Journal: Frontiers in Immunology

Article Title: Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation

doi: 10.3389/fimmu.2026.1739660

Figure Lengend Snippet: Identification of core genes associated with macrophage immune training and heart failure. (A) Schematic overview of human-derived macrophage trained immunity model and transcriptomic profiling workflow ( GSE235897 ). (B) The volcano plot and (C) DEGs heatmap of hMDMs from trained (n=3) and untrained (n=3) samples in the macrophage-trained immunity dataset GSE235897 (|log2FC| ≥ 0.585, p < 0.05). (D) Sample clustering dendrogram of GSE135055 dataset based on gene expression profiles. (E) Scale-free topology fit index and (F) mean connectivity analysis across a range of soft-thresholding powers. (G) Cluster dendrogram of genes showing co-expression modules identified by WGCNA in database GSE135055 . (H) Module-trait heatmap values represent correlation coefficients between healthy controls and HF samples (* p < 0.05, ** p < 0.01). (I) Venn diagram showing the overlap among heart failure DEGs, trained-immunity DEGs, and WGCNA module genes.

Article Snippet: For single-cell transcriptomic data, we accessed the SCP1303 project from the Broad Institute ( https://singlecell.broadinstitute.org/single_cell ), which includes raw scRNA-seq data from failing human hearts with dilated and hypertrophic cardiomyopathy.

Techniques: Derivative Assay, Gene Expression, Expressing

Journal: Frontiers in Immunology

Article Title: Identification of MTURN as a trained immunity-related biomarker for heart failure via integrative transcriptomic machine learning analysis and experimental validation

doi: 10.3389/fimmu.2026.1739660

Figure Lengend Snippet: Five heart failure transcriptomic datasets were integrated with a macrophage-trained immunity model to identify immune-related biomarkers. Through DEGs analysis, WGCNA, CIBERSORT, and six machine learning algorithms, hub genes were prioritized with MTURN emerging as the top candidate. Its potential was further validated by scRNA-seq analysis, which confirmed MTURN enrichment in cardiac macrophages. Finally, MTURN expression was validated using previously published heart failure transcriptomic data and in vitro experiments.

Article Snippet: For single-cell transcriptomic data, we accessed the SCP1303 project from the Broad Institute ( https://singlecell.broadinstitute.org/single_cell ), which includes raw scRNA-seq data from failing human hearts with dilated and hypertrophic cardiomyopathy.

Techniques: Expressing, In Vitro

Journal: bioRxiv

Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

doi: 10.1101/2025.07.25.666708

Figure Lengend Snippet: (a) Simplified cross-section of the human epidermis, highlighting squamous cells, melanocytes and basal cells. Coloured regions represent cSCC (green), which originates from squamous cells, melanoma (orange), which originates from melanocytes, and BCC (blue), which originates from basal cells. Two orange melanocytes are shown in the dermal region as occurs in invasive melanoma; other cells in the lower dermis layer are not depicted. (b) Overview of sample design and technologies used to generate data for this project. ROI - region of interest; FOV - field of view; S - cSCC; B - BCC; M - melanoma; HC - healthy (cancer patient); HNC - healthy (non-cancer patient donor). Technologies included are single cell RNA sequencing for fresh samples, single nuclei sequencing for formalin-fixed samples, Visium, Xenium, CosMX, GeoMX DSP for whole transcriptome, GeoMX DSP for proteins, Polaris, RNAscope, the proximal ligation assay, spatial glycomics and CODEX.

Article Snippet: Cells expressing the two genes are visualized on single-cell level resolution spatial data from STOmics and Curio-Seeker (Takara Bio, USA) melanoma samples and appear to be in spatial proximity ( ).

Techniques: RNA Sequencing, Sequencing, RNAscope, Ligation

Journal: bioRxiv

Article Title: Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

doi: 10.1101/2025.07.25.666708

Figure Lengend Snippet: (a) Gene specificity score (GSS) and association of spatial spots with skin cancer heritability. GSS score for each gene in a spot/cell represents the enrichment of the gene as a top rank most abundant gene in the spot/cell and its neighbour spots/cells in an anatomical region, a spatial domain, or a cell type. The p-value shows the spatial heritability enrichment significance of a spot with a trait based on SNPs mapped to the genes with high GSS scores (one-sided Z-test for stratified coefficient different to 0). The p-value is more significant if the SNPs that are mapped to the high GSS genes explain a higher proportion of heritability for the trait. (b) Cell types with the highest enrichment of heritability explained by SNPs tagged to GSS genes of cells in a cell type. The white asterisks indicate the most enriched cell-type for heritability of cutaneous melanoma, cSCC and BCC traits. (c) gsMAP significance spatial heritability enrichment is shown at single-cell resolution across the tissue (upper tissue plots) or per annotated skin regions (lower violin plots) from the cosMx data of the sample mel48974. (d) LR pairs with significant association with SNP heritability explained by the corresponding cell types. The rectangles show cases where both L and R genes had PCC >0.3 between GSS of the gene and the gsMAP P-values (the significance level for the LD stratified coefficients for the spot bigger than 0). The results suggest which LR pairs are related with the heritability of a cell type pairs. (e) GSS of two LR pairs showing specificity of the L and R genes to tissue regions at the immune-rich dermal layers and the epidermis of the skin. (f) Manhattan plot showing top significant GWAS SNPs co-localizing with genes in melanocytes (red) and T cells (blue) that had the highest Pearson correlation between GSS and the gsMAP trait association P-value or associated with SNPs with genome-wide significance. The Y-axis shows the -log(P-value) from GWAS analysis.

Article Snippet: Cells expressing the two genes are visualized on single-cell level resolution spatial data from STOmics and Curio-Seeker (Takara Bio, USA) melanoma samples and appear to be in spatial proximity ( ).

Techniques: Genome Wide

Journal: Stem Cells

Article Title: Advantages of cell proliferation and immune regulation in CD146 + NESTIN + HUMSCs: insights from single-cell RNA sequencing

doi: 10.1093/stmcls/sxae063

Figure Lengend Snippet: Analysis of single-cell transcriptome data. (A) Violin plots display the expression patterns of surface markers in different clusters, including MSCs (CD105/ENG + , CD90/THY1 + , CD73/NT5E + , CD45/PTPRC − , CD34 − , CD19 − , HLA − DRA − , and CD11b/ITGAM − ), fibroblasts (VIM + , CD31/PECAM1 − , CD34 − , CD45/PTPRC − , EPCAM − , and MYH11 − ), and pericytes (CD146/MCAM + , CD31/PECAM1 − , CD34 − , and CD45/PTPRC − ). (B) Chord plots illustrate the phenotypic overlap between MSCs, fibroblasts, and pericytes in different clusters. (C) Scatter plots demonstrate cell annotations on UMAP dimensions. (D) Bar charts depict the proportions of different MSC subpopulations from various sample sources. (E) Bar charts show the proportions of different cell cycle phases within different MSC subpopulations. (F) The large scatter plot on the left highlights CD146 + NES + cells, while the smaller scatter plot on the right displays the expression of S-phase representative genes (PCNA and CLSPN) and G2M-phase representative genes (CDK1 and CCNB2) in different MSC subpopulations. (G) Volcano plots exhibit the top 5 differentially expressed genes in different MSC subpopulations.

Article Snippet: The single-cell transcriptome data of HUMSCs (22 + 5, 28W) are shared in Mendeley Data ( https://data.mendeley.com/drafts/36tfjc42hm ).

Techniques: Expressing