Journal: bioRxiv

Article Title: Catestatin ameliorates tauopathy and amyloidogenesis via adrenergic inhibition

doi: 10.64898/2026.01.04.697519

Figure Lengend Snippet: UMAP visualization of snRNA-seq data. ( A ) Merged dataset showing annotated neuronal and glial clusters. ( B ) Non-transgenic (nTg) brains display normal cellular distributions. ( C ) Sal/PS19 mice show loss of CA1-ProS and SUB-ProS neurons and expansion of Astro and Micro-PVM clusters. ( D ) CST treatment partially restores CA1-ProS/SUB-ProS populations and reduces Astro and Micro-PVM clustering. (E&F) Number of Downregulated and Upregulated differentially expressed genes (DEG) in the reference group (top) vs comparison group (bottom) for the identified neuronal and glial clusters. ( G ) Enriched upregulated pathways with GO terms in CA1-ProS cluster of nTg and CST/PS19 compared to Sal/PS19. ( H ) Enriched upregulated pathways with GO terms in SUB-ProS cluster of nTg and CST/PS19 compared to Sal/PS19. (I&J) CD68 immunostaining depicts increased hippocampal microglial activation in Sal/PS19 mice, which is significantly reduced by CST (nTg: n=3, Sal/PS19: n=5, CST/PS19: n=5). Scale bars = 200µM. (K&L) GFAP staining reveals robust hippocampal astrogliosis in Sal/PS19 mice; CST markedly decreases GFAP⁺ area (nTg: n=3, Sal/PS19: n=5, CST/PS19: n=5). Scale bars = 200µM. (M&N) GFAP staining in the entorhinal cortex also shows elevated astrocytosis in Sal/PS19 mice, partially rescued by CST (nTg: n=3, Sal/PS19: n=5, CST/PS19: n=5). Scale bars = 200µM. Data are mean ± SEM; Significance indicated as *p < 0.05, **p < 0.01, *** p < 0.001, ****p < 0.0001; NS, not significant.

Article Snippet: To obtain an unbiased and high-resolution view of CST’s impact on cellular composition and transcriptional states in Tauopathy, we performed single-nucleus RNA sequencing (snRNA-seq) on hippocampal tissue from nTg, saline-treated PS19 (Sal/PS19), and CST-treated PS19 (CST/PS19) mice at 9 months of age the same cohort used for structural and biochemical analyses.

Techniques: Transgenic Assay, Comparison, Immunostaining, Activation Assay, Staining

Journal: European Journal of Medical Research

Article Title: The S1PR1–CCN1 axis drives endothelial-to-mesenchymal transition and vascular instability in brain arteriovenous malformations

doi: 10.1186/s40001-025-03484-5

Figure Lengend Snippet: Endothelial enrichment of S1PR1 in the human brain and its downregulation in ruptured bAVMs. A Normalized transcript levels of S1PR1 across multiple brain regions in the Human Protein Atlas (HPA) dataset, with peak expression observed in the cortex, hippocampus, thalamus, and basal ganglia. B Independent validation of regional S1PR1 expression patterns in the Genotype-Tissue Expression (GTEx) dataset. C Preferential enrichment of S1PR1 in endothelial cells shown by single-cell RNA-seq from the HPA brain atlas. D Immunohistochemistry (IHC) analysis of human bAVM tissues showing strong and continuous endothelial S1PR1 expression in unruptured tissues, and reduced staining in ruptured tissues. Right panel: Quantification of IHC staining intensity for endothelial S1PR1 expression ( n = 3). E Dual immunofluorescence staining of S1PR1 and CD31 (red) shows extensive colocalization in unruptured bAVMs, whereas ruptured bAVMs tissues exhibit reduced colocalization and fragmented endothelial S1PR1 expression

Article Snippet: Single-nucleus RNA sequencing (snRNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets were retrieved from the Human Protein Atlas (HPA, https://www.proteinatlas.org/ ) and integrated to assess cell type-resolved expression profiles.

Techniques: Expressing, Biomarker Discovery, RNA Sequencing, Immunohistochemistry, Staining, Immunofluorescence

Journal: European Journal of Medical Research

Article Title: The S1PR1–CCN1 axis drives endothelial-to-mesenchymal transition and vascular instability in brain arteriovenous malformations

doi: 10.1186/s40001-025-03484-5

Figure Lengend Snippet: CCN1 is upregulated upon S1PR1 silencing and correlates with EndoMT-associated endothelial activation in human bAVMs. A Proteomic profiling identifies CCN1 as significantly upregulated following S1PR1 knockdown in HUVECs. B , C Validation of CCN1 upregulation at the mRNA (qRT-PCR) and protein (Western blot) levels in S1PR1 knockdown endothelial cells ( n = 3). D Quantification of CCN1 expression in ruptured versus unruptured human bAVM tissues ( n = 3). E Representative Immunohistochemistry images of CCN1 in human bAVM tissues, showing elevated expression in ruptured versus unruptured samples. F Immunofluorescence staining of human bAVM tissue shows co-localization of CCN1 with the endothelial marker CD31. G , H Re-analysis of publicly available single-cell RNA sequencing data comparing CCN1 expression across normal and bAVM vascular endothelium. I Schematic illustration of endothelial-specific ccn1 overexpression in zebrafish embryos using Tol2-mediated transgenesis. J Quantification of ccn1 mRNA levels in control and ccn1 -overexpressing embryos at 2 days post-fertilization (2 dpf) by qRT-PCR ( n = 3). K Light field images of zebrafish embryos at 2 dpf. Cranial hemorrhage (yellow arrow) is observed in the ccn1 -overexpressing group but not in controls. Right panel: Quantification of cerebral hemorrhage incidence ( n = 100). L Confocal images of cranial vasculature and red blood cells in Tg (gata1:Ds Red; flk1:EGFP) embryos at 2 dpf. Overexpression of ccn1 led to erythrocyte leakage and blood pooling in the cranial region. M Representative images of trunk vasculature in 5 dpf embryos. Overexpression of ccn1 caused significant dilation of intersomitic vessels (ISVs), as shown by increased vessel diameter. Right panel: Quantification of ISV diameter ( n = 6). N Confocal images of cerebral vasculature at 5 dpf showing increased cerebrovascular density in ccn1-overexpressing embryos. Right panel: Quantification of cerebrovascular fluorescence area ( n = 6). Scale bars: 100 μm

Article Snippet: Single-nucleus RNA sequencing (snRNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets were retrieved from the Human Protein Atlas (HPA, https://www.proteinatlas.org/ ) and integrated to assess cell type-resolved expression profiles.

Techniques: Activation Assay, Knockdown, Biomarker Discovery, Quantitative RT-PCR, Western Blot, Expressing, Immunohistochemistry, Immunofluorescence, Staining, Marker, RNA Sequencing, Over Expression, Control, Fluorescence

Journal: bioRxiv

Article Title: Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs

doi: 10.1101/2025.09.08.675012

Figure Lengend Snippet: A) This study re-analysed a publicly available dataset comprising 11 matched pairs of BMMCs samples from healthy donors. These samples were processed across four (A1) different laboratories, where each donor’s sample was divided into two aliquots: one processed using scRNA-seq and the other using snRNA-seq (A2). Libraries for both scRNA-seq and snRNA-seq were sequenced on the Illumina NextSeq 2000 platform (A3). B) Comparative analyses between the two approaches were conducted using the Seurat pipeline and other R packages. For sample-specific analyses, the raw data were downloaded (B1) and aligned to the reference genome using 10x Genomics’ Cell Ranger software (B2). The QC steps include doublet detection (B3), removing cells with high mitochondrial gene percentage (B4), ambient RNA removal (B5) and a biology-aware QC approach (B6), which was performed first on each sample separately. Multi-sample analyses, followed by merging different samples (B7), applying batch effect removal, clustering (B8), cell type annotation (B9), differential expression (DE) analysis (B10) and subsequent downstream analyses (B11). Figure created with BioRender.com .

Article Snippet: Among the most widely adopted of these are singlecell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), commercialised by 10x Genomics ( ).

Techniques: Software, Quantitative Proteomics

Journal: bioRxiv

Article Title: Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs

doi: 10.1101/2025.09.08.675012

Figure Lengend Snippet: A–C) Split violin plots display the distribution of key sequencing and cell quality metrics, with red representing snRNA-seq and blue representing scRNA-seq. Each pair of samples is connected by a line. Samples are coloured by the laboratory to indicate batch origin (A, B, C, F, H, I, L). Median number of genes detected per cell for each sample (A), Library size per sample after removing MT genes (B) and Sequencing saturation (C). D–E) Scatter plots showing an example of the correlation between library size and number of genes per cell in scRNA-seq (D) and snRNA-seq (E), coloured by cell type. Scatter plots for all samples are shown in Figure S7. F) Split violin plots showing proportion of reads mapped to intronic regions per sample. G) Distribution of total UMI counts per gene length bin across 50 gene length bins across all 22 samples. H–I) Split violin plots comparing the percentage of MT gene content (H) and ambient RNA (I). J) Correlation between the percentage of detected doublets and the total number of cells recovered per sample. K) Boxplots showing Jaccard scores representing similarity of HVGs identified within each method, based on the variance-stabilising transformation method. L) Comparison of the average number of clusters detected in three comparable resolutions in each sample.

Article Snippet: Among the most widely adopted of these are singlecell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), commercialised by 10x Genomics ( ).

Techniques: Sequencing, Transformation Assay, Comparison

Journal: bioRxiv

Article Title: Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs

doi: 10.1101/2025.09.08.675012

Figure Lengend Snippet: A–B) Histograms showing results from pseudo-bulk DE analysis between scRNA-seq and snRNA-seq across 15 cell types. Grey bars indicate non-DE genes, blue bars genes upregulated in scRNA-seq, and red bars genes upregulated in snRNA-seq, grouped by cell type (A) and distribution of log fold-change values for each group (B). C) Jaccard similarity heatmap based on top 200 upregulated DEGs in each methodology, across different cell types. Samples are clustered using Ward.D2 on MDS-embedded Jaccard distances; annotations indicate cell type and methodology. D) Line plots show the number of upregulated genes in scRNA-seq and snRNA-seq datasets across midpoints of 50 gene-length bins. E-F) Volcano plots representing the distribution of DEGs and top marker genes for T cell comparisons within scRNA-seq (E) and snRNA-seq (F) G) Venn diagrams showing the overlap of DEGs between scRNA-seq and snRNA-seq in T cells. H) Gene Set Enrichment Analysis (GSEA) with the MSigDB C7 immunologic signature collection results. Bar plots show the normalised enrichment scores (NES) of the top 4 significantly enriched gene sets (adjusted p< 0.05, ranked by NES).

Article Snippet: Among the most widely adopted of these are singlecell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq), commercialised by 10x Genomics ( ).

Techniques: Marker