Metabolic Flux Profiling of Single Cells

Single-cell flux estimation analysis (scFEA; Alghamdi et al., 2021 (link)) was used to identify the single-cell metabolic flux profiles. A total of 168 metabolic modules were directly downloaded from the algorithm’s official GitHub page.⁴ Using default parameters, the FindMarkers function (the nonparametric Wilcoxon rank sum test) was used for differential expression analysis. Differential expression genes with p_val_adj < 0.05 were then selected for futher analysis. Using default parameters, the Monocle2 (version 2.22.0; Trapnell et al., 2014 (link)) algorithm was used to construct a single-cell pseudotime trajectory. We used the 2,000 most highly variable genes for analysis and identified 5 distinct cell states. State-specific genes were identified using differentialGeneTest, which compares the expression of genes between each state and the remaining four states. The trajectories were constructed using DDRTree.