10x genomics visium dataset  (10X Genomics)

Journal: bioRxiv

Article Title: MatriSpace: Identification and visualization of spatially resolved ECM gene expression patterns in health and disease

doi: 10.64898/2026.04.26.720198

Figure Lengend Snippet: We illustrate the functionalities of MatriSpace in to using the following BrK dataset from https://cf.10xgenomics.com/samples/spatial-exp/2.0.0/CytAssist_FFPE_Human_Breast_Cancer/CytAssist_FFPE_Human_Breast_Cancer_web_summary.html . A. Hematoxylin and eosin (H&E) staining conducted pre-CytAssist is shown alongside the Visium spatial plot with default Seurat-derived cell type clusters. B. Spatial visualization of matrisome signature expression, illustrated here by ECM glycoproteins, is shown as either a spatial distribution ( left ) or a hotspot map ( right ). Users can switch to an interactive plot and/or adjust spot size. Within the interactive plot, users can zoom into regions of interest, select specific areas, or download the image using the interactive panel. Hovering over individual spots displays expression value and annotated cell type.

Article Snippet: The preloaded dataset collection comprises 198 10x Genomics Visium datasets obtained from public repositories, including 10x Genomics ( https://www.10xgenomics.com/datasets ), the National Cancer Institute’s Human Tumor Atlas Network ( https://humantumoratlas.org/ ; ( )), the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo ; ( )), and Zenodo ( https://zenodo.org/ ) ( Supplemental Table S1 ).

Techniques: Staining, Derivative Assay, Expressing

Journal: Nature Communications

Article Title: Tumor cell villages define the co-dependency of tumor and microenvironment in liver cancer

doi: 10.1038/s41467-026-69797-z

Figure Lengend Snippet: A Schematic overview of the workflow in this study. A total of 50 samples collected from the tumor core (T), tumor border (B), and adjacent non-tumor tissue (N) of 7 liver cancer patients (4 HCC, 3 iCCA) were profiled. Sample IDs were named based on histological subtypes of liver cancer, where H represents HCC and C represents iCCA. Single-cell transcriptome data, 10X Visium spatial transcriptome data, and bulk transcriptome data were used for validation, with sample numbers indicated. Illustration was partially created using BioRender. B UMAP embeddings of all profiled single cells colored by cell types ( n = 2,460,095, top panel), and the cells colored by profiling methods ( n = 2,347,589, CosMx TM SMI; n = 112,506, scRNA-seq, bottom panel). C , D A representative tumor sample (1CT) colored by cell types ( C ) and gene score of each cell type ( D ). Gene score was determined based on the average expression of marker genes specific to each cell type. E Cell type annotation and protein staining of a selected window in ( C ). CD68 (red) and Pan-cytokeratin (Pan-CK, green) represent markers for macrophages and epithelial cells. DAPI (light gray) and CD298/B2M (blue) were used for nuclei and membrane staining. A total of 15 samples were profiled using CosMx TM SMI. Scale bars, 50 µm. F Comparison of cell type compositions based on scRNA-seq and CosMx TM SMI from the same set of liver cancer patients in our cohort.

Article Snippet: The publicly available 10X genomics visium datasets used in this study include samples from Liu et al. (Mendeley Data: skrx2fz79n, https://data.mendeley.com/datasets/skrx2fz79n ), Wu et al. ( http://lifeome.net/supp/livercancer-st/data.htm ), Zhang et al. (GEO accession: GSE238264 , https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE238264 ), and Mo et al. (HTAN DCC Portal under the HTAN WUSTL Atlas, https://data.humantumoratlas.org/ ).

Techniques: Single Cell, Biomarker Discovery, Expressing, Marker, Staining, Membrane, Comparison

Journal: Nature Communications

Article Title: Tumor cell villages define the co-dependency of tumor and microenvironment in liver cancer

doi: 10.1038/s41467-026-69797-z

Figure Lengend Snippet: A Enrichment of each tumor cell state on SDNs. Red lines indicate observed enrichment while gray lines stand for randomized enrichment score. P -values were calculated based on the observed and randomized values ( n = 100 iterations per condition, see Methods for details). No adjustment was made for multiple comparisons. **, p -value < 0.01. Source data and exact p -values are provided as a Source data file. B The distances between EMT-like malignant cells and endothelial cells ( n = 20,312), as well as the distances between other malignant cells (cell cycle, cholangiocyte-like, or immune response and locomotion) and endothelial cells ( n = 433,456). Each box shows the median (center line), interquartile range (box), and data range (whiskers). Only tumor cell states with no significant enrichment in the SDN-T1–SDN-T5 were included in the comparison with EMT-like malignant cells. P -value was calculated with a one-sided Student’s t -test. ***, p -value < 0.001. Detailed statistics and source data are provided as a Source Data file. C The number (left) and the proportion (right) of malignant cells in the surrounding (within 40 µm distance) of cell cycle-related malignant cells ( n = 125,867) compared to that of other malignant cells ( n = 665,674). Each box shows the median (center line), interquartile range (box), and data range (whiskers). P -value was calculated using one-sided Student’s t -test. ***, p -value < 0.001. Detailed statistics and source data are provided as a Source Data file. D Validation of the spatial preference of tumor cell states using 10X Visium data from Liu et al. (left), Wu et al. (middle), and Zhang et al. (right). Each pair of connected dot and triangle represents a tumor cell state. For each tumor cell state, “Hit” represents the significantly associated SDNs in (A), while “non-Hit” indicates the rest of the SDNs ( n = 12 tumor cell states). Violin plots of the distributions of the enrichment scores were shown. p -value was calculated with a one-sided paired Student’s t -test. ***, p -value < 0.001. Source data and detailed statistics are provided as a Source Data file.

Article Snippet: The publicly available 10X genomics visium datasets used in this study include samples from Liu et al. (Mendeley Data: skrx2fz79n, https://data.mendeley.com/datasets/skrx2fz79n ), Wu et al. ( http://lifeome.net/supp/livercancer-st/data.htm ), Zhang et al. (GEO accession: GSE238264 , https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE238264 ), and Mo et al. (HTAN DCC Portal under the HTAN WUSTL Atlas, https://data.humantumoratlas.org/ ).

Techniques: Comparison, Biomarker Discovery

Journal: Discover Oncology

Article Title: Dissecting PTPN7‐driven aggressiveness in IDH‐wildtype astrocytomas: multi‐omics, clinical validation, and spatial transcriptomics for prognostic insights

doi: 10.1007/s12672-025-02662-5

Figure Lengend Snippet: Spatial transcriptomic profiling of an adult glioblastoma via the 10 × Genomics Visium platform. A H&E image showing the tissue section. B A pseudocolor map of the PTPN7 expression is overlaid in the same section. Key tumor subregions—leading edge, infiltrating border, cellular tumor, peri‐necrotic area, and necrotic core—are delineated by dashed lines

Article Snippet: To visualize PTPN7 expression in a spatial context, we examined a publicly available 10 × Genomics Visium dataset of an adult glioblastoma ( https://www.10xgenomics.com/datasets/human-glioblastoma-whole-transcriptome-analysis-1-standard-1-2-0 ).

Techniques: Expressing