10x visium spatial transcriptomics scrna binning  (Spatial Transcriptomics Inc)

Journal: Frontiers in Allergy

Article Title: MRGPRX2-expressing mast cells are increased in the GI tract of individuals with active inflammatory bowel disease and hereditary α-tryptasemia

doi: 10.3389/falgy.2025.1726096

Figure Lengend Snippet: Mast cells from IBD patients with HαT demonstrate increased MRGPRX2 expression. Spatial transcriptomics (10x Xenium) was performed on 8 descending colon biopsies from the University of Pennsylvania IBD biobank (4 HαT, 4 non-HαT; balanced UC/CD). (A) UMAP embedding showing major cellular populations. (B) Mast cells (MCs), defined as TPSAB1 + MS4A2 + KIT + , are more abundant in HαT samples. (C) Feature map of isolated MCs demonstrating increased MRGPRX2 transcript levels in HαT. (D) Digital droplet PCR (ddPCR) of representative tissues from the same cohort confirms upregulated MRGPRX2 expression in HαT vs. non-HαT. (E) Spatial transcriptomics images showing increased MRGPRX2 transcripts (red dots) in HαT-positive IBD tissue compared with non-HαT tissue. (F) ddPCR validation on matched samples (HαT: n = 4; non-HαT: n = 4) showing elevated MRGPRX2 mRNA. (G) Pseudobulk differential expression demonstrates significantly increased MRGPRX2 in HαT samples. For transcriptomic analyses, differential expression was calculated using DESeq2 with Benjamini–Hochberg FDR correction (FDR < 0.05). Effect sizes are shown as log₂ fold-change with 95% CIs. For ddPCR comparisons, Welch's t -test was used with Cohen's d reported.

Article Snippet: Spatial transcriptomics cohort , 8 , 4 , 4 , Severe IBD: UC ( n = 4), CD ( n = 4)—balanced across HαT and non-HαT , Descending colon , 10x Visium Spatial Transcriptomics + scRNA-binning , Selected from genotyped cohort; used to evaluate MC abundance and MRGPRX2 expression patterns..

Techniques: Expressing, Isolation, Biomarker Discovery, Quantitative Proteomics

Journal: Frontiers in Allergy

Article Title: MRGPRX2-expressing mast cells are increased in the GI tract of individuals with active inflammatory bowel disease and hereditary α-tryptasemia

doi: 10.3389/falgy.2025.1726096

Figure Lengend Snippet: Individuals with IBD and HαT exhibit increased SIGLEC8 expression in colon tissue. Spatial transcriptomics and pseudobulk analysis were performed on 8 representative descending colon samples (4 HαT, 4 non-HαT; balanced UC/CD). (A) Pseudobulk counts aggregated by sample show higher SIGLEC8 expression in the HαT group (Wilcoxon test; Cohen's d and 95% CI reported). (B) Volcano plot of DESeq2 pseudobulk differential expression analysis contrasting non-HαT (blue) and HαT (red) samples. Genes surpassing FDR < 0.05 (Benjamini–Hochberg correction) are highlighted. SIGLEC8 is prominently upregulated in HαT, consistent with findings from CyTOF and ddPCR validation.

Article Snippet: Spatial transcriptomics cohort , 8 , 4 , 4 , Severe IBD: UC ( n = 4), CD ( n = 4)—balanced across HαT and non-HαT , Descending colon , 10x Visium Spatial Transcriptomics + scRNA-binning , Selected from genotyped cohort; used to evaluate MC abundance and MRGPRX2 expression patterns..

Techniques: Expressing, Quantitative Proteomics, Biomarker Discovery

Journal: bioRxiv

Article Title: Extracellular matrix regulates lineage plasticity in prostate cancer through YAP/TEAD

doi: 10.64898/2025.12.30.697072

Figure Lengend Snippet: (A) Western blot analysis confirming disruption of Trp53 , Rb1 and Pten , as well as overexpression of cMYC in engineered mutant organoid lines. (B) Hematoxylin and eosin staining (H&E) and Ascl1 immunohistochemistry (IHC) of TKO, RPM and TKOM organoids, as well as their corresponding orthotopic transplant tumors. (C) Vimentin IHC and Masson’s trichrome staining of early- and late-stage orthotopic transplants derived from TKO and RPM organoids. (D) Deconvolved NEPC theta estimates and corresponding stromal theta estimates across eight specimens. Spatial maps highlight the distribution of NEPC and fibroblast-like content across Visium spots.

Article Snippet: To explore whether a similar depletion of fibroblasts is observed in human NEPC, we analyzed a recently published prostate cancer cohort profiled using 10X Visium spatial transcriptomics ( ) for stromal content in regions of PRAD versus NEPC.

Techniques: Western Blot, Disruption, Over Expression, Mutagenesis, Staining, Immunohistochemistry, Derivative Assay

Journal: bioRxiv

Article Title: Extracellular matrix regulates lineage plasticity in prostate cancer through YAP/TEAD

doi: 10.64898/2025.12.30.697072

Figure Lengend Snippet: (A) IHC of Vimentin and Fibronectin, along with Masson’s trichrome staining of normal prostate tissue, adenocarcinoma regions, and neuroendocrine regions from TKO tumors. (B) Deconvolved NEPC theta estimates and corresponding stromal theta estimates across two human CRPC specimens. Spatial maps highlight the distribution of NEPC and fibroblast-like content across Visium spots. (C) Proportion of spots classified as both tumor-high and stroma-high for PRAD compared to NEPC. (D) ASCL1 IHC of TKOM organoids cultured in Matrigel versus suspension conditions. (E) Volcano plot showing differentially expressed genes from RNA-seq analysis comparing TKOM organoids cultured in suspension versus Matrigel. (F) Gene set enrichment analysis (GSEA) of RNA-seq data from TKOM organoids in suspension versus Matrigel culture using previously reported NEPC signatures derived from GEMMs .

Article Snippet: To explore whether a similar depletion of fibroblasts is observed in human NEPC, we analyzed a recently published prostate cancer cohort profiled using 10X Visium spatial transcriptomics ( ) for stromal content in regions of PRAD versus NEPC.

Techniques: Staining, Cell Culture, Suspension, RNA Sequencing, Derivative Assay

Journal: bioRxiv

Article Title: Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

doi: 10.1101/2025.10.08.681087

Figure Lengend Snippet: (a) Overview of the clustering approach and characterization of identified regions. i) Features are extracted from each tiles of H&E slides using the histology foundation model H-optimus-0. ii) Tile features from each slide are clustered using K-means, trained on the discovery cohort (cohort A) and applied to the validation cohorts (cohorts B and C). iii) Expert neuropathologists review and annotate each cluster to define distinct regions. (b) Methods overview. Left : Graphical representation of the methods used to align paired H&E and Visium spatial transcriptomics for cell type annotation in 31 samples from Cohort B from the MOSAIC consortium. Right: Overview of the method used to categorize tiles according to their tile score and tile prediction in 3 classes: high score + high prediction = Long Survival, high score + low prediction = Short Survival, all others = Non-informative. (c) Schematic representation of common spatial arrangements of the regions within tumor sections. (d) Proportion of tiles associated with short survival, long survival, and non-informative categories in the validation cohort in the different regions (excluding the 4 regions with necrosis, hemorrhage, and artefacts) (cohorts B and C). (e) Distribution of regions among Long Survival and Short Survival tiles in the validation cohorts (cohorts B and C). (f) Average of cell count per cell type in the different tissue regions as estimated using the output weights of the cell2loc deconvolution algorithm and 31 samples from cohort B with paired H&E, Visium spatial transcriptomics, and scRNAseq. Rare cell types (median counts < 0.1) were excluded.

Article Snippet: Differential expression analysis (DEA) was performed on 10x Visium spatial transcriptomics data using a 2-step approach.

Techniques: Biomarker Discovery, Cell Counting

Journal: bioRxiv

Article Title: Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

doi: 10.1101/2025.10.08.681087

Figure Lengend Snippet: (a) Distributions of the number of nuclei per tile and the median nuclear area per tile across 457 samples in cohort A (training dataset). Only tiles with a tile score > 0 were included. Purple lines denote thresholds optimized on the training set; arrows indicate the direction associated with shorter survival. *** indicates p value < 0.001 for a Mann Whitney U test between two boxplots. (b) Proportion of biomarker-positive tiles defined as infiltrated white matter (liWM) regions with >30 nuclei and a median nuclear area <40 µm² among all tumor tiles in short- and long-surviving patients from cohorts B and C (testing dataset). (c) Representative H&E slide and corresponding Visium spatial transcriptomics overlay (validation dataset) showing biomarker positive liWM tiles (dark purple) in comparison to adjacent liWM tissue (purple). (d) Cell type composition per tile in biomarker-positive liWM tiles versus non informative liWM tiles in the n=31 patients of the MOSAIC dataset. Cell types with median count per tile <0.1 were excluded for clarity. (e) Pathways enriched in short survival liWM vs non informative liWM. Raster plot of GSEA showing leading genes of each pathways by log2FoldChange. Only pathways with FDR<0.2 were included (See GSEA results and all genes in Supplementary table I and Table J [Supplementary material]). (f) Volcano plot for DEA of short survival liWM vs non informative liWM.

Article Snippet: Differential expression analysis (DEA) was performed on 10x Visium spatial transcriptomics data using a 2-step approach.

Techniques: MANN-WHITNEY, Biomarker Discovery, Comparison

Journal: bioRxiv

Article Title: Histology and spatial transcriptomic integration revealed infiltration zone with specific cell composition as a prognostic hotspot in glioblastoma

doi: 10.1101/2025.10.08.681087

Figure Lengend Snippet: (a). In cohort A, proportion of tiles associated with short survival, long survival, and non-informative categories in pooled tissue subtypes Tumor 1.0, Tumor 1.1, and Tumor 2.1. (b). Within pooled tissue subtypes Tumor 1.0, Tumor 1.1, and Tumor 2.1, distributions of nuclear morphology features like nuclear density (number of nuclei per tile) and nuclear size (median nuclear area in µm²) and median nuclear circularity. (c) Representative images of the annotated vessels subtypes patterns on H&E slides (20X): (i) thin endothelium capillary (TEC), (ii) hyperplasic endothelium capillary (HEC), (iii) Microvascular Proliferation (MVP) (iv) Thin Endothelium Wide Lumen (TEWL) (v) Hyperplasic Endothelium Wide Lumen (HEWL) (d) Relative ratios of tiles with vessels or specific vessels subtypes within tumor regions (1.0, 1.1 and 2.1) for each patients across the two survival group (2y < OS and OS > 3y). Distributions are computed for the training set (cohort A) and validation set (cohort B&C). Vessels presence and vessels subtypes detection are obtained after inference by the vessels detection and vessels subtypes classifier models on the entire cohorts. (e) Distribution of cell count per cell type in the tumor regions (1.0, 1.1 and 2.1) for short-survival, long-survival and non-informative tiles. Distributions of cell counts statistically significantly different (Mann–Whitney test; threshold at 0.05) are indicated with a star. 31 samples from Cohort B with paired H&E, Visium spatial transcriptomics, and scRNAseq were used. Cell types with low range of mean count variations across tissue regions were removed for clarity. (f) Illustration of the pathway enrichment analyses of the differential expressed genes between long- and short-survival. Negative fold change is associated with worse survival outcomes and positive with better survival outcomes. (See GSEA results and all genes in supplementary table L [Supplementary material])

Article Snippet: Differential expression analysis (DEA) was performed on 10x Visium spatial transcriptomics data using a 2-step approach.

Techniques: Biomarker Discovery, Cell Counting, MANN-WHITNEY

Journal: Nature Communications

Article Title: Macrophage ferroptosis potentiates GCN2 deficiency induced pulmonary venous arterialization

doi: 10.1038/s41467-025-64035-4

Figure Lengend Snippet: a H&E-stained images of 10X Visium spatial transcriptomics sections from Control ( n = 2 individuals) and PVOD ( n = 1 individual) lung tissues. The two control samples represent the upper and lower halves of the same slide (stitched together). Scale bar = 2 mm. b Spatial mapping of tissue region clusters (Alveoli, Bronchi, Vessel, Unspecified) on spatial transcriptomics spots from Control (left) and PVOD (right) lung samples. c Violin plot showing HMOX1 expression levels across tissue regions in Control and PVOD lung samples. P values were determined via two-sided Wilcoxon rank-sum test. d Violin plots depicting expression of arterial endothelial markers ( KDR, CXCL12 ) and venous related marker ( ACKR1 ) in vessel regions comparing Control and PVOD groups. P values were determined via two-sided Wilcoxon rank-sum test. e Violin plots showing arterial and venous endothelial gene set scores in vessel regions of Control versus PVOD samples. P values were determined via two-sided Wilcoxon rank-sum test. f Volcano plot of differentially expressed genes in vessel regions between PVOD and Control groups. P values were determined via two-sided Wilcoxon rank-sum test with Benjamini–Hochberg correction for multiple testing. Significance thresholds were set at |log2 fold change| > 0.5 and adjusted p -value < 0.05. The top 5 upregulated and top 5 downregulated genes are annotated in the plot. GO biological processes ( g ) and KEGG pathways ( h ) significantly enriched (FDR < 0.05) from upregulated genes in PVOD vessel regions. P values were calculated using the hypergeometric test with Benjamini–Hochberg correction for multiple testing. Ten relevant terms associated with pulmonary vascular disease are shown, ranked by combined score. Dot size represents the percentage of genes in the gene set, and dot color indicates –log10(FDR). i Volcano plot of transcription factor activity differences (z-score normalized AUC scores) between Control and PVOD vessel regions analyzed by the limma method. j Violin plot showing ETS1 AUC scores in Control and PVOD vessel regions. k Violin plot of ETS1 expression in venous endothelial cells from scRNA-seq data comparing Control and PVOD groups. P values were determined via two-sided Wilcoxon rank-sum test. l ETS1 transcription factor binding motif (metacluster_183.1) obtained from the cisTarget motif collection (v10nr_clust). m Spatial distribution of cell type proportions (EC_arterial, EC_venous, Macrophages, Muscular cells, Fibroblasts) inferred by RCTD deconvolution. Color intensity corresponds to the relative abundance of each cell type, with darker colors indicating higher proportions. n Heatmaps showing Pearson correlation between RCTD cell type scores and cell death pathway gene set scores in Alveoli (top) and Vessel (bottom) region of the PVOD lung sample (* P < 0.05, ** P < 0.01, *** P < 0.001). P values are indicated in the figures. Source data are provided as a file.

Article Snippet: Fig. 6 Spatial transcriptomics reveals enhanced venous arterialization and ETS1-mediated gene regulation in PVOD lung vessels. a H&E-stained images of 10X Visium spatial transcriptomics sections from Control ( n = 2 individuals) and PVOD ( n = 1 individual) lung tissues.

Techniques: Staining, Control, Expressing, Marker, Activity Assay, Binding Assay