Deep characterization of <i>in vitro</i> chronically stimulated T cells via single-cell multiomic analysis
Bibliographic record
Abstract
Abstract 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. To comprehensively characterize in vitro chronically expanded human T cells, we performed single-cell multiomic analysis using BD® AbSeq and BD Rhapsody™ Single-Cell Analysis system for simultaneous measurement of the expression of 38 proteins and 399 genes. Comprehensive immunophenotypic and transcriptomic analysis at day 0 enabled a refined characterization of T-cell maturational states. T-cell activation induced by anti-CD3/CD28 beads and recombinant human IL-2 led to 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. © 2019 BD. All rights reserved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".