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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.

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

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.

Objective The developing mammalian retina initially contains undifferentiated cells, providing a model for investigating the mechanisms of differentiation. Notch signaling, mediated by four Notch receptors ( Notch 1–4 ) in mammals, has been studied in the differentiation of neural progenitor cells. Among the four Notch receptors, the frequency, rather than the peak level, of Notch1 -mediated signaling has been suggested to promote the activation of neural progenitor cells. In contrast to Notch1 , the involvement of Notch3 in this process is poorly documented, although Notch3 is known for its role in vascular integrity. Results By re-analyzing publicly available single-cell RNA-seq data from one mouse retinal dataset, two human retinal organoid datasets and two human embryonic retinal datasets, we found that, along with Notch1 , Notch3 is expressed in neural progenitor cells in the retina. In addition, the results of the co-expression profile analyses varied among the datasets, leaving uncertainty regarding the regulatory mechanisms of Notch1 and Notch3 . Our findings shed light on Notch3 in neurogenic progenitor cells of the developing mammalian retina. Since Notch3 has been suggested to cause ligand-independent signaling, Notch3 expression might antagonize Notch1 -mediated signaling oscillations, maintaining the quiescent state of neurogenic progenitor cells.

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.

Background Muscle satellite cells (MuSCs) exert essential roles in skeletal muscle adaptation to growth, injury and ageing, and their functions are extensively modulated by microenvironmental factors. However, the current knowledge about the interaction of MuSCs with niche cells is quite limited. Methods A 10× single‐cell RNA sequencing (scRNA‐seq) was performed on porcine longissimus dorsi and soleus (SOL) muscles to generate a single‐cell transcriptomic dataset of myogenic cells and other cell types. Sophisticated bioinformatic analyses, including unsupervised clustering analysis, marker gene, gene set variation analysis (GSVA), AUCell, pseudotime analysis and RNA velocity analysis, were performed to explore the heterogeneity of myogenic cells. CellChat analysis was used to demonstrate cell–cell communications across myogenic cell subpopulations and niche cells, especially fibro‐adipogenic progenitors (FAPs). Integrated analysis with human and mice datasets was performed to verify the expression of FGF7 across diverse species. The role of FGF7 on MuSC proliferation was evaluated through administering recombinant FGF7 to porcine MuSCs, C2C12, cardiotoxin (CTX)‐injured muscle and d‐galactose (d‐gal)‐induced ageing model. Results ScRNA‐seq totally figured out five cell types including myo‐lineage cells and FAPs, and myo‐lineage cells were further classified into six subpopulations, termed as RCN3+, S100A4+, ID3+, cycling (MKI67+), MYF6+ and MYMK+ satellite cells, respectively. There was a higher proportion of cycling and MYF6+ cells in the SOL population. CellChat analysis uncovered a particular impact of FAPs on myogenic cells mediated by FGF7, which was relatively highly expressed in SOL samples. Administration of FGF7 (10 ng/mL) significantly increased the proportion of EdU+ porcine MuSCs and C2C12 by 4.03 ± 0.81% (P < 0.01) and 6.87 ± 2.17% (P < 0.05), respectively, and knockdown of FGFR2 dramatically abolished the pro‐proliferating effects (P < 0.05). In CTX‐injured muscle, FGF7 significantly increased the ratio of EdU+/Pax7+ cells by 15.68 ± 5.45% (P < 0.05) and elevated the number of eMyHC+ regenerating myofibres by 19.7 ± 4.25% (P < 0.01). Under d‐gal stimuli, FGF7 significantly reduced γH2AX+ cells by 17.19 ± 3.05% (P < 0.01) in porcine MuSCs, induced EdU+ cells by 4.34 ± 1.54% (P < 0.05) in C2C12, and restored myofibre size loss and running exhaustion in vivo (all P < 0.05). Conclusions Our scRNA‐seq reveals a novel interaction between muscle FAPs and satellite cells mediated by FGF7–FGFR2. Exogenous FGF7 augments the proliferation of satellite cells and thus benefits muscle regeneration and counteracts age‐related myopathy.

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.

Understanding the liver stem cells (LSCs) holds great promise for new insights into liver diseases and liver regeneration. However, the heterogenicity and plasticity of liver cells have made it controversial. Here, by employing single‐cell RNA‐sequencing technology, transcriptome features of Krt19+ bile duct lineage cells isolated from Krt19CreERT; Rosa26R‐GFP reporter mouse livers are examined. Distinct biliary epithelial cells which include adult LSCs, as well as their downstream hepatocytes and cholangiocytes are identified. Importantly, a novel cell surface LSCs marker, CD63, as well as CD56, which distinguished active and quiescent LSCs are discovered. Cell expansion and bi‐potential differentiation in culture demonstrate the stemness ability of CD63+ cells in vitro. Transplantation and lineage tracing of CD63+ cells confirm their contribution to liver cell mass in vivo upon injury. Moreover, CD63+CD56+ cells are proved to be activated LSCs with vigorous proliferation ability. Further studies confirm that CD63+CD56− quiescent LSCs express VEGFR2 and FGFR1, and they can be activated to proliferation and differentiation through combination of growth factors: VEGF‐A and bFGF. These findings define an authentic adult liver stem cells compartment, make a further understanding of fate regulation on LSCs, and highlight its contribution to liver during pathophysiologic processes. Single‐cell RNA sequencing analysis reveals and describes a stem cell population in the biliary epithelial cells of the liver. Liver stem cells contain quiescent (CD63+CD56−) and active states (CD63+CD56+). Quiescent liver stem cells express receptors VEGFR2 and FGFR1, which can be further activated through VEGF and FGF signaling, and participate in the liver injury repair.