Isolation and adipogenic differentiation of murine mesenchymal stem cells harvested from macrophage-depleted bone marrow and adipose tissue
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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 it