Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software

Chemical derivatization is a widely employed strategy
in metabolomics
to enhance metabolite coverage by improving chromatographic behavior
and increasing the ionization rates in mass spectroscopy (MS). However,
derivatization might complicate MS data, posing challenges for data
mining due to the lack of a corresponding benchmark database. To address
this issue, we developed a triple-dimensional combinatorial derivatization
strategy for nontargeted metabolomics. This strategy utilizes three
structurally similar derivatization reagents and is supported by MS-TDF
software for accelerated data processing. Notably, simultaneous derivatization
of specific metabolite functional groups in biological samples produced
compounds with stable but distinct chromatographic retention times
and mass numbers, facilitating discrimination by MS-TDF, an in-house
MS data processing software. In this study, carbonyl analogues in
human plasma were derivatized using a combination of three hydrazide-based
derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine,
and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This
approach was applied to identify potential carbonyl biomarkers in
lung cancer. Analysis and validation of human plasma samples demonstrated
that our strategy improved the recognition accuracy of metabolites
and reduced the risk of false positives, providing a useful method
for nontargeted metabolomics studies. The MATLAB code for MS-TDF is
available on GitHub at https://github.com/CaixiaYuan/MS-TDF.