Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) Deficiency Attenuates Phagocytic Activities of Microglia and Exacerbates Ischemic Damage in Experimental Stroke
Bibliographic record
Abstract
Clearing cellular debris after brain injury represents an important mechanism in regaining tissue homeostasis and promoting functional recovery. Triggering receptor expressed on myeloid cells-2 (TREM2) is a newly identified receptor expressed on microglia and is thought to phagocytose damaged brain cells. The precise role of TREM2 during ischemic stroke has not been fully understood. We explore TREM2 in both in vitro and in vivo stroke models and identify a potential endogenous TREM2 ligand. TREM2 knockdown in microglia reduced microglial activation to an amoeboid phenotype and decreased the phagocytosis of injured neurons. Phagocytosis and infarcted brain tissue resorption was reduced in TREM2 knock-out (KO) mice compared with wild-type (WT) mice. TREM2 KO mice also had worsened neurological recovery and decreased viable brain tissue in the ipsilateral hemisphere. The numbers of activated microglia and phagocytes in TREM2 KO mice were decreased compared with WT mice, and foamy macrophages were nearly absent in the TREM2 KO mice. Postischemia, TREM2 was highly expressed on microglia and TREM2-Fc fusion protein (used as a probe to identify potential TREM2 binding partners) bound to an unknown TREM2 ligand that colocalized to neurons. Oxygen glucose deprivation-exposed neuronal media, or cellular fractions containing nuclei or purified DNA, but not cytosolic fractions, stimulated signaling through TREM2. TREM2-Fc fusion protein pulled down nucleic acids from ischemic brain lysate. These findings establish the relevance of TREM2 in the phagocytosis of the infarcted brain and emphasize its role in influencing neurological outcomes following stroke. Further, nucleic acids may be one potential ligand of TREM2 in brain ischemia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".