Adipocyte death triggers a pro-inflammatory response and induces metabolic activation of resident macrophages
Bibliographic record
Abstract
A chronic low-grade inflammation within adipose tissue (AT) seems to be the link between obesity and some of its associated diseases. One hallmark of this AT inflammation is the accumulation of AT macrophages (ATMs) around dead or dying adipocytes, forming so-called crown-like structures (CLS). To investigate the dynamics of CLS and their direct impact on the activation state of ATMs, we established a laser injury model to deplete individual adipocytes in living AT from double reporter mice (GFP-labeled ATMs and tdTomato-labeled adipocytes). Hence, we were able to detect early ATM-adipocyte interactions by live imaging and to determine a precise timeline for CLS formation after adipocyte death. Further, our data indicate metabolic activation and increased lipid metabolism in ATMs upon forming CLS. Most importantly, adipocyte death, even in lean animals under homeostatic conditions, leads to a locally confined inflammation, which is in sharp contrast to other tissues. We identified cell size as cause for the described pro-inflammatory response, as the size of adipocytes is above a critical threshold size for efferocytosis, a process for anti-inflammatory removal of dead cells during tissue homeostasis. Finally, experiments on parabiotic mice verified that adipocyte death leads to a pro-inflammatory response of resident ATMs in vivo, without significant recruitment of blood monocytes. Our data indicate that adipocyte death triggers a unique degradation process and locally induces a metabolically activated ATM phenotype that is globally observed with obesity.
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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) | 0.001 | 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".