An approach to optimize sample preparation for MALDI imaging MS of FFPE sections using fractional factorial design of experiments View Full Text


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Article Info

DATE

2016-08-02

AUTHORS

Janina Oetjen, Delf Lachmund, Andrew Palmer, Theodore Alexandrov, Michael Becker, Tobias Boskamp, Peter Maass

ABSTRACT

A standardized workflow for matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI imaging MS) is a prerequisite for the routine use of this promising technology in clinical applications. We present an approach to develop standard operating procedures for MALDI imaging MS sample preparation of formalin-fixed and paraffin-embedded (FFPE) tissue sections based on a novel quantitative measure of dataset quality. To cover many parts of the complex workflow and simultaneously test several parameters, experiments were planned according to a fractional factorial design of experiments (DoE). The effect of ten different experiment parameters was investigated in two distinct DoE sets, each consisting of eight experiments. FFPE rat brain sections were used as standard material because of low biological variance. The mean peak intensity and a recently proposed spatial complexity measure were calculated for a list of 26 predefined peptides obtained by in silico digestion of five different proteins and served as quality criteria. A five-way analysis of variance (ANOVA) was applied on the final scores to retrieve a ranking of experiment parameters with increasing impact on data variance.Graphical abstractMALDI imaging experiments were planned according to fractional factorial design of experiments for the parameters under study. Selected peptide images were evaluated by the chosen quality metric (structure and intensity for a given peak list), and the calculated values were used as an input for the ANOVA. The parameters with the highest impact on the quality were deduced and SOPs recommended. More... »

PAGES

6729-6740

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    http://scigraph.springernature.com/pub.10.1007/s00216-016-9793-4

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    http://dx.doi.org/10.1007/s00216-016-9793-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1043902527

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27485623


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