Metabolic profiling using benchtop NMR identifies the metabolomic signature of persistent CAR-T cells

Abstract

Analytical technologies for engineered biological systems hold great promise in addressing various challenges in modern pharmaceuticals and biomedical therapies. These endeavors often follow a design-build-test-learn approach, utilizing biological data from genetic circuits, signal pathways, metabolites, and proteins to optimize biological systems. Deciphering large and intricate datasets can prove to be a formidable task. Principal component analysis (PCA) tool is an invaluable method for reducing dataset complexity and enhancing interpretability while minimizing information loss, simultaneously. PCA tool achieves this by creating new, uncorrelated variables that capture the maximum variance in the data. Herein chimeric antigen receptors (CARs) T cells metabolomic study presents a slightly inverse problem, where PCA is applied to model sparse extracellular metabolites data from CAR-T cells, resulting in a two-component model. Using an benchtop NMR spectrometer only six metabolites could be annotated, nevertheless, the PCA model could identify differences in the metabolites of CAR-T cells based on the design of CARs, specifically the combinations of the intracellular domain (ICDs). Its noteworthy that the behavior and fate of CAR-T cells are distinctly influenced by the type of ICDs used upon antigen recognition.