Improving the Data Quality of Untargeted Metabolomics through a Targeted Data-Dependent Acquisition Based on an Inclusion List of Differential and Preidentified Ions

Metabolomics
based on high-resolution mass spectrometry has become
a powerful technique in biomedical research. The development of various
analytical tools and online libraries has promoted the identification
of biomarkers. However, how to make mass spectrometry collect more
data information is an important but underestimated research topic.
Herein, we combined full-scan and data-dependent acquisition (DDA)
modes to develop a new targeted DDA based on the inclusion list of
differential and preidentified ions (dpDDA). In this workflow, the
MS1 datasets for statistical analysis and metabolite preidentification
were first obtained using full-scan, and then, the MS/MS datasets
for metabolite identification were obtained using targeted DDA of
quality control samples based on the inclusion list. Compared with
the current methods (DDA, data-independent acquisition, targeted DDA
with time-staggered precursor ion list, and iterative exclusion DDA),
dpDDA showed better stability, higher characteristic ion coverage,
higher differential metabolites’ MS/MS coverage, and higher
quality MS/MS spectra. Moreover, the same trend was verified in the
analysis of large-scale clinical samples. More surprisingly, dpDDA
can distinguish patients with different severities of coronary heart
disease (CHD) based on the Canadian Cardiovascular Society angina
classification, which we cannot distinguish through conventional metabolomics
data collection. Finally, dpDDA was employed to differentiate CHD
from healthy control, and targeted metabolomics confirmed that dpDDA
could identify a more complete metabolic pathway network. At the same
time, four unreported potential CHD biomarkers were identified, and
the area under the receiver operating characteristic curve was greater
than 0.85. These results showed that dpDDA would expand the discovery
of biomarkers based on metabolomics, more comprehensively explore
the key metabolites and their association with diseases, and promote
the development of precision medicine.