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Imaging MS MIAPE

This document provides a detailed guide for the imaging, histological classification, and data analysis of tissue specimens, including tissue preparation, data acquisition, and data analysis workflows.

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Imaging MS MIAPE

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

  2. General • Responsible person

  3. Tissue Collection & Histomorphological Classification • Origin - institution • Specimen – species, organ • Fixed / fresh / embedded – incl. method • Morphological classification – e.g. WHO • Sample randomization – Yes/No – if yes, then how • Additional comments received • 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

  4. Tissue Preparation Tissue Sectioning • Tissue thickness • If embedded give method Tissue Wash • Provide procedure if applicable On- tissue chemistry (enymaticdigestion, internal standards) • Provide detailed procedure if applicable - supplementary Matrix deposition • Matrix solution • Deposition method and device • Provide detailed procedure - supplementary

  5. Histology • Histoarchitectural overview of tissue • Staining method • 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

  6. Data Acquisition • Pixel size • Mass analyzer type, model, and laser/ionizing beam • Software packages – incl. versions • Mass range and polarity • Scanning pattern (random, left-right, right-left, serpentine) • Number of shots (incl. random walk if applicable) • Continuous – mention scanning speed if applicable • Oversampling - if applicable provide laser spot size • Representative mass spectrum, linked to MSI image and histology 7A. SRM – isolation and MS/MS method Additional comments received • Resolution in pixels of all images – Gyorgy • Internal reference points for eventual alignment – Gyorgy

  7. Data Analysis Workflowshould be provided

  8. MS Pre-processing • Software – including version • Baseline subtraction algorithm and settings • Smoothing algorithm and settings • Alignment / re-calibration – if yes, how? • Normalization/Quantitation method – TIC… • Peak-picking method – algorithm and settings • 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

  9. Visualization • Peak evaluation method (area, peak height,..) • If area define m/z (or ppm) integration range • Provide intensity scale, and color scheme, for each MS image. • Interpolation and image smoothing – provide method if applicable • Scale bar

  10. Data Analysis • Software package – incl. version number • Data analysis algorithm plus parameters • Provide loading plots if applicable

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