Single-nuclei RNA Profiling Reveals Disruption of Adipokine and Inflammatory Signaling in Adipose Tissue of Burn Patients
Bibliographic record
Abstract
OBJECTIVE: We conducted a large-scale investigation of the systemic and adipose tissue-specific alterations in a clinical population of burn patients to identify factors that may influence hypermetabolism. BACKGROUND: Previous research has identified chronic disturbances in adipose tissue inflammation, lipolysis, and browning, which may drive the perpetuation of hypermetabolism following the severe adrenergic stress of a burn injury. Given that adipose tissue is thought to be a central node in the regulation of systemic metabolism, we believe that systematically delineating the pathologic role of adipose tissue postburn, will lead to the identification of novel interventions to mitigate morbidity and mortality from severe burns. METHODS: This was a single-institution cohort study, which obtained plasma and subcutaneous adipose tissue samples from severely burn adult patients over various time points during acute hospitalization. Whole-body clinical, metabolic, and inflammatory mediators were assessed in plasma, while genetic analyses through RT-qPCR and single-nuclei RNA sequencing were conducted in adipose tissue. RESULTS: Systemic inflammation and adrenergic stress increase IL-6 signaling, lipolysis, browning, and adipokine dysfunction in the adipose tissue of adult burn patients, which may further propagate the long-term hypermetabolic response. Moreover, using single-nuclei RNA sequencing, we provide the first comprehensive characterization of alterations in the adipose tissue microenvironment occurring at acute and chronic stages postburn. CONCLUSION: We provide novel insight toward the effect of burns on adipokine release, inflammatory signaling pathways, and adipose heterogeneity over the trajectory of acute and chronic stages.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".