Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.

muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

Single-cell RNA sequencing (scRNA-seq) has become an empowering technology to profile the transcriptomes of individual cells on a large scale. Early analyses of differential expression have aimed at identifying differences between subpopulations to identify subpopulation markers. More generally, such methods compare expression levels across sets of cells, thus leading to cross-condition analyses. Given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state analysis; however, it is not clear which statistical framework best handles this situation. Here, we surveyed methods to perform cross-condition differential state analyses, including cell-level mixed models and methods based on aggregated pseudobulk data. To evaluate method performance, we developed a flexible simulation that mimics multi-sample scRNA-seq data. We analyzed scRNA-seq data from mouse cortex cells to uncover subpopulation-specific responses to lipopolysaccharide treatment, and provide robust tools for multi-condition analysis within the muscat R package.

Single-cell transcriptomic profiling provides insights into retinal endothelial barrier properties

Purpose

To better characterize retinal endothelial barrier properties through analysis of individual transcriptomes of primary bovine retinal microvascular endothelial cells (RMECs).

Methods

Individual RMECs were captured on the Fluidigm C1 system, cDNA libraries were prepared using a Nextera XT kit, and sequencing was performed on a NextSeq system (Illumina). Data analysis was performed using R packages Scater, SC3, and Seurat, and the browser application Automated Single-cell Analysis Pipeline (ASAP). Alternative splicing events in single cells were quantified with Outrigger. Cytoscape was used for network analyses.

Results

Application of a single-cell RNA sequencing (scRNA-seq) analysis workflow showed that RMECs form a relatively homogeneous population in culture, with the main differences related to proliferation status. Expression of markers from along the arteriovenous tree suggested that most cells originated from capillaries. Average gene expression levels across all cells were used to develop an in silico model of the inner blood-retina barrier incorporating junctional proteins not previously reported within the retinal vasculature. Correlation of barrier gene expression among individual cells revealed a subgroup of genes highly correlated with PECAM-1 at the center of the correlation network. Numerous alternative splicing events involving exons within microvascular barrier genes were observed, and in many cases, individual cells expressed one isoform exclusively.

Conclusions

We optimized a workflow for single-cell transcriptomics in primary RMECs. The results provide fundamental insights into the genes involved in formation of the retinal-microvascular barrier.

Common diseases alter the physiological age-related blood microRNA profile

Aging is a key risk factor for chronic diseases of the elderly. MicroRNAs regulate post-transcriptional gene silencing through base-pair binding on their target mRNAs. We identified nonlinear changes in age-related microRNAs by analyzing whole blood from 1334 healthy individuals. We observed a larger influence of the age as compared to the sex and provide evidence for a shift to the 5' mature form of miRNAs in healthy aging. The addition of 3059 diseased patients uncovered pan-disease and disease-specific alterations in aging profiles. Disease biomarker sets for all diseases were different between young and old patients. Computational deconvolution of whole-blood miRNAs into blood cell types suggests that cell intrinsic gene expression changes may impart greater significance than cell abundance changes to the whole blood miRNA profile. Altogether, these data provide a foundation for understanding the relationship between healthy aging and disease, and for the development of age-specific disease biomarkers.

Looking at the developing lung in single-cell resolution

Lung development is a complicated and delicate process, facilitated by spatially and temporarily coordinated cross talk of up to 40 cell types. Developmental origin and heterogeneity of lung cell lineages in context of lung development have been a focus of research efforts for decades. Bulk RNA and protein measurements, RNA and protein labeling, and lineage tracing techniques have been traditionally employed. However, the complex and heterogeneous nature of lung tissue presents a particular challenge when identifying subtle changes in gene expression in individual cell types. Rapidly developing single-cell RNA sequencing (scRNA-seq) techniques allow for unbiased and robust assessment of complex cellular dynamics during biological processes in unprecedented ways. Discovered a decade ago, scRNA-seq has been applied in respiratory research to understand lung cellular composition and to identify novel cell types. Still, very few studies to date have addressed the single-cell transcriptome in healthy or aberrantly developing lung. In this review, we discuss principal discoveries with scRNA-seq in the field of prenatal and postnatal lung development. In addition, we examine challenges and expectations, and propose future steps associated with the use of scRNA-seq to study developmental lung diseases.

Interactive analysis of single-cell epigenomic landscapes with ChromSCape

Chromatin modifications orchestrate the dynamic regulation of gene expression during development and in disease. Bulk approaches have characterized the wide repertoire of histone modifications across cell types, detailing their role in shaping cell identity. However, these population-based methods do not capture cell-to-cell heterogeneity of chromatin landscapes, limiting our appreciation of the role of chromatin in dynamic biological processes. Recent technological developments enable the mapping of histone marks at single-cell resolution, opening up perspectives to characterize the heterogeneity of chromatin marks in complex biological systems over time. Yet, existing tools used to analyze bulk histone modifications profiles are not fit for the low coverage and sparsity of single-cell epigenomic datasets. Here, we present ChromSCape, a user-friendly interactive Shiny/R application distributed as a Bioconductor package, that processes single-cell epigenomic data to assist the biological interpretation of chromatin landscapes within cell populations. ChromSCape analyses the distribution of repressive and active histone modifications as well as chromatin accessibility landscapes from single-cell datasets. Using ChromSCape, we deconvolve chromatin landscapes within the tumor micro-environment, identifying distinct H3K27me3 landscapes associated with cell identity and breast tumor subtype.

Single-Cell RNA Sequencing Unravels Heterogeneity of the Stromal Niche in Cutaneous Melanoma Heterogeneous Spheroids

Heterogeneous spheroids have recently acquired a prominent position in melanoma research because they incorporate microenvironmental cues relevant for melanoma. In this study, we focused on the analysis of microenvironmental factors introduced in melanoma heterogeneous spheroids by different dermal fibroblasts. We aimed to map the fibroblast diversity resulting from previously acquired damage caused by exposure to extrinsic and intrinsic stimuli. To construct heterogeneous melanoma spheroids, we used normal dermal fibroblasts from the sun-protected skin of a juvenile donor. We compared them to the fibroblasts from the sun-exposed photodamaged skin of an adult donor. Further, we analysed the spheroids by single-cell RNA sequencing. To validate transcriptional data, we also compared the immunohistochemical analysis of heterogeneous spheroids to melanoma biopsies. We have distinguished three functional clusters in primary human fibroblasts from melanoma spheroids. These clusters differed in the expression of (a) extracellular matrix-related genes, (b) pro-inflammatory factors, and (c) TGFβ signalling superfamily. We observed a broader deregulation of gene transcription in previously photodamaged cells. We have confirmed that pro-inflammatory cytokine IL-6 significantly enhances melanoma invasion to the extracellular matrix in our model. This supports the opinion that the aspects of ageing are essential for reliable melanoma 3D modelling in vitro.

Benchmarking of cell type deconvolution pipelines for transcriptomics data

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance.

Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.

The bone marrow microenvironment of pre-B acute lymphoblastic leukemia at single-cell resolution

The bone marrow microenvironment (BMM) plays a key role in leukemia progression, but its molecular complexity in pre-B cell acute lymphoblastic leukemia (B-ALL), the most common cancer in children, remains poorly understood. To gain further insight, we used single-cell RNA sequencing to characterize the kinetics of the murine BMM during B-ALL progression. Normal pro- and pre-B cells were found to be the most affected at the earliest stages of disease and this was associated with changes in expression of genes regulated by the AP1-transcription factor complex and regulatory factors NELFE, MYC and BCL11A. Granulocyte–macrophage progenitors show reduced expression of the tumor suppressor long non-coding RNA Neat1 and disruptions in the rate of transcription. Intercellular communication networks revealed monocyte-dendritic precursors to be consistently active during B-ALL progression, with enriched processes including cytokine-mediated signaling pathway, neutrophil-mediated immunity and regulation of cell migration and proliferation. In addition, we confirmed that the hematopoietic stem and progenitor cell compartment was perturbed during leukemogenesis. These findings extend our understanding of the complexity of changes and molecular interactions among the normal cells of the BMM during B-ALL progression.

Systems Biology Methods Applied to Blood and Tissue for a Comprehensive Analysis of Immune Response to Hepatitis B Vaccine in Adults

Conventional vaccine design has been based on trial-and-error approaches, which have been generally successful. However, there have been some major failures in vaccine development and we still do not have highly effective licensed vaccines for tuberculosis, HIV, respiratory syncytial virus, and other major infections of global significance. Approaches at rational vaccine design have been limited by our understanding of the immune response to vaccination at the molecular level. Tools now exist to undertake in-depth analysis using systems biology approaches, but to be fully realized, studies are required in humans with intensive blood and tissue sampling. Methods that support this intensive sampling need to be developed and validated as feasible. To this end, we describe here a detailed approach that was applied in a study of 15 healthy adults, who were immunized with hepatitis B vaccine. Sampling included ~350 mL of blood, 12 microbiome samples, and lymph node fine needle aspirates obtained over a ~7-month period, enabling comprehensive analysis of the immune response at the molecular level, including single cell and tissue sample analysis. Samples were collected for analysis of immune phenotyping, whole blood and single cell gene expression, proteomics, lipidomics, epigenetics, whole blood response to key immune stimuli, cytokine responses, in vitro T cell responses, antibody repertoire analysis and the microbiome. Data integration was undertaken using different approaches-NetworkAnalyst and DIABLO. Our results demonstrate that such intensive sampling studies are feasible in healthy adults, and data integration tools exist to analyze the vast amount of data generated from a multi-omics systems biology approach. This will provide the basis for a better understanding of vaccine-induced immunity and accelerate future rational vaccine design.

Statistical significance of cluster membership for unsupervised evaluation of cell identities

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) allows us to dissect transcriptional heterogeneity arising from cellular types, spatio-temporal contexts and environmental stimuli. Transcriptional heterogeneity may reflect phenotypes and molecular signatures that are often unmeasured or unknown a priori. Cell identities of samples derived from heterogeneous subpopulations are then determined by clustering of scRNA-seq data. These cell identities are used in downstream analyses. How can we examine if cell identities are accurately inferred? Unlike external measurements or labels for single cells, using clustering-based cell identities result in spurious signals and false discoveries.

RESULTS

We introduce non-parametric methods to evaluate cell identities by testing cluster memberships in an unsupervised manner. Diverse simulation studies demonstrate accuracy of the jackstraw test for cluster membership. We propose a posterior probability that a cell should be included in that clustering-based subpopulation. Posterior inclusion probabilities (PIPs) for cluster memberships can be used to select and visualize samples relevant to subpopulations. The proposed methods are applied on three scRNA-seq datasets. First, a mixture of Jurkat and 293T cell lines provides two distinct cellular populations. Second, Cell Hashing yields cell identities corresponding to eight donors which are independently analyzed by the jackstraw. Third, peripheral blood mononuclear cells are used to explore heterogeneous immune populations. The proposed P-values and PIPs lead to probabilistic feature selection of single cells that can be visualized using principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and others. By learning uncertainty in clustering high-dimensional data, the proposed methods enable unsupervised evaluation of cluster membership.

AVAILABILITY AND IMPLEMENTATION

https://cran.r-project.org/package=jackstraw.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-Cell Transcriptomics Reveals Early Emergence of Liver Parenchymal and Non-parenchymal Cell Lineages

The cellular complexity and scale of the early liver have constrained analyses examining its emergence during organogenesis. To circumvent these issues, we analyzed 45,334 single-cell transcriptomes from embryonic day (E)7.5, when endoderm progenitors are specified, to E10.5 liver, when liver parenchymal and non-parenchymal cell lineages emerge. Our data detail divergence of vascular and sinusoidal endothelia, including a distinct transcriptional profile for sinusoidal endothelial specification by E8.75. We characterize two distinct mesothelial cell types as well as early hepatic stellate cells and reveal distinct spatiotemporal distributions for these populations. We capture transcriptional profiles for hepatoblast specification and migration, including the emergence of a hepatomesenchymal cell type and evidence for hepatoblast collective cell migration. Further, we identify cell-cell interactions during the organization of the primitive sinusoid. This study provides a comprehensive atlas of liver lineage establishment from the endoderm and mesoderm through to the organization of the primitive sinusoid at single-cell resolution.

An R-based reproducible and user-friendly preprocessing pipeline for CyTOF data

Mass cytometry (CyTOF) has become a method of choice for in-depth characterization of tissue heterogeneity in health and disease, and is currently implemented in multiple clinical trials, where higher quality standards must be met. Currently, preprocessing of raw files is commonly performed in independent standalone tools, which makes it difficult to reproduce. Here, we present an R pipeline based on an updated version of CATALYST that covers all preprocessing steps required for downstream mass cytometry analysis in a fully reproducible way. This new version of CATALYST is based on Bioconductor's SingleCellExperiment class and fully unit tested. The R-based pipeline includes file concatenation, bead-based normalization, single-cell deconvolution, spillover compensation and live cell gating after debris and doublet removal. Importantly, this pipeline also includes different quality checks to assess machine sensitivity and staining performance while allowing also for batch correction. This pipeline is based on open source R packages and can be easily be adapted to different study designs. It therefore has the potential to significantly facilitate the work of CyTOF users while increasing the quality and reproducibility of data generated with this technology.

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework

Background

The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets.

Results

Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal.

Conclusions

The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

A discrete subset of epigenetically primed human NK cells mediates antigen-specific immune responses

Adaptive features of natural killer (NK) cells have been reported in various species with different underlying mechanisms. It is unclear, however, which NK cell populations are capable of mounting antigen-specific recall responses and how such functions are regulated at the molecular level. Here, we identify and characterize a discrete population of CD49a+CD16- NK cells in the human liver that displays increased epigenetic potential to elicit memory responses and has the functional properties to exert antigen-specific immunity in the skin as an effector site. Integrated chromatin-based epigenetic and transcriptomic profiling revealed unique characteristics of hepatic CD49a+CD16- NK cells when compared with conventional CD49a-CD16+ NK cells, thereby defining active genomic regions and molecules underpinning distinct NK cell reactivity. In contrast to conventional NK cells, our results suggest that adaptive CD49a+CD16- NK cells are able to bypass the KIR receptor-ligand system upon antigen-specific stimulation. Furthermore, these cells were highly migratory toward chemokine gradients expressed in epicutaneous patch test lesions as an effector site of adaptive immune responses in the skin. These results define pathways operative in human antigen-specific adaptive NK cells and provide a roadmap for harnessing this NK cell subset for specific therapeutic or prophylactic vaccine strategies.

TreeSummarizedExperiment: a S4 class for data with hierarchical structure

Data organized into hierarchical structures (e.g., phylogenies or cell types) arises in several biological fields. It is therefore of interest to have data containers that store the hierarchical structure together with the biological profile data, and provide functions to easily access or manipulate data at different resolutions. Here, we present TreeSummarizedExperiment, a R/S4 class that extends the commonly used SingleCellExperiment class by incorporating tree representations of rows and/or columns (represented by objects of the phylo class). It follows the convention of the SummarizedExperiment class, while providing links between the assays and the nodes of a tree to allow data manipulation at arbitrary levels of the tree. The package is designed to be extensible, allowing new functions on the tree (phylo) to be contributed. As the work is based on the SingleCellExperiment class and the phylo class, both of which are popular classes used in many R packages, it is expected to be able to interact seamlessly with many other tools.

TreeSummarizedExperiment: a S4 class for data with hierarchical structure

Data organized into hierarchical structures (e.g., phylogenies or cell types) arises in several biological fields. It is therefore of interest to have data containers that store the hierarchical structure together with the biological profile data, and provide functions to easily access or manipulate data at different resolutions. Here, we present TreeSummarizedExperiment, a R/S4 class that extends the commonly used SingleCellExperiment class by incorporating tree representations of rows and/or columns (represented by objects of the phylo class). It follows the convention of the SummarizedExperiment class, while providing links between the assays and the nodes of a tree to allow data manipulation at arbitrary levels of the tree. The package is designed to be extensible, allowing new functions on the tree (phylo) to be contributed. As the work is based on the SingleCellExperiment class and the phylo class, both of which are popular classes used in many R packages, it is expected to be able to interact seamlessly with many other tools.

Drosophila Voltage-Gated Sodium Channels Are Only Expressed in Active Neurons and Are Localized to Distal Axonal Initial Segment-like Domains

In multipolar vertebrate neurons, action potentials (APs) initiate close to the soma, at the axonal initial segment. Invertebrate neurons are typically unipolar with dendrites integrating directly into the axon. Where APs are initiated in the axons of invertebrate neurons is unclear. Voltage-gated sodium (NaV) channels are a functional hallmark of the axonal initial segment in vertebrates. We used an intronic Minos-Mediated Integration Cassette to determine the endogenous gene expression and subcellular localization of the sole NaV channel in both male and female Drosophila, para Despite being the only NaV channel in the fly, we show that only 23 ± 1% of neurons in the embryonic and larval CNS express para, while in the adult CNS para is broadly expressed. We generated a single-cell transcriptomic atlas of the whole third instar larval brain to identify para expressing neurons and show that it positively correlates with markers of differentiated, actively firing neurons. Therefore, only 23 ± 1% of larval neurons may be capable of firing NaV-dependent APs. We then show that Para is enriched in an axonal segment, distal to the site of dendritic integration into the axon, which we named the distal axonal segment (DAS). The DAS is present in multiple neuron classes in both the third instar larval and adult CNS. Whole cell patch clamp electrophysiological recordings of adult CNS fly neurons are consistent with the interpretation that Nav-dependent APs originate in the DAS. Identification of the distal NaV localization in fly neurons will enable more accurate interpretation of electrophysiological recordings in invertebrates.SIGNIFICANCE STATEMENT The site of action potential (AP) initiation in invertebrates is unknown. We tagged the sole voltage-gated sodium (NaV) channel in the fly, para, and identified that Para is enriched at a distal axonal segment. The distal axonal segment is located distal to where dendrites impinge on axons and is the likely site of AP initiation. Understanding where APs are initiated improves our ability to model neuronal activity and our interpretation of electrophysiological data. Additionally, para is only expressed in 23 ± 1% of third instar larval neurons but is broadly expressed in adults. Single-cell RNA sequencing of the third instar larval brain shows that para expression correlates with the expression of active, differentiated neuronal markers. Therefore, only 23 ± 1% of third instar larval neurons may be able to actively fire NaV-dependent APs.

Interleukin-6 trans-signaling is a candidate mechanism to drive progression of human DCCs during clinical latency

Although thousands of breast cancer cells disseminate and home to bone marrow until primary surgery, usually less than a handful will succeed in establishing manifest metastases months to years later. To identify signals that support survival or outgrowth in patients, we profile rare bone marrow-derived disseminated cancer cells (DCCs) long before manifestation of metastasis and identify IL6/PI3K-signaling as candidate pathway for DCC activation. Surprisingly, and similar to mammary epithelial cells, DCCs lack membranous IL6 receptor expression and mechanistic dissection reveals IL6 trans-signaling to regulate a stem-like state of mammary epithelial cells via gp130. Responsiveness to IL6 trans-signals is found to be niche-dependent as bone marrow stromal and endosteal cells down-regulate gp130 in premalignant mammary epithelial cells as opposed to vascular niche cells. PIK3CA activation renders cells independent from IL6 trans-signaling. Consistent with a bottleneck function of microenvironmental DCC control, we find PIK3CA mutations highly associated with late-stage metastatic cells while being extremely rare in early DCCs. Our data suggest that the initial steps of metastasis formation are often not cancer cell-autonomous, but also depend on microenvironmental signals.

Metastatic dissemination in breast cancer patients occurs early in malignant transformation, raising questions about how disseminated cancer cells (DCC) progress at distant sites. Here, the authors show that DCCs in bone marrow are activated via IL6-trans-signaling and thereby acquire stemness traits relevant for metastasis formation.

Single-cell sequencing reveals clonal expansions of pro-inflammatory synovial CD8 T cells expressing tissue-homing receptors in psoriatic arthritis

Psoriatic arthritis (PsA) is a debilitating immune-mediated inflammatory arthritis of unknown pathogenesis commonly affecting patients with skin psoriasis. Here we use complementary single-cell approaches to study leukocytes from PsA joints. Mass cytometry demonstrates a 3-fold expansion of memory CD8 T cells in the joints of PsA patients compared to peripheral blood. Meanwhile, droplet-based and plate-based single-cell RNA sequencing of paired T cell receptor alpha and beta chain sequences show pronounced CD8 T cell clonal expansions within the joints. Transcriptome analyses find these expanded synovial CD8 T cells to express cycling, activation, tissue-homing and tissue residency markers. T cell receptor sequence comparison between patients identifies clonal convergence. Finally, chemokine receptor CXCR3 is upregulated in the expanded synovial CD8 T cells, while two CXCR3 ligands, CXCL9 and CXCL10, are elevated in PsA synovial fluid. Our data thus provide a quantitative molecular insight into the cellular immune landscape of psoriatic arthritis.

Variance-adjusted Mahalanobis (VAM): a fast and accurate method for cell-specific gene set scoring

Statistical analysis of single cell RNA-sequencing (scRNA-seq) data is hindered by high levels of technical noise and inflated zero counts. One promising approach for addressing these challenges is gene set testing, or pathway analysis, which can mitigate sparsity and noise, and improve interpretation and power, by aggregating expression data to the pathway level. Unfortunately, methods optimized for bulk transcriptomics perform poorly on scRNA-seq data and progress on single cell-specific techniques has been limited. Importantly, no existing methods support cell-level gene set inference. To address this challenge, we developed a new gene set testing method, Variance-adjusted Mahalanobis (VAM), that integrates with the Seurat framework and can accommodate the technical noise, sparsity and large sample sizes characteristic of scRNA-seq data. The VAM method computes cell-specific pathway scores to transform a cell-by-gene matrix into a cell-by-pathway matrix that can be used for both data visualization and statistical enrichment analysis. Because the distribution of these scores under the null of uncorrelated technical noise has an accurate gamma approximation, both population and cell-level inference is supported. As demonstrated using simulated and real scRNA-seq data, the VAM method provides superior classification accuracy at a lower computation cost relative to existing single sample gene set testing approaches.

Methodologies for Following EMT In Vivo at Single Cell Resolution

An epithelial-mesenchymal transition (EMT) occurs in almost every metazoan embryo at the time mesoderm begins to differentiate. Several embryos have a long record as models for studying an EMT given that a known population of cells enters the EMT at a known time thereby enabling a detailed study of the process. Often, however, it is difficult to learn the molecular details of these model EMT systems because the transitioning cells are a minority of the population of cells in the embryo and in most cases there is an inability to isolate that population. Here we provide a method that enables an examination of genes expressed before, during, and after the EMT with a focus on just the cells that undergo the transition. Single cell RNA-seq (scRNA-seq) has advanced as a technology making it feasible to study the trajectory of gene expression specifically in the cells of interest, in vivo, and without the background noise of other cell populations. The sea urchin skeletogenic cells constitute only 5% of the total number of cells in the embryo yet with scRNA-seq it is possible to study the genes expressed by these cells without background noise. This approach, though not perfect, adds a new tool for uncovering the mechanism of EMT in this cell type.

Signalling codes for the maintenance and lineage commitment of embryonic gastric epithelial progenitors

The identity of embryonic gastric epithelial progenitors is unknown. We used single-cell RNA-sequencing, genetic lineage tracing and organoid assays to assess whether Axin2- and Lgr5-expressing cells are gastric progenitors in the developing mouse stomach. We show that Axin2 ⁺ cells represent a transient population of embryonic epithelial cells in the forestomach. Lgr5 ⁺ cells generate both glandular corpus and squamous forestomach organoids ex vivo Only Lgr5 ⁺ progenitors give rise to zymogenic cells in culture. Modulating the activity of the WNT, BMP and Notch pathways in vivo and ex vivo, we found that WNTs are essential for the maintenance of Lgr5 ⁺ epithelial cells. Notch prevents differentiation of the embryonic epithelial cells along all secretory lineages and hence ensures their maintenance. Whereas WNTs promote differentiation of the embryonic progenitors along the zymogenic cell lineage, BMPs enhance their differentiation along the parietal lineage. In contrast, WNTs and BMPs are required to suppress differentiation of embryonic gastric epithelium along the pit cell lineage. Thus, coordinated action of the WNT, BMP and Notch pathways controls cell fate determination in the embryonic gastric epithelium.

Type I interferon signaling in fibroblastic reticular cells prevents exhaustive activation of antiviral CD8+ T cells

Fibroblastic reticular cells (FRCs) are stromal cells that actively promote the induction of immune responses by coordinating the interaction of innate and adaptive immune cells. However, whether and to which extent immune cell activation is determined by lymph node FRC reprogramming during acute viral infection has remained unexplored. Here, we genetically ablated expression of the type I interferon-α receptor (Ifnar) in Ccl19-Cre⁺ cells and found that sensing of type I interferon imprints an antiviral state in FRCs and thereby preserves myeloid cell composition in lymph nodes of naive mice. During localized lymphocytic choriomeningitis virus infection, IFNAR signaling precipitated profound phenotypic adaptation of all FRC subsets enhancing antigen presentation, chemokine-driven immune cell recruitment, and immune regulation. The IFNAR-dependent shift of all FRC subsets toward an immunostimulatory state reduced exhaustive CD8⁺ T cell activation. In sum, these results unveil intricate circuits underlying type I IFN sensing in lymph node FRCs that enable protective antiviral immunity.

An era of single-cell genomics consortia

The human body consists of 37 trillion single cells represented by over 50 organs that are stitched together to make us who we are, yet we still have very little understanding about the basic units of our body: what cell types and states make up our organs both compositionally and spatially. Previous efforts to profile a wide range of human cell types have been attempted by the FANTOM and GTEx consortia. Now, with the advancement in genomic technologies, profiling the human body at single-cell resolution is possible and will generate an unprecedented wealth of data that will accelerate basic and clinical research with tangible applications to future medicine. To date, several major organs have been profiled, but the challenges lie in ways to integrate single-cell genomics data in a meaningful way. In recent years, several consortia have begun to introduce harmonization and equity in data collection and analysis. Herein, we introduce existing and nascent single-cell genomics consortia, and present benefits to necessitate single-cell genomic consortia in a regional environment to achieve the universal human cell reference dataset.

ILC2-driven innate immune checkpoint mechanism antagonizes NK cell antimetastatic function in the lung

Metastasis constitutes the primary cause of cancer-related deaths, with the lung being a commonly affected organ. We found that activation of lung-resident group 2 innate lymphoid cells (ILC2s) orchestrated suppression of natural killer (NK) cell-mediated innate antitumor immunity, leading to increased lung metastases and mortality. Using multiple models of lung metastasis, we show that interleukin (IL)-33-dependent ILC2 activation in the lung is involved centrally in promoting tumor burden. ILC2-driven innate type 2 inflammation is accompanied by profound local suppression of interferon-γ production and cytotoxic function of lung NK cells. ILC2-dependent suppression of NK cells is elaborated via an innate regulatory mechanism, which is reliant on IL-5-induced lung eosinophilia, ultimately limiting the metabolic fitness of NK cells. Therapeutic targeting of IL-33 or IL-5 reversed NK cell suppression and alleviated cancer burden. Thus, we reveal an important function of IL-33 and ILC2s in promoting tumor metastasis via their capacity to suppress innate type 1 immunity.

Single-cell RNA sequencing identifies shared differentiation paths of mouse thymic innate T cells

Invariant natural killer T (iNKT), mucosal-associated invariant T (MAIT), and γδ T cells are innate T cells that acquire memory phenotype in the thymus and share similar biological characteristics. However, how their effector differentiation is developmentally regulated is still unclear. Here, we identify analogous effector subsets of these three innate T cell types in the thymus that share transcriptional profiles. Using single-cell RNA sequencing, we show that iNKT, MAIT and γδ T cells mature via shared, branched differentiation rather than linear maturation or TCR-mediated instruction. Simultaneous TCR clonotyping analysis reveals that thymic maturation of all three types is accompanied by clonal selection and expansion. Analyses of mice deficient of TBET, GATA3 or RORγt and additional in vivo experiments corroborate the predicted differentiation paths, while human innate T cells from liver samples display similar features. Collectively, our data indicate that innate T cells share effector differentiation processes in the thymus.

Innate T cells such as iNKT, MAIT and γδ T cells all develop in the thymus, but their differentiation paths are still unclear. Here, the authors show, using single-cell RNA sequencing, that all three cell types develop via shared and branched differentiation paths that are corroborated by additional results from gene-deficient mice and human liver T cells.

Heterotypic cell-cell communication regulates glandular stem cell multipotency

Glandular epithelia, including the mammary and prostate glands, are composed of basal cells (BCs) and luminal cells (LCs)^1,2. Many glandular epithelia develop from multipotent basal stem cells (BSCs) that are replaced in adult life by distinct pools of unipotent stem cells^1,3-8. However, adult unipotent BSCs can reactivate multipotency under regenerative conditions and upon oncogene expression^3,9-13. This suggests that an active mechanism restricts BSC multipotency under normal physiological conditions, although the nature of this mechanism is unknown. Here we show that the ablation of LCs reactivates the multipotency of BSCs from multiple epithelia both in vivo in mice and in vitro in organoids. Bulk and single-cell RNA sequencing revealed that, after LC ablation, BSCs activate a hybrid basal and luminal cell differentiation program before giving rise to LCs-reminiscent of the genetic program that regulates multipotency during embryonic development⁷. By predicting ligand-receptor pairs from single-cell data¹⁴, we find that TNF-which is secreted by LCs-restricts BC multipotency under normal physiological conditions. By contrast, the Notch, Wnt and EGFR pathways were activated in BSCs and their progeny after LC ablation; blocking these pathways, or stimulating the TNF pathway, inhibited regeneration-induced BC multipotency. Our study demonstrates that heterotypic communication between LCs and BCs is essential to maintain lineage fidelity in glandular epithelial stem cells.

Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis

BACKGROUND

Many computational methods have been developed recently to analyze single-cell RNA-seq (scRNA-seq) data. Several benchmark studies have compared these methods on their ability for dimensionality reduction, clustering, or differential analysis, often relying on default parameters. Yet, given the biological diversity of scRNA-seq datasets, parameter tuning might be essential for the optimal usage of methods, and determining how to tune parameters remains an unmet need.

RESULTS

Here, we propose a benchmark to assess the performance of five methods, systematically varying their tunable parameters, for dimension reduction of scRNA-seq data, a common first step to many downstream applications such as cell type identification or trajectory inference. We run a total of 1.5 million experiments to assess the influence of parameter changes on the performance of each method, and propose two strategies to automatically tune parameters for methods that need it.

CONCLUSIONS

We find that principal component analysis (PCA)-based methods like scran and Seurat are competitive with default parameters but do not benefit much from parameter tuning, while more complex models like ZinbWave, DCA, and scVI can reach better performance but after parameter tuning.

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Disease classification based on machine learning has become a crucial research topic in the fields of genetics and molecular biology. Generally, disease classification involves a supervised learning style; i.e., it requires a large number of labelled samples to achieve good classification performance. However, in the majority of the cases, labelled samples are hard to obtain, so the amount of training data are limited. However, many unclassified (unlabelled) sequences have been deposited in public databases, which may help the training procedure. This method is called semi-supervised learning and is very useful in many applications. Self-training can be implemented using high- to low-confidence samples to prevent noisy samples from affecting the robustness of semi-supervised learning in the training process. The deep forest method with the hyperparameter settings used in this paper can achieve excellent performance. Therefore, in this work, we propose a novel combined deep learning model and semi-supervised learning with self-training approach to improve the performance in disease classification, which utilizes unlabelled samples to update a mechanism designed to increase the number of high-confidence pseudo-labelled samples. The experimental results show that our proposed model can achieve good performance in disease classification and disease-causing gene identification.

Differential IRF8 Transcription Factor Requirement Defines Two Pathways of Dendritic Cell Development in Humans

The formation of mammalian dendritic cells (DCs) is controlled by multiple hematopoietic transcription factors, including IRF8. Loss of IRF8 exerts a differential effect on DC subsets, including plasmacytoid DCs (pDCs) and the classical DC lineages cDC1 and cDC2. In humans, cDC2-related subsets have been described including AXL+SIGLEC6+ pre-DC, DC2 and DC3. The origin of this heterogeneity is unknown. Using high-dimensional analysis, in vitro differentiation, and an allelic series of human IRF8 deficiency, we demonstrated that cDC2 (CD1c+DC) heterogeneity originates from two distinct pathways of development. The lymphoid-primed IRF8hi pathway, marked by CD123 and BTLA, carried pDC, cDC1, and DC2 trajectories, while the common myeloid IRF8lo pathway, expressing SIRPA, formed DC3s and monocytes. We traced distinct trajectories through the granulocyte-macrophage progenitor (GMP) compartment showing that AXL+SIGLEC6+ pre-DCs mapped exclusively to the DC2 pathway. In keeping with their lower requirement for IRF8, DC3s expand to replace DC2s in human partial IRF8 deficiency.

Fusion transcript detection using spatial transcriptomics

BACKGROUND

Fusion transcripts are involved in tumourigenesis and play a crucial role in tumour heterogeneity, tumour evolution and cancer treatment resistance. However, fusion transcripts have not been studied at high spatial resolution in tissue sections due to the lack of full-length transcripts with spatial information. New high-throughput technologies like spatial transcriptomics measure the transcriptome of tissue sections on almost single-cell level. While this technique does not allow for direct detection of fusion transcripts, we show that they can be inferred using the relative poly(A) tail abundance of the involved parental genes.

METHOD

We present a new method STfusion, which uses spatial transcriptomics to infer the presence and absence of poly(A) tails. A fusion transcript lacks a poly(A) tail for the 5' gene and has an elevated number of poly(A) tails for the 3' gene. Its expression level is defined by the upstream promoter of the 5' gene. STfusion measures the difference between the observed and expected number of poly(A) tails with a novel C-score.

RESULTS

We verified the STfusion ability to predict fusion transcripts on HeLa cells with known fusions. STfusion and C-score applied to clinical prostate cancer data revealed the spatial distribution of the cis-SAGe SLC45A3-ELK4 in 12 tissue sections with almost single-cell resolution. The cis-SAGe occurred in disease areas, e.g. inflamed, prostatic intraepithelial neoplastic, or cancerous areas, and occasionally in normal glands.

CONCLUSIONS

STfusion detects fusion transcripts in cancer cell line and clinical tissue data, and distinguishes chimeric transcripts from chimeras caused by trans-splicing events. With STfusion and the use of C-scores, fusion transcripts can be spatially localised in clinical tissue sections on almost single cell level.

Chronic myelomonocytic leukaemia stem cell transcriptomes anticipate disease morphology and outcome

BACKGROUND

Chronic myelomonocytic leukaemia (CMML) is a clinically heterogeneous stem cell malignancy with overlapping features of myelodysplasia and myeloproliferation. Over 90% of patients carry mutations in epigenetic and/or splicing genes, typically detectable in the Lin-CD34+CD38- immunophenotypic stem cell compartment in which the leukaemia-initiating cells reside. Transcriptional dysregulation at the stem cell level is likely fundamental to disease onset and progression.

METHODS

We performed single-cell RNA sequencing on 6826 Lin-CD34+CD38-stem cells from CMML patients and healthy controls using the droplet-based, ultra-high-throughput 10x platform.

FINDINGS

We found substantial inter- and intra-patient heterogeneity, with CMML stem cells displaying distinctive transcriptional programs. Compared with normal controls, CMML stem cells exhibited transcriptomes characterized by increased expression of myeloid-lineage and cell cycle genes, and lower expression of genes selectively expressed by normal haematopoietic stem cells. Neutrophil-primed progenitor genes and a MYC transcription factor regulome were prominent in stem cells from CMML-1 patients, whereas CMML-2 stem cells exhibited strong expression of interferon-regulatory factor regulomes, including those associated with IRF1, IRF7 and IRF8. CMML-1 and CMML-2 stem cells (stages distinguished by proportion of downstream blasts and promonocytes) differed substantially in both transcriptome and pseudotime, indicating fundamentally different biology underpinning these disease states. Gene expression and pathway analyses highlighted potentially tractable therapeutic vulnerabilities for downstream investigation. Importantly, CMML patients harboured variably-sized subpopulations of transcriptionally normal stem cells, indicating a potential reservoir to restore functional haematopoiesis.

INTERPRETATION

Our findings provide novel insights into the CMML stem cell compartment, revealing an unexpected degree of heterogeneity and demonstrating that CMML stem cell transcriptomes anticipate disease morphology, and therefore outcome.

FUNDING

Project funding was supported by Oglesby Charitable Trust, Cancer Research UK, Blood Cancer UK, and UK Medical Research Council.

Single-cell RNA sequencing in cardiovascular development, disease and medicine

Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.

Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis

Liver disease is a major global health-care problem, affecting an estimated 844 million people worldwide. Despite this substantial burden, therapeutic options for liver disease remain limited, in part owing to a paucity of detailed analyses defining the cellular and molecular mechanisms that drive these conditions in humans. Single-cell transcriptomic technologies are transforming our understanding of cellular diversity and function in health and disease. In this Review, we discuss how these technologies have been applied in hepatology, advancing our understanding of cellular heterogeneity and providing novel insights into fundamental liver biology such as the metabolic zonation of hepatocytes, endothelial cells and hepatic stellate cells, and the cellular mechanisms underpinning liver regeneration. Application of these methodologies is also uncovering critical pathophysiological changes driving disease states such as hepatic fibrosis, where distinct populations of macrophages, endothelial cells and mesenchymal cells reside within a spatially distinct fibrotic niche and interact to promote scar formation. In addition, single-cell approaches are starting to dissect key cellular and molecular functions in liver cancer. In the near future, new techniques such as spatial transcriptomics and multiomic approaches will further deepen our understanding of disease pathogenesis, enabling the identification of novel therapeutic targets for patients across the spectrum of liver diseases.

Identification of hub genes in diabetic kidney disease via multiple-microarray analysis

Background

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease; however, the underlying molecular mechanisms remain unclear. Recently, bioinformatics analysis has provided a comprehensive insight toward the molecular mechanisms of DKD. Here, we re-analyzed three mRNA microarray datasets including a single-cell RNA sequencing (scRNA-seq) dataset, with the aim of identifying crucial genes correlated with DKD and contribute to a better understanding of DKD pathogenesis.

Methods

Three datasets including GSE131882, GSE30122, and GSE30529 were utilized to find differentially expressed genes (DEGs). The potential functions of DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed, and hub genes were selected with the top three molecular complex detection (MCODE) score. A correlation analysis between hub genes and clinical indicators was also performed.

Results

In total, 84 upregulated DEGs and 49 downregulated DEGs were identified. Enriched pathways of the upregulated DEGs included extracellular matrix (ECM) receptor interaction, focal adhesion, human papillomavirus infection, malaria, and cell adhesion molecules. The downregulated DEGs were mainly enriched in ascorbate and aldarate metabolism, arginine and proline metabolism, endocrine- and other factor-regulated calcium reabsorption, mineral absorption and longevity regulating pathway, and multiple species signaling pathway. Seventeen hub genes were identified, and correlation analysis between unexplored hub genes and clinical features of DKD suggested that EGF, KNG1, GADD45B, and CDH2 might have reno-protective roles in DKD. Meanwhile, ATF3, B2M, VCAM1, CLDN4, SPP1, SOX9, JAG1, C3, and CD24 might promote the progression of DKD. Finally, most hub genes were found present in the immune cells of diabetic kidneys, which suggest the important role of inflammation infiltration in DKD pathogenesis.

Conclusions

In this study, we found seventeen hub genes using a scRNA-seq contained multiple-microarray analysis, which enriched the present understanding of molecular mechanisms underlying the pathogenesis of DKD in cells' level and provided candidate targets for diagnosis and treatment of DKD.

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia

Altered olfactory function is a common symptom of COVID-19, but its etiology is unknown. A key question is whether SARS-CoV-2 (CoV-2) - the causal agent in COVID-19 - affects olfaction directly, by infecting olfactory sensory neurons or their targets in the olfactory bulb, or indirectly, through perturbation of supporting cells. Here we identify cell types in the olfactory epithelium and olfactory bulb that express SARS-CoV-2 cell entry molecules. Bulk sequencing demonstrated that mouse, non-human primate and human olfactory mucosa expresses two key genes involved in CoV-2 entry, ACE2 and TMPRSS2. However, single cell sequencing revealed that ACE2 is expressed in support cells, stem cells, and perivascular cells, rather than in neurons. Immunostaining confirmed these results and revealed pervasive expression of ACE2 protein in dorsally-located olfactory epithelial sustentacular cells and olfactory bulb pericytes in the mouse. These findings suggest that CoV-2 infection of non-neuronal cell types leads to anosmia and related disturbances in odor perception in COVID-19 patients.

Mechanisms of stretch-mediated skin expansion at single-cell resolution

The ability of the skin to grow in response to stretching has been exploited in reconstructive surgery¹. Although the response of epidermal cells to stretching has been studied in vitro^2,3, it remains unclear how mechanical forces affect their behaviour in vivo. Here we develop a mouse model in which the consequences of stretching on skin epidermis can be studied at single-cell resolution. Using a multidisciplinary approach that combines clonal analysis with quantitative modelling and single-cell RNA sequencing, we show that stretching induces skin expansion by creating a transient bias in the renewal activity of epidermal stem cells, while a second subpopulation of basal progenitors remains committed to differentiation. Transcriptional and chromatin profiling identifies how cell states and gene-regulatory networks are modulated by stretching. Using pharmacological inhibitors and mouse mutants, we define the step-by-step mechanisms that control stretch-mediated tissue expansion at single-cell resolution in vivo.

Human Pluripotent Stem Cell-Derived Neural Cells and Brain Organoids Reveal SARS-CoV-2 Neurotropism

Neurological complications are common in patients with COVID-19. While SARS-CoV-2, the causal pathogen of COVID-19, has been detected in some patient brains, its ability to infect brain cells and impact their function are not well understood, and experimental models using human brain cells are urgently needed. Here we investigated the susceptibility of human induced pluripotent stem cell (hiPSC)-derived monolayer brain cells and region-specific brain organoids to SARS-CoV-2 infection. We found modest numbers of infected neurons and astrocytes, but greater infection of choroid plexus epithelial cells. We optimized a protocol to generate choroid plexus organoids from hiPSCs, which revealed productive SARS-CoV-2 infection that leads to increased cell death and transcriptional dysregulation indicative of an inflammatory response and cellular function deficits. Together, our results provide evidence for SARS-CoV-2 neurotropism and support use of hiPSC-derived brain organoids as a platform to investigate the cellular susceptibility, disease mechanisms, and treatment strategies for SARS-CoV-2 infection.

CORAZON: a web server for data normalization and unsupervised clustering based on expression profiles

Objective

Data normalization and clustering are mandatory steps in gene expression and downstream analyses, respectively. However, user-friendly implementations of these methodologies are available exclusively under expensive licensing agreements, or in stand-alone scripts developed, reflecting on a great obstacle for users with less computational skills.

Results

We developed an online tool called CORAZON (Correlations Analyses Zipper Online), which implements three unsupervised learning methods to cluster gene expression datasets in a friendly environment. It allows the usage of eight gene expression normalization/transformation methodologies and the attribute’s influence. The normalizations requiring the gene length only could be performed to RNA-seq, meanwhile the others can be used with microarray and/or NanoString data. Clustering methodologies performances were evaluated through five models with accuracies between 92 and 100%. We applied our tool to obtain functional insights of non-coding RNAs (ncRNAs) based on Gene Ontology enrichment of clusters in a dataset generated by the ENCODE project. The clusters where the majority of transcripts are coding genes were enriched in Cellular, Metabolic, Transports, and Systems Development categories. Meanwhile, the ncRNAs were enriched in the Detection of Stimulus, Sensory Perception, Immunological System, and Digestion categories. CORAZON source-code is freely available at https://gitlab.com/integrativebioinformatics/corazon and the web-server can be accessed at http://corazon.integrativebioinformatics.me.

MYC Drives Temporal Evolution of Small Cell Lung Cancer Subtypes by Reprogramming Neuroendocrine Fate

Small cell lung cancer (SCLC) is a neuroendocrine tumor treated clinically as a single disease with poor outcomes. Distinct SCLC molecular subtypes have been defined based on expression of ASCL1, NEUROD1, POU2F3, or YAP1. Here, we use mouse and human models with a time-series single-cell transcriptome analysis to reveal that MYC drives dynamic evolution of SCLC subtypes. In neuroendocrine cells, MYC activates Notch to dedifferentiate tumor cells, promoting a temporal shift in SCLC from ASCL1+ to NEUROD1+ to YAP1+ states. MYC alternatively promotes POU2F3+ tumors from a distinct cell type. Human SCLC exhibits intratumoral subtype heterogeneity, suggesting that this dynamic evolution occurs in patient tumors. These findings suggest that genetics, cell of origin, and tumor cell plasticity determine SCLC subtype.

Immune cell infiltration features and related marker genes in lung cancer based on single-cell RNA-seq

PURPOSE

Immune cells in the immune microenvironment of lung cancer have a great impact on the development of lung cancer. Our purpose was to analyze the immune cell infiltration features and related marker genes for lung cancer.

METHODS

Single cell RNA sequencing data of 11,485 lung cancer cells were retrieved from the Gene Expression Omnibus. After quality control and data normalization, cell clustering was performed using the Seurat package. Based on the marker genes of each cell type from the CellMarker database, each cell was divided into G1, G2M, and S phases. Then, differential expression and functional enrichment analyses were performed. CIBERSORT was used to reconstruct immune cell types.

RESULTS

Following cell filtering, highly variable genes were identified for all cells. 14 cell types were clustered. Among them, CD4 + T cell, B cell, plasma cell, natural killer cell and cancer stem cell were the top five cell types. Up-regulated genes were mainly enriched in immune-related biological processes and pathways. Using CIBERSORT, we identified the significantly higher fractions of naïve B cell, memory CD4 + T cell, T follicular helper cell, T regulatory helper cell and M1 macrophage in lung cancer tissues compared to normal tissues. Furthermore, the fractions of resting NK cell, monocyte, M0 macrophage, resting mast cell, eosinophil and neutrophil were significantly lower in tumor tissues than normal tissues.

CONCLUSION

Our findings dissected the immune cell infiltration features and related marker genes for lung cancer, which might provide novel insights for the immunotherapy of lung cancer.

Genome-wide studies reveal the essential and opposite roles of ARID1A in controlling human cardiogenesis and neurogenesis from pluripotent stem cells

BACKGROUND

Early human heart and brain development simultaneously occur during embryogenesis. Notably, in human newborns, congenital heart defects strongly associate with neurodevelopmental abnormalities, suggesting a common gene or complex underlying both cardiogenesis and neurogenesis. However, due to lack of in vivo studies, the molecular mechanisms that govern both early human heart and brain development remain elusive.

RESULTS

Here, we report ARID1A, a DNA-binding subunit of the SWI/SNF epigenetic complex, controls both neurogenesis and cardiogenesis from human embryonic stem cells (hESCs) through distinct mechanisms. Knockout-of-ARID1A (ARID1A-/-) leads to spontaneous differentiation of neural cells together with globally enhanced expression of neurogenic genes in undifferentiated hESCs. Additionally, when compared with WT hESCs, cardiac differentiation from ARID1A -/- hESCs is prominently suppressed, whereas neural differentiation is significantly promoted. Whole genome-wide scRNA-seq, ATAC-seq, and ChIP-seq analyses reveal that ARID1A is required to open chromatin accessibility on promoters of essential cardiogenic genes, and temporally associated with key cardiogenic transcriptional factors T and MEF2C during early cardiac development. However, during early neural development, transcription of most essential neurogenic genes is dependent on ARID1A, which can interact with a known neural restrictive silencer factor REST/NRSF.

CONCLUSIONS

We uncover the opposite roles by ARID1A to govern both early cardiac and neural development from pluripotent stem cells. Global chromatin accessibility on cardiogenic genes is dependent on ARID1A, whereas transcriptional activity of neurogenic genes is under control by ARID1A, possibly through ARID1A-REST/NRSF interaction.

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq

Systematic measurement biases make normalization an essential step in single-cell RNA sequencing (scRNA-seq) analysis. There may be multiple competing considerations behind the assessment of normalization performance, of which some may be study specific. We have developed "scone"- a flexible framework for assessing performance based on a comprehensive panel of data-driven metrics. Through graphical summaries and quantitative reports, scone summarizes trade-offs and ranks large numbers of normalization methods by panel performance. The method is implemented in the open-source Bioconductor R software package scone. We show that top-performing normalization methods lead to better agreement with independent validation data for a collection of scRNA-seq datasets. scone can be downloaded at http://bioconductor.org/packages/scone/.

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update

Galaxy (https://galaxyproject.org) is a web-based computational workbench used by tens of thousands of scientists across the world to analyze large biomedical datasets. Since 2005, the Galaxy project has fostered a global community focused on achieving accessible, reproducible, and collaborative research. Together, this community develops the Galaxy software framework, integrates analysis tools and visualizations into the framework, runs public servers that make Galaxy available via a web browser, performs and publishes analyses using Galaxy, leads bioinformatics workshops that introduce and use Galaxy, and develops interactive training materials for Galaxy. Over the last two years, all aspects of the Galaxy project have grown: code contributions, tools integrated, users, and training materials. Key advances in Galaxy's user interface include enhancements for analyzing large dataset collections as well as interactive tools for exploratory data analysis. Extensions to Galaxy's framework include support for federated identity and access management and increased ability to distribute analysis jobs to remote resources. New community resources include large public servers in Europe and Australia, an increasing number of regional and local Galaxy communities, and substantial growth in the Galaxy Training Network.

Single-cell transcriptomics identifies multiple pathways underlying antitumor function of TCR- and CD8αβ-engineered human CD4+ T cells

Transgenic coexpression of a class I-restricted tumor antigen-specific T cell receptor (TCR) and CD8αβ (TCR8) redirects antigen specificity of CD4+ T cells. Reinforcement of biophysical properties and early TCR signaling explain how redirected CD4+ T cells recognize target cells, but the transcriptional basis for their acquired antitumor function remains elusive. We, therefore, interrogated redirected human CD4+ and CD8+ T cells by single-cell RNA sequencing and characterized them experimentally in bulk and single-cell assays and a mouse xenograft model. TCR8 expression enhanced CD8+ T cell function and preserved less differentiated CD4+ and CD8+ T cells after tumor challenge. TCR8+CD4+ T cells were most potent by activating multiple transcriptional programs associated with enhanced antitumor function. We found sustained activation of cytotoxicity, costimulation, oxidative phosphorylation- and proliferation-related genes, and simultaneously reduced differentiation and exhaustion. Our study identifies molecular features of TCR8 expression that can guide the development of enhanced immunotherapies.

Heterochromatin establishment during early mammalian development is regulated by pericentromeric RNA and characterized by non-repressive H3K9me3

Upon fertilization in mammals the gametes are reprogrammed to create a totipotent zygote, a process that involves de novo establishment of chromatin domains. A major feature occurring during preimplantation development is the dramatic remodeling of constitutive heterochromatin, although the functional relevance of this is unknown. Here we show that heterochromatin establishment relies on the stepwise expression and regulated activity of Suv39h enzymes. Enforcing precocious acquisition of constitutive heterochromatin results in compromised development and epigenetic reprogramming, demonstrating that heterochromatin remodeling is essential for natural reprogramming at fertilization. We find that de novo H3K9 trimethylation in the paternal pronucleus after fertilization is catalyzed by Suv39h2 and that pericentromeric RNAs inhibit Suv39h2 activity and reduce H3K9me3. De novo H3K9me3 is initially non-repressive for gene expression but instead can bookmark promoters for compaction. Overall, we uncover the functional importance for the restricted transmission of constitutive heterochromatin during reprogramming and a non-repressive role for H3K9me3.

Machine Learning of Single-Cell Transcriptome Highly Identifies mRNA Signature by Comparing F-Score Selection with DGE Analysis

Human preimplantation development is a complex process involving dramatic changes in transcriptional architecture. For a better understanding of their time-spatial development, it is indispensable to identify key genes. Although the single-cell RNA sequencing (RNA-seq) techniques could provide detailed clustering signatures, the identification of decisive factors remains difficult. Additionally, it requires high experimental cost and a long experimental period. Thus, it is highly desired to develop computational methods for identifying effective genes of development signature. In this study, we first developed a predictor called EmPredictor to identify developmental stages of human preimplantation embryogenesis. First, we compared the F-score of feature selection algorithms with differential gene expression (DGE) analysis to find specific signatures of the development stage. In addition, by training the support vector machine (SVM), four types of signature subsets were comprehensively discussed. The prediction results showed that a feature subset with 1,881 genes from the F-score algorithm obtained the best predictive performance, which achieved the highest accuracy of 93.3% on the cross-validation set. Further function enrichment demonstrated that the gene set selected by the feature selection method was involved in more development-related pathways and cell fate determination biomarkers. This indicates that the F-score algorithm should be preferentially proposed for detecting key genes of multi-period data in mammalian early development.

Myeloid cell-synthesized coagulation factor X dampens antitumor immunity

Immune evasion in the tumor microenvironment (TME) is a crucial barrier for effective cancer therapy, and plasticity of innate immune cells may contribute to failures of targeted immunotherapies. Here, we show that rivaroxaban, a direct inhibitor of activated coagulation factor X (FX), promotes antitumor immunity by enhancing infiltration of dendritic cells and cytotoxic T cells at the tumor site. Profiling FX expression in the TME identifies monocytes and macrophages as crucial sources of extravascular FX. By generating mice with immune cells lacking the ability to produce FX, we show that myeloid cell-derived FX plays a pivotal role in promoting tumor immune evasion. In mouse models of cancer, we report that the efficacy of rivaroxaban is comparable with anti-programmed cell death ligand 1 (PD-L1) therapy and that rivaroxaban synergizes with anti-PD-L1 in improving antitumor immunity. Mechanistically, we demonstrate that FXa promotes immune evasion by signaling through protease-activated receptor 2 and that rivaroxaban specifically targets this cell-autonomous signaling pathway to reprogram tumor-associated macrophages. Collectively, our results have uncovered the importance of FX produced in the TME as a regulator of immune cell activation and suggest translational potential of direct oral anticoagulants to remove persisting roadblocks for immunotherapy and provide extravascular benefits in other diseases.