Precision Health Resource of Control iPSC Lines for Versatile Multilineage Differentiation
Bibliographic record
Abstract
Induced pluripotent stem cells (iPSC) derived from healthy individuals are important controls for disease-modeling studies. Here we apply precision health to create a high-quality resource of control iPSCs. Footprint-free lines were reprogrammed from four volunteers of the Personal Genome Project Canada (PGPC). Multilineage-directed differentiation efficiently produced functional cortical neurons, cardiomyocytes and hepatocytes. Pilot users demonstrated versatility by generating kidney organoids, T lymphocytes, and sensory neurons. A frameshift knockout was introduced into MYBPC3 and these cardiomyocytes exhibited the expected hypertrophic phenotype. Whole-genome sequencing-based annotation of PGPC lines revealed on average 20 coding variants. Importantly, nearly all annotated PGPC and HipSci lines harbored at least one pre-existing or acquired variant with cardiac, neurological, or other disease associations. Overall, PGPC lines were efficiently differentiated by multiple users into cells from six tissues for disease modeling, and variant-preferred healthy control lines were identified for specific disease settings.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".