Immune function assessing of TIM3/CD28‐modified CD19 CAR‐T cells and general CD19 CAR‐T cells through a high‐throughput single‐cell microarray platform
Bibliographic record
Abstract
Abstract Chimeric antigen receptor (CAR) T cells are widely used to treat hematological tumors due to their powerful ability to target and kill cancer cells, of which accurate function evaluation at the single‐cell level is crucial to ensuring the efficacy of diagnosis and treatment. Currently, a universal platform to evaluate the efficacy of immune single cells rapidly, efficiently, and systematically is urgently needed. Here, the cytotoxicity, proliferative potential, and persistence of TIM3/CD28‐modified CD19 CAR‐T cells are evaluated in comparison with ordinary CD19 CAR‐T cells through high‐performance and throughput graphene oxide quantum dot (GOQD)‐based single‐cell microfluidic chips. Overall secretory factor expression, immune‐therapy effect of different effector‐target ratios, spatial immune‐therapy effects, and subgroup type profiling are demonstrated to explicit the immunotherapy effect of TIM3/CD28‐modified CD19 CAR‐T cells. TIM3/CD28‐modified CD19 CAR‐T cells show stronger anti‐tumor ability and maintain excellent immunotherapy effects even at low effector‐target ratios and remote distances. TIM3/CD28 also strengthens the local targeting ability of TIM3/CD28‐modified CD19 CAR‐T. Importantly, TIM3/CD28‐modified CD19 CAR‐T exhibits more distinct Th1/Th2 long‐term persistent and potent killer subgroups, which is very helpful for personalized therapy. Overall, this study provides a valuable approach that can be widely implemented to analyze current CAR‐T combinations and evaluate the function of innovative CAR treatments in the future.
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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".