TIGER: A tdTomato in vivo genome-editing reporter mouse for investigating precision-editor delivery approaches
Bibliographic record
Abstract
In vivo genome editing has the potential to address many inherited and environmental disorders. However, a major hurdle for the clinical translation of genome editing is safe, efficient delivery to disease-relevant tissues. A modality-agnostic reporter animal model that facilitates rapid, precise, and quantifiable assessment of functional delivery and editing could greatly enhance the evaluation and translation of delivery technologies. Here, we present the development of the tdTomato in vivo genome-editing reporter (TIGER) mouse, a reporter strain that harbors an integrated and constitutively expressed mutated tdTomato gene in the Polr2a locus. The mutations (Q115X, Q357X) abolish fluorescence, but successful adenine base editing (ABE) or prime editing (PE) restores tdTomato fluorescence. This mouse model facilitates the tissue- and cell type–specific assessment of genome editing agent delivery. We describe several editing strategies validated in vitro and demonstrate efficient ABE and PE in vivo using viral and nonviral delivery vectors targeting four cell types within the mouse eye: the retinal pigment epithelium, photoreceptors, Müller glia, and the trabecular meshwork. We show direct editing characterization in the ocular tissues via in vivo and ex vivo two-photon confocal microscopy and verify the spectral and fluorescence lifetime properties of tdTomato reporter in other mouse tissues. Additionally, we demonstrate successful adeno-associated virus (AAV)-mediated PE of extraocular tissues, including hepatocytes, skeletal muscle, and brain neurons by intravenous injection. Thus, the TIGER mouse facilitates the direct development, comparison, and optimization of delivery platforms for efficient and productive ABE or PE broadly applicable in vivo across multiple tissues tested in this study.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".