<scp>iPSC</scp>‐derived microglia carrying the <scp>TREM2 R47H</scp>/+ mutation are proinflammatory and promote synapse loss
Bibliographic record
Abstract
Genetic findings have highlighted key roles for microglia in the pathology of neurodegenerative conditions such as Alzheimer's disease (AD). A number of mutations in the microglial protein triggering receptor expressed on myeloid cells 2 (TREM2) have been associated with increased risk for developing AD, most notably the R47H/+ substitution. We employed gene editing and stem cell models to gain insight into the effects of the TREM2 R47H/+ mutation on human-induced pluripotent stem cell-derived microglia. We found transcriptional changes affecting numerous cellular processes, with R47H/+ cells exhibiting a proinflammatory gene expression signature. TREM2 R47H/+ also caused impairments in microglial movement and the uptake of multiple substrates, as well as rendering microglia hyperresponsive to inflammatory stimuli. We developed an in vitro laser-induced injury model in neuron-microglia cocultures, finding an impaired injury response by TREM2 R47H/+ microglia. Furthermore, mouse brains transplanted with TREM2 R47H/+ microglia exhibited reduced synaptic density, with upregulation of multiple complement cascade components in TREM2 R47H/+ microglia suggesting inappropriate synaptic pruning as one potential mechanism. These findings identify a number of potentially detrimental effects of the TREM2 R47H/+ mutation on microglial gene expression and function likely to underlie its association with AD.
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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 | 0.000 | 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 | 0.001 |
| 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".