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Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data

INTRODUCTION: Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis. OBJECTIVES: Present spectral binning as a pragmatic approach to p...

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Autores principales: Finch, Jasen P., Wilson, Thomas, Lyons, Laura, Phillips, Helen, Beckmann, Manfred, Draper, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345815/
https://www.ncbi.nlm.nih.gov/pubmed/35917032
http://dx.doi.org/10.1007/s11306-022-01923-6
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author Finch, Jasen P.
Wilson, Thomas
Lyons, Laura
Phillips, Helen
Beckmann, Manfred
Draper, John
author_facet Finch, Jasen P.
Wilson, Thomas
Lyons, Laura
Phillips, Helen
Beckmann, Manfred
Draper, John
author_sort Finch, Jasen P.
collection PubMed
description INTRODUCTION: Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis. OBJECTIVES: Present spectral binning as a pragmatic approach to post-acquisition procession of FIE-HRMS metabolome fingerprinting data. METHODS: A spectral binning approach was developed that included the elimination of single scan m/z events, the binning of spectra and the averaging of spectra across the infusion profile. The modal accurate m/z was then extracted for each bin. This approach was assessed using four different biological matrices and a mix of 31 known chemical standards analysed by FIE-HRMS using an Exactive Orbitrap. Bin purity and centrality metrics were developed to objectively assess the distribution and position of accurate m/z within an individual bin respectively. RESULTS: The optimal spectral binning width was found to be 0.01 amu. 80.8% of the extracted accurate m/z matched to predicted ionisation products of the chemical standards mix were found to have an error of below 3 ppm. The open-source R package binneR was developed as a user friendly implementation of the approach. This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB. CONCLUSION: Spectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open-source R package binneR allows users to efficiently process data from FIE-HRMS experiments with the resources available on a standard desktop computer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-022-01923-6.
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spelling pubmed-93458152022-08-04 Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data Finch, Jasen P. Wilson, Thomas Lyons, Laura Phillips, Helen Beckmann, Manfred Draper, John Metabolomics Original Article INTRODUCTION: Flow infusion electrospray high resolution mass spectrometry (FIE-HRMS) fingerprinting produces complex, high dimensional data sets which require specialist in-silico software tools to process the data prior to analysis. OBJECTIVES: Present spectral binning as a pragmatic approach to post-acquisition procession of FIE-HRMS metabolome fingerprinting data. METHODS: A spectral binning approach was developed that included the elimination of single scan m/z events, the binning of spectra and the averaging of spectra across the infusion profile. The modal accurate m/z was then extracted for each bin. This approach was assessed using four different biological matrices and a mix of 31 known chemical standards analysed by FIE-HRMS using an Exactive Orbitrap. Bin purity and centrality metrics were developed to objectively assess the distribution and position of accurate m/z within an individual bin respectively. RESULTS: The optimal spectral binning width was found to be 0.01 amu. 80.8% of the extracted accurate m/z matched to predicted ionisation products of the chemical standards mix were found to have an error of below 3 ppm. The open-source R package binneR was developed as a user friendly implementation of the approach. This was able to process 100 data files using 4 Central Processing Units (CPU) workers in only 55 seconds with a maximum memory usage of 1.36 GB. CONCLUSION: Spectral binning is a fast and robust method for the post-acquisition processing of FIE-HRMS data. The open-source R package binneR allows users to efficiently process data from FIE-HRMS experiments with the resources available on a standard desktop computer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-022-01923-6. Springer US 2022-08-02 2022 /pmc/articles/PMC9345815/ /pubmed/35917032 http://dx.doi.org/10.1007/s11306-022-01923-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Finch, Jasen P.
Wilson, Thomas
Lyons, Laura
Phillips, Helen
Beckmann, Manfred
Draper, John
Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data
title Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data
title_full Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data
title_fullStr Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data
title_full_unstemmed Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data
title_short Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data
title_sort spectral binning as an approach to post-acquisition processing of high resolution fie-ms metabolome fingerprinting data
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9345815/
https://www.ncbi.nlm.nih.gov/pubmed/35917032
http://dx.doi.org/10.1007/s11306-022-01923-6
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