single cell transcriptomics datasets for lgg cancer type  (Broad Institute Inc)

Journal: iScience

Article Title: Single-cell transcriptomics of neuroinflammation and cerebrovascular endothelial cells in the aged rat hippocampus

doi: 10.1016/j.isci.2025.113332

Figure Lengend Snippet: Single-cell transcriptome analysis of the microglia (A) UMAP shows the distribution of each subtype of microglia. (B) The sector graph shows the composition of cells in subclusters by groups. (C) Violin plot depicts the expression levels of known core signature genes for each microglia subcluster. (D) Representative immunofluorescence double staining images of NFKBIA (red), IBA1 (green), and nuclei were labeled with DAPI located in hippocampus in the NC and LPS groups. Scale bar = 75 μm or 25 μm. Quantitative analysis of the proportion of NFKBIA + cells in microglia (IBA1+) in hippocampal DG subregion. Data are shown as mean ± SEM, independent samples t-test, n = 4, ∗∗ p < 0.01. (E) Marker genes enriched KEGG pathway analyses in various microglia subpopulations. (F) GO analysis shows the top five signaling pathways across the four subpopulations, MG0, MG4, MG5 and MG7.

Article Snippet: Based on these results, we conducted single-cell transcriptome analysis (scRNA-seq) on aging rat hippocampus on day 3 after LPS or vehicle injection using the 10X Genomics platform to examine changes in the neuroinflammatory microenvironment ( H).

Techniques: Expressing, Immunofluorescence, Double Staining, Labeling, Marker, Protein-Protein interactions

Journal: iScience

Article Title: Single-cell transcriptomics of neuroinflammation and cerebrovascular endothelial cells in the aged rat hippocampus

doi: 10.1016/j.isci.2025.113332

Figure Lengend Snippet: Single-cell transcriptome analysis of the cerebral vascular endothelial cells (A–C) UMAP plot and bar plot showing the distribution of 6 subpopulations of cerebral vascular endothelial cells in the LPS and NC groups. (D) Violin plot shows the gene expression related to vascular origin (arterial, venous, and capillary), including arterial endothelial cell marker genes Fbln5, Bmx, Efnb2 , Vegfc . The venous endothelial cells highly expressed gene Nr2f and capillary endothelial cells highly expressed gene Rgcc and Slc16a1 . (E) Expression profiles of EC0 Marker genes including Mfge8, Lrg1, Lgals9, Cldn5, Ocln, Tjp1, Ddit4/Redd1, Mfsd2a are shown using the UMAP visualization approach. (F) Marker genes in the EC0 subpopulation are enriched with GO functional analysis. (G) Volcano plot depicts the DEGs at overall level of cerebral vascular endothelial cells between LPS and NC groups. DEGs (|log2(fold change)| > 1, p Value FDR <0.05, Difference = |pct.1- pct.2 | > 0.2) were colored (red for upregulated DEGs and blue for downregulated DEGs. (H and I) GO analysis shows the upregulated signaling pathway at overall level of cerebral vascular endothelial cells and EC0 subpopulation respectively.

Article Snippet: Based on these results, we conducted single-cell transcriptome analysis (scRNA-seq) on aging rat hippocampus on day 3 after LPS or vehicle injection using the 10X Genomics platform to examine changes in the neuroinflammatory microenvironment ( H).

Techniques: Gene Expression, Marker, Expressing, Functional Assay

Journal: JACC: Basic to Translational Science

Article Title: TIMD4 hi MHCⅡ hi Macrophages Preserve Heart Function Through Retnla

doi: 10.1016/j.jacbts.2024.08.009

Figure Lengend Snippet: Single-Cell RNA Sequencing Revealed That DP MΦs Had High Expression of Retnla Cardiac CD45 + immune cells were sorted from the sham group, 1 week post-TAC group (acute stress phase), and 9 weeks post-TAC group (chronic heart failure phase), and single-cell transcriptomic analysis was performed using the 10X Genomics platform. (A) Uniform manifold approximation and projection dimensionality reduction analysis identified that mononuclear MΦs consisted of Lyve1 hi MΦs, Lyve1 hi MHCⅡ hi MΦs, MHCⅡ hi MΦs, and monocytes after reclustering under “Lyve1score” and “MHCⅡscore” dimensions. (B) Nineteen representative differentially expressed genes are plotted as a heatmap. (C) The percentage of MΦ subsets among the total MΦs across conditions. (D) Volcano plots of differentially expressed genes across conditions including both up-regulated and down-regulated genes (minimum percentage: 0.1; logFC threshold: 0.1; adjusted P value < 0.05). Genes of interest are highlighted in red. (E) The percentage of Retlna-positive cells among the MΦ subsets detected with flow cytometry. (F) The relative expression levels of Retnla and Mgl2 in heart tissues were measured by quantitative polymerase chain reaction 3 days after diphtheria toxin injection between the sham and TAC groups (4 weeks post-TAC) using a DP MΦ–reduced model (TAC reduced). Values were analyzed by using analysis of variance with Tukey post hoc analysis. (G) Pathway enrichment analysis (gProfiler, Gene Ontology biological processes) using differentially expressed genes across conditions. ∗∗ P < 0.01; ∗∗∗ P < 0.001. avg = average; FC = fold change; mRNA = messenger RNA; other abbreviations as in Figure 1 , Figure 2 , Figure 3 .

Article Snippet: Figure 5 Single-Cell RNA Sequencing Revealed That DP MΦs Had High Expression of Retnla Cardiac CD45 + immune cells were sorted from the sham group, 1 week post-TAC group (acute stress phase), and 9 weeks post-TAC group (chronic heart failure phase), and single-cell transcriptomic analysis was performed using the 10X Genomics platform. (A) Uniform manifold approximation and projection dimensionality reduction analysis identified that mononuclear MΦs consisted of Lyve1 hi MΦs, Lyve1 hi MHCII hi MΦs, MHCII hi MΦs, and monocytes after reclustering under “Lyve1score” and “MHCIIscore” dimensions. (B) Nineteen representative differentially expressed genes are plotted as a heatmap. (C) The percentage of MΦ subsets among the total MΦs across conditions. (D) Volcano plots of differentially expressed genes across conditions including both up-regulated and down-regulated genes (minimum percentage: 0.1; logFC threshold: 0.1; adjusted P value < 0.05).

Techniques: RNA Sequencing, Expressing, Flow Cytometry, Real-time Polymerase Chain Reaction, Injection