RNA sequencing (RNA-seq) is a genomic approach for the detection and quantitative analysis of messenger RNA molecules in a biological sample and is useful for studying cellular responses. RNA-seq has fueled much discovery and innovation in medicine over recent years. For practical reasons, the technique is usually conducted on samples comprising thousands to millions of cells. However, this has hindered direct assessment of the fundamental unit of biology—the cell. Since the first single-cell RNA-sequencing (scRNA-seq) study was published in 2009, many more have been conducted, mostly by specialist laboratories with unique skills in wet-lab single-cell genomics, bioinformatics, and computation. However, with the increasing commercial availability of scRNA-seq platforms, and the rapid ongoing maturation of bioinformatics approaches, a point has been reached where any biomedical researcher or clinician can use scRNA-seq to make exciting discoveries. In this review, we present a practical guide to help researchers design their first scRNA-seq studies, including introductory information on experimental hardware, protocol choice, quality control, data analysis and biological interpretation.

Rationale: Atherosclerotic lesions are known for their cellular heterogeneity, yet the molecular complexity within the cells of human plaques have not been fully assessed. Objective: Using single-cell transcriptomics and chromatin accessibility we gained a better understanding of the pathophysiology underlying human atherosclerosis. Methods and Results: We performed single-cell RNA and single-cell ATAC sequencing on human carotid atherosclerotic plaques to define the cells at play and determine their transcriptomic and epigenomic characteristics. We identified 14 distinct cell populations including endothelial cells, smooth muscle cells, mast cells, B cells, myeloid cells, and T cells and identified multiple cellular activation states and suggested cellular interconversions. Within the endothelial cell population we defined subsets with angiogenic capacity plus clear signs of endothelial to mesenchymal transition. CD4 + and CD8 + T cells showed activation-based subclasses, each with a gradual decline from a cytotoxic to a more quiescent phenotype. Myeloid cells included two populations of pro-inflammatory macrophages showing IL1B or TNF expression as well as a foam cell-like population expressing TREM2 and displaying a fibrosis-promoting phenotype. ATACseq data identified specific transcription factors associated with the myeloid subpopulation and T cell cytokine profiles underlying mutual activation between both cell types. Finally, cardiovascular disease susceptibility genes identified using public GWAS data were particularly enriched in lesional macrophages, endothelial and smooth muscle cells. Conclusions: This study provides a transcriptome-based cellular landscape of human atherosclerotic plaques and highlights cellular plasticity and intercellular communication at the site of disease. This detailed definition of cell communities at play in atherosclerosis will facilitate cell-based mapping of novel interventional targets with direct functional relevance for the treatment of human disease.

The enormous sequence diversity within T cell receptor (TCR) repertoires allows specific TCR sequences to be used as lineage markers for T cells that derive from a common progenitor. We have developed a computational method, called TraCeR, to reconstruct full-length, paired TCR sequences from T lymphocyte single-cell RNA-seq by combining existing assembly and alignment programs with “combinatorial recombinome” sequences comprising all possible TCR combinations. We validate this method to quantify its accuracy and sensitivity. Inferred TCR sequences reveal clonal relationships between T cells whilst the cells’ complete transcriptional landscapes can be quantified from the remaining RNA-seq data. This provides a powerful tool to link T cell specificity with functional response and we demonstrate this by determining the distribution of members of expanded T cell clonotypes in a mouse Salmonella infection model. Members of the same clonotype span early activated CD4+ T cells, as well as mature effector and memory cells.

Differentiation of naïve CD4 + T cells into functionally distinct T helper subsets is crucial for the orchestration of immune responses. Due to extensive heterogeneity and multiple overlapping transcriptional programs in differentiating T cell populations, this process has remained a * Correspondence to: st9@sanger.ac.uk, Ashraful.Haque@qimrberghofer.edu.au or stegle@ebi.ac.uk. # denotes equal contribution † denotes equal contribution Author contributions TL and KRJ performed the single-cell RNA-seq experiments. VS developed the GPfates model in collaboration with MZ, NDL, OS and SAT. DFR and WRH generated the PbTII mouse model. KRJ, RM, IS, MSFS, LGF, ASN, UL, FSFG, PTB and CRE performed the mouse experiments. TL, VS, KRJ, LHL and FOB analysed the data and interpreted the results MJTS performed the TCR clonality analysis. TL, KRJ, RM, OB, AH and SAT designed the experiments. OS, AH and SAT cosupervised the study. TL, VS, KRJ, OS, AH and SAT wrote the manuscript. All authors have read and approved the manuscript. Competing interestsThe authors declare no competing interests. Data and materials availabilityThe data presented in this paper is publically available in the ArrayExpress database with accession number E-MTAB-4388. Europe PMC Funders Group Europe PMC Funders Author ManuscriptsEurope PMC Funders Author Manuscripts challenge for systematic dissection in vivo. By using single-cell transcriptomics and computational analysis using a temporal mixtures of Gaussian processes model, termed GPfates, we reconstructed the developmental trajectories of Th1 and Tfh cells during blood-stage Plasmodium infection in mice. By tracking clonality using endogenous TCR sequences, we first demonstrated that Th1/Tfh bifurcation had occurred at both population and single-clone levels. Next, we identified genes whose expression was associated with Th1 or Tfh fates, and demonstrated a T-cell intrinsic role for Galectin-1 in supporting a Th1 differentiation. We also revealed the close molecular relationship between Th1 and IL-10-producing Tr1 cells in this infection. Th1 and Tfh fates emerged from a highly proliferative precursor that upregulated aerobic glycolysis and accelerated cell cycling as cytokine expression began. Dynamic gene expression of chemokine receptors around bifurcation predicted roles for cell-cell in driving Th1/Tfh fates. In particular, we found that precursor Th cells were coached towards a Th1 but not a Tfh fate by inflammatory monocytes. Thus, by integrating genomic and computational approaches, our study has provided two unique resources, a database www.PlasmoTH.org, which facilitates discovery of novel factors controlling Th1/Tfh fate commitment, and more generally, GPfates, a modelling framework for characterizing cell differentiation towards multiple fates.

Highlights d Single-cell RNA sequencing of human lymph nodes unveils six types of LECs d LECs lining the floor and ceiling of the SCS, MS, and valve are the main types d LECs of the SCS floor and MS highly express neutrophil chemoattractants d Human MS LECs support neutrophil adhesion in the LN medulla via CD209

SUMMARY Naïve CD4+ T cells can differentiate into specific helper and regulatory T cell lineages in order to combat infection and disease. The correct response to cytokines and a controlled balance of these populations is critical for the immune system and the avoidance of autoimmune disorders. To investigate how early cell fate commitment is regulated, we generated the first human genome-wide maps of histone modifications that reveal enhancer elements after 72 hrs of in vitro polarization toward T helper-1 (Th1) and T helper-2 (Th2) cell lineages. Our analysis indicated that even at this very early time point, cell-specific gene regulation and enhancers were at work directing lineage commitment. Further examination of lineage-specific enhancers identified transcription factors (TFs) with known and unknown T cell roles as putative drivers of lineage-specific gene expression. Lastly, an integrative analysis of immunopathogenic associated single nucleotide polymorphisms (SNPs) suggests a role for distal regulatory elements in disease etiology.

Rationale: Genome-wide association studies (GWAS) have identified hundreds of loci associated with coronary artery disease (CAD). Many of these loci are enriched in cis-regulatory elements (CREs) but not linked to cardiometabolic risk factors nor to candidate causal genes, complicating their functional interpretation. Objective: Single nucleus chromatin accessibility profiling of the human atherosclerotic lesions was used to investigate cell type-specific patterns of CREs, to understand transcription factors establishing cell identity and to interpret CAD-relevant, non-coding genetic variation. Methods and Results: We used single nucleus ATAC-seq to generate DNA accessibility maps in > 7,000 cells derived from human atherosclerotic lesions. We identified five major lesional cell types including endothelial cells, smooth muscle cells, monocyte/macrophages, NK/T-cells and B-cells and further investigated subtype characteristics of macrophages and smooth muscle cells transitioning into fibromyocytes. We demonstrated that CAD associated genetic variants are particularly enriched in endothelial and smooth muscle cell-specific open chromatin. Using single cell co-accessibility and cis-eQTL information, we prioritized putative target genes and candidate regulatory elements for ~30% of all known CAD loci. Finally, we performed genome-wide experimental fine-mapping of the CAD GWAS variants using epigenetic QTL analysis in primary human aortic endothelial cells and STARR-Seq massively parallel reporter assay in smooth muscle cells. This analysis identified potential causal SNP(s) and the associated target gene for over 30 CAD loci. We present several examples where the chromatin accessibility and gene expression could be assigned to one cell type predicting the cell type of action for CAD loci. Conclusions: These findings highlight the potential of applying snATAC-seq to human tissues in revealing relative contributions of distinct cell types to diseases and in identifying genes likely to be influenced by non-coding GWAS variants.

The appearance of type 1 diabetes (T1D)-associated autoantibodies is the first and only measurable parameter to predict progression toward T1D in genetically susceptible individuals. However, autoantibodies indicate an active autoimmune reaction, wherein the immune tolerance is already broken. Therefore, there is a clear and urgent need for new biomarkers that predict the onset of the autoimmune reaction preceding autoantibody positivity or reflect progressive β-cell destruction. Here we report the mRNA sequencing–based analysis of 306 samples including fractionated samples of CD4+ and CD8+ T cells as well as CD4−CD8− cell fractions and unfractionated peripheral blood mononuclear cell samples longitudinally collected from seven children who developed β-cell autoimmunity (case subjects) at a young age and matched control subjects. We identified transcripts, including interleukin 32 (IL32), that were upregulated before T1D-associated autoantibodies appeared. Single-cell RNA sequencing studies revealed that high IL32 in case samples was contributed mainly by activated T cells and NK cells. Further, we showed that IL32 expression can be induced by a virus and cytokines in pancreatic islets and β-cells, respectively. The results provide a basis for early detection of aberrations in the immune system function before T1D and suggest a potential role for IL32 in the pathogenesis of T1D.