## 2 Results
### Metabolomi Analysis of Distinct Tissue Sites in Aquilaria sinensis  
In order to further investigate the variations in metabolites across different tissue components, a total of 367 volatile compounds were analyzed using gas chromatography-mass spectrometry (GC-MS) in six distinct tissue parts. These metabolites encompassed various classes, including 8 acids, 29 alcohols, 27 aldehydes, 6 amines, 19 aromatics, 57 esters, 2 halogenated hydrocarbons, 49 heterocyclic compounds, 45 hydrocarbons, 22 ketones, 3 nitrogen compounds, 10 phenols, 4 sulfur compounds, 84 terpenoids, and 2 others. Among them, terpenoids species accounted for the majority, representing 22.89% of the total. Esters, heterocyclic compounds, and hydrocarbons were the next, accounting for 15.53%, 13.35%, and 12.26%, respectively. Alcohols, aldehydes, ketones, aromatics, and phenols comprised only 7.9%, 7.36%, 5.99%, 5.18%, and 2.72%, respectively. The remaining compound species made up a relatively small proportion (Figure 2A). The ketone cashmeran displayed its peak levels in roots, seeds, husks, and leaf callus. Additionally, the ketone 4-hexen-3-one exhibited the highest concentration in leaves, while the phenolic compound coniferyl alcohol showed the highest abundance in stems.  
Quality control (QC) samples were incorporated into every 10 monitored analysis samples during the analysis process to ensure the reproducibility and reliability of data across different tissue sites. The overlap plot of total ion current (TIC) and various quality control samples demonstrated a high degree of concurrence in the total ion current curve for metabolite detection, with consistent retention time and peak intensity. This indicates excellent signal stability of the mass spectrometer when detecting the same sample at different time points (Figure S1).  
The principal component analysis (PCA) of the samples, including the quality control samples, can preliminarily determine the overall difference in metabolites between sample groups and assess the extent of variation within each group. The PCA results demonstrate the discernible separation pattern among metabolic groups and provide insights into potential variations within each group, thereby serving as an effective approach for comparing different sample cohorts in the field of metabolic research [56]. The principal component analysis revealed that PC1 and PC2 accounted for 45.53% and 17.83% of the total variance contribution rate, respectively, effectively distinguishing between different tissue samples (Figure 2B). The sample group within each tissue site exhibited minimal variation, while a distinct pattern of differentiation was observed between different tissue sites. The patterns of metabolite accumulation exhibited the highest similarity between roots and stems, while showing the greatest dissimilarity in leaves compared to other tissue sites.  
The identification of differential metabolites involved the application of orthogonal partial least squares discriminant analysis (OPLS-DA) to extract components from both the independent variable X and the dependent variable Y, followed by the computation of their correlation [57]. In this study, the OPLS-DA method was employed to identify the variables accountable for the disparities observed across six distinct tissue sites (Figure S2). The results demonstrated that the Q2 values of all control groups exceeded 0.9, indicating  
Figure 1: Morphological characteristics observation of _Aquilaria sinensis._ (**A**) Seedling; (**B**) leaf before callus induction; (**C**) leaf callus; (**D**) husk; (**E**) seed.  
the stability of the model and the reliability of the data. In addition, OPLS-DA score plots showed that the six tissue sites were well separated in pairs (Figure S2).