Investigation of microglial diversity in a LRRK2 G2019S mouse model of Parkinson's disease
Bibliographic record
Abstract
Microglia contribute to the outcomes of various pathological conditions including Parkinson's disease (PD). Microglia are heterogenous, with a variety of states recently identified in aging and neurodegenerative disease models. Here, we delved into the diversity of microglia in a preclinical PD model featuring the G2019S mutation in LRRK2, a known pathological mutation associated with PD. Specifically, we investigated the 'dark microglia' (DM) and the 'disease-associated microglia' (DAM) which present a selective enrichment of CLEC7A expression. In the dorsal striatum - a region affected by PD pathology - extensive ultrastructural features of cellular stress as well as reduced direct cellular contacts, were observed for microglia from old LRRK2 G2019S mice versus controls. In addition, DM were more prevalent while CLEC7A-positive microglia had extensive phagocytic ultrastructural characteristics in the LRRK2 G2019S mice. Furthermore, our findings revealed a higher proportion of DM in LRRK2 G2019S mice, and an increased number of CLEC7A-positive cells with age, exacerbated by the pathological mutation. These CLEC7A-positive cells exhibited a selective enrichment of ameboid morphology and tended to cluster in the affected animals. In summary, we provide novel insights into the occurrence and features of recently defined microglial states, CLEC7A-positive cells and DM, in the context of LRRK2 G2019S PD pathology.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".