1 research outputs found
msPurity: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry-Based Fragmentation in Metabolomics
Tandem mass spectrometry
(MS/MS or MS<sup>2</sup>) 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<sup>2</sup> 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<sup>2</sup> 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<sup>2</sup> 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<sup>2</sup> 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<sup>2</sup> metabolomics data sets derived
from three metabolomics data repositories. For the 10 LC-MS<sup>2</sup> 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