Stem Cell Engineering Using Transducible Cre Recombinase
Bibliographic record
Abstract
Embryonic stem (ES) cells have become a major focus of scientific interest both as a potential donor source for regenerative medicine and as a model system for tissue development and pathobiology. Tight and efficient methods for genetic engineering are required to exploit ES cells as disease models and to generate specific somatic phenotypes by lineage selection or instruction. In 1990s, the application of site-specific recombinases (SSRs) such as Cre has revolutionized mammalian genetics by providing a reliable and efficient means to delete, insert, invert, or exchange chromosomal DNA in a conditional manner. Despite these significant advances, the available technology still suffers from limitations, including unwanted side effects elicited by the random integration of Cre expression vectors and leak activity of inducible or presumptive cell type-specific Cre expression systems. These challenges can be met by combining the Cre/loxP recombination system with direct intracellular delivery of Cre by protein transduction, thus enabling rapid and highly efficient conditional mutagenesis in ES cells and ES cell-derived somatic progeny. Modified recombinant variants of Cre protein induce recombination in virtually 100% of human ES (hES) and mouse ES (mES) cells. Here, we present methods for generating purified transducible Cre protein from Escherichia coli and its transduction into ES cells and their neural progeny.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".