhibits a unique tissue distribution, being abundant in peripheral blood, vasculature, skin, and skin-draining lymph nodes, but uncommon in the intestines and in viscera-draining lymph nodes. CD49b+ Treg cells, which display superior functionality revealed by in vitro and in vivo assays, appear to develop after multiple rounds of cell division and TCR-dependent activation. Accordingly, single-cell RNA-seq analysis placed these cells at the apex of the Treg developmental trajectory. These results shed light on the identity and development of a functionally potent subset of mature effector Treg cells that recirculate through and survey peripheral tissues.

Single-cell analysis uncovers convergence of cell identities during axolotl limb regeneration

Amputation of the axolotl forelimb results in the formation of a blastema, a transient tissue where progenitor cells accumulate prior to limb regeneration. However, the molecular understanding of blastema formation had previously been hampered by the inability to identify and isolate blastema precursor cells in the adult tissue. We have used a combination of Cre-loxP reporter lineage tracking and single-cell messenger RNA sequencing (scRNA-seq) to molecularly track mature connective tissue (CT) cell heterogeneity and its transition to a limb blastema state. We have uncovered a multiphasic molecular program where CT cell types found in the uninjured adult limb revert to a relatively homogenous progenitor state that recapitulates an embryonic limb bud-like phenotype including multipotency within the CT lineage. Together, our data illuminate molecular and cellular reprogramming during complex organ regeneration in a vertebrate.

Cytofast: A workflow for visual and quantitative analysis of flow and mass cytometry data to discover immune signatures and correlations

Multi-parametric flow and mass cytometry allows exceptional high-resolution exploration of the cellular composition of the immune system. A large panel of computational tools have been developed to analyze the high-dimensional landscape of the data generated. Analysis frameworks such as FlowSOM or Cytosplore incorporate clustering and dimensionality reduction techniques and include algorithms allowing visualization of multi-parametric cytometric analysis. To additionally provide means to quantify specific cell clusters and correlations between samples, we developed an R-package, called cytofast, for further downstream analysis. Specifically, cytofast enables the visualization and quantification of cell clusters for an efficient discovery of cell populations associated with diseases or physiology. We used cytofast on mass and flow cytometry datasets based on the modulation of the immune system upon immunotherapy. With cytofast, we rapidly generated visual representations of group-related immune cell clusters and showed correlations with the immune system composition. We discovered macrophage subsets that significantly decrease upon cancer immunotherapy and distinct prime-boost effects of prophylactic vaccines on the myeloid compartment. Cytofast is a time-efficient tool for comprehensive cytometric analysis to reveal immune signatures and correlations. Cytofast is available at Bioconductor.

Myelo-lymphoid lineage restriction occurs in the human haematopoietic stem cell compartment before lymphoid-primed multipotent progenitors

Capturing where and how multipotency is lost is crucial to understand how blood formation is controlled. Blood lineage specification is currently thought to occur downstream of multipotent haematopoietic stem cells (HSC). Here we show that, in human, the first lineage restriction events occur within the CD19^-CD34⁺CD38^-CD45RA^-CD49f⁺CD90⁺ (49f⁺) HSC compartment to generate myelo-lymphoid committed cells with no erythroid differentiation capacity. At single-cell resolution, we observe a continuous but polarised organisation of the 49f⁺ compartment, where transcriptional programmes and lineage potential progressively change along a gradient of opposing cell surface expression of CLEC9A and CD34. CLEC9A^hiCD34^lo cells contain long-term repopulating multipotent HSCs with slow quiescence exit kinetics, whereas CLEC9A^loCD34^hi cells are restricted to myelo-lymphoid differentiation and display infrequent but durable repopulation capacity. We thus propose that human HSCs gradually transition to a discrete lymphoid-primed state, distinct from lymphoid-primed multipotent progenitors, representing the earliest entry point into lymphoid commitment.

Heterogeneous combinatorial expression of Hoxd genes in single cells during limb development

BACKGROUND

Global analyses of gene expression during development reveal specific transcription patterns associated with the emergence of various cell types, tissues, and organs. These heterogeneous patterns are instrumental to ensure the proper formation of the different parts of our body, as shown by the phenotypic effects generated by functional genetic approaches. However, variations at the cellular level can be observed within each structure or organ. In the developing mammalian limbs, expression of Hox genes from the HoxD cluster is differentially controlled in space and time, in cells that will pattern the digits and the forearms. While the Hoxd genes broadly share a common regulatory landscape and large-scale analyses have suggested a homogenous Hox gene transcriptional program, it has not previously been clear whether Hoxd genes are expressed together at the same levels in the same cells.

RESULTS

We report a high degree of heterogeneity in the expression of the Hoxd11 and Hoxd13 genes. We analyzed single-limb bud cell transcriptomes and show that Hox genes are expressed in specific combinations that appear to match particular cell types. In cells giving rise to digits, we find that the expression of the five relevant Hoxd genes (Hoxd9 to Hoxd13) is unbalanced, despite their control by known global enhancers. We also report that specific combinatorial expression follows a pseudo-time sequence, which is established based on the transcriptional diversity of limb progenitors.

CONCLUSIONS

Our observations reveal the existence of distinct combinations of Hoxd genes at the single-cell level during limb development. In addition, we document that the increasing combinatorial expression of Hoxd genes in this developing structure is associated with specific transcriptional signatures and that these signatures illustrate a temporal progression in the differentiation of these cells.

Continuous Immune Cell Differentiation Inferred From Single-Cell Measurements Following Allogeneic Stem Cell Transplantation

The process of immune system regeneration after allogeneic stem cell transplantation is slow, complex, and insufficiently understood. An entire immune system with all of its cell populations must regenerate from infused donor hematopoietic stem cells over the course of weeks and months post-transplantation. Both innate and adaptive arms of the immune system differ in their capacity and speed to reconstitiute in the recipient, which contributes to inadequacy in global immunity during the delayed reconstitution period. Systems-level analyses of immune systems in human patients have been made possible by high-throughput and high-dimensional, state-of-the-art, single-cell methodologies such as mass cytometry. Mass cytometry has revolutionized our ability to comprehensively profile all immune cell populations simultaneously in blood or tissue samples, providing signatures of differentially regulated cells in a range of clinical conditions. Such kind of systems immunology analyses promise not only for more accurate descriptions of variation between patients but also within individual patients over time, inter-dependencies between cell populations and the inference of developmental trajectories for specific cell populations. Here, we took advantage of a recently performed longitudinal mass cytometry analysis in 26 patients with hematological malignancies followed during the first 12 months following allogeneic stem cell transplantation. We present a proof-of-principle analysis to understand the evolution of individual immune cell populations. By applying non-linear dimensionality reduction and feauture extraction algorithms, we infer trajectories for individual immune cell populations, and map continuous marker expression changes occuring during immune cell regeneration that add novel information about this developmental process.

GraphDDP: a graph-embedding approach to detect differentiation pathways in single-cell-data using prior class knowledge

Cell types can be characterized by expression profiles derived from single-cell RNA-seq. Subpopulations are identified via clustering, yielding intuitive outcomes that can be validated by marker genes. Clustering, however, implies a discretization that cannot capture the continuous nature of differentiation processes. One could give up the detection of subpopulations and directly estimate the differentiation process from cell profiles. A combination of both types of information, however, is preferable. Crucially, clusters can serve as anchor points of differentiation trajectories. Here we present GraphDDP, which integrates both viewpoints in an intuitive visualization. GraphDDP starts from a user-defined cluster assignment and then uses a force-based graph layout approach on two types of carefully constructed edges: one emphasizing cluster membership, the other, based on density gradients, emphasizing differentiation trajectories. We show on intestinal epithelial cells and myeloid progenitor data that GraphDDP allows the identification of differentiation pathways that cannot be easily detected by other approaches.

Inference and representation of differentiation trajectories from single cell RNA-seq data remains a challenge. Here, the authors offer a visualization approach that captures both continuous differentiation trajectories and discrete clusters representing metastable states along the trajectories.

Single-cell genomics to guide human stem cell and tissue engineering

To understand human development and disease, as well as to regenerate damaged tissues, scientists are working to engineer certain cell types in vitro and to create 3D microenvironments in which cells behave physiologically. Single-cell genomics (SCG) technologies are being applied to primary human organs and to engineered cells and tissues to generate atlases of cell diversity in these systems at unparalleled resolution. Moving beyond atlases, SCG methods are powerful tools for gaining insight into the engineering and disease process. Here we discuss how scientists can use single-cell sequencing to optimize human cell and tissue engineering by measuring precision, detecting inefficiencies, and assessing accuracy. We also provide a perspective on how emerging SCG methods can be used to reverse-engineer human cells and tissues and unravel disease mechanisms.

Creating Lineage Trajectory Maps Via Integration of Single-Cell RNA-Sequencing and Lineage Tracing: Integrating transgenic lineage tracing and single-cell RNA-sequencing is a robust approach for mapping developmental lineage trajectories and cell fate changes

Mapping the paths that stem and progenitor cells take en route to differentiate and elucidating the underlying molecular controls are key goals in developmental and stem cell biology. However, with population level analyses it is difficult - if not impossible - to define the transition states and lineage trajectory branch points within complex developmental lineages. Single-cell RNA-sequencing analysis can discriminate heterogeneity in a population of cells and even identify rare or transient intermediates. In this review, we propose that using these data, one can infer the lineage trajectories of individual stem cells and identify putative branch points. Clonal lineage tracing of stem cells allows one to define the outcome of differentiation. Integrating these single cell-based approaches provides a robust strategy for establishing and testing models of how an individual stem cell changes through time to differentiate and self-renew.

A revised airway epithelial hierarchy includes CFTR-expressing ionocytes

The airways of the lung are the primary sites of disease in asthma and cystic fibrosis. Here we study the cellular composition and hierarchy of the mouse tracheal epithelium by single-cell RNA-sequencing (scRNA-seq) and in vivo lineage tracing. We identify a rare cell type, the Foxi1+ pulmonary ionocyte; functional variations in club cells based on their location; a distinct cell type in high turnover squamous epithelial structures that we term 'hillocks'; and disease-relevant subsets of tuft and goblet cells. We developed 'pulse-seq', combining scRNA-seq and lineage tracing, to show that tuft, neuroendocrine and ionocyte cells are continually and directly replenished by basal progenitor cells. Ionocytes are the major source of transcripts of the cystic fibrosis transmembrane conductance regulator in both mouse (Cftr) and human (CFTR). Knockout of Foxi1 in mouse ionocytes causes loss of Cftr expression and disrupts airway fluid and mucus physiology, phenotypes that are characteristic of cystic fibrosis. By associating cell-type-specific expression programs with key disease genes, we establish a new cellular narrative for airways disease.

Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations

Single-cell RNA-seq's (scRNA-seq) unprecedented cellular resolution at a genome-wide scale enables us to address questions about cellular heterogeneity that are inaccessible using methods that average over bulk tissue extracts. However, scRNA-seq data sets also present additional challenges such as high transcript dropout rates, stochastic transcription events, and complex population substructures. Here, we present a single-cell RNA-seq analysis and klustering evaluation (SAKE), a robust method for scRNA-seq analysis that provides quantitative statistical metrics at each step of the analysis pipeline. Comparing SAKE to multiple single-cell analysis methods shows that most methods perform similarly across a wide range of cellular contexts, with SAKE outperforming these methods in the case of large complex populations. We next applied the SAKE algorithms to identify drug-resistant cellular populations as human melanoma cells respond to targeted BRAF inhibitors (BRAFi). Single-cell RNA-seq data from both the Fluidigm C1 and 10x Genomics platforms were analyzed with SAKE to dissect this problem at multiple scales. Data from both platforms indicate that BRAF inhibitor-resistant cells can emerge from rare populations already present before drug application, with SAKE identifying both novel and known markers of resistance. These experimentally validated markers of BRAFi resistance share overlap with previous analyses in different melanoma cell lines, demonstrating the generality of these findings and highlighting the utility of single-cell analysis to elucidate mechanisms of BRAFi resistance.

Single-cell transcriptome analysis of lineage diversity in high-grade glioma

BACKGROUND

Despite extensive molecular characterization, we lack a comprehensive understanding of lineage identity, differentiation, and proliferation in high-grade gliomas (HGGs).

METHODS

We sampled the cellular milieu of HGGs by profiling dissociated human surgical specimens with a high-density microwell system for massively parallel single-cell RNA-Seq. We analyzed the resulting profiles to identify subpopulations of both HGG and microenvironmental cells and applied graph-based methods to infer structural features of the malignantly transformed populations.

RESULTS

While HGG cells can resemble glia or even immature neurons and form branched lineage structures, mesenchymal transformation results in unstructured populations. Glioma cells in a subset of mesenchymal tumors lose their neural lineage identity, express inflammatory genes, and co-exist with marked myeloid infiltration, reminiscent of molecular interactions between glioma and immune cells established in animal models. Additionally, we discovered a tight coupling between lineage resemblance and proliferation among malignantly transformed cells. Glioma cells that resemble oligodendrocyte progenitors, which proliferate in the brain, are often found in the cell cycle. Conversely, glioma cells that resemble astrocytes, neuroblasts, and oligodendrocytes, which are non-proliferative in the brain, are generally non-cycling in tumors.

CONCLUSIONS

These studies reveal a relationship between cellular identity and proliferation in HGG and distinct population structures that reflects the extent of neural and non-neural lineage resemblance among malignantly transformed cells.

Visualization of Biomedical Data

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.

Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics

Single-cell genomics has recently emerged as a powerful tool for observing multicellular systems at a much higher level of resolution and depth than previously possible. High-throughput single-cell RNA sequencing techniques are able to simultaneously quantify expression levels of several thousands of genes within individual cells for tens of thousands of cells within a complex tissue. This has led to development of novel computational methods to analyze this high-dimensional data, investigating longstanding and fundamental questions regarding the granularity of cell types, the definition of cell states, and transitions from one cell type to another along developmental trajectories. In this perspective, we outline this emerging field starting from the "input data" (e.g., quantifying transcription levels in single cells), which are analyzed to define "identities" (e.g., cell types, states, and key genes) and to build "interactions" using models that can infer relations and transitions between cells.

Machine Learning Helps Identify New Drug Mechanisms in Triple-Negative Breast Cancer

This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment on in the laboratory to establish new drug mechanisms. As a case study, this paper uses MDA-MB-231 breast cancer single-cells treated with the antidiabetic drug metformin. We show that mixture-model-based unsupervised methods with validation from hierarchical clustering can identify single-cell subpopulations (clusters). These clusters are characterized by a small set of genes (1% of the genome) that have significant differential expression across the clusters and are also highly correlated with pathways with anticancer effects driven by metformin. Among the identified small set of genes associated with reduced breast cancer incidence, laboratory experiments on one of the genes, CDC42, showed that its downregulation by metformin inhibited cancer cell migration and proliferation, thus validating the ability of machine learning approaches to identify biologically relevant candidates for laboratory experiments. Given the large size of the human genome and limitations in cost and skilled resources, the broader impact of this work in identifying a small set of differentially expressed genes after drug treatment lies in augmenting the drug-disease knowledge of pharmacogenomics experts in laboratory investigations, which could help establish novel biological mechanisms associated with drug response in diseases beyond breast cancer.

The impact of single-cell RNA sequencing on understanding the functional organization of the immune system

Application of single-cell genomics technologies has revolutionized our approach to study the immune system. Unravelling the functional diversity of immune cells and their coordinated response is key to understanding immunity. Single-cell transcriptomics technologies provide high-dimensional assessment of the transcriptional states of immune cells and have been successfully applied to discover new immune cell types, reveal haematopoietic lineages, identify gene modules dictating immune responses and investigate lymphocyte antigen receptor diversity. In this review, we discuss the impact and applications of single-cell RNA sequencing technologies in immunology.

The Human Cell Atlas: Technical approaches and challenges

The Human Cell Atlas is a large, international consortium that aims to identify and describe every cell type in the human body. The comprehensive cellular maps that arise from this ambitious effort have the potential to transform many aspects of fundamental biology and clinical practice. Here, we discuss the technical approaches that could be used today to generate such a resource and also the technical challenges that will be encountered.

Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment

Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We profiled 45,000 immune cells from eight breast carcinomas, as well as matched normal breast tissue, blood, and lymph nodes, using single-cell RNA-seq. We developed a preprocessing pipeline, SEQC, and a Bayesian clustering and normalization method, Biscuit, to address computational challenges inherent to single-cell data. Despite significant similarity between normal and tumor tissue-resident immune cells, we observed continuous phenotypic expansions specific to the tumor microenvironment. Analysis of paired single-cell RNA and T cell receptor (TCR) sequencing data from 27,000 additional T cells revealed the combinatorial impact of TCR utilization on phenotypic diversity. Our results support a model of continuous activation in T cells and do not comport with the macrophage polarization model in cancer. Our results have important implications for characterizing tumor-infiltrating immune cells.

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

Single-cell RNA sequencing technologies suffer from many sources of technical noise, including under-sampling of mRNA molecules, often termed "dropout," which can severely obscure important gene-gene relationships. To address this, we developed MAGIC (Markov affinity-based graph imputation of cells), a method that shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. We validate MAGIC on several biological systems and find it effective at recovering gene-gene relationships and additional structures. Applied to the epithilial to mesenchymal transition, MAGIC reveals a phenotypic continuum, with the majority of cells residing in intermediate states that display stem-like signatures, and infers known and previously uncharacterized regulatory interactions, demonstrating that our approach can successfully uncover regulatory relations without perturbations.

Single cell transcriptome sequencing: A new approach for the study of mammalian sex determination

Mammalian sex determination is a highly complex developmental process that is particularly difficult to study due to the limited number of gonadal cells present at the bipotential stage, the large cellular heterogeneity in both testis and ovaries and the rapid sex-dependent differentiation processes. Single-cell RNA-sequencing (scRNA-seq) circumvents the averaging artifacts associated with methods traditionally used to profile bulk populations of cells. It is a powerful tool that allows the identification and classification of cell populations in a comprehensive and unbiased manner. In particular, scRNA-seq enables the tracing of cells along developmental trajectories and characterization of the transcriptional dynamics controlling their differentiation. In this review, we describe the current state-of-the-art experimental methods used for scRNA-seq and discuss their strengths and limitations. Additionally, we summarize the multiple key insights that scRNA-seq has provided to the understanding of mammalian sex determination. Finally, we briefly discuss the future of this technology, as well as complementary applications in single cell -omics in the context of mammalian sex determination.

Transcriptional Dynamics of Hair-Bundle Morphogenesis Revealed with CellTrails

Protruding from the apical surface of inner ear sensory cells, hair bundles carry out mechanotransduction. Bundle growth involves sequential and overlapping cellular processes, which are concealed within gene expression profiles of individual cells. To dissect such processes, we developed CellTrails, a tool for uncovering, analyzing, and visualizing single-cell gene-expression dynamics. Utilizing quantitative gene-expression data for key bundle proteins from single cells of the developing chick utricle, we reconstructed de novo a bifurcating trajectory that spanned from progenitor cells to mature striolar and extrastriolar hair cells. Extraction and alignment of developmental trails and association of pseudotime with bundle length measurements linked expression dynamics of individual genes with bundle growth stages. Differential trail analysis revealed high-resolution dynamics of transcripts that control striolar and extrastriolar bundle development, including those that encode proteins that regulate [Ca2+]i or mediate crosslinking and lengthening of actin filaments.

A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice

Hematopoietic stem and progenitor cells (HSPCs) maintain the adult blood system, and their dysregulation causes a multitude of diseases. However, the differentiation journeys toward specific hematopoietic lineages remain ill defined, and system-wide disease interpretation remains challenging. Here, we have profiled 44 802 mouse bone marrow HSPCs using single-cell RNA sequencing to provide a comprehensive transcriptional landscape with entry points to 8 different blood lineages (lymphoid, megakaryocyte, erythroid, neutrophil, monocyte, eosinophil, mast cell, and basophil progenitors). We identified a common basophil/mast cell bone marrow progenitor and characterized its molecular profile at the single-cell level. Transcriptional profiling of 13 815 HSPCs from the c-Kit mutant (W41/W41) mouse model revealed the absence of a distinct mast cell lineage entry point, together with global shifts in cell type abundance. Proliferative defects were accompanied by reduced Myc expression. Potential compensatory processes included upregulation of the integrated stress response pathway and downregulation of proapoptotic gene expression in erythroid progenitors, thus providing a template of how large-scale single-cell transcriptomic studies can bridge between molecular phenotypes and quantitative population changes.

HopLand: single-cell pseudotime recovery using continuous Hopfield network-based modeling of Waddington's epigenetic landscape

Motivation

The interpretation of transcriptional dynamics in single-cell data, especially pseudotime estimation, could help understand the transition of gene expression profiles. The recovery of pseudotime increases the temporal resolution of single-cell transcriptional data, but is challenging due to the high variability in gene expression between individual cells. Here, we introduce HopLand, a pseudotime recovery method using continuous Hopfield network to map cells to a Waddington’s epigenetic landscape. It reveals from the single-cell data the combinatorial regulatory interactions among genes that control the dynamic progression through successive cell states.

Results

We applied HopLand to different types of single-cell transcriptomic data. It achieved high accuracies of pseudotime prediction compared with existing methods. Moreover, a kinetic model can be extracted from each dataset. Through the analysis of such a model, we identified key genes and regulatory interactions driving the transition of cell states. Therefore, our method has the potential to generate fundamental insights into cell fate regulation.

Availability and implementation

The MATLAB implementation of HopLand is available at https://github.com/NetLand-NTU/HopLand.

ShinyCortex: Exploring Single-Cell Transcriptome Data From the Developing Human Cortex

Single-cell mRNA sequencing (scRNA-seq) is a powerful method to identify and classify cell types and reconstruct differentiation trajectories within complex tissues, such as the developing human cortex. scRNA-seq data also enables the discovery of cell type-specific marker genes and genes that regulate developmental transitions. Here we provide a brief overview of how scRNA-seq has been shaping the study of human cortex development, and present ShinyCortex, a resource that brings together data from recent scRNA-seq studies of the developing cortex for further analysis. ShinyCortex is based in R and displays recently published scRNA-seq data from the human and mouse cortex in a comprehensible, dynamic and accessible way, suitable for data exploration by biologists.

Hematopoietic stem cells can differentiate into restricted myeloid progenitors before cell division in mice

Hematopoietic stem cells (HSCs) continuously replenish all blood cell types through a series of differentiation steps and repeated cell divisions that involve the generation of lineage-committed progenitors. However, whether cell division in HSCs precedes differentiation is unclear. To this end, we used an HSC cell-tracing approach and Ki67^RFP knock-in mice, in a non-conditioned transplantation model, to assess divisional history, cell cycle progression, and differentiation of adult HSCs. Our results reveal that HSCs are able to differentiate into restricted progenitors, especially common myeloid, megakaryocyte-erythroid and pre-megakaryocyte progenitors, without undergoing cell division and even before entering the S phase of the cell cycle. Additionally, the phenotype of the undivided but differentiated progenitors correlated with the expression of lineage-specific genes and loss of multipotency. Thus HSC fate decisions can be uncoupled from physical cell division. These results facilitate a better understanding of the mechanisms that control fate decisions in hematopoietic cells.

Dependence of hematopoietic stem cell (HSC) fate on the phase of the cell cycle has not been demonstrated in vivo. Here, the authors find that HSCs can differentiate into a downstream progenitor without physical division, even before progressing into the S phase of the cell cycle.

Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis

During embryogenesis, cells acquire distinct fates by transitioning through transcriptional states. To uncover these transcriptional trajectories during zebrafish embryogenesis, we sequenced 38,731 cells and developed URD, a simulated diffusion-based computational reconstruction method. URD identified the trajectories of 25 cell types through early somitogenesis, gene expression along them, and their spatial origin in the blastula. Analysis of Nodal signaling mutants revealed that their transcriptomes were canalized into a subset of wild-type transcriptional trajectories. Some wild-type developmental branch points contained cells that express genes characteristic of multiple fates. These cells appeared to trans-specify from one fate to another. These findings reconstruct the transcriptional trajectories of a vertebrate embryo, highlight the concurrent canalization and plasticity of embryonic specification, and provide a framework with which to reconstruct complex developmental trees from single-cell transcriptomes.

Using single-cell genomics to understand developmental processes and cell fate decisions

High-throughput -omics techniques have revolutionised biology, allowing for thorough and unbiased characterisation of the molecular states of biological systems. However, cellular decision-making is inherently a unicellular process to which "bulk" -omics techniques are poorly suited, as they capture ensemble averages of cell states. Recently developed single-cell methods bridge this gap, allowing high-throughput molecular surveys of individual cells. In this review, we cover core concepts of analysis of single-cell gene expression data and highlight areas of developmental biology where single-cell techniques have made important contributions. These include understanding of cell-to-cell heterogeneity, the tracing of differentiation pathways, quantification of gene expression from specific alleles, and the future directions of cell lineage tracing and spatial gene expression analysis.

Kinetic models of hematopoietic differentiation

As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning "knobs" in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous-valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi-scale models integrating cell dynamical and transcriptional models. This article is categorized under: Models of Systems Properties and Processes > Mechanistic Models Developmental Biology > Stem Cell Biology and Regeneration.

Developmental diversification of cortical inhibitory interneurons

Diverse subsets of cortical interneurons have vital roles in higher-order brain functions. To investigate how this diversity is generated, here we used single-cell RNA sequencing to profile the transcriptomes of mouse cells collected along a developmental time course. Heterogeneity within mitotic progenitors in the ganglionic eminences is driven by a highly conserved maturation trajectory, alongside eminence-specific transcription factor expression that seeds the emergence of later diversity. Upon becoming postmitotic, progenitors diverge and differentiate into transcriptionally distinct states, including an interneuron precursor state. By integrating datasets across developmental time points, we identified shared sources of transcriptomic heterogeneity between adult interneurons and their precursors, and uncovered the embryonic emergence of cardinal interneuron subtypes. Our analysis revealed that the transcription factor Mef2c, which is linked to various neuropsychiatric and neurodevelopmental disorders, delineates early precursors of parvalbumin-expressing neurons, and is essential for their development. These findings shed new light on the molecular diversification of early inhibitory precursors, and identify gene modules that may influence the specification of human interneuron subtypes.

Myeloid cell heterogeneity in cancer: not a single cell alike

Tumors of various histological origins show abundant infiltration of myeloid cells from early stages of disease progression. These cells have a profound impact on antitumor immunity and influence fundamental processes that underlie malignancy, including neoangiogenesis, sustained cancer cell proliferation, metastasis and therapy resistance. For these reasons, development of therapeutic approaches to deplete or reprogram myeloid cells in cancer is an emerging field of interest. However, knowledge about the heterogeneity of myeloid cells in tumors and their variability between patients and disease stages is still limited. In this review, we summarize the most recent advances in our understanding about how the phenotype of tumor-associated macrophages, monocytes, neutrophils, myeloid-derived suppressor cells and dendritic cells is dictated by their ontogeny, activation status and localization. We also outline major open questions that will only be resolved by applying high-dimensional single-cell technologies and systems biology approaches in the analysis of the tumor microenvironment.

Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems

Function at the organ level manifests itself from a heterogeneous collection of cell types. Cellular heterogeneity emerges from developmental processes by which multipotent progenitor cells make fate decisions and transition to specific cell types through intermediate cell states. Although genetic experimental strategies such as lineage tracing have provided insights into cell lineages, recent developments in single-cell technologies have greatly increased our ability to interrogate distinct cell types, as well as transitional cell states in tissue systems. From single-cell data that describe these intermediate cell states, computational tools have been developed to reconstruct cell-state transition trajectories that model cell developmental processes. These algorithms, although powerful, are still in their infancy, and attention must be paid to their strengths and weaknesses when they are used. Here, we review some of these tools, also referred to as pseudotemporal ordering algorithms, and their associated assumptions and caveats. We hope to provide a rational and generalizable workflow for single-cell trajectory analysis that is intuitive for experimental biologists.

CellCycleTRACER accounts for cell cycle and volume in mass cytometry data

Recent studies have shown that cell cycle and cell volume are confounding factors when studying biological phenomena in single cells. Here we present a combined experimental and computational method, CellCycleTRACER, to account for these factors in mass cytometry data. CellCycleTRACER is applied to mass cytometry data collected on three different cell types during a TNFα stimulation time-course. CellCycleTRACER reveals signaling relationships and cell heterogeneity that were otherwise masked.

Mass cytometry is a powerful method of single cell analysis, but potential confounding effects of cell cycle and cell volume are not taken into account. Here the authors present a combined experimental and computational method to correct for these effects and reveal features of TNFα stimulation that are otherwise masked.

SCANPY: large-scale single-cell gene expression data analysis

SCANPY is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells ( https://github.com/theislab/Scanpy ). Along with SCANPY, we present ANNDATA, a generic class for handling annotated data matrices ( https://github.com/theislab/anndata ).

From haematopoietic stem cells to complex differentiation landscapes

The development of mature blood cells from haematopoietic stem cells has long served as a model for stem-cell research, with the haematopoietic differentiation tree being widely used as a model for the maintenance of hierarchically organized tissues. Recent results and new technologies have challenged the demarcations between stem and progenitor cell populations, the timing of cell-fate choices and the contribution of stem and multipotent progenitor cells to the maintenance of steady-state blood production. These evolving views of haematopoiesis have broad implications for our understanding of the functions of adult stem cells, as well as the development of new therapies for malignant and non-malignant haematopoietic diseases.

Single-cell analysis reveals the continuum of human lympho-myeloid progenitor cells

The hierarchy of human hemopoietic progenitor cells that produce lymphoid and granulocytic-monocytic (myeloid) lineages is unclear. Multiple progenitor populations produce lymphoid and myeloid cells, but they remain incompletely characterized. Here we demonstrated that lympho-myeloid progenitor populations in cord blood - lymphoid-primed multi-potential progenitors (LMPPs), granulocyte-macrophage progenitors (GMPs) and multi-lymphoid progenitors (MLPs) - were functionally and transcriptionally distinct and heterogeneous at the clonal level, with progenitors of many different functional potentials present. Although most progenitors had the potential to develop into only one mature cell type ('uni-lineage potential'), bi- and rarer multi-lineage progenitors were present among LMPPs, GMPs and MLPs. Those findings, coupled with single-cell expression analyses, suggest that a continuum of progenitors execute lymphoid and myeloid differentiation, rather than only uni-lineage progenitors' being present downstream of stem cells.

High Throughput Single Cell RNA Sequencing, Bioinformatics Analysis and Applications

Single cell sequencing (SCS) can be harnessed to acquire the genomes, transcriptomes and epigenomes from individual cells. Next generation sequencing (NGS) technology is the driving force for single cell sequencing. scRNA-seq requires a lengthy pipeline comprising of single cell sorting, RNA extraction, reverse transcription, amplification, library construction, sequencing and subsequent bioinformatic analysis. Computational algorithms are essential to fulfill many tasks of interest using scRNA-seq data. scRNA-seq has already enabled researchers to revisit long-standing questions in cancer biology, including cancer metastasis, heterogeneity and evolution. Circulating Tumor Cells (CTC) are not only an important mechanism for cancer metastasis, but also provide a possibility to diagnose and monitor cancer in a convenient way independent of surgical resection of the cancer.

Reverse-engineering of gene networks for regulating early blood development from single-cell measurements

BACKGROUND

Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information.

METHODS

This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression.

RESULTS

The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks.

CONCLUSION

The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations.

Illuminating stem cell transcription factor dynamics: long-term single-cell imaging of fluorescent protein fusions

Most single-cell approaches to date are based on destructive snapshot measurements which do not permit to correlate a current molecular state with future fate. However, to understand how cell fate choices are established by transcription factor networks (TFNs) regulating cell fates, TFN dynamics must be continuously monitored in single cells. Here we review how quantitative time-lapse imaging can contribute to understanding TFN dependent cell fate regulation at the single-cell level. We outline potentials of the technology and highlight challenges for interpreting the dynamics of fluorescent protein reporters that may interfere with endogenous TF function. We provide an outlook on how continuous observation of TF dynamics and single-cell fates may be complemented by perturbation studies and be linked to multidimensional molecular profiling in the future.

Single-cell RNA-sequencing uncovers transcriptional states and fate decisions in haematopoiesis

The success of marker-based approaches for dissecting haematopoiesis in mouse and human is reliant on the presence of well-defined cell surface markers specific for diverse progenitor populations. An inherent problem with this approach is that the presence of specific cell surface markers does not directly reflect the transcriptional state of a cell. Here, we used a marker-free approach to computationally reconstruct the blood lineage tree in zebrafish and order cells along their differentiation trajectory, based on their global transcriptional differences. Within the population of transcriptionally similar stem and progenitor cells, our analysis reveals considerable cell-to-cell differences in their probability to transition to another committed state. Once fate decision is executed, the suppression of transcription of ribosomal genes and upregulation of lineage-specific factors coordinately controls lineage differentiation. Evolutionary analysis further demonstrates that this haematopoietic programme is highly conserved between zebrafish and higher vertebrates.

Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing

Characterising the hierarchy of mammary epithelial cells (MECs) and how they are regulated during adult development is important for understanding how breast cancer arises. Here we report the use of single-cell RNA sequencing to determine the gene expression profile of MECs across four developmental stages; nulliparous, mid gestation, lactation and post involution. Our analysis of 23,184 cells identifies 15 clusters, few of which could be fully characterised by a single marker gene. We argue instead that the epithelial cells-especially in the luminal compartment-should rather be conceptualised as being part of a continuous spectrum of differentiation. Furthermore, our data support the existence of a common luminal progenitor cell giving rise to intermediate, restricted alveolar and hormone-sensing progenitors. This luminal progenitor compartment undergoes transcriptional changes in response to a full pregnancy, lactation and involution. In summary, our results provide a global, unbiased view of adult mammary gland development.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.