LC-HRMS Coupling to Feature-Based Molecular Networking to Efficiently Annotate Monoterpene Indole Alkaloids of Alstonia scholaris

Monoterpene indole alkaloids (MIAs) exhibit diverse structures and pharmacological effects. Annotating MIAs in herbal medicines remains challenging when using liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS). This study introduced a new annotation strategy employing LC-HRMS to efficiently identify MIAs in herbal medicines. Briefly, MS² spectra under multiple collision energies (MCEs/MS²) helped capture high-quality product ions across a range of mass-to-charge (<i>m</i>/<i>z</i>) values, revealing key MS² features such as diagnostic product ions (DPIs), characteristic cleavages (CCs), and neutral/radical losses (NLs/RLs). Next, feature-based molecular networking (FBMN) was created to map the structural relationships among MIAs across large MS datasets. Potential MIAs were then graded and annotated through systematic comparison with known biosynthetic pathways (BPs), derived skeletons, and their characteristic substituents. The MCEs/MS²-FBMN/BPs workflow was first applied to annotate MIAs in the alkaloids from the leaf of <i>Alstonia scholaris</i> (ALAS), a new botanical drug for respiratory diseases. A total of 229 MIAs were systematically annotated and classified, forming a solid basis for future clinical research on ALAS. This study offers an effective strategy that enhances the structural annotation of MIAs within complex herbal medicines.