A dynamic <i>in vitro</i> model of Down Syndrome neurogenesis with Trisomy 21 gene dosage correction
Bibliographic record
Abstract
ABSTRACT/SUMMARY Excess gene dosage from human chromosome 21 (chr21) causes Down syndrome (DS), spanning developmental as well as acute phenotypes in terminal cell types. Which phenotypes remain amenable to intervention after development is unknown. To address this question in a model of DS neurogenesis, we generated trisomy 21 (T21) human induced pluripotent stem cells (hiPSCs) alongside otherwise isogenic euploid controls from mosaic DS fibroblasts, and integrated an inducible XIST transgene on one chr21 copy. Monoallelic chr21 silencing by XIST was near-complete and irreversible in hiPSCs. Differential expression reveals T21 neural lineages and T21 hiPSCs suppress similar translation and mitochondrial pathways, and activate cellular stress responses. When XIST is induced before the neural progenitor stage, T21 dosage correction mitigates a pronounced skew towards astrogenesis in differentiation. Because our transgene remained inducible in post-mitotic T21 neurons and astrocytes, we demonstrate XIST efficiently represses genes even after terminal differentiation, which will empower exploration of cell type-specific T21 phenotypes that remain responsive to chr21 dosage.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".