A Digital PCR-Based Method for Efficient and Highly Specific Screening of Genome Edited Cells
Bibliographic record
Abstract
The rapid adoption of gene editing tools such as CRISPRs and TALENs for research and eventually therapeutics necessitates assays that can rapidly detect and quantitate the desired alterations. Currently, the most commonly used assay employs "mismatch nucleases" T7E1 or "Surveyor" that recognize and cleave heteroduplexed DNA amplicons containing mismatched base-pairs. However, this assay is prone to false positives due to cancer-associated mutations and/or SNPs and requires large amounts of starting material. Here we describe a powerful alternative wherein droplet digital PCR (ddPCR) can be used to decipher homozygous from heterozygous mutations with superior levels of both precision and sensitivity. We use this assay to detect knockout inducing alterations to stem cell associated proteins, NODAL and SFRP1, generated using either TALENs or an "all-in-one" CRISPR/Cas plasmid that we have modified for one-step cloning and blue/white screening of transformants. Moreover, we highlight how ddPCR can be used to assess the efficiency of varying TALEN-based strategies. Collectively, this work highlights how ddPCR-based screening can be paired with CRISPR and TALEN technologies to enable sensitive, specific, and streamlined approaches to gene editing and validation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".