Protocol for CRISPR-based endogenous protein tagging in mammalian cells
Bibliographic record
Abstract
Tracking the localization and proximal interaction partners of endogenous proteins provides valuable functional insight. Here, we present a protocol for CRISPR-based endogenous protein tagging in mammalian cells. We describe steps for endogenously tagging human TSC22D2 and MAP4, including designing Cas9 and Cas12a guides for knockin, modularized repair template design and cloning, and procedures for lipid transfection and electroporation. This protocol accommodates Cas nucleases in plasmid expression or ribonucleoprotein complex (RNP) formats. This “endo-tagging” approach offers flexibility and broad applicability. For complete details on the use and execution of this protocol, please refer to Xiao et al. 1 • Instructions for endogenously tagging human TSC22D2 and MAP4 • Steps for designing Cas9 and Cas12a guides for knockin • Steps for modularized repair template design and cloning • Steps for lipid transfection and electroporation in mammalian cells Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Tracking the localization and proximal interaction partners of endogenous proteins provides valuable functional insight. Here, we present a protocol for CRISPR-based endogenous protein tagging in mammalian cells. We describe steps for endogenously tagging human TSC22D2 and MAP4, including designing Cas9 and Cas12a guides for knockin, modularized repair template design and cloning, and procedures for lipid transfection and electroporation. This protocol accommodates Cas nucleases in plasmid expression or ribonucleoprotein complex (RNP) formats. This “endo-tagging” approach offers flexibility and broad applicability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.061 | 0.057 |
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".