MGB probe-based multiplex droplet digital PCR for the interspecific identification of Notopterygii Rhizoma et Radix in herbal materials and preparations.

BACKGROUND

Owing to high sensitivity and ability for absolute quantification, the droplet digital polymerase chain reaction (ddPCR) is widely used for viral and bacterial detection. However, few studies have been conducted on the application of ddPCR to identify the original plant species used in traditional Chinese medicine and Chinese patent medicine.

PURPOSE

In this study, we investigated the feasibility of using ddPCR to differentiate between Notopterygium incisum and N. franchetii to establish a sensitive and quantitative method for quality control of herbal materials and preparations.

METHODS

Specific minor groove binding (MGB) probes and primers were designed based on stable single nucleotide polymorphisms. The ddPCR experimental conditions were designed and optimised according to the results of multiplex PCR and qPCR, which ultimately confirmed the limits of detection and quantification (LOD and LOQ, respectively) of the method for Notopterygii Rhizoma et Radix. Additionally, the original plant species of Notopterygii Rhizoma et Radix in Jiuwei Qianghuo pills circulating in the market were identified.

RESULTS

The results of the multiplex PCR and qPCR indicated that the probes and primers were specific. Furthermore, a Qsep analyser and Sanger sequencing were used to confirm that the specific amplification products of N. incisum and N. franchetii were 283 and 206 bp, respectively. The optimised ddPCR system was employed to determine the LOD to be 0.000816 ng/µl, and LOQ of N. incisum and N. franchetii to be 0.00408 and 0.003312 ng/µl, respectively. In addition, Notopterygii Rhizoma et Radix in four Jiuwei Qianghuo pills was amplified and successfully identified using ddPCR assays.

CONCLUSION

This study established a multiplex ddPCR method using MGB probes to identify Notopterygii Rhizoma et Radix, providing a foundation for the identification and quantification of multi-source Chinese herbal medicines.

Copyright © 2024. Published by Elsevier GmbH.