Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments

Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars

A key goal of developmental biology is to understand how a single cell transforms into a full-grown organism comprising many different cell types. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ1. However, organizing the resulting taxonomy of cell types into lineage trees to understand developmental origin of cells remains challenging. Here we present LINNAEUS (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences)—a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae, and in heart, liver, pancreas and telencephalon of adult fish. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Age-related islet inflammation marks the proliferative decline of pancreatic beta-cells in zebrafish

The pancreatic islet, a cellular community harboring the insulin-producing beta-cells, is known to undergo age-related alterations. However, only a handful of signals associated with aging have been identified. By comparing beta-cells from younger and older zebrafish, here we show that the aging islets exhibit signs of chronic inflammation. These include recruitment of tnfα-expressing macrophages and the activation of NF-kB signaling in beta-cells. Using a transgenic reporter, we show that NF-kB activity is undetectable in juvenile beta-cells, whereas cells from older fish exhibit heterogeneous NF-kB activity. We link this heterogeneity to differences in gene expression and proliferation. Beta-cells with high NF-kB signaling proliferate significantly less compared to their neighbors with low activity. The NF-kB signaling^hi cells also exhibit premature upregulation of socs2, an age-related gene that inhibits beta-cell proliferation. Together, our results show that NF-kB activity marks the asynchronous decline in beta-cell proliferation with advancing age.

Comprehensive Identification and Spatial Mapping of Habenular Neuronal Types Using Single-Cell RNA-Seq

The identification of cell types and marker genes is critical for dissecting neural development and function, but the size and complexity of the brain has hindered the comprehensive discovery of cell types. We combined single-cell RNA-seq (scRNA-seq) with anatomical brain registration to create a comprehensive map of the zebrafish habenula, a conserved forebrain hub involved in pain processing and learning. Single-cell transcriptomes of ∼13,000 habenular cells with 4× cellular coverage identified 18 neuronal types and dozens of marker genes. Registration of marker genes onto a reference atlas created a resource for anatomical and functional studies and enabled the mapping of active neurons onto neuronal types following aversive stimuli. Strikingly, despite brain growth and functional maturation, cell types were retained between the larval and adult habenula. This study provides a gene expression atlas to dissect habenular development and function and offers a general framework for the comprehensive characterization of other brain regions.

Molecular transitions in early progenitors during human cord blood hematopoiesis

Hematopoietic stem cells (HSCs) give rise to diverse cell types in the blood system, yet our molecular understanding of the early trajectories that generate this enormous diversity in humans remains incomplete. Here, we leverage Drop-seq, a massively parallel single-cell RNA sequencing (scRNA-seq) approach, to individually profile 20,000 progenitor cells from human cord blood, without prior enrichment or depletion for individual lineages based on surface markers. Our data reveal a transcriptional compendium of progenitor states in human cord blood, representing four committed lineages downstream from HSC, alongside the transcriptional dynamics underlying fate commitment. We identify intermediate stages that simultaneously co-express "primed" programs for multiple downstream lineages, and also observe striking heterogeneity in the early molecular transitions between myeloid subsets. Integrating our data with a recently published scRNA-seq dataset from human bone marrow, we illustrate the molecular similarity between these two commonly used systems and further explore the chromatin dynamics of "primed" transcriptional programs based on ATAC-seq. Finally, we demonstrate that Drop-seq data can be utilized to identify new heterogeneous surface markers of cell state that correlate with functional output.

An accurate and robust imputation method scImpute for single-cell RNA-seq data

The emerging single-cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at the single-cell resolution. ScRNA-seq data analysis is complicated by excess zero counts, the so-called dropouts due to low amounts of mRNA sequenced within individual cells. We introduce scImpute, a statistical method to accurately and robustly impute the dropouts in scRNA-seq data. scImpute automatically identifies likely dropouts, and only perform imputation on these values without introducing new biases to the rest data. scImpute also detects outlier cells and excludes them from imputation. Evaluation based on both simulated and real human and mouse scRNA-seq data suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropouts. scImpute is shown to identify likely dropouts, enhance the clustering of cell subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.

Despite being widely performed in exploring cell heterogeneity and gene expression stochasticity, single cell RNA-seq analysis is complicated by excess zero counts (dropouts). Here, Li and Li develop scImpute for statistical imputation of dropouts in scRNA-seq data.

Conserved and Divergent Features of Mesenchymal Progenitor Cell Types within the Cortical Nephrogenic Niche of the Human and Mouse Kidney

Cellular interactions among nephron, interstitial, and collecting duct progenitors drive mammalian kidney development. In mice, Six2⁺ nephron progenitor cells (NPCs) and Foxd1⁺ interstitial progenitor cells (IPCs) form largely distinct lineage compartments at the onset of metanephric kidney development. Here, we used the method for analyzing RNA following intracellular sorting (MARIS) approach, single-cell transcriptional profiling, in situ hybridization, and immunolabeling to characterize the presumptive NPC and IPC compartments of the developing human kidney. As in mice, each progenitor population adopts a stereotypical arrangement in the human nephron-forming niche: NPCs capped outgrowing ureteric branch tips, whereas IPCs were sandwiched between the NPCs and the renal capsule. Unlike mouse NPCs, human NPCs displayed a transcriptional profile that overlapped substantially with the IPC transcriptional profile, and key IPC determinants, including FOXD1, were readily detected within SIX2⁺ NPCs. Comparative gene expression profiling in human and mouse Six2/SIX2⁺ NPCs showed broad agreement between the species but also identified species-biased expression of some genes. Notably, some human NPC-enriched genes, including DAPL1 and COL9A2, are linked to human renal disease. We further explored the cellular diversity of mesenchymal cell types in the human nephrogenic niche through single-cell transcriptional profiling. Data analysis stratified NPCs into two main subpopulations and identified a third group of differentiating cells. These findings were confirmed by section in situ hybridization with novel human NPC markers predicted through the single-cell studies. This study provides a benchmark for the mesenchymal progenitors in the human nephrogenic niche and highlights species-variability in kidney developmental programs.

Conserved and Divergent Features of Mesenchymal Progenitor Cell Types within the Cortical Nephrogenic Niche of the Human and Mouse Kidney

Cellular interactions among nephron, interstitial, and collecting duct progenitors drive mammalian kidney development. In mice, Six2⁺ nephron progenitor cells (NPCs) and Foxd1⁺ interstitial progenitor cells (IPCs) form largely distinct lineage compartments at the onset of metanephric kidney development. Here, we used the method for analyzing RNA following intracellular sorting (MARIS) approach, single-cell transcriptional profiling, in situ hybridization, and immunolabeling to characterize the presumptive NPC and IPC compartments of the developing human kidney. As in mice, each progenitor population adopts a stereotypical arrangement in the human nephron-forming niche: NPCs capped outgrowing ureteric branch tips, whereas IPCs were sandwiched between the NPCs and the renal capsule. Unlike mouse NPCs, human NPCs displayed a transcriptional profile that overlapped substantially with the IPC transcriptional profile, and key IPC determinants, including FOXD1, were readily detected within SIX2⁺ NPCs. Comparative gene expression profiling in human and mouse Six2/SIX2⁺ NPCs showed broad agreement between the species but also identified species-biased expression of some genes. Notably, some human NPC-enriched genes, including DAPL1 and COL9A2, are linked to human renal disease. We further explored the cellular diversity of mesenchymal cell types in the human nephrogenic niche through single-cell transcriptional profiling. Data analysis stratified NPCs into two main subpopulations and identified a third group of differentiating cells. These findings were confirmed by section in situ hybridization with novel human NPC markers predicted through the single-cell studies. This study provides a benchmark for the mesenchymal progenitors in the human nephrogenic niche and highlights species-variability in kidney developmental programs.

A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments

Background

qPCR has established itself as the technique of choice for the quantification of gene expression. Procedures for conducting qPCR have received significant attention; however, more rigorous approaches to the statistical analysis of qPCR data are needed.

Results

Here we develop a mathematical model, termed the Common Base Method, for analysis of qPCR data based on threshold cycle values (C_q) and efficiencies of reactions (E). The Common Base Method keeps all calculations in the logscale as long as possible by working with log₁₀(E) ∙ C_q, which we call the efficiency-weighted C_q value; subsequent statistical analyses are then applied in the logscale. We show how efficiency-weighted C_q values may be analyzed using a simple paired or unpaired experimental design and develop blocking methods to help reduce unexplained variation.

Conclusions

The Common Base Method has several advantages. It allows for the incorporation of well-specific efficiencies and multiple reference genes. The method does not necessitate the pairing of samples that must be performed using traditional analysis methods in order to calculate relative expression ratios. Our method is also simple enough to be implemented in any spreadsheet or statistical software without additional scripts or proprietary components.

Derivation of Diverse Hormone-Releasing Pituitary Cells from Human Pluripotent Stem Cells

Human pluripotent stem cells (hPSCs) provide an unlimited cell source for regenerative medicine. Hormone-producing cells are particularly suitable for cell therapy, and hypopituitarism, a defect in pituitary gland function, represents a promising therapeutic target. Previous studies have derived pituitary lineages from mouse and human ESCs using 3D organoid cultures that mimic the complex events underlying pituitary gland development in vivo. Instead of relying on unknown cellular signals, we present a simple and efficient strategy to derive human pituitary lineages from hPSCs using monolayer culture conditions suitable for cell manufacturing. We demonstrate that purified placode cells can be directed into pituitary fates using defined signals. hPSC-derived pituitary cells show basal and stimulus-induced hormone release in vitro and engraftment and hormone release in vivo after transplantation into a murine model of hypopituitarism. This work lays the foundation for future cell therapy applications in patients with hypopituitarism.

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Defined, cGMP-ready protocol to derive anterior pituitary-lineage cells from hPSCs

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FGF8 and BMP2 patterning enables enrichment for specific hormone-producing cells

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Pituitary cells secrete multiple hormones and respond to physiological stimuli

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hPSC-pituitary cells partially rescue a rat model of hypopituitarism

Targeted reconstruction of T cell receptor sequence from single cell RNA-seq links CDR3 length to T cell differentiation state

The T cell compartment must contain diversity in both T cell receptor (TCR) repertoire and cell state to provide effective immunity against pathogens. However, it remains unclear how differences in the TCR contribute to heterogeneity in T cell state. Single cell RNA-sequencing (scRNA-seq) can allow simultaneous measurement of TCR sequence and global transcriptional profile from single cells. However, current methods for TCR inference from scRNA-seq are limited in their sensitivity and require long sequencing reads, thus increasing the cost and decreasing the number of cells that can be feasibly analyzed. Here we present TRAPeS, a publicly available tool that can efficiently extract TCR sequence information from short-read scRNA-seq libraries. We apply it to investigate heterogeneity in the CD8⁺ T cell response in humans and mice, and show that it is accurate and more sensitive than existing approaches. Coupling TRAPeS with transcriptome analysis of CD8⁺ T cells specific for a single epitope from Yellow Fever Virus (YFV), we show that the recently described ‘naive-like’ memory population have significantly longer CDR3 regions and greater divergence from germline sequence than do effector-memory phenotype cells. This suggests that TCR usage is associated with the differentiation state of the CD8⁺ T cell response to YFV.

Massively parallel single-nucleus RNA-seq with DroNc-seq

Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

Diversity amongst trigeminal neurons revealed by high throughput single cell sequencing

The trigeminal ganglion contains somatosensory neurons that detect a range of thermal, mechanical and chemical cues and innervate unique sensory compartments in the head and neck including the eyes, nose, mouth, meninges and vibrissae. We used single-cell sequencing and in situ hybridization to examine the cellular diversity of the trigeminal ganglion in mice, defining thirteen clusters of neurons. We show that clusters are well conserved in dorsal root ganglia suggesting they represent distinct functional classes of somatosensory neurons and not specialization associated with their sensory targets. Notably, functionally important genes (e.g. the mechanosensory channel Piezo2 and the capsaicin gated ion channel Trpv1) segregate into multiple clusters and often are expressed in subsets of cells within a cluster. Therefore, the 13 genetically-defined classes are likely to be physiologically heterogeneous rather than highly parallel (i.e., redundant) lines of sensory input. Our analysis harnesses the power of single-cell sequencing to provide a unique platform for in silico expression profiling that complements other approaches linking gene-expression with function and exposes unexpected diversity in the somatosensory system.

On-slide detection of enzymatic activities in selected single cells

With increasing recognition of the importance in addressing cell-to-cell heterogeneity for the understanding of complex biological systems, there is a growing need for assays capable of single cell analyses. In the current study, we describe the measurement of human topoisomerase I activity in single CD44 positive Caco2 cells specifically captured from a mixed population on glass slides, which were dual functionalized with anti-CD44-antibodies and specific DNA primers. On-slide lysis of captured CD44 positive cells, resulted in the release of human topoisomerase I, allowing the enzyme to circularize a specific linear DNA substrate added to the slides. The generated circles hybridized to the anchored DNA primers and acted as templates for a solid support rolling circle amplification reaction leading to the generation of long tandem repeat products that were detected at the single molecule level in a fluorescent microscope upon hybridization of fluorescent labelled probes. The on-slide detection system was demonstrated to be directly quantitative and specific towards CD44 positive cells. Moreover, it allowed reproducible detection of human topoisomerase I activity in single cells.

MITF-High and MITF-Low Cells and a Novel Subpopulation Expressing Genes of Both Cell States Contribute to Intra- and Intertumoral Heterogeneity of Primary Melanoma

Purpose: Understanding tumor heterogeneity is an important challenge in current cancer research. Transcription and epigenetic profiling of cultured melanoma cells have defined at least two distinct cell phenotypes characterized by distinctive gene expression signatures associated with high or low/absent expression of microphthalmia-associated transcription factor (MITF). Nevertheless, heterogeneity of cell populations and gene expression in primary human tumors is much less well characterized.Experimental Design: We performed single-cell gene expression analyses on 472 cells isolated from needle biopsies of 5 primary human melanomas, 4 superficial spreading, and one acral melanoma. The expression of MITF-high and MITF-low signature genes was assessed and compared to investigate intra- and intertumoral heterogeneity and correlated gene expression profiles.Results: Single-cell gene expression analyses revealed varying degrees of intra- and intertumor heterogeneity conferred by the variable expression of distinct sets of genes in different tumors. Expression of MITF partially correlated with that of its known target genes, while SOX10 expression correlated best with PAX3 and ZEB2 Nevertheless, cells simultaneously expressing MITF-high and MITF-low signature genes were observed both by single-cell analyses and RNAscope.Conclusions: Single-cell analyses can be performed on limiting numbers of cells from primary human melanomas revealing their heterogeneity. Although tumors comprised variable proportions of cells with the MITF-high and MITF-low gene expression signatures characteristic of melanoma cultures, primary tumors also comprised cells expressing markers of both signatures defining a novel cell state in tumors in vivoClin Cancer Res; 23(22); 7097-107. ©2017 AACR.

Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer

Single cell RNA sequencing has emerged as a powerful tool for resolving transcriptional diversity in tumors, but is limited by throughput, cost and the ability to process archival frozen tissue samples. Here we develop a high-throughput 3' single-nucleus RNA sequencing approach that combines nanogrid technology, automated imaging, and cell selection to sequence up to ~1800 single nuclei in parallel. We compare the transcriptomes of 485 single nuclei to 424 single cells in a breast cancer cell line, which shows a high concordance (93.34%) in gene levels and abundance. We also analyze 416 nuclei from a frozen breast tumor sample and 380 nuclei from normal breast tissue. These data reveal heterogeneity in cancer cell phenotypes, including angiogenesis, proliferation, and stemness, and a minor subpopulation (19%) with many overexpressed cancer genes. Our studies demonstrate the utility of nanogrid single-nucleus RNA sequencing for studying the transcriptional programs of tumor nuclei in frozen archival tissue samples.Single cell RNA sequencing is a powerful tool for understanding cellular diversity but is limited by cost, throughput and sample preparation. Here the authors use nanogrid technology with integrated imaging to sequence thousands of cancer nuclei in parallel from fresh or frozen tissue.

IFNγ-Dependent Tissue-Immune Homeostasis Is Co-opted in the Tumor Microenvironment

Homeostatic programs balance immune protection and self-tolerance. Such mechanisms likely impact autoimmunity and tumor formation, respectively. How homeostasis is maintained and impacts tumor surveillance is unknown. Here, we find that different immune mononuclear phagocytes share a conserved steady-state program during differentiation and entry into healthy tissue. IFNγ is necessary and sufficient to induce this program, revealing a key instructive role. Remarkably, homeostatic and IFNγ-dependent programs enrich across primary human tumors, including melanoma, and stratify survival. Single-cell RNA sequencing (RNA-seq) reveals enrichment of homeostatic modules in monocytes and DCs from human metastatic melanoma. Suppressor-of-cytokine-2 (SOCS2) protein, a conserved program transcript, is expressed by mononuclear phagocytes infiltrating primary melanoma and is induced by IFNγ. SOCS2 limits adaptive anti-tumoral immunity and DC-based priming of T cells in vivo, indicating a critical regulatory role. These findings link immune homeostasis to key determinants of anti-tumoral immunity and escape, revealing co-opting of tissue-specific immune development in the tumor microenvironment.

Genetically Distinct Parallel Pathways in the Entopeduncular Nucleus for Limbic and Sensorimotor Output of the Basal Ganglia

The basal ganglia (BG) integrate inputs from diverse sensorimotor, limbic, and associative regions to guide action-selection and goal-directed behaviors. The entopeduncular nucleus (EP) is a major BG output nucleus and has been suggested to channel signals from distinct BG nuclei to target regions involved in diverse functions. Here we use single-cell transcriptional and molecular analyses to demonstrate that the EP contains at least three classes of projection neurons-glutamate/GABA co-releasing somatostatin neurons, glutamatergic parvalbumin neurons, and GABAergic parvalbumin neurons. These classes comprise functionally and anatomically distinct output pathways that differentially affect EP target regions, such as the lateral habenula (LHb) and thalamus. Furthermore, LHb- and thalamic-projecting EP neurons are differentially innervated by subclasses of striatal and pallidal neurons. Therefore, we identify previously unknown subdivisions within the EP and reveal the existence of cascading, molecularly distinct projections through striatum and globus pallidus to EP targets within epithalamus and thalamus.

Disentangling neural cell diversity using single-cell transcriptomics

Cellular specialization is particularly prominent in mammalian nervous systems, which are composed of millions to billions of neurons that appear in thousands of different 'flavors' and contribute to a variety of functions. Even in a single brain region, individual neurons differ greatly in their morphology, connectivity and electrophysiological properties. Systematic classification of all mammalian neurons is a key goal towards deconstructing the nervous system into its basic components. With the recent advances in single-cell gene expression profiling technologies, it is now possible to undertake the enormous task of disentangling neuronal heterogeneity. High-throughput single-cell RNA sequencing and multiplexed quantitative RT-PCR have become more accessible, and these technologies enable systematic categorization of individual neurons into groups with similar molecular properties. Here we provide a conceptual and practical guide to classification of neural cell types using single-cell gene expression profiling technologies.

Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells

CD4 T cells harboring HIV-1/SIV represent a formidable hurdle to eradicating infection, and yet their detailed phenotype remains unknown. Here we integrate two single-cell technologies, flow cytometry and highly multiplexed quantitative RT-PCR, to characterize SIV-infected CD4 T cells directly ex vivo. Within individual cells, we correlate the cellular phenotype, in terms of host protein and RNA expression, with stages of the viral life cycle defined by combinatorial expression of viral RNAs. Spliced RNA⁺ infected cells display multiple memory and activation phenotypes, indicating virus production by diverse CD4 T cell subsets. In most (but not all) cells, progressive infection accompanies post-transcriptional downregulation of CD4 protein, while surface MHC class I is largely retained. Interferon-stimulated genes were also commonly upregulated. Thus, we demonstrate that combined quantitation of transcriptional and post-transcriptional regulation at the single-cell level informs in vivo mechanisms of viral replication and immune evasion.

HIV-1, and its simian counterpart, SIV, infect and kill CD4 T cells, resulting in their massive depletion that ultimately leads to AIDS in the absence of antiretroviral therapy. With effective therapy, these cells are largely preserved, but a subset harbors latent virus that can persist for decades and reemerge upon therapy interruption, preventing HIV-1 cure. To prevent or eliminate productive cellular infection, there is tremendous demand to identify host factors expressed by these cells in vivo, which may serve as unique biomarkers or drug targets. Here we provide the first detailed combined transcriptomic and protein expression profile of SIV-infected cells directly ex vivo using novel single-cell technologies. Our survey of activation markers, interferon-stimulated genes, and viral restriction factors identified multiple host genes differentially expressed by SIV-infected cells and will inform future therapeutic strategies.

Single-cell gene expression patterns in lupus monocytes independently indicate disease activity, interferon and therapy

Objectives

Important findings can be masked in gene expression studies of mixed cell populations. We examined single-cell gene expression in SLE patient monocytes in the context of clinical and immunological features.

Methods

Monocytes were purified from patients with SLE and controls, and individually isolated for single-cell gene expression measurement. A panel of monocyte-related transcripts were measured in individual classical (CL) and non-classical (NCL) monocytes.

Results

Analyses of both CL and NCL monocytes demonstrated that many genes had a lower expression rate in SLE monocytes than in controls. Unsupervised hierarchical clustering of the CL and NCL data sets demonstrated independent clusters of cells from the patients with SLE that were related to disease activity, type I interferon (IFN) and medication use. Thus, each of these factors exerted a different impact on monocyte gene expression that could be identified separately, and a number of genes correlated uniquely with disease activity. We found within-cell correlations between genes directly induced by type I IFN-induced and other non–IFN-induced genes, suggesting the downstream biological effects of type I IFN in individual human SLE monocytes which differed between CLs and NCLs.

Conclusions

In summary, single-cell gene expression in monocytes was associated with a wide range of clinical and biological features in SLE, providing much greater detail and insight into the cellular biology underlying the disease than previous mixed-cell population studies.

Revealing the vectors of cellular identity with single-cell genomics

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

Defining epithelial cell dynamics and lineage relationships in the developing lacrimal gland

The tear-producing lacrimal gland is a tubular organ that protects and lubricates the ocular surface. The lacrimal gland possesses many features that make it an excellent model in which to investigate tubulogenesis, but the cell types and lineage relationships that drive lacrimal gland formation are unclear. Using single-cell sequencing and other molecular tools, we reveal novel cell identities and epithelial lineage dynamics that underlie lacrimal gland development. We show that the lacrimal gland from its earliest developmental stages is composed of multiple subpopulations of immune, epithelial and mesenchymal cell lineages. The epithelial lineage exhibits the most substantial cellular changes, transitioning through a series of unique transcriptional states to become terminally differentiated acinar, ductal and myoepithelial cells. Furthermore, lineage tracing in postnatal and adult glands provides the first direct evidence of unipotent KRT5⁺ epithelial cells in the lacrimal gland. Finally, we show conservation of developmental markers between the developing mouse and human lacrimal gland, supporting the use of mice to understand human development. Together, our data reveal crucial features of lacrimal gland development that have broad implications for understanding epithelial organogenesis.

Single-Cell RNA Sequencing Reveals Expanded Clones of Islet Antigen-Reactive CD4+ T Cells in Peripheral Blood of Subjects with Type 1 Diabetes

The significance of islet Ag-reactive T cells found in peripheral blood of type 1 diabetes (T1D) subjects is unclear, partly because similar cells are also found in healthy control (HC) subjects. We hypothesized that key disease-associated cells would show evidence of prior Ag exposure, inferred from expanded TCR clonotypes, and essential phenotypic properties in their transcriptomes. To test this, we developed single-cell RNA sequencing procedures for identifying TCR clonotypes and transcript phenotypes in individual T cells. We applied these procedures to analysis of islet Ag-reactive CD4⁺ memory T cells from the blood of T1D and HC individuals after activation with pooled immunodominant islet peptides. We found extensive TCR clonotype sharing in Ag-activated cells, especially from individual T1D subjects, consistent with in vivo T cell expansion during disease progression. The expanded clonotype from one T1D subject was detected at repeat visits spanning >15 mo, demonstrating clonotype stability. Notably, we found no clonotype sharing between subjects, indicating a predominance of "private" TCR specificities. Expanded clones from two T1D subjects recognized distinct IGRP peptides, implicating this molecule as a trigger for CD4⁺ T cell expansion. Although overall transcript profiles of cells from HC and T1D subjects were similar, profiles from the most expanded clones were distinctive. Our findings demonstrate that islet Ag-reactive CD4⁺ memory T cells with unique Ag specificities and phenotypes are expanded during disease progression and can be detected by single-cell analysis of peripheral blood.

Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

Dendritic cells (DCs) and monocytes play a central role in pathogen sensing, phagocytosis, and antigen presentation and consist of multiple specialized subtypes. However, their identities and interrelationships are not fully understood. Using unbiased single-cell RNA sequencing (RNA-seq) of ~2400 cells, we identified six human DCs and four monocyte subtypes in human blood. Our study reveals a new DC subset that shares properties with plasmacytoid DCs (pDCs) but potently activates T cells, thus redefining pDCs; a new subdivision within the CD1C+ subset of DCs; the relationship between blastic plasmacytoid DC neoplasia cells and healthy DCs; and circulating progenitor of conventional DCs (cDCs). Our revised taxonomy will enable more accurate functional and developmental analyses as well as immune monitoring in health and disease.

Environment Tunes Propagation of Cell-to-Cell Variation in the Human Macrophage Gene Network

Cell-to-cell variation in gene expression and the propagation of such variation (PoV or "noise propagation") from one gene to another in the gene network, as reflected by gene-gene correlation across single cells, are commonly observed in single-cell transcriptomic studies and can shape the phenotypic diversity of cell populations. While gene network "rewiring" is known to accompany cellular adaptation to different environments, how PoV changes between environments and its underlying regulatory mechanisms are less understood. Here, we systematically explored context-dependent PoV among genes in human macrophages, utilizing different cytokines as natural perturbations of multiple molecular parameters that may influence PoV. Our single-cell, epigenomic, computational, and stochastic simulation analyses reveal that environmental adaptation can tune PoV to potentially shape cellular heterogeneity by changing parameters such as the degree of phosphorylation and transcription factor-chromatin interactions. This quantitative tuning of PoV may be a widespread, yet underexplored, property of cellular adaptation to distinct environments.

Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

Single-cell RNA-Sequencing (scRNA-Seq) is a fast-evolving technology that enables the understanding of biological processes at an unprecedentedly high resolution. However, well-suited bioinformatics tools to analyze the data generated from this new technology are still lacking. Here we investigate the performance of non-negative matrix factorization (NMF) method to analyze a wide variety of scRNA-Seq datasets, ranging from mouse hematopoietic stem cells to human glioblastoma data. In comparison to other unsupervised clustering methods including K-means and hierarchical clustering, NMF has higher accuracy in separating similar groups in various datasets. We ranked genes by their importance scores (D-scores) in separating these groups, and discovered that NMF uniquely identifies genes expressed at intermediate levels as top-ranked genes. Finally, we show that in conjugation with the modularity detection method FEM, NMF reveals meaningful protein-protein interaction modules. In summary, we propose that NMF is a desirable method to analyze heterogeneous single-cell RNA-Seq data. The NMF based subpopulation detection package is available at: https://github.com/lanagarmire/NMFEM.

Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics

Patterns of gene expression can be used to characterize and classify neuronal types. It is challenging, however, to generate taxonomies that fulfill the essential criteria of being comprehensive, harmonizing with conventional classification schemes, and lacking superfluous subdivisions of genuine types. To address these challenges, we used massively parallel single-cell RNA profiling and optimized computational methods on a heterogeneous class of neurons, mouse retinal bipolar cells (BCs). From a population of ∼25,000 BCs, we derived a molecular classification that identified 15 types, including all types observed previously and two novel types, one of which has a non-canonical morphology and position. We validated the classification scheme and identified dozens of novel markers using methods that match molecular expression to cell morphology. This work provides a systematic methodology for achieving comprehensive molecular classification of neurons, identifies novel neuronal types, and uncovers transcriptional differences that distinguish types within a class.

Human dendritic cells (DCs) are derived from distinct circulating precursors that are precommitted to become CD1c+ or CD141+ DCs

Breton et al. identify CD172a as a lineage marker that distinguishes human cDC precursor (pre-cDC) subpopulations committed to the CD1c⁺ lineage (CD172a⁺ pre-cDCs) or CD141⁺ lineage (CD172a⁻ pre-cDCs).

In humans, conventional dendritic cells (cDCs) exist as two unique populations characterized by expression of CD1c and CD141. cDCs arise from increasingly restricted but well-defined bone marrow progenitors that include the common DC progenitor that differentiates into the pre-cDC, which is the direct precursor of cDCs. In this study, we show that pre-cDCs in humans are heterogeneous, consisting of two distinct populations of precursors that are precommitted to become either CD1c⁺ or CD141⁺ cDCs. The two groups of lineage-primed precursors can be distinguished based on differential expression of CD172a. Both subpopulations of pre-cDCs arise in the adult bone marrow and can be found in cord blood and adult peripheral blood. Gene expression analysis revealed that CD172a⁺ and CD172a⁻ pre-cDCs represent developmentally discrete populations that differentially express lineage-restricted transcription factors. A clinical trial of Flt3L injection revealed that this cytokine increases the number of both CD172a⁻ and CD172a⁺ pre-cDCs in human peripheral blood.

Highly Multiplexed, Single Cell Transcriptomic Analysis of T-Cells by Microfluidic PCR

Recently, technologies have been developed to measure expression of 96 (or more) mRNA transcripts at once from a single cell. Here we describe methods and important considerations for use of Fluidigm's BioMark platform for multiplexed single cell gene expression. We describe how to qualify primer/probes, select genes to examine in 96-parameter panels, perform the reverse transcription/cDNA synthesis step, and operate the instrument. In addition, we describe data analysis considerations. This technology has enormous value for characterizing the heterogeneity of T-cells, thereby providing a useful tool for immune monitoring.

Massive and parallel expression profiling using microarrayed single-cell sequencing

Single-cell transcriptome analysis overcomes problems inherently associated with averaging gene expression measurements in bulk analysis. However, single-cell analysis is currently challenging in terms of cost, throughput and robustness. Here, we present a method enabling massive microarray-based barcoding of expression patterns in single cells, termed MASC-seq. This technology enables both imaging and high-throughput single-cell analysis, characterizing thousands of single-cell transcriptomes per day at a low cost (0.13 USD/cell), which is two orders of magnitude less than commercially available systems. Our novel approach provides data in a rapid and simple way. Therefore, MASC-seq has the potential to accelerate the study of subtle clonal dynamics and help provide critical insights into disease development and other biological processes.

Currently available single-cell transcriptomic analyses are expensive and low throughput. Here, Vickovic et al. describe a new method called MASC-seq that is based on microarray barcoding of expression pattern and of low cost with high robustness.

TGF-β receptor maintains CD4 T helper cell identity during chronic viral infections

Suppression of CD8 and CD4 T cells is a hallmark in chronic viral infections, including hepatitis C and HIV. While multiple pathways are known to inhibit CD8 T cells, the host molecules that restrict CD4 T cell responses are less understood. Here, we used inducible and CD4 T cell-specific deletion of the gene encoding the TGF-β receptor during chronic lymphocytic choriomeningitis virus infection in mice, and determined that TGF-β signaling restricted proliferation and terminal differentiation of antiviral CD4 T cells. TGF-β signaling also inhibited a cytotoxic program that includes granzymes and perforin expression at both early and late stages of infection in vivo and repressed the transcription factor eomesodermin. Overexpression of eomesodermin was sufficient to recapitulate in great part the phenotype of TGF-β receptor-deficient CD4 T cells, while SMAD4 was necessary for CD4 T cell accumulation and differentiation. TGF-β signaling also restricted accumulation and differentiation of CD4 T cells and reduced the expression of cytotoxic molecules in mice and humans infected with other persistent viruses. These data uncovered an eomesodermin-driven CD4 T cell program that is continuously suppressed by TGF-β signaling. During chronic viral infection, this program limits CD4 T cell responses while maintaining CD4 T helper cell identity.

Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction

We present a scalable, integrated strategy for coupled protein and RNA detection from single cells. Our approach leverages the DNA polymerase activity of reverse transcriptase to simultaneously perform proximity extension assays and complementary DNA synthesis in the same reaction. Using the Fluidigm C1™ system, we profile the transcriptomic and proteomic response of a human breast adenocarcinoma cell line to a chemical perturbation, benchmarking against in situ hybridizations and immunofluorescence staining, as well as recombinant proteins, ERCC Spike-Ins, and population lysate dilutions. Through supervised and unsupervised analyses, we demonstrate synergies enabled by simultaneous measurement of single-cell protein and RNA abundances. Collectively, our generalizable approach highlights the potential for molecular metadata to inform highly-multiplexed single-cell analyses.

Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling

Quantifying heterogeneity in gene expression among single cells can reveal information inaccessible to cell-population averaged measurements. However, the expression level of many genes in single cells fall below the detection limit of even the most sensitive technologies currently available. One proposed approach to overcome this challenge is to measure random pools of k cells (e.g., 10) to increase sensitivity, followed by computational “deconvolution” of cellular heterogeneity parameters (CHPs), such as the biological variance of single-cell expression levels. Existing approaches infer CHPs using either single-cell or k-cell data alone, and typically within a single population of cells. However, integrating both single- and k-cell data may reap additional benefits, and quantifying differences in CHPs across cell populations or conditions could reveal novel biological information. Here we present a Bayesian approach that can utilize single-cell, k-cell, or both simultaneously to infer CHPs within a single condition or their differences across two conditions. Using simulated as well as experimentally generated single- and k-cell data, we found situations where each data type would offer advantages, but using both together can improve precision and better reconcile CHP information contained in single- and k-cell data. We illustrate the utility of our approach by applying it to jointly generated single- and k-cell data to reveal CHP differences in several key inflammatory genes between resting and inflammatory cytokine-activated human macrophages, delineating differences in the distribution of ‘ON’ versus ‘OFF’ cells and in continuous variation of expression level among cells. Our approach thus offers a practical and robust framework to assess and compare cellular heterogeneity within and across biological conditions using modern multiplexed technologies.

Different cells can make different amounts of biomolecules such as RNA transcripts of genes. New technologies are emerging to measure the transcript level of many genes in single cells. However, accurate quantification of the biological variation from cell to cell can be challenging due to the low transcript level of many genes and the presence of substantial measurement noise. Here we present a flexible, novel computational approach to quantify biological cell-to-cell variation that can use different types of data, namely measurements directly obtained from single cells, and/or those from random pools of k-cells (e.g., k = 10). Assessment of these different inputs using simulated and real data revealed that each data type can offer advantages under different scenarios, but combining both single- and k-cell measurements tend to offer the best of both. Application of our approach to single- and k-cell data obtained from resting and inflammatory macrophages, an important type of immune cells implicated in diverse diseases, revealed interesting changes in cell-to-cell variation in transcript levels upon inflammatory stimulation, thus suggesting that inflammation can shape not only the average expression level of a gene but also the gene’s degree of expression variation among single cells.

Distinct activation thresholds of human conventional and innate-like memory T cells

Conventional memory CD8⁺ T cells and mucosal-associated invariant T cells (MAIT cells) are found in blood, liver, and mucosal tissues and have similar effector potential following activation, specifically expression of IFN-γ and granzyme B. To better understand each subset's unique contributions to immunity and pathology, we interrogated inflammation- and TCR-driven activation requirements using human memory CD8⁺ T and MAIT cells isolated from blood and mucosal tissue biopsies in ex vivo functional assays and single cell gene expression experiments. We found that MAIT cells had a robust IFN-γ and granzyme B response to inflammatory signals but limited responsiveness when stimulated directly via their TCR. Importantly, this is not due to an overall hyporesponsiveness to TCR signals. When delivered together, TCR and inflammatory signals synergize to elicit potent effector function in MAIT cells. This unique control of effector function allows MAIT cells to respond to the same TCR signal in a dichotomous and situation-specific manner. We propose that this could serve to prevent responses to antigen in noninflamed healthy mucosal tissue, while maintaining responsiveness and great sensitivity to inflammation-eliciting infections. We discuss the implications of these findings in context of inflammation-inducing damage to tissues such as BM transplant conditioning or HIV infection.

A Generic and Cell-Type-Specific Wound Response Precedes Regeneration in Planarians

Regeneration starts with injury. Yet how injuries affect gene expression in different cell types and how distinct injuries differ in gene expression remain unclear. We defined the transcriptomes of major cell types of planarians--flatworms that regenerate from nearly any injury--and identified 1,214 tissue-specific markers across 13 cell types. RNA sequencing on 619 single cells revealed that wound-induced genes were expressed either in nearly all cell types or specifically in one of three cell types (stem cells, muscle, or epidermis). Time course experiments following different injuries indicated that a generic wound response is activated with any injury regardless of the regenerative outcome. Only one gene, notum, was differentially expressed early between anterior- and posterior-facing wounds. Injury-specific transcriptional responses emerged 30 hr after injury, involving context-dependent patterning and stem-cell-specialization genes. The regenerative requirement of every injury is different; however, our work demonstrates that all injuries start with a common transcriptional response.

Two FGFRL-Wnt circuits organize the planarian anteroposterior axis

How positional information instructs adult tissue maintenance is poorly understood. Planarians undergo whole-body regeneration and tissue turnover, providing a model for adult positional information studies. Genes encoding secreted and transmembrane components of multiple developmental pathways are predominantly expressed in planarian muscle cells. Several of these genes regulate regional identity, consistent with muscle harboring positional information. Here, single-cell RNA-sequencing of 115 muscle cells from distinct anterior-posterior regions identified 44 regionally expressed genes, including multiple Wnt and ndk/FGF receptor-like (ndl/FGFRL) genes. Two distinct FGFRL-Wnt circuits, involving juxtaposed anterior FGFRL and posterior Wnt expression domains, controlled planarian head and trunk patterning. ndl-3 and wntP-2 inhibition expanded the trunk, forming ectopic mouths and secondary pharynges, which independently extended and ingested food. fz5/8-4 inhibition, like that of ndk and wntA, caused posterior brain expansion and ectopic eye formation. Our results suggest that FGFRL-Wnt circuits operate within a body-wide coordinate system to control adult axial positioning.

DOI:http://dx.doi.org/10.7554/eLife.12845.001

Some animals can regrow tissues that have been amputated. A group of flatworms called planarians are often used as a model to study the regeneration process because they are able to restore any lost tissue or even an entire animal from tiny pieces of the body. For regeneration to be successful, it is important to ensure that the new tissues form in the correct locations in the body.

The planarian body is divided into three main parts: head, trunk and tail. Several gene products involved in specifying what tissues regenerate are made by muscle cells along the planarian body. Some of the genes are involved in mechanisms that allow cells to communicate with each other, such as the Wnt signaling pathway. These genes could form a coordinated system to control regeneration, but their precise roles remain poorly understood.

Two groups of researchers have now independently identified genes that provide cells with information about their location in the flatworm body. Scimone, Cote et al. used a technique called RNA sequencing in individual muscle cells to identify 44 genes that have different levels of expression across the head, trunk and tail regions. These genes included multiple components of the Wnt signaling pathway and others that encode members of the FGFRL family of signaling proteins.

Further experiments revealed two distinct sets of genes, or “gene circuits”, that provide information to correctly position tissues in the head and trunk regions of the worm. For example, inhibiting the activity of the wntP-2 or ndl-3 genes increased the size of the trunk of the worms and caused extra mouths and pharynges (muscular organ used for eating) to form. On the other hand, blocking the activity of genes in the other gene circuit caused the brain to expand and extra eyes to form.

Another study by Lander and Petersen found that wntP-2 and ndl-3 act with another gene called ptk7, which encodes another component of the Wnt signaling pathway. Together these findings suggest that the Wnt-FGFRL circuits act in a body-wide system that co-ordinates where and which new tissues form during regeneration. A future challenge is to find out how the genes identified in these studies interact and how the cells of the animal interpret this information to properly regenerate missing tissues.

DOI:http://dx.doi.org/10.7554/eLife.12845.002

Single-cell transcriptome sequencing: recent advances and remaining challenges

Single-cell RNA-sequencing methods are now robust and economically practical and are becoming a powerful tool for high-throughput, high-resolution transcriptomic analysis of cell states and dynamics. Single-cell approaches circumvent the averaging artifacts associated with traditional bulk population data, yielding new insights into the cellular diversity underlying superficially homogeneous populations. Thus far, single-cell RNA-sequencing has already shown great effectiveness in unraveling complex cell populations, reconstructing developmental trajectories, and modeling transcriptional dynamics. Ongoing technical improvements to single-cell RNA-sequencing throughput and sensitivity, the development of more sophisticated analytical frameworks for single-cell data, and an increasing array of complementary single-cell assays all promise to expand the usefulness and potential applications of single-cell transcriptomic profiling.

Promises and Pitfalls of High-Throughput Biological Assays

This chapter discusses some of the pitfalls encountered when performing biomedical research involving high-throughput "omics" data and presents some strategies and guidelines that researchers should follow when undertaking such studies. We discuss common errors in experimental design and data analysis that lead to irreproducible and non-replicable research and provide some guidelines to avoid these common mistakes so that researchers may have confidence in study outcomes, even if the results are negative. We discuss the importance of ranking and prespecifying hypotheses, performing power analysis, careful experimental design, and preplanning of statistical analyses in order to avoid the "fishing expedition" data analysis strategy, which is doomed to fail. The impact of multiple testing on false-positive rates is discussed, particularly in the context of the analysis of high-throughput data, and methods to correct for it are presented, as well as approaches to detect and correct for experimental biases and batch effects, which often plague high-throughput assays. We highlight the importance of sharing data and analysis code to facilitate reproducibility and present tools and software that are appropriate for this purpose.

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST.

SCExV: a webtool for the analysis and visualisation of single cell qRT-PCR data

Background

Single cell gene expression assays have become a powerful tool with which to dissect heterogeneous populations. While methods and software exist to interrogate such data, what has been lacking is a unified solution combining analysis and visualisation which is also accessible and intuitive for use by non-bioinformaticians, as well as bioinformaticians.

Results

We present the Single cell expression visualiser (SCExV), a webtool developed to expedite the analysis of single cell qRT-PCR data. SCExV is able to take any data matrix of Ct values as an input, but can handle files exported by the Fluidigm Biomark platform directly. In addition, SCExV also accepts and automatically integrates cell surface marker intensity values which are measured during index sorting. This allows the user to directly visualise relationships between a single cell gene expression profile and the immunophenotype of the interrogated cell.

Conclusions

SCExV is a freely available webtool created to import, filter, analyse, and visualise single cell gene expression data whilst being able to simultaneously consider cellular immunophenotype. SCExV is designed to be intuitive to use whilst maintaining advanced functionality and flexibility in how analyses are performed.

Single-cell analysis reveals a stem-cell program in human metastatic breast cancer cells

Despite major advances in understanding the molecular and genetic basis of cancer, metastasis remains the cause of >90% of cancer-related mortality. Understanding metastasis initiation and progression is critical to developing new therapeutic strategies to treat and prevent metastatic disease. Prevailing theories hypothesize that metastases are seeded by rare tumour cells with unique properties, which may function like stem cells in their ability to initiate and propagate metastatic tumours. However, the identity of metastasis-initiating cells in human breast cancer remains elusive, and whether metastases are hierarchically organized is unknown. Here we show at the single-cell level that early stage metastatic cells possess a distinct stem-like gene expression signature. To identify and isolate metastatic cells from patient-derived xenograft models of human breast cancer, we developed a highly sensitive fluorescence-activated cell sorting (FACS)-based assay, which allowed us to enumerate metastatic cells in mouse peripheral tissues. We compared gene signatures in metastatic cells from tissues with low versus high metastatic burden. Metastatic cells from low-burden tissues were distinct owing to their increased expression of stem cell, epithelial-to-mesenchymal transition, pro-survival, and dormancy-associated genes. By contrast, metastatic cells from high-burden tissues were similar to primary tumour cells, which were more heterogeneous and expressed higher levels of luminal differentiation genes. Transplantation of stem-like metastatic cells from low-burden tissues showed that they have considerable tumour-initiating capacity, and can differentiate to produce luminal-like cancer cells. Progression to high metastatic burden was associated with increased proliferation and MYC expression, which could be attenuated by treatment with cyclin-dependent kinase (CDK) inhibitors. These findings support a hierarchical model for metastasis, in which metastases are initiated by stem-like cells that proliferate and differentiate to produce advanced metastatic disease.

Bioinformatics approaches to single-cell analysis in developmental biology

Individual cells within the same population show various degrees of heterogeneity, which may be better handled with single-cell analysis to address biological and clinical questions. Single-cell analysis is especially important in developmental biology as subtle spatial and temporal differences in cells have significant associations with cell fate decisions during differentiation and with the description of a particular state of a cell exhibiting an aberrant phenotype. Biotechnological advances, especially in the area of microfluidics, have led to a robust, massively parallel and multi-dimensional capturing, sorting, and lysis of single-cells and amplification of related macromolecules, which have enabled the use of imaging and omics techniques on single cells. There have been improvements in computational single-cell image analysis in developmental biology regarding feature extraction, segmentation, image enhancement and machine learning, handling limitations of optical resolution to gain new perspectives from the raw microscopy images. Omics approaches, such as transcriptomics, genomics and epigenomics, targeting gene and small RNA expression, single nucleotide and structural variations and methylation and histone modifications, rely heavily on high-throughput sequencing technologies. Although there are well-established bioinformatics methods for analysis of sequence data, there are limited bioinformatics approaches which address experimental design, sample size considerations, amplification bias, normalization, differential expression, coverage, clustering and classification issues, specifically applied at the single-cell level. In this review, we summarize biological and technological advancements, discuss challenges faced in the aforementioned data acquisition and analysis issues and present future prospects for application of single-cell analyses to developmental biology.

Single-Cell Transcriptomics Reveals a Population of Dormant Neural Stem Cells that Become Activated upon Brain Injury

Heterogeneous pools of adult neural stem cells (NSCs) contribute to brain maintenance and regeneration after injury. The balance of NSC activation and quiescence, as well as the induction of lineage-specific transcription factors, may contribute to diversity of neuronal and glial fates. To identify molecular hallmarks governing these characteristics, we performed single-cell sequencing of an unbiased pool of adult subventricular zone NSCs. This analysis identified a discrete, dormant NSC subpopulation that already expresses distinct combinations of lineage-specific transcription factors during homeostasis. Dormant NSCs enter a primed-quiescent state before activation, which is accompanied by downregulation of glycolytic metabolism, Notch, and BMP signaling and a concomitant upregulation of lineage-specific transcription factors and protein synthesis. In response to brain ischemia, interferon gamma signaling induces dormant NSC subpopulations to enter the primed-quiescent state. This study unveils general principles underlying NSC activation and lineage priming and opens potential avenues for regenerative medicine in the brain.

Geometry of the Gene Expression Space of Individual Cells

There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.

In the past, biological experiments usually pooled together millions of cells, masking the differences between individual cells. Current technology takes a big step forward by measuring gene expression from individual cells. Interpreting this data is challenging because we need to understand how cells are arranged in a high dimensional gene expression space. Here we test recent theory that suggests that cells facing multiple tasks should be arranged in simple low dimensional polygons or polyhedra (generally called polytopes). The vertices of the polytopes are gene expression profiles optimal for each of the tasks. We find evidence for such simplicity in a variety of tissues—spleen, bone marrow, intestine—analyzed by different single-cell technologies. We find that cells are distributed inside polytopes, such as tetrahedrons or four-dimensional simplexes, with cells closest to each vertex responsible for a different key task. For example, intestinal progenitor cells that give rise to the other cell types show a continuous distribution in a tetrahedron whose vertices correspond to several key sub-tasks. Immune dendritic cells likewise are continuously distributed between key immune tasks. This approach of testing whether data falls in polytopes may be useful for interpreting a variety of single-cell datasets in terms of biological tasks.

Bacterial Internalization, Localization, and Effectors Shape the Epithelial Immune Response during Shigella flexneri Infection

Intracellular pathogens are differentially sensed by the compartmentalized host immune system. Nevertheless, gene expression studies of infected cells commonly average the immune responses, neglecting the precise pathogen localization. To overcome this limitation, we dissected the transcriptional immune response to Shigella flexneri across different infection stages in bulk and single cells. This identified six distinct transcriptional profiles characterizing the dynamic, multilayered host response in both bystander and infected cells. These profiles were regulated by external and internal danger signals, as well as whether bacteria were membrane bound or cytosolic. We found that bacterial internalization triggers a complex, effector-independent response in bystander cells, possibly to compensate for the undermined host gene expression in infected cells caused by bacterial effectors, particularly OspF. Single-cell analysis revealed an important bacterial strategy to subvert host responses in infected cells, demonstrating that OspF disrupts concomitant gene expression of proinflammatory, apoptosis, and stress pathways within cells. This study points to novel mechanisms through which bacterial internalization, localization, and injected effectors orchestrate immune response transcriptional signatures.