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

doi:10.1021/acs.analchem.9b03333

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 journal › Article › Academic › peer-review

Abstract

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 language	English
Pages (from-to)	13112-13118
Number of pages	7
Journal	Analytical Chemistry
Volume	91
Issue number	20
DOIs	https://doi.org/10.1021/acs.analchem.9b03333
Publication status	Published - 15 Oct 2019

Keywords

BRAIN SECTIONS
LIPIDOMICS
PHOSPHOLIPIDS
SPECTRA
TOOL

Access to Document

10.1021/acs.analchem.9b03333

Cite this

Kune, C., McCann, A., Raphael, L. R., Arias, A. A., Tiquet, M., Van Kruining, D., Martinez, P. M., Ongena, M., Eppe, G., Quinton, L., Far, J., & De Pauw, E. (2019). Rapid Visualization of Chemically Related Compounds Using Kendrick Mass Defect As a Filter in Mass Spectrometry Imaging. Analytical Chemistry, 91(20), 13112-13118. https://doi.org/10.1021/acs.analchem.9b03333

@article{fba7538400b845c18be323242d176339,

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

abstract = "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.",

keywords = "BRAIN SECTIONS, LIPIDOMICS, PHOSPHOLIPIDS, SPECTRA, TOOL",

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

note = "Funding Information: We thank Prof. Philippe Jacques, from the Microbial Processes and Interactions (MiPI) research unit of Gembloux Agro-Bio Tech (Belgium) and Lipofabrik (Lille, France) for providing lipopeptides samples. We acknowledge financial support from the Excellence Of Science Program of the FNRS F.R.S (Rhizoclip EOS 18000802), from the Interreg EMR project: EURLIPIDS (R-8598) and from the European Union{\textquoteright}s Horizon 2020 research and innovation program under grant agreement No. 731077. Finally, we acknowledge the European Union and Wallonia program FEDER for the founding of the instruments (SolariX XR 9.4T). All experiments were done with permission from the Committee on Animal Welfare of Maastricht University, according to Dutch governmental rules. Publisher Copyright: {\textcopyright} 2019 American Chemical Society.",

year = "2019",

month = oct,

day = "15",

doi = "10.1021/acs.analchem.9b03333",

language = "English",

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T1 - Rapid Visualization of Chemically Related Compounds Using Kendrick Mass Defect As a Filter in Mass Spectrometry Imaging

AU - Kune, Christopher

AU - McCann, Andrea

AU - Raphael, La Rocca

AU - Arias, Anthony Arguelles

AU - Tiquet, Mathieu

AU - Van Kruining, Daan

AU - Martinez, Pilar Martinez

AU - Ongena, Marc

AU - Eppe, Gauthier

AU - Quinton, Loic

AU - Far, Johann

AU - De Pauw, Edwin

N1 - Funding Information: We thank Prof. Philippe Jacques, from the Microbial Processes and Interactions (MiPI) research unit of Gembloux Agro-Bio Tech (Belgium) and Lipofabrik (Lille, France) for providing lipopeptides samples. We acknowledge financial support from the Excellence Of Science Program of the FNRS F.R.S (Rhizoclip EOS 18000802), from the Interreg EMR project: EURLIPIDS (R-8598) and from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 731077. Finally, we acknowledge the European Union and Wallonia program FEDER for the founding of the instruments (SolariX XR 9.4T). All experiments were done with permission from the Committee on Animal Welfare of Maastricht University, according to Dutch governmental rules. Publisher Copyright: © 2019 American Chemical Society.

PY - 2019/10/15

Y1 - 2019/10/15

N2 - 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.

AB - 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.

KW - BRAIN SECTIONS

KW - LIPIDOMICS

KW - PHOSPHOLIPIDS

KW - SPECTRA

KW - TOOL

U2 - 10.1021/acs.analchem.9b03333

DO - 10.1021/acs.analchem.9b03333

M3 - Article

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SN - 0003-2700

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JO - Analytical Chemistry

JF - Analytical Chemistry

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ER -