Somatic Genome-Engineered Mouse Models Using <em>In Vivo</em> Microinjection and Electroporation
Bibliographic record
Abstract
Germline genetically engineered mouse models (G-GEMMs) have provided valuable insight into in vivo gene function in development, homeostasis, and disease. However, the time and cost associated with colony creation and maintenance are high. Recent advances in CRISPR-mediated genome editing have allowed the generation of somatic GEMMs (S-GEMMs) by directly targeting the cell/tissue/organ of interest. The oviduct, or fallopian tube in humans, is considered the tissue-of-origin of the most common ovarian cancer, high-grade serous ovarian carcinomas (HGSCs). HGSCs initiate in the region of the fallopian tube distal to the uterus, located adjacent to the ovary, but not the proximal fallopian tube. However, traditional mouse models of HGSC target the entire oviduct, and thus do not recapitulate the human condition. We present a method of DNA, RNA, or ribonucleoprotein (RNP) solution microinjection into the oviduct lumen and in vivo electroporation to target mucosal epithelial cells in restricted regions along the oviduct. There are several advantages of this method for cancer modeling, such as 1) high adaptability in targeting the area/tissue/organ and region of electroporation, 2) high flexibility in targeted cell types (cellular pliancy) when used in combination with specific promoters for Cas9 expression, 3) high flexibility in the number of electroporated cells (relatively low frequency), 4) no specific mouse line is required (immunocompetent disease modeling), 5) high flexibility in gene mutation combination, and 6) possibility of tracking electroporated cells when used in combination with a Cre reporter line. Thus, this cost-effective method recapitulates human cancer initiation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".