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

Christopher Kune, Andrea McCann, La Rocca Raphael, Anthony Arguelles Arias, Mathieu Tiquet, Daan Van Kruining, Pilar Martinez Martinez, Marc Ongena, Gauthier Eppe, Loic Quinton, Johann Far, Edwin De Pauw*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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 data set. The method allow automated, easy and efficient data processing, enabling the reconstruction of 2D distributions of families of homologous compounds from MSI images. We show that KMD filtering, based on 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 cocultures, and brain tissue sections.

Original languageEnglish
Pages (from-to)13112-13118
Number of pages7
JournalAnalytical Chemistry
Issue number20
Publication statusPublished - 15 Oct 2019


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