New insights into the Van Krevelen diagram: Automated molecular formula determination from HRMS for a large chemical profiling of lichen extracts
Simon Ollivier, Philippe Jéhan, Damien Olivier‐Jimenez, Fabian Lambert, Joël Boustié, Françoise Lohézic‐Le Dévéhat, Nicolas Le Yondre
Abstract
INTRODUCTION: In recent years, LC-MS has become the golden standard for metabolomic studies. Indeed, LC is relatively easy to couple with the soft electrospray ionization. As a consequence, many tools have been developed for the structural annotation of tandem mass spectra. However, it is sometimes difficult to do data-dependent acquisition (DDA), especially when developing new methods that stray from the classical LC-MS workflow. OBJECTIVE: An old tool from petroleomics that has recently gained popularity in metabolomics, the Van Krevelen diagram, is adapted for an overview of the molecular diversity profile in lichens through high-resolution mass spectrometry (HRMS). METHODS: A new method is benchmarked against the state-of-the-art classification tool ClassyFire using a database containing most known lichen metabolites (n ≈ 2,000). Four lichens known for their contrasted chemical composition were selected, and extractions with apolar, aprotic polar, and protic polar solvents were performed to cover a wide range of polarities. Extracts were analyzed with direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) and atmospheric solids analysis probe mass spectrometry (ASAP-MS) techniques to be compared with the chemical composition described in the literature. RESULTS: The most common lichen metabolites were efficiently classified, with more than 90% of the molecules in some classes being matched with ClassyFire. Results from this method are consistent with the various extraction protocols in the present case study. CONCLUSION: This approach is a rapid and efficient tool to gain structural insight regarding lichen metabolites analyzed by HRMS without relying on DDA by LC-MS/MS analysis. It may notably be of use during the development phase of novel MS-based metabolomic approaches.