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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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Detalles Bibliográficos
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
Descripción
Sumario: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.