Abstract
We present a new, quantification-driven proteomic approach to identifying biomarkers. In contrast to the identification-driven approach, limited in scope to peptides that are identified by database searching in the first step, all MS data are considered to select biomarker candidates. The endopeptidome of cerebrospinal fluid from 40 Alzheimer’s disease (AD) patients, 40 subjects with mild cognitive impairment, and 40 controls with subjective cognitive decline was analyzed using multiplex isobaric labeling. Spectral clustering was used to match MS/MS spectra. The top biomarker candidate cluster (215% higher in AD compared to controls, area under ROC curve = 0.96) was identified as a fragment of pleiotrophin located near the protein’s C-terminus. Analysis of another cohort (n = 60 over four clinical groups) verified that the biomarker was increased in AD patients while no change in controls, Parkinson’s disease or progressive supranuclear palsy was observed. The identification of the novel biomarker pleiotrophin 151–166 demonstrates that our quantification-driven proteomic approach is a promising method for biomarker discovery, which may be universally applicable in clinical proteomics.
Highlights
We present a new, quantification-driven proteomic approach to identifying biomarkers
While study size was previously limited by the lengthy liquid chromatography-mass spectrometry (LC-MS) analyses required in discovery proteomics workflows, performing large studies comprising hundreds of participants is feasible through the development of timesaving multiplex isobaric labeling techniques, such as the tandem mass tag (TMT) approach[5]
At follow-up, 14 of the mild cognitive imapairment (MCI) patients had progressed to Alzheimer’s disease (AD) dementia (MCI-AD) while 23 remained stable MCI (MCI-S)
Summary
An identification-driven strategy is most commonly used for proteomic data analysis, including in studies based on isobaric labeling According to this strategy, peptide identification is performed as the first step by searching fragment ion (MS/MS) spectra against protein sequence databases[7]. While not permitting immediate peptide identification, these spectra may contain quantitative information on biomarker candidates, which is overlooked because their identity could not be established in the first step of the analysis To address this limitation, we developed a new quantification-driven proteomic approach that uses spectral clustering[8] (instead of identified peptide sequence) to match MS/MS spectra representing the same peptide in isobaric labeling proteomic LC-MS data sets. All clustered data are evaluated quantitatively for their ability to separate the study groups, identifying biomarker clusters that can be subsequently identified in targeted follow-up experiments
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