• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

定量蛋白质组学分析微切割乳腺癌组织:无标记和 SILAC 定量与 shotgun、定向和靶向 MS 方法的比较。

Quantitative proteomic analysis of microdissected breast cancer tissues: comparison of label-free and SILAC-based quantification with shotgun, directed, and targeted MS approaches.

机构信息

Department of Medical Oncology, Erasmus MC Cancer Institute and ‡Department of Neurology, Erasmus University Medical Center , Dr. Molewaterplein 50, Be-401, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.

出版信息

J Proteome Res. 2013 Oct 4;12(10):4627-41. doi: 10.1021/pr4005794. Epub 2013 Sep 13.

DOI:10.1021/pr4005794
PMID:23957277
Abstract

Quantitative proteomics plays an important role in validation of breast-cancer-related biomarkers. In this study, we systematically compared the performance of label-free quantification (LFQ) and SILAC with shotgun and directed methods for quantifying breast-cancer-related markers in microdissected tissues. We show that LFQ leads to slightly higher coefficient of variation (CV) for protein quantification (median CV = 16.3%) than SILAC quantification (median CV = 13.7%) (P < 0.0001), but LFQ method enables ∼60% more protein quantification and is also more reproducible (∼20% more proteins were quantified in all replicate samples). Furthermore, we describe a method to accurately quantify multiple proteins within one pathway, that is, "focal adhesion pathway", in trace amounts of breast cancer tissues using a SILAC-based SRM assay. Using this SILAC-based SRM assay, we precisely quantified five "focal adhesion" proteins with good quantitative precision (CV range: 2.4-5.9%) in replicate whole tissue lysate samples and replicate microdissected samples (CV range: 5.8-16.1%). Our results show that in microdissected breast cancer tissues LFQ in combination with shotgun proteomics performed the best overall and is therefore suitable for both biomarker discovery and validation in these types of specimens. The SILAC-based SRM method can be used for the development of clinically relevant protein assays in tumor biopsies.

摘要

定量蛋白质组学在验证乳腺癌相关生物标志物方面发挥着重要作用。在这项研究中,我们系统地比较了无标记定量(LFQ)和 SILAC 与鸟枪法和定向方法在定量微切割组织中乳腺癌相关标记物的性能。我们表明,LFQ 导致蛋白质定量的变异系数(CV)略高于 SILAC 定量(中位数 CV=16.3%比 SILAC 定量(中位数 CV=13.7%)(P<0.0001),但 LFQ 方法可实现约 60%更多的蛋白质定量,并且更具重现性(所有重复样本中定量的蛋白质约增加 20%)。此外,我们描述了一种方法,该方法可使用基于 SILAC 的 SRM 测定法,在痕量乳腺癌组织中准确地定量一条途径(即“黏附斑途径”)中的多个蛋白质。使用这种基于 SILAC 的 SRM 测定法,我们在重复的全组织裂解物样本和重复的微切割样本中精确地定量了五个“黏附斑”蛋白,具有良好的定量精度(CV 范围:2.4-5.9%)(CV 范围:5.8-16.1%)。我们的结果表明,在微切割的乳腺癌组织中,LFQ 与鸟枪法蛋白质组学相结合总体表现最佳,因此适用于此类标本中的生物标志物发现和验证。基于 SILAC 的 SRM 方法可用于开发肿瘤活检中临床相关的蛋白质测定法。

相似文献

1
Quantitative proteomic analysis of microdissected breast cancer tissues: comparison of label-free and SILAC-based quantification with shotgun, directed, and targeted MS approaches.定量蛋白质组学分析微切割乳腺癌组织:无标记和 SILAC 定量与 shotgun、定向和靶向 MS 方法的比较。
J Proteome Res. 2013 Oct 4;12(10):4627-41. doi: 10.1021/pr4005794. Epub 2013 Sep 13.
2
Use of universal stable isotope labeling by amino acids in cell culture (SILAC)-based selected reaction monitoring (SRM) approach for verification of breast cancer-related protein markers.使用基于细胞培养中氨基酸的通用稳定同位素标记(SILAC)的选择反应监测(SRM)方法来验证乳腺癌相关蛋白质标志物。
Methods Mol Biol. 2014;1156:307-22. doi: 10.1007/978-1-4939-0685-7_21.
3
Biomarker discovery in low-grade breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-2DLC-MS/MS) based quantitative proteomic analysis.使用等压稳定同位素标签和基于二维液相色谱-串联质谱(iTRAQ-2DLC-MS/MS)的定量蛋白质组学分析在低级别乳腺癌中发现生物标志物
J Proteome Res. 2009 Jan;8(1):362-73. doi: 10.1021/pr800622b.
4
Comparison of liquid chromatography-tandem mass spectrometry-based targeted proteomics and conventional analytical methods for the determination of P-glycoprotein in human breast cancer cells.基于液相色谱-串联质谱的靶向蛋白质组学与传统分析方法测定人乳腺癌细胞 P-糖蛋白的比较。
J Chromatogr B Analyt Technol Biomed Life Sci. 2013 Oct 1;936:18-24. doi: 10.1016/j.jchromb.2013.07.023. Epub 2013 Aug 3.
5
SWATH enables precise label-free quantification on proteome scale.SWATH技术能够在蛋白质组规模上实现精确的无标记定量分析。
Proteomics. 2015 Apr;15(7):1215-23. doi: 10.1002/pmic.201400270.
6
Systematic evaluation of label-free and super-SILAC quantification for proteome expression analysis.用于蛋白质组表达分析的无标记和超级稳定同位素标记氨基酸定量法的系统评估。
Rapid Commun Mass Spectrom. 2015 May 15;29(9):795-801. doi: 10.1002/rcm.7160.
7
Proteomic profiles of human lung adeno and squamous cell carcinoma using super-SILAC and label-free quantification approaches.使用超级稳定同位素标记氨基酸在细胞培养中(SILAC)和无标记定量方法对人肺腺癌和鳞状细胞癌进行蛋白质组学分析。
Proteomics. 2014 Mar;14(6):795-803. doi: 10.1002/pmic.201300382.
8
Application of the SILAC (stable isotope labelling with amino acids in cell culture) technique in quantitative comparisons for tissue proteome expression.细胞培养中氨基酸稳定同位素标记(SILAC)技术在组织蛋白质组表达定量比较中的应用。
Biotechnol Appl Biochem. 2009 Jul 6;54(1):11-20. doi: 10.1042/BA20090007.
9
Novel comprehensive approach for accessible biomarker identification and absolute quantification from precious human tissues.从珍贵人体组织中进行可及生物标志物的鉴定和绝对定量的新型综合方法。
J Proteome Res. 2011 Jul 1;10(7):3160-82. doi: 10.1021/pr200212r. Epub 2011 May 20.
10
SILAC-based proteomic analysis to dissect the "histone modification signature" of human breast cancer cells.基于 SILAC 的蛋白质组学分析解析人类乳腺癌细胞的“组蛋白修饰特征”。
Amino Acids. 2011 Jul;41(2):387-99. doi: 10.1007/s00726-010-0668-2. Epub 2010 Jul 9.

引用本文的文献

1
Comparative Proteomics of ccRCC Cell Lines to Identify Kidney Cancer Progression Factors.ccRCC 细胞系的比较蛋白质组学分析以鉴定肾癌进展因子。
Cancer Genomics Proteomics. 2024 Nov-Dec;21(6):645-652. doi: 10.21873/cgp.20480.
2
Systematic Review: Urine Biomarker Discovery for Inflammatory Bowel Disease Diagnosis.系统评价:用于炎症性肠病诊断的尿液生物标志物发现。
Int J Mol Sci. 2023 Jun 15;24(12):10159. doi: 10.3390/ijms241210159.
3
A Practical and Analytical Comparative Study of Gel-Based Top-Down and Gel-Free Bottom-Up Proteomics Including Unbiased Proteoform Detection.
基于凝胶的自上而下和无凝胶的自下而上蛋白质组学的实用分析比较研究,包括无偏的蛋白质形式检测。
Cells. 2023 Feb 26;12(5):747. doi: 10.3390/cells12050747.
4
Quantitative proteomic study of mitoxantrone-resistant NCI-H460 cell-xenograft tumors.米托蒽醌耐药的NCI-H460细胞异种移植瘤的定量蛋白质组学研究
Int J Clin Exp Pathol. 2018 May 1;11(5):2377-2388. eCollection 2018.
5
Quantitative Microproteomics Based Characterization of the Central and Peripheral Nervous System of a Mouse Model of Krabbe Disease.基于定量微量蛋白质组学的克氏病小鼠模型中枢及外周神经系统特征描述。
Mol Cell Proteomics. 2019 Jun;18(6):1227-1241. doi: 10.1074/mcp.RA118.001267. Epub 2019 Mar 29.
6
Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival.乳腺癌的整合蛋白质组学转录组学研究揭示了与亚型和生存相关的全局蛋白质-mRNA 一致性增加。
Genome Med. 2018 Dec 3;10(1):94. doi: 10.1186/s13073-018-0602-x.
7
Approaches to the discovery of non-invasive urinary biomarkers of prostate cancer.前列腺癌非侵入性尿液生物标志物的发现方法。
Oncotarget. 2018 Aug 21;9(65):32534-32550. doi: 10.18632/oncotarget.25946.
8
Chemical cross-linking with mass spectrometry: a tool for systems structural biology.化学交联与质谱联用:系统结构生物学的研究工具。
Curr Opin Chem Biol. 2019 Feb;48:8-18. doi: 10.1016/j.cbpa.2018.08.006. Epub 2018 Aug 30.
9
Proteomic alterations in early stage cervical cancer.早期宫颈癌的蛋白质组学改变
Oncotarget. 2018 Apr 6;9(26):18128-18147. doi: 10.18632/oncotarget.24773.
10
Application of targeted mass spectrometry in bottom-up proteomics for systems biology research.靶向质谱在系统生物学研究中的蛋白质组学中的应用。
J Proteomics. 2018 Oct 30;189:75-90. doi: 10.1016/j.jprot.2018.02.008. Epub 2018 Feb 13.