DOI: 10.6084/m9.figshare.29237660
title: <b>Visium spatial transcriptomics data for individual oral squamous cell carcinoma (OSCC) patients</b>

description: <b>How to Cite</b>If you use this dataset in your research, please cite our primary publication in <i>PLOS Genetics</i>:Furudate K, Kasai S, Yoshizawa T, Sasaki Y, Fujikura K, <i>et al</i>. (2025) Spatial colocalization and molecular crosstalk of myofroblastic CAFs and tumor cells shape lymph node metastasis in oral squamous cell carcinoma. <i>PLOS Genetics</i> 21(9): e1011791. https://doi.org/10.1371/journal.pgen.1011791<br><b>Dataset Citation (Optional but Recommended):</b> For data traceability, you may also cite this Figshare dataset directly using the citation generated by the "Cite" button on this page. <b>However, please ensure the primary publication above is also cited.</b><br><b>Dataset Description</b>This dataset contains the 10x Genomics Space Ranger output files for four individual human oral squamous cell carcinoma (OSCC) samples, supporting the findings of our study: <b>"Spatial colocalization and molecular crosstalk of myofibroblastic CAFs and tumor cells shape lymph node metastasis in oral squamous cell carcinoma" </b>(https://doi.org/10.1371/journal.pgen.1011791).For each of the four samples (Sample A [HUH001-P], Sample B [HUH001-P2], Sample C [HUH001-met], and Sample D [HUH002-P]), the following files and folders are provided:The filtered feature-barcode matrix in HDF5 format (filtered_feature_bc_matrix.h5).<br>A spatial subfolder containing:High-resolution tissue image (tissue_hires_image.png)Low-resolution tissue image (tissue_lowres_image.png)Fiducial alignment image (aligned_fiducials.jpg)Tissue detection image (detected_tissue_image.jpg)Scale factors JSON file (scalefactors_json.json)Tissue spot coordinates CSV file (tissue_positions_list.csv)<br>A patho_annot subfolder containing annotation files in CSV format for each respective sample. These files link annotations to spot barcodes and provide detailed information for each spot, including columns for:Barcode (the unique spot identifier)patho (primary pathological annotation ID)graph-based cluster (computationally assigned cluster ID based on gene expression)patho_diag (detailed pathological diagnostic information or feature)category (a broader classification category for the spot, e.g., tumor, peritumor, non_tumor)description (additional descriptive text or notes for the spot)<br>These files allow for the direct import, reprocessing, and reanalysis of individual samples using standard spatial transcriptomics software packages such as Seurat (e.g., the Load10X_Spatial function) or Scanpy/Squidpy (e.g., the read_visium function).<b>Interactive Data Exploration with Loupe Browser</b>For users who prefer interactive visualization without programming, this dataset also includes Loupe Browser (.cloupe) files for each of the four samples. Loupe Browser is a free desktop application from 10x Genomics that allows for easy exploration of the spatial gene expression data.To get started:Download and install Loupe Browser from the 10x Genomics website:https://www.10xgenomics.com/support/software/loupe-browser/downloadsOpen the .cloupe file for the sample you wish to explore (e.g., A.cloupe).Import pathologist annotations: To view our custom pathology annotations on the tissue image, navigate to the "Categories" tab, click the three-dot menu (...) next to a clustering (e.g., "Graph-Based"), and select "Import Categories". Then, select the corresponding annotation file from the patho_annot folder provided in this deposit.You can now explore gene expression spatially and overlay it with our pathological annotations. <b>Please note:</b> The gene expression data visualized in Loupe Browser is based on raw, un-normalized UMI counts.<b>Related Data Repositories</b>The raw sequencing reads for these samples are available at DDBJ under BioProject accession PRJDB13905 (https://ddbj.nig.ac.jp/search/entry/bioproject/PRJDB13905).A processed and integrated AnnData object (.h5ad) combining these four samples is available at GEA under accession E-GEAD-511 (https://ddbj.nig.ac.jp/public/ddbj_database/gea/experiment/E-GEAD-000/E-GEAD-511/).Supplementary spatial metadata components (pickled Python objects) for use with the integrated AnnData object are available at Figshare (https://doi.org/10.6084/m9.figshare.20408067).The trained scVI model used for data integration is available at Figshare <b>(</b>https://doi.org/10.6084/m9.figshare.20279025.v1).All patient-derived images included in this dataset were de-identified prior to deposition.