Single-nuclei transcriptomics of schizophrenia prefrontal cortex primarily implicates neuronal subtypes
Bibliographic record
Abstract
Abstract Transcriptomic studies of bulk neural tissue homogenates from persons with schizophrenia and controls have identified differentially expressed genes in multiple brain regions. However, the brain’s heterogeneous nature prevents identification of relevant cell types. This study analyzed single-nuclei transcriptomics of ~275,000 nuclei from frozen human postmortem dorsolateral prefrontal cortex samples from males with schizophrenia (n = 12) and controls (n = 14). 4,766 differential expression events were identified in 2,994 unique genes in 16 of 20 transcriptomically-distinct cell populations. ~96% of differentially expressed genes occurred in five neuronal cell types, and differentially expressed genes were enriched for genes associated with schizophrenia and bipolar GWAS loci. Downstream analyses identified cluster-specific enriched gene ontologies, KEGG pathways, and canonical pathways. Additionally, microRNAs and transcription factors with overrepresented neuronal cell type-specific targets were identified. These results expand our knowledge of disrupted gene expression in specific cell types and permit new insight into the pathophysiology of schizophrenia.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".