FACS for the isolation of individual cells from transgenic mice harboring a fluorescent protein reporter
Bibliographic record
Abstract
Flow cytometry is extensively used for the isolation of discreet populations of cells from complex pools. The advent of autofluorescent (AFP) reporters such as wild type Green Fluorescent Protein (wtGFP) (Chalfie et al., 1994) and its variants, including enhanced green fluorescent protein (EGFP) and enhanced yellow fluorescent protein (EYFP) (Cormack et al., 1996), as vital reporters opens up the possibility of sorting live reporter-expressing cells. Moreover the use of these reporters in transgenics (Okabe et al., 1997) or mice carrying homologously targeted loci (Godwin et al., 1998) should enable the direct isolation of reporter-expressing cells from any desired lineage. Here we have assessed this approach in transgenic mice. ES cell-mediated transgenesis was used for generating a line of mice that express an autofluorescent EYFP reporter in the heart and part of the neural tube at midgestation. Pools of fluorescent cells harboring and expressing the EYFP reporter were isolated from defined regions of embryos and their origin confirmed by assaying the expression of domain-defined marker genes. Such a tool should prove useful for gaining access to any given lineage that can be fluorescent protein reporter tagged.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.021 |
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".