Histology 1)Histoarchitectural overview of tissue – Staining method 2)Cytological view of regions of interest – Correlated histology and MSI images (same tissue / adjacent tissue section) – Representative images of histological features referred to in manuscript – Scale bars must always be included
Data Acquisition 1)Pixel size 2)Mass analyzer type, model, and laser/ionizing beam 3)Software packages – incl. versions 4)Mass range and polarity 5)Number of shots (incl. random walk if applicable) 6)Continuous – mention scanning speed if applicable 7)Oversampling - if applicable provide laser spot size 8)Representative mass spectrum, linked to MSI image and histology 7A. SRM – isolation and MS/MS method
Data Analysis Workflow should be provided MS processing Virtual microdissection of cell types A and B Univariate analysis for biomarker discovery – cell type A vs. cell type B Visualization of lead features – comparison with histology Virtual microdissection of cell types A Hierarchical cluster analysis for intratumor heterogeneity Comparison with histology
MS Pre-processing 1)Software – including version 2)Baseline subtraction algorithm and settings 3)Smoothing algorithm and settings 4)Alignment / re-calibration – if yes, how? 5)Normalization method – TIC… 6)Peak-picking method – algorithm and settings 7)MS Data reduction method if applicable (incl. integration width/peak height if applicable, m/z binning width if applicable) Univariate filtering – report if applicable Multivariate/projection methods – if applicable include method and parameters
Visualization 1)Define m/z or ppm integration range 2)Provide intensity scale, and color scheme, for each MS image. 3)Interpolation and image smoothing – provide method if applicable 4)Scale bar
Data Analysis Software package – incl. version number Data analysis algorithm plus parameters Provide loading plots if applicable
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