Improved cloning-free one-step CRISPR-Cas12a-assisted tagging of mammalian genes using PCR generated reagents (PCR tagging)
Bibliographic record
Abstract
Abstract Precise DNA integration in mammalian genomes using CRISPR/Cas based strategies remains challenging due to competing DNA repair pathways. We present an update for our CRISPR/Cas12a-assisted PCR-tagging method using linear donor cassettes and self-targeting gRNAs for gene tagging. By incorporating 2A-linked selection markers and chemically modifying PCR cassettes with phosphorothioate and biotin, along with the inhibition of NHEJ, we improve precise insertions while reducing unwanted integration products and we devise a variant of the method for seamless gene tagging. To profile integration outcomes, we further developed our integration site sequencing protocol (Tn5-Anchor-Seq, Meurer et al., 2018) to obtain a protocol using long-read (ONT) sequencing coupled with a custom computational pipeline. This enables high-resolution quantification of all integration events with respect to integration by HDR or NHEJ, as well as the presence of concatemers and off-target integrations. Our data reveal gene-specific editing profiles and demonstrate that combining cassette design with DNA repair modulation yields up to 5-fold increases in HDR and suppression of concatemers and indels. Together, our improvements outline an robust strategy for efficient and high-fidelity gene tagging in mammalian cells, facilitating functional genomics and cell engineering applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.003 | 0.004 |
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".