Characterizing oligodendrocyte-lineage cells and myelination in the basolateral amygdala: Insights from a novel methodology in postmortem human brain
Bibliographic record
Abstract
The basolateral amygdala (BLA) plays a key role in the pathophysiology of depressive disorders and trauma, yet oligodendrocyte (OL)-lineage cells and myelin in this region remain understudied in humans. This may be due in part to the lack of a cost-effective, antibody-based method to isolate OL and OL precursor cells (OPC) from postmortem brain tissue. This study aimed to 1) create and validate a method for isolating OPC and OL nuclei from postmortem grey matter; 2) compare OPC and OL gene expression in the BLA between individuals with depression who died by suicide (with or without a history of childhood abuse) and matched controls; and 3) provide histological characterizations of OPCs, OLs, and myelin in the BLA. Frozen left-hemisphere BLA samples were obtained from brain donors with well-characterized phenotypic information. Immunolabeled nuclei were sorted into OPC (SOX10+/CRYAB-) and OL (SOX10+/CRYAB+) populations, and RNA was measured using a custom Nanostring codeset. OPC (PDGFRα+) and OL (MYRF+) densities were determined using RNAScope, and axons (NF-H) and myelin (MBP) were labeled by immunofluorescence to assess myelin area fraction. This method successfully isolated OPC and OL nuclei with correct transcriptomic profiles. In the OL fraction, MOBP expression was significantly decreased in depressed individuals with a history of childhood abuse compared to controls. No other genes showed group differences in either fraction, though significant age-related expression patterns were observed. Furthermore, no group differences were seen in cell densities or myelin coverage. This study validates a novel sorting method and provides a comprehensive characterization of OL-lineage gene expression, cell densities, and myelin in the human BLA.
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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 | 0.001 | 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".