Correlation-Based Deconvolution (CorrDec) To Generate High-Quality MS2 Spectra from Data-Independent Acquisition in Multisample Studies

Data-independent
acquisition mass spectrometry (DIA-MS) is essential
for information-rich spectral annotations in untargeted metabolomics.
However, the acquired MS2 spectra are highly complex, posing significant
annotation challenges. We have developed a correlation-based deconvolution
(CorrDec) method that uses ion abundance correlations in multisample
studies using DIA-MS as an update of our MS-DIAL software. CorrDec
is based on the assumption that peak intensities of precursor and
fragment ions correlate across samples and exploits this quantitative
information to deconvolute complex DIA spectra. CorrDec clearly improved
deconvolution of the original MS-DIAL deconvolution method (MS2Dec)
in a dilution series of chemical standards and a 224-sample urinary
metabolomics study. The primary advantage of CorrDec over MS2Dec is
the ability to discriminate coeluting low-abundance compounds. CorrDec
requires the measurement of multiple samples to successfully deconvolute
DIA spectra; however, our randomized assessment demonstrated that
CorrDec can contribute to studies with as few as 10 unique samples.
The presented methodology improves compound annotation and identification
in multisample studies and will be useful for applications in large
cohort studies.