Faculty Opinions recommendation of Optimizing Nervous System-Specific Gene Targeting with Cre Driver Lines: Prevalence of Germline Recombination and Influencing Factors.
Bibliographic record
Abstract
The Cre-loxP system is invaluable for spatial and temporal control of gene knockout, knockin, and reporter expression in the mouse nervous system.However, we report varying probabilities of unexpected germline recombination in distinct Cre driver lines designed for nervous systemspecific recombination.Selective maternal or paternal germline recombination is showcased with sample Cre lines.Collated data reveal germline recombination in over half of 64 commonly used Cre driver lines, in most cases with a parental sex bias related to Cre expression in sperm or oocytes.Slight differences among Cre driver lines utilizing common transcriptional control elements affect germline recombination rates.Specific target loci demonstrated differential recombination; thus, reporters are not reliable proxies for another locus of interest.Similar principles apply to other recombinase systems and other genetically targeted organisms.We hereby draw attention to the prevalence of germline recombination and provide guidelines to inform future research for the neuroscience and broader molecular genetics communities. In BriefLuo et al. report variable rates of germline recombination in commonly used mouse Cre driver lines, influenced by sex of Cre-carrying parents and target loci.Guidelines are provided to optimize cell-type-specific recombination in genetically targeted organisms expressing sitespecific recombinases.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.146 | 0.124 |
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".