Imaging MS MIAPE Working Document Helmholtz Institute, Munich, April 16 th 2012

General 1)Responsible person

Tissue Collection & Histomorphological Classification 1)Origin - institution 2)Specimen – species, organ 3)Fixed / fresh / embedded – incl. method 4)Morphological classification – e.g. WHO 5)Sample randomization – Yes/No – if yes, then how Additional comments received i)Time and date of sampling – Markus Liam’s input Clinical samples – patient number arguably more important for validation Preclinical work – easier to record times and as normally involve fewer specimens may be more significant. So Liam’s opinion Pre-clinical Yes / Clinical No ii) Index of sample amount – Gyorgy iii) Usage history – Gyorgy Liam’s input maybe important but would add an enormous workload to clinical studies, and is likely to be incomplete

Tissue Preparation Tissue Sectioning 1)Tissue thickness 2)If embedded give method Tissue Wash 1)Provide procedure if applicable On- tissue chemistry (enymatic digestion, internal standards) 1)Provide detailed procedure if applicable - supplementary Matrix deposition 1)Matrix solution 2)Deposition method and device 3)Provide detailed procedure - supplementary

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)Scanning pattern (random, left-right, right-left, serpentine) 6)Number of shots (incl. random walk if applicable) 7)Continuous – mention scanning speed if applicable 8)Oversampling - if applicable provide laser spot size 9)Representative mass spectrum, linked to MSI image and histology 7A. SRM – isolation and MS/MS method Additional comments received i)Resolution in pixels of all images – Gyorgy ii)Internal reference points for eventual alignment – Gyorgy

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/Quantitation 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)Peak evaluation method (area, peak height,..) a.If area 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