Summary 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.

Highlights d We define two multiplet errors in single-cell RNA-seq data: ''embedded'' and ''neotypic'' d Neotypic errors can lead to misidentification of cell types or transitional states d Scrublet code identifies neotypic doublets and predicts the overall doublet rate d The algorithm is tested against several experimental methods for labeling multiplets

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 Scrublet (Single-Cell Remover of Doublets), 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.

SUMMARY Hematopoiesis, the process of mature blood and immune cell production, is functionally organized as a hierarchy, with self-renewing hematopoietic stem cells (HSCs) and multipotent progenitor (MPP) cells sitting at the very top1,2. Multiple models have been proposed as to what the earliest lineage choices are in these primitive hematopoietic compartments, the cellular intermediates, and the resulting lineage trees that emerge from them3–10. Given that the bulk of studies addressing lineage outcomes have been performed in the context of hematopoietic transplantation, current lineage branching models are more likely to represent roadmaps of lineage potential rather than native fate. Here, we utilize transposon (Tn) tagging to clonally trace the fates of progenitors and stem cells in unperturbed hematopoiesis. Our results describe a distinct clonal roadmap in which the megakaryocyte (Mk) lineage arises largely independently of other hematopoietic fates. Our data, combined with single cell RNAseq, identify a functional hierarchy of uni- and oligolineage producing clones within the MPP population. Finally, our results demonstrate that traditionally defined long-term HSCs (LT-HSCs) are a significant source of Mk-restricted progenitors, suggesting that the Mk-lineage is the predominant native fate of LT-HSCs. Our study provides evidence for a substantially revised roadmap for unperturbed hematopoiesis, and highlights unique properties of MPPs and HSCs in situ.

Red cell formation begins with the differentiation of multipotent hematopoietic progenitors. Reconstructing the steps of differentiation represents a stereotypical challenge in stem cell biology. Combining single-cell transcriptomics, fate assays, and theory for predicting fate from population snapshots, we inferred a continuous, hierarchical structure of murine hematopoietic progenitors committing to seven blood lineages. We uncovered coupling between erythroid and basophil/mast cell fates, a global hematopoietic response to erythroid stress, and novel growth factor receptor regulators of erythropoiesis. We also defined a new flow-cytometric sorting strategy to purify progressive early stages of erythroid differentiation, completely isolating classically-defined burst-forming (BFU-e) and colony-forming progenitors (CFU-e). Intriguingly, profound remodeling of the cell cycle is intimately entwined with erythroid development and with a sharp transcriptional switch that extinguishes the CFU-e stage and activates terminal differentiation. Our work showcases the utility of theory linking transcriptomic data to predictive fate models, providing insights into lineage development in vivo.

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.

SignificanceSeeing a snapshot of individuals at different stages of a dynamic process can reveal what the process would look like for a single individual over time. Biologists apply this principle to infer temporal sequences of gene expression states in cells from measurements made at a single moment in time. However, the sparsity and high dimensionality of single-cell data have made inference difficult using formal approaches. Here, we apply recent innovations in spectral graph theory to devise a simple and asymptotically exact algorithm for inferring the unique dynamic solution under defined approximations and apply it to data from bone marrow stem cells.

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 (W/W) 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 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.