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Expression Atlas in 2026: enabling FAIR and open expression data through community collaboration and integration.
Expression Atlas (https://www.ebi.ac.uk/gxa/home) is EMBL-EBI's comprehensive knowledgebase for gene and protein expression across tissues, cell types, conditions, and multiple species. Since our last update, Expression Atlas has expanded substantially in both content and functionality, now comprising >4500 studies from 67 species, with increased proteomics coverage and updated Genotype-Tissue Expression (GTEx) tissue profiles. The resource also includes hundreds of single-cell RNA-seq experiments spanning 21 species, among them externally analysed community datasets such as Tabula Sapiens and GTEx single-nucleus profiles, allowing exploration of curated atlases while maintaining their original analytical framework. Key methodological advances include a new marker gene analysis module for bulk baseline experiments, alongside workflow updates that improve reproducibility. Expression Atlas data are integrated into EMBL-EBI resources such as Ensembl, UniProt, and Europe PMC and disseminated through collaboration with model organism communities such as FlyBase and Gramene. The resource also supports translational research through the European Diagnostic Transcriptomic Library and integration with the Open Targets platform. Future directions include modernizing analysis pipelines, enhancing programmatic access, and delivering AI-ready data formats, strengthening Expression Atlas as a findable, accessible, interoperable, and reusable (FAIR) community-driven resource for both fundamental and translational discovery.
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SHCBP1 drives tumor progression in triple-negative breast cancer.
BACKGROUNDS: Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype. The limited treatment options underscore the urgent need to explore novel molecular targets to combat TNBC progression. This study investigates the oncogenic functions of SHCBP1 in TNBC. MATERIALS AND METHODS: Bulk RNA-seq and single-cell sequencing (scRNA-seq) data for TNBC samples were acquired from the Cancer Genome Atlas (TCGA) dataset and GSE161529, respectively. SHCBP1 expression at the mRNA and protein levels was compared between TNBC and normal breast tissues. The prognostic significance of SHCBP1 in TNBC was assessed using Kaplan-Meier analysis. The potential biological functions of SHCBP1 were explored through gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Immunofluorescence was utilized to determine the subcellular localization of SHCBP1 during cell division. Quantitative PCR (qPCR) and western blotting were employed to measure SHCBP1 expression in breast cancer cell lines. Subsequently, the impact of SHCBP1 on TNBC cell proliferation and migration was evaluated in vitro. Finally, scRNA-seq analysis was conducted to characterize SHCBP1 expression patterns at the single-cell resolution. RESULTS: SHCBP1 is markedly upregulated in TNBC tissues, and its overexpression is associated with poorer survival outcomes. Functional enrichment analysis reveals that SHCBP1-related genes are significantly enriched in pathways involved in cell-cycle regulation and DNA damage response. In vitro studies demonstrate that SHCBP1 enhances TNBC cell proliferation and migration. The scRNA-seq analysis displays the cell clusters in which SHCBP1 is primarily expressed. Cancer epithelial cells exhibiting higher SHCBP1 expression display stronger interactions with stromal cells in the tumor microenvironment. CONCLUSIONS: This study elucidates the critical role of SHCBP1 in TNBC progression, highlighting its potential as a therapeutic target. These findings provide a foundation for further exploration of SHCBP1 in TNBC treatment strategies.
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Single cell RNA-seq study of wild type and Hox9,10,11 mutant developing uterus.
The uterus is a remarkable organ that must guard against infections while maintaining the ability to support growth of a fetus without rejection. The Hoxa10 and Hoxa11 genes have previously been shown to play essential roles in uterus development and function. In this report we show that the Hoxa9,10,11, Hoxc9,10,11, Hoxd9,10,11 genes play a redundant role in the formation of uterine glands. In addition, we use single cell RNA-seq to create a high resolution gene expression atlas of the developing wild type mouse uterus. Cell types and subtypes are defined, for example dividing endothelial cells into arterial, venous, capillary, and lymphatic, while epithelial cells separate into luminal and glandular subtypes. Further, a surprising heterogeneity of stromal and myocyte cell types are identified. Transcription factor codes and ligand/receptor interactions are characterized. We also used single cell RNA-seq to globally define the altered gene expression patterns in all developing uterus cell types for two Hox mutants, with 8 or 9 mutant Hox genes. The mutants show a striking disruption of Wnt signaling as well as the Cxcl12/Cxcr4 ligand/receptor axis.
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Investigating the basis of lineage decisions and developmental trajectories in the dorsal spinal cord through pseudotime analyses.
Dorsal interneurons (dIs) in the spinal cord encode the perception of touch, pain, heat, itchiness and proprioception. Previous studies using genetic strategies in animal models have revealed important insights into dI development, but the molecular details of how dIs arise as distinct populations of neurons remain incomplete. We have developed a resource to investigate dI fate specification by combining a single-cell RNA-Seq atlas of mouse embryonic stem cell-derived dIs with pseudotime analyses. To validate this in silico resource as a useful tool, we used it to first identify genes that are candidates for directing the transition states that lead to distinct dI lineage trajectories, and then validated them using in situ hybridization analyses in the developing mouse spinal cord in vivo. We have also identified an endpoint of the dI5 lineage trajectory and found that dIs become more transcriptionally homogeneous during terminal differentiation. This study introduces a valuable tool for further discovery about the timing of gene expression during dI differentiation and demonstrates its utility in clarifying dI lineage relationships.
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Alignment of single-cell RNA-seq samples without overcorrection using kernel density matching.
Single-cell RNA sequencing (scRNA-seq) technology is poised to replace bulk cell RNA sequencing for many biological and medical applications as it allows users to measure gene expression levels in a cell type-specific manner. However, data produced by scRNA-seq often exhibit batch effects that can be specific to a cell type, to a sample, or to an experiment, which prevent integration or comparisons across multiple experiments. Here, we present Dmatch, a method that leverages an external expression atlas of human primary cells and kernel density matching to align multiple scRNA-seq experiments for downstream biological analysis. Dmatch facilitates alignment of scRNA-seq data sets with cell types that may overlap only partially and thus allows integration of multiple distinct scRNA-seq experiments to extract biological insights. In simulation, Dmatch compares favorably to other alignment methods, both in terms of reducing sample-specific clustering and in terms of avoiding overcorrection. When applied to scRNA-seq data collected from clinical samples in a healthy individual and five autoimmune disease patients, Dmatch enabled cell type-specific differential gene expression comparisons across biopsy sites and disease conditions and uncovered a shared population of pro-inflammatory monocytes across biopsy sites in RA patients. We further show that Dmatch increases the number of eQTLs mapped from population scRNA-seq data. Dmatch is fast, scalable, and improves the utility of scRNA-seq for several important applications. Dmatch is freely available online.
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DISCERN: deep single-cell expression reconstruction for improved cell clustering and cell subtype and state detection.
BACKGROUND: Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering and cell type identification. RESULTS: Here, we present DISCERN, a novel deep generative network that precisely reconstructs missing single-cell gene expression using a reference dataset. DISCERN outperforms competing algorithms in expression inference resulting in greatly improved cell clustering, cell type and activity detection, and insights into the cellular regulation of disease. We show that DISCERN is robust against differences between batches and is able to keep biological differences between batches, which is a common problem for imputation and batch correction algorithms. We use DISCERN to detect two unseen COVID-19-associated T cell types, cytotoxic CD4+ and CD8+ Tc2 T helper cells, with a potential role in adverse disease outcome. We utilize T cell fraction information of patient blood to classify mild or severe COVID-19 with an AUROC of 80% that can serve as a biomarker of disease stage. DISCERN can be easily integrated into existing single-cell sequencing workflow. CONCLUSIONS: Thus, DISCERN is a flexible tool for reconstructing missing single-cell gene expression using a reference dataset and can easily be applied to a variety of data sets yielding novel insights, e.g., into disease mechanisms.
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Visualizing Cluster-specific Genes from Single-cell Transcriptomics Data Using Association Plots.
Visualizing single-cell transcriptomics data in an informative way is a major challenge in biological data analysis. Clustering of cells is a prominent analysis step and the results are usually visualized in a planar embedding of the cells using methods like PCA, t-SNE, or UMAP. Given a cluster of cells, one frequently searches for the genes highly expressed specifically in that cluster. At this point, visualization is usually replaced by studying a list of differentially expressed genes. Association Plots are derived from correspondence analysis and constitute a planar visualization of the features which characterize a given cluster of observations. We have adapted Association Plots to address the challenge of visualizing cluster-specific genes in large single-cell data sets. Our method is made available as a free R package called APL. We demonstrate the application of APL and Association Plots to single-cell RNA-seq data on two example data sets. First, we present how to delineate novel marker genes using Association Plots with the example of Peripheral Blood Mononuclear Cell data. Second, we show how to apply Association Plots for annotating cell clusters to known cell types using Association Plots and a predefined list of marker genes. To do this we will use data from the human cell atlas of fetal gene expression. Results from Association Plots will also be compared to methods for deriving differentially expressed genes, and we will show the integration of APL with Gene Ontology Enrichment.
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A single-cell transcriptome atlas reveals the trajectory of early cell fate transition during callus induction in Arabidopsis.
The acquisition of pluripotent callus from somatic cells plays an important role in plant development studies and crop genetic improvement. This developmental process incorporates a series of cell fate transitions and reprogramming. However, our understanding of cell heterogeneity and mechanisms of cell fate transition during callus induction remains quite limited. Here, we report a time-series single-cell transcriptome experiment on Arabidopsis root explants that were induced in callus induction medium for 0, 1, and 4 days, and the construction of a detailed single-cell transcriptional atlas of the callus induction process. We identify the cell types responsible for initiating the early callus: lateral root primordium-initiating (LRPI)-like cells and quiescent center (QC)-like cells. LRPI-like cells are derived from xylem pole pericycle cells and are similar to lateral root primordia. We delineate the developmental trajectory of the dedifferentiation of LRPI-like cells into QC-like cells. QC-like cells are undifferentiated pluripotent acquired cells that appear in the early stages of callus formation and play a critical role in later callus development and organ regeneration. We also identify the transcription factors that regulate QC-like cells and the gene expression signatures that are related to cell fate decisions. Overall, our cell-lineage transcriptome atlas for callus induction provides a distinct perspective on cell fate transitions during callus formation, significantly improving our understanding of callus formation.
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Identification of a prognostic signature consisting of three macrophage-related genes for glioblastoma based on bulk and single-cell transcriptomes analyses.
BACKGROUND: Tumor-associated macrophages have been implicated in the progression and treatment resistance of glioblastoma (GBM). This study aimed to identify macrophage-related genes associated with prognosis and therapeutic response in GBM. MATERIALS AND METHODS: Bulk RNA-seq data from 533 patients with GBM were downloaded from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Bioinformatic tools were used to detect the co-expression gene modules associated with the infiltration of immune cells, identify a prognostic macrophage-related gene signature, and explore their association with sensitivity to chemotherapeutic drugs and immune checkpoint blockade. Single-cell RNA-seq data and multiplexed immunofluorescence were used to validate ISG20 expression (a member of the identified gene signature) in macrophages. RESULTS: We detected gene modules associated with macrophages and identified a signature consisting of three macrophage-related genes (ISG20, PARP12 and IFIT5) in the discovery set (TCGA-GBM, n = 159), and validated its prognostic value in the validation set (CGGA-GBM, n = 374). This gene signature demonstrated favorable accuracy in predicting prognosis and resistance of immuno- and chemo-therapy. The co-expression of ISG20 and PD-1 in macrophages was verified by single-cell RNA-seq data and multiplex immunofluorescence. CONCLUSIONS: This study presents a macrophage-related gene signature to predict prognosis and therapeutic response in GBM. ISG20, PARP12 and IFIT5 are interferon-stimulated genes, and further investigations may provide new insights into the interplay between macrophages and interferon signaling in GBM.
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EREG is the core onco-immunological biomarker of cuproptosis and mediates the cross-talk between VEGF and CD99 signaling in glioblastoma.
BACKGROUND: Glioma is the most prevalent primary tumor of the central nervous system. Glioblastoma multiforme (GBM) is the most malignant form of glioma with an extremely poor prognosis. A novel, regulated cell death form of copper-induced cell death called "cuproptosis" provides a new prospect for cancer treatment by regulating cuproptosis. METHODS: Data from bulk RNA sequencing (RNA-seq) analysis (The Cancer Genome Atlas cohort and Chinese Glioma Genome Atlas cohort) and single cell RNA-seq (scRNA-seq) analysis were integrated to reveal their relationships. A scoring system was constructed according to the cuproptosis-related gene expression, and core genes were experimentally verified using real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR), Western blot (WB), immunohistochemistry (IHC), and immunofluorescence (IF). Moreover, cell counting kit-8 (CCK8), colony formation, 5-ethynyl-2'-deoxyuridine (EdU) incorporation, transwell, and flow cytometry cell cycle were performed to evaluate cell proliferation, invasion, and migration. RESULTS: The Cuproptosis Activation Scoring (CuAS) Model has stable and independent prognostic efficacy, as verified by two CGGA datasets. Epiregulin (EREG), the gene of the model has the most contributions in the principal component analysis (PCA), is an onco-immunological gene that can affect immunity by influencing the expression of programmed death-ligand 1 (PD-L1) and mediate the process of cuproptosis by influencing the expression of ferredoxin 1 (FDX1). Single cell transcriptome analysis revealed that high CuAS GBM cells are found in vascular endothelial growth factor A (VEGFA) + malignant cells. Oligodendrocyte transcription factor 1 (OLIG1) + malignant is the original clone, and VEGF and CD99 are the differential pathways of specific cell communication between the high and low CuAS groups. This was also demonstrated by immunofluorescence in the tissue sections. Furthermore, CuAS has therapeutic potential for immunotherapy, and we predict that many drugs (methotrexate, NU7441, KU -0063794, GDC-0941, cabozantinib, and NVP-BEZ235) may be used in patients with high CuAS. CONCLUSION: EREG is the core onco-immunological biomarker of CuAS and modulates the cross-talk between VEGF and CD99 signaling in glioblastoma, and CuAS may provide support for immunotherapy and chemotherapy.