Molecular cartography of the human down syndrome and trisomic mouse brain
Bibliographic record
Abstract
Down syndrome (DS, or Trisomy 21) is one of the most common genetic causes of intellectual disability. DS results in both abnormal neurodevelopment and accelerated neurodegeneration, but the molecular mechanisms underlying abnormal corticogenesis are incompletely understood. To gain molecular insight into the prenatal neurobiology of DS, we performed single-nucleus sequencing, spatial transcriptomics, and proteomics on mid-gestational prenatal human cortex. We captured altered expression dynamics of lineage commitment genes and de-repression of transposable elements in DS neural progenitor cells, which suggest changes to the fate and functionality of neuronal and glial cells. Given the importance of linking human and model system pathobiology, we also performed highly multiplexed RNA in situ spatial transcriptomics on a well-established trisomic mouse model (Ts65Dn) to study the cellular landscape of the trisomic brain during early development and maturation. We profiled the spatial transcriptome of > 240,000 cells in the mouse brain and identified trisomy-associated gene expression patterns in the molecular control of neurogenesis and gliogenesis. Together, our study provides an extensive resource for understanding of the complex multicellular processes underlying DS neurodevelopment. Down Syndrome is a common genetic cause of intellectual disability and is associated with altered fetal neurodevelopment. Here, the authors identify altered expression dynamics of lineage commitment genes and de-repression of transposable elements in DS neural progenitor cells.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".