A curated database reveals trends in single-cell transcriptomics.

The more than 1000 single-cell transcriptomics studies that have been published to date constitute a valuable and vast resource for biological discovery. While various 'atlas' projects have collated some of the associated datasets, most questions related to specific tissue types, species or other attributes of studies require identifying papers through manual and challenging literature search. To facilitate discovery with published single-cell transcriptomics data, we have assembled a near exhaustive, manually curated database of single-cell transcriptomics studies with key information: descriptions of the type of data and technologies used, along with descriptors of the biological systems studied. Additionally, the database contains summarized information about analysis in the papers, allowing for analysis of trends in the field. As an example, we show that the number of cell types identified in scRNA-seq studies is proportional to the number of cells analysed. Database URL: www.nxn.se/single-cell-studies/gui.

Transcriptome dynamics of CD4+ T cells during malaria maps gradual transit from effector to memory.

The dynamics of CD4+ T cell memory development remain to be examined at genome scale. In malaria-endemic regions, antimalarial chemoprevention protects long after its cessation and associates with effects on CD4+ T cells. We applied single-cell RNA sequencing and computational modelling to track memory development during Plasmodium infection and treatment. In the absence of central memory precursors, two trajectories developed as T helper 1 (TH1) and follicular helper T (TFH) transcriptomes contracted and partially coalesced over three weeks. Progeny of single clones populated TH1 and TFH trajectories, and fate-mapping suggested that there was minimal lineage plasticity. Relationships between TFH and central memory were revealed, with antimalarials modulating these responses and boosting TH1 recall. Finally, single-cell epigenomics confirmed that heterogeneity among effectors was partially reset in memory. Thus, the effector-to-memory transition in CD4+ T cells is gradual during malaria and is modulated by antiparasitic drugs. Graphical user interfaces are presented for examining gene-expression dynamics and gene-gene correlations ( http://haquelab.mdhs.unimelb.edu.au/cd4_memory/ ).

Single-cell transcriptomics of alloreactive CD4+ T cells over time reveals divergent fates during gut graft-versus-host disease.

Acute gastrointestinal (GI) graft-versus-host disease (GVHD) is a primary determinant of mortality after allogeneic hematopoietic stem cell transplantation (alloSCT). The condition is mediated by alloreactive donor CD4+ T cells that differentiate into pathogenic subsets expressing IFN-γ, IL-17A, or GM-CSF and is regulated by subsets expressing IL-10 and/or Foxp3. Developmental relationships between Th cell states during priming in mesenteric lymph nodes (mLNs) and effector function in the GI tract remain undefined at genome scale. We applied scRNA-Seq and computational modeling to a mouse model of donor DC-mediated GVHD exacerbation, creating an atlas of putative CD4+ T cell differentiation pathways in vivo. Computational trajectory inference suggested emergence of pathogenic and regulatory states along a single developmental trajectory in mLNs. Importantly, we inferred an unexpected second trajectory, categorized by little proliferation or cytokine expression, reduced glycolysis, and high tcf7 expression. TCF1hi cells upregulated α4ß7 before gut migration and failed to express cytokines. These cells exhibited recall potential and plasticity following secondary transplantation, including cytokine or Foxp3 expression, but reduced T cell factor 1 (TCF1). Thus, scRNA-Seq suggested divergence of alloreactive CD4+ T cells into quiescent and effector states during gut GVHD exacerbation by donor DC, reflecting putative heterogeneous priming in vivo. These findings, which are potentially the first at a single-cell level during GVHD over time, may assist in examination of T cell differentiation in patients undergoing alloSCT.

Interpretable factor models of single-cell RNA-seq via variational autoencoders.

MOTIVATION: Single-cell RNA-seq makes possible the investigation of variability in gene expression among cells, and dependence of variation on cell type. Statistical inference methods for such analyses must be scalable, and ideally interpretable. RESULTS: We present an approach based on a modification of a recently published highly scalable variational autoencoder framework that provides interpretability without sacrificing much accuracy. We demonstrate that our approach enables identification of gene programs in massive datasets. Our strategy, namely the learning of factor models with the auto-encoding variational Bayes framework, is not domain specific and may be useful for other applications. AVAILABILITY AND IMPLEMENTATION: The factor model is available in the scVI package hosted at https://github.com/YosefLab/scVI/. CONTACT: v@nxn.se. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Protein velocity and acceleration from single-cell multiomics experiments.

The simultaneous quantification of protein and RNA makes possible the inference of past, present, and future cell states from single experimental snapshots. To enable such temporal analysis from multimodal single-cell experiments, we introduce an extension of the RNA velocity method that leverages estimates of unprocessed transcript and protein abundances to extrapolate cell states. We apply the model to six datasets and demonstrate consistency among cell landscapes and phase portraits. The analysis software is available as the protaccel Python package.

Single-cell transcriptomics identifies CD44 as a marker and regulator of endothelial to haematopoietic transition.

The endothelial to haematopoietic transition (EHT) is the process whereby haemogenic endothelium differentiates into haematopoietic stem and progenitor cells (HSPCs). The intermediary steps of this process are unclear, in particular the identity of endothelial cells that give rise to HSPCs is unknown. Using single-cell transcriptome analysis and antibody screening, we identify CD44 as a marker of EHT enabling us to isolate robustly the different stages of EHT in the aorta-gonad-mesonephros (AGM) region. This allows us to provide a detailed phenotypical and transcriptional profile of CD44-positive arterial endothelial cells from which HSPCs emerge. They are characterized with high expression of genes related to Notch signalling, TGFbeta/BMP antagonists, a downregulation of genes related to glycolysis and the TCA cycle, and a lower rate of cell cycle. Moreover, we demonstrate that by inhibiting the interaction between CD44 and its ligand hyaluronan, we can block EHT, identifying an additional regulator of HSPC development.

RNA Velocity: Molecular Kinetics from Single-Cell RNA-Seq.

Applying a kinetic model of RNA transcription and splicing, La Manno et al. (2018) predict changes in mRNA levels of individual cells from single-cell RNA-seq data.

SpatialDE: identification of spatially variable genes.

Technological advances have made it possible to measure spatially resolved gene expression at high throughput. However, methods to analyze these data are not established. Here we describe SpatialDE, a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA-sequencing data. SpatialDE also implements 'automatic expression histology', a spatial gene-clustering approach that enables expression-based tissue histology.

Exponential scaling of single-cell RNA-seq in the past decade.

Measurement of the transcriptomes of single cells has been feasible for only a few years, but it has become an extremely popular assay. While many types of analysis can be carried out and various questions can be answered by single-cell RNA-seq, a central focus is the ability to survey the diversity of cell types in a sample. Unbiased and reproducible cataloging of gene expression patterns in distinct cell types requires large numbers of cells. Technological developments and protocol improvements have fueled consistent and exponential increases in the number of cells that can be studied in single-cell RNA-seq analyses. In this Perspective, we highlight the key technological developments that have enabled this growth in the data obtained from single-cell RNA-seq experiments.

Single-cell RNA-sequencing resolves self-antigen expression during mTEC development.

The crucial capability of T cells for discrimination between self and non-self peptides is based on negative selection of developing thymocytes by medullary thymic epithelial cells (mTECs). The mTECs purge autoreactive T cells by expression of cell-type specific genes referred to as tissue-restricted antigens (TRAs). Although the autoimmune regulator (AIRE) protein is known to promote the expression of a subset of TRAs, its mechanism of action is still not fully understood. The expression of TRAs that are not under the control of AIRE also needs further characterization. Furthermore, expression patterns of TRA genes have been suggested to change over the course of mTEC development. Herein we have used single-cell RNA-sequencing to resolve patterns of TRA expression during mTEC development. Our data indicated that mTEC development consists of three distinct stages, correlating with previously described jTEC, mTEChi and mTEClo phenotypes. For each subpopulation, we have identified marker genes useful in future studies. Aire-induced TRAs were switched on during jTEC-mTEC transition and were expressed in genomic clusters, while otherwise the subsets expressed largely overlapping sets of TRAs. Moreover, population-level analysis of TRA expression frequencies suggested that such differences might not be necessary to achieve efficient thymocyte selection.

Computational approaches for interpreting scRNA-seq data.

The recent developments in high-throughput single-cell RNA sequencing technology (scRNA-seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high-dimensional data mining techniques. Here, we consider biological questions for which scRNA-seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene-level analyses are particularly informative areas of computational analysis.

Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria.

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 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.

Power analysis of single-cell RNA-sequencing experiments.

Single-cell RNA sequencing (scRNA-seq) has become an established and powerful method to investigate transcriptomic cell-to-cell variation, thereby revealing new cell types and providing insights into developmental processes and transcriptional stochasticity. A key question is how the variety of available protocols compare in terms of their ability to detect and accurately quantify gene expression. Here, we assessed the protocol sensitivity and accuracy of many published data sets, on the basis of spike-in standards and uniform data processing. For our workflow, we developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). We compared 15 protocols computationally and 4 protocols experimentally for batch-matched cell populations, in addition to investigating the effects of spike-in molecular degradation. Our analysis provides an integrated framework for comparing scRNA-seq protocols.

MERVL/Zscan4 Network Activation Results in Transient Genome-wide DNA Demethylation of mESCs.

Mouse embryonic stem cells are dynamic and heterogeneous. For example, rare cells cycle through a state characterized by decondensed chromatin and expression of transcripts, including the Zscan4 cluster and MERVL endogenous retrovirus, which are usually restricted to preimplantation embryos. Here, we further characterize the dynamics and consequences of this transient cell state. Single-cell transcriptomics identified the earliest upregulated transcripts as cells enter the MERVL/Zscan4 state. The MERVL/Zscan4 transcriptional network was also upregulated during induced pluripotent stem cell reprogramming. Genome-wide DNA methylation and chromatin analyses revealed global DNA hypomethylation accompanying increased chromatin accessibility. This transient DNA demethylation was driven by a loss of DNA methyltransferase proteins in the cells and occurred genome-wide. While methylation levels were restored once cells exit this state, genomic imprints remained hypomethylated, demonstrating a potential global and enduring influence of endogenous retroviral activation on the epigenome.