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Mass Spectrometry Imaging (MSI) is an analytical method employed to map the distribution
of molecules inside a sample based on their mass-to-charge ratio. When working with
biological sample, classical histology is often combined due to the complementarity of the
data.
Unfortunately, bioinformatic tools used to treat those data are not open source and are
often limited in terms of their analytic possibilities. The lack of means to analyse imagery
by mass spectrometry data forces scientists to build their own tools.
In this work, we have developed a tool from existing methods to process image by mass
spectrometry data in order to identify lipids in an image by MALDI FT-ICR mass spec-
trometry from a biological section. The different methods used to achieve this goal are peak
picking methods, to process the mass spectra signals, followed by a FDR controlled anno-
tion of lipids. The identifications are made by the LIPID MAPS database. Furthermore,
not recorded lipids are infered according to the lipids already identified.
Moreover, we used the identification process as a feature reduction method to allow efficient
segmentation of an image by mass spectrometry. Then, the regions of interest, discovered
by the segmentation , can be analyzed in term of their lipid composition.
The pipeline is implemented in R and is tested on 3 biological sections of zebrafish.

Sciences du vivant > Biochimie, biophysique & biologie moléculaire