Comparison of Human Adipose-Derived Stem Cells Isolated from Subcutaneous, Omental, and Intrathoracic Adipose Tissue Depots for Regenerative Applications
Bibliographic record
Abstract
Adipose tissue is an abundant source of multipotent progenitor cells that have shown promise in regenerative medicine. In humans, fat is primarily distributed in the subcutaneous and visceral depots, which have varying biochemical and functional properties. In most studies to date, subcutaneous adipose tissue has been investigated as the adipose-derived stem cell (ASC) source. In this study, we sought to develop a broader understanding of the influence of specific adipose tissue depots on the isolated ASC populations through a systematic comparison of donor-matched abdominal subcutaneous fat and omentum, and donor-matched pericardial adipose tissue and thymic remnant samples. We found depot-dependent and donor-dependent variability in the yield, viability, immunophenotype, clonogenic potential, doubling time, and adipogenic and osteogenic differentiation capacities of the ASC populations. More specifically, ASCs isolated from both intrathoracic depots had a longer average doubling time and a significantly higher proportion of CD34(+) cells at passage 2, as compared with cells isolated from subcutaneous fat or the omentum. Furthermore, ASCs from subcutaneous and pericardial adipose tissue demonstrated enhanced adipogenic differentiation capacity, whereas ASCs isolated from the omentum displayed the highest levels of osteogenic markers in culture. Through cell culture analysis under hypoxic (5% O(2)) conditions, oxygen tension was shown to be a key mediator of colony-forming unit-fibroblast number and osteogenesis for all depots. Overall, our results suggest that depot selection is an important factor to consider when applying ASCs in tissue-specific cell-based regenerative therapies, and also highlight pericardial adipose tissue as a potential new ASC source.
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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 | 0.001 | 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) | 0.001 | 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".