Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq

Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). State-of-the-art pipelines perform differential analysis after clustering on the same dataset. We observe that because clustering "forces" separation, reusing the same dataset generates artificially low p values and hence false discoveries. We introduce a valid post-clustering differential analysis framework, which corrects for this problem. We provide software at https://github.com/jessemzhang/tn_test.

The Malaria Cell Atlas: Single parasite transcriptomes across the complete Plasmodium life cycle

Malaria parasites adopt a remarkable variety of morphological life stages as they transition through multiple mammalian host and mosquito vector environments. We profiled the single-cell transcriptomes of thousands of individual parasites, deriving the first high-resolution transcriptional atlas of the entire Plasmodium berghei life cycle. We then used our atlas to precisely define developmental stages of single cells from three different human malaria parasite species, including parasites isolated directly from infected individuals. The Malaria Cell Atlas provides both a comprehensive view of gene usage in a eukaryotic parasite and an open-access reference dataset for the study of malaria parasites.

CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons

CRISPR/Cas9-based functional genomics have transformed our ability to elucidate mammalian cell biology. However, most previous CRISPR-based screens were conducted in cancer cell lines rather than healthy, differentiated cells. Here, we describe a CRISPR interference (CRISPRi)-based platform for genetic screens in human neurons derived from induced pluripotent stem cells (iPSCs). We demonstrate robust and durable knockdown of endogenous genes in such neurons and present results from three complementary genetic screens. First, a survival-based screen revealed neuron-specific essential genes and genes that improved neuronal survival upon knockdown. Second, a screen with a single-cell transcriptomic readout uncovered several examples of genes whose knockdown had strikingly cell-type-specific consequences. Third, a longitudinal imaging screen detected distinct consequences of gene knockdown on neuronal morphology. Our results highlight the power of unbiased genetic screens in iPSC-derived differentiated cell types and provide a platform for systematic interrogation of normal and disease states of neurons. VIDEO ABSTRACT.

Single-Cell Transcriptome Analysis of T Cells

Single-cell RNA-seq (scRNA-seq) has provided novel routes to investigate the heterogeneous populations of T cells and is rapidly becoming a common tool for molecular profiling and identification of novel subsets and functions. This chapter offers an experimental and computational workflow for scRNA-seq analysis of T cells. We focus on the analyses of scRNA-seq data derived from plate-based sorted T cells using flow cytometry and full-length transcriptome protocols such as Smart-Seq2. However, the proposed pipeline can be applied to other high-throughput approaches such as UMI-based methods. We describe a detailed bioinformatics pipeline that can be easily reproduced and discuss future directions and current limitations of these methods in the context of T cell biology.

Mono- and multi-nucleated ventricular cardiomyocytes constitute a transcriptionally homogenous cell population

Individual adult ventricular cardiomyocytes are either mono- or multi-nucleated and undergo morphological changes during cardiac hypertrophy. However, corresponding transcriptional signatures, reflecting potentially different functions or the ability for cell-cycle entry, are not known. The aim of this study was to determine the transcriptional profile of mono- and multi-nucleated adult cardiomyocytes by single-cell RNA-sequencing (scRNA-seq) and to investigate heterogeneity among cardiomyocytes under baseline conditions and in pressure-induced cardiac hypertrophy. We developed an array-based approach for scRNA-seq of rod-shaped multi-nucleated cardiomyocytes from both healthy and hypertrophic hearts. Single-cell transcriptomes of mono- or multi-nucleated cardiomyocytes were highly similar, although a certain degree of variation was noted across both populations. Non-image-based quality control allowing inclusion of damaged cardiomyocytes generated artificial cell clusters demonstrating the need for strict exclusion criteria. In contrast, cardiomyocytes isolated from hypertrophic heart after transverse aortic constriction showed heterogeneous transcriptional signatures, characteristic for hypoxia-induced responses. Immunofluorescence analysis revealed an inverse correlation between HIF1α⁺ cells and CD31-stained vessels, suggesting that imbalanced vascular growth in the hypertrophied heart induces cellular heterogeneity. Our study demonstrates that individual mono- and multi-nucleated cardiomyocytes express nearly identical sets of genes. Homogeneity among cardiomyocytes was lost after induction of hypertrophy due to differential HIF1α-dependent responses most likely caused by none-homogenous vessel growth.

Spatiotemporal Gene Coexpression and Regulation in Mouse Cardiomyocytes of Early Cardiac Morphogenesis

Background Heart tube looping to form a 4-chambered heart is a critical stage of embryonic heart development, but the gene drivers and their regulatory targets have not been extensively characterized at the cell-type level. Methods and Results To study the interaction of signaling pathways, transcription factors (TFs), and genetic networks in the process, we constructed gene co-expression networks and identified gene modules highly activated in individual cardiomyocytes at multiple anatomical regions and developmental stages using previously published single-cell RNA-seq data. Function analyses of the modules uncovered major pathways important for spatiotemporal cardiomyocyte differentiation. Interestingly, about half of the pathways were highly active in cardiomyocytes at the outflow tract (OFT) and atrioventricular canal, including well-known pathways for cardiac development and many newly identified ones. We predicted that these OFT-atrioventricular canal pathways were regulated by a large number of TFs actively expressed at the OFT-atrioventricular canal cardiomyocytes, with the prediction supported by motif enrichment analysis, including 10 TFs that have not been previously associated with cardiac development (eg, Etv5, Rbpms, and Baz2b). Furthermore, we found that TF targets in the OFT-atrioventricular canal modules were most significantly enriched with genes associated with mouse heart developmental abnormalities and human congenital heart defects, in comparison with TF targets in other modules, consistent with the critical developmental roles of OFT. Conclusions By analyzing gene co-expression at single cardiomyocytes, our systematic study has uncovered many known and additional new important TFs and their regulated molecular signaling pathways that are spatiotemporally active during heart looping.

ascend: R package for analysis of single-cell RNA-seq data

BACKGROUND

Recent developments in single-cell RNA sequencing (scRNA-seq) platforms have vastly increased the number of cells typically assayed in an experiment. Analysis of scRNA-seq data is multidisciplinary in nature, requiring careful consideration of the application of statistical methods with respect to the underlying biology. Few analysis packages exist that are at once robust, are computationally fast, and allow flexible integration with other bioinformatics tools and methods.

FINDINGS

ascend is an R package comprising tools designed to simplify and streamline the preliminary analysis of scRNA-seq data, while addressing the statistical challenges of scRNA-seq analysis and enabling flexible integration with genomics packages and native R functions, including fast parallel computation and efficient memory management. The package incorporates both novel and established methods to provide a framework to perform cell and gene filtering, quality control, normalization, dimension reduction, clustering, differential expression, and a wide range of visualization functions.

CONCLUSIONS

ascend is designed to work with scRNA-seq data generated by any high-throughput platform and includes functions to convert data objects between software packages. The ascend workflow is simple and interactive, as well as suitable for implementation by a broad range of users, including those with little programming experience.

Joint analysis of heterogeneous single-cell RNA-seq dataset collections

Single-cell RNA sequencing is often applied in study designs that include multiple individuals, conditions or tissues. To identify recurrent cell subpopulations in such heterogeneous collections, we developed Conos, an approach that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph enables identification of recurrent cell clusters and propagation of information between datasets in multi-sample or atlas-scale collections.

Deciphering Brain Complexity Using Single-cell Sequencing

The human brain contains billions of highly differentiated and interconnected cells that form intricate neural networks and collectively control the physical activities and high-level cognitive functions, such as memory, decision-making, and social behavior. Big data is required to decipher the complexity of cell types, as well as connectivity and functions of the brain. The newly developed single-cell sequencing technology, which provides a comprehensive landscape of brain cell type diversity by profiling the transcriptome, genome, and/or epigenome of individual cells, has contributed substantially to revealing the complexity and dynamics of the brain and providing new insights into brain development and brain-related disorders. In this review, we first introduce the progresses in both experimental and computational methods of single-cell sequencing technology. Applications of single-cell sequencing-based technologies in brain research, including cell type classification, brain development, and brain disease mechanisms, are then elucidated by representative studies. Lastly, we provided our perspectives into the challenges and future developments in the field of single-cell sequencing. In summary, this mini review aims to provide an overview of how big data generated from single-cell sequencing have empowered the advancements in neuroscience and shed light on the complex problems in understanding brain functions and diseases.

Gene expression atlas of a developing tissue by single cell expression correlation analysis

The Drosophila wing disc has been a fundamental model system for the discovery of key signaling pathways and for our understanding of developmental processes. However, a complete map of gene expression in this tissue is lacking. To obtain a complete gene expression atlas in the wing disc, we employed single-cell sequencing (scRNA-seq) and developed a new method for analyzing scRNA-seq data based on gene expression correlations rather than cell mapping. This enables us to compute expression maps for all detected genes in the wing disc and to discover 824 genes with spatially restricted expression patterns. This approach identifies both known and new clusters of genes with similar expression patterns and functional relevance. As proof of concept, we characterize the previously unstudied gene CG5151 and show that it regulates Wnt signaling. This novel method will enable the leveraging of scRNA-seq data for generating expression atlases of undifferentiated tissues during development.

Single cell analysis of human foetal liver captures the transcriptional profile of hepatobiliary hybrid progenitors

The liver parenchyma is composed of hepatocytes and bile duct epithelial cells (BECs). Controversy exists regarding the cellular origin of human liver parenchymal tissue generation during embryonic development, homeostasis or repair. Here we report the existence of a hepatobiliary hybrid progenitor (HHyP) population in human foetal liver using single-cell RNA sequencing. HHyPs are anatomically restricted to the ductal plate of foetal liver and maintain a transcriptional profile distinct from foetal hepatocytes, mature hepatocytes and mature BECs. In addition, molecular heterogeneity within the EpCAM⁺ population of freshly isolated foetal and adult human liver identifies diverse gene expression signatures of hepatic and biliary lineage potential. Finally, we FACS isolate foetal HHyPs and confirm their hybrid progenitor phenotype in vivo. Our study suggests that hepatobiliary progenitor cells previously identified in mice also exist in humans, and can be distinguished from other parenchymal populations, including mature BECs, by distinct gene expression profiles.

The liver parenchyma consists of several cell types, but the origin of this tissue in humans is unclear. Here, the authors perform single cell RNA sequencing of human fetal and adult liver to identify a hepatobiliary hybrid progenitor population of cells, which have a similar gene signature to mouse oval cells.

Genome-wide analysis of androgen receptor binding and transcriptomic analysis in mesenchymal subsets during prostate development

Prostate development is controlled by androgens, the androgen receptor (AR) and mesenchymal–epithelial signalling. We used chromatin immunoprecipitation sequencing (ChIP-seq) to define AR genomic binding in the male and female mesenchyme. Tissue- and single-cell-based transcriptional profiling was used to define mesenchymal AR target genes. We observed significant AR genomic binding in females and a strong enrichment at proximal promoters in both sexes. In males, there was greater AR binding to introns and intergenic regions as well as to classical AR binding motifs. In females, there was increased proximal promoter binding and involvement of cofactors. Comparison of AR-bound genes with transcriptomic data enabled the identification of novel sexually dimorphic AR target genes. We validated the dimorphic expression of AR target genes using published datasets and confirmed regulation by androgens using ex vivo organ cultures. AR targets showed variable expression in patients with androgen insensitivity syndrome. We examined AR function at single-cell resolution using single-cell RNA sequencing (scRNA-seq) in male and female mesenchyme. Surprisingly, both AR and target genes were distributed throughout cell subsets, with few positive cells within each subset. AR binding was weakly correlated with target gene expression.

Summary: A study of how androgens lead to sexually dimorphic development of the prostate using transcription factor genome binding and transcriptome analysis in mesenchymal subsets.

CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data

We develop CellSIUS (Cell Subtype Identification from Upregulated gene Sets) to fill a methodology gap for rare cell population identification for scRNA-seq data. CellSIUS outperforms existing algorithms for specificity and selectivity for rare cell types and their transcriptomic signature identification in synthetic and complex biological data. Characterization of a human pluripotent cell differentiation protocol recapitulating deep-layer corticogenesis using CellSIUS reveals unrecognized complexity in human stem cell-derived cellular populations. CellSIUS enables identification of novel rare cell populations and their signature genes providing the means to study those populations in vitro in light of their role in health and disease.

A statistical simulator scDesign for rational scRNA-seq experimental design

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) has revolutionized biological sciences by revealing genome-wide gene expression levels within individual cells. However, a critical challenge faced by researchers is how to optimize the choices of sequencing platforms, sequencing depths and cell numbers in designing scRNA-seq experiments, so as to balance the exploration of the depth and breadth of transcriptome information.

RESULTS

Here we present a flexible and robust simulator, scDesign, the first statistical framework for researchers to quantitatively assess practical scRNA-seq experimental design in the context of differential gene expression analysis. In addition to experimental design, scDesign also assists computational method development by generating high-quality synthetic scRNA-seq datasets under customized experimental settings. In an evaluation based on 17 cell types and 6 different protocols, scDesign outperformed four state-of-the-art scRNA-seq simulation methods and led to rational experimental design. In addition, scDesign demonstrates reproducibility across biological replicates and independent studies. We also discuss the performance of multiple differential expression and dimension reduction methods based on the protocol-dependent scRNA-seq data generated by scDesign. scDesign is expected to be an effective bioinformatic tool that assists rational scRNA-seq experimental design and comparison of scRNA-seq computational methods based on specific research goals.

AVAILABILITY AND IMPLEMENTATION

We have implemented our method in the R package scDesign, which is freely available at https://github.com/Vivianstats/scDesign.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity

The trillions of cells in the human body can be viewed as elementary but essential biological units that achieve different body states, but the low resolution of previous cell isolation and measurement approaches limits our understanding of the cell-specific molecular profiles. The recent establishment and rapid growth of single-cell sequencing technology has facilitated the identification of molecular profiles of heterogeneous cells, especially on the transcription level of single cells [single-cell RNA sequencing (scRNA-seq)]. As a novel method, the robustness of scRNA-seq under changing conditions will determine its practical potential in major research programs and clinical applications. In this review, we first briefly presented the scRNA-seq-related methods from the point of view of experiments and computation. Then, we compared several state-of-the-art scRNA-seq analysis frameworks mainly by analyzing their performance robustness on independent scRNA-seq datasets for the same complex disease. Finally, we elaborated on our hypothesis on consensus scRNA-seq analysis and summarized the potential indicative and predictive roles of individual cells in understanding disease heterogeneity by single-cell technologies.

Cell type-dependent differential activation of ERK by oncogenic KRAS in colon cancer and intestinal epithelium

Oncogenic mutations in KRAS or BRAF are frequent in colorectal cancer and activate the ERK kinase. Here, we find graded ERK phosphorylation correlating with cell differentiation in patient-derived colorectal cancer organoids with and without KRAS mutations. Using reporters, single cell transcriptomics and mass cytometry, we observe cell type-specific phosphorylation of ERK in response to transgenic KRAS^G12V in mouse intestinal organoids, while transgenic BRAF^V600E activates ERK in all cells. Quantitative network modelling from perturbation data reveals that activation of ERK is shaped by cell type-specific MEK to ERK feed forward and negative feedback signalling. We identify dual-specificity phosphatases as candidate modulators of ERK in the intestine. Furthermore, we find that oncogenic KRAS, together with β-Catenin, favours expansion of crypt cells with high ERK activity. Our experiments highlight key differences between oncogenic BRAF and KRAS in colorectal cancer and find unexpected heterogeneity in a signalling pathway with fundamental relevance for cancer therapy.

KRAS^G12V and BRAF^V600E are oncogenic mutations that activate ERK signalling. Here, the authors use single cell analysis in intestinal organoids and show that BRAF^V600E activates ERK in all intestinal cell types, while KRAS^G12V induces ERK activation in only a subset of cells, depending on cell differentiation state.

MVApp-Multivariate Analysis Application for Streamlined Data Analysis and Curation

Modern phenotyping techniques yield vast amounts of data that are challenging to manage and analyze. When thoroughly examined, this type of data can reveal genotype-to-phenotype relationships and meaningful connections among individual traits. However, efficient data mining is challenging for experimental biologists with limited training in curating, integrating, and exploring complex datasets. Additionally, data transparency, accessibility, and reproducibility are important considerations for scientific publication. The need for a streamlined, user-friendly pipeline for advanced phenotypic data analysis is pressing. In this article we present an open-source, online platform for multivariate analysis (MVApp), which serves as an interactive pipeline for data curation, in-depth analysis, and customized visualization. MVApp builds on the available R-packages and adds extra functionalities to enhance the interpretability of the results. The modular design of the MVApp allows for flexible analysis of various data structures and includes tools underexplored in phenotypic data analysis, such as clustering and quantile regression. MVApp aims to enhance findable, accessible, interoperable, and reproducible data transparency, streamline data curation and analysis, and increase statistical literacy among the scientific community.

Estimating Differentiation Potency of Single Cells Using Single-Cell Entropy (SCENT)

The ability to measure molecular properties (e.g., mRNA expression) at the single-cell level is revolutionizing our understanding of cellular developmental processes and how these are altered in diseases like cancer. The need for computational methods aimed at extracting biological knowledge from such single-cell data has never been greater. Here, we present a detailed protocol for estimating differentiation potency of single cells, based on our Single-Cell ENTropy (SCENT) algorithm. The estimation of differentiation potency is based on an explicit biophysical model that integrates the RNA-Seq profile of a single cell with an interaction network to approximate potency as the entropy of a diffusion process on the network. We here focus on the implementation, providing a step-by-step introduction to the method and illustrating it on a real scRNA-Seq dataset profiling human embryonic stem cells and multipotent progenitors representing the 3 main germ layers. SCENT is aimed particularly at single-cell studies trying to identify novel stem-or-progenitor like phenotypes, and may be particularly valuable for the unbiased identification of cancer stem cells. SCENT is implemented in R, licensed under the GNU General Public Licence v3, and freely available from https://github.com/aet21/SCENT .

Aberrant fucosylation enables breast cancer clusterin to interact with dendritic cell-specific ICAM-grabbing non-integrin (DC-SIGN)

Clusterin is a glycoprotein able to mediate different physiological functions such as control of complement activation, promotion of unfolded protein clearance and modulation of cell survival. Clusterin is overexpressed in many types of cancers and a large body of evidence suggests that it promotes carcinogenesis and tumor progression. We have previously described a novel clusterin glycoform present in human semen, but not in serum, highly enriched in terminal fucose motifs. Here we show that human luminal breast cancer (LBC) clusterin also bears terminal fucosylated glycans, conferring clusterin the ability to interact with DC-SIGN, a C-type lectin receptor expressed by myeloid cells. This clusterin glycosylation pattern was absent or diminished in non-involved juxtatumoral tissue, suggesting that fucosylated clusterin might represent a cancer associated glycoform. We also found that DC-SIGN is expressed by luminal breast cancer intratumoral macrophages. Moreover, experiments performed in vitro using semen fucosylated clusterin and monocyte derived macrophages showed that the interaction of semen clusterin with DC-SIGN promoted a proangiogenic profile, characterized by a high production of VEGF, IL-8 and TNF-α. Our results reveal an unexpected complexity on the structure and function of secretory clusterin produced by tumors and suggest that fucosylated clusterin produced by luminal breast cancer cells might play a role in tumor progression by promoting the release of pro-angiogenic factors by intratumoral macrophages.

Neuro-mesodermal progenitors (NMPs): a comparative study between pluripotent stem cells and embryo-derived populations

The mammalian embryo's caudal lateral epiblast (CLE) harbours bipotent progenitors, called neural mesodermal progenitors (NMPs), that contribute to the spinal cord and the paraxial mesoderm throughout axial elongation. Here, we performed a single cell analysis of different in vitro NMP populations produced either from embryonic stem cells (ESCs) or epiblast stem cells (EpiSCs) and compared them with E8.25 CLE mouse embryos. In our analysis of this region, our findings challenge the notion that NMPs can be defined by the exclusive co-expression of Sox2 and T at mRNA level. We analyse the in vitro NMP-like populations using a purpose-built support vector machine (SVM) based on the embryo CLE and use it as a classification model to compare the in vivo and in vitro populations. Our results show that NMP differentiation from ESCs leads to heterogeneous progenitor populations with few NMP-like cells, as defined by the SVM algorithm, whereas starting with EpiSCs yields a high proportion of cells with the embryo NMP signature. We find that the population from which the Epi-NMPs are derived in culture contains a node-like population, which suggests that this population probably maintains the expression of T in vitro and thereby a source of NMPs. In conclusion, differentiation of EpiSCs into NMPs reproduces events in vivo and suggests a sequence of events for the emergence of the NMP population.

Targeting enhancer switching overcomes non-genetic drug resistance in acute myeloid leukaemia

Non-genetic drug resistance is increasingly recognised in various cancers. Molecular insights into this process are lacking and it is unknown whether stable non-genetic resistance can be overcome. Using single cell RNA-sequencing of paired drug naïve and resistant AML patient samples and cellular barcoding in a unique mouse model of non-genetic resistance, here we demonstrate that transcriptional plasticity drives stable epigenetic resistance. With a CRISPR-Cas9 screen we identify regulators of enhancer function as important modulators of the resistant cell state. We show that inhibition of Lsd1 (Kdm1a) is able to overcome stable epigenetic resistance by facilitating the binding of the pioneer factor, Pu.1 and cofactor, Irf8, to nucleate new enhancers that regulate the expression of key survival genes. This enhancer switching results in the re-distribution of transcriptional co-activators, including Brd4, and provides the opportunity to disable their activity and overcome epigenetic resistance. Together these findings highlight key principles to help counteract non-genetic drug resistance.

Current best practices in single-cell RNA-seq analysis: a tutorial

Single-cell RNA-seq has enabled gene expression to be studied at an unprecedented resolution. The promise of this technology is attracting a growing user base for single-cell analysis methods. As more analysis tools are becoming available, it is becoming increasingly difficult to navigate this landscape and produce an up-to-date workflow to analyse one's data. Here, we detail the steps of a typical single-cell RNA-seq analysis, including pre-processing (quality control, normalization, data correction, feature selection, and dimensionality reduction) and cell- and gene-level downstream analysis. We formulate current best-practice recommendations for these steps based on independent comparison studies. We have integrated these best-practice recommendations into a workflow, which we apply to a public dataset to further illustrate how these steps work in practice. Our documented case study can be found at https://www.github.com/theislab/single-cell-tutorial This review will serve as a workflow tutorial for new entrants into the field, and help established users update their analysis pipelines.

Aims, Study Design, and Enrollment Results From the Assessing Predictors of Infant Respiratory Syncytial Virus Effects and Severity Study

BACKGROUND

The majority of infants hospitalized with primary respiratory syncytial virus (RSV) infection have no obvious risk factors for severe disease.

OBJECTIVE

The aim of this study (Assessing Predictors of Infant RSV Effects and Severity, AsPIRES) was to identify factors associated with severe disease in full-term healthy infants younger than 10 months with primary RSV infection.

METHODS

RSV infected infants were enrolled from 3 cohorts during consecutive winters from August 2012 to April 2016 in Rochester, New York. A birth cohort was prospectively enrolled and followed through their first winter for development of RSV infection. An outpatient supplemental cohort was enrolled in the emergency department or pediatric offices, and a hospital cohort was enrolled on admission with RSV infection. RSV was diagnosed by reverse transcriptase-polymerase chain reaction. Demographic and clinical data were recorded and samples collected for assays: buccal swab (cytomegalovirus polymerase chain reaction, PCR), nasal swab (RSV qualitative PCR, complete viral gene sequence, 16S ribosomal ribonucleic acid [RNA] amplicon microbiota analysis), nasal wash (chemokine and cytokine assays), nasal brush (nasal respiratory epithelial cell gene expression using RNA sequencing [RNAseq]), and 2 to 3 ml of heparinized blood (flow cytometry, RNAseq analysis of purified cluster of differentiation [CD]4+, CD8+, B cells and natural killer cells, and RSV-specific antibody). Cord blood (RSV-specific antibody) was also collected for the birth cohort. Univariate and multivariate logistic regression will be used for analysis of data using a continuous Global Respiratory Severity Score (GRSS) as the outcome variable. Novel statistical methods will be developed for integration of the large complex datasets.

RESULTS

A total of 453 infants were enrolled into the 3 cohorts; 226 in the birth cohort, 60 in the supplemental cohort, and 78 in the hospital cohort. A total of 126 birth cohort infants remained in the study and were evaluated for 150 respiratory illnesses. Of the 60 RSV positive infants in the supplemental cohort, 42 completed the study, whereas all 78 of the RSV positive hospital cohort infants completed the study. A GRSS was calculated for each RSV-infected infant and is being used to analyze each of the complex datasets by correlation with disease severity in univariate and multivariate methods.

CONCLUSIONS

The AsPIRES study will provide insights into the complex pathogenesis of RSV infection in healthy full-term infants with primary RSV infection. The analysis will allow assessment of multiple factors potentially influencing the severity of RSV infection including the level of RSV specific antibodies, the innate immune response of nasal epithelial cells, the adaptive response by various lymphocyte subsets, the resident airway microbiota, and viral factors. Results of this study will inform disease interventions such as vaccines and antiviral therapies.

Application of single-cell sequencing in human cancer

Precision medicine is emerging as a cornerstone of future cancer care with the objective of providing targeted therapies based on the molecular phenotype of each individual patient. Traditional bulk-level molecular phenotyping of tumours leads to significant information loss, as the molecular profile represents an average phenotype over large numbers of cells, while cancer is a disease with inherent intra-tumour heterogeneity at the cellular level caused by several factors, including clonal evolution, tissue hierarchies, rare cells and dynamic cell states. Single-cell sequencing provides means to characterize heterogeneity in a large population of cells and opens up opportunity to determine key molecular properties that influence clinical outcomes, including prognosis and probability of treatment response. Single-cell sequencing methods are now reliable enough to be used in many research laboratories, and we are starting to see applications of these technologies for characterization of human primary cancer cells. In this review, we provide an overview of studies that have applied single-cell sequencing to characterize human cancers at the single-cell level, and we discuss some of the current challenges in the field.

Polychromic Reporter Mice Reveal Unappreciated Innate Lymphoid Cell Progenitor Heterogeneity and Elusive ILC3 Progenitors in Bone Marrow

Innate lymphoid cells (ILCs) play strategic roles in tissue homeostasis and immunity. ILCs arise from lymphoid progenitors undergoing lineage restriction and the development of specialized ILC subsets. We generated “5x polychromILC” transcription factor reporter mice to delineate ILC precursor states by revealing the multifaceted expression of key ILC-associated transcription factors (Id2, Bcl11b, Gata3, RORγt, and RORα) during ILC development in the bone marrow. This approach allowed previously unattained enrichment of rare progenitor subsets and revealed hitherto unappreciated ILC precursor heterogeneity. In vivo and in vitro assays identified precursors with potential to generate all ILC subsets and natural killer (NK) cells, and also permitted discrimination of elusive ILC3 bone marrow antecedents. Single-cell gene expression analysis identified a discrete ILC2-committed population and delineated transition states between early progenitors and a highly heterogeneous ILC1, ILC3, and NK precursor cell cluster. This diversity might facilitate greater lineage potential upon progenitor recruitment to peripheral tissues.

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Five-color “polychromILC” transcription factor reporter mice define ILC precursors

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ILC precursors give rise to ILC1, ILC2, and ILC3 and retain NK potential

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A Rorc^Kat allele allows resolution of extremely rare ILC3 progenitors

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Detection of divergent trajectories for ILC2 and common ILC1, ILC3, and NK development

scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets

Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

Single-cell transcriptome analysis of CD8 + T-cell memory inflation

Background: Persistent viruses such as murine cytomegalovirus (MCMV) and adenovirus-based vaccines induce strong, sustained CD8 + T-cell responses, described as memory "inflation". These retain functionality, home to peripheral organs and are associated with a distinct transcriptional program. Methods: To further define the nature of the transcriptional mechanisms underpinning memory inflation at different sites we used single-cell RNA sequencing of tetramer-sorted cells from MCMV-infected mice, analyzing transcriptional networks in virus-specific populations in the spleen and gut intra-epithelial lymphocytes (IEL). Results: We provide a transcriptional map of T-cell memory and define a module of gene expression, which distinguishes memory inflation in spleen from resident memory T-cells (T RM) in the gut. Conclusions: These data indicate that CD8 + T-cell memory in the gut epithelium induced by persistent viruses and vaccines has a distinct quality from both conventional memory and "inflationary" memory which may be relevant to protection against mucosal infections.

Single-cell transcriptomes of the regenerating intestine reveal a revival stem cell

The turnover of the intestinal epithelium is driven by multipotent LGR5⁺ crypt-base columnar cells (CBCs) located at the bottom of crypt zones¹. However, CBCs are lost following injury, such as irradiation², but the intestinal epithelium is nevertheless able to recover³. Thus, a second population of quiescent '+4' cells, or reserve stem cells (RSCs), has previously been proposed to regenerate the damaged intestine^4-7. Although CBCs and RSCs were thought to be mutually exclusive^4,8, subsequent studies have found that LGR5⁺ CBCs express RSC markers⁹ and that RSCs were dispensable-whereas LGR5⁺ cells were essential-for repair of the damaged intestine³. In addition, progenitors of absorptive enterocytes¹⁰, secretory cells^11-15 and slow cycling LGR5⁺ cells¹⁶ have been shown to contribute to regeneration whereas the transcriptional regulator YAP1, which is important for intestinal regeneration, was suggested to induce a pro-survival phenotype in LGR5⁺ cells¹⁷. Thus, whether cellular plasticity or distinct cell populations are critical for intestinal regeneration remains unknown. Here we applied single-cell RNA sequencing to profile the regenerating mouse intestine and identified a distinct, damage-induced quiescent cell type that we term the revival stem cell (revSC). revSCs are marked by high clusterin expression and are extremely rare under homoeostatic conditions, yet give rise-in a temporal hierarchy-to all the major cell types of the intestine, including LGR5⁺ CBCs. After intestinal damage by irradiation, targeted ablation of LGR5⁺ CBCs, or treatment with dextran sodium sulfate, revSCs undergo a YAP1-dependent transient expansion, reconstitute the LGR5⁺ CBC compartment and are required to regenerate a functional intestine. These studies thus define a unique stem cell that is mobilized by damage to revive the homoeostatic stem cell compartment and regenerate the intestinal epithelium.

Single-cell transcriptomes of the regenerating intestine reveal a revival stem cell

The turnover of the intestinal epithelium is driven by multipotent LGR5⁺ crypt-base columnar cells (CBCs) located at the bottom of crypt zones¹. However, CBCs are lost following injury, such as irradiation², but the intestinal epithelium is nevertheless able to recover³. Thus, a second population of quiescent '+4' cells, or reserve stem cells (RSCs), has previously been proposed to regenerate the damaged intestine^4-7. Although CBCs and RSCs were thought to be mutually exclusive^4,8, subsequent studies have found that LGR5⁺ CBCs express RSC markers⁹ and that RSCs were dispensable-whereas LGR5⁺ cells were essential-for repair of the damaged intestine³. In addition, progenitors of absorptive enterocytes¹⁰, secretory cells^11-15 and slow cycling LGR5⁺ cells¹⁶ have been shown to contribute to regeneration whereas the transcriptional regulator YAP1, which is important for intestinal regeneration, was suggested to induce a pro-survival phenotype in LGR5⁺ cells¹⁷. Thus, whether cellular plasticity or distinct cell populations are critical for intestinal regeneration remains unknown. Here we applied single-cell RNA sequencing to profile the regenerating mouse intestine and identified a distinct, damage-induced quiescent cell type that we term the revival stem cell (revSC). revSCs are marked by high clusterin expression and are extremely rare under homoeostatic conditions, yet give rise-in a temporal hierarchy-to all the major cell types of the intestine, including LGR5⁺ CBCs. After intestinal damage by irradiation, targeted ablation of LGR5⁺ CBCs, or treatment with dextran sodium sulfate, revSCs undergo a YAP1-dependent transient expansion, reconstitute the LGR5⁺ CBC compartment and are required to regenerate a functional intestine. These studies thus define a unique stem cell that is mobilized by damage to revive the homoeostatic stem cell compartment and regenerate the intestinal epithelium.

Origin and differentiation trajectories of fibroblastic reticular cells in the splenic white pulp

The splenic white pulp is underpinned by poorly characterized stromal cells that demarcate distinct immune cell microenvironments. Here we establish fibroblastic reticular cell (FRC)-specific fate-mapping in mice to define their embryonic origin and differentiation trajectories. Our data show that all reticular cell subsets descend from multipotent progenitors emerging at embryonic day 19.5 from periarterial progenitors. Commitment of FRC progenitors is concluded during the first week of postnatal life through occupation of niches along developing central arterioles. Single cell transcriptomic analysis facilitated deconvolution of FRC differentiation trajectories and indicated that perivascular reticular cells function both as adult lymphoid organizer cells and mural cell progenitors. The lymphotoxin-β receptor-independent sustenance of postnatal progenitor stemness unveils that systemic immune surveillance in the splenic white pulp is governed through subset specification of reticular cells from a multipotent periarterial progenitor cell. In sum, the finding that discrete signaling events in perivascular niches determine the differentiation trajectories of reticular cell networks explains the development of distinct microenvironmental niches in secondary and tertiary lymphoid tissues that are crucial for the induction and regulation of innate and adaptive immune processes.

The white pulp of spleen is an important immune structure dynamically modulated during development and immune responses. Here the authors define, using multi-color lineage tracing and single-cell transcriptome analysis, the subset distribution and differentiation trajectory of fibroblastic reticular cells to serve structural insights for splenic white pulps.

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 RNAseq reveals seven classes of colonic sensory neuron

OBJECTIVE

Integration of nutritional, microbial and inflammatory events along the gut-brain axis can alter bowel physiology and organism behaviour. Colonic sensory neurons activate reflex pathways and give rise to conscious sensation, but the diversity and division of function within these neurons is poorly understood. The identification of signalling pathways contributing to visceral sensation is constrained by a paucity of molecular markers. Here we address this by comprehensive transcriptomic profiling and unsupervised clustering of individual mouse colonic sensory neurons.

DESIGN

Unbiased single-cell RNA-sequencing was performed on retrogradely traced mouse colonic sensory neurons isolated from both thoracolumbar (TL) and lumbosacral (LS) dorsal root ganglia associated with lumbar splanchnic and pelvic spinal pathways, respectively. Identified neuronal subtypes were validated by single-cell qRT-PCR, immunohistochemistry (IHC) and Ca²⁺-imaging.

RESULTS

Transcriptomic profiling and unsupervised clustering of 314 colonic sensory neurons revealed seven neuronal subtypes. Of these, five neuronal subtypes accounted for 99% of TL neurons, with LS neurons almost exclusively populating the remaining two subtypes. We identify and classify neurons based on novel subtype-specific marker genes using single-cell qRT-PCR and IHC to validate subtypes derived from RNA-sequencing. Lastly, functional Ca²⁺-imaging was conducted on colonic sensory neurons to demonstrate subtype-selective differential agonist activation.

CONCLUSIONS

We identify seven subtypes of colonic sensory neurons using unbiased single-cell RNA-sequencing and confirm translation of patterning to protein expression, describing sensory diversity encompassing all modalities of colonic neuronal sensitivity. These results provide a pathway to molecular interrogation of colonic sensory innervation in health and disease, together with identifying novel targets for drug development.

Dissecting Cellular Heterogeneity Using Single-Cell RNA Sequencing

Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological system is developed, homeo-statically regulated, and responds to external perturbations. Single-cell RNA sequencing (scRNA-seq) allows the quantitative and unbiased characterization of cellular heterogeneity by providing genome-wide molecular profiles from tens of thousands of individual cells. A major question in analyzing scRNA-seq data is how to account for the observed cell-to-cell variability. In this review, we provide an overview of scRNA-seq protocols, computational approaches for dissecting cellular heterogeneity, and future directions of single-cell transcriptomic analysis.

EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data

Droplet-based single-cell RNA sequencing protocols have dramatically increased the throughput of single-cell transcriptomics studies. A key computational challenge when processing these data is to distinguish libraries for real cells from empty droplets. Here, we describe a new statistical method for calling cells from droplet-based data, based on detecting significant deviations from the expression profile of the ambient solution. Using simulations, we demonstrate that EmptyDrops has greater power than existing approaches while controlling the false discovery rate among detected cells. Our method also retains distinct cell types that would have been discarded by existing methods in several real data sets.

Complementary networks of cortical somatostatin interneurons enforce layer specific control

The neocortex is functionally organized into layers. Layer four receives the densest bottom up sensory inputs, while layers 2/3 and 5 receive top down inputs that may convey predictive information. A subset of cortical somatostatin (SST) neurons, the Martinotti cells, gate top down input by inhibiting the apical dendrites of pyramidal cells in layers 2/3 and 5, but it is unknown whether an analogous inhibitory mechanism controls activity in layer 4. Using high precision circuit mapping, in vivo optogenetic perturbations, and single cell transcriptional profiling, we reveal complementary circuits in the mouse barrel cortex involving genetically distinct SST subtypes that specifically and reciprocally interconnect with excitatory cells in different layers: Martinotti cells connect with layers 2/3 and 5, whereas non-Martinotti cells connect with layer 4. By enforcing layer-specific inhibition, these parallel SST subnetworks could independently regulate the balance between bottom up and top down input.

West Nile Virus-Inclusive Single-Cell RNA Sequencing Reveals Heterogeneity in the Type I Interferon Response within Single Cells

West Nile virus (WNV) is a neurotropic mosquito-borne flavivirus of global importance. Neuroinvasive WNV infection results in encephalitis and can lead to prolonged neurological impairment or death. Type I interferon (IFN-I) is crucial for promoting antiviral defenses through the induction of antiviral effectors, which function to restrict viral replication and spread. However, our understanding of the antiviral response to WNV infection is mostly derived from analysis of bulk cell populations. It is becoming increasingly apparent that substantial heterogeneity in cellular processes exists among individual cells, even within a seemingly homogenous cell population. Here, we present WNV-inclusive single-cell RNA sequencing (scRNA-seq), an approach to examine the transcriptional variation and viral RNA burden across single cells. We observed that only a few cells within the bulk population displayed robust transcription of IFN-β mRNA, and this did not appear to depend on viral RNA abundance within the same cell. Furthermore, we observed considerable transcriptional heterogeneity in the IFN-I response, with genes displaying high unimodal and bimodal expression patterns. Broadly, IFN-stimulated genes negatively correlated with viral RNA abundance, corresponding with a precipitous decline in expression in cells with high viral RNA levels. Altogether, we demonstrated the feasibility and utility of WNV-inclusive scRNA-seq as a high-throughput technique for single-cell transcriptomics and WNV RNA detection. This approach can be implemented in other models to provide insights into the cellular features of protective immunity and identify novel therapeutic targets.IMPORTANCE West Nile virus (WNV) is a clinically relevant pathogen responsible for recurrent epidemics of neuroinvasive disease. Type I interferon is essential for promoting an antiviral response against WNV infection; however, it is unclear how heterogeneity in the antiviral response at the single-cell level impacts viral control. Specifically, conventional approaches lack the ability to distinguish differences across cells with varying viral abundance. The significance of our research is to demonstrate a new technique for studying WNV infection at the single-cell level. We discovered extensive variation in antiviral gene expression and viral abundance across cells. This protocol can be applied to primary cells or in vivo models to better understand the underlying cellular heterogeneity following WNV infection for the development of targeted therapeutic strategies.

DEvis: an R package for aggregation and visualization of differential expression data

Background

Existing tools for the aggregation and visualization of differential expression data have discrete functionality and require that end-users rely on multiple software packages with complex dependencies or manually manipulate data for analysis and interpretation. Furthermore, at present, data aggregation and visualization are laborious, time consuming, and subject to human error. This is a serious limitation on the current state of differential transcriptomic analysis, which makes it necessary to expend extensive time and resources to reach the point where biological meaning can be interpreted. Such an approach for analysis also leads to scattered and non-standardized code, unsystematic project management and non-reproducible result sets.

Results

Here, we present a differential expression analysis toolkit, DEvis, that provides a powerful, integrated solution for the analysis of differential expression data with a rapid turnaround time. DEvis has simple installation requirements and provides a convenient, user-friendly R package that addresses the issues inherent to complex multi-factor experiments, such as multiple contrast aggregation and integration, result sorting and selection, visualization, project management, and reproducibility. This tool increases the capabilities of differential expression analysis while reducing workload and the potential for manual error. Furthermore, it provides a much-needed encapsulation of scattered functionality, making large and complex analysis more efficient and reproducible.

Conclusion

DEvis provides a wide range of powerful visualization, data aggregation, and project management tools that provide flexibility and speed in analysis. The functionality provided by DEVis increases efficiency of analysis and supplies researchers with new and relevant means for the analysis of large and complicated transcriptomic experiments. DEvis furthermore incorporates automatic project management capabilities, which standardizes analysis and ensures the reproducibility of results. After the establishment of statistical frameworks that identify differentially expressed genes, this package is the next logical step for differential transcriptomic analysis, establishing the critical framework necessary to manipulate, explore, and extract biologically relevant meaning from differential expression data.

The transcription factor Duxbl mediates elimination of pre-T cells that fail β-selection

During β-selection, T cells without productive TCRβ rearrangements are eliminated. Klein et al. show that the transcription factor Duxbl regulates this process by inducing apoptosis through activation of the Oas/RNaseL pathway. Successful TCRβ rearrangement rescues cells by pre-TCR–mediated Duxbl suppression.

T cell development is critically dependent on successful rearrangement of antigen-receptor chains. At the β-selection checkpoint, only cells with a functional rearrangement continue in development. However, how nonselected T cells proceed in their dead-end fate is not clear. We identified low CD27 expression to mark pre-T cells that have failed to rearrange their β-chain. Expression profiling and single-cell transcriptome clustering identified a developmental trajectory through β-selection and revealed specific expression of the transcription factor Duxbl at a stage of high recombination activity before β-selection. Conditional transgenic expression of Duxbl resulted in a developmental block at the DN3-to-DN4 transition due to reduced proliferation and enhanced apoptosis, whereas RNA silencing of Duxbl led to a decrease in apoptosis. Transcriptome analysis linked Duxbl to elevated expression of the apoptosis-inducing Oas/RNaseL pathway. RNaseL deficiency or sustained Bcl2 expression led to a partial rescue of cells in Duxbl transgenic mice. These findings identify Duxbl as a regulator of β-selection by inducing apoptosis in cells with a nonfunctional rearrangement.

Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity

BACKGROUND

Alternative splicing is a key regulatory mechanism in eukaryotic cells and increases the effective number of functionally distinct gene products. Using bulk RNA sequencing, splicing variation has been studied across human tissues and in genetically diverse populations. This has identified disease-relevant splicing events, as well as associations between splicing and genomic features, including sequence composition and conservation. However, variability in splicing between single cells from the same tissue or cell type and its determinants remains poorly understood.

RESULTS

We applied parallel DNA methylation and transcriptome sequencing to differentiating human induced pluripotent stem cells to characterize splicing variation (exon skipping) and its determinants. Our results show that variation in single-cell splicing can be accurately predicted based on local sequence composition and genomic features. We observe moderate but consistent contributions from local DNA methylation profiles to splicing variation across cells. A combined model that is built based on genomic features as well as DNA methylation information accurately predicts different splicing modes of individual cassette exons. These categories include the conventional inclusion and exclusion patterns, but also more subtle modes of cell-to-cell variation in splicing. Finally, we identified and characterized associations between DNA methylation and splicing changes during cell differentiation.

CONCLUSIONS

Our study yields new insights into alternative splicing at the single-cell level and reveals a previously underappreciated link between DNA methylation variation and splicing.

CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA sequencing

Recent technical improvements in single-cell RNA sequencing (scRNA-seq) have enabled massively parallel profiling of transcriptomes, thereby promoting large-scale studies encompassing a wide range of cell types of multicellular organisms. With this background, we propose CellFishing.jl, a new method for searching atlas-scale datasets for similar cells and detecting noteworthy genes of query cells with high accuracy and throughput. Using multiple scRNA-seq datasets, we validate that our method demonstrates comparable accuracy to and is markedly faster than the state-of-the-art software. Moreover, CellFishing.jl is scalable to more than one million cells, and the throughput of the search is approximately 1600 cells per second.

IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis

Next-Generation Sequencing has made available substantial amounts of large-scale Omics data, providing unprecedented opportunities to understand complex biological systems. Specifically, the value of RNA-Sequencing (RNA-Seq) data has been confirmed in inferring how gene regulatory systems will respond under various conditions (bulk data) or cell types (single-cell data). RNA-Seq can generate genome-scale gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies. While these analyses can provide invaluable information related to gene expression, integration and interpretation of the results can prove challenging. Here we present a tool called IRIS-EDA, which is a Shiny web server for expression data analysis. It provides a straightforward and user-friendly platform for performing numerous computational analyses on user-provided RNA-Seq or Single-cell RNA-Seq (scRNA-Seq) data. Specifically, three commonly used R packages (edgeR, DESeq2, and limma) are implemented in the DGE analysis with seven unique experimental design functionalities, including a user-specified design matrix option. Seven discovery-driven methods and tools (correlation analysis, heatmap, clustering, biclustering, Principal Component Analysis (PCA), Multidimensional Scaling (MDS), and t-distributed Stochastic Neighbor Embedding (t-SNE)) are provided for gene expression exploration which is useful for designing experimental hypotheses and determining key factors for comprehensive DGE analysis. Furthermore, this platform integrates seven visualization tools in a highly interactive manner, for improved interpretation of the analyses. It is noteworthy that, for the first time, IRIS-EDA provides a framework to expedite submission of data and results to NCBI's Gene Expression Omnibus following the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. IRIS-EDA is freely available at http://bmbl.sdstate.edu/IRIS/.

Deterministic column subset selection for single-cell RNA-Seq

Analysis of single-cell RNA sequencing (scRNA-Seq) data often involves filtering out uninteresting or poorly measured genes and dimensionality reduction to reduce noise and simplify data visualization. However, techniques such as principal components analysis (PCA) fail to preserve non-negativity and sparsity structures present in the original matrices, and the coordinates of projected cells are not easily interpretable. Commonly used thresholding methods to filter genes avoid those pitfalls, but ignore collinearity and covariance in the original matrix. We show that a deterministic column subset selection (DCSS) method possesses many of the favorable properties of common thresholding methods and PCA, while avoiding pitfalls from both. We derive new spectral bounds for DCSS. We apply DCSS to two measures of gene expression from two scRNA-Seq experiments with different clustering workflows, and compare to three thresholding methods. In each case study, the clusters based on the small subset of the complete gene expression profile selected by DCSS are similar to clusters produced from the full set. The resulting clusters are informative for cell type.

Single-cell analysis reveals congruence between kidney organoids and human fetal kidney

BACKGROUND

Human kidney organoids hold promise for studying development, disease modelling and drug screening. However, the utility of stem cell-derived kidney tissues will depend on how faithfully these replicate normal fetal development at the level of cellular identity and complexity.

METHODS

Here, we present an integrated analysis of single cell datasets from human kidney organoids and human fetal kidney to assess similarities and differences between the component cell types.

RESULTS

Clusters in the combined dataset contained cells from both organoid and fetal kidney with transcriptional congruence for key stromal, endothelial and nephron cell type-specific markers. Organoid enriched neural, glial and muscle progenitor populations were also evident. Major transcriptional differences between organoid and human tissue were likely related to technical artefacts. Cell type-specific comparisons revealed differences in stromal, endothelial and nephron progenitor cell types including expression of WNT2B in the human fetal kidney stroma.

CONCLUSIONS

This study supports the fidelity of kidney organoids as models of the developing kidney and affirms their potential in disease modelling and drug screening.

Single-cell analysis reveals congruence between kidney organoids and human fetal kidney

Background

Human kidney organoids hold promise for studying development, disease modelling and drug screening. However, the utility of stem cell-derived kidney tissues will depend on how faithfully these replicate normal fetal development at the level of cellular identity and complexity.

Methods

Here, we present an integrated analysis of single cell datasets from human kidney organoids and human fetal kidney to assess similarities and differences between the component cell types.

Results

Clusters in the combined dataset contained cells from both organoid and fetal kidney with transcriptional congruence for key stromal, endothelial and nephron cell type-specific markers. Organoid enriched neural, glial and muscle progenitor populations were also evident. Major transcriptional differences between organoid and human tissue were likely related to technical artefacts. Cell type-specific comparisons revealed differences in stromal, endothelial and nephron progenitor cell types including expression of WNT2B in the human fetal kidney stroma.

Conclusions

This study supports the fidelity of kidney organoids as models of the developing kidney and affirms their potential in disease modelling and drug screening.

Electronic supplementary material

The online version of this article (10.1186/s13073-019-0615-0) contains supplementary material, which is available to authorized users.

Single cell sequencing reveals gene expression signatures associated with bone marrow stromal cell subpopulations and time in culture

Background

Bone marrow stromal cells (BMSCs) are a heterogeneous population that participates in wound healing, immune modulation and tissue regeneration. Next generation sequencing was used to analyze transcripts from single BMSCs in order to better characterize BMSC subpopulations.

Methods

Cryopreserved passage 2 BMSCs from one healthy subject were cultured through passage 10. The transcriptomes of bulk BMSCs from designated passages were analyzed with microarrays and RNA sequencing (RNA-Seq). For some passages, single BMSCs were separated using microfluidics and their transcriptomes were analyzed by RNA-Seq.

Results

Transcriptome analysis by microarray and RNA-Seq of unseparated BMSCs from passages 2, 4, 6, 8, 9 and 10 yielded similar results; both data sets grouped passages 4 and 6 and passages 9 and 10 together and genes differentially expressed among these early and late passage BMSCs were similar. 3D Diffusion map visualization of single BMSCs from passages 3, 4, 6, 8 and 9 clustered passages 3 and 9 into two distinct groups, but there was considerable overlap for passages 4, 6 and 8 cells. Markers for early passage, FGFR2, and late passage BMSCs, PLAT, were able to identify three subpopulations within passage 3 BMSCs; one that expressed high levels of FGFR2 and low levels of PLAT; one that expressed low levels of FGFR2 and high levels of PLAT and one that expressed intermediate levels of FGFR2 and low levels of PLAT.

Conclusions

Single BMSCs can be separated by microfluidics and their transcriptome analyzed by next generation sequencing. Single cell analysis of early passage BMSCs identified a subpopulation of cells expressing high levels of FGFR2 that might include skeletal stem cells.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1766-2) contains supplementary material, which is available to authorized users.

Single-Cell Sequencing of iPSC-Dopamine Neurons Reconstructs Disease Progression and Identifies HDAC4 as a Regulator of Parkinson Cell Phenotypes

Induced pluripotent stem cell (iPSC)-derived dopamine neurons provide an opportunity to model Parkinson's disease (PD), but neuronal cultures are confounded by asynchronous and heterogeneous appearance of disease phenotypes in vitro. Using high-resolution, single-cell transcriptomic analyses of iPSC-derived dopamine neurons carrying the GBA-N370S PD risk variant, we identified a progressive axis of gene expression variation leading to endoplasmic reticulum stress. Pseudotime analysis of genes differentially expressed (DE) along this axis identified the transcriptional repressor histone deacetylase 4 (HDAC4) as an upstream regulator of disease progression. HDAC4 was mislocalized to the nucleus in PD iPSC-derived dopamine neurons and repressed genes early in the disease axis, leading to late deficits in protein homeostasis. Treatment of iPSC-derived dopamine neurons with HDAC4-modulating compounds upregulated genes early in the DE axis and corrected PD-related cellular phenotypes. Our study demonstrates how single-cell transcriptomics can exploit cellular heterogeneity to reveal disease mechanisms and identify therapeutic targets.

Analysis of Technical and Biological Variability in Single-Cell RNA Sequencing

Profiling the transcriptomes of individual cells with single-cell RNA sequencing (scRNA-seq) has been widely applied to provide a detailed molecular characterization of cellular heterogeneity within a population of cells. Despite recent technological advances of scRNA-seq, technical variability of gene expression in scRNA-seq is still much higher than that in bulk RNA-seq. Accounting for technical variability is therefore a prerequisite for correctly analyzing single-cell data. This chapter describes a computational pipeline for detecting highly variable genes exhibiting higher cell-to-cell variability than expected by technical noise. The basic pipeline using the scater and scran R/Bioconductor packages includes deconvolution-based normalization, fitting the mean-variance trend, testing for nonzero biological variability, and visualization with highly variable genes. An outline of the underlying theory of detecting highly variable genes is also presented. We illustrate how the pipeline works by using two case studies, one from mouse embryonic stem cells with external RNA spike-ins, and the other from mouse dentate gyrus cells without spike-ins.

Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction

Macrophages promote both injury and repair after myocardial infarction, but discriminating functions within mixed populations remains challenging. Here we used fate mapping, parabiosis and single-cell transcriptomics to demonstrate that at steady state, TIMD4+LYVE1+MHC-IIloCCR2- resident cardiac macrophages self-renew with negligible blood monocyte input. Monocytes partially replaced resident TIMD4-LYVE1-MHC-IIhiCCR2- macrophages and fully replaced TIMD4-LYVE1-MHC-IIhiCCR2+ macrophages, revealing a hierarchy of monocyte contribution to functionally distinct macrophage subsets. Ischemic injury reduced TIMD4+ and TIMD4- resident macrophage abundance, whereas CCR2+ monocyte-derived macrophages adopted multiple cell fates within infarcted tissue, including those nearly indistinguishable from resident macrophages. Recruited macrophages did not express TIMD4, highlighting the ability of TIMD4 to track a subset of resident macrophages in the absence of fate mapping. Despite this similarity, inducible depletion of resident macrophages using a Cx3cr1-based system led to impaired cardiac function and promoted adverse remodeling primarily within the peri-infarct zone, revealing a nonredundant, cardioprotective role of resident cardiac macrophages.

A descriptive marker gene approach to single-cell pseudotime inference

Motivation

Pseudotime estimation from single-cell gene expression data allows the recovery of temporal information from otherwise static profiles of individual cells. Conventional pseudotime inference methods emphasize an unsupervised transcriptome-wide approach and use retrospective analysis to evaluate the behaviour of individual genes. However, the resulting trajectories can only be understood in terms of abstract geometric structures and not in terms of interpretable models of gene behaviour.

Results

Here we introduce an orthogonal Bayesian approach termed 'Ouija' that learns pseudotimes from a small set of marker genes that might ordinarily be used to retrospectively confirm the accuracy of unsupervised pseudotime algorithms. Crucially, we model these genes in terms of switch-like or transient behaviour along the trajectory, allowing us to understand why the pseudotimes have been inferred and learn informative parameters about the behaviour of each gene. Since each gene is associated with a switch or peak time the genes are effectively ordered along with the cells, allowing each part of the trajectory to be understood in terms of the behaviour of certain genes. We demonstrate that this small panel of marker genes can recover pseudotimes that are consistent with those obtained using the entire transcriptome. Furthermore, we show that our method can detect differences in the regulation timings between two genes and identify 'metastable' states-discrete cell types along the continuous trajectories-that recapitulate known cell types.

Availability and implementation

An open source implementation is available as an R package at http://www.github.com/kieranrcampbell/ouija and as a Python/TensorFlow package at http://www.github.com/kieranrcampbell/ouijaflow.

Supplementary information

Supplementary data are available at Bioinformatics online.