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Metabolomics has been used as a powerful tool for the analysis, and drug-lead identification in medicinal plants and herbal medicines. For the coverage of a broader range of plant-based metabolites using LC-MS, one of the important parameters is the selection of analysis mode and data processing for identification. This paper describes the utility of two distinctive acquisition modes in combination, a classic data-dependent acquisition (DDA) mode and a data-independent acquisition (DIA) mode for obtaining the mass spectrometry data of plant extracts using LC-ESI-QTOF/MS. Tribulus terrestris plant was used as a case study. We have applied three-step data analysis pipeline 1-annotation and putative identification of metabolites, 2-validation, and relative quantification, and 3-multivariate analysis using open-access MS-DIAL, Skyline, and Perseus software. A total of four samples of T. terrestris (aqueous extracts), two fruits, and two whole plant samples, from two different regions, were used. By combining data analysis results of plant fruit samples from two different regions, a total of 95 and 77 metabolites were identified in positive and negative ionization modes, respectively. Similarly, in the analysis of the whole plant from two different regions, 75 and 76 metabolites were identified in positive and negative ionization modes, respectively. We suggested the use of DDA mode for annotation, identification of metabolites, and generation of transition lists in MS-DIAL, furthermore, the use of DIA acquisition mode for enhancing metabolites sensitivity in complex samples, deconvolute MS1/MS2 spectra in Skyline for the quantitative performance and analytical reliability. The developed protocol can be used for the broader coverage of plant-based metabolites.

Natural product screening in plants has always been a difficult task due to the complex nature of the plant material and diverse structures of the compounds present in them. Flavonoids are important and diverse class of plant secondary metabolites with numerous medicinal activities. The present study focuses on the development of a high-resolution tandem mass spectral library for the rapid and authentic identification of common flavonoids. A total of forty flavonoid standards belong to class flavones, isoflavones, flavanones, flavanols and anthocyanins were pooled into two solutions applying logP-based strategy. The flavonoids were analyzed using LC-QTOF-MS high-resolution mass spectrometer with optimization of different instrumental parameters to achieve good sensitivity. The library was built by incorporating names, molecular formulae, exact masses, and MS, and MS/MS spectra of analyzed flavonoids using Bruker Library Editor tool. The fragmentation pattern observed for the standard compounds were compared to the fragments reported in the literature. To assess the practical implications, an extract of tea sample was analyzed and screened using the developed library, which resulted in the identification of three common flavonoids based on their HR-ESI-MS/MS spectral features. The established LC-HR-MS/MS method can be used for the targeted identification of flavonoids in complex samples like food material from different botanical families.

Poisonous plants are widely distributed and may have risk of phytotoxicity upon mixing with medicinal plants. Several species of Cestrum genus are poisonous and linked with many serious health issues. In the present study, cross-mixing of a toxic plant, Cestrum diurnum with morphologically resembling medicinal plant, Adhatoda vasica was studied using chemical fingerprinting approach. LC-ESI-MS/MS tool was used to develop the chemical fingerprints of three toxic species of Cestrum, including, C. diurnum, C. nocturnum and C. parqui. Total forty-three compounds were identified using high-resolution LC-ESI-MS/MS data comparison. Chemometric analyses were done to compare the distribution of identified compounds present in these Cestrum species. One of the identified compounds, nornicotine (a toxic compound) was also quantified using LC-IT-MS/MS. Adulteration study was conducted by mixing toxic C. diurnum in A. vasica with various ratios (w/w) and five differentiable compounds were identified to detect the adulteration. The method was able to detect up to the limit of 5% mixing of toxic C. diurnum. Moreover, cytotoxicity of the methanolic extracts of these three species were also studied on normal human PBMC (peripheral blood mononuclear cells) and all found to be toxic, while the C. nocturnum showed the highest level of toxicity with the IC50 12.5 μg/mL.

[Display omitted] Metabolomics has been used as a powerful tool for the analysis, and drug-lead identification in medicinal plants and herbal medicines. For the coverage of a broader range of plant-based metabolites using LC-MS, one of the important parameters is the selection of analysis mode and data processing for identification. This paper describes the utility of two distinctive acquisition modes in combination, a classic data-dependent acquisition (DDA) mode and a data-independent acquisition (DIA) mode for obtaining the mass spectrometry data of plant extracts using LC-ESI-QTOF/MS. Tribulus terrestris plant was used as a case study. We have applied three-step data analysis pipeline 1-annotation and putative identification of metabolites, 2-validation, and relative quantification, and 3-multivariate analysis using open-access MS-DIAL, Skyline, and Perseus software. A total of four samples of T. terrestris (aqueous extracts), two fruits, and two whole plant samples, from two different regions, were used. By combining data analysis results of plant fruit samples from two different regions, a total of 95 and 77 metabolites were identified in positive and negative ionization modes, respectively. Similarly, in the analysis of the whole plant from two different regions, 75 and 76 metabolites were identified in positive and negative ionization modes, respectively. We suggested the use of DDA mode for annotation, identification of metabolites, and generation of transition lists in MS-DIAL, furthermore, the use of DIA acquisition mode for enhancing metabolites sensitivity in complex samples, deconvolute MS1/MS2 spectra in Skyline for the quantitative performance and analytical reliability. The developed protocol can be used for the broader coverage of plant-based metabolites. [ABSTRACT FROM AUTHOR]