定量斑点印迹分析(QDB),一种通用的高通量免疫印迹方法。

Quantitative dot blot analysis (QDB), a versatile high throughput immunoblot method.

作者信息

Tian Geng, Tang Fangrong, Yang Chunhua, Zhang Wenfeng, Bergquist Jonas, Wang Bin, Mi Jia, Zhang Jiandi

机构信息

Medicine and Pharmacy Research Center, Binzhou Medical University, Yantai, P. R. China.

Yantai Zestern Biotechnique Co. LTD, Yantai, P. R. China.

出版信息

Oncotarget. 2017 Apr 19;8(35):58553-58562. doi: 10.18632/oncotarget.17236. eCollection 2017 Aug 29.

Abstract

Lacking access to an affordable method of high throughput immunoblot analysis for daily use remains a big challenge for scientists worldwide. We proposed here Quantitative Dot Blot analysis (QDB) to meet this demand. With the defined linear range, QDB analysis fundamentally transforms traditional immunoblot method into a true quantitative assay. Its convenience in analyzing large number of samples also enables bench scientists to examine protein expression levels from multiple parameters. In addition, the small amount of sample lysates needed for analysis means significant saving in research sources and efforts. This method was evaluated at both cellular and tissue levels with unexpected observations otherwise would be hard to achieve using conventional immunoblot methods like Western blot analysis. Using QDB technique, we were able to observed an age-dependent significant alteration of CAPG protein expression level in TRAMP mice. We believe that the adoption of QDB analysis would have immediate impact on biological and biomedical research to provide much needed high-throughput information at protein level in this "Big Data" era.

摘要

缺乏一种经济实惠的日常高通量免疫印迹分析方法,这对全球科学家来说仍然是一个巨大的挑战。我们在此提出定量斑点印迹分析(QDB)以满足这一需求。凭借定义的线性范围,QDB分析从根本上将传统免疫印迹方法转变为真正的定量检测。它在分析大量样品方面的便利性也使实验台科学家能够从多个参数检测蛋白质表达水平。此外,分析所需的少量样品裂解物意味着在研究资源和精力方面的显著节省。该方法在细胞和组织水平上进行了评估,得到了意想不到的观察结果,而使用传统免疫印迹方法如蛋白质免疫印迹分析则很难实现这些结果。使用QDB技术,我们能够观察到TRAMP小鼠中CAPG蛋白表达水平随年龄的显著变化。我们相信,在这个“大数据”时代,采用QDB分析将对生物学和生物医学研究产生直接影响,在蛋白质水平上提供急需的高通量信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4af0/5601674/3aa9e908c7f2/oncotarget-08-58553-g001.jpg

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