Differentiation of Functional Osteoclasts from Human Peripheral Blood CD14<sup>+</sup> Monocytes
Bibliographic record
Abstract
Osteoclasts (OCs) are bone-resorbing cells that play a pivotal role in skeletal development and adult bone remodeling. Several bone disorders are caused by increased differentiation and activation of OCs, so the inhibition of this pathobiology is a key therapeutic principle.Two key factors drive the differentiation of OCs from myeloid precursors: macrophage colony-stimulating factor (M-CSF) and receptor activator of nuclear factor kappa-B ligand (RANKL). Human circulating CD14+ monocytes have long been known to differentiate into OCs in vitro. However, the exposure time and the concentration of RANKL influence the differentiation efficiency. Indeed, protocols for the generation of human OCs in vitro have been described, but they often result in a poor and lengthy differentiation process. Herein, a robust and standardized protocol for generating functionally active mature human OCs in a timely manner is provided. CD14+ monocytes are enriched from human peripheral blood mononuclear cells (PBMCs) and primed with M-CSF to upregulate RANK. Subsequent exposure to RANKL generates OCs in a dose- and time-dependent manner. OCs are identified and quantified by staining with tartrate acid-resistant phosphatase (TRAP) and light microscopy analysis. Immunofluorescence staining of nuclei and F-actin is used to identify functionally active OCs. In addition, OSCAR+CD14− mature OCs are further enriched via flow cytometry cell sorting, and OC functionality quantified by mineral (or dentine/bone) resorption assays and actin ring formation. Finally, a known OC inhibitor, rotenone, is used on mature OCs, demonstrating that adenosine triphosphate (ATP) production is essential for actin ring integrity and OC function. In conclusion, a robust assay for differentiating high numbers of OCs is established in this work, which in combination with actin ring staining and an ATP assay provides a useful in vitro model to evaluate OC function and to screen for novel therapeutic compounds that can modulate the differentiation process.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".