An adaptable plasmid scaffold for CRISPR-based endogenous tagging
Bibliographic record
Abstract
Abstract Endogenous tagging makes it possible to study a protein’s localization, dynamics, and function within its native regulatory context. This is typically accomplished via CRISPR, which involves inserting a sequence encoding a functional tag into the reading frame of a gene. However, this process is often inefficient. Here, we introduce the “quickTAG,” or qTAG system, a versatile collection of optimized repair cassettes designed to make CRISPR-mediated tagging more accessible. By including a desired tag sequence linked to a selectable marker in the cassette, integrations can be quickly isolated post-editing. The core sequence scaffold within these constructs incorporates several key features that enhance flexibility and ease of use, such as: specific cassette designs for N- and C-terminus tagging; standardized cloning sequences to simplify the incorporation of homology arms for HDR or MMEJ-based repairs; restriction sites next to each genetic element within the cassette for easy modification of tags and selectable markers; and the inclusion of lox sites flanking the selectable marker to allow for marker gene removal following integration. We showcase the versatility of these cassettes with a diverse range of tags, demonstrating their applications in fluorescence imaging, proximity-dependent biotinylation, epitope tagging, and targeted protein degradation. The adaptability of this scaffold is also exhibited by incorporating novel tags such as mStayGold, which offer enhanced brightness and photostability, reconciling prolonged live-cell imaging of proteins at their endogenous levels. Finally, by leveraging the restriction sites, entirely distinct cassette structures and editing schemes were developed. These enabled scenarios that included conditional expression tagging, selectable knockout tagging, and safe-harbor expression. Our existing and forthcoming collection of plasmids will be accessible through Addgene. It includes ready-to-use constructs targeting common subcellular marker genes, as well as an assortment of tagging cassettes for the tagging of genes of interest. The qTAG system offers an accessible framework to streamline endogenous tagging and will serve as an open resource for researchers to adapt and tailor for 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".