Single-cell analysis of insulin-regulated fatty acid uptake in adipocytes
Bibliographic record
Abstract
Increased body fat correlates with the enlargement of average fat cell size and reduced adipose tissue insulin sensitivity. It is currently unclear whether adipocytes, as they accumulate more triglycerides and grow in size, gradually become less insulin sensitive or whether obesity-related factors independently cause both the enlargement of adipocyte size and reduced adipose tissue insulin sensitivity. In the first instance, large and small adipocytes in the same tissue would exhibit differences in insulin sensitivity, whereas, in the second instance, adipocyte size per se would not necessarily correlate with insulin response. To analyze the effect of adipocyte size on insulin sensitivity, we employed a new single-cell imaging assay that resolves fatty acid uptake and insulin response in single adipocytes in subcutaneous adipose tissue explants. Here, we report that subcutaneous adipocytes are heterogeneous in size and intrinsic insulin sensitivity. Whereas smaller adipocytes respond to insulin by increasing lipid uptake, adipocytes with cell diameters larger than 80-100 microm are insulin resistant. We propose that, when cell size approaches a critical boundary, adipocytes lose insulin-dependent fatty acid transport. This negative feedback mechanism may protect adipocytes from lipid overload and restrict further expansion of adipose tissue, which leads to obesity and metabolic complications.
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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".