Related Experiment Video
Updated: Nov 3, 2025

07:10
Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
Published on: March 13, 2020
10.3K
A Comprehensive Targeted Metabolomics Assay for Crop Plant Sample Analysis.
Jiamin Zheng1, Mathew Johnson1, Rupasri Mandal1
1Departments of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.
Metabolites
|June 2, 2021
Summary
This study introduces a quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay for plant metabolomics. The high-coverage assay enables precise analysis of 206 plant metabolites, supporting phenotyping and disease diagnosis.
Area of Science:
- Plant biology
- Biochemistry
- Analytical chemistry
Background:
- Metabolomics is crucial in agriculture and health, but comprehensive quantitative plant metabolite analysis is limited.
- Existing methods lack the sensitivity and breadth for detailed plant phenotyping and disease diagnosis.
Purpose of the Study:
- To develop and validate a high-coverage, quantitative mass spectrometry (MS)-based assay for plant metabolite analysis.
- To enable precise quantification of primary and secondary plant metabolites, including hormones.
Main Methods:
- Utilized liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) in both positive and negative modes.
- Employed multiple reaction monitoring (MRM) for targeted quantification of 206 metabolites.
- Developed assay in a 96-well plate format for high-throughput analysis.
Main Results:
- Quantified 206 plant metabolites: 28 amino acids, 27 organic acids, 20 biogenic amines, 40 acylcarnitines, 90 phospholipids, and C-6 sugars.
- Achieved recovery rates of 80-120% and precision below 20% for spiked samples.
- Successfully applied the assay to diverse plant samples including needles, roots, and cannabis.
Conclusions:
- The developed LC-MS/MS assay provides a robust, high-throughput method for quantitative plant metabolomics.
- This assay facilitates detailed plant phenotyping, disease diagnosis, and analysis of large sample cohorts.

