Literature DB >> 19183795

Systematic internal standard selection for capillary liquid chromatography-mass spectrometry time normalization to facilitate serum proteomics.

Karen Merrell1, Craig D Thulin, M Sean Esplin, Steven W Graves.   

Abstract

Because blood interacts with almost all tissues of the body, it is likely that changes in the overall health of an organism will be reflected in the quantities of specific serum peptides and proteins, making them biomarkers. Due to the complexity of serum, pre-analytical sample simplification and separation are needed prior to mass spectrometric analysis. Use of a reverse-phase capillary column coupled to a mass spectrometer allows for separation and analysis of serum as part of efforts to discover biomarkers. Even after sample simplification by organic solvent precipitation, data files for a single sample typically exceed one gigabyte, making it difficult to analyze complete serum mass spectrometry profiles with currently available software. However, with adequate safeguards, it appears possible to consider portions of mass spectra to find differences in peak intensities between clinical comparison groups visually. To facilitate this, the elution profile was divided into 2-min intervals in which mass spectrometry data were averaged. This required that molecular species had defined reproducible elution times. Given liquid chromatography coupled to mass spectrometry variation, misalignment of elution times of individual peaks occurred often. Hence, internal time controls were identified within each window and used for elution time normalization. This significantly reduced variability in data. This approach allowed for peak alignment across samples, improving biomarker discovery.

Keywords:  LCMS; biomarkers; proteomics; serum

Mesh:

Substances:

Year:  2008        PMID: 19183795      PMCID: PMC2628071     

Source DB:  PubMed          Journal:  J Biomol Tech        ISSN: 1524-0215


  14 in total

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Review 2.  Clinical applications of proteomics: proteomic pattern diagnostics.

Authors:  Emanuel E Petricoin; Cloud P Paweletz; Lance A Liotta
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Authors:  Lance A Liotta; Mauro Ferrari; Emanuel Petricoin
Journal:  Nature       Date:  2003-10-30       Impact factor: 49.962

4.  Mining the low molecular weight proteome of blood.

Authors:  Richard R Drake; Lisa Cazares; O John Semmes
Journal:  Proteomics Clin Appl       Date:  2007-07-10       Impact factor: 3.494

5.  Proteomics in ocular fluids.

Authors:  Franz H Grus; Stephanie C Joachim; Norbert Pfeiffer
Journal:  Proteomics Clin Appl       Date:  2007-07-18       Impact factor: 3.494

6.  High-resolution proteome/peptidome analysis of peptides and low-molecular-weight proteins in urine.

Authors:  Harald Mischak; Bruce A Julian; Jan Novak
Journal:  Proteomics Clin Appl       Date:  2007-07-10       Impact factor: 3.494

7.  Proteomics of nipple aspirate fluid, breast cyst fluid, milk, and colostrum.

Authors:  Rachel L Ruhlen; Edward R Sauter
Journal:  Proteomics Clin Appl       Date:  2007-07-10       Impact factor: 3.494

8.  Clinical application of tear proteomics: Present and future prospects.

Authors:  Kaili Wu; Yanli Zhang
Journal:  Proteomics Clin Appl       Date:  2007-08-10       Impact factor: 3.494

Review 9.  Human body fluid proteome analysis.

Authors:  Shen Hu; Joseph A Loo; David T Wong
Journal:  Proteomics       Date:  2006-12       Impact factor: 3.984

10.  Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer.

Authors:  Jinong Li; Zhen Zhang; Jason Rosenzweig; Young Y Wang; Daniel W Chan
Journal:  Clin Chem       Date:  2002-08       Impact factor: 8.327

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  1 in total

1.  Proteomic identification of serum peptides predicting subsequent spontaneous preterm birth.

Authors:  M Sean Esplin; Karen Merrell; Robert Goldenberg; Yinglei Lai; Jay D Iams; Brian Mercer; Catherine Y Spong; Menachem Miodovnik; Hygriv N Simhan; Peter van Dorsten; Mitchell Dombrowski
Journal:  Am J Obstet Gynecol       Date:  2010-11-11       Impact factor: 8.661

  1 in total

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