Profiling Small RNA From Brain Extracellular Vesicles in Individuals With Depression
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is a leading cause of disability with significant mortality risk. Despite progress in our understanding of the etiology of MDD, the underlying molecular changes in the brain remain poorly understood. Extracellular vesicles (EVs) are lipid-bound particles that can reflect the molecular signatures of the tissue of origin. We aimed to optimize a streamlined EV isolation protocol from postmortem brain tissue and determine whether EV RNA cargo, particularly microRNAs (miRNAs), have an MDD-specific profile. METHODS: EVs were isolated from postmortem human brain tissue. Quality was assessed using western blots, transmission electron microscopy, and microfluidic resistive pulse sensing. EV RNA was extracted and sequenced on Illumina platforms. Functional follow-up was performed in silico. RESULTS: Quality assessment showed an enrichment of EV markers, as well as a size distribution of 30 to 200 nm in diameter, and no contamination with cellular debris. Small RNA profiling indicated the presence of several RNA biotypes, with miRNAs and transfer RNAs being the most prominent. Exploring miRNA levels between groups revealed decreased expression of miR-92a-3p and miR-129-5p, which was validated by qPCR and was specific to EVs and not seen in bulk tissue. Finally, in silico functional analyses indicate potential roles for these 2 miRNAs in neurotransmission and synaptic plasticity. CONCLUSION: We provide a streamlined isolation protocol that yields EVs of high quality that are suitable for molecular follow-up. Our findings warrant future investigations into brain EV miRNA dysregulation in MDD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 | 0.000 |
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".