Mass recalibration for desorption electrospray ionization mass spectrometry imaging using endogenous reference ions
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Published version
Author(s)
Inglese, Paolo
Huang, Helen Xuexia
Wu, Vincen
Lewis, Matthew R
Takats, Zoltan
Type
Journal Article
Abstract
Background
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.
Results
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.
Conclusion
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.
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.
Results
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.
Conclusion
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.
Date Issued
2022-04-15
Date Acceptance
2022-03-30
Citation
BMC Bioinformatics, 2022, 23 (1), pp.1-17
ISSN
1471-2105
Publisher
BioMed Central
Start Page
1
End Page
17
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.
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.
License URL
Sponsor
Medical Research Council
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000782776000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
MR/S010483/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Biochemical Research Methods
Biotechnology & Applied Microbiology
Mathematical & Computational Biology
Biochemistry & Molecular Biology
CALIBRATION
ALIGNMENT
Publication Status
Published
Article Number
ARTN 133
Date Publish Online
2022-04-15