Defining the lineage of thermogenic perivascular adipose tissue
Bibliographic record
Abstract
Summary Brown adipose tissue can expend large amounts of energy and thus increasing its amount or activity is a promising therapeutic approach to combat metabolic disease. In humans, major deposits of brown fat cells are found intimately associated with large blood vessels, corresponding to perivascular adipose tissue (PVAT). However, the cellular origins of PVAT are poorly understood. Here, we applied single cell transcriptomic analyses, ex vivo adipogenesis assays, and genetic fate mapping in vivo to determine the identity of perivascular adipocyte progenitors. We found that thoracic PVAT initially develops from a fibroblastic lineage, comprising progenitor cells ( Pdgfra +; Ly6a +; Pparg −) and preadipocytes ( Pdgfra +; Ly6a −; Pparg +). Progenitors and preadipocytes in PVAT shared transcriptional similarity with analogous cell types in white adipose tissue, pointing towards a conserved adipose cell lineage hierarchy. Interestingly, the aortic adventitia of adult animals contained a novel population of adipogenic smooth muscle cells ( Myh11 +; Pdgfra −; Pparg +) possessing the capacity to generate adipocytes in vitro and in vivo . Taken together, these studies define distinct populations of fibroblastic and smooth muscle progenitor cells for thermogenic PVAT, providing a crucial foundation for developing strategies to augment brown fat activity.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".