High-dimensional imaging of vestibular schwannoma reveals distinctive immunological networks across histomorphic niches in NF2-related schwannomatosis

In this dataset, we have used imaging mass cytometry to identify and map the various populations in the human vestibular schwannomas tumor microenvironment (TME) using known markers for myeloid and schwann cells in vestibular schwannoma. This dataset consists of imaging mass cytometry data (16-bit TIFF images) for 17 vestibular schwannomas sourced from the Salford Royal NHS Trust Biobank. We identified various schwann, myeloid and T-cell subsets that has distinct spatial locations within the TME.

Methods
In brief, 5 um FFPE sections from 17 vestibular schwannomas (see cases.csv) 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, labelling them Antoni A or Antoni B (see cases.csv). 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). Denoised images are provided (images.zip). 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 raw mean single-cell expression of each of the markers in the panel, along with their X and Y locations in the region of interest. Meta-data for cells is also provided (cell_level_metadata.csv). Segmentation masks created by the Bodenmiller pipeline are also provided (masks.zip). Processed data is also provided as an AnnData object (VS_NF2_IMC_AnnData.h5ad), where markers were normalised to the 99th percentile, and population were identified by leiden clustering.