Spatial genomics of AAVs reveals mechanism of transcriptional crosstalk that enables targeted delivery of large genetic cargo
Bibliographic record
Abstract
Abstract Integrating cell type-specific regulatory elements (e.g. enhancers) with recombinant adeno-associated viruses (AAVs) can provide broad and efficient genetic access to specific cell types. However, the packaging capacity of AAVs restricts the size of both the enhancers and the cargo that can be delivered. Transcriptional crosstalk offers a novel paradigm for cell type-specific expression of large cargo, by separating distally-acting regulatory elements into a second AAV genome. Here, we identify and profile transcriptional crosstalk in AAV genomes carrying 11 different enhancers active in mouse brain. To understand transcriptional crosstalk, we develop spatial genomics methods to identify and localize AAV genomes and their concatemeric forms in cultured cells and in tissue. Using these methods, we construct detailed views of the dynamics of AAV transduction and demonstrate that transcriptional crosstalk is dependent upon concatemer formation. Finally, we leverage transcriptional crosstalk to drive expression of a large Cas9 cargo in a cell type-specific manner with systemically-administered engineered AAVs and demonstrate AAV-delivered, minimally-invasive, cell type-specific gene editing in wildtype animals that recapitulates known disease phenotypes. Highlights Transcriptional crosstalk between enhancers and promoters delivered in trans by AAVs is a generalized phenomenon. Spatial genomics techniques, AAV-Zombie and SpECTr, reveal that AAV genome concatemerization facilitates transcriptional crosstalk. Transcriptional crosstalk can be leveraged for minimally-invasive, targeted AAV delivery of large cargo, including machinery for CRISPR-based gene editing and manipulation. Transcriptional crosstalk enables cell-type specific gene disruption in wildtype animals, recapitulating behavioural phenotypes of genetic knockouts.
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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 | 0.001 |
| 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".