GNrep mouse: A reporter mouse for front–rear cell polarity
Bibliographic record
Abstract
Cell migration is essential during development, regeneration, homeostasis, and disease. Depending on the microenvironment, cells use different mechanisms to migrate. Yet, all modes of migration require the establishment of an intracellular front-rear polarity axis for directional movement. Although front-rear polarity can be easily identified in in vitro conditions, its assessment in vivo by live-imaging is challenging due to tissue complexity and lack of reliable markers. Here, we describe a novel and unique double fluorescent reporter mouse line to study front-rear cell polarity in living tissues, called GNrep. This mouse line simultaneously labels Golgi complexes and nuclei allowing the assignment of a nucleus-to-Golgi axis to each cell, which functions as a readout for cell front-rear polarity. As a proof-of-principle, we validated the efficiency of the GNrep line using an endothelial-specific Cre mouse line. We show that the GNrep labels the nucleus and the Golgi apparatus of endothelial cells with very high efficiency and high specificity. Importantly, the features of fluorescent intensity and localization for both mCherry and eGFP fluorescent intensity and localization allow automated segmentation and assignment of polarity vectors in complex tissues, making GNrep a great tool to study cell behavior in large-scale automated analyses. Altogether, the GNrep mouse line, in combination with different Cre recombinase lines, is a novel and unique tool to study of front-rear polarity in mice, both in fixed tissues or in intravital live imaging. This new line will be instrumental to understand cell migration and polarity in development, homeostasis, and disease.
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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) | 0.002 | 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 | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".