Abstract
BackgroundBiomarkers 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.Methods/ResultsFourteen 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.ConclusionsQuantitative 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.
Highlights
Dementia of the Alzheimer type (DAT) currently affects an estimated 30 million people worldwide
Quantitative label-free LC-MS/MS can measure scores of Cerebrospinal fluid (CSF) proteins with low technical variability and can segregate samples according to desired criteria
The major goals of this study were: first, to identify the major reparable sources of technical variability within this complex proteomic workflow; second, to quantify the effect sizes of interindividual and residual technical variability on measurements of protein abundances; third, to compare two strategies for protein quantification from peptide data generated using label free proteomics; and fourth, to evaluate the potential of the data generated by this proteomic workflow to segregate biological samples on the basis of desired biomarker characteristics
Summary
Dementia of the Alzheimer type (DAT) currently affects an estimated 30 million people worldwide. In addition to those affected by DAT, many more are afflicted by Alzheimer’s disease (AD, the pathological process responsible for DAT) but have not yet begun to experience symptoms Individuals in this 10- to 15-year pre-symptomatic or ‘pre-clinical’ phase of AD are at increased risk to develop dementia [2,3,4,5] but have not yet experienced significant neuronal damage [6,7]. Biomarkers should estimate an individual’s risk of impending cognitive decline (prognosis) and even allow monitoring of pathological progression and response to treatment Once such biomarkers are developed, clinical trials should become more efficient and effective treatments will be identified more quickly. 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
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