Research Article | Open Access

Volume 2023 |Article ID 0098 | https://doi.org/10.34133/plantphenomics.0098

Phenotyping of Salvia miltiorrhiza Roots Reveals Associations between Root Traits and Bioactive Components

Junfeng Chen,^1,9 Yun Wang,^2,9 Peng Di,^3,9 Yulong Wu,^4,9 Shi Qiu,¹ Zongyou Lv,¹ Yuqi Qiao,¹ Yajing Li,¹ Jingfu Tan,⁵ Weixu Chen,⁵ Ma Yu,⁶ Ping Wei,⁷ Ying Xiao ,¹ Wansheng Chen ^1,8

¹The SATCM Key Laboratory for New Resources & Quality Evaluation of Chinese Medicine, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
²School of Medicine, Shanghai University, Shanghai 200444, China
³State Local Joint Engineering Research Center of Ginseng Breeding and Application, Jilin Agricultural University, Changchun 130118, China
⁴School of Computer Science, Sichuan Normal University, Chengdu 610066, China
⁵Shangyao Huayu (Linyi) Traditional Chinese Resources Co., Ltd., Linyi 276000, China
⁶School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, China
⁷Sichuan Academy of Traditional Chinese Medicine, Chengdu 610041, China
⁸Department of Pharmacy, Changzheng Hospital, Second Military Medical University, Shanghai 200003, China
⁹These authors contributed equally to this work.

Abstract

Plant phenomics aims to perform high-throughput, rapid, and accurate measurement of plant traits, facilitating the identification of desirable traits and optimal genotypes for crop breeding. Salvia miltiorrhiza (Danshen) roots possess remarkable therapeutic effect on cardiovascular diseases, with huge market demands. Although great advances have been made in metabolic studies of the bioactive metabolites, investigation for S. miltiorrhiza roots on other physiological aspects is poor. Here, we developed a framework that utilizes image feature extraction software for in-depth phenotyping of S. miltiorrhiza roots. By employing multiple software programs, S. miltiorrhiza roots were described from 3 aspects: agronomic traits, anatomy traits, and root system architecture. Through K-means clustering based on the diameter ranges of each root branch, all roots were categorized into 3 groups, with primary root-associated key traits. As a proof of concept, we examined the phenotypic components in a series of randomly collected S. miltiorrhiza roots, demonstrating that the total surface of root was the best parameter for the biomass prediction with high linear regression correlation (R2 = 0.8312), which was sufficient for subsequently estimating the production of bioactive metabolites without content determination. This study provides an important approach for further grading of medicinal materials and breeding practices.