Chinese medicine · 2026
Systematic identification of characteristic chemical constituents in Cistanches Herba from saline-alkali land based on hierarchical filtering of mass spectrometry data and spatial metabolomics technology.
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Li Mengjiao, Wang Yaqi, Bi Chenghao, Wang Ruifen, Guo Fengdan, Shen Guiqing, Hu Zihan, Liu Kairui, Li Xinrui, Guan Renwei, Wang Songsong, Zhong Hao, Wang Xingjun, Han Liwen
Аннотация
BACKGROUND: The traditional habitat of the Chinese medicinal plant Cistanches Herba (CH) is the deserts of northwestern China. Recently, CH cultivation has been introduced to saline-alkali land (SAL) in the Yellow River delta region. However, the effects of this SAL environment on the accumulation of secondary metabolites in CH remain unclear.
OBJECTIVE: This research aims to systematically characterize the chemical profiles of CH grown in SAL and to identify key chemical markers associated with the SAL environment.
METHODS: CH samples were collected from both SAL and traditional sandy land (SL). Untargeted metabolomics was performed using ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) to characterize the global metabolic profiles of CH from two habitats. Chemical markers associated with SAL environments were screened via weighted gene co-expression network analysis (WGCNA), targeted quantification by ultra performance liquid chromatography-triple quadrupole mass spectrometry (UPLC-TQ-MS), and compound loading contributions of principal component analysis (PCA). Subsequently, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was employed to visualize the in situ spatial distribution of partial screened constituents in mouse organs.
RESULTS: Untargeted UPLC-QTOF-MS metabolomics identified 74 chemical constituents from CH samples, including 40 phenylethanoid glycosides, 13 iridoid glycosides, 9 lignans, and other minor constituents. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) clearly separated the SAL and SL sample groups, with 30 constituents showing prominent contributions to the discrimination. WGCNA revealed 23 metabolites correlated with SAL. By integrating these two screening methods, 12 constituents were preliminarily selected as candidate markers associated with SAL. Subsequently, combining PCA loading coefficients (with the PCA model validated by Kaiser-Meyer-Olkin and Bartlett's sphericity tests) and targeted UPLC-TQ-MS quantification, 4 constituents-verbascoside, echinacoside, isoacteoside, and pinoresinol- were ultimately identified as characteristic chemical markers, all exhibiting significantly higher levels in SAL samples. DESI-MSI results further demonstrated that echinacoside predominantly accumulated in brain tissue, whereas verbascoside or isoacteoside displayed the strongest ion signal in the lung.
CONCLUSION: This study developed an integrated strategy that combines hierarchical filtering of mass spectrometry data to successfully identify 4 characteristic chemical markers of CH associated with SAL. This strategy provides a scientific basis for future quality control and resource development.
OBJECTIVE: This research aims to systematically characterize the chemical profiles of CH grown in SAL and to identify key chemical markers associated with the SAL environment.
METHODS: CH samples were collected from both SAL and traditional sandy land (SL). Untargeted metabolomics was performed using ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) to characterize the global metabolic profiles of CH from two habitats. Chemical markers associated with SAL environments were screened via weighted gene co-expression network analysis (WGCNA), targeted quantification by ultra performance liquid chromatography-triple quadrupole mass spectrometry (UPLC-TQ-MS), and compound loading contributions of principal component analysis (PCA). Subsequently, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was employed to visualize the in situ spatial distribution of partial screened constituents in mouse organs.
RESULTS: Untargeted UPLC-QTOF-MS metabolomics identified 74 chemical constituents from CH samples, including 40 phenylethanoid glycosides, 13 iridoid glycosides, 9 lignans, and other minor constituents. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) clearly separated the SAL and SL sample groups, with 30 constituents showing prominent contributions to the discrimination. WGCNA revealed 23 metabolites correlated with SAL. By integrating these two screening methods, 12 constituents were preliminarily selected as candidate markers associated with SAL. Subsequently, combining PCA loading coefficients (with the PCA model validated by Kaiser-Meyer-Olkin and Bartlett's sphericity tests) and targeted UPLC-TQ-MS quantification, 4 constituents-verbascoside, echinacoside, isoacteoside, and pinoresinol- were ultimately identified as characteristic chemical markers, all exhibiting significantly higher levels in SAL samples. DESI-MSI results further demonstrated that echinacoside predominantly accumulated in brain tissue, whereas verbascoside or isoacteoside displayed the strongest ion signal in the lung.
CONCLUSION: This study developed an integrated strategy that combines hierarchical filtering of mass spectrometry data to successfully identify 4 characteristic chemical markers of CH associated with SAL. This strategy provides a scientific basis for future quality control and resource development.