Single-nucleus profiling of the human brain to identify therapeutic targets
Bibliographic record
Abstract
The human brain is underpinned by a massive cellular complexity. A diverse conglomerate of cells, over 100 billion of them, functionally interact to power the most uniquely human organ. Unfortunately, the brain often encounters difficulties, and these neurological disorders drive significant clinical challenges. Most neurological disorders have no consistently effective therapeutic treatments. The work of this dissertation has been conducted with a single goal in mind: to improve the understanding of the human brain, in turn enabling the development of effective therapeutics to treat neurological disorders. To accomplish this, we conducted method development to enable effective single-nucleus profiling of the human brain, outlined tools for analyzing this data, carefully selected targets that may drive functional improvements, and developed and tested therapeutics capable of changing the brain. Here, we have profiled human brains with Down syndrome and matched controls to identify microglial overactivation, and a unique transcription factor, RUNX1, that appear to drive memory deficits. Additionally, we show that a potential therapeutic, targeting RUNX1, can reverse certain aspects of this biology. This work establishes a foundation for drug discovery, utilizing single-nucleus RNA-sequencing data to guide target selection and providing conceptual proof that these efforts can yield efficacious therapeutics.
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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 | 0.000 |
| 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) | 0.003 | 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".