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Kune and McCann et al Rapid visualization of chemically related compounds using KMD as a filter in MSI.pdf (1.43 MB)

Rapid Visualization of Chemically Related Compounds Using Kendrick Mass Defect as a Filter in Mass Spectrometry Imaging

preprint
revised on 18.07.2019, 14:16 and posted on 18.07.2019, 15:10 by Christopher Kune, Andréa Mc Cann, Raphaël La Rocca, Anthony Arguelles Arias, Mathieu Tiquet, Daan van Kruining, Pilar Martinez-Martinez, Marc Ongena, Gauthier Eppe, Loïc Quinton, Johann Far, Edwin De Pauw

Kendrick mass defect (KMD) analysis is widely used for helping the detection and identification of chemically related compounds based on exact mass measurements. We report here the use of KMD as a criterion for filtering complex mass spectrometry dataset. The method enables an automated, easy and efficient data processing, enabling the reconstruction of 2D distributions of family of homologous compounds from MSI images. We show that the KMD filtering, based on an in-house software, is suitable and robust for high resolution (full width at half-maximum, FWHM, at m/z 410 of 20 000) and very high-resolution (FWHM, at m/z 410 of 160 000) MSI data. This method has been successfully applied to two different types of samples, bacteria co-cultures and brain tissue section

Funding

Excellence Of Science Program of the FNRS F.R.S (Rhizoclip EOS2018000802)

Interreg EMR project: EURLIPIDS (R-8598)

European Union’s Horizon 2020 research and innovation program No. 731077

History

Email Address of Submitting Author

c.kune@uliege.be

Institution

University of Liege

Country

Belgique

ORCID For Submitting Author

0000-0002-3010-8173

Declaration of Conflict of Interest

no conflict of Interest

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