Twenty-Seven Tamoxifen-Inducible iCre-Driver Mouse Strains for Eye and Brain, Including Seventeen Carrying a New Inducible-First Constitutive-Ready Allele
Bibliographic record
Abstract
Abstract To understand gene function, the cre/loxP conditional system is the most powerful available for temporal and spatial control of expression in mouse. However, the research community requires more cre recombinase expressing transgenic mouse strains (cre-drivers) that restrict expression to specific cell types. To address these problems, a high-throughput method for large-scale production that produces high-quality results is necessary. Further, endogenous promoters need to be chosen that drive cell type specific expression, or we need to further focus the expression by manipulating the promoter. Here we test the suitability of using knock-ins at the docking site 5′ of Hprt for rapid development of numerous cre-driver strains focused on expression in adulthood, using an improved cre tamoxifen inducible allele (icre/ERT2), and testing a novel inducible-first, constitutive-ready allele (icre/f3/ERT2/f3). In addition, we test two types of promoters either to capture an endogenous expression pattern (MaxiPromoters), or to restrict expression further using minimal promoter element(s) designed for expression in restricted cell types (MiniPromoters). We provide new cre-driver mouse strains with applicability for brain and eye research. In addition, we demonstrate the feasibility and applicability of using the locus 5′ of Hprt for the rapid generation of substantial numbers of cre-driver strains. We also provide a new inducible-first constitutive-ready allele to further speed cre-driver generation. Finally, all these strains are available to the research community through The Jackson Laboratory.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".