Population snapshots predict early haematopoietic and erythroid hierarchies.

The formation of red blood cells begins with the differentiation of multipotent haematopoietic progenitors. Reconstructing the steps of this differentiation represents a general challenge in stem-cell biology. Here we used single-cell transcriptomics, fate assays and a theory that allows the prediction of cell fates from population snapshots to demonstrate that mouse haematopoietic progenitors differentiate through a continuous, hierarchical structure into seven blood lineages. We uncovered coupling between the erythroid and the basophil or mast cell fates, a global haematopoietic response to erythroid stress and novel growth factor receptors that regulate erythropoiesis. We defined a flow cytometry sorting strategy to purify early stages of erythroid differentiation, completely isolating classically defined burst-forming and colony-forming progenitors. We also found that the cell cycle is progressively remodelled during erythroid development and during a sharp transcriptional switch that ends the colony-forming progenitor stage and activates terminal differentiation. Our work showcases the utility of linking transcriptomic data to predictive fate models, and provides insights into lineage development in vivo.

Clonal analysis of lineage fate in native haematopoiesis.

Haematopoiesis, the process of mature blood and immune cell production, is functionally organized as a hierarchy, with self-renewing haematopoietic stem cells and multipotent progenitor cells sitting at the very top. Multiple models have been proposed as to what the earliest lineage choices are in these primitive haematopoietic compartments, the cellular intermediates, and the resulting lineage trees that emerge from them. Given that the bulk of studies addressing lineage outcomes have been performed in the context of haematopoietic transplantation, current models of lineage branching are more likely to represent roadmaps of lineage potential than native fate. Here we use transposon tagging to clonally trace the fates of progenitors and stem cells in unperturbed haematopoiesis. Our results describe a distinct clonal roadmap in which the megakaryocyte lineage arises largely independently of other haematopoietic fates. Our data, combined with single-cell RNA sequencing, identify a functional hierarchy of unilineage- and oligolineage-producing clones within the multipotent progenitor population. Finally, our results demonstrate that traditionally defined long-term haematopoietic stem cells are a significant source of megakaryocyte-restricted progenitors, suggesting that the megakaryocyte lineage is the predominant native fate of long-term haematopoietic stem cells. Our study provides evidence for a substantially revised roadmap for unperturbed haematopoiesis, and highlights unique properties of multipotent progenitors and haematopoietic stem cells in situ.

Fundamental limits on dynamic inference from single-cell snapshots.

Single-cell expression profiling reveals the molecular states of individual cells with unprecedented detail. Because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. However, some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single-cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, population balance analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high-dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements.

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.

SPRING: a kinetic interface for visualizing high dimensional single-cell expression data.

Motivation: Single-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, visualizing continuous gene expression topologies can be improved, since current tools tend to fragment gene expression continua or capture only limited features of complex population topologies. Results: Force-directed layouts of k-nearest-neighbor graphs can visualize continuous gene expression topologies in a manner that preserves high-dimensional relationships and captures complex population topologies. We describe SPRING, a pipeline for data filtering, normalization and visualization using force-directed layouts and show that it reveals more detailed biological relationships than existing approaches when applied to branching gene expression trajectories from hematopoietic progenitor cells and cells of the upper airway epithelium. Visualizations from SPRING are also more reproducible than those of stochastic visualization methods such as tSNE, a state-of-the-art tool. We provide SPRING as an interactive web-tool with an easy to use GUI. Availability and implementation: https://kleintools.hms.harvard.edu/tools/spring.html, https://github.com/AllonKleinLab/SPRING/. Contact: calebsw@gmail.com or allon_klein@hms.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Molecular Genetic Evidence for Shared Etiology of Autism and Prodigy.

Child prodigies are rare individuals with an exceptional working memory and unique attentional skills that may facilitate the attainment of professional skill levels at an age well before what is observed in the general population. Some characteristics of prodigy have been observed to be quantitatively similar to those observed in autism spectrum disorder (ASD), suggesting possible shared etiology, though objectively validated prodigies are so rare that evidence has been sparse. We performed a family-based genome-wide linkage analysis on 5 nuclear and extended families to search for genetic loci that influence the presence of both prodigy and ASD, assuming that the two traits have the same genetic etiology in the analysis model in order to find shared loci. A shared locus on chromosome 1p31-q21 reached genome-wide significance with two extended family-based linkage methods consisting of the Bayesian PPL method and the LOD score maximized over the trait parameters (i.e., MOD), yielding a simulation-based empirical significance of p = 0.000742 and p = 0.000133, respectively. Within linkage regions, we performed association analysis and assessed if copy number variants could account for the linkage signal. No evidence of specificity for either the prodigy or the ASD trait was observed. This finding suggests that a locus on chromosome 1 increases the likelihood of both prodigy and autism in these families.

Addressing the unmet need for visualizing conditional random fields in biological data.

BACKGROUND: The biological world is replete with phenomena that appear to be ideally modeled and analyzed by one archetypal statistical framework - the Graphical Probabilistic Model (GPM). The structure of GPMs is a uniquely good match for biological problems that range from aligning sequences to modeling the genome-to-phenome relationship. The fundamental questions that GPMs address involve making decisions based on a complex web of interacting factors. Unfortunately, while GPMs ideally fit many questions in biology, they are not an easy solution to apply. Building a GPM is not a simple task for an end user. Moreover, applying GPMs is also impeded by the insidious fact that the "complex web of interacting factors" inherent to a problem might be easy to define and also intractable to compute upon. DISCUSSION: We propose that the visualization sciences can contribute to many domains of the bio-sciences, by developing tools to address archetypal representation and user interaction issues in GPMs, and in particular a variety of GPM called a Conditional Random Field(CRF). CRFs bring additional power, and additional complexity, because the CRF dependency network can be conditioned on the query data. CONCLUSIONS: In this manuscript we examine the shared features of several biological problems that are amenable to modeling with CRFs, highlight the challenges that existing visualization and visual analytics paradigms induce for these data, and document an experimental solution called StickWRLD which, while leaving room for improvement, has been successfully applied in several biological research projects. Software and tutorials are available at http://www.stickwrld.org/.

Gene × smoking interactions on human brain gene expression: finding common mechanisms in adolescents and adults.

BACKGROUND: Numerous studies have examined gene × environment interactions (G × E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here, we assessed G × E interactions using two publically available datasets to assess if DNA variation is associated with post-mortem brain gene expression changes based on smoking behavior, a biobehavioral construct that is part of a complex system of genetic and environmental influences. METHODS: We conducted an expression quantitative trait locus (eQTL) study on two independent human brain gene expression datasets assessing G × E for selected psychiatric genes and smoking status. We employed linear regression to model the significance of the Gene × Smoking interaction term, followed by meta-analysis across datasets. RESULTS: Overall, we observed that the effect of DNA variation on gene expression is moderated by smoking status. Expression of 16 genes was significantly associated with single nucleotide polymorphisms that demonstrated G × E effects. The strongest finding (p = 1.9 × 10⁻¹¹) was neurexin 3-alpha (NRXN3), a synaptic cell-cell adhesion molecule involved in maintenance of neural connections (such as the maintenance of smoking behavior). Other significant G × E associations include four glutamate genes. CONCLUSIONS: This is one of the first studies to demonstrate G × E effects within the human brain. In particular, this study implicated NRXN3 in the maintenance of smoking. The effect of smoking on NRXN3 expression and downstream behavior is different based upon SNP genotype, indicating that DNA profiles based on SNPs could be useful in understanding the effects of smoking behaviors. These results suggest that better measurement of psychiatric conditions, and the environment in post-mortem brain studies may yield an important avenue for understanding the biological mechanisms of G × E interactions in psychiatry.

Metabolic alterations mediated by STAT3 promotes drug persistence in CML.

Leukemic stem cells (LSCs) can acquire non-mutational resistance following drug treatment leading to therapeutic failure and relapse. However, oncogene-independent mechanisms of drug persistence in LSCs are incompletely understood, which is the primary focus of this study. We integrated proteomics, transcriptomics, and metabolomics to determine the contribution of STAT3 in promoting metabolic changes in tyrosine kinase inhibitor (TKI) persistent chronic myeloid leukemia (CML) cells. Proteomic and transcriptional differences in TKI persistent CML cells revealed BCR-ABL-independent STAT3 activation in these cells. While knockout of STAT3 inhibited the CML cells from developing drug-persistence, inhibition of STAT3 using a small molecule inhibitor sensitized the persistent CML cells to TKI treatment. Interestingly, given the role of phosphorylated STAT3 as a transcription factor, it localized uniquely to genes regulating metabolic pathways in the TKI-persistent CML stem and progenitor cells. Subsequently, we observed that STAT3 dysregulated mitochondrial metabolism forcing the TKI-persistent CML cells to depend on glycolysis, unlike TKI-sensitive CML cells, which are more reliant on oxidative phosphorylation. Finally, targeting pyruvate kinase M2, a rate-limiting glycolytic enzyme, specifically eradicated the TKI-persistent CML cells. By exploring the role of STAT3 in altering metabolism, we provide critical insight into identifying potential therapeutic targets for eliminating TKI-persistent LSCs.

Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data.

Single-cell RNA-sequencing has become a widely used, powerful approach for studying cell populations. However, these methods often generate multiplet artifacts, where two or more cells receive the same barcode, resulting in a hybrid transcriptome. In most experiments, multiplets account for several percent of transcriptomes and can confound downstream data analysis. Here, we present Single-Cell Remover of Doublets (Scrublet), a framework for predicting the impact of multiplets in a given analysis and identifying problematic multiplets. Scrublet avoids the need for expert knowledge or cell clustering by simulating multiplets from the data and building a nearest neighbor classifier. To demonstrate the utility of this approach, we test Scrublet on several datasets that include independent knowledge of cell multiplets. Scrublet is freely available for download at github.com/AllonKleinLab/scrublet.

Mapping Distinct Bone Marrow Niche Populations and Their Differentiation Paths.

The bone marrow microenvironment is composed of heterogeneous cell populations of non-hematopoietic cells with complex phenotypes and undefined trajectories of maturation. Among them, mesenchymal cells maintain the production of stromal, bone, fat, and cartilage cells. Resolving these unique cellular subsets within the bone marrow remains challenging. Here, we used single-cell RNA sequencing of non-hematopoietic bone marrow cells to define specific subpopulations. Furthermore, by combining computational prediction of the cell state hierarchy with the known expression of key transcription factors, we mapped differentiation paths to the osteocyte, chondrocyte, and adipocyte lineages. Finally, we validated our findings using lineage-specific reporter strains and targeted knockdowns. Our analysis reveals differentiation hierarchies for maturing stromal cells, determines key transcription factors along these trajectories, and provides an understanding of the complexity of the bone marrow microenvironment.

A comprehensive single cell transcriptional landscape of human hematopoietic progenitors.

Hematopoietic Stem/Progenitor cells (HSPCs) are endowed with the role of maintaining a diverse pool of blood cells throughout the human life. Despite recent efforts, the nature of the early cell fate decisions remains contentious. Using single-cell RNA-Seq, we show that existing approaches to stratify bone marrow CD34+ cells reveal a hierarchically-structured transcriptional landscape of hematopoietic differentiation. Still, this landscape misses important early fate decisions. We here provide a broader transcriptional profiling of bone marrow lineage negative hematopoietic progenitors that recovers a key missing branchpoint into basophils and expands our understanding of the underlying structure of early adult human haematopoiesis. We also show that this map has strong similarities in topology and gene expression to that found in mouse. Finally, we identify the sialomucin CD164, as a reliable marker for the earliest branches of HSPCs specification and we showed how its use can foster the design of alternative transplantation cell products.

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.

Although the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures. Here we implemented a droplet-based, single-cell RNA-seq method to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two mouse strains. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare epsilon-cells; exocrine cell types; vascular cells; Schwann cells; quiescent and activated stellate cells; and four types of immune cells. We detected subpopulations of ductal cells with distinct expression profiles and validated their existence with immuno-histochemistry stains. Moreover, among human beta- cells, we detected heterogeneity in the regulation of genes relating to functional maturation and levels of ER stress. Finally, we deconvolved bulk gene expression samples using the single-cell data to detect disease-associated differential expression. Our dataset provides a resource for the discovery of novel cell type-specific transcription factors, signaling receptors, and medically relevant genes.

StickWRLD as an Interactive Visual Pre-Filter for Canceromics-Centric Expression Quantitative Trait Locus Data.

As datasets increase in complexity, the time required for analysis (both computational and human domain-expert) increases. One of the significant impediments introduced by such burgeoning data is the difficulty in knowing what features to include or exclude from statistical models. Simple tables of summary statistics rarely provide an adequate picture of the patterns and details of the dataset to enable researchers to make well-informed decisions about the adequacy of the models they are constructing. We have developed a tool, StickWRLD, which allows the user to visually browse through their data, displaying all possible correlations. By allowing the user to dynamically modify the retention parameters (both P and the residual, r), StickWRLD allows the user to identify significant correlations and disregard potential correlations that do not meet those same criteria - effectively filtering through all possible correlations quickly and identifying possible relationships of interest for further analysis. In this study, we applied StickWRLD to a semi-synthetic dataset constructed from two published human datasets. In addition to detecting high-probability correlations in this dataset, we were able to quickly identify gene-SNP correlations that would have gone undetected using more traditional approaches due to issues of low penetrance.