Insight into chemical basis of traditional Chinese medicine based on the state-of-the-art techniques of liquid chromatography-mass spectrometry.

Traditional Chinese medicine (TCM) has been an indispensable source of drugs for curing various human diseases. However, the inherent chemical diversity and complexity of TCM restricted the safety and efficacy of its usage. Over the past few decades, the combination of liquid chromatography with mass spectrometry has contributed greatly to the TCM qualitative analysis. And novel approaches have been continuously introduced to improve the analytical performance, including both the data acquisition methods to generate a large and informative dataset, and the data post-processing tools to extract the structure-related MS information. Furthermore, the fast-developing computer techniques and big data analytics have markedly enriched the data processing tools, bringing benefits of high efficiency and accuracy. To provide an up-to-date review of the latest techniques on the TCM qualitative analysis, multiple data-independent acquisition methods and data-dependent acquisition methods (precursor ion list, dynamic exclusion, mass tag, precursor ion scan, neutral loss scan, and multiple reaction monitoring) and post-processing techniques (mass defect filtering, diagnostic ion filtering, neutral loss filtering, mass spectral trees similarity filter, molecular networking, statistical analysis, database matching, etc.) were summarized and categorized. Applications of each technique and integrated analytical strategies were highlighted, discussion and future perspectives were proposed as well.

Binary code, a flexible tool for diagnostic metabolite sequencing of medicinal plants.

The principle of chromatographic fingerprint is that certain diagnostic metabolites should be always distributed in a given plant and currently, it has been widely accepted as a promising means for medicinal plant authentication. Moreover, the chemical profile is the only evidence to clarify the ingredients of those consumable plant products, e.g. traditional Chinese medicine (TCM) prescriptions. Herein, efforts were made to describe the diagnostic metabolome of medicinal plant or TCM prescription using a binary code sequence. Forty-five well-known medicinal plants along with six relevant prescriptions were employed for concept illustration and proof. Each plant was subjected to chemical characterization, and diagnostic metabolites of all plants were gathered into a chemical pool containing 595 compounds. A robust method enabling the detection of all 595 constituents was then developed using LC coupled to scheduled multiple reaction monitoring. Analyst™ software was responsible for automatically judging the presence (defined as "1") or absence (defined as "0") of each analyte with a defined signal-to-noise threshold (S/N > 100). After converting each medicinal plant to a binary sequence consisting of 595 codes, an in-house database was built by involving all sequences. The potentials of sequence library retrieval towards plant authentication, preliminary chemical characterization, and deformulation of TCM prescriptions were demonstrated after that the diagnostic metabolome of each test sample was translated to a binary code sequence. Above all, binary code is a flexible tool for diagnostic metabolite sequencing of medicinal plants, and it should be an alternative tool of DNA barcoding towards plant authentication.

Source attribution and structure classification-assisted strategy for comprehensively profiling Chinese herbal formula: Ganmaoling granule as a case.

Chinese herbal formula (CHF) has extremely complex chemical composition. Herein, a source attribution and structure classification-assisted strategy was established based on reductionism for rapidly and comprehensively profiling CHF, and Ganmaoling granule (GMLG) was selected as a representative case to illustrate such a strategy and to confirm its applicability. Firstly, comprehensive data acquisition was achieved using neutral losses along with full scan on a liquid chromatography coupled with hybrid ion trap-time of flight mass spectrometer (LC-IT-TOF-MS). Then, the detected precursor and product ions were paired to construct a list of ion transitions for profiling GMLG and its constituent herbs using the scheduled multiple reaction monitoring (sMRM) mode on a LC coupled with hybrid triple quadrupole-linear ion trap mass spectrometer (LC-Q-Trap-MS). The mass parameters of sMRM were optimized using an online optimization strategy to achieve the highest sensitivity, and the automated source attribution was performed with the assistant of the "Quantitate" function of Analyst software. The target peaks were then structurally classified into seven classes through integrating the mass defect filtering (MDF) and diagnostic fragment ion filtering (DFIF), and identified by combination of the mass fragmentation rules and a 'structure extension' approach. Using this strategy, 261 components, including 148 trace ones (with the intensity lower than 100,000 cps), were tentatively characterized. The findings demonstrated that such a comprehensive source attribution and structure classification-assisted strategy is qualified to be an efficient approach for rapidly and globally characterizing the chemical profile of CHF.

Homolog-focused profiling of ginsenosides based on the integration of step-wise formate anion-to-deprotonated ion transition screening and scheduled multiple reaction monitoring.

Homolog-focused profiling is a favored option to bridge targeted metabolomics toward non-targeted metabolomics. In current study, an attempt was made for the large-scale ginsenoside-specific analysis in ginseng (G) and American ginseng (AG). When formic acid (0.1%, v/v) was introduced as the mobile phase additive, formate anion-to-deprotonated ion transitions ([M+HCOO](-)>[M-H](-)) with an optimal collision energy (-32eV) could result in satisfactory responses for ginsenosides. Therefore, a step-wise multiple reaction monitoring (MRM)-based method employing [M+HCOO](-)>[M-H](-) ion pairs was constructed to screen ginsenosides among 501-1250u (for Q1) with a step-size of 2u, and MRM also served as a survey experiment to trigger enhanced product ion scans for MS(2) spectrum acquisition on a hybrid triple quadrupole-linear ion trap mass spectrometer; then, the identification of those observed ginsenosides was achieved on the basis of the well-defined mass cracking patterns for ginsenosides; afterwards, scheduled MRM was introduced for large-scale relatively quantitative analysis of all detected ginsenosides. Finally, comparative metabolomics were performed to differentiate G, AG, and their processed products. Method validation was carried out using thirteen authentic compounds. A total of 221 ginsenosides, among which 185 ones were annotated, were observed and relatively quantitated. All crude materials were obviously classified into groups I-III. Above all, the MRM-based homolog-focused profiling of ginsenosides could be used as a reliable tool to gain an in-depth view for ginsenoside-enriched herbal products.