A Flow Cytometry–Based Method for Assessing CAR Cell Binding Kinetics Using Stable CAR Jurkat Cells
Bibliographic record
Abstract
Chimeric antigen receptors (CARs) are synthetic fusion proteins that can reprogram immune cells to target specific antigens. CAR-expressing T cells have emerged as an effective treatment method for hematological cancers; despite this success, the mechanisms and structural properties that govern CAR responses are not fully understood. Here, we provide a simple assay to assess cellular avidity using a standard flow cytometer. This assay measures the interaction kinetics of CAR-expressing T cells and targets antigen-expressing target cells. By co-culturing stably transfected CAR Jurkat cells with target positive and negative cells for short periods of time in a varying effector-target gradient, we were able to observe the formation of CAR-target cell doublets, providing a readout of actively bound cells. When using the optimized protocol reported here, we observed unique cellular binding curves that varied between CAR constructs with differing antigen binding domains. The cellular binding kinetics of unique CARs remained consistent, were dependent on specific target antigen expression, and required active biological signaling. While existing literature is not clear at this time whether higher or lower CAR cell binding is beneficial to CAR therapeutic activity, the application of this simplified protocol for assessing CAR binding could lead to a better understanding of the proximal signaling events that regulate CAR functionality. Key features • Determines CAR receptor cellular interaction kinetics using a Jurkat cell model. • Can be used for a wide variety of CAR target antigens, including both hematological and solid tumor targets. • Experiments can be performed in under two hours with no staining using a standard flow cytometer. • Requires stable CAR Jurkat cells and target cells with stable fluorescent marker expression for optimal results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".