首页> 外文期刊>Plant and cell physiology >Peptide separation methodologies for in-depth proteomics in Arabidopsis.
【24h】

Peptide separation methodologies for in-depth proteomics in Arabidopsis.

机译:Peptide separation methodologies for in-depth proteomics in Arabidopsis.

获取原文
获取原文并翻译 | 示例
           

摘要

In the post-genome era, several tools that have increased our global understanding of the molecular basis of several cell-based phenomena have been developed. However, proteomics has not been efficiently integrated with the other 'omics' (e.g. transcriptomics and metabolomics), because of the relatively low number of proteins identified by mass spectrometry (MS). Peptides from low-abundance proteins are often not detected by MS due to ionization suppression. To improve the number of peptide identifications in MS analyses, we propose three separation methodologies; namely, OFFGEL electrophoresis, 2D-liquid chromatography (LC) and the long monolithic silica-C18 capillary column method, with the common aim to decrease peptide complexity prior to MS analyses. Proteomics using the above three peptide separation methods were separately applied to protoplasts collected from the epidermal cell layer of Arabidopsis roots using fluorescence-activated cell sorting. In each method alone, 1,132, 836 and 795 proteins were specifically identified, respectively. This has allowed the identification of 1,493 proteins with no redundancy and with <1.0% false discovery rate. Moreover, approximately two-thirds of these proteins are identified here for the first time in the epidermal cell layer. These results show that use of different proteomic approaches can increase the total number of proteins identified. We propose that the integration of data from these methodologies represents a powerful tool for generation of proteome maps by enabling identification of low-abundance proteins in the various Arabidopsis root cell layers.Digital Object Identifier http://dx.doi.org/10.1093/pcp/pct033

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号