An In Silico Database for Automated Feature Identification of High-Resolution Tandem Mass Spectrometry 13C Trimethylation Enhancement Using Diazomethane (13CTrEnDi)- Modified Lipid Data

13C-Trimethylation enhancement using diazomethane (13C-TrEnDi) is a chemical derivatization technique that uses 13C-labeled diazomethane to increase mass spectrometry (MS) signal intensities for phosphatidylcholine (PC) and phosphatidylethanolamine (PE) lipid classes, both of which are of major interest in biochemistry. In silico mass spectrometry databases have become mainstays in lipidomics experiments; however, 13C-TrEnDi modified PC and PE species have altered m/z and fragmentation patterns from their native counterparts. To build a database of 13C-TrEnDi modified PC and PE species, a lipid extract from nutritional yeast was derivatized and fragmentation spectra of modified PC and PE species were mined using diagnostic fragmentation filtering by searching 13C-TrEnDi modified headgroups with m/z 199 (PC) and 202 (PE). Identities of 25 PC and 10 PE species were assigned after comparing to predicted masses from the Lipid Maps Structure Database with no false positive identifications observed; neutral lipids could still be annotated after derivatization. Collision energies from 16 to 52 eV were examined, resulting in three additional class-specific fragment ions emerging, as well as a combined sn1/sn2 fragment ion, allowing sum-composition level annotations to be assigned. Using the Lipid Blast templates, a NIST-compatible 13C-TrEnDi database was produced based on fragmentation spectra observed at 36 eV and tested on HEK 293T cell lipid extracts, identifying 47 PC and 24 PE species, representing a 1.8-fold and 2.2-fold increase in annotations, respectively. The 13C-TrEnDi database is freely available, MS vendor-independent and widely compatible with MS data processing pipelines, increasing the throughput and accessibility of TrEnDi for lipidomics applications.