Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Transcriptomic and TME characteristics associated with ECMSig in TCGA-GBM cohort (A) Volcano plot showing DEGs between ECMSig-high and ECMSig-low groups. Red dots: upregulated in high-risk; blue dots: upregulated in low-risk. Benjamini-Hochberg adjusted. (B) Gene set enrichment analysis (GSEA) plots showing enrichment of hallmark pathways. Pathways enriched in ECMSig-high and ECMSig-low groups are shown with their running enrichment scores (ESs) and ranked gene lists. Benjamini-Hochberg adjusted. (C) Heatmap showing the activity scores of selected oncogenic and tumor-related signaling pathways (rows) across TCGA-GBM samples (columns), annotated by ECMSig group and ECMSig score. Red indicates high activity, blue indicates low activity. ∗ p < 0.05. Wilcoxon signed-rank test. (D) Heatmap depicting the estimated infiltration levels of various immune and stromal cell types (rows) in TCGA-GBM samples (columns), stratified by ECMSig group and score. Red indicates high infiltration, blue indicates low infiltration. Cells significantly highly infiltrated in ECMSig-high are labeled in red, and those high in ECMSig-low group are in blue. ∗q < 0.05, ∗∗q < 0.01, ∗∗∗q < 0.001. Wilcoxon signed-rank test. Benjamini-Hochberg adjusted. (E and F) Scatterplots showing the spearman correlation between ECMSig score and (E) Macrophage_XCELL infiltration score and (F) immune_score_XCELL. The blue line represents the linear regression fit with 95% confidence interval bands. Spearman correlation test.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Activity Assay, Protein-Protein interactions, Labeling

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Single-cell RNA sequencing analysis revealing ECMSig expression across cell types and identification of prognostically relevant cell states in GBM (A) UMAP visualization of major cell types identified in GBM scRNA-seq data. (B) Dot plot showing the scaled average expression (color intensity) and percentage of cells expressing (dot size) canonical marker genes for each major cell type. (C) Dot plot showing the scaled average expression and percentage of cells expressing the seven ECMSig genes across major cell types. (D) UMAP plots showing the expression levels of individual ECMSig genes and overall ECMSig score across all cells. (E–G) UMAP plots illustrating Scissor-identified prognostically unfavorable (Scissor_Pos, red dashed circle) and favorable (Scissor_Neg, blue dashed circle; Scissor_Others, gray) cell subpopulations within (E) tumor cells, (F) myeloid cells, and (G) endothelial cells. (H–K) Violin plots comparing ECMSig scores among tumor cells grouped by Scissor status (H) and tumor type (I), and myeloid cells (J) or endothelial cells (K) grouped by Scissor status. ∗∗∗∗ p < 0.0001. Wilcoxon signed-rank test. (L) Dot plot showing differentially expressed marker genes between myeloid Scissor_Pos and other myeloid cells. Dot size indicates the fraction of cells in the group expressing the gene; color indicates average expression level.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Single Cell, RNA Sequencing, Expressing, Marker

Journal: iScience

Article Title: Multi-omics profiling-derived signature links cellular ecosystem to glioblastoma prognosis

doi: 10.1016/j.isci.2026.115982

Figure Lengend Snippet: Spatial transcriptomic analysis revealing co-localization of ECMSig, hypoxia, Scissor-Positive cells, and pericytes in GBM (A) Spatial feature plots for four GBM samples. Each row represents a sample. Columns show spatial heatmaps of: ECMSig score, hypoxia signature score, tumor Scissor_Pos signature score, myeloid Scissor_Pos signature score, endothelial Scissor Pos signature score, and pericyte marker signature score. Color scale indicates scaled expression or score (low to high). Each dot represents a spatial barcoded spot.

Article Snippet: The single-cell transcriptomic sequencing dataset utilizing technology from the 10X Genomics platform was available under the accession number GEO: GSE182109 at the Gene Expression Omnibus (GEO) repository.

Techniques: Marker, Expressing

Journal: Research

Article Title: Trabecular-Like Scaffold Dictates Osteogenesis via Fluid Shear Stress-Induced Metabolic Reprogramming through the CAV1–HIF-1α Axis

doi: 10.34133/research.1307

Figure Lengend Snippet: Transcriptomic and proteomic profiling of mouse BMSCs under HFSS. (A) Schematic workflow of RNA-seq and proteomic analyses. Created with BioRender.com . (B) Heatmap of DEGs between HFSS and control groups. (C) Volcano plot showing up-regulated and down-regulated genes. (D) GO enrichment analysis of DEGs in biological process, cellular component, and molecular function categories. (E) KEGG pathway enrichment analysis of DEGs. (F) Heatmap of DEPs between HFSS and control groups. (G) Volcano plot of proteins substantially altered by HFSS. (H) GO enrichment analysis of DEPs. (I) KEGG pathway enrichment analysis of DEPs. (J) GSEA plot for the GO biological process related to oxidative phosphorylation, showing negative enrichment in the HFSS group. (K) GSEA plot for the KEGG oxidative phosphorylation pathway, demonstrating substantial down-regulation under HFSS. RNA-seq and proteomic data were obtained from n = 3 independent samples per group. Differential expression and enrichment analyses were conducted using standard statistical criteria as described in Materials and Methods.

Article Snippet: Transcriptomic, proteomic, and untargeted metabolomic analyses were performed by Hipro Life Sciences Co., Ltd. (China).

Techniques: RNA Sequencing, Control, Phospho-proteomics, Quantitative Proteomics

Journal: Research

Article Title: Trabecular-Like Scaffold Dictates Osteogenesis via Fluid Shear Stress-Induced Metabolic Reprogramming through the CAV1–HIF-1α Axis

doi: 10.34133/research.1307

Figure Lengend Snippet: Integrated transcriptomic and proteomic analyses and in vitro validation of mechano-metabolic remodeling in BMSCs under HFSS. (A) Schematic workflow of the integrated analysis. DEGs and DEPs between the HFSS and Ctrl groups were identified from RNA-seq and proteomic datasets, respectively, and concordantly regulated targets showing the same direction of change at both transcriptomic and proteomic levels were selected for downstream analyses. Created with BioRender.com . (B) Venn diagram illustrating the overlap between DEGs and DEPs. (C) Nine-quadrant plot comparing log₂ fold changes at the RNA and protein levels, highlighting concordant and discordant regulation patterns. (D) GO enrichment analysis of concordantly regulated genes and proteins. (E) KEGG pathway enrichment analysis of concordant targets. (F) GSEA plots for glycolysis, HIF-1 signaling, and PI3K–AKT signaling, showing positive enrichment of all 3 pathways in the HFSS group. (G) Heatmap of selected genes and proteins involved in glycolysis and mechanosignaling. (H) Phalloidin staining of the actin cytoskeleton in BMSCs cultured on Voronoi scaffolds under dynamic FSS. (I) Seahorse extracellular flux analysis of BMSCs cultured on V50, V60, and V70 scaffolds. (J) Representative ECAR curves and quantitative analysis of glycolysis rate, glycolytic reserve, and glycolytic capacity. (K) Western blot analysis of GLUT1, HIF1A, and CAV1, and (L) PI3K, p-PI3K, AKT, and p-AKT in BMSCs cultured on scaffolds, with corresponding quantitative analyses of Western blot protein bands. (M) Quantitative analysis of multiple glycolytic flux fractions in BMSCs seeded onto scaffolds. (N) Representative immunofluorescence images of CAV1 expression in BMSCs cultured on V50, V60, and V70 scaffolds. Scale bar, 100 μm. (O) qPCR analysis of Hif1a , Ldha , Slc2a1 , and Cav1 gene expression. Data are presented as mean ± SD ( n = 3 per group). Statistical significance was assessed using unpaired 2-tailed Welch’s t test and one-way ANOVA with Tukey’s HSD post hoc test.

Article Snippet: Transcriptomic, proteomic, and untargeted metabolomic analyses were performed by Hipro Life Sciences Co., Ltd. (China).

Techniques: In Vitro, Biomarker Discovery, RNA Sequencing, Staining, Cell Culture, Western Blot, Immunofluorescence, Expressing, Gene Expression

Journal: Molecular Therapy. Nucleic Acids

Article Title: TiSMeD: A tissue-specific methylation and expression database for biomarker and translational applications

doi: 10.1016/j.omtn.2026.102884

Figure Lengend Snippet: Overview of TiSMeD TiSMeD is a comprehensive resource that integrates 6,782 methylomes, 16,894 transcriptomes, and 241 proteomes obtained from multiple platforms. All datasets were processed using a standardized pipeline, resulting in the identification of 67,427 TSMs, 4,607 TSGs, and 2,833 TSPs based on tissue specificity and confidence score evaluation. In addition, TiSMeD includes 11,411,136 HKMs. TiSMeD features a user-friendly interface that enables data searching, browsing, downloading, and visualization.

Article Snippet: The transcriptome datasets were obtained from GEO, , the Genotype-Tissue Expression (GTEx) database, the Human Protein Atlas (HPA), and the RNA-seq Atlas.

Techniques: