Stage-Specific Conditional Mutagenesis in Mouse Embryonic Stem Cell-Derived Neural Cells and Postmitotic Neurons by Direct Delivery of Biologically Active Cre Recombinase
Bibliographic record
Abstract
Conditional mutagenesis using Cre/loxP recombination is a powerful tool to investigate genes involved in neural development and function. However, the efficient delivery of biologically active Cre recombinase to neural cells, particularly to postmitotic neurons, represents a limiting factor. In this study, we devised a protocol enabling highly efficient conditional mutagenesis in ESC-derived neural progeny. Using a stepwise in vitro differentiation paradigm, we demonstrate that recombinant cell-permeable Cre protein can be used to efficiently induce recombination at defined stages of neural differentiation. Recombination rates of more than 90% were achieved in multipotent pan-neural and glial precursors derived from the Z/EG reporter mouse ESC line, in which Cre recombination activates enhanced green fluorescent proteinexpression. Recombined precursor cells displayed a normal phenotype and were able to differentiate into neurons and/or glial cells, indicating that Cre treatment has no overt side effects on proliferation and neural differentiation. Our data further demonstrate that recombination via Cre protein transduction is not restricted to dividing cells but can even be applied to postmitotic neurons. The ability to conduct Cre/loxP recombination at defined stages of stem cell differentiation in an expression-independent manner provides new prospects for studying the role of individual genes under stringent temporal control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
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".