Single-cell roadmap of human gonadal development.

Gonadal development is a complex process that involves sex determination followed by divergent maturation into either testes or ovaries1. Historically, limited tissue accessibility, a lack of reliable in vitro models and critical differences between humans and mice have hampered our knowledge of human gonadogenesis, despite its importance in gonadal conditions and infertility. Here, we generated a comprehensive map of first- and second-trimester human gonads using a combination of single-cell and spatial transcriptomics, chromatin accessibility assays and fluorescent microscopy. We extracted human-specific regulatory programmes that control the development of germline and somatic cell lineages by profiling equivalent developmental stages in mice. In both species, we define the somatic cell states present at the time of sex specification, including the bipotent early supporting population that, in males, upregulates the testis-determining factor SRY and sPAX8s, a gonadal lineage located at the gonadal-mesonephric interface. In females, we resolve the cellular and molecular events that give rise to the first and second waves of granulosa cells that compartmentalize the developing ovary to modulate germ cell differentiation. In males, we identify human SIGLEC15+ and TREM2+ fetal testicular macrophages, which signal to somatic cells outside and inside the developing testis cords, respectively. This study provides a comprehensive spatiotemporal map of human and mouse gonadal differentiation, which can guide in vitro gonadogenesis.

Single-cell Atlas of common variable immunodeficiency shows germinal center-associated epigenetic dysregulation in B-cell responses.

Common variable immunodeficiency (CVID), the most prevalent symptomatic primary immunodeficiency, displays impaired terminal B-cell differentiation and defective antibody responses. Incomplete genetic penetrance and ample phenotypic expressivity in CVID suggest the participation of additional pathogenic mechanisms. Monozygotic (MZ) twins discordant for CVID are uniquely valuable for studying the contribution of epigenetics to the disease. Here, we generate a single-cell epigenomics and transcriptomics census of naïve-to-memory B cell differentiation in a CVID-discordant MZ twin pair. Our analysis identifies DNA methylation, chromatin accessibility and transcriptional defects in memory B-cells mirroring defective cell-cell communication upon activation. These findings are validated in a cohort of CVID patients and healthy donors. Our findings provide a comprehensive multi-omics map of alterations in naïve-to-memory B-cell transition in CVID and indicate links between the epigenome and immune cell cross-talk. Our resource, publicly available at the Human Cell Atlas, gives insight into future diagnosis and treatments of CVID patients.

Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro.

The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated dense single-cell and spatial reference maps of the human uterus and three-dimensional endometrial organoid cultures. We dissect the signaling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids reveals the pathways and cell states regulating differentiation of the secretory and ciliated lineages both in vivo and in vitro. In vitro downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. We utilize our cellular maps to deconvolute bulk data from endometrial cancers and endometriotic lesions, illuminating the cell types dominating in each of these disorders. These mechanistic insights provide a platform for future development of treatments for common conditions including endometriosis and endometrial carcinoma.

Fast searches of large collections of single-cell data using scfind.

Single-cell technologies have made it possible to profile millions of cells, but for these resources to be useful they must be easy to query and access. To facilitate interactive and intuitive access to single-cell data we have developed scfind, a single-cell analysis tool that facilitates fast search of biologically or clinically relevant marker genes in cell atlases. Using transcriptome data from six mouse cell atlases, we show how scfind can be used to evaluate marker genes, perform in silico gating, and identify both cell-type-specific and housekeeping genes. Moreover, we have developed a subquery optimization routine to ensure that long and complex queries return meaningful results. To make scfind more user friendly, we use indices of PubMed abstracts and techniques from natural language processing to allow for arbitrary queries. Finally, we show how scfind can be used for multi-omics analyses by combining single-cell ATAC-seq data with transcriptome data.

Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench.

As the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects. Although there are several computational methods available that can remove batch effects, evaluating which method performs best is not straightforward. Here, we present BatchBench (https://github.com/cellgeni/batchbench), a modular and flexible pipeline for comparing batch correction methods for single-cell RNA-seq data. We apply BatchBench to eight methods, highlighting their methodological differences and assess their performance and computational requirements through a compendium of well-studied datasets. This systematic comparison guides users in the choice of batch correction tool, and the pipeline makes it easy to evaluate other datasets.

Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. However, the analysis of the large volumes of data generated from these experiments requires specialized statistical and computational methods. Here we present an overview of the computational workflow involved in processing scRNA-seq data. We discuss some of the most common tasks and the tools available for addressing central biological questions. In this article and our companion website ( https://scrnaseq-course.cog.sanger.ac.uk/website/index.html ), we provide guidelines regarding best practices for performing computational analyses. This tutorial provides a hands-on guide for experimentalists interested in analyzing their data as well as an overview for bioinformaticians seeking to develop new computational methods.

Comparison of visualization tools for single-cell RNAseq data.

In the last decade, single cell RNAseq (scRNAseq) datasets have grown in size from a single cell to millions of cells. Due to its high dimensionality, it is not always feasible to visualize scRNAseq data and share it in a scientific report or an article publication format. Recently, many interactive analysis and visualization tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review several of the currently available scRNAseq visualization tools and benchmark the subset that allows to visualize the data on the web and share it with others. We consider the memory and time required to prepare datasets for sharing as the number of cells increases, and additionally review the user experience and features available in the web interface. To address the problem of format compatibility we have also developed a user-friendly R package, sceasy, which allows users to convert their own scRNAseq datasets into a specific data format for visualization.

Challenges in unsupervised clustering of single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.

Publisher Correction: Challenges in unsupervised clustering of single-cell RNA-seq data.

During typesetting of this article, errors were inadvertently introduced to the hyperlinked URLs of some of the clustering tools in table 1 (Seurat, CIDR, pcaReduce and mpath), as well as to the numbering of the bold-text annotations in the reference list. The article has now been corrected online. The editors apologize for this error.

scmap: projection of single-cell RNA-seq data across data sets.

Single-cell RNA-seq (scRNA-seq) allows researchers to define cell types on the basis of unsupervised clustering of the transcriptome. However, differences in experimental methods and computational analyses make it challenging to compare data across experiments. Here we present scmap (http://bioconductor.org/packages/scmap; web version at http://www.sanger.ac.uk/science/tools/scmap), a method for projecting cells from an scRNA-seq data set onto cell types or individual cells from other experiments.

Tia1 dependent regulation of mRNA subcellular location and translation controls p53 expression in B cells.

Post-transcriptional regulation of cellular mRNA is essential for protein synthesis. Here we describe the importance of mRNA translational repression and mRNA subcellular location for protein expression during B lymphocyte activation and the DNA damage response. Cytoplasmic RNA granules are formed upon cell activation with mitogens, including stress granules that contain the RNA binding protein Tia1. Tia1 binds to a subset of transcripts involved in cell stress, including p53 mRNA, and controls translational silencing and RNA granule localization. DNA damage promotes mRNA relocation and translation in part due to dissociation of Tia1 from its mRNA targets. Upon DNA damage, p53 mRNA is released from stress granules and associates with polyribosomes to increase protein synthesis in a CAP-independent manner. Global analysis of cellular mRNA abundance and translation indicates that this is an extended ATM-dependent mechanism to increase protein expression of key modulators of the DNA damage response.Sequestering mRNA in cytoplasmic stress granules is a mechanism for translational repression. Here the authors find that p53 mRNA, present in stress granules in activated B lymphocytes, is released upon DNA damage and is translated in a CAP-independent manner.

SC3: consensus clustering of single-cell RNA-seq data.

Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple clustering solutions through a consensus approach (http://bioconductor.org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.

Perturbations of PIP3 signalling trigger a global remodelling of mRNA landscape and reveal a transcriptional feedback loop.

PIP3 is synthesized by the Class I PI3Ks and regulates complex cell responses, such as growth and migration. Signals that drive long-term reshaping of cell phenotypes are difficult to resolve because of complex feedback networks that operate over extended times. PIP3-dependent modulation of mRNA accumulation is clearly important in this process but is poorly understood. We have quantified the genome-wide mRNA-landscape of non-transformed, breast epithelium-derived MCF10a cells and its response to acute regulation by EGF, in the presence or absence of a PI3Kα inhibitor, compare it to chronic activation of PI3K signalling by cancer-relevant mutations (isogenic cells expressing an oncomutant PI3Kα allele or lacking the PIP3-phosphatase/tumour-suppressor, PTEN). Our results show that whilst many mRNAs are changed by long-term genetic perturbation of PIP3 signalling ('butterfly effect'), a much smaller number do so in a coherent fashion with the different PIP3 perturbations. This suggests a subset of more directly regulated mRNAs. We show that mRNAs respond differently to given aspects of PIP3 regulation. Some PIP3-sensitive mRNAs encode PI3K pathway components, thus suggesting a transcriptional feedback loop. We identify the transcription factor binding motifs SRF and PRDM1 as important regulators of PIP3-sensitive mRNAs involved in cell movement.

Lateral dynamics of charged lipids and peripheral proteins in spatially heterogeneous membranes: comparison of continuous and Monte Carlo approaches.

Biological membranes are complex environments whose physico-chemical properties are of utmost importance for the understanding of many crucial biological processes. Much attention has been given in the literature to the description of membranes along the z-axis perpendicular to the membrane. Here, we instead consider the lateral dynamics of lipids and peripheral proteins due to their electrostatic interaction. Previously, we constructed a Monte Carlo automaton capable of simulating mutual diffusive dynamics of charged lipids and associated positively charged peptides. Here, we derive and numerically analyze a system of Poisson-Boltzmann-Nernst-Planck (PBNP) equations that provide a mean-field approximation compatible with our Monte Carlo model. The thorough comparison between the mean-field PBNP equations and Monte Carlo simulations demonstrates that both the approaches are in a good qualitative agreement in all tested scenarios. We find that the two methods quantitatively deviate when the local charge density is high, presumably because the Poisson-Boltzmann formalism is applicable in the so-called weak coupling limit, whose validity is restricted to low charge densities. Nevertheless, we conclude that the mean-field PBNP approach provides a good approximation for the considerably more detailed Monte Carlo model at only a fraction of the associated computational cost and allows simulation of the membrane lateral dynamics on the space and time scales relevant for the realistic biological problems.

Lateral dynamics of proteins with polybasic domain on anionic membranes: a dynamic Monte-Carlo study.

Positively charged polybasic domains are essential for recruiting multiple signaling proteins, such as Ras GTPases and Src kinase, to the negatively charged cellular membranes. Much less, however, is known about the influence of electrostatic interactions on the lateral dynamics of these proteins. We developed a dynamic Monte-Carlo automaton that faithfully simulates lateral diffusion of the adsorbed positively charged oligopeptides as well as the dynamics of mono- (phosphatidylserine) and polyvalent (PIP(2)) anionic lipids within the bilayer. In agreement with earlier results, our simulations reveal lipid demixing that leads to the formation of a lipid shell associated with the peptide. The computed association times and average numbers of bound lipids demonstrate that tetravalent PIP(2) interacts with the peptide much more strongly than monovalent lipid. On the spatially homogeneous membrane, the lipid shell affects the behavior of the peptide only by weakly reducing its lateral mobility. However, spatially heterogeneous distributions of monovalent lipids are found to produce peptide drift, the velocity of which is determined by the total charge of the peptide-lipid complex. We hypothesize that this predicted phenomenon may affect the spatial distribution of proteins with polybasic domains in the context of cell-signaling events that alter the local density of monovalent anionic lipids.