Spatial transcriptomics of the human dorsolateral prefrontal cortex in major depressive disorder
Bibliographic record
Abstract
Major depressive disorder (MDD) is a debilitating, heterogeneous disease characterized by depressed mood, diminished interests, impaired cognitive function, and vegetative symptoms. Roughly 300 million people worldwide are currently living with MDD, with the lifetime prevalence of MDD significantly higher in women than in men. The most dramatic consequence of MDD remains suicide, with psychological autopsy studies suggesting that >50% of adults who died by suicide have had a previous diagnosis of depression. In Canada alone, nearly 4000 deaths occur by means of suicide annually: representing a significant and pertinent public health concern. Despite the burden of this disorder and despite extensive research conducted, critical gaps in our understanding of the complex genetic and biological mechanisms underlying MDD remain. Rapidly advancing technologies including snRNAseq and spatial transcriptomic approaches allow for the resolution needed to study the fine molecular differences among different cell-types and/or disease states like never before. Though questions still remain to understand the complex interplay of cells and their localization in the context of the brain, and mental illness.This thesis presents an optimized and validated, spatially resolved transcriptomic workflow: incorporating postmortem human brain tissue and reliable cell-type specific immunofluorescent markers. We successfully generate next-generation sequence-ready (NGS), spatially-barcoded libraries using our workflow.Lastly, we optimize and reliably demonstrate bioinformatic analysis pipelines using R to filter, normalize, and transcriptomically define the laminar organization of the human dorsolateral prefrontal cortex (dlPFC) using unsupervised clustering
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".