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

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