Development of a gene‐trap vector with a highly sensitive fluorescent protein reporter system for expression profiling
Bibliographic record
Abstract
SUMMARY: Combining high-content screening (HCS) with random gene-trap mutagenesis could be a powerful tool to investigate transcriptional networks, cell signaling, chemical genetics, and developmental processes. However, a critical limitation has been poor quantification of reporter expression per cell. To overcome this hurdle, we generated a variety of Gtx-based expression cassettes and re-evaluated translational enhancement of arrayed Gtx segments in tandem by HCS. We then modified the cassette into a new polyA trap vector, which consists of a variant of yellow fluorescent protein, Venus, in combination with the Gtx segments. Expression of Venus was detected in about 60% of trapped genes assayed in embryonic stem cell (ESC) cultures, comparable to expression screening of LacZ-based vectors. Furthermore, tetraploid aggregations using a clone encoding a gene-trap insertion into Twist2 demonstrated identical spatiotemporal expression between Venus and Twist2. This highly sensitive reporter system is amenable to high-throughput expression-based real-time HCS including single cell analyses.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".