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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. Graphical Abstract This Tutorial details the steps of a typical single‐cell RNA‐seq analysis. Best‐practice recommendations are provided and illustrated with a workflow provided in the form of an open source code repository.

High‐throughput ‐omics techniques have revolutionised biology, allowing for thorough and unbiased characterisation of the molecular states of biological systems. However, cellular decision‐making is inherently a unicellular process to which “bulk” ‐omics techniques are poorly suited, as they capture ensemble averages of cell states. Recently developed single‐cell methods bridge this gap, allowing high‐throughput molecular surveys of individual cells. In this review, we cover core concepts of analysis of single‐cell gene expression data and highlight areas of developmental biology where single‐cell techniques have made important contributions. These include understanding of cell‐to‐cell heterogeneity, the tracing of differentiation pathways, quantification of gene expression from specific alleles, and the future directions of cell lineage tracing and spatial gene expression analysis. Graphical Abstract Single‐cell genomic techniques have advanced our understanding of several developmental processes. This Review summarises advances related to generating and analyzing single‐cell transcriptome data and discusses areas of developmental biology that benefited from such technologies.

Female fertility is inversely correlated with maternal age due to a depletion of the oocyte pool and a reduction in oocyte developmental competence. Few studies have addressed the effect of maternal age on the human mature oocyte (MII) transcriptome, which is established during oocyte growth and maturation, however, the pathways involved remain unclear. Here, we characterize and compare the transcriptomes of a large cohort of fully grown germinal vesicle stage (GV) and in vitro matured (IVM‐MII) oocytes from women of varying reproductive age. First, we identified two clusters of cells reflecting the oocyte maturation stage (GV and IVM‐MII) with 4445 and 324 putative marker genes, respectively. Furthermore, we identified genes for which transcript representation either progressively increased or decreased with age. Our results indicate that the transcriptome is more affected by age in IVM‐MII oocytes (1219 genes) than in GV oocytes (596 genes). In particular, we found that transcripts of genes involved in chromosome segregation and RNA splicing significantly increased representation with age, while genes related to mitochondrial activity showed a lower representation. Gene regulatory network analysis facilitated the identification of potential upstream master regulators of the genes involved in those biological functions. Our analysis suggests that advanced maternal age does not globally affect the oocyte transcriptome at GV or IVM‐MII stages. Nonetheless, hundreds of genes displayed altered transcript representation, particularly in IVM‐MII oocytes, which might contribute to the age‐related quality decline in human oocytes. Here we used single‐oocyte RNA‐Seq to study the transcriptome changes occurring during human oocyte aging. We sampled Germinal Vesicle (GV) stage oocytes from women of age 18–43 and analyzed them directly (GV) or after in vitro maturation (IVM‐MII). We observed that age has a bigger impact on the IVM‐MII transcriptome. Transcripts belonging to biological pathways critical for oocyte maturation and function either increased (green, top) or decreased (red, bottom) in representation with age.

Background Recently, pioneering expression quantitative trait loci (eQTL) studies on single cell RNA sequencing (scRNA-seq) data have revealed new and cell-specific regulatory single nucleotide variants (SNVs). Here, we present an alternative QTL-related approach applicable to transcribed SNV loci from scRNA-seq data: scReQTL. ScReQTL uses Variant Allele Fraction (VAF RNA ) at expressed biallelic loci, and corelates it to gene expression from the corresponding cell. Results Our approach employs the advantage that, when estimated from multiple cells, VAF RNA can be used to assess effects of SNVs in a single sample or individual. In this setting scReQTL operates in the context of identical genotypes, where it is likely to capture RNA-mediated genetic interactions with cell-specific and transient effects. Applying scReQTL on scRNA-seq data generated on the 10 × Genomics Chromium platform using 26,640 mesenchymal cells derived from adipose tissue obtained from three healthy female donors, we identified 1272 unique scReQTLs. ScReQTLs common between individuals or cell types were consistent in terms of the directionality of the relationship and the effect size. Comparative assessment with eQTLs from bulk sequencing data showed that scReQTL analysis identifies a distinct set of SNV-gene correlations, that are substantially enriched in known gene-gene interactions and significant genome-wide association studies (GWAS) loci. Conclusion ScReQTL is relevant to the rapidly growing source of scRNA-seq data and can be applied to outline SNVs potentially contributing to cell type-specific and/or dynamic genetic interactions from an individual scRNA-seq dataset. Availability: https://github.com/HorvathLab/NGS/tree/master/scReQTL

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. Synopsis Single‐cell transcriptome profiling of hematopoietic progenitors collected from human cord blood provides molecular evidence for multi‐lineage transcriptomic priming in early progenitors, which correlates with epigenetic state, surface marker expression, and functional output. Unsupervised reconstruction of transcriptomic cell states in human CD34 + hematopoietic progenitors from cord blood is performed using Drop‐seq. “Primed” and “ de novo ” programs that accompany specification into four downstream lineages are identified. Integration of cord blood and bone marrow single cell datasets reveals strong conservation of molecular programs. Heterogeneous surface markers are identified within early lymphoid‐primed multipotent progenitors (LMPPs) and their expression correlates with transcriptomic state and functional potential. Graphical Abstract Single‐cell transcriptome profiling of hematopoietic progenitors collected from human cord blood provides molecular evidence for multi‐lineage transcriptomic priming in early progenitors, which correlates with epigenetic state, surface marker expression, and functional output.

The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single‐cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single‐cell transcriptomes ( n  = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid ( n  = 124) and plasma ( n  = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type‐2 epithelial cell health status in lavage fluid and plasma. Using cross‐modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes. Synopsis Multi‐modal integration of single‐cell RNA‐seq data from lung tissue and proteomic data from body fluids across independent lung fibrosis patient cohorts revealed biomarker signatures that correspond with cell state changes during disease progression. Changes in gene expression and cell type frequency are reproducible across cohorts. Protein biomarker signatures of lung function decline in pulmonary fibrosis. Multi‐modal data transfer identifies cellular source of regulated proteins. An activated pericyte state features inflammatory and complement regulators. CRTAC1 levels in body fluids indicate AT2 cell dedifferentiation in disease. Graphical Abstract Multi‐modal integration of single‐cell RNA‐seq data from lung tissue and proteomic data from body fluids across independent lung fibrosis patient cohorts revealed biomarker signatures that correspond with cell state changes during disease progression.

Single‐cell transcriptomic studies are identifying novel cell populations with exciting functional roles in various in vivo contexts, but identification of succinct gene marker panels for such populations remains a challenge. In this work, we introduce COMET, a computational framework for the identification of candidate marker panels consisting of one or more genes for cell populations of interest identified with single‐cell RNA‐seq data. We show that COMET outperforms other methods for the identification of single‐gene panels and enables, for the first time, prediction of multi‐gene marker panels ranked by relevance. Staining by flow cytometry assay confirmed the accuracy of COMET's predictions in identifying marker panels for cellular subtypes, at both the single‐ and multi‐gene levels, validating COMET's applicability and accuracy in predicting favorable marker panels from transcriptomic input. COMET is a general non‐parametric statistical framework and can be used as‐is on various high‐throughput datasets in addition to single‐cell RNA‐sequencing data. COMET is available for use via a web interface ( http://www.cometsc.com/ ) or a stand‐alone software package ( https://github.com/MSingerLab/COMETSC ). Synopsis COMET is a computational tool for marker‐panel selection from single‐cell RNA‐seq data. It generates ranked predictions of single‐ and multiple‐gene marker panels for a cell population of interest. COMET is a computational tool for combinatorial prediction of marker panels from single‐cell transcriptomic data. COMET's statistical framework enables controlling for specificity and sensitivity in predicted marker panels. Staining by flow‐cytometry validates that COMET identifies novel and favorable single‐ and multi‐gene marker panels for cellular subtypes. COMET is available via a web interface ( http://www.cometsc.com/ ) or downloadable software package ( https://github.com/MSingerLab/COMETSC ). Graphical Abstract COMET is a computational tool for marker‐panel selection from single‐cell RNA‐seq data. It generates ranked predictions of single‐ and multiple‐gene marker panels for a cell population of interest.

The assembly of primordial follicles in mammals represents one of the most critical processes in ovarian biology. It directly affects the number of oocytes available to a female throughout her reproductive life. Premature depletion of primordial follicles contributes to the ovarian pathology primary ovarian insufficiency (POI). To delineate the developmental trajectory and regulatory mechanisms of oocytes during the process, we performed RNA‐seq on single germ cells from newborn (P0.5) ovaries. Three cell clusters were classified which corresponded to three cell states (germ cell cyst, cyst breakdown, and follicle) in the newborn ovary. By Monocle analysis, a uniform trajectory of oocyte development was built with a series of genes showed dynamic changes along the pseudo‐timeline. Gene Ontology term enrichment revealed a significant decrease in meiosis‐related genes and a dramatic increase in oocyte‐specific genes which marked the transition from a germ cell to a functional oocyte. We then established a network of regulons by using single‐cell regulatory network inference and clustering (SCENIC) algorithm and identified possible candidate transcription factors that may maintain transcription programs during follicle formation. Following functional studies further revealed the differential regulation of the identified regulon Id2 and its family member Id1, on the establishment of primordial follicle pool by using siRNA knockdown and genetic modified mouse models. In summary, our study systematically reconstructed molecular cascades in oocytes and identified a series of genes and molecular pathways in follicle formation and development. The assembly of primordial follicle determines the number of oocytes available to a female throughout her reproductive life. Premature depletion of primordial follicles contributes to the ovarian pathology primary ovarian insufficiency. We performed RNA‐seq on single germ cells from newborn mouse ovary and the data revealed dynamic gene expressions along with the transition of germ cells to a functional oocyte. Our study highlights the importance of transcriptional regulations on the process.

Osteoclasts (OCs) and regulatory CD4+ T cells (CD4+Tregs) are important components in the tumor microenvironment (TME) of osteosarcoma. In this study, we collected six osteosarcoma samples from our previous study (GSE162454). We also integrated a public database (GSE152048), which included single cell sequencing data of 11 osteosarcoma patients. We obtained 138,192 cells and then successfully identified OCs and CD4+Tregs. Based on the interaction gene set between OCs and CD4+Tregs, patients from GSE21257 were distinguished into two clusters by consensus clustering analysis. Both the tumor immune microenvironment and survival prognosis between the two clusters were significantly different. Subsequently, five model genes were identified by protein–protein interaction network based on differentially upregulated genes of cluster 2. Quantitative RT‐PCR was used to detect their expression in human osteoblast and osteosarcoma cells. A prognostic model was successfully established using these five genes. Kaplan–Meier survival analysis found that patients in the high‐risk group had worse survival (p = 0.029). Therefore, our study first found that cell–cell communication between OCs and CD4+Tregs significantly alters TME and is connected to poor prognosis of OS. The model we constructed can accurately predict prognosis for osteosarcoma patients. This research found that cell–cell communication between osteoclasts and regulatory CD4+ T cells significantly alter the tumor microenvironment and is connected to poor prognosis of osteosarcoma. The model can accurately predict patient prognosis.

Metabolism is a highly compartmentalized process that provides building blocks for biomass generation during development, homeostasis, and wound healing, and energy to support cellular and organismal processes. In metazoans, different cells and tissues specialize in different aspects of metabolism. However, studying the compartmentalization of metabolism in different cell types in a whole animal and for a particular stage of life is difficult. Here, we present MEtabolic models Reconciled with Gene Expression (MERGE), a computational pipeline that we used to predict tissue‐relevant metabolic function at the network, pathway, reaction, and metabolite levels based on single‐cell RNA‐sequencing (scRNA‐seq) data from the nematode Caenorhabditis elegans . Our analysis recapitulated known tissue functions in C. elegans , captured metabolic properties that are shared with similar tissues in human, and provided predictions for novel metabolic functions. MERGE is versatile and applicable to other systems. We envision this work as a starting point for the development of metabolic network models for individual cells as scRNA‐seq continues to provide higher‐resolution gene expression data. Synopsis Monitoring tissue‐ and cell‐type specific metabolic activities by metabolomics remains challenging. MERGE is a computational pipeline that can convert tissue‐level RNA‐seq data from Caenorhabditis elegans into predicted tissue flux potentials that indicate metabolic function. Network‐level flux distributions are successfully fitted to categorized gene expression data for seven tissues in L2 stage animals. Tissue metabolic networks are defined using these integrated flux distributions as the basis. Flux potentials of individual reactions are quantitatively predicted for each tissue. Predicted tissue‐level flux potentials recapitulate known tissue metabolic functions in C. elegans and match key functions to mammalian tissues. Graphical Abstract Monitoring tissue‐ and cell‐type specific metabolic activities by metabolomics remains challenging. MERGE is a computational pipeline that can convert tissue‐level RNA‐seq data from Caenorhabditis elegans into predicted tissue flux potentials that indicate metabolic function.