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Abstract Multiomic droplet-based technologies allow different molecular modalities, such as chromatin accessibility and gene expression (scATAC-seq and scRNA-seq), to be probed in the same nucleus. We develop EmptyDropsMultiome, an approach that distinguishes true nuclei-containing droplets from background. Using simulations, we show that EmptyDropsMultiome has higher statistical power and accuracy than existing approaches, including CellRanger-arc and EmptyDrops. On real datasets, we observe that CellRanger-arc misses more than half of the nuclei identified by EmptyDropsMultiome and, moreover, is biased against certain cell types, some of which have a retrieval rate lower than 20%.

Abstract Cis-genetic effects are key determinants of transcriptional divergence in discrete tissues and cell types. However, how cis- and trans-effects act across continuous trajectories of cellular differentiation in vivo is poorly understood. Here, we quantify allele-specific expression during spermatogenic differentiation at single-cell resolution in an F1 hybrid mouse system, allowing for the comprehensive characterisation of cis- and trans-genetic effects, including their dynamics across cellular differentiation. Collectively, almost half of the genes subject to genetic regulation show evidence for dynamic cis-effects that vary during differentiation. Our system also allows us to robustly identify dynamic trans-effects, which are less pervasive than cis-effects. In aggregate, genetic effects were strongest in round spermatids, which parallels their increased transcriptional divergence we identified between species. Our approach provides a comprehensive quantification of the variability of genetic effects in vivo, and demonstrates a widely applicable strategy to dissect the impact of regulatory variants on gene regulation in dynamic systems.

Abstract CRISPR screens with single-cell transcriptomic readouts are a valuable tool to understand the effect of genetic perturbations including single nucleotide variants (SNVs) associated with diseases. Interpretation of these data is currently limited as genotypes cannot be accurately inferred from guide RNA identity alone. scSNV-seq overcomes this limitation by coupling single-cell genotyping and transcriptomics of the same cells enabling accurate and high-throughput screening of SNVs. Analysis of variants across the JAK1 gene with scSNV-seq demonstrates the importance of determining the precise genetic perturbation and accurately classifies clinically observed missense variants into three functional categories: benign, loss of function, and separation of function.

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.

Abstract Understanding of the molecular drivers of lineage diversification and tissue patterning during primary germ layer development requires in-depth knowledge of the dynamic molecular trajectories of cell lineages across a series of developmental stages of gastrulation. Through computational modeling, we constructed at single-cell resolution, a spatio-temporal transcriptome of cell populations in the germ-layers of gastrula-stage mouse embryos. This molecular atlas enables the inference of molecular network activity underpinning the specification and differentiation of the germ-layer tissue lineages. Heterogeneity analysis of cellular composition at defined positions in the epiblast revealed progressive diversification of cell types. The single-cell transcriptome revealed an enhanced BMP signaling activity in the right-side mesoderm of late-gastrulation embryo. Perturbation of asymmetric BMP signaling activity at late gastrulation led to randomization of left-right molecular asymmetry in the lateral mesoderm of early-somite-stage embryo. These findings indicate the asymmetric BMP activity during gastrulation may be critical for the symmetry breaking process.

Traumatic spinal cord injuries (SCI) are a group of highly debilitating pathologies affecting thousands annually, and adversely affecting quality of life. Currently, no fully restorative therapies exist, and SCI still results in significant personal, societal and financial burdens. Inflammation plays a major role in the evolution of SCI, with myeloid cells, including bone marrow derived macrophages (BMDMs) and microglia (MG) being primary drivers of both early secondary pathogenesis and delayed wound healing events.The precise role of myeloid cell subsets is unclear as upon crossing the blood-spinal cord barrier, infiltrating bone marrow derived macrophages (BMDMs) may take on the morphology of resident microglia, and upregulate canonical microglia markers, thus making the two populations difficult to distinguish.Here, we used time-resolved scRNAseq and transgenic fate-mapping to chart the transcriptional profiles of tissue-resident and -infiltrating myeloid cells in a mouse model of thoracic contusion SCI.Our work identifies a novel subpopulation of foam cell-like inflammatory myeloid cells with increased expression of Fatty Acid Binding Protein 5 (Fabp5) and comprise both tissue-resident and -infiltrating cells. Fabp5+ inflammatory myeloid cells display a delayed cytotoxic profile that is predominant at the lesion epicentre and extends into the chronic phase of SCI.

Abstract Background Perturbation of DNA methyltransferases (DNMTs) and of the active DNA demethylation pathway via ten-eleven translocation (TET) methylcytosine dioxygenases results in severe developmental defects and embryonic lethality. Dynamic control of DNA methylation is therefore vital for embryogenesis, yet the underlying mechanisms remain poorly understood. Results Here we report a single-cell transcriptomic atlas from Dnmt and Tet mutant mouse embryos during early organogenesis. We show that both the maintenance and de novo methyltransferase enzymes are dispensable for the formation of all major cell types at E8.5. However, DNA methyltransferases are required for silencing of prior or alternative cell fates such as pluripotency and extraembryonic programmes. Deletion of all three TET enzymes produces substantial lineage biases, in particular, a failure to generate primitive erythrocytes. Single-cell multi-omics profiling moreover reveals that this is linked to a failure to demethylate distal regulatory elements in Tet triple-knockout embryos. Conclusions This study provides a detailed analysis of the effects of perturbing DNA methylation on mouse organogenesis at a whole organism scale and affords new insights into the regulatory mechanisms of cell fate decisions.

Abstract scRNA-seq datasets are increasingly used to identify gene panels that can be probed using alternative technologies, such as spatial transcriptomics, where choosing the best subset of genes is vital. Existing methods are limited by a reliance on pre-existing cell type labels or by difficulties in identifying markers of rare cells. We introduce an iterative approach, geneBasis, for selecting an optimal gene panel, where each newly added gene captures the maximum distance between the true manifold and the manifold constructed using the currently selected gene panel. Our approach outperforms existing strategies and can resolve cell types and subtle cell state differences.

Abstract Background Single-cell technologies are transforming biomedical research, including the recent demonstration that unspliced pre-mRNA present in single-cell RNA-Seq permits prediction of future expression states. Here we apply this RNA velocity concept to an extended timecourse dataset covering mouse gastrulation and early organogenesis. Results Intriguingly, RNA velocity correctly identifies epiblast cells as the starting point, but several trajectory predictions at later stages are inconsistent with both real-time ordering and existing knowledge. The most striking discrepancy concerns red blood cell maturation, with velocity-inferred trajectories opposing the true differentiation path. Investigating the underlying causes reveals a group of genes with a coordinated step-change in transcription, thus violating the assumptions behind current velocity analysis suites, which do not accommodate time-dependent changes in expression dynamics. Using scRNA-Seq analysis of chimeric mouse embryos lacking the major erythroid regulator Gata1, we show that genes with the step-changes in expression dynamics during erythroid differentiation fail to be upregulated in the mutant cells, thus underscoring the coordination of modulating transcription rate along a differentiation trajectory. In addition to the expected block in erythroid maturation, the Gata1-chimera dataset reveals induction of PU.1 and expansion of megakaryocyte progenitors. Finally, we show that erythropoiesis in human fetal liver is similarly characterized by a coordinated step-change in gene expression. Conclusions By identifying a limitation of the current velocity framework coupled with in vivo analysis of mutant cells, we reveal a coordinated step-change in gene expression kinetics during erythropoiesis, with likely implications for many other differentiation processes.

BRCA1 driven breast cancer arises from luminal progenitor cells but how BRCA1 loss-of-function affects the luminal progenitor cell state during premalignant stages of the disease is still unclear. Here, the authors demonstrate an aberrant differentiation of luminal progenitors towards a partial secretory luminal cell phenotype that occurs in a Brca1 deficient mouse model of breast cancer at early stages of tumour initiation and in breast cells from BRCA1 carriers.

Summary: Clotting Factor V (FV) is primarily synthesized in the liver and when cleaved by thrombin forms pro-coagulant Factor Va (FVa). Using whole blood RNAseq and scRNAseq of peripheral blood mononuclear cells, we find that FV mRNA is expressed in leukocytes, and identify neutrophils, monocytes, and T regulatory cells as sources of increased FV in hospitalized patients with COVID-19. Proteomic analysis confirms increased FV in circulating neutrophils in severe COVID-19, and immunofluorescence microscopy identifies FV in lung-infiltrating leukocytes in COVID-19 lung disease. Increased leukocyte FV expression in severe disease correlates with T-cell lymphopenia. Both plasma-derived and a cleavage resistant recombinant FV, but not thrombin cleaved FVa, suppress T-cell proliferation in vitro. Anticoagulants that reduce FV conversion to FVa, including heparin, may have the unintended consequence of suppressing the adaptive immune system.

Abstract Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.

Long noncoding RNAs (lncRNAs) regulate key steps of cell division. Here, the authors perform a comprehensive RNAi imaging screen targeting more than 2,000 human lncRNAs, and suggest a role of chromatin-associated linc00899 in regulation of cell division by suppressing the transcription of microtubule-binding protein TPPP/p25.

Abstract The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands—or even millions—of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Studying the genetic effects on early stages of human development is challenging due to a scarcity of biological material. Here, the authors utilise induced pluripotent stem cells from 125 donors to track gene expression changes and expression quantitative trait loci at single cell resolution during in vitro endoderm differentiation.

The transcriptional regulation of murine spermatogenesis is not well understood. Here, the authors use single-cell and bulk RNA-Sequencing of juvenile and adult mice to characterise somatic and germ cell development, and chromatin profile the X chromosome to show that spermatid-specific genes are repressed by H3K9me3 during meiosis.

Abstract 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.

SRPK1, a kinase involved in splicing regulation, is a potential therapeutic target for AML patients. Here, the authors show that SRPK1 inhibition changes isoform levels of key epigenetic regulators, including BRD4, and it has anti-tumor effects specifically against MLL-rearranged AML cells.

Abstract Background CTCF binding to DNA helps partition the mammalian genome into discrete structural and regulatory domains. Complete removal of CTCF from mammalian cells causes catastrophic genome dysregulation, likely due to widespread collapse of 3D chromatin looping and alterations to inter- and intra-TAD interactions within the nucleus. In contrast, Ctcf hemizygous mice with lifelong reduction of CTCF expression are viable, albeit with increased cancer incidence. Here, we exploit chronic Ctcf hemizygosity to reveal its homeostatic roles in maintaining genome function and integrity. Results We find that Ctcf hemizygous cells show modest but robust changes in almost a thousand sites of genomic CTCF occupancy; these are enriched for lower affinity binding events with weaker evolutionary conservation across the mouse lineage. Furthermore, we observe dysregulation of the expression of several hundred genes, which are concentrated in cancer-related pathways, and are caused by changes in transcriptional regulation. Chromatin structure is preserved but some loop interactions are destabilized; these are often found around differentially expressed genes and their enhancers. Importantly, the transcriptional alterations identified in vitro are recapitulated in mouse tumors and also in human cancers. Conclusions This multi-dimensional genomic and epigenomic profiling of a Ctcf hemizygous mouse model system shows that chronic depletion of CTCF dysregulates steady-state gene expression by subtly altering transcriptional regulation, changes which can also be observed in primary tumors.

Summary: Iterative liver injury results in progressive fibrosis disrupting hepatic architecture, regeneration potential, and liver function. Hepatic stellate cells (HSCs) are a major source of pathological matrix during fibrosis and are thought to be a functionally homogeneous population. Here, we use single-cell RNA sequencing to deconvolve the hepatic mesenchyme in healthy and fibrotic mouse liver, revealing spatial zonation of HSCs across the hepatic lobule. Furthermore, we show that HSCs partition into topographically diametric lobule regions, designated portal vein-associated HSCs (PaHSCs) and central vein-associated HSCs (CaHSCs). Importantly we uncover functional zonation, identifying CaHSCs as the dominant pathogenic collagen-producing cells in a mouse model of centrilobular fibrosis. Finally, we identify LPAR1 as a therapeutic target on collagen-producing CaHSCs, demonstrating that blockade of LPAR1 inhibits liver fibrosis in a rodent NASH model. Taken together, our work illustrates the power of single-cell transcriptomics to resolve the key collagen-producing cells driving liver fibrosis with high precision. : Dobie et al. use scRNA-seq to reveal spatial and functional zonation of hepatic stellate cells (HSCs) across the hepatic lobule, identifying central vein-associated HSCs as the dominant pathogenic collagen-producing cells during centrilobular injury-induced fibrosis. This illustrates the power of scRNA-seq to resolve the key collagen-producing cells driving liver fibrosis. Keywords: liver fibrosis, mesenchyme, hepatic stellate cells, single-cell RNA sequencing, zonation

DNA barcode swapping results in mislabelling of sequencing reads between multiplexed samples. Here, the authors investigate the severity and consequences of barcode swapping for single-cell RNA-seq data, and develop a computational method to exclude swapped reads.

Abstract Background Population phenotypic variation can arise from genetic differences between individuals, or from cellular heterogeneity in an isogenic group of cells or organisms. The emergence of gene expression differences between genetically identical cells is referred to as gene expression noise, the sources of which are not well understood. Results In this work, by studying gene expression noise between multiple cell lineages and mammalian species, we find consistent evidence of a role for CpG islands as sources of gene expression noise. Variation in noise among CpG island promoters can be partially attributed to differences in island size, in which short islands have noisier gene expression. Building on these findings, we investigate the potential for short CpG islands to act as fast response elements to environmental stimuli. Specifically, we find that these islands are enriched amongst primary response genes in SWI/SNF-independent stimuli, suggesting that expression noise is an indicator of promoter responsiveness. Conclusions Thus, through the integration of single-cell RNA expression profiling, chromatin landscape and temporal gene expression dynamics, we have uncovered a role for short CpG island promoters as fast response elements.

Relationships between DNA methylation and transcription, and methylation and DNA accessibility can be probed but interrogating all three in the same single cells has not been possible. Here, the authors report the first single-cell method for parallel chromatin accessibility, DNA methylation and transcriptome profiling.

There is a need to understand how mammary epithelial cells respond to changes at various developmental stages. Here, the authors use single-cell RNA sequencing of mammary epithelial cells at different adult developmental stages, identifying different cell types and charting their developmental trajectory.

Summary: Loss of cone photoreceptors, crucial for daylight vision, has the greatest impact on sight in retinal degeneration. Transplantation of stem cell-derived L/M-opsin cones, which form 90% of the human cone population, could provide a feasible therapy to restore vision. However, transcriptomic similarities between fetal and stem cell-derived cones remain to be defined, in addition to development of cone cell purification strategies. Here, we report an analysis of the human L/M-opsin cone photoreceptor transcriptome using an AAV2/9.pR2.1:GFP reporter. This led to the identification of a cone-enriched gene signature, which we used to demonstrate similar gene expression between fetal and stem cell-derived cones. We then defined a cluster of differentiation marker combination that, when used for cell sorting, significantly enriches for cone photoreceptors from the fetal retina and stem cell-derived retinal organoids, respectively. These data may facilitate more efficient isolation of human stem cell-derived cones for use in clinical transplantation studies. : Welby et al. define a cone-enriched gene signature within a human fetal L/M-opsin cone population, which is used as a baseline reference to demonstrate similar cone gene expression between bona fide and stem cell-derived L/M-opsin cone cells. Furthermore, profiling of cell surface molecules in human fetal cones led to the generation of a cluster of differentiation marker panel, which provides enrichment of fetal and stem cell-derived cones. Keywords: retinal dystrophies, cone photoreceptor cells, transcriptome, human pluripotent stem cells, retinal organoids, cell surface markers, cell transplantation therapy

Abstract Single-cell RNA-sequencing (scRNA-seq) allows studying heterogeneity in gene expression in large cell populations. Such heterogeneity can arise due to technical or biological factors, making decomposing sources of variation difficult. We here describe f-scLVM (factorial single-cell latent variable model), a method based on factor analysis that uses pathway annotations to guide the inference of interpretable factors underpinning the heterogeneity. Our model jointly estimates the relevance of individual factors, refines gene set annotations, and infers factors without annotation. In applications to multiple scRNA-seq datasets, we find that f-scLVM robustly decomposes scRNA-seq datasets into interpretable components, thereby facilitating the identification of novel subpopulations.

Abstract Background Aneuploidies are copy number variants that affect entire chromosomes. They are seen commonly in cancer, embryonic stem cells, human embryos, and in various trisomic diseases. Aneuploidies frequently affect only a subset of cells in a sample; this is known as “mosaic” aneuploidy. A cell that harbours an aneuploidy exhibits disrupted gene expression patterns which can alter its behaviour. However, detection of aneuploidies using conventional single-cell DNA-sequencing protocols is slow and expensive. Methods We have developed a method that uses chromosome-wide expression imbalances to identify aneuploidies from single-cell RNA-seq data. The method provides quantitative aneuploidy calls, and is integrated into an R software package available on GitHub and as an Additional file of this manuscript. Results We validate our approach using data with known copy number, identifying the vast majority of aneuploidies with a low rate of false discovery. We show further support for the method’s efficacy by exploiting allele-specific gene expression levels, and differential expression analyses. Conclusions The method is quick and easy to apply, straightforward to interpret, and represents a substantial cost saving compared to single-cell genome sequencing techniques. However, the method is less well suited to data where gene expression is highly variable. The results obtained from the method can be used to investigate the consequences of aneuploidy itself, or to exclude aneuploidy-affected expression values from conventional scRNA-seq data analysis.

“Variation in the noncoding regulatory sequences in the genome plays important roles in human disease and evolution. Here, the authors use F1 mouse hybrids to shed light on the regulatory mechanisms mediating transcription factor binding, chromatin state and gene expression in mammalian cells.”

Summary: Alternative splicing is a critical determinant of genome complexity and, by implication, is assumed to engender proteomic diversity. This notion has not been experimentally tested in a targeted, quantitative manner. Here, we have developed an integrative approach to ask whether perturbations in mRNA splicing patterns alter the composition of the proteome. We integrate RNA sequencing (RNA-seq) (to comprehensively report intron retention, differential transcript usage, and gene expression) with a data-independent acquisition (DIA) method, SWATH-MS (sequential window acquisition of all theoretical spectra-mass spectrometry), to capture an unbiased, quantitative snapshot of the impact of constitutive and alternative splicing events on the proteome. Whereas intron retention is accompanied by decreased protein abundance, alterations in differential transcript usage and gene expression alter protein abundance proportionate to transcript levels. Our findings illustrate how RNA splicing links isoform expression in the human transcriptome with proteomic diversity and provides a foundation for studying perturbations associated with human diseases. : Liu et al. have developed an integrative approach to ask whether perturbations in mRNA splicing patterns alter the composition of the proteome. Their findings illustrate how RNA splicing links isoform expression in the human transcriptome with proteomic diversity and provides a foundation for studying perturbations associated with human diseases. Keywords: alternative splicing, proteomics, RNA

Summary: The mouse inner cell mass (ICM) segregates into the epiblast and primitive endoderm (PrE) lineages coincident with implantation of the embryo. The epiblast subsequently undergoes considerable expansion of cell numbers prior to gastrulation. To investigate underlying regulatory principles, we performed systematic single-cell RNA sequencing (seq) of conceptuses from E3.5 to E6.5. The epiblast shows reactivation and subsequent inactivation of the X chromosome, with Zfp57 expression associated with reactivation and inactivation together with other candidate regulators. At E6.5, the transition from epiblast to primitive streak is linked with decreased expression of polycomb subunits, suggesting a key regulatory role. Notably, our analyses suggest elevated transcriptional noise at E3.5 and within the non-committed epiblast at E6.5, coinciding with exit from pluripotency. By contrast, E6.5 primitive streak cells became highly synchronized and exhibit a shortened G1 cell-cycle phase, consistent with accelerated proliferation. Our study systematically charts transcriptional noise and uncovers molecular processes associated with early lineage decisions. : Mohammed et al. chart mouse embryonic development from implantation to early gastrulation at single-cell resolution. They describe regulatory processes associated with lineage commitment. An increased level of transcriptional noise is observed prior to lineage commitment, an observation that provides fresh insights into cell fate decision-making processes. Keywords: gastrulation, embryo, single-cell RNA-seq, epiblast, primitive endoderm, primitive streak, X-chromosome, transcriptional noise

Polycomb repressive complexes modify histones but it is unclear how changes in chromatin states alter kinetics of transcription. Here, the authors use single-cell RNAseq and ChIPseq to find that actively transcribed genes with Polycomb marks have greater cell-to-cell variation in expression.

Expression of the Autoimmune regulator (AIRE) outside of the thymus has long been suggested in both humans and mice, but the cellular source in humans has remained undefined. Here we identify AIRE expression in human tonsils and extensively analyzed these “extra-thymic AIRE expressing cells” (eTACs) using combinations of flow cytometry, CyTOF and single cell RNA-sequencing. We identified AIRE+ cells as dendritic cells (DCs) with a mature and migratory phenotype including high levels of antigen presenting molecules and costimulatory molecules, and specific expression of CD127, CCR7, and PDL1. These cells also possessed the ability to stimulate and re-stimulate T cells and displayed reduced responses to toll-like receptor (TLR) agonists compared to conventional DCs. While expression of AIRE was enriched within CCR7+CD127+ DCs, single-cell RNA sequencing revealed expression of AIRE to be transient, rather than stable, and associated with the differentiation to a mature phenotype. The role of AIRE in central tolerance induction within the thymus is well-established, however our study shows that AIRE expression within the periphery is not associated with an enriched expression of tissue-restricted antigens (TRAs). This unexpected finding, suggestive of wider functions of AIRE, may provide an explanation for the non-autoimmune symptoms of APECED patients who lack functional AIRE.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Fertilization triggers global erasure of paternal 5-methylcytosine as part of epigenetic reprogramming during the transition from gametic specialization to totipotency. This involves oxidation by TET3, but our understanding of its targets and the wider context of demethylation is limited to a small fraction of the genome. We employed an optimized bisulfite strategy to generate genome-wide methylation profiles of control and TET3-deficient zygotes, using SNPs to access paternal alleles. This revealed that in addition to pervasive removal from intergenic sequences and most retrotransposons, gene bodies constitute a major target of zygotic demethylation. Methylation loss is associated with zygotic genome activation and at gene bodies is also linked to increased transcriptional noise in early development. Our data map the primary contribution of oxidative demethylation to a subset of gene bodies and intergenic sequences and implicate redundant pathways at many loci. Unexpectedly, we demonstrate that TET3 activity also protects certain CpG islands against methylation buildup.

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

Multicellular eukaryotes have evolved a range of mechanisms for immune recognition. A widespread family involved in innate immunity are the NACHT-domain and leucine-rich-repeat-containing (NLR) proteins. Mammals have small numbers of NLR proteins, whereas in some species, mostly those without adaptive immune systems, NLRs have expanded into very large families. We describe a family of nearly 400 NLR proteins encoded in the zebrafish genome. The proteins share a defining overall structure, which arose in fishes after a fusion of the core NLR domains with a B30.2 domain, but can be subdivided into four groups based on their NACHT domains. Gene conversion acting differentially on the NACHT and B30.2 domains has shaped the family and created the groups. Evidence of positive selection in the B30.2 domain indicates that this domain rather than the leucine-rich repeats acts as the pathogen recognition module. In an unusual chromosomal organization, the majority of the genes are located on one chromosome arm, interspersed with other large multigene families, including a new family encoding zinc-finger proteins. The NLR-B30.2 proteins represent a new family with diversity in the specific recognition module that is present in fishes in spite of the parallel existence of an adaptive immune system.

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.

Nature Communications 6: Article number: 8687 (2015); Published: 22 October 2015; Updated: 11 January 2016. The original version of this Article contained an error in the spelling of the author Tomislav Ilicic, which was incorrectly given as Tomislav Illicic. This has now been corrected in both the PDF and HTML versions of the Article.

Cell-to-cell gene expression variability is an inherent feature of complex biological systems, such as immunity and development. Single-cell RNA sequencing is a powerful tool to quantify this heterogeneity, but it is prone to strong technical noise. In this article, we describe a step-by-step computational workflow that uses the BASiCS Bioconductor package to robustly quantify expression variability within and between known groups of cells (such as experimental conditions or cell types). BASiCS uses an integrated framework for data normalisation, technical noise quantification and downstream analyses, propagating statistical uncertainty across these steps. Within a single seemingly homogeneous cell population, BASiCS can identify highly variable genes that exhibit strong heterogeneity as well as lowly variable genes with stable expression. BASiCS also uses a probabilistic decision rule to identify changes in expression variability between cell populations, whilst avoiding confounding effects related to differences in technical noise or in overall abundance. Using a publicly available dataset, we guide users through a complete pipeline that includes preliminary steps for quality control, as well as data exploration using the scater and scran Bioconductor packages. The workflow is accompanied by a Docker image that ensures the reproducibility of our results.