Comprehensive profiling of Stephania tetrandra (Fangji) by stepwise DFI and NL-dependent structure annotation algorithm-based UHPLC-Q-TOF-MS and direct authentication by LMJ-HRMS
Graphical abstract
Introduction
Stephania tetrandra S. Moore, commonly referred as “Fangji”, has long been used as a diuretic, antirheumatic, and analgesic medicine in China [[1], [2], [3]]. The major marker compounds of S. tetrandra are tetrandrine and fangchinoline (Fig. 1), and these possess anti-cancer, anti-inflammatory, and anti-hyperglycemic activities [4,5]. Although the therapeutic benefits of S. tetrandra have been increasingly reported [[1], [2], [3]], its phytochemical properties have received little attention. It is therefore necessary to determine the chemical constitution of S. tetrandra to study its effective material basis and mechanisms. In addition, S. tetrandra is often confused with the nephrotoxic “Fangji,” Aristolochia fangchi, because of the similarities of their names and morphologies [6,7]. However, the aristolochic acids present in A. fangchi (Fig. 1) are known nephrotoxins and carcinogens that can cause kidney failure, urothelial carcinoma, and liver cancer [8,9]. The development of a reliable and effective method for the quality assessment of S. tetrandra is therefore of high practical significance.
Ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) combines the efficient separation capabilities of UHPLC and the powerful structural characterization of HRMS, and is the most selective technique for profiling the chemical constitutions of complex matrices [10]. However, large data sets containing tens of thousands of data points generate a tedious workload in the post-acquisition data process. In addition, the isoquinoline components in S. tetrandra are formed through the same biosynthetic pathway [11,12], thereby resulting in similar chemical skeletons and mass spectra, which confuse the chemical characterization process. As filtering strategies based on diagnostic fragment ions (DFIs) and/or neutral losses (NLs) have been employed to process the complex LC-MS datasets [13], a structure annotation algorithm was proposed here to mitigate the problems by classifying the unknown components into known structural classes, utilizing the relevant DFIs and NLs in a step-by-step manner.
Considering the phytochemical differences of the two Fangji [6], S. tetrandra and A. fangchi can be easily distinguished by LC-MS based on their structurally diverse markers (Fig. 1). However, the LC-MS-based quality analysis of herbal medicines (HMs) remains challenging, as the corresponding sample preparation techniques involve multiple steps, e.g., mechanical pulverization, extraction, and/or fractionation [14]. Moreover, sample pretreatment is destructive and time-/solvent-consuming, sometimes resulting in component degradation and/or modification. Conversely, liquid microjunction surface sampling-high-resolution mass spectrometry (LMJ-HRMS), the developed ambient ionization techniques, can allow direct sampling and ionization in the open air with no or minimal sample preparation [15]. The obvious benefit of LMJ-HRMS is the possibility to analyze directly surfaces with relatively less complex and time-consuming sample preparation compared to UHPLC-HRMS analysis. However, as a chromatography-free approach, LMJ-HRMS creates ionization suppression effects thus unables the identification of the low abundance analytes. Moreover, as compounds are co-desorbed during LMJ-HRMS analysis, it also suffers from the absence of distinguishing the structural isomers and/or isobaric compounds. LMJ-HRMS can therefore be considered a promising alternative method for authenticating the origin of HMs by the direct and rapid profiling of the marker components in their crude slices.
Thus, we herein report the use of UHPLC-Q-TOF-MS for the initial comprehensive phytochemical profiling of the isoquinolines present in S. tetrandra, followed by the application of LMJ-HRMS to authenticate S. tetrandra through the direct profiling of the marker constituents in its crude slices. In addition, the relevant DFIs and NLs of the isoquinolines are summarized and utilized herein to classify the unknown components into known classes in a step-by-step manner, which well mitigate the problems posed by the large mass dataset and the inherent structural similarities and complexities of the isoquinolines.
Section snippets
Chemicals and reagents
Fourteen reference standards (Table S1) were purchased from Chengdu Must Bio-Tech Co., Ltd. (Chengdu, China). S. tetrandra was purchased from Tongrentang Chinese Medicine (Zhengzhou, China). Methanol (MeOH), acetonitrile (ACN), and formic acid were of HPLC grade (Merck, Darmstadt, Germany). Ultra-pure water (18.2 MΩ cm at 25 °C) was prepared in-house using a Milli-Q integral water purification system (Millipore, Bedford, MA, USA).
Preparation of reference standards
Standard stock solutions were prepared by dissolving the
Chemical profiling of S. Tetrandra by UHPLC-Q-TOF-MS analysis
Phytochemical constituents, particularly secondary metabolites, are responsible for the therapeutic effects of HMs and are of key importance in the screening of lead compounds. In addition, the unique active components of HMs are often chosen as marker components for quality evaluation [16]. Although LC-MS is the most selective technique for comprehensive chemical profiling and quality assessment of HM, it fails to detect the constituents with low abundance, and so a simple sample pretreatment
Conclusions
We herein reported the use of ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) to comprehensively profile the isoquinolines from S. tetrandra, followed by the use of liquid microjunction surface sampling high-resolution mass spectrometry (LMJ-HRMS) to directly authenticate S. tetrandra by the chemical profiling of its crude slice. Overall, LMJ-HRMS can provide a direct, nondestructive, high-throughput, and environmentally friendly
CRediT authorship contribution statement
Jinfeng Chen: Conceptualization, Methodology, Writing - original draft, Writing - review & editing, Visualization, Project administration, Funding acquisition. Qian Zhao: Methodology, Investigation. Dandan Si: Methodology, Data curation. Anzheng Nie: Investigation, Resources. Yuanyuan Wang: Investigation, Data curation. Zhifen Deng: Investigation, Data curation. Yibo Wen: Data curation. Fengmei Chen: Data curation. Lei Zhang: Data curation. Bowen Dong: Data curation. Jinghua Yang: Writing -
Declaration of competing interest
The authors have declared no conflict of interest
Acknowledgments
Funding: This work was funded by the Research grants (No. 81903773) from the National Natural Science Foundation of China and the Medical Science and Technology Program of Henan province, China (No. 201802140).
We would like to thank the Academy of Medical Sciences of Zhengzhou University Translational Medicine Platform for their kind help, the Medical Research Center of the First Affiliated Hospital of Zhengzhou University, and ASPEC Technologies Limited Corporation for providing an excellent
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