Characterization of adipose depot-specific stromal cell populations by single-cell mass cytometry
Bibliographic record
Abstract
The increased prevalence of obesity and metabolic diseases has heightened interest in adipose tissue biology and its potential as a therapeutic target. To better understand cellular heterogeneity and complexity of white adipose tissue (WAT), we employed cytometry by time-of-flight (CyTOF) to characterize immune and stromal cells in visceral and subcutaneous WAT depots under normal and high-fat diet feeding, by quantifying the expression levels of 32 surface marker proteins. We observed comparable proportions of immune cells in two WAT depots under steady state, but depot-distinct subtypes of adipose precursor cells (APC), suggesting differences in their adipogenic and fibrogenic potential. Furthermore, in addition to pro-inflammatory immune cell shifts, significant pro-fibrotic changes were observed in APCs under high-fat diet, suggesting that APCs are early responders to dietary challenges. We propose CyTOF as a complementary and alternative tool to current high-throughput single-cell transcriptomic analyses to better understand the function and plasticity of adipose tissue.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".