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Usage Information

Identification of Postn+ periosteal progenitor cells with bone regenerative potential
Bei Yin, Fangyuan Shen, Qingge Ma, Yongcheng Liu, Xianglong Han, Xuyu Cai, Yu Shi, Ling Ye
Bei Yin, Fangyuan Shen, Qingge Ma, Yongcheng Liu, Xianglong Han, Xuyu Cai, Yu Shi, Ling Ye
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Research Article Bone biology

Identification of Postn+ periosteal progenitor cells with bone regenerative potential

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Abstract

Bone contains multiple pools of skeletal stem/progenitor cells (SSPCs), and SSPCs in periosteal compartments are known to exhibit higher regenerative potential than those in BM and endosteal compartments. However, the in vivo identity and hierarchical relationships of periosteal SSPCs (P-SSPCs) remain unclear due to a lack of reliable markers to distinguish BM SSPCs and P-SSPCs. Here, we found that periosteal mesenchymal progenitor cells (P-MPs) in periosteum can be identified based on Postn-CreERT2 expression. Postn-expressing periosteal subpopulation produces osteolineage descendants that fuel bones to maintain homeostasis and support regeneration. Notably, Postn+ P-MPs are likely derived from Gli1+ skeletal stem cells (SSCs). Ablation of Postn+ cells results in impairments in homeostatic cortical bone architecture and defects in fracture repair. Genetic deletion of Igf1r in Postn+ cells dampens bone fracture healing. In summary, our study provides a mechanistic understanding of bone regeneration through the regulation of region-specific Postn+ P-MPs.

Authors

Bei Yin, Fangyuan Shen, Qingge Ma, Yongcheng Liu, Xianglong Han, Xuyu Cai, Yu Shi, Ling Ye

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Usage data is cumulative from February 2025 through February 2026.

Usage JCI PMC
Text version 1,893 538
PDF 299 129
Figure 622 13
Supplemental data 202 35
Citation downloads 140 0
Totals 3,156 715
Total Views 3,871

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.

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