Protocol for the efficient and inducible generation of CRISPR-Cas9-edited human cortical neurons from the iCas9-iNgn2 hPSCs
Bibliographic record
Abstract
Generation of CRISPR-Cas9-edited cortical neurons from human pluripotent stem cells (hPSCs) enables the study of gene functions and neural disease mechanisms. Here, we present a protocol for developing iCas9-iNgn2 hPSC, an inducible cell line that allows the simultaneous induction of the neuralizing transcription factor Ngn2 and the Cas9 nuclease to rapidly generate edited human cortical neurons. We describe the steps of the protocol from transducing iCas9-iNgn2 with guide RNA-containing lentivirus to producing edited cortical neurons in about 2 weeks. For complete details on the use and execution of this protocol, please refer to Dhaliwal et al. 1 • iCas9-iNgn2 hPSCs allow the simultaneous induction of Cas9 and Ngn2 expression • Steps for the generation of sgRNA-containing lentivirus for iCas9-iNgn2 transduction • Techniques for the transduction, selection, and induction of iCas9-iNgn2 • Functional validation to assess neuronal differentiation and gene-editing efficiency Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Generation of CRISPR-Cas9-edited cortical neurons from human pluripotent stem cells (hPSCs) enables the study of gene functions and neural disease mechanisms. Here, we present a protocol for developing iCas9-iNgn2 hPSC, an inducible cell line that allows the simultaneous induction of the neuralizing transcription factor Ngn2 and the Cas9 nuclease to rapidly generate edited human cortical neurons. We describe the steps of the protocol from transducing iCas9-iNgn2 with guide RNA-containing lentivirus to producing edited cortical neurons in about 2 weeks.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.021 |
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".