Intersectional Cre Driver Lines Generated Using Split-Intein Mediated Split-Cre Reconstitution
Bibliographic record
Abstract
Tissue and cell type highly specific Cre drivers are very rare due to the fact that most genes or promoters used to direct Cre expressions are generally expressed in more than one tissues and/or in multiple cell types. We developed a split-intein based split-Cre system for highly efficient Cre-reconstitution through protein splicing. This split-intein-split-Cre system can be used to intersect the expression patterns of two genes or promoters to restrict full-length Cre reconstitution in their overlapping domains. To test this system in vivo, we selected several conserved human enhancers to drive the expression of either Cre-N-intein-N, or intein-C-Cre-C transgene in different brain regions. In all paired CreN/CreC transgenic mice, Cre-dependent reporter was efficiently induced specifically in the intersectional expression domains of two enhancers. This split-intein based method is simpler to implement compared with other strategies for generating highly-restricted intersectional Cre drivers to study complex tissues such as the nervous system.
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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) | 0.000 | 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) | 0.000 | 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".