Prefrontal interneuron genes underlie neurobiological processes shared between psychiatric disorders.
Bibliographic record
Abstract
The use of bulk tissue in gene expression analyses underplays the diversity of cell populations and cellular components involved in single-cell preparations. On the other hand, single-cell RNA sequencing provides a finer-grained resolution when interrogating brain cell types, dynamic states, and functional processes. To identify cell-type specific co-expression patterns relevant to major depressive disorder (MDD) etiopathogenesis, we analyzed single-nucleus transcriptomes from the prefrontal cortex of male patients with MDD who died by suicide. We report distinct co-expression patterns in PV- and SST-expressing interneurons, which correlated with genetic risk for MDD and schizophrenia (SCZ) and differed between patients and controls. We found similar co-expression patterns in an independent tissue homogenate cohort analyzed including both patients with MDD and SCZ. These results suggest a molecular pathway in these neurons that might clarify how the shared genetic risk of these disorders influences specific gene activity in the prefrontal cortex neural circuit.
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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".