A strategy for genome-wide seamless tagging of human protein-coding genes
Bibliographic record
Abstract
Abstract Comprehensive and systematic proteome-wide experiments require financial and technical resources unavailable to most researchers. Here we describe a scalable CRISPR/Cas9-non-homologous end joining (NHEJ) based method for P ooled R ecombinant I ntegration of S eamless M arkers (PRISM) into protein coding genes. We created two gRNA libraries for 5’- and 3’-tagging of 18,804 human protein-coding genes. Selection for in-frame integration of the donor cassette can be guaranteed by fusing it to an antibiotic resistance enzyme (ARE) and P2A self-cleaving peptide, resulting in tagged proteins and free ARE to select for clones with inserted donor cassettes. We achieved a library integration rate of 19.75% and tagging of ∼80% for genes expressed in Hek293T cells and notably, 89.7% of essential genes, with donor DNA. Our strategy is scalable, specific, and selective, paving the way for genome-scale construction of human cell lines tagged with different types of reporter genes for protein functional characterization. Significance statement Here we report the first practical method to achieve near complete 5’- or 3’-end integration of reporter protein-coding sequences to express seamless fusions of human protein and reporter proteins that we call P ooled R ecombinant I ntegration of S eamless M arkers ( PRISM ). PRISM is independent of homology templates, yet it works with high efficiency and precision. Using gRNA-directed CRISPR/Cas9 genome editing and mini-plasmid donor cassettes we were able to tag about 80% of the protein-coding sequences of human genes with a green florescent protein at an integration efficiency of about 20% and most notably, of 90% of essential genes in the human cell line HEK 293T. This is important because the characterization of gene functions in a particular cell type usually begins with essential genes.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".