Application of CRISPR/Cas9 editing and digital droplet PCR in human iPSCs to generate novel knock-in reporter lines to visualize dopaminergic neurons
Bibliographic record
Abstract
Human induced pluripotent stem cells (hiPSCs) have become indispensable for disease modelling. They are an important resource to access patient cells harbouring disease-causing mutations. Derivation of midbrain dopaminergic (DAergic) neurons from hiPSCs of PD patients represents the only option to model physiological processes in a cell type that is not otherwise accessible from human patients. However, differentiation does not produce a homogenous population of DA neurons and contaminant cell types may interfere with the readout of the in vitro system. Here, we use CRISPR/Cas9 to generate novel knock-in reporter lines for DA neurons, engineered with an endogenous fluorescent tyrosine hydroxylase - enhanced green fluorescent protein (TH-eGFP) reporter. We present a reproducible knock-in strategy combined with a highly specific homologous directed repair (HDR) screening approach using digital droplet PCR (ddPCR). The knock-in cell lines that we created show a functioning fluorescent reporter system for DA neurons that are identifiable by flow cytometry.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".