Ex Vivo Phenotyping and Potency Monitoring of CD19 CAR T Cells With a combined Flow Cytometry and Impedance-based Real-Time Cell Analysis Workflow
Bibliographic record
Abstract
Abstract Cancer immunotherapy is increasingly utilized for cancer treatment. CARs (Chimeric antigen receptors) targeting CD19 have produced strong antitumor responses in hematologic malignancies, but tumor regression has seldom occurred when CARs targeting other antigens. The state of T-cell differentiation and T cell exhaustion influences the engraftment and persistence of CAR-expressing T cells following adoptive transfer. Here we report the use of a combined impendence-based Real-Time Cell Analysis (RTCA) and flow cytometry cell analysis workflow for ex vivo cytolytic potency monitoring of CD19 CAR T cell, as well as the examination of phenotypic and functional responses to antigen exposure over time. CD19 CAR T cell killing of HEK293 CD19 with different E:T ratio was monitored by RTCA system. Cytokine productions were measured by NovoCyte® Quanteon™ flow cytometer after 24 hrs. Co-incubation with HEK293 CD19, phenotypic and functional responses of CD19 CAR T cells towards exposure of different antigen levels were systematically examined over time. Both assays reveal that the CD19 CAR T cells show high dose-dependent killing activities against HEK293 CD19 but not against HEK293. Using the multi-color Flow Cytometry immunophenotyping, CD4 dim and CD8 dim populations are identified as effective killing cells which are enriched in central memory (CD45RA+CCR7−) cells while normal CD4+ or CD8+ T cells consist of predominant naïve-like (CD45RA+CCR7+) T cells after co-incubation with HEK293 CD19 cells. Exhaustion marker (PD-1, LAG-3, TIM-3) are overexpressed in normal CD4+ and CD8+ populations but not in CD4 dim and CD8 dim populations with CD19 antigen exposure. After antigen exposure, T cell activation levels change significantly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".