Microglia Transcriptional Profiling in Major Depressive Disorder Shows Inhibition of Cortical Gray Matter Microglia
Bibliographic record
Abstract
BACKGROUND: Microglia have been implicated in the pathophysiology of major depressive disorder (MDD), but information on biological mechanisms is limited. Therefore, we investigated the gene expression profile of microglial cells in relation to neuronal regulators of microglia activity in well-characterized MDD and control autopsy brains. METHODS: Pure, intact microglia were isolated at brain autopsy from occipital cortex gray matter (GM) and corpus callosum white matter of 13 donors with MDD and 10 age-matched control donors for RNA sequencing. Top differentially expressed genes were validated using immunohistochemistry staining. Because gene expression changes were only detected in GM microglia, neuronal regulators of microglia were investigated in cortical tissue and synaptosomes from the cortex by reverse transcriptase-quantitative polymerase chain reaction and Western blot. RESULTS: Transcriptome analysis revealed 92 genes differentially expressed in microglia isolated from GM, but none in microglia from white matter in donors with MDD, compared with control donors. Of these, 81 genes were less abundantly expressed in GM in MDD, including CD163, MKI67, SPP1, CD14, FCGR1A/C, and C1QA/B/C. Accordingly, pathways related to effector mechanisms, such as the complement system and phagocytosis, were differentially regulated in GM microglia in MDD. Immunohistochemistry staining revealed significantly lower expression of CD163 protein in MDD. Whole tissue analysis showed an increase in CD200 (p = .0009) and CD47 (p = .068) messenger RNA, and CD47 protein was significantly elevated (p = .0396) in synaptic fractions of MDD cases. CONCLUSIONS: Transcriptional profiling indicates an immune-suppressed microglial phenotype in MDD that is possibly caused by neuronal regulation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".