Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach

Direct-infusion
Fourier transform ion cyclotron resonance mass
spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis
due to ultrahigh mass accuracy and resolution. However, most of the
DI-FTICR MS approaches focused on high-throughput metabolomics analysis
at the expense of sensitivity and resolution and the potential for
metabolome characterization has not been fully explored. Here, we
proposed a novel deep characterization approach of serum metabolome
using a segment-optimized spectral-stitching DI-FTICR MS method integrated
with high-confidence and database-independent formula assignments.
With varied acquisition parameters for each segment, a highly efficient
acquisition was achieved for the whole mass range with sub-ppm mass
accuracy. In a pooled human serum sample, thousands of features were
assigned with unambiguous formulas and possible candidates based on
highly accurate mass measurements. Furthermore, a reaction network
was used to select confidently unique formulas from possible candidates,
which was constructed by unambiguous formulas and possible candidates
connected by the formula differences resulting from biochemical and
MS transformation. Compared with full-range and conventional segment
acquisition, 8- and 1.2-fold increases in observed features were achieved,
respectively. Assignment accuracy was 93–94% for both a standard
mixture containing 190 metabolites and a spiked serum sample with
the root mean square mass error of 0.15–0.16 ppm. In total,
3534 unequivocal neutral molecular formulas were assigned in the pooled
serum sample, 35% of which are contained in the HMDB. This method
offers great enhancement in the deep characterization of serum metabolome
by DI-FTICR MS.