4.7 Article

Method development of efficient protein extraction in bone tissue for proteome analysis

期刊

JOURNAL OF PROTEOME RESEARCH
卷 6, 期 6, 页码 2287-2294

出版社

AMER CHEMICAL SOC
DOI: 10.1021/pr070056t

关键词

protein extraction; bone proteome; shotgun proteomics; tandem mass spectrometry; bone diseases; biomarker discovery

向作者/读者索取更多资源

Exploring bone proteome is an important and challenging task for understanding the mechanisms of physiological/pathological process of bone tissue. However, classical methods of protein extraction for soft tissues and cells are not applicable for bone tissue. Therefore, method development of efficient protein extraction is critical for bone proteome analysis. We found in this study that the protein extraction efficiency was improved significantly when bone tissue was demineralized by hydrochloric acid (HCl). A sequential protein extraction method was developed for large-scale proteome analysis of bone tissue. The bone tissue was first demineralized by HCl solution and then extracted using three different lysis buffers. As large amounts of acid soluble proteins also presented in the HCl solution, besides collection of proteins in the extracted lysis buffers, the proteins in the demineralized HCl solution were also collected for proteome analysis. Automated 2D-LC-MS/MS analysis of the collected protein fractions resulted in the identification of 6202 unique peptides which matched 2479 unique proteins. The identified proteins revealed a broad diversity in the protein identity and function. More than 40 bone-specific proteins and 15 potential protein biomarkers previously reported were observed in this study. It was demonstrated that the developed extraction method of proteins in bone tissue, which was also the first large-scale proteomic study of bone, was very efficient for comprehensive analysis of bone proteome and might be helpful for clarifying the mechanisms of bone diseases.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据