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

• Published in: Metabolomics 2022-08, Vol.18 (8), p.64-64, Article 64
• Main Authors: Finch, Jasen P., Wilson, Thomas, Lyons, Laura, Phillips, Helen, Beckmann, Manfred, Draper, John
• Format: Article
• Language: English
• Subjects: Biochemistry; Biomedical and Life Sciences; Biomedicine; Cell Biology; Chromatography; Developmental Biology; Fingerprinting; Life Sciences; Mass spectrometry; Mass spectroscopy; Molecular Medicine; Original; Original Article; Scientific imaging; Software; Solvents
• ISSN: 1573-3890; 1573-3882; 1573-3890
• DOI: 10.1007/s11306-022-01923-6

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.