A key step in the clinical production of CAR T cells is the expansion of engineered T cells. To generate enough cells for viable adoptive cell therapy, cells must be robustly stimulated, which raises the risk of inducing T-cell exhaustion and reducing therapeutic efficacy. We sought to answer fundamental questions about the impact of in vitro manipulation on T-cell identity to by performing single-cell multiomic analysis using BD® AbSeq and BD Rhapsody™ Single-Cell Analysis system to simultaneously measure expression of 38 proteins and 399 genes in human T cells expanded in vitro. Comprehensive immunophenotypic and transcriptomic analysis at day 0 enabled a refined characterization of T-cell maturational states and the identification of a donor-specific subset of terminally differentiated T cells that would have been otherwise overlooked using canonical cell classification schema. As expected, T-cell activation induced downregulation of naïve-associated markers and upregulation of effector molecules, proliferation regulators, co-inhibitory and co-stimulatory receptors. Our deep kinetic analysis further revealed clusters of proteins and genes identifying unique states of activation defined by markers temporarily expressed upon 3 days of stimulation, markers constitutively expressed throughout chronic activation, and markers uniquely up-regulated upon 14 days of stimulation. These data indicate heterogeneity and plasticity of chronically stimulated T cells in response to different kinetics of activation. We demonstrate the power of a single-cell multiomic approach to comprehensively characterize T cells and to precisely monitor changes in differentiation, activation and exhaustion signatures in response to different activation protocols. For Research Use Only. Not for use in diagnostic or therapeutic procedures. BD, the BD Logo, and Rhapsody are trademarks of Becton, Dickinson and Company or its affiliates. © 2020 BD. All rights reserved.
1. The complexity of T-cell exhaustion and utility of multiomic approaches in studying complex biological processes such as T-cell exhaustion
2. Use of unsupervised data analysis approaches in analyzing heterogeneous cell population
3. Comparing and contrasting data from traditional flow cytometry and BD® AbSeq