The raw SCMS data were subjected to pretreatment using a customized R script reported in our previous studies . The data pretreatment includes background removal (to remove signals originating from solvents and cell culture media), noise reduction (to remove instrument noise), ion intensity normalization (to normalize the intensity of each ion to the total ion current (TIC)). Deisotope was performed with Python package ms_deisotope v0.0.053 (mobiusklein.github.io/ms_deisotope). After deisotope, peak alignment was performed using in-house Python script. To extract essential biological information and perform comparison of metabolomic profiles among different groups of cells, pretreated SCMS data were processed for visualization (by Principal Component Analysis (PCA), heat map, and volcano plot) and pathway analysis using MetaboAnalyst 6.0 . PCA was used for dimensionality reduction and visualization of SCMS data, allowing for intuitive comparison of the overall metabolites' profiles of cells from multiple groups. Heat map was generated to visualize the relative abundances of metabolites among cells. The volcano plot was used to illustrate significantly changed (p < 0.05 from t-test, FC > 1.5) species of cells in two different groups. Pathway analysis was employed to determine which metabolomic pathway significantly altered (FDR < 0.05) in pairwise comparison of cells in two groups. Pathway analysis examined the correlation between p-values (from pathway enrichment analysis) and pathway impact scores (from pathway topology analysis mapped against KEGG using Homo sapiens as the model organism). This comprehensive approach allowed for gaining deeper insights into the nuanced variations within the metabolic landscapes of the studied cell groups.