ScRNA-Seq Reveals a Distinct Osteogenic Progenitor of Alveolar Bone
Bibliographic record
Abstract
The metabolism and remodeling of alveolar bone are the most active among the whole skeletal system, which is related to the biological characteristics and heterogeneity of the bone mesenchymal stromal cells (MSCs). However, there is a lack of systematic description of the heterogeneity of MSC-derived osteoblastic lineage cells as well as their distinct osteogenic differentiation trajectory of alveolar bone. In this study, we constructed a single-cell atlas of the mouse alveolar bone cells through single-cell RNA sequencing (scRNA-seq). Remarkably, by comparing the cell compositions between the alveolar bone and long bone, we uncovered a previously undescribed cell population that exhibits a high expression of protocadherin Fat4 (Fat4+ cells) and is specifically enriched around alveolar bone marrow cavities. ScRNA-seq analysis indicated that Fat4+ cells may initiate a distinct osteogenic differentiation trajectory in the alveolar bone. By isolating and cultivating Fat4+ cells in vitro, we demonstrated that they possess colony-forming, osteogenic, and adipogenic capabilities. Moreover, FAT4 knockdown could significantly inhibit the osteogenic differentiation of alveolar bone MSCs. Furthermore, we revealed that the Fat4+ cells exhibit a core transcriptional signature consisting of several key transcription factors, such as SOX6, which are involved in osteogenesis, and further demonstrated that SOX6 is required for the efficient osteogenic differentiation of the Fat4+ cells. Collectively, our high-resolution single-cell atlas of the alveolar bone reveals a distinct osteogenic progenitor that may contribute to the unique physiological characteristics of alveolar bone.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".