single-cell resolution transcriptomics analysis (scrna-seq)  (Xtraordinary Home Products LLC)

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: Advanced Science

Article Title: Multiscale Cell–Cell Interactive Spatial Transcriptomics Analysis

doi: 10.1002/advs.202508358

Figure Lengend Snippet: Overview of MCIST workflow. Gene expression data are treated as a point cloud of cells, from which we construct a sequence of multiscale cell‐cell interaction graphs based on an affinity measure between expression profiles and k‐nearest neighbors (kNNs). These graphs give rise to an ensemble of low‐dimensional multiscale topological PCA representations of the gene expression data, each characterizing a specific combination of cell–cell connectivities. A latent space representation of the spatially resolved gene expression data is also constructed from a deep learning model to pair with the multiscale topological representation. These representations are then aligned for downstream ensemble clustering‐enabling spatial domain detection, residue‐similarity index (RSI)‐optimized trajectory inference, and differential gene expression analysis.

Article Snippet: In this study, we present the MultiScale Cell‐Cell Interactive Spatial Transcriptomics Analysis method, which unites the strengths of spatially resolved deep learning techniques with a topological representation of multi‐scale cell‐cell similarity relations.

Techniques: Gene Expression, Construct, Sequencing, Expressing, Residue