Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.

Biomarkers are required for pre-symptomatic diagnosis, treatment, and monitoring of neurodegenerative diseases such as Alzheimer's disease. Cerebrospinal fluid (CSF) is a favored source because its proteome reflects the composition of the brain. Ideal biomarkers have low technical and inter-ind...

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Main Authors: Richard J Perrin, Jacqueline E Payton, James P Malone, Petra Gilmore, Alan E Davis, Chengjie Xiong, Anne M Fagan, R Reid Townsend, David M Holtzman
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3659127?pdf=render
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spelling doaj-18320a1706b94b3aab85df95fd4776fb2020-11-25T01:24:02ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0185e6431410.1371/journal.pone.0064314Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.Richard J PerrinJacqueline E PaytonJames P MalonePetra GilmoreAlan E DavisChengjie XiongAnne M FaganR Reid TownsendDavid M HoltzmanBiomarkers are required for pre-symptomatic diagnosis, treatment, and monitoring of neurodegenerative diseases such as Alzheimer's disease. Cerebrospinal fluid (CSF) is a favored source because its proteome reflects the composition of the brain. Ideal biomarkers have low technical and inter-individual variability (subject variance) among control subjects to minimize overlaps between clinical groups. This study evaluates a process of multi-affinity fractionation (MAF) and quantitative label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) for CSF biomarker discovery by (1) identifying reparable sources of technical variability, (2) assessing subject variance and residual technical variability for numerous CSF proteins, and (3) testing its ability to segregate samples on the basis of desired biomarker characteristics.Fourteen aliquots of pooled CSF and two aliquots from six cognitively normal individuals were randomized, enriched for low-abundance proteins by MAF, digested endoproteolytically, randomized again, and analyzed by nano-LC-MS. Nano-LC-MS data were time and m/z aligned across samples for relative peptide quantification. Among 11,433 aligned charge groups, 1360 relatively abundant ones were annotated by MS2, yielding 823 unique peptides. Analyses, including Pearson correlations of annotated LC-MS ion chromatograms, performed for all pairwise sample comparisons, identified several sources of technical variability: i) incomplete MAF and keratins; ii) globally- or segmentally-decreased ion current in isolated LC-MS analyses; and iii) oxidized methionine-containing peptides. Exclusion of these sources yielded 609 peptides representing 81 proteins. Most of these proteins showed very low coefficients of variation (CV<5%) whether they were quantified from the mean of all or only the 2 most-abundant peptides. Unsupervised clustering, using only 24 proteins selected for high subject variance, yielded perfect segregation of pooled and individual samples.Quantitative label-free LC-MS/MS can measure scores of CSF proteins with low technical variability and can segregate samples according to desired criteria. Thus, this technique shows potential for biomarker discovery for neurological diseases.http://europepmc.org/articles/PMC3659127?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Richard J Perrin
Jacqueline E Payton
James P Malone
Petra Gilmore
Alan E Davis
Chengjie Xiong
Anne M Fagan
R Reid Townsend
David M Holtzman
spellingShingle Richard J Perrin
Jacqueline E Payton
James P Malone
Petra Gilmore
Alan E Davis
Chengjie Xiong
Anne M Fagan
R Reid Townsend
David M Holtzman
Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.
PLoS ONE
author_facet Richard J Perrin
Jacqueline E Payton
James P Malone
Petra Gilmore
Alan E Davis
Chengjie Xiong
Anne M Fagan
R Reid Townsend
David M Holtzman
author_sort Richard J Perrin
title Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.
title_short Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.
title_full Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.
title_fullStr Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.
title_full_unstemmed Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.
title_sort quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2013-01-01
description Biomarkers are required for pre-symptomatic diagnosis, treatment, and monitoring of neurodegenerative diseases such as Alzheimer's disease. Cerebrospinal fluid (CSF) is a favored source because its proteome reflects the composition of the brain. Ideal biomarkers have low technical and inter-individual variability (subject variance) among control subjects to minimize overlaps between clinical groups. This study evaluates a process of multi-affinity fractionation (MAF) and quantitative label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) for CSF biomarker discovery by (1) identifying reparable sources of technical variability, (2) assessing subject variance and residual technical variability for numerous CSF proteins, and (3) testing its ability to segregate samples on the basis of desired biomarker characteristics.Fourteen aliquots of pooled CSF and two aliquots from six cognitively normal individuals were randomized, enriched for low-abundance proteins by MAF, digested endoproteolytically, randomized again, and analyzed by nano-LC-MS. Nano-LC-MS data were time and m/z aligned across samples for relative peptide quantification. Among 11,433 aligned charge groups, 1360 relatively abundant ones were annotated by MS2, yielding 823 unique peptides. Analyses, including Pearson correlations of annotated LC-MS ion chromatograms, performed for all pairwise sample comparisons, identified several sources of technical variability: i) incomplete MAF and keratins; ii) globally- or segmentally-decreased ion current in isolated LC-MS analyses; and iii) oxidized methionine-containing peptides. Exclusion of these sources yielded 609 peptides representing 81 proteins. Most of these proteins showed very low coefficients of variation (CV<5%) whether they were quantified from the mean of all or only the 2 most-abundant peptides. Unsupervised clustering, using only 24 proteins selected for high subject variance, yielded perfect segregation of pooled and individual samples.Quantitative label-free LC-MS/MS can measure scores of CSF proteins with low technical variability and can segregate samples according to desired criteria. Thus, this technique shows potential for biomarker discovery for neurological diseases.
url http://europepmc.org/articles/PMC3659127?pdf=render
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