Plant alkaloids represent a diverse group of nitrogen-containing natural products. These compounds are considered valuable in drug discovery and development. High-throughput identification of such plant secondary metabolites in complex plant extracts is essential for drug discovery, lead optimization, and understanding the biological pathway. The present study aims to rapidly identify different classes of alkaloids in plant extracts through the liquid chromatography with electrospray ionization-tandem mass spectrometry (LC−ESI-MS/MS) approach using 161 isolated and purified alkaloids. These are biologically important unique alkaloids belonging to different sub-classes such as isoquinoline, quinoline, indole, tropane, pyridine, piperidine, quinolizidine, aporphine, steroidal, and terpenoid. The majority of these are not available commercially and are known to manifest valuable biological activities. Four pools of a maximum of 50 phytostandards each were prepared, based on their log P value to minimize co-elution for rapid and cost-effective analyses. MS/MS spectra were acquired in the positive ionization mode by using their [M + H] + and/or [M + Na] + with both the average collisional energy (25.5−62 eV) and individual collisional energies (10, 20, 30, and 40 eV). Accurate mass, high-resolution mass spectrometry (HR-MS) data, MS/MS data, and retention times were curated for each compound. The developed LC−MS/MS method was successfully used to interrogate and fast dereplicate alkaloids in 13 medicinal plant extracts and a herbal formulation. A total of 56 alkaloids were identified based on the reference standard retention times (RTs), HR-MS spectra, and/or MS/MS spectra. The MS data have been submitted to the MetaboLights online database (MTBLS2914). The mass spectrometric and chromatographic data will be useful for the discovery of new congeners and the study of biological pathways of alkaloids in the plant kingdom.

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Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software. The results of acquisition methods for quantitative analysis validated the remarkable analytical performance of the constructed workflow, proving it to be a sensitive and reproducible pipeline for biological complex fluids.