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Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions

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Title: Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions
Authors: Inglese, P
Huang, HX
Wu, V
Lewis, MR
Takats, Z
Item Type: Journal Article
Abstract: <jats:title>Abstract</jats:title><jats:sec> <jats:title>Background</jats:title> <jats:p>Mass spectrometry imaging (MSI) data often consist of tens of thousands of mass spectra collected from a sample surface. During the time necessary to perform a single acquisition, it is likely that uncontrollable factors alter the validity of the initial mass calibration of the instrument, resulting in mass errors of magnitude significantly larger than their theoretical values. This phenomenon has a two-fold detrimental effect: (a) it reduces the ability to interpret the results based on the observed signals, (b) it can affect the quality of the observed signal spatial distributions.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>We present a post-acquisition computational method capable of reducing the observed mass drift by up to 60 ppm in biological samples, exploiting the presence of typical molecules with a known mass-to-charge ratio. The procedure, tested on time-of-flight and Orbitrap mass spectrometry analyzers interfaced to a desorption electrospray ionization (DESI) source, improves the molecular annotation quality and the spatial distributions of the detected ions.</jats:p> </jats:sec><jats:sec> <jats:title>Conclusion</jats:title> <jats:p>The presented method represents a robust and accurate tool for performing post-acquisition mass recalibration of DESI-MSI datasets and can help to increase the reliability of the molecular assignment and the data quality.</jats:p> </jats:sec>
Issue Date: Dec-2022
URI: http://hdl.handle.net/10044/1/96651
DOI: 10.1186/s12859-022-04671-5
Publisher: Springer Science and Business Media LLC
Journal / Book Title: BMC Bioinformatics
Volume: 23
Issue: 1
Copyright Statement: © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Sponsor/Funder: Medical Research Council
Funder's Grant Number: MR/S010483/1
Keywords: Bioinformatics
01 Mathematical Sciences
06 Biological Sciences
08 Information and Computing Sciences
Publication Status: Published online
Open Access location: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-04671-5#citeas
Article Number: 133
Online Publication Date: 2022-04-15
Appears in Collections:Department of Metabolism, Digestion and Reproduction
Faculty of Medicine

This item is licensed under a Creative Commons License Creative Commons