The chemical complexity of traditional Chinese medicines (TCMs) makes the active and functional annotation of natural compounds challenging. Herein, we developed the TCMs-Compounds Functional Annotation platform (TCMs-CFA) for large-scale predicting active compounds with potential mechanisms from TCM complex system, without isolating and activity testing every single compound one by one. The platform was established based on the integration of TCMs knowledge base, chemome profiling, and high-content imaging. It mainly included: (1) selection of herbal drugs of target based on TCMs knowledge base; (2) chemome profiling of TCMs extract library by LC‒MS; (3) cytological profiling of TCMs extract library by high-content cell-based imaging; (4) active compounds discovery by combining each mass signal and multi-parametric cell phenotypes; (5) construction of functional annotation map for predicting the potential mechanisms of lead compounds. In this stud TCMs with myocardial protection were applied as a case study, and validated for the feasibility and utility of the platform. Seven frequently used herbal drugs (Ginseng, etc.) were screened from 100,000 TCMs formulas for myocardial protection and subsequently prepared as a library of 700 extracts. By using TCMs-CFA platform, 81 lead compounds, including 10 novel bioactive ones, were quickly identified by correlating 8089 mass signals with 170,100 cytological parameters from an extract library. The TCMs-CFA platform described a new evidence-led tool for the rapid discovery process by data mining strategies, which is valuable for novel lead compounds from TCMs. All computations are done through Python and are publicly available on GitHub.
Keywords: Cell phenotype; Ginseng; Ginsenosides; High content screening; Knowledge discovery; Metabolomics.
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