Rapid Visualization of Chemically Related Compounds Using Kendrick Mass Defect As a Filter in Mass Spectrometry Imaging.
Christopher KuneAndréa McCannLa Rocca RaphaëlAnthony Arguelles AriasMathieu TiquetDaan Van KruiningPilar Martinez MartinezMarc OngenaGauthier EppeLoïc QuintonJohann FarEdwin De PauwPublished in: Analytical chemistry (2019)
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.
Keyphrases
- high resolution
- mass spectrometry
- electronic health record
- liquid chromatography
- deep learning
- big data
- data analysis
- loop mediated isothermal amplification
- dna damage
- tandem mass spectrometry
- high performance liquid chromatography
- high speed
- density functional theory
- fluorescence imaging
- multiple sclerosis
- photodynamic therapy
- blood brain barrier
- resting state