mouse brain visium hd dataset  (10X Genomics)

Journal: NAR Genomics and Bioinformatics

Article Title: SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics

doi: 10.1093/nargab/lqag039

Figure Lengend Snippet: Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, Visium HD, MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.

Article Snippet: Mouse brain Visium HD dataset: https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-mouse-brain-fresh-frozen .

Techniques: Expressing, Extraction, Construct, Quantitative Proteomics

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Overall sketching performance for 0.10 sampling fraction across datasets. ( A ) Spatial scatter plots of real datasets colored by cell type or cluster label. ( B ) Heatmap of rank-sums for each method aggregated by metric across all real world datasets. Low rank indicates best performance for that metric. ( C ) Spatial scatter plots of simulated Visium HD-like and Xenium like datasets. ( D ) Heatmap of rank-sums for each method aggregated by metric across all simulated datasets. Low rank indicates best performance for that metric.

Article Snippet: Mouse embryo: We downloaded the whole mouse embryo Visium HD dataset from the publicly available datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-mouse-embryo-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Retained cell type/cluster label proportions at 0.10 sketching fraction for ( A ) Merfish mouse ovary; ( B ) Merfish sagittal mouse brain; ( C ) Xenium human breast cancer; ( D ) Xenium human lung; ( E ) Xenium whole mouse pup; ( F ) Visium HD coronal mouse brain; ( G ) Visium HD mouse embryo; ( H ) Visium HD ovarian cancer.

Article Snippet: Mouse embryo: We downloaded the whole mouse embryo Visium HD dataset from the publicly available datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-mouse-embryo-fresh-frozen (last accessed date: 12 February 2025).

Techniques:

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Quantification of transcriptomic and coordinate Hausdorff distance at 0.10 sampling fraction for real datasets. ( A ) Quantification of imaging based (Merfish, Xenium) dataset’s Hausdorff distances. ( B ) Quntification of sequencing/spot based (Visium HD) dataset’s Hausdorff distances. Each boxplot represents one sketching method, with individual points corresponding to results from 10 independent runs with different random seeds.

Article Snippet: Mouse embryo: We downloaded the whole mouse embryo Visium HD dataset from the publicly available datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-mouse-embryo-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling, Imaging, Sequencing

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Overall sketching performance for 0.10 sampling fraction across datasets. ( A ) Spatial scatter plots of real datasets colored by cell type or cluster label. ( B ) Heatmap of rank-sums for each method aggregated by metric across all real world datasets. Low rank indicates best performance for that metric. ( C ) Spatial scatter plots of simulated Visium HD-like and Xenium like datasets. ( D ) Heatmap of rank-sums for each method aggregated by metric across all simulated datasets. Low rank indicates best performance for that metric.

Article Snippet: Human ovarian cancer: We downloaded the human ovarian cancer Visium HD dataset from the publicly avaiable datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-ovarian-cancer-discovery-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Retained cell type/cluster label proportions at 0.10 sketching fraction for ( A ) Merfish mouse ovary; ( B ) Merfish sagittal mouse brain; ( C ) Xenium human breast cancer; ( D ) Xenium human lung; ( E ) Xenium whole mouse pup; ( F ) Visium HD coronal mouse brain; ( G ) Visium HD mouse embryo; ( H ) Visium HD ovarian cancer.

Article Snippet: Human ovarian cancer: We downloaded the human ovarian cancer Visium HD dataset from the publicly avaiable datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-ovarian-cancer-discovery-fresh-frozen (last accessed date: 12 February 2025).

Techniques:

Journal: Nucleic Acids Research

Article Title: Benchmarking sketching methods on spatial transcriptomics data

doi: 10.1093/nar/gkag434

Figure Lengend Snippet: Quantification of transcriptomic and coordinate Hausdorff distance at 0.10 sampling fraction for real datasets. ( A ) Quantification of imaging based (Merfish, Xenium) dataset’s Hausdorff distances. ( B ) Quntification of sequencing/spot based (Visium HD) dataset’s Hausdorff distances. Each boxplot represents one sketching method, with individual points corresponding to results from 10 independent runs with different random seeds.

Article Snippet: Human ovarian cancer: We downloaded the human ovarian cancer Visium HD dataset from the publicly avaiable datasets on the 10x website https://www.10xgenomics.com/datasets/visium-hd-three-prime-ovarian-cancer-discovery-fresh-frozen (last accessed date: 12 February 2025).

Techniques: Sampling, Imaging, Sequencing

Journal: bioRxiv

Article Title: MilliMap: interactive closed-loop analysis for spatial omics

doi: 10.64898/2026.05.01.722104

Figure Lengend Snippet: (a) sST: Visium HD mouse brain, grid expression over H&E. (b) iST: Xenium human breast cancer; DAPI/IF morphology (left) and cluster-colored centroids (right). (c) SP: CODEX human intestine with protein-defined clusters. (d) scRNA-seq: honey bee brain, 3D UMAP. (e, f) Lasso-defined inner (e) and large (f) Kenyon cell (KC) ROIs (left); linked embedding confirms molecular coherence (right). (g) Differential expression between inner and large KCs (left: Dop3 -colored spatial view; right: DEG heatmap). (h) Spatially varying gene CHIT1 expression: whole tissue (left), ROI1 (middle), ROI2 (right). (i) Same layout as h, CD83 . (j) Spatially resolved ROI1 cell-type clusters (left) and TAMs (cluster 11) sub-clusters (right). (k) Spatially resolved ROI2 cell-type clusters. (l) Cell type composition of ROI1 and ROI2. (m) Volcano of ROI1-core-specific TAMs (11.1) vs other TAMs (11.0 and 11.2).

Article Snippet: The Visium HD Mouse Brain dataset (FFPE; C57BL/6; Space Ranger v3.0.0) is available from 10x Genomics at https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he , licensed under CC BY 4.0.

Techniques: Expressing, Quantitative Proteomics

Journal: Nature Communications

Article Title: Charting spatial ligand-target activity using Renoir

doi: 10.1038/s41467-026-72388-7

Figure Lengend Snippet: a Preparation of FFPE fetal liver tissue. Created in BioRender. Zafar, H. (2026) ( https://BioRender.com/gmj8e7g ) b Spatial communication domains inferred by Renoir based on ligand-target neighborhood scores obtained from the 10x Genomics Visium HD dataset (sample D1). Black scale bar, 1 mm. c Distribution of UMI count across the tissue section. d Spatial map of communication domains and neighborhood activity scores for differentially active ligand-target pairs ( COL18A1:BCL6 , PLG:MARCO , and MDK:KLF6 ). e Ranking of ligands based on their cumulative activities over target genes expressed by major cell types in domain 0. f UMAP plot of latent embedding for hepatocyte population in scRNA-seq data, cells are colored as either PLG + or PLG − . g Volcano plot depicting differentially expressed genes between PLG + and PLG − Hepatocytes calculated using non-parametric Wilcoxon rank sum test ( − log 10 P threshold = 50 and log 2 foldchange threshold = 0.3). h Spatial feature plot of PLG expression and cell type abundances of Hepatocytes ( PLG + and PLG − ) and FOLR2 + Macrophages overlaid onto the spots in ST data. i Spatial similarity measures between PLG:MARCO neighborhood activity scores and cell type abundances of PLG + and PLG − Hepatocytes across neighborhoods containing FOLR2 + Macrophages ( n = 39651 spots). Source data are provided as a Source Data file.

Article Snippet: Zafar, H. (2026) ( https://BioRender.com/gmj8e7g ) b Spatial communication domains inferred by Renoir based on ligand-target neighborhood scores obtained from the 10x Genomics Visium HD dataset (sample D1).

Techniques: Activity Assay, Expressing

Journal: bioRxiv

Article Title: Synora: vector-based boundary detection for spatial omics

doi: 10.64898/2026.02.26.708395

Figure Lengend Snippet: a , Representative examples of Synora boundary identification in Visium HD datasets. Top row, H&E histology images. Bottom row, Synora boundary detection. Each image is shown at two magnifications (overview and zoomed region). Scale bars, 200 μm and 1 mm. b , Dot plot showing pan-cancer and cancer-type-specific boundary-enriched genes across six cancer types. Dot size represents percentage of datasets showing enrichment; color intensity indicates expression level. c , Bar plots summarizing overrepresentation analysis results across cancer types. For each indicated pathway, bars show the fold enrichment of the gene set in each cancer type, whereas the fill color encodes statistical significance. d , Distance-binned cell-type composition profiles showing the proportion of different cell types as a function of signed distance to boundary (negative = outside tumor, positive = inside tumor) for each cancer type. e , Comparison of boundary identification methods in a CODEX colorectal cancer dataset. Left, original cellular neighborhood (CN) annotation. Right, Synora annotation showing boundary, nest, outside and noise classifications. f , Heatmap showing cellular neighborhood (CN) clustering incorporating Synora-derived spatial features (BoundaryScore and distance to boundary). Left panel, Jaccard similarity index comparing original CNs to Synora-enhanced CNs. Right panel, CN composition by cell-type frequency or Synora-derived features (z-scored). Red arrows indicate Synora-derived features. g , Scatter plot showing correlation between TME cell types and boundary abundance in patient groups (Crohn’s-like lymphoid reaction (CLR) versus diffuse inflammatory infiltration (DII)). Each point represents a cell type; significant correlations in the CLR group (outer circle) or DII group (inner circle) are highlighted. h , Differential abundance analysis of CNs (CN1–CN10) between CLR and DII groups. Box plots show the proportion of each CN in CLR versus DII patient samples. P values from two-sided Wilcoxon rank-sum tests are indicated.

Article Snippet: We analyzed 15 publicly available Visium HD datasets from https://www.10xgenomics.com/datasets (details in Supplementary Table 3).

Techniques: Expressing, Comparison, Derivative Assay

Journal: bioRxiv

Article Title: FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching

doi: 10.64898/2025.12.22.696108

Figure Lengend Snippet: (a) Abundance invariance test. Ranking stability of oligodendrocyte markers (top 20 genes by expression in oligodendrocytes) in mouse brain scRNA-seq (31,053 genes) as cell population is downsampled from 26.7% to 0.4%. Rank denotes average position when all genes are sorted by score (rank 1 = highest). Variance-based ranking (blue) degrades from rank 115 to 240 as abundance decreases—a two-fold deterioration. Leverage-score ranking (red) remains stable at rank ~ 150 regardless of population size, demonstrating true decoupling of biological identity from numerical prevalence. (b) The variance-leverage plane. Classification of 31,053 genes by variance (x-axis) and leverage score (y-axis). Four quadrants emerge: structurally informative “GOLD” genes (green, low variance/high leverage) include vascular markers ( Cldn5, Rgs5, Ly6a, Abcb1a, Hspb1 ) that define rare anatomical structures; variance-dominated “NOISE” genes (red, high variance/low leverage) contain 35% unannotated Gm -series transcripts compared to only 6% in the GOLD set, indicating that high variance alone does not ensure cell-type discriminative power. (c) Functional enrichment analysis. GO Biological Process enrichment reveals GOLD genes are significantly enriched for regulation of angiogenesis (FDR-adjusted p = 2.8 × 10 −6 ), endothelial cell differentiation (FDR-adjusted p = 2.1 × 10 −4 ), vasculogenesis, and blood vessel morphogenesis. NOISE genes show zero significant GO terms at FDR-adjusted p < 0.05. Genome-wide cell type specificity analysis further confirms that GOLD genes systematically target rare populations (median 0.27% abundance) versus NOISE genes (0.51%; p = 3.25 × 10 −25 ), with Endothelial cells as the top target—validating leverage as an unsupervised metric for biological distinctiveness. (d) Spatial verification on Visium. Top row: GOLD genes ( Cldn5, Ly6a, Rgs5 ) reconstruct clear vascular anatomical structures on mouse brain Visium sections (spatial structure score = 1.33). Bottom row: NOISE genes exhibit random, speckle-like distribution patterns (structure score = 0.87; Mann-Whitney p = 5.6 × 10 −5 ). This visual contrast demonstrates that leverage selects for genuine biological structure rather than technical variation.

Article Snippet: The Visium HD Mouse Small Intestine (FFPE) dataset was obtained from 10x Genomics ( https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-intestine ).

Techniques: Expressing, Functional Assay, Cell Differentiation, Genome Wide, MANN-WHITNEY

Journal: bioRxiv

Article Title: FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching

doi: 10.64898/2025.12.22.696108

Figure Lengend Snippet: (a) FlashDeconv performance across resolutions. Left: Number of measurement units at each bin size. Middle: Processing time demonstrating scalability to 350,000+ spots. Right: Signal purity (fraction of spots with > 80% single cell type) collapses from 61.5% at 8 µ m to 13.3% at 16 µ m. (b) Spatial maps of enterocyte proportions at 8, 16, and 32 µ m resolution on mouse small intestine. Fine anatomical detail visible at 8 µ m becomes progressively blurred at coarser resolutions. (c) Resolution sensitivity varies by cell type. Stem cells (red) show the steepest decline in spatial coherence (Moran’s I), while Paneth cells (blue) retain spatial structure. Shaded region indicates the 8–16 µ m transition zone. (d) Cryptvillus boundary validation. Gradient sharpness decreases by 77% from 8 µ m to 16 µ m, quantifying anatomical blurring. (e) Spatial binning induces spurious colocalization. Paneth and Goblet cells show weak mutual exclusion at 8 µ m ( r = −0.12, p < 10 −100 ) but appear strongly colocalized at 64 µ m ( r = +0.80, p < 10 −100 )—a correlation sign flip that could lead to incorrect biological conclusions about cell-cell interactions. Data: Visium HD Mouse Small Intestine (10x Genomics), scRNA-seq reference from Haber et al. 2017.

Article Snippet: The Visium HD Mouse Small Intestine (FFPE) dataset was obtained from 10x Genomics ( https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-intestine ).

Techniques: Biomarker Discovery

Journal: bioRxiv

Article Title: FlashDeconv enables atlas-scale, multi-resolution spatial deconvolution via structure-preserving sketching

doi: 10.64898/2025.12.22.696108

Figure Lengend Snippet: (a) HVG blindness ranking across intestinal cell types. HVG blindness is defined as the difference in mean percentile rank of a cell type’s marker genes under variance-based versus leverage-based selection; positive values indicate systematic underweighting by HVG. Tuft (brush) cells exhibit the highest HVG blindness (21 percentile points). (b) Spatial distribution of Tuft cells at 8 µ m resolution reveals focal niches (red spots) with proportions up to 61%. (c) Stem cell distribution at 8 µ m shows concentration at crypt bases. (d) Resolution sensitivity of Tuft cell detection. Maximum proportion decreases from 61% (8 µ m) to 4% (128 µ m), rendering focal niches undetectable at conventional resolution. (e) Colocalization analysis reveals Tuft cell hotspots are enriched 16.8-fold for stem cells and 15.3-fold for enteroendocrine cells ( p < 10 −4 , permutation test), but depleted for differentiated cell types (enterocytes 0.11×, goblet cells 0.10×). (f) Spatial zoom showing Tuft-Stem co-localization at crypt bases (blue: Stem-high, pink: co-localized). Tuft cells rarely appear without adjacent stem cells, consistent with their intimate niche association. Data: Visium HD Mouse Small Intestine (10x Genomics).

Article Snippet: The Visium HD Mouse Small Intestine (FFPE) dataset was obtained from 10x Genomics ( https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-intestine ).

Techniques: Marker, Selection, Concentration Assay