Isolation and adipogenic differentiation of murine mesenchymal stem cells harvested from macrophage-depleted bone marrow and adipose tissue
Bibliographic record
Abstract
INTRODUCTION AND PURPOSE: Mouse mesenchymal stem cells (MSCs) provide a resourceful tool to study physiological and pathological aspects of adipogenesis. Bone marrow-derived MSCs (BM-MSCs) and adipose tissue-derived MSCs (ASCs) are widely used for these studies. Since there is a wide spectrum of methods available, the purpose is to provide a focused hands-on procedural guide for isolation and characterization of murine BM-MSCs and ASCs and to effectively differentiate them into adipocytes. METHODS AND RESULTS: Optimized harvesting procedures for murine BM-MSCs and ASCs are described and graphically documented. Since macrophages reside in bone-marrow and fat tissues and regulate the biological behaviour of BM-MSCs and ASCs, we included a procedure to deplete macrophages from the MSC preparations. The identity and stemness of BM-MSCs and ASCs were confirmed by flow cytometry using established markers. Since the composition and concentrations of adipogenic differentiation cocktails differ widely, we present a standardized four-component adipogenic cocktail, consisting of insulin, dexamethasone, 3-isobutyl-1-methylxanthine, and indomethacin to efficiently differentiate freshly isolated or frozen/thawed BM-MSCs and ASCs into adipocytes. We further included visualization and quantification protocols of the differentiated adipocytes. CONCLUSION: This laboratory protocol was designed as a step-by-step procedure for harvesting murine BM-MSCs and ASCs and differentiating them into adipocytes.
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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) | 0.001 | 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) | 0.002 | 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".