A Rapid Pipeline to Model Rare Neurodevelopmental Disorders with Simultaneous CRISPR/Cas9 Gene Editing
Bibliographic record
Abstract
The development of targeted therapeutics for rare neurodevelopmental disorders (NDDs) faces significant challenges due to the scarcity of subjects and the difficulty of obtaining human neural cells. Here, we illustrate a rapid, simple protocol by which patient derived cells can be reprogrammed to induced pluripotent stem cells (iPSCs) using an episomal vector and differentiated into neurons. Using this platform enables patient somatic cells to be converted to physiologically active neurons in less than two months with minimal labor. This platform includes a method to combine somatic cell reprogramming with CRISPR/Cas9 gene editing at single cell resolution, which enables the concurrent development of clonal knockout or knock-in models that can be used as isogenic control lines. This platform reduces the logistical barrier for using iPSC technology, allows for the development of appropriate control lines for use in rare neurodevelopmental disease research, and establishes a fundamental component to targeted therapeutics and precision medicine. Stem Cells Translational Medicine 2017;6:886-896.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.003 |
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".