Imaging CRISPR-Edited CAR-T Cell Therapies with Optical and Positron Emission Tomography Reporters
Bibliographic record
Abstract
Abstract Rationale Chimeric antigen receptor (CAR) T cell therapies have shown remarkable success in treating hematological cancers and are increasingly demonstrating potential for solid tumors. CRISPR-based genome editing offers a promising approach to enhance the potency and safety of CAR-T cells. However, several challenges persist, including inefficient tumor homing and treatment-related toxicities in normal tissues, which continue to hinder widespread adoption. Advanced imaging technologies, including bioluminescence imaging (BLI) and positron emission tomography (PET), provide real-time insights into CAR-T cell distribution and activity in vivo, both in preclinical models and in patients. Here, we developed Trackable Reporter Adaptable CRISPR-Edited CAR (tRACE-CAR) T cells, a modular system for site-specific integration of CARs and imaging reporters. Methods The luciferase reporter AkaLuciferase (AkaLuc) or the human sodium iodide symporter (NIS) were cloned downstream of the CAR in adeno-associated virus (AAV) donors for BLI or PET tracking, respectively. CARs with imaging reporters were knocked into the TRAC locus of primary human T cells via CRISPR editing and AAV transduction. Editing efficiency was evaluated by flow cytometry and junction PCR. In vitro cytotoxicity was assessed by BLI using firefly luciferase (Fluc)-expressing cancer cells co-cultured with CAR-T cells at varying effector-to-target ratios. In vivo, BLI and PET imaging assessed CAR-AkaLuc and CAR-NIS T cell expansion and trafficking in Nod-SCID-gamma mice bearing xenograft tumors. Results T cell receptor (TCR) knockout efficiency exceeded 85%, with CAR expression observed in 70–80% of cells, depending on the reporter used. Reporter-engineered CAR-T cells retained functionality in vitro and exhibited significant cytotoxicity against target cancer cells, outperforming naïve T cells. In vivo, AkaLuc BLI and 18 F-tetrafluoroborate PET enabled non-invasive tracking of viable CAR-T cells. Notably, the route of administration (intravenous, peritumoral, or intraperitoneal) significantly influenced the distribution of CAR-T cells and their therapeutic effectiveness. Conclusion tRACE-CAR enabled precise optical and PET tracking of CAR-T cells in models of B cell leukemia and ovarian cancer, allowing dynamic, non-invasive monitoring of cell distribution in both tumors and off-target tissues. This imaging platform could lead to more personalized, effective CRISPR-edited CAR cell therapies.
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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 | 0.001 | 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 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".