Imaging mass cytometry data from IDH wildtype glioblastomas

Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We now have limited knowledge of the tumor microenvironment (TME) determinants that influence the localization and the functions of the diverse myeloid cell populations in GBM. In this dataset, we have used imaging mass cytometry to identify and map the various myeloid populations in the human GBM tumor microenvironment (TME) using known markers for myeloid and neoplastic cells in GBM. Our analyses of these data found that different myeloid populations had distinct and reproducible compartmentalization patterns in the GBM TME that were driven by tissue hypoxia and varied homotypic and heterotypic cellular interactions. This dataset consists of imaging mass cytometry data (16-bit TIFF images) for 8 glioblastomas and 1 tonsil sourced from the Salford Royal NHS Trust Biobank., Full details can be found in the accompanying publication. However, in brief, 5 um FFPE sections from 8 primary glioblastomas (see Cases.xls) and 1 tonsil (as a control) were stained using the protocol recommended by Standard BioTools (https://www.standardbio.com/products/instruments/hyperion) using a panel of metal-conjugated antibodies. They were then imaged on the Hyperion using the standard settings. Regions of interest were identified on serial-cut H&E stained sections by a neuropathologist, targeting regions either in the tumor or infiltrating the edge of the tumor. Raw TIFF images were then extracted from MCD files, and denoised using the IMC-Denoise method (https://www.nature.com/articles/s41467-023-37123-6). Single-cell information for each of the channels was extracted using the Bodenmiller pipeline (https://github.com/BodenmillerGroup/ImcSegmentationPipeline), with the resulting cell table (see cell_table.csv) detailing the single-cell expression of each of the markers in..., Denoised images are supplied as 32-bit TIFF files, with individual files for each metal channel. Each pixel is 1 um2, and pixel values correspond to the total counts for that metal in that pixel. The cell table (cell_table.csv) is the output of the Bodenmiller pipeline, in which each cell is one row, and the columns are either the mean marker expression for that channel, the cells' location (Location_Center_X, Location_Center_Y), the region name (ROI), or cell ID within that ROI (ObjectNumber). The details for the different regions can be found in the ROI tab of the Cases.xls file., # Imaging mass cytometry data from IDH wildtype glioblastomas

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## Description of the data and file structure

Denoised images are supplied as 32-bit TIFF files, with individual files for each metal channel. Each pixel is 1 um2, and pixel values correspond to the total counts for that metal in that pixel. The cell table (cell_table.csv) is the output of the Bodenmiller pipeline, in which each cell is one row, and the columns are either the mean marker expression for that channel, the cells' location (Location_Center_X, Location_Center_Y), the region name (ROI), or cell ID within that ROI (ObjectNumber). The details for the different regions can be found in the ROI tab of the Cases.xls file.

Cases.xls - Information about the glioblastoma cases and regions of interest
cell_table.csv - Single-cell data, with each row for one cell
images.zip - Zipped and denoised IMC images, with each channel is an individual .tiff file, with each region of interest (ROI) having its own folder.

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