A Single-Cell Omics Technical Guide for Advancing Neuropsychiatric Research
Bibliographic record
Abstract
Single-cell omics technology has advanced rapidly since its inception, offering increasing precision, resolution, and technical diversity to explore cell-specific molecular features in the human brain and neuropsychiatric disorders. While traditional bulk genomic analyses have provided valuable insights into the molecular processes of these disorders, single-cell omics allows for the investigation of cellular heterogeneity in the brain, which is crucial for dissecting underlying pathology. Neuropsychiatric disorders-such as dementia and depression-are complex and heterogenous brain disorders driven by intricate interactions of genetic and environmental factors. Methodological developments in single-cell omic technologies have enabled their application directly to human brain tissue for the study of neuropsychiatric disorders, yielding cell-specific insights in transcriptomics and epigenomics, with emerging findings in proteomics, metabolomics, multi-omics, and beyond. This review discusses different single-cell omic technologies, focusing on their application to postmortem human brain tissue, highlighting key findings from the use of these methods in neuropsychiatric disorders, and providing considerations for future implementation to elucidate the molecular landscape of brain changes associated with these conditions.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.035 | 0.053 |
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".