msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in Metabolomics

Abstract

Tandem mass spectrometry (MS/MS or MS) is a widely used approach for structural annotation and identification of metabolites in complex biological samples. The importance of assessing the contribution of the precursor ion within an isolation window for MS experiments has been previously detailed in proteomics, where precursor ion purity influences the quality and accuracy of matching to mass spectral libraries, but to date, there has been little attention to this data-processing technique in metabolomics. Here, we present msPurity, a vendor-independent R package for liquid chromatography (LC) and direct infusion (DI) MS that calculates a simple metric to describe the contribution of the selected precursor. The precursor purity metric is calculated as "intensity of a selected precursor divided by the summed intensity of the isolation window". The metric is interpolated at the recorded point of MS acquisition using bordering full-scan spectra. Isotopic peaks of the selected precursor can be removed, and low abundance peaks that are believed to have limited contribution to the resulting MS spectra are removed. Additionally, the isolation efficiency of the mass spectrometer can be taken into account. The package was applied to Data Dependent Acquisition (DDA)-based MS metabolomics data sets derived from three metabolomics data repositories. For the 10 LC-MS DDA data sets with > ±1 Da isolation windows, the median precursor purity score ranged from 0.67 to 0.96 (scale = 0 to +1). The R package was also used to assess precursor purity of theoretical isolation windows from LC-MS data sets of differing sample types. The theoretical isolation windows being the same width used for an anticipated DDA experiment (±0.5 Da). The most complex sample had a median precursor purity score of 0.46 for the 64,498 XCMS determined features, in comparison to the less spectrally complex sample that had a purity score of 0.66 for 5071 XCMS features. It has been previously reported in proteomics that a purity score of <0.5 can produce unreliable spectra matching results. With this assumption, we show that for complex samples there will be a large number of metabolites where traditional DDA approaches will struggle to provide reliable annotations or accurate matches to mass spectral libraries.

Identification of small molecules using accurate mass MS/MS search

“…There are a number of R‐language computational packages hosted on BioConductor (http://www.bioconductor.org/) that can be used to manipulate and process MS/MS spectra. That includes MSnID, MSnbase, msPurity, RMassBank, SwathXtend, and RAMClustR …”

“…There are a number of R-language computational packages hosted on BioConductor (http://www.bioconductor.org/) that can be used to manipulate and process MS/MS spectra. That includes MSnID, 84 MSnbase, 85 msPurity, 86 RMassBank, 87 SwathXtend, 88 and RAMClustR. 89 The "automatic curation" of MS/MS spectra in order to create high-quality and high-accuracy data has been a focus especially for MassBank and recently the MoNA database (http://mona.fiehnlab.…”

A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics

“…In the case of human studies, this is in large part due to the fact that these databases and tools are not entirely applicable to clinical metabolomics, where inclusion of such categories as theoretical human, xenobiotic, and non-dietary plant compounds may not be relevant. To date, it is challenging to effectively take sample preparation and instrument-generated artifacts into account, although this is changing and resources for understanding artifacts are becoming available [23][24][25][26]. Since a significant portion of a dataset may be composed of artifacts and non-biologically relevant compounds [10,27], deciphering and understanding these signals in a sample-specific manner, is vital.…”

“…These cannot be based on theoretical or in silico data, they require empirically-derived datasets. Indeed, as mentioned previously, resources such as these are becoming available [24,39]. These types of databases are also critical to the field of metabolomics as a whole, where such DBs can be cross-checked by any metabolomics laboratory.…”

New tools and resources in metabolomics: 2016–2017

“…msPurity is a vendor‐independent R package for LC‐MS and direct infusion (DI) MS 2 that calculates a simple metric to describe the contribution of the selected precursor . The package was applied to data dependent acquisition (DDA)‐based MS 2 metabolomics data sets derived from three metabolomics data repositories and can be used to assess precursor purity of theoretical isolation windows from LC‐MS data sets of differing sample types.…”