<scp>Human‐Specific</scp> Transcriptome of Ventral and Dorsal Midbrain Dopamine Neurons
Bibliographic record
Abstract
OBJECTIVE: Neuronal loss in the substantia nigra pars compacta (SNpc) in Parkinson disease (PD) is not uniform, as dopamine neurons from the ventral tier are lost more rapidly than those of the dorsal tier. Identifying the intrinsic differences that account for this differential vulnerability may provide a key for developing new treatments for PD. METHODS: Here, we compared the RNA-sequenced transcriptomes of ~100 laser captured microdissected SNpc neurons from each tier from 7 healthy controls. RESULTS: Expression levels of dopaminergic markers were similar across the tiers, whereas markers specific to the neighboring ventral tegmental area were virtually undetected. After accounting for unwanted sources of variation, we identified 106 differentially expressed genes (DEGs) between the SNpc tiers. The genes higher in the dorsal/resistant SNpc tier neurons displayed coordinated patterns of expression across the human brain, their protein products had more interactions than expected by chance, and they demonstrated evidence of functional convergence. No significant shared functionality was found for genes higher in the ventral/vulnerable SNpc tier. Surprisingly but importantly, none of the identified DEGs was among the familial PD genes or genome-wide associated loci. Finally, we found some DEGs in opposite tier orientation between human and analogous mouse populations. INTERPRETATION: Our results highlight functional enrichments of vesicular trafficking, ion transport/homeostasis and oxidative stress genes showing higher expression in the resistant neurons of the SNpc dorsal tier. Furthermore, the comparison of gene expression variation in human and mouse SNpc populations strongly argues for the need of human-focused omics studies. ANN NEUROL 2020;87:853-868.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".