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Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue
Mass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while inappropriate...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3428526/ https://www.ncbi.nlm.nih.gov/pubmed/22644111 http://dx.doi.org/10.1007/s10911-012-9252-6 |
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author | Liu, Ning Qing Braakman, René B. H. Stingl, Christoph Luider, Theo M. Martens, John W. M. Foekens, John A. Umar, Arzu |
author_facet | Liu, Ning Qing Braakman, René B. H. Stingl, Christoph Luider, Theo M. Martens, John W. M. Foekens, John A. Umar, Arzu |
author_sort | Liu, Ning Qing |
collection | PubMed |
description | Mass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while inappropriate statistical methods will lead to false positive hits. All these issues have hampered the identification of reliable protein markers. A workflow, which integrates reproducible and robust sample preparation and data handling methods, is highly desirable in clinical proteomics investigations. Here we describe a label-free tissue proteomics pipeline, which encompasses laser capture microdissection (LCM) followed by nanoscale liquid chromatography and high resolution MS. This pipeline routinely identifies on average ∼10,000 peptides corresponding to ∼1,800 proteins from sub-microgram amounts of protein extracted from ∼4,000 LCM breast cancer epithelial cells. Highly reproducible abundance data were generated from different technical and biological replicates. As a proof-of-principle, comparative proteome analysis was performed on estrogen receptor α positive or negative (ER+/−) samples, and commonly known differentially expressed proteins related to ER expression in breast cancer were identified. Therefore, we show that our tissue proteomics pipeline is robust and applicable for the identification of breast cancer specific protein markers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10911-012-9252-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-3428526 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-34285262013-01-02 Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue Liu, Ning Qing Braakman, René B. H. Stingl, Christoph Luider, Theo M. Martens, John W. M. Foekens, John A. Umar, Arzu J Mammary Gland Biol Neoplasia Article Mass spectrometry (MS)-based label-free proteomics offers an unbiased approach to screen biomarkers related to disease progression and therapy-resistance of breast cancer on the global scale. However, multi-step sample preparation can introduce large variation in generated data, while inappropriate statistical methods will lead to false positive hits. All these issues have hampered the identification of reliable protein markers. A workflow, which integrates reproducible and robust sample preparation and data handling methods, is highly desirable in clinical proteomics investigations. Here we describe a label-free tissue proteomics pipeline, which encompasses laser capture microdissection (LCM) followed by nanoscale liquid chromatography and high resolution MS. This pipeline routinely identifies on average ∼10,000 peptides corresponding to ∼1,800 proteins from sub-microgram amounts of protein extracted from ∼4,000 LCM breast cancer epithelial cells. Highly reproducible abundance data were generated from different technical and biological replicates. As a proof-of-principle, comparative proteome analysis was performed on estrogen receptor α positive or negative (ER+/−) samples, and commonly known differentially expressed proteins related to ER expression in breast cancer were identified. Therefore, we show that our tissue proteomics pipeline is robust and applicable for the identification of breast cancer specific protein markers. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10911-012-9252-6) contains supplementary material, which is available to authorized users. Springer US 2012-05-30 2012 /pmc/articles/PMC3428526/ /pubmed/22644111 http://dx.doi.org/10.1007/s10911-012-9252-6 Text en © The Author(s) 2012 https://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | Article Liu, Ning Qing Braakman, René B. H. Stingl, Christoph Luider, Theo M. Martens, John W. M. Foekens, John A. Umar, Arzu Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue |
title | Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue |
title_full | Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue |
title_fullStr | Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue |
title_full_unstemmed | Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue |
title_short | Proteomics Pipeline for Biomarker Discovery of Laser Capture Microdissected Breast Cancer Tissue |
title_sort | proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3428526/ https://www.ncbi.nlm.nih.gov/pubmed/22644111 http://dx.doi.org/10.1007/s10911-012-9252-6 |
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