Single-cell mRNA quantification and differential analysis with Census

The Major Risk Factors for Alzheimer's Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques

Gene expression profiles of more than 10,000 individual microglial cells isolated from cortex and hippocampus of male and female AppNL-G-F mice over time demonstrate that progressive amyloid-β accumulation accelerates two main activated microglia states that are also present during normal aging. Activated response microglia (ARMs) are composed of specialized subgroups overexpressing MHC type II and putative tissue repair genes (Dkk2, Gpnmb, and Spp1) and are strongly enriched with Alzheimer's disease (AD) risk genes. Microglia from female mice progress faster in this activation trajectory. Similar activated states are also found in a second AD model and in human brain. Apoe, the major genetic risk factor for AD, regulates the ARMs but not the interferon response microglia (IRMs). Thus, the ARMs response is the converging point for aging, sex, and genetic AD risk factors.

Ex vivo Dynamics of Human Glioblastoma Cells in a Microvasculature-on-a-Chip System Correlates with Tumor Heterogeneity and Subtypes

The perivascular niche (PVN) plays an essential role in brain tumor stem-like cell (BTSC) fate control, tumor invasion, and therapeutic resistance. Here, a microvasculature-on-a-chip system as a PVN model is used to evaluate the ex vivo dynamics of BTSCs from ten glioblastoma patients. BTSCs are found to preferentially localize in the perivascular zone, where they exhibit either the lowest motility, as in quiescent cells, or the highest motility, as in the invasive phenotype, with migration over long distance. These results indicate that PVN is a niche for BTSCs, while the microvascular tracks may serve as a path for tumor cell migration. The degree of colocalization between tumor cells and microvessels varies significantly across patients. To validate these results, single-cell transcriptome sequencing (10 patients and 21 750 single cells in total) is performed to identify tumor cell subtypes. The colocalization coefficient is found to positively correlate with proneural (stem-like) or mesenchymal (invasive) but not classical (proliferative) tumor cells. Furthermore, a gene signature profile including PDGFRA correlates strongly with the "homing" of tumor cells to the PVN. These findings demonstrate that the model can recapitulate in vivo tumor cell dynamics and heterogeneity, representing a new route to study patient-specific tumor cell functions.

Ex vivo Dynamics of Human Glioblastoma Cells in a Microvasculature-on-a-Chip System Correlates with Tumor Heterogeneity and Subtypes

The perivascular niche (PVN) plays an essential role in brain tumor stem-like cell (BTSC) fate control, tumor invasion, and therapeutic resistance. Here, a microvasculature-on-a-chip system as a PVN model is used to evaluate the ex vivo dynamics of BTSCs from ten glioblastoma patients. BTSCs are found to preferentially localize in the perivascular zone, where they exhibit either the lowest motility, as in quiescent cells, or the highest motility, as in the invasive phenotype, with migration over long distance. These results indicate that PVN is a niche for BTSCs, while the microvascular tracks may serve as a path for tumor cell migration. The degree of colocalization between tumor cells and microvessels varies significantly across patients. To validate these results, single-cell transcriptome sequencing (10 patients and 21 750 single cells in total) is performed to identify tumor cell subtypes. The colocalization coefficient is found to positively correlate with proneural (stem-like) or mesenchymal (invasive) but not classical (proliferative) tumor cells. Furthermore, a gene signature profile including PDGFRA correlates strongly with the "homing" of tumor cells to the PVN. These findings demonstrate that the model can recapitulate in vivo tumor cell dynamics and heterogeneity, representing a new route to study patient-specific tumor cell functions.

Single cell transcriptomics based-MacSpectrum reveals novel macrophage activation signatures in diseases

Adipose tissue macrophages (ATM) are crucial for maintaining adipose tissue homeostasis and mediating obesity-induced metabolic abnormalities, including prediabetic conditions and type 2 diabetes mellitus. Despite their key functions in regulating adipose tissue metabolic and immunologic homeostasis under normal and obese conditions, a high-resolution transcriptome annotation system that can capture ATM multifaceted activation profiles has not yet been developed. This is primarily attributed to the complexity of their differentiation/activation process in adipose tissue and their diverse activation profiles in response to microenvironmental cues. Although the concept of multifaceted macrophage action is well-accepted, no current model precisely depicts their dynamically regulated in vivo features. To address this knowledge gap, we generated single-cell transcriptome data from primary bone marrow-derived macrophages under polarizing and non-polarizing conditions to develop new high-resolution algorithms. The outcome was creation of a two-index platform, MacSpectrum (https://macspectrum.uconn.edu), that enables comprehensive high-resolution mapping of macrophage activation states from diverse mixed cell populations. MacSpectrum captured dynamic transitions of macrophage subpopulations under both in vitro and in vivo conditions. Importantly, MacSpectrum revealed unique "signature" gene sets in ATMs and circulating monocytes that displayed significant correlation with BMI and homeostasis model assessment of insulin resistance (HOMA-IR) in obese human patients. Thus, MacSpectrum provides unprecedented resolution to decode macrophage heterogeneity and will open new areas of clinical translation.

Hypoxia tolerance in the Norrin-deficient retina and the chronically hypoxic brain studied at single-cell resolution

The mammalian CNS is capable of tolerating chronic hypoxia, but cell type-specific responses to this stress have not been systematically characterized. In the Norrin KO (Ndp ^KO ) mouse, a model of familial exudative vitreoretinopathy (FEVR), developmental hypovascularization of the retina produces chronic hypoxia of inner nuclear-layer (INL) neurons and Muller glia. We used single-cell RNA sequencing, untargeted metabolomics, and metabolite labeling from ¹³C-glucose to compare WT and Ndp ^KO retinas. In Ndp ^KO retinas, we observe gene expression responses consistent with hypoxia in Muller glia and retinal neurons, and we find a metabolic shift that combines reduced flux through the TCA cycle with increased synthesis of serine, glycine, and glutathione. We also used single-cell RNA sequencing to compare the responses of individual cell types in Ndp ^KO retinas with those in the hypoxic cerebral cortex of mice that were housed for 1 week in a reduced oxygen environment (7.5% oxygen). In the hypoxic cerebral cortex, glial transcriptome responses most closely resemble the response of Muller glia in the Ndp ^KO retina. In both retina and brain, vascular endothelial cells activate a previously dormant tip cell gene expression program, which likely underlies the adaptive neoangiogenic response to chronic hypoxia. These analyses of retina and brain transcriptomes at single-cell resolution reveal both shared and cell type-specific changes in gene expression in response to chronic hypoxia, implying both shared and distinct cell type-specific physiologic responses.

Modeling the Pathological Long-Range Regulatory Effects of Human Structural Variation with Patient-Specific hiPSCs

The pathological consequences of structural variants disrupting 3D genome organization can be difficult to elucidate in vivo due to differences in gene dosage sensitivity between mice and humans. This is illustrated by branchiooculofacial syndrome (BOFS), a rare congenital disorder caused by heterozygous mutations within TFAP2A, a neural crest regulator for which humans, but not mice, are haploinsufficient. Here, we present a BOFS patient carrying a heterozygous inversion with one breakpoint located within a topologically associating domain (TAD) containing enhancers essential for TFAP2A expression in human neural crest cells (hNCCs). Using patient-specific hiPSCs, we show that, although the inversion shuffles the TFAP2A hNCC enhancers with novel genes within the same TAD, this does not result in enhancer adoption. Instead, the inversion disconnects one TFAP2A allele from its cognate enhancers, leading to monoallelic and haploinsufficient TFAP2A expression in patient hNCCs. Our work illustrates the power of hiPSC differentiation to unveil long-range pathomechanisms.

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.

Single-Cell RNA Sequencing Resolves Molecular Relationships Among Individual Plant Cells

Single-cell RNA sequencing (scRNA-seq) has been used extensively to study cell-specific gene expression in animals, but it has not been widely applied to plants. Here, we describe the use of a commercially available droplet-based microfluidics platform for high-throughput scRNA-seq to obtain single-cell transcriptomes from protoplasts of more than 10,000 Arabidopsis (Arabidopsis thaliana) root cells. We find that all major tissues and developmental stages are represented in this single-cell transcriptome population. Further, distinct subpopulations and rare cell types, including putative quiescent center cells, were identified. A focused analysis of root epidermal cell transcriptomes defined developmental trajectories for individual cells progressing from meristematic through mature stages of root-hair and nonhair cell differentiation. In addition, single-cell transcriptomes were obtained from root epidermis mutants, enabling a comparative analysis of gene expression at single-cell resolution and providing an unprecedented view of the impact of the mutated genes. Overall, this study demonstrates the feasibility and utility of scRNA-seq in plants and provides a first-generation gene expression map of the Arabidopsis root at single-cell resolution.

PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https://github.com/theislab/paga ). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.

Single-Cell Analysis Reveals Heterogeneity of High Endothelial Venules and Different Regulation of Genes Controlling Lymphocyte Entry to Lymph Nodes

High-endothelial venules (HEVs) are specialized blood vessels allowing recirculation of naive lymphocytes through lymphoid organs. Here, using full-length, single-cell RNA sequencing, RNA fluorescence in situ hybridization (FISH), flow cytometry, and immunohistofluorescence, we reveal the heterogeneity of HEVs in adult mouse peripheral lymph nodes (PLNs) under conditions of homeostasis, antigenic stimulation, and after inhibition of lymphotoxin-β receptor (LTβR) signaling. We demonstrate that HEV endothelial cells are in an activated state during homeostasis, and we identify the genes characteristic of the differentiated HEV phenotype. We show that LTβR signaling regulates many HEV genes and pathways in resting PLNs and that immune stimulation induces a global and temporary inflammatory phenotype in HEVs without compromising their ability to recruit naive lymphocytes. Most importantly, we uncover differences in the regulation of genes controlling lymphocyte trafficking, Glycam1, Fut7, Gcnt1, Chst4, B3gnt3, and Ccl21a, that have implications for HEV function and regulation in health and disease.

A novel in vitro model of primary human pediatric lung epithelial cells

BACKGROUND

Current in vitro human lung epithelial cell models derived from adult tissues may not accurately represent all attributes that define homeostatic and disease mechanisms relevant to the pediatric lung.

METHODS

We report methods for growing and differentiating primary Pediatric Human Lung Epithelial (PHLE) cells from organ donor infant lung tissues. We use immunohistochemistry, flow cytometry, quantitative RT-PCR, and single cell RNA sequencing (scRNAseq) analysis to characterize the cellular and transcriptional heterogeneity of PHLE cells.

RESULTS

PHLE cells can be expanded in culture up to passage 6, with a doubling time of ~4 days, and retain attributes of highly enriched epithelial cells. PHLE cells can form resistant monolayers, and undergo differentiation when placed at air-liquid interface. When grown at Air-Liquid Interface (ALI), PHLE cells expressed markers of airway epithelial cell lineages. scRNAseq suggests the cultures contained 4 main sub-phenotypes defined by expression of FOXJ1, KRT5, MUC5B, and SFTPB. These cells are available to the research community through the Developing Lung Molecular Atlas Program Human Tissue Core.

CONCLUSION

Our data demonstrate that PHLE cells provide a novel in vitro human cell model that represents the pediatric airway epithelium, which can be used to study perinatal developmental and pediatric disease mechanisms.

Diabetes relief in mice by glucose-sensing insulin-secreting human α-cells

Cell identity switches, where terminally-differentiated cells convert into different cell-types when stressed, represent a widespread regenerative strategy in animals, yet they are poorly documented in mammals. In mice, some glucagon-producing pancreatic α-cells and somatostatin-producing δ-cells become insulin expressers upon ablation of insulin-secreting β-cells, promoting diabetes recovery. Whether human islets also display this plasticity, especially in diabetic conditions, remains unknown. Here we show that islet non-β-cells, namely α-cells and PPY-producing γ–cells, obtained from deceased non-diabetic or diabetic human donors, can be lineage-traced and reprogrammed by the transcription factors Pdx1 and MafA to produce and secrete insulin in response to glucose. When transplanted into diabetic mice, converted human α-cells reverse diabetes and remain producing insulin even after 6 months. Surprisingly, insulin-producing α-cells maintain α-cell markers, as seen by deep transcriptomic and proteomic characterization. These observations provide conceptual evidence and a molecular framework for a mechanistic understanding of in situ cell plasticity as a treatment for diabetes and other degenerative diseases.

Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds

During wound healing in adult mouse skin, hair follicles and then adipocytes regenerate. Adipocytes regenerate from myofibroblasts, a specialized contractile wound fibroblast. Here we study wound fibroblast diversity using single-cell RNA-sequencing. On analysis, wound fibroblasts group into twelve clusters. Pseudotime and RNA velocity analyses reveal that some clusters likely represent consecutive differentiation states toward a contractile phenotype, while others appear to represent distinct fibroblast lineages. One subset of fibroblasts expresses hematopoietic markers, suggesting their myeloid origin. We validate this finding using single-cell western blot and single-cell RNA-sequencing on genetically labeled myofibroblasts. Using bone marrow transplantation and Cre recombinase-based lineage tracing experiments, we rule out cell fusion events and confirm that hematopoietic lineage cells give rise to a subset of myofibroblasts and rare regenerated adipocytes. In conclusion, our study reveals that wounding induces a high degree of heterogeneity among fibroblasts and recruits highly plastic myeloid cells that contribute to adipocyte regeneration.

The diversity of fibroblasts contributing to wound healing is unclear. Here, the authors use single-cell RNA-sequencing to identify heterogeneity among murine fibroblasts in the wound and find that recruited myeloid cells contribute to adipocyte regeneration during healing.

Specification of diverse cell types during early neurogenesis of the mouse cerebellum

We applied single-cell RNA sequencing to profile genome-wide gene expression in about 9400 individual cerebellar cells from the mouse embryo at embryonic day 13.5. Reiterative clustering identified the major cerebellar cell types and subpopulations of different lineages. Through pseudotemporal ordering to reconstruct developmental trajectories, we identified novel transcriptional programs controlling cell fate specification of populations arising from the ventricular zone and the rhombic lip, two distinct germinal zones of the embryonic cerebellum. Together, our data revealed cell-specific markers for studying the cerebellum, gene-expression cascades underlying cell fate specification, and a number of previously unknown subpopulations that may play an integral role in the formation and function of the cerebellum. Our findings will facilitate new discovery by providing insights into the molecular and cell type diversity in the developing cerebellum.

A Peninsular Structure Coordinates Asynchronous Differentiation with Morphogenesis to Generate Pancreatic Islets

The pancreatic islets of Langerhans regulate glucose homeostasis. The loss of insulin-producing β cells within islets results in diabetes, and islet transplantation from cadaveric donors can cure the disease. In vitro production of whole islets, not just β cells, will benefit from a better understanding of endocrine differentiation and islet morphogenesis. We used single-cell mRNA sequencing to obtain a detailed description of pancreatic islet development. Contrary to the prevailing dogma, we find islet morphology and endocrine differentiation to be directly related. As endocrine progenitors differentiate, they migrate in cohesion and form bud-like islet precursors, or "peninsulas" (literally "almost islands"). α cells, the first to develop, constitute the peninsular outer layer, and β cells form later, beneath them. This spatiotemporal collinearity leads to the typical core-mantle architecture of the mature, spherical islet. Finally, we induce peninsula-like structures in differentiating human embryonic stem cells, laying the ground for the generation of entire islets in vitro.

Memory CD4+ T cells are generated in the human fetal intestine

The fetus is thought to be protected from exposure to foreign antigens, yet CD45RO⁺ T cells reside in the fetal intestine. Here we combined functional assays with mass cytometry, single-cell RNA-sequencing and high-throughput T cell antigen receptor (TCR) sequencing to characterize the CD4⁺ T cell compartment in the human fetal intestine. We identified 22 CD4⁺ T cell clusters, including naive-like, regulatory-like and memory-like subpopulations, which were confirmed and further characterized at the transcriptional level. Memory-like CD4⁺ T cells had high expression of Ki-67, indicative of cell division, and CD5, a surrogate marker of TCR avidity, and produced the cytokines IFN-γ and IL-2. Pathway analysis revealed a differentiation trajectory associated with cellular activation and proinflammatory effector functions, and TCR repertoire analysis indicated clonal expansions, distinct repertoire characteristics and interconnections between subpopulations of memory-like CD4⁺ T cells. Imaging-mass cytometry indicated that memory-like CD4⁺ T cells colocalized with antigen-presenting cells. Collectively, these results provide evidence for the generation of memory-like CD4⁺ T cells in the human fetal intestine that is consistent with exposure to foreign antigens.

Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data

Background

The analysis of single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the detection of differentially expressed (DE) genes. scRNAseq data, however, are highly heterogeneous and have a large number of zero counts, which introduces challenges in detecting DE genes. Addressing these challenges requires employing new approaches beyond the conventional ones, which are based on a nonzero difference in average expression. Several methods have been developed for differential gene expression analysis of scRNAseq data. To provide guidance on choosing an appropriate tool or developing a new one, it is necessary to evaluate and compare the performance of differential gene expression analysis methods for scRNAseq data.

Results

In this study, we conducted a comprehensive evaluation of the performance of eleven differential gene expression analysis software tools, which are designed for scRNAseq data or can be applied to them. We used simulated and real data to evaluate the accuracy and precision of detection. Using simulated data, we investigated the effect of sample size on the detection accuracy of the tools. Using real data, we examined the agreement among the tools in identifying DE genes, the run time of the tools, and the biological relevance of the detected DE genes.

Conclusions

In general, agreement among the tools in calling DE genes is not high. There is a trade-off between true-positive rates and the precision of calling DE genes. Methods with higher true positive rates tend to show low precision due to their introducing false positives, whereas methods with high precision show low true positive rates due to identifying few DE genes. We observed that current methods designed for scRNAseq data do not tend to show better performance compared to methods designed for bulk RNAseq data. Data multimodality and abundance of zero read counts are the main characteristics of scRNAseq data, which play important roles in the performance of differential gene expression analysis methods and need to be considered in terms of the development of new methods.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2599-6) contains supplementary material, which is available to authorized users.

A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens

Over one million candidate regulatory elements have been identified across the human genome, but nearly all are unvalidated and their target genes uncertain. Approaches based on human genetics are limited in scope to common variants and in resolution by linkage disequilibrium. We present a multiplex, expression quantitative trait locus (eQTL)-inspired framework for mapping enhancer-gene pairs by introducing random combinations of CRISPR/Cas9-mediated perturbations to each of many cells, followed by single-cell RNA sequencing (RNA-seq). Across two experiments, we used dCas9-KRAB to perturb 5,920 candidate enhancers with no strong a priori hypothesis as to their target gene(s), measuring effects by profiling 254,974 single-cell transcriptomes. We identified 664 (470 high-confidence) cis enhancer-gene pairs, which were enriched for specific transcription factors, non-housekeeping status, and genomic and 3D conformational proximity to their target genes. This framework will facilitate the large-scale mapping of enhancer-gene regulatory interactions, a critical yet largely uncharted component of the cis-regulatory landscape of the human genome.

A transcriptomic roadmap to alpha- and beta cell differentiation in the embryonic pancreas

During pancreatic development, endocrine cells appear from the pancreatic epithelium when Neurog3 positive cells delaminate and differentiate into alpha, beta, gamma and delta cells. The mechanisms involved in this process are still incompletely understood. We characterized the temporal, lineage-specific developmental programs during pancreatic development by sequencing the transcriptome of thousands of individual pancreatic cells from embryonic day E12.5 to E18.5 in mice, and identified all known cell types that are present in the embryonic pancreas, but focused specifically on alpha and beta cell differentiation by enrichment of a MIP-GFP reporter. We characterized transcriptomic heterogeneity in the tip domain based on proliferation, and characterized two endocrine precursor clusters marked by expression of Neurog3 and Fev. Pseudotime analysis revealed specific branches for developing alpha- and beta cells, which allowed identification of specific gene regulation patterns. These include some known and many previously unreported genes that appear to define pancreatic cell fate transitions. This resource allows dynamic profiling of embryonic pancreas development at single cell resolution and reveals novel gene signatures during pancreatic differentiation into alpha and beta cells.

ddSeeker: a tool for processing Bio-Rad ddSEQ single cell RNA-seq data

Background

New single-cell isolation technologies are facilitating studies on the transcriptomics of individual cells. Bio-Rad ddSEQ is a droplet-based microfluidic system that, when coupled with downstream Illumina library preparation and sequencing, enables the monitoring of thousands of genes per cell. Sequenced reads show unique features that do not permit the use of freely available tools to perform single cell demultiplexing.

Results

We present ddSeeker, a tool to perform initial processing and quality metrics of reads generated through Bio-Rad ddSEQ/Illumina experiments. Its application to the Illumina test dataset demonstrates that ddSeeker performs better than Illumina BaseSpace software, enabling a higher recovery of valid reads. We also show its utility in the analysis of an in-house dataset including two read sets characterized by low and high sequencing quality. ddSeeker and its source code are available at https://github.com/cgplab/ddSeeker.

Conclusions

ddSeeker is a freely available tool to perform initial processing and quality metrics of reads generated through Bio-Rad ddSEQ/Illumina single cell transcriptomic experiments.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5249-x) contains supplementary material, which is available to authorized users.

Metabolic heterogeneity underlies reciprocal fates of TH17 cell stemness and plasticity

A defining feature of adaptive immunity is the development of long-lived memory T cells to curtail infection. Recent studies have identified a unique stem-like T cell subset in exhausted CD8⁺ T cells in chronic infection^1–3, but it remains unclear whether CD4⁺ T cell subsets with similar features exist in chronic inflammatory conditions. Among helper T cells, T_H17 cells play prominent roles in autoimmunity and tissue inflammation and are characterized by inherent plasticity^4–7, although the regulation of plasticity is poorly understood. Here we demonstrate that T_H17 cells in autoimmune disease are functionally and metabolically heterogeneous and contain a subset with stemness-associated features but lower anabolic metabolism, and a reciprocal subset with higher metabolic activity that supports the transdifferentiation into T_H1 cells. These two T_H17 cell subsets are defined by selective expression of transcription factors TCF-1 and T-bet, and discrete CD27 expression levels. Moreover, we identify mTORC1 signaling as a central regulator to orchestrate T_H17 cell fates by coordinating metabolic and transcriptional programs. T_H17 cells with disrupted mTORC1 or anabolic metabolism fail to induce autoimmune neuroinflammation or develop into T_H1-like cells, but instead upregulate TCF-1 expression and activity and acquire stemness-associated features. Single cell RNA-sequencing and experimental validation reveal heterogeneity in fate-mapped T_H17 cells, and a developmental arrest in the T_H1 transdifferentiation trajectory upon mTORC1 deletion or metabolic perturbation. Our results establish that the dichotomy of stemness and effector function underlies the heterogeneous T_H17 responses and autoimmune pathogenesis, and point to previously unappreciated metabolic control of helper T cell plasticity.

Direct reprogramming of fibroblasts into antigen-presenting dendritic cells

Ectopic expression of transcription factors has been used to reprogram differentiated somatic cells toward pluripotency or to directly reprogram them to other somatic cell lineages. This concept has been explored in the context of regenerative medicine. Here, we set out to generate dendritic cells (DCs) capable of presenting antigens from mouse and human fibroblasts. By screening combinations of 18 transcription factors that are expressed in DCs, we have identified PU.1, IRF8, and BATF3 transcription factors as being sufficient to reprogram both mouse and human fibroblasts to induced DCs (iDCs). iDCs acquire a conventional DC type 1–like transcriptional program, with features of interferon-induced maturation. iDCs secrete inflammatory cytokines and have the ability to engulf, process, and present antigens to T cells. Furthermore, we demonstrate that murine iDCs generated here were able to cross-present antigens to CD8+ T cells. Our reprogramming system should facilitate better understanding of DC specification programs and serve as a platform for the development of patient-specific DCs for immunotherapy.

Human bone marrow assessment by single-cell RNA sequencing, mass cytometry, and flow cytometry

New techniques for single-cell analysis have led to insights into hematopoiesis and the immune system, but the ability of these techniques to cross-validate and reproducibly identify the biological variation in diverse human samples is currently unproven. We therefore performed a comprehensive assessment of human bone marrow cells using both single-cell RNA sequencing and multiparameter flow cytometry from 20 healthy adult human donors across a broad age range. These data characterize variation between healthy donors as well as age-associated changes in cell population frequencies. Direct comparison of techniques revealed discrepancy in the quantification of T lymphocyte and natural killer cell populations. Orthogonal validation of immunophenotyping using mass cytometry demonstrated a strong correlation with flow cytometry. Technical replicates using single-cell RNA sequencing matched robustly, while biological replicates showed variation. Given the increasing use of single-cell technologies in translational research, this resource serves as an important reference data set and highlights opportunities for further refinement.

Longitudinal single-cell RNA sequencing of patient-derived primary cells reveals drug-induced infidelity in stem cell hierarchy

Chemo-resistance is one of the major causes of cancer-related deaths. Here we used single-cell transcriptomics to investigate divergent modes of chemo-resistance in tumor cells. We observed that higher degree of phenotypic intra-tumor heterogeneity (ITH) favors selection of pre-existing drug-resistant cells, whereas phenotypically homogeneous cells engage covert epigenetic mechanisms to trans-differentiate under drug-selection. This adaptation was driven by selection-induced gain of H3K27ac marks on bivalently poised resistance-associated chromatin, and therefore not expressed in the treatment-naïve setting. Mechanistic interrogation of this phenomenon revealed that drug-induced adaptation was acquired upon the loss of stem factor SOX2, and a concomitant gain of SOX9. Strikingly we observed an enrichment of SOX9 at drug-induced H3K27ac sites, suggesting that tumor evolution could be driven by stem cell-switch-mediated epigenetic plasticity. Importantly, JQ1 mediated inhibition of BRD4 could reverse drug-induced adaptation. These results provide mechanistic insights into the modes of therapy-induced cellular plasticity and underscore the use of epigenetic inhibitors in targeting tumor evolution.

Drug resistance is one of the major causes of cancer-related deaths. Here, the authors using single cell RNA-seq of oral squamous cell carcinoma patient samples pre- and post-cisplatin treatment show that phenotypically homogenous cell populations display cell state plasticity, with poised chromatin marks at mesenchymal genes in epithelial cells, and that the loss of stem factor Sox2 but gain of Sox9 expression (with de novo gain of H3K27ac sites) is associated with drug-induced adaptation.

Characterization of human mosaic Rett syndrome brain tissue by single-nucleus RNA sequencing

In females with X-linked genetic disorders, wild-type and mutant cells coexist within brain tissue because of X-chromosome inactivation, posing challenges for interpreting the effects of X-linked mutant alleles on gene expression. We present a single-nucleus RNA sequencing approach that resolves mosaicism by using single-nucleotide polymorphisms in genes expressed in cis with the X-linked mutation to determine which nuclei express the mutant allele even when the mutant gene is not detected. This approach enables gene expression comparisons between mutant and wild-type cells within the same individual, eliminating variability introduced by comparisons to controls with different genetic backgrounds. We apply this approach to mosaic female mouse models and humans with Rett syndrome, an X-linked neurodevelopmental disorder caused by mutations in the gene encoding the methyl-DNA-binding protein MECP2, and observe that cell-type-specific DNA methylation predicts the degree of gene upregulation in MECP2-mutant neurons. This approach can be broadly applied to study gene expression in mosaic X-linked disorders.

Single-cell RNA-sequencing reveals transcriptional dynamics of estrogen-induced dysplasia in the ovarian surface epithelium

Estrogen therapy increases the risk of ovarian cancer and exogenous estradiol accelerates the onset of ovarian cancer in mouse models. Both in vivo and in vitro, ovarian surface epithelial (OSE) cells exposed to estradiol develop a subpopulation that loses cell polarity, contact inhibition, and forms multi-layered foci of dysplastic cells with increased susceptibility to transformation. Here, we use single-cell RNA-sequencing to characterize this dysplastic subpopulation and identify the transcriptional dynamics involved in its emergence. Estradiol-treated cells were characterized by up-regulation of genes associated with proliferation, metabolism, and survival pathways. Pseudotemporal ordering revealed that OSE cells occupy a largely linear phenotypic spectrum that, in estradiol-treated cells, diverges towards cell state consistent with the dysplastic population. This divergence is characterized by the activation of various cancer-associated pathways including an increase in Greb1 which was validated in fallopian tube epithelium and human ovarian cancers. Taken together, this work reveals possible mechanisms by which estradiol increases epithelial cell susceptibility to tumour initiation.

T-ALL leukemia stem cell 'stemness' is epigenetically controlled by the master regulator SPI1

Leukemia stem cells (LSCs) are regarded as the origins and key therapeutic targets of leukemia, but limited knowledge is available on the key determinants of LSC 'stemness'. Using single-cell RNA-seq analysis, we identify a master regulator, SPI1, the LSC-specific expression of which determines the molecular signature and activity of LSCs in the murine Pten-null T-ALL model. Although initiated by PTEN-controlled β-catenin activation, Spi1 expression and LSC 'stemness' are maintained by a β-catenin-SPI1-HAVCR2 regulatory circuit independent of the leukemogenic driver mutation. Perturbing any component of this circuit either genetically or pharmacologically can prevent LSC formation or eliminate existing LSCs. LSCs lose their 'stemness' when Spi1 expression is silenced by DNA methylation, but Spi1 expression can be reactivated by 5-AZ treatment. Importantly, similar regulatory mechanisms may be also present in human T-ALL.

CDC20B is required for deuterosome-mediated centriole production in multiciliated cells

Multiciliated cells (MCCs) harbor dozens to hundreds of motile cilia, which generate hydrodynamic forces important in animal physiology. In vertebrates, MCC differentiation involves massive centriole production by poorly characterized structures called deuterosomes. Here, single-cell RNA sequencing reveals that human deuterosome stage MCCs are characterized by the expression of many cell cycle-related genes. We further investigated the uncharacterized vertebrate-specific cell division cycle 20B (CDC20B) gene, which hosts microRNA-449abc. We show that CDC20B protein associates to deuterosomes and is required for centriole release and subsequent cilia production in mouse and Xenopus MCCs. CDC20B interacts with PLK1, a kinase known to coordinate centriole disengagement with the protease Separase in mitotic cells. Strikingly, over-expression of Separase rescues centriole disengagement and cilia production in CDC20B-deficient MCCs. This work reveals the shaping of deuterosome-mediated centriole production in vertebrate MCCs, by adaptation of canonical and recently evolved cell cycle-related molecules.

Cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure

Pressure overload induces a transition from cardiac hypertrophy to heart failure, but its underlying mechanisms remain elusive. Here we reconstruct a trajectory of cardiomyocyte remodeling and clarify distinct cardiomyocyte gene programs encoding morphological and functional signatures in cardiac hypertrophy and failure, by integrating single-cardiomyocyte transcriptome with cell morphology, epigenomic state and heart function. During early hypertrophy, cardiomyocytes activate mitochondrial translation/metabolism genes, whose expression is correlated with cell size and linked to ERK1/2 and NRF1/2 transcriptional networks. Persistent overload leads to a bifurcation into adaptive and failing cardiomyocytes, and p53 signaling is specifically activated in late hypertrophy. Cardiomyocyte-specific p53 deletion shows that cardiomyocyte remodeling is initiated by p53-independent mitochondrial activation and morphological hypertrophy, followed by p53-dependent mitochondrial inhibition, morphological elongation, and heart failure gene program activation. Human single-cardiomyocyte analysis validates the conservation of the pathogenic transcriptional signatures. Collectively, cardiomyocyte identity is encoded in transcriptional programs that orchestrate morphological and functional phenotypes.

Lineage tracking reveals dynamic relationships of T cells in colorectal cancer

T cells are key elements of cancer immunotherapy¹ but certain fundamental properties, such as the development and migration of T cells within tumours, remain unknown. The enormous T cell receptor (TCR) repertoire, which is required for the recognition of foreign and self-antigens², could serve as lineage tags to track these T cells in tumours³. Here we obtained transcriptomes of 11,138 single T cells from 12 patients with colorectal cancer, and developed single T cell analysis by RNA sequencing and TCR tracking (STARTRAC) indices to quantitatively analyse the dynamic relationships among 20 identified T cell subsets with distinct functions and clonalities. Although both CD8⁺ effector and 'exhausted' T cells exhibited high clonal expansion, they were independently connected with tumour-resident CD8⁺ effector memory cells, implicating a TCR-based fate decision. Of the CD4⁺ T cells, most tumour-infiltrating T regulatory (T_reg) cells showed clonal exclusivity, whereas certain T_reg cell clones were developmentally linked to several T helper (T_H) cell clones. Notably, we identified two IFNG⁺ T_H1-like cell clusters in tumours that were associated with distinct IFNγ-regulating transcription factors -the GZMK⁺ effector memory T cells, which were associated with EOMES and RUNX3, and CXCL13⁺BHLHE40⁺ T_H1-like cell clusters, which were associated with BHLHE40. Only CXCL13⁺BHLHE40⁺ T_H1-like cells were preferentially enriched in patients with microsatellite-instable tumours, and this might explain their favourable responses to immune-checkpoint blockade. Furthermore, IGFLR1 was highly expressed in both CXCL13⁺BHLHE40⁺ T_H1-like cells and CD8⁺ exhausted T cells and possessed co-stimulatory functions. Our integrated STARTRAC analyses provide a powerful approach to dissect the T cell properties in colorectal cancer comprehensively, and could provide insights into the dynamic relationships of T cells in other cancers.

Discovery of a periosteal stem cell mediating intramembranous bone formation

Bone consists of separate inner endosteal and outer periosteal compartments, each with distinct contributions to bone physiology and each maintaining separate pools of cells owing to physical separation by the bone cortex. The skeletal stem cell that gives rise to endosteal osteoblasts has been extensively studied; however, the identity of periosteal stem cells remains unclear^1-5. Here we identify a periosteal stem cell (PSC) that is present in the long bones and calvarium of mice, displays clonal multipotency and self-renewal, and sits at the apex of a differentiation hierarchy. Single-cell and bulk transcriptional profiling show that PSCs display transcriptional signatures that are distinct from those of other skeletal stem cells and mature mesenchymal cells. Whereas other skeletal stem cells form bone via an initial cartilage template using the endochondral pathway⁴, PSCs form bone via a direct intramembranous route, providing a cellular basis for the divergence between intramembranous versus endochondral developmental pathways. However, there is plasticity in this division, as PSCs acquire endochondral bone formation capacity in response to injury. Genetic blockade of the ability of PSCs to give rise to bone-forming osteoblasts results in selective impairments in cortical bone architecture and defects in fracture healing. A cell analogous to mouse PSCs is present in the human periosteum, raising the possibility that PSCs are attractive targets for drug and cellular therapy for skeletal disorders. The identification of PSCs provides evidence that bone contains multiple pools of stem cells, each with distinct physiologic functions.

A Single-Cell Transcriptional Atlas of the Developing Murine Cerebellum

The cerebellum develops from a restricted number of cell types that precisely organize to form the circuitry that controls sensory-motor coordination and some higher-order cognitive processes. To acquire an enhanced understanding of the molecular processes that mediate cerebellar development, we performed single-cell RNA-sequencing of 39,245 murine cerebellar cells at twelve critical developmental time points. Using recognized lineage markers, we confirmed that the single-cell data accurately recapitulate cerebellar development. We then followed distinct populations from emergence through migration and differentiation, and determined the associated transcriptional cascades. After identifying key lineage commitment decisions, focused analyses uncovered waves of transcription factor expression at those branching points. Finally, we created Cell Seek, a flexible online interface that facilitates exploration of the dataset. Our study provides a transcriptional summarization of cerebellar development at single-cell resolution that will serve as a valuable resource for future investigations of cerebellar development, neurobiology, and disease.

Single-cell transcriptomics reveals distinct inflammation-induced microglia signatures

Microglia are specialized parenchymal‐resident phagocytes of the central nervous system (CNS) that actively support, defend and modulate the neural environment. Dysfunctional microglial responses are thought to worsen CNS diseases; nevertheless, their impact during neuroinflammatory processes remains largely obscure. Here, using a combination of single‐cell RNA sequencing and multicolour flow cytometry, we comprehensively profile microglia in the brain of lipopolysaccharide (LPS)‐injected mice. By excluding the contribution of other immune CNS‐resident and peripheral cells, we show that microglia isolated from LPS‐injected mice display a global downregulation of their homeostatic signature together with an upregulation of inflammatory genes. Notably, we identify distinct microglial activated profiles under inflammatory conditions, which greatly differ from neurodegenerative disease‐associated profiles. These results provide insights into microglial heterogeneity and establish a resource for the identification of specific phenotypes in CNS disorders, such as neuroinflammatory and neurodegenerative diseases.

Single-cell RNA sequencing of adult mouse testes

Spermatogenesis is an efficient and complex system of continuous cell differentiation. Previous studies investigating the transcriptomes of different cell populations in the testis relied either on sorting cells, cell depletion, or juvenile animals where not all stages of spermatogenesis have been completed. We present single-cell RNA sequencing (scRNA-Seq) data of 2,500 cells from the testes of two 8-week-old C57Bl/6J mice. Our dataset includes all spermatogenic stages from preleptotene to condensing spermatids as well as individual spermatogonia, Sertoli and Leydig cells. The data capture the full continuity of the meiotic and postmeiotic stages of spermatogenesis, and is thus ideally suited for marker discovery, network inference and similar analyses for which temporal ordering of differentiation processes can be exploited. Furthermore, it can serve as a reference for future studies involving single-cell RNA-Seq in mice where spermatogenesis is perturbed.

Cicero Predicts cis-Regulatory DNA Interactions from Single-Cell Chromatin Accessibility Data

Linking regulatory DNA elements to their target genes, which may be located hundreds of kilobases away, remains challenging. Here, we introduce Cicero, an algorithm that identifies co-accessible pairs of DNA elements using single-cell chromatin accessibility data and so connects regulatory elements to their putative target genes. We apply Cicero to investigate how dynamically accessible elements orchestrate gene regulation in differentiating myoblasts. Groups of Cicero-linked regulatory elements meet criteria of "chromatin hubs"-they are enriched for physical proximity, interact with a common set of transcription factors, and undergo coordinated changes in histone marks that are predictive of changes in gene expression. Pseudotemporal analysis revealed that most DNA elements remain in chromatin hubs throughout differentiation. A subset of elements bound by MYOD1 in myoblasts exhibit early opening in a PBX1- and MEIS1-dependent manner. Our strategy can be applied to dissect the architecture, sequence determinants, and mechanisms of cis-regulation on a genome-wide scale.

Aligning Single-Cell Developmental and Reprogramming Trajectories Identifies Molecular Determinants of Myogenic Reprogramming Outcome

Cellular reprogramming through manipulation of defined factors holds great promise for large-scale production of cell types needed for use in therapy and for revealing principles of gene regulation. However, most reprogramming systems are inefficient, converting only a fraction of cells to the desired state. Here, we analyze MYOD-mediated reprogramming of human fibroblasts to myotubes, a well-characterized model system for direct conversion by defined factors, at pseudotemporal resolution using single-cell RNA-seq. To expose barriers to efficient conversion, we introduce a novel analytic technique, trajectory alignment, which enables quantitative comparison of gene expression kinetics across two biological processes. Reprogrammed cells navigate a trajectory with branch points that correspond to two alternative decision points, with cells that select incorrect branches terminating at aberrant or incomplete reprogramming outcomes. Analysis of these branch points revealed insulin and BMP signaling as crucial molecular determinants of reprogramming. Single-cell trajectory alignment enables rigorous quantitative comparisons between biological trajectories found in diverse processes in development, reprogramming, and other contexts.

Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by extensive intratumoral heterogeneity. To investigate the underlying biology, we conducted single-cell RNA-sequencing (scRNA-seq) of >1500 cells from six primary TNBC. Here, we show that intercellular heterogeneity of gene expression programs within each tumor is variable and largely correlates with clonality of inferred genomic copy number changes, suggesting that genotype drives the gene expression phenotype of individual subpopulations. Clustering of gene expression profiles identified distinct subgroups of malignant cells shared by multiple tumors, including a single subpopulation associated with multiple signatures of treatment resistance and metastasis, and characterized functionally by activation of glycosphingolipid metabolism and associated innate immunity pathways. A novel signature defining this subpopulation predicts long-term outcomes for TNBC patients in a large cohort. Collectively, this analysis reveals the functional heterogeneity and its association with genomic evolution in TNBC, and uncovers unanticipated biological principles dictating poor outcomes in this disease.

Memory B cells are reactivated in subcapsular proliferative foci of lymph nodes

Vaccine-induced immunity depends on the generation of memory B cells (MBC). However, where and how MBCs are reactivated to make neutralising antibodies remain unknown. Here we show that MBCs are prepositioned in a subcapsular niche in lymph nodes where, upon reactivation by antigen, they rapidly proliferate and differentiate into antibody-secreting plasma cells in the subcapsular proliferative foci (SPF). This novel structure is enriched for signals provided by T follicular helper cells and antigen-presenting subcapsular sinus macrophages. Compared with contemporaneous secondary germinal centres, SPF have distinct single-cell molecular signature, cell migration pattern and plasma cell output. Moreover, SPF are found both in human and mouse lymph nodes, suggesting that they are conserved throughout mammalian evolution. Our data thus reveal that SPF is a seat of immunological memory that may be exploited to rapidly mobilise secondary antibody responses and improve vaccine efficacy.

Endocrine lineage biases arise in temporally distinct endocrine progenitors during pancreatic morphogenesis

Decoding the molecular composition of individual Ngn3 + endocrine progenitors (EPs) during pancreatic morphogenesis could provide insight into the mechanisms regulating hormonal cell fate. Here, we identify population markers and extensive cellular diversity including four EP subtypes reflecting EP maturation using high-resolution single-cell RNA-sequencing of the e14.5 and e16.5 mouse pancreas. While e14.5 and e16.5 EPs are constantly born and share select genes, these EPs are overall transcriptionally distinct concomitant with changes in the underlying epithelium. As a consequence, e16.5 EPs are not the same as e14.5 EPs: e16.5 EPs have a higher propensity to form beta cells. Analysis of e14.5 and e16.5 EP chromatin states reveals temporal shifts, with enrichment of beta cell motifs in accessible regions at later stages. Finally, we provide transcriptional maps outlining the route progenitors take as they make cell fate decisions, which can be applied to advance the in vitro generation of beta cells.

Endocrine progenitors form early in pancreatic development but the diversity of this cell population is unclear. Here, the authors use single cell RNA sequencing of the mouse pancreas at e14.5 and e16.5 to show that endocrine progenitors are temporally distinct and those formed later are more likely to become beta cells

Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors

Messenger RNA encodes cellular function and phenotype. In the context of human cancer, it defines the identities of malignant cells and the diversity of tumor tissue. We studied 72,501 single-cell transcriptomes of human renal tumors and normal tissue from fetal, pediatric, and adult kidneys. We matched childhood Wilms tumor with specific fetal cell types, thus providing evidence for the hypothesis that Wilms tumor cells are aberrant fetal cells. In adult renal cell carcinoma, we identified a canonical cancer transcriptome that matched a little-known subtype of proximal convoluted tubular cell. Analyses of the tumor composition defined cancer-associated normal cells and delineated a complex vascular endothelial growth factor (VEGF) signaling circuit. Our findings reveal the precise cellular identities and compositions of human kidney tumors.

Single-cell RNAseq for the study of isoforms-how is that possible?

Single-cell RNAseq and alternative splicing studies have recently become two of the most prominent applications of RNAseq. However, the combination of both is still challenging, and few research efforts have been dedicated to the intersection between them. Cell-level insight on isoform expression is required to fully understand the biology of alternative splicing, but it is still an open question to what extent isoform expression analysis at the single-cell level is actually feasible. Here, we establish a set of four conditions that are required for a successful single-cell-level isoform study and evaluate how these conditions are met by these technologies in published research.

powsimR: power analysis for bulk and single cell RNA-seq experiments

Summary

Power analysis is essential to optimize the design of RNA-seq experiments and to assess and compare the power to detect differentially expressed genes in RNA-seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses.

Availability and implementation

The R package and associated tutorial are freely available at https://github.com/bvieth/powsimR.

Contact

vieth@bio.lmu.de or hellmann@bio.lmu.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

From Tissues to Cell Types and Back: Single-Cell Gene Expression Analysis of Tissue Architecture

With the recent transformative developments in single-cell genomics and, in particular, single-cell gene expression analysis, it is now possible to study tissues at the single-cell level, rather than having to rely on data from bulk measurements. Here we review the rapid developments in single-cell RNA sequencing (scRNA-seq) protocols that have the potential for unbiased identification and profiling of all cell types within a tissue or organism. In addition, novel approaches for spatial profiling of gene expression allow us to map individual cells and cell types back into the three-dimensional context of organs. The combination of in-depth single-cell and spatial gene expression data will reveal tissue architecture in unprecedented detail, generating a wealth of biological knowledge and a better understanding of many diseases.

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.

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.

Quantitative single-cell transcriptomics

Single-cell RNA sequencing (scRNA-seq) is currently transforming our understanding of biology, as it is a powerful tool to resolve cellular heterogeneity and molecular networks. Over 50 protocols have been developed in recent years and also data processing and analyzes tools are evolving fast. Here, we review the basic principles underlying the different experimental protocols and how to benchmark them. We also review and compare the essential methods to process scRNA-seq data from mapping, filtering, normalization and batch corrections to basic differential expression analysis. We hope that this helps to choose appropriate experimental and computational methods for the research question at hand.

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics

BACKGROUND

Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve.

RESULTS

We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods.

CONCLUSIONS

Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.