Overexpressing GhADC2-WT-Silencing GhADC2 - Non-target Metabolite Mass Spectrometry Detection and Analysis

This project conducts non-targeted metabolomics research based on liquid chromatography-mass spectrometry (LC-MS) technology. The experimental process mainly includes: metabolite extraction from samples, LC-MS/MS detection, and data analysis. Due to the characteristics of metabolomics, such as rapid changes in metabolite levels, a wide variety of metabolites, significant concentration differences, and diverse chemical properties, each step from sample collection, preservation, metabolite extraction, to mass spectrometry detection may potentially affect the quality of the data. And the quality of the data directly affects the results of subsequent information analysis. To ensure the accuracy and reliability of the detection data from the very beginning, Norho Zhiyuan strictly controls every experimental step and implements standardized operation of metabolomics, fundamentally ensuring the output of high-quality data. The first 3 quality control (QC) tests before sample injection are used to monitor the instrument status before sample injection and to balance the chromatography-mass spectrometry system. The following 3 QC tests perform segmented scanning, along with the secondary spectra obtained from the experimental samples, to determine the metabolites. The QC tests inserted during sample detection are used to evaluate the stability of the system throughout the entire experiment and to conduct data quality control analysis. Firstly, the original files (in .raw format) obtained from the mass spectrometry detection are imported into the Compound Discoverer 3.1 (hereinafter referred to as CD3.1) software for spectral processing and database search, to obtain the qualitative and quantitative results of the metabolites. Then, quality control of the data is carried out to ensure the accuracy and reliability of the data results. Next, multivariate statistical analysis of the metabolites is conducted, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), etc., to reveal the differences in metabolic patterns among different groups. Hierarchical clustering (HCA) and metabolite correlation analysis are used to reveal the relationships between samples and between metabolites and metabolites. Finally, the biological significance related to the metabolites is explained through functional analysis of metabolic pathways and other functions.