Usage Information
Abstract
We present a transcriptomic analysis that provides a better understanding of regulatory mechanisms within the healthy and injured periosteum. The focus of this work is on characterizing early events controlling bone healing during formation of periosteal callus on day 3 after fracture. Building on our previous findings showing that induced Notch1 signaling in osteoprogenitors leads to better healing, we compared samples in which the Notch 1 intracellular domain is overexpressed by periosteal stem/progenitor cells, with control intact and fractured periosteum. Molecular mechanisms and changes in skeletal stem/progenitor cells (SSPCs) and other cell populations within the callus, including hematopoietic lineages, were determined. Notably, Notch ligands were differentially expressed in endothelial and mesenchymal populations, with Dll4 restricted to endothelial cells, whereas Jag1 was expressed by mesenchymal populations. Targeted deletion of Dll4 in endothelial cells using Cdh5CreER resulted in negative effects on early fracture healing, while deletion in SSPCs using α-smooth muscle actin–CreER did not impact bone healing. Translating these observations into a clinically relevant model of bone healing revealed the beneficial effects of delivering Notch ligands alongside the osteogenic inducer, BMP2. These findings provide insights into the regulatory mechanisms within the healthy and injured periosteum, paving the way for novel translational approaches to bone healing.
Authors
Sanja Novak, Hitoshi Tanigawa, Vijender Singh, Sierra H. Root, Tannin A. Schmidt, Kurt D. Hankenson, Ivo Kalajzic
Usage data is cumulative from July 2025 through July 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 2,600 | 349 |
| 402 | 60 | |
| Figure | 977 | 4 |
| Supplemental data | 330 | 14 |
| Citation downloads | 286 | 0 |
| Totals | 4,595 | 427 |
| Total Views | 5,022 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
