Heptafluorobutyl Chloroformate Derivatization Enables Molecular-Ion-Centric Metabolite Screening and Fluorine-Assisted Elemental-Composition Assignment by GC–APCI–HRMS

Abstract Metabolomics enables the simultaneous monitoring of hundreds to thousands of metabolites in complex biological matrices; however, confident identification of low-abundance and unknown compounds remains challenging. Gas chromatography–mass spectrometry (GC–MS) workflows based on electron ionization (EI) often provide limited molecular-ion information because of extensive fragmentation. Here, we present a novel rapid screening workflow combining 1 min heptafluorobutyl chloroformate (HFBCF) derivatization of protic metabolites and concurrent liquid–liquid microextraction with gas chromatography–atmospheric pressure chemical ionization mass spectrometry (GC–APCI–MS). The proven HFBCF–mediated reaction produces stable heptafluorobutyl derivatives that yield abundant protonated molecular ions in the APCI mass spectra, predictable class-specific fragmentation, and characteristic fluorine-specific mass signatures. Compared with conventional full-scan GC–EI–MS, full-scan GC–APCI–MS provided up to 10–1000-fold higher sensitivity, enabling metabolite screening from extremely limited sample amounts. In low-input HeLa cell extracts, at least 100 metabolites were consistently detected from as few as 15,000 cells. Specific heptafluorobutyl-derived mass shifts and fluorine-based elemental constraints facilitated determination of the number and, in selected cases, the type of derivatized functional groups and supported elemental-composition assignment of unknown metabolites, even at mass accuracies of several tens of ppm. To aid compound annotation, we established an openly accessible database comprising more than 620 derivatizable metabolites and experimentally characterized retention and mass-spectral data for 240 reference standards. Together, these results establish HFBCF–GC–APCI–MS as a sensitive molecular-ion-centric workflow for exploratory metabolomics, enabling annotation of unknown metabolites across defined confidence levels, from standard-confirmed identifications to database-supported candidate assignments, suitable for limited biological samples.