Flow cytometry data mining by cytoChain identifies determinants of exhaustion and stemness in TCR-engineered T cells.

The phenotype of infused cells is a major determinant of Adoptive T-cell therapy (ACT) efficacy. Yet, the difficulty in deciphering multiparametric cytometry data limited the fine characterization of cellular products. To allow the analysis of dynamic and complex flow cytometry samples, we developed cytoChain, a novel dataset mining tool and a new analytical workflow. CytoChain was challenged to compare state-of-the-art and innovative culture conditions to generate stem-like memory cells (T ) suitable for ACT. Noticeably, the combination of IL-7/15 and superoxides scavenging sustained the emergence of a previously unidentified nonexhausted Fit-T signature, overlooked by manual gating and endowed with superior expansion potential. CytoChain proficiently traced back this population in independent datasets, and in T-cell receptor engineered lymphocytes. CytoChain flexibility and function were then further validated on a published dataset from circulating T cells in COVID-19 patients. Collectively, our results support the use of cytoChain to identify novel, functionally critical immunophenotypes for ACT and patients immunomonitoring.