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Expression Atlas update: from tissues to single cells.
Expression Atlas is EMBL-EBI's resource for gene and protein expression. It sources and compiles data on the abundance and localisation of RNA and proteins in various biological systems and contexts and provides open access to this data for the research community. With the increased availability of single cell RNA-Seq datasets in the public archives, we have now extended Expression Atlas with a new added-value service to display gene expression in single cells. Single Cell Expression Atlas was launched in 2018 and currently includes 123 single cell RNA-Seq studies from 12 species. The website can be searched by genes within or across species to reveal experiments, tissues and cell types where this gene is expressed or under which conditions it is a marker gene. Within each study, cells can be visualized using a pre-calculated t-SNE plot and can be coloured by different features or by cell clusters based on gene expression. Within each experiment, there are links to downloadable files, such as RNA quantification matrices, clustering results, reports on protocols and associated metadata, such as assigned cell types.
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Single-Cell RNA Sequencing of the Cynomolgus Macaque Testis Reveals Conserved Transcriptional Profiles during Mammalian Spermatogenesis.
Spermatogenesis is highly orchestrated and involves the differentiation of diploid spermatogonia into haploid sperm. The process is driven by spermatogonial stem cells (SSCs). SSCs undergo mitotic self-renewal, whereas sub-populations undergo differentiation and later gain competence to initiate meiosis. Here, we describe a high-resolution single-cell RNA-seq atlas of cells derived from Cynomolgus macaque testis. We identify gene signatures that define spermatogonial populations and explore self-renewal versus differentiation dynamics. We detail transcriptional changes occurring over the entire process of spermatogenesis and highlight the concerted activity of DNA damage response (DDR) pathway genes, which have dual roles in maintaining genomic integrity and effecting meiotic sex chromosome inactivation (MSCI). We show remarkable similarities and differences in gene expression during spermatogenesis with two other eutherian mammals, i.e., mouse and humans. Sex chromosome expression in the male germline in all three species demonstrates conserved features of MSCI but divergent multicopy and ampliconic gene content.
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Integrating single-cell transcriptomic data across different conditions, technologies, and species.
Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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scASK: A Novel Ensemble Framework for Classifying Cell Types Based on Single-cell RNA-seq Data.
The Human Cell Atlas (HCA) is a large project that aims to identify all cell types in the human body. The dimension reduction and clustering for identification of cell types from single-cell RNA-sequencing (scRNA-seq) data have become foundational approaches to HCA. The major challenges of current computational analyses are of poor performance on large scale data and sensitive to initial data. We present a new ensemble framework called Adaptive Slice KNNs (scASK) to address the challenges for analyzing scRNA-seq data with high dimensionality. scASK consists of three innovational modules, called DAS (Data Adaptive Slicing), MCS (Meta Classifiers Selecting) and EMS (Ensemble Mode Switching), respectively, which facilitate scASK to approximate a bias-variance tradeoff beyond classification. Thirteen real scRNA-seq datasets are used to evaluate the performance of scASK. Compared with five popular classification algorithms, our experimental results indicate that scASK achieves the best accuracy and robustness among all competing methods. In conclusion, adaptive slicing is an effective structural reduction procedure, and meanwhile scASK provides novel and robust ensemble framework especially for classifying cell types based on scRNA-seq data. scASK is now publically available at https://github.com/liubo2358/scASKcmd.
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Robust and adaptive non-parametric tests for detecting general distributional shifts in gene expression.
Differential expression analysis is crucial in genomics, yet most methods focus only on mean shifts. Variance shifts in gene expression-especially in cellular signaling and aging-are increasingly recognized as being biologically important. We present QRscore (quantile rank score), a general non-parametric framework that extends the Mann-Whitney test to detect both mean and variance shifts through model-informed weights derived from negative binomial (NB) and zero-inflated NB (ZINB) distributions. QRscore offers high statistical power with false discovery rate (FDR) control, surpassing existing methods in detecting both types of distributional changes. When applied to bulk RNA sequencing (RNA-seq) data from Genotype-Tissue Expression (GTEx), QRscore uncovers numerous genes with dispersion shifts that are missed by mean-shift analysis. In pseudo-bulked single-cell RNA-seq data from the Asian Immune Diversity Atlas (AIDA), QRscore further reveals cell-type-specific variance shifts across age groups that remain undetectable in bulk analyses. QRscore augments the genome bioinformatics toolkit by offering a powerful and flexible approach for differential expression analysis.
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A Single-Cell Transcriptomic Atlas of Thymus Organogenesis Resolves Cell Types and Developmental Maturation.
Thymus development is critical to the adaptive immune system, yet a comprehensive transcriptional framework capturing thymus organogenesis at single-cell resolution is still needed. We applied single-cell RNA sequencing (RNA-seq) to capture 8 days of thymus development, perturbations of T cell receptor rearrangement, and in vitro organ cultures, producing profiles of 24,279 cells. We resolved transcriptional heterogeneity of developing lymphocytes, and genetic perturbation confirmed T cell identity of conventional and non-conventional lymphocytes. We characterized maturation dynamics of thymic epithelial cells in vivo, classified cell maturation state in a thymic organ culture, and revealed the intrinsic capacity of thymic epithelium to preserve transcriptional regularity despite exposure to exogenous retinoic acid. Finally, by integrating the cell atlas with human genome-wide association study (GWAS) data and autoimmune-disease-related genes, we implicated embryonic thymus-resident cells as possible participants in autoimmune disease etiologies. This resource provides a single-cell transcriptional framework for biological discovery and molecular analysis of thymus organogenesis.
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Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma.
BACKGROUND: Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice. METHODS: We studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model. RESULTS: We identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients. CONCLUSION: We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.
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Cell-type-specific responses to fungal infection in plants revealed by single-cell transcriptomics.
Pathogen infection is a dynamic process. Here, we employ single-cell transcriptomics to investigate plant response heterogeneity. By generating an Arabidopsis thaliana leaf atlas encompassing 95,040 cells during infection by a fungal pathogen, Colletotrichum higginsianum, we unveil cell-type-specific gene expression, notably an enrichment of intracellular immune receptors in vasculature cells. Trajectory inference identifies cells that had different interactions with the invading fungus. This analysis divulges transcriptional reprogramming of abscisic acid signaling specifically occurring in guard cells, which is consistent with a stomatal closure dependent on direct contact with the fungus. Furthermore, we investigate the transcriptional plasticity of genes involved in glucosinolate biosynthesis in cells at the fungal infection sites, emphasizing the contribution of the epidermis-expressed MYB122 to disease resistance. This work underscores spatially dynamic, cell-type-specific plant responses to a fungal pathogen and provides a valuable resource that supports in-depth investigations of plant-pathogen interactions.
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High content of nuclei-free low-quality cells in reference single-cell atlases: a call for more stringent quality control using nuclear fraction.
The advent of droplet-based single-cell RNA-sequencing (scRNA-seq) has dramatically increased data throughput, enabling the release of a diverse array of tissue cell atlases to the public. However, we will show that prominent initiatives such as the Human Cell Atlas [1], the Tabula Sapiens [2] and the Tabula Muris [3] contain a significant amount of contamination products (frequently affecting the whole organ) in their data portals due to suboptimal quality filtering. Our work addresses a critical gap by advocating for more stringent quality filtering, highlighting the imperative for a shift from existing standards, which currently lean towards greater permissiveness. We will show the importance of incorporating cell intronic fraction in quality control -or MALAT1 expression otherwise- showcasing its informative nature and potential to elevate cell atlas data reliability. In summary, here, we unveil the hidden intronic landscape of every tissue and highlight the importance of more rigorous single-cell RNA-sequencing quality assessment in cell atlases to enhance their applicability in diverse downstream analyses.
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Cell-Type Specific miRNA Regulatory Network Responses to ABA Stress Revealed by Time Series Transcriptional Atlases in Arabidopsis.
In plants, microRNAs (miRNAs) participate in complex gene regulatory networks together with the transcription factors (TFs) in response to biotic and abiotic stresses. To date, analyses of miRNAs-induced transcriptome remodeling are at the whole plant or tissue levels. Here, Arabidopsis's ABA-induced single-cell RNA-seq (scRNA-seq) is performed at different stages of time points-early, middle, and late. Single-cell level primary miRNAs (pri-miRNAs) atlas supported the rapid, dynamic, and cell-type specific miRNA responses under ABA treatment. MiRNAs respond rapidly and prior to target gene expression dynamics, and these rapid response miRNAs are highly cell-type specific, especially in mesophyll and vascular cells. MiRNA-TF-mRNA regulation modules are identified by identifying miRNA-contained feed-forward loops (M-FFLs) in the regulatory network, and regulatory networks with M-FFLs have higher co-expression and clustering coefficient (CC) values than those without M-FFLs, suggesting the hub role of miRNAs in regulatory networks. The cell-type-specific M-FFLs are regulated by these hub miRNAs rather than TFs through sc-RNA-seq network analysis. MiR858a-FBH3-MYB module inhibited the expression of MYB63 and MYB20, which related to the formation of plant secondary wall and the production of lignin, through M-FFL specifically in vascular. These results can provide prominent insights into miRNAs' dynamic and cell-type-specific roles in plant development and stress responses.