A three-dimensional human adipocyte model of fatty acid-induced obesity
Bibliographic record
Abstract
Abstract Obesity prevalence has reached pandemic proportions, leaving individuals at high risk for the development of diseases such as cancer and type 2 diabetes. In obesity, to accommodate excess lipid storage, adipocytes become hypertrophic, which is associated with an increased pro-inflammatory cytokine secretion and dysfunction of metabolic processes such as insulin signaling and lipolysis. Targeting adipocyte dysfunction is an important strategy to prevent the development of obesity-associated disease. However, it is unclear how accurately animal models reflect human biology, and the long-term culture of human hypertrophic adipocytes in an in vitro 2D monolayer is challenging due to the buoyant nature of adipocytes. Here we describe the development of a human 3D in vitro disease model that recapitulates hallmarks of obese adipocyte dysfunction. First, primary human adipose-derived mesenchymal stromal cells are embedded in hydrogel, and infiltrated into a thin cellulose scaffold. The thin microtissue profile allows for efficient assembly and image-based analysis. After adipocyte differentiation, the scaffold is stimulated with oleic or palmitic acid to mimic caloric overload. Using functional assays, we demonstrated that this treatment induced important obese adipocyte characteristics such as a larger lipid droplet size, increased basal lipolysis, insulin resistance and a change in macrophage gene expression through adipocyte-conditioned media. This 3D disease model mimics physiologically relevant hallmarks of obese adipocytes, to enable investigations into the mechanisms by which dysfunctional adipocytes contribute to disease.
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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 | 0.001 | 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".