Kendrick Mass Defect Variation to Decipher Isotopic Labeling in Brain Metastases Studied by Mass Spectrometry Imaging

2021 ◽  
Author(s):  
Landry Blanc ◽  
Gino B. Ferraro ◽  
Michael Tuck ◽  
Brendan Prideaux ◽  
Véronique Dartois ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Brain Metastases ◽  
Mass Spectrometry Imaging ◽  
Isotopic Labeling ◽  
Mass Defect ◽  
Kendrick Mass Defect

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

2019 ◽  
Vol 91 (20) ◽  
pp. 13112-13118 ◽  
Author(s):  
Christopher Kune ◽  
Andréa McCann ◽  
La Rocca Raphaël ◽  
Anthony Arguelles Arias ◽  
Mathieu Tiquet ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Mass Defect ◽  
Kendrick Mass Defect ◽  
Related Compounds

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

2019 ◽  
Author(s):  
Christopher Kune ◽  
Andréa Mc Cann ◽  
Raphaël La Rocca ◽  
Anthony Arguelles Arias ◽  
Mathieu Tiquet ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Mass Spectrometry Data ◽  
Mass Defect ◽  
Imaging Data ◽  
Exact Mass ◽  
Detection And Identification ◽  
Efficient Data ◽  
Kendrick Mass Defect ◽  
Related Compounds

Draft article concerning our work about the use of Kendrick mass defect for filtering mass spectrometry imaging data. 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. The method enables an automated, faster and efficient data processing, enabling the reconstruction of 2D distributions of family of homologous compound in MSI images. We show that the KMD filtering, based on a homemade software, is suitable for low resolution and high resolution MSI data. This method has been successfully applied to two different types of samples, bacteria co-cultures and brain tissue section.

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

2019 ◽  
Author(s):  
Christopher Kune ◽  
Andréa Mc Cann ◽  
Raphaël La Rocca ◽  
Anthony Arguelles Arias ◽  
Mathieu Tiquet ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Mass Defect ◽  
Exact Mass ◽  
Detection And Identification ◽  
Homologous Compounds ◽  
Brain Tissue Section ◽  
Efficient Data ◽  
Kendrick Mass Defect ◽  
Related Compounds

<div> <p>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 <i>m/z</i> 410 of 20 000) and very high-resolution (FWHM, at <i>m/z</i> 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</p> </div>

scholarly journals Analyzing Mass Spectrometry Imaging Data of 13C-Labeled Phospholipids in Camelina sativa and Thlaspi arvense (Pennycress) Embryos

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 148
Author(s):  
Trevor B. Romsdahl ◽  
Shrikaar Kambhampati ◽  
Somnath Koley ◽  
Umesh P. Yadav ◽  
Ana Paula Alonso ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Mass Spectrometry Imaging ◽  
Metabolic Flux ◽  
Isotopic Labeling ◽  
Membrane Lipid ◽  
Camelina Sativa ◽  
Complex Data ◽  
Imaging Data ◽  
Thlaspi Arvense

The combination of 13C-isotopic labeling and mass spectrometry imaging (MSI) offers an approach to analyze metabolic flux in situ. However, combining isotopic labeling and MSI presents technical challenges ranging from sample preparation, label incorporation, data collection, and analysis. Isotopic labeling and MSI individually create large, complex data sets, and this is compounded when both methods are combined. Therefore, analyzing isotopically labeled MSI data requires streamlined procedures to support biologically meaningful interpretations. Using currently available software and techniques, here we describe a workflow to analyze 13C-labeled isotopologues of the membrane lipid and storage oil lipid intermediate―phosphatidylcholine (PC). Our results with embryos of the oilseed crops, Camelina sativa and Thlaspi arvense (pennycress), demonstrated greater 13C-isotopic labeling in the cotyledons of developing embryos compared with the embryonic axis. Greater isotopic enrichment in PC molecular species with more saturated and longer chain fatty acids suggest different flux patterns related to fatty acid desaturation and elongation pathways. The ability to evaluate MSI data of isotopically labeled plant embryos will facilitate the potential to investigate spatial aspects of metabolic flux in situ.

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

2019 ◽  
Author(s):  
Christopher Kune ◽  
Andréa Mc Cann ◽  
Raphaël La Rocca ◽  
Anthony Arguelles Arias ◽  
Mathieu Tiquet ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Mass Defect ◽  
Exact Mass ◽  
Detection And Identification ◽  
Homologous Compounds ◽  
Brain Tissue Section ◽  
Efficient Data ◽  
Kendrick Mass Defect ◽  
Related Compounds

<div> <p>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 <i>m/z</i> 410 of 20 000) and very high-resolution (FWHM, at <i>m/z</i> 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</p> </div>

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