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Multimodal imaging of undecalcified tissue sections by MALDI MS and µXRF


Matrix-assisted laser desorbtion/ionisation mass spectrometric imaging (MALDI MSI) is a technique providing localized information on intact molecules in a sample. Micro X-ray fluorescence (µXRF) imaging allows to examine the spatial distribution of elements in a sample without any morphological changes. These methods have already been applied separately to different tissues, organs, plants and bacterial films, but, to the best of our knowledge, they have yet to be coupled in a multimodal analysis. In this proof-of-principle study, we established and tested sample preparation strategies, allowing multimodal analysis of lipids (sphingomyelin and phosphatidylcholines) and elements relevant for bone structures as calcium, phosphorous and sulphur in the very same sample section of a chicken phalanx without tissue decalcification. The results of the investigation of such parameters as adhesive tapes supporting tissue sections, and sequence of the imaging experiments are presented. We show specific lipid distributions in skin, cartilage, muscle, nail, and intact morphology of bone by calcium and phosphorous images. Combination of molecular and elemental imaging was achieved, thus, providing now for the first time the possibility to gather MALDI MSI and µXRF information from the very same sample without any washing steps omitting therefore analytical artifacts that inevitably occur in approaches using consecutive tissue sections. The proposed combination can benefit in researches regarding bone diseases, osteoporosis, osteoarthritis, cartilage failure, bone/tendon distinguishing, were elemental and lipid interaction play an essential role.

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Publication details

The article was received on 18 Feb 2018, accepted on 10 Apr 2018 and first published on 16 Apr 2018

Article type: Paper
DOI: 10.1039/C8AN00313K
Citation: Analyst, 2018, Accepted Manuscript
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    Multimodal imaging of undecalcified tissue sections by MALDI MS and µXRF

    A. Svirkova, A. Turyanskaya, L. Perneczky, C. Streli and M. Marchetti-Deschmann, Analyst, 2018, Accepted Manuscript , DOI: 10.1039/C8AN00313K

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