Multimodal dataset for cohort-wide spatial autocorrelation-based MIR imaging analysis with correlative MSI lipidomics

This record contains the multimodal raw datasets of which analysis results are published in "Cohort-scale Spatial Autcorrelation for Tumor Prediction in Mid-infrared Pathology and Spatial Biomarker Discovery using MALDI Imaging Lipidomics" in Advanced Science by Rittel et al. The study was conducted on twelve surgical tissue specimens of colorectal cancer liver metastasis (CRLM) obtained from patients with informed consent, prepared as fresh frozen (FF) material. The dataset includes images of hematoxylin and eosin (H&E) staining conducted on adjacent sections and MIR imaging as well as MS imaging (positive and negative ionization mode) data obtained subsequently on the same section for all twelve tumor samples. In short data was generated via Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) on a timsTOF flex mass spectrometer (Bruker Daltonics), imzML format Fourier-transform infrared (FT-IR) imaging (Spotlight 400, Perkin Elmer), fsm format Optical images of hematoxylin and eosin (H&E) stainings recorded with an Aperio CS2 Scanner (objective: 20×, Leica Biosystems), svs format (more detailed information can be obtained from the corresponding publication) Additionally, an example dataset to showcase basic functionality of the published code (https://github.com/CeMOS-Mannheim/InSpIRe) is provided here.