qTAG: an adaptable plasmid scaffold for CRISPR-based endogenous tagging
Bibliographic record
Abstract
Endogenous tagging enables the study of proteins within their native regulatory context, typically using CRISPR to insert tag sequences directly into the gene sequence. Here, we introduce qTAG, a collection of repair cassettes that makes endogenous tagging more accessible. The cassettes support N- and C-terminal tagging with commonly used selectable markers and feature restriction sites for easy modification. Lox sites also enable the removal of the marker gene after successful integration. We demonstrate the utility of qTAG with a range of diverse tags for applications in fluorescence imaging, proximity labeling, epitope tagging, and targeted protein degradation. The system includes novel tags like mStayGold, offering enhanced brightness and photostability for live-cell imaging of native protein dynamics. Additionally, we explore alternative cassette designs for conditional expression tagging, selectable knockout tagging, and safe-harbor expression. The plasmid collection is available through Addgene, featuring ready-to-use constructs for common subcellular markers and tagging cassettes to target genes of interest. The qTAG system will serve as an open resource for researchers to adapt and tailor their own experiments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".