single cell transcriptomic datasets  (Human Protein Atlas)

Journal: medRxiv

Article Title: THR-6E: A Six-Gene Cell-of-Origin Signature Stratifies Risk and Predicts Systemic Therapy Response in ER+/HER2− Breast Cancer

doi: 10.64898/2026.01.31.26345244

Figure Lengend Snippet: (a) THR-6E and MKI67 (Ki67) expression across normal breast glandular epithelial cell subsets in the Human Protein Atlas (HPA) single-cell RNA-seq dataset (v23). Values are reported as normalized transcripts per million (nTPM) for the indicated epithelial clusters (C1, C4, C9, C11, C16, C17, C21). (b) Fraction of cells expressing ESR1 (ER), AR, and VDR across the same glandular breast epithelial clusters in HPA (v23). Percent expression denotes the proportion of cells within each cluster with detectable transcript. (c) Gene–gene correlation matrices for THR-6E and PAM50 gene sets in normal breast tissue (top) and breast tumors (bottom) from TNMplot. Colors represent Spearman correlation coefficients (−1 to 1). (d) Genomic alteration frequencies for THR-6E, Oncotype DX, and PAM-50 gene sets in breast cancer using combined TCGA-BRCA and METABRIC cohorts (total n=3,593). Alterations include mutations and copy-number changes as reported by cBioPortal. THR-6E genes show low alteration frequency (mean 1.1%), whereas Oncotype DX and PAM-50 include multiple genes with recurrent copy-number gains (15–20% in queried genes). (e) Protein–protein interaction network of THR-6E with ESR1, AR, and VDR generated using STRING (v12.0) with k-means clustering. Orange arrows indicate literature-supported regulatory relationships overlaid on the STRING network. (f) CancerGeneNet (SIGNOR) network linking THR-6E genes to cancer-associated phenotypes. Query proteins are shown in yellow, first neighbors in green; protein families are shown as white circles and protein complexes as blue clover symbols. Solid edges denote direct interactions and dashed edges denote indirect interactions; blue arrows indicate up-regulation and red T-bars indicate down-regulation.

Article Snippet: To resolve THR-6E at cellular resolution within the normal breast, we analyzed the Human Protein Atlas single-cell transcriptomic dataset ( , ).

Techniques: Expressing, Single Cell, RNA Sequencing, Generated

Journal: Frontiers in Genetics

Article Title: Integrative bioinformatics analysis and experimental validation reveals key genes and regulatory mechanisms in the development of gout

doi: 10.3389/fgene.2025.1598835

Figure Lengend Snippet: Preprocessing and quality control of single-cell RNA sequencing data. (A) Scatter plot of the number of detected genes and the percentage of mitochondrial genes per cell. (B) Principal component analysis plot of single-cell gene expression profiles of the normal group and Gout group. (C) Feature selection of highly variable genes. Red dots represent highly variable genes, and black dots represent non-variable genes. (D) TOP30 genes with cell differentiation. (E) PCA plot of single-cell gene expression data. (F) Elbow plot showing the standard deviations of the first 50 principal components (PCs).

Article Snippet: GSE211783 is a single-cell transcriptome dataset based on the GPL24676 platform (Illumina NovaSeq 6000), including peripheral blood samples from 3 gout patients and 3 normal individuals.

Techniques: Control, RNA Sequencing, Gene Expression, Selection, Cell Differentiation

Journal: Advanced Science

Article Title: Revealing the Transcriptional and Metabolic Characteristics of Sebocytes Based on the Donkey Cell Transcriptome Atlas

doi: 10.1002/advs.202413819

Figure Lengend Snippet: Transcriptional characterization of inter‐tissue epithelial cells. A) UMAP plot showing clustering results of epithelial cells colored by cell type. B) Dendrogram depicting the hierarchical clustering of various epithelial cell types. C) Distribution of metabolic pathway activity in different epithelial cell types in the donkey single‐cell transcriptome dataset. D) Butanoate metabolism activity score in different epithelial cell types. Cell type coloring is consistent with (C). E) Violin plots visualizing the expression of AACS , ACAT2 , ECHS1 , HADHA , and HMGCS1 across various cell types in donkey skin. F) Representative images of AACS and HADHA staining in donkey sebocytes. Nuclei were counterstained with DAPI (blue). Sebocytes were labeled with KRT14 (green). G) The top accumulated and depleted metabolites predicted in sebocytes. The y ‐axis denotes metabolism stress level (or level of accumulation and depletion), where a positive value represents accumulation and a negative value represents depletion. The x ‐axis denotes metabolites in a decreasing order of accumulation level. H) Heatmap showing predicted cell‐wise flux results for acetyl‐CoA‐related metabolic reactions in different epithelial cell types. Cell type coloring is consistent with (C).

Article Snippet: [ ] In light of these factors, we constructed the first single‐cell transcriptome dataset of tissue types from adult Dezhou donkeys (Wutou strain) and established a Donkey Cell Atlas (DCA) database to facilitate the sharing and utilization of this valuable resource ( https://egrb.qau.edu.cn/ ).

Techniques: Activity Assay, Expressing, Staining, Labeling