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- Hölttä, Mikko, et al.
(författare)
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An Integrated Workflow for Multiplex CSF Proteomics and Peptidomics-Identification of Candidate Cerebrospinal Fluid Biomarkers of Alzheimer's Disease
- 2015
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Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 14:2, s. 654-663
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Tidskriftsartikel (refereegranskat)abstract
- Many disease processes in the brain are reflected in the protein composition of the cerebrospinal fluid (CSF). In addition to proteins, CSF also contains a large number of endogenous peptides whose potential as disease biomarkers largely remains to be explored. We have developed a novel workflow in which multiplex isobaric labeling is used for simultaneous quantification of endogenous CSF peptides and proteins by liquid chromatography coupled with mass spectrometry. After the labeling of CSF samples, endogenous peptides are separated from proteins by ultrafiltration. The proteins retained on the filters are trypsinized, and the tryptic peptides are collected separately. We evaluated this technique in a comparative pilot study of CSF peptide and protein profiles in eight patients with Alzheimer's disease (AD) and eight nondemented controls. We identified several differences between the AD and control group among endogenous peptides derived from proteins known to be associated with AD, including neurosecretory protein VGF (ratios AD/controls 0.45-0.81), integral membrane protein 2B (ratios AD/controls 0.72-0.84), and metallothionein-3 (ratios AD/controls 0.51-0.61). Analysis of tryptic peptides identified several proteins that were altered in the AD group, some of which have previously been reported as changed in AD, for example, VGF (ratio AD/controls 0.70).
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| 2. |
- Magdalinou, N. K., et al.
(författare)
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Identification of candidate cerebrospinal fluid biomarkers in parkinsonism using quantitative proteomics
- 2017
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Ingår i: Parkinsonism & Related Disorders. - : Elsevier BV. - 1353-8020 .- 1873-5126. ; 37, s. 65-71
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: Neurodegenerative parkinsonian syndromes have significant clinical and pathological overlap, making early diagnosis difficult. Cerebrospinal fluid (CSF) biomarkers may aid the differentiation of these disorders, but other than a-synuclein and neurofilament light chain protein, which have limited diagnostic power, specific protein biomarkers remain elusive. Objectives: To study disease mechanisms and identify possible CSF diagnostic biomarkers through discovery proteomics, which discriminate parkinsonian syndromes from healthy controls. Methods: CSF was collected consecutively from 134 participants; Parkinson's disease (n = 26), atypical parkinsonian syndromes (n = 78, including progressive supranuclear palsy (n = 36), multiple system atrophy (n = 28), corticobasal syndrome (n = 14)), and elderly healthy controls (n = 30). Participants were divided into a discovery and a validation set for analysis. The samples were subjected to tryptic digestion, followed by liquid chromatography-mass spectrometry analysis for identification and relative quantification by isobaric labelling. Candidate protein biomarkers were identified based on the relative abundances of the identified tryptic peptides. Their predictive performance was evaluated by analysis of the validation set. Results: 79 tryptic peptides, derived from 26 proteins were found to differ significantly between atypical parkinsonism patients and controls. They included acute phase/inflammatory markers and neuronal/synaptic markers, which were respectively increased or decreased in atypical parkinsonism, while their levels in PD subjects were intermediate between controls and atypical parkinsonism. Conclusion: Using an unbiased proteomic approach, proteins were identified that were able to differentiate atypical parkinsonian syndrome patients from healthy controls. Our study indicates that markers that may reflect neuronal function and/or plasticity, such as the amyloid precursor protein, and inflammatory markers may hold future promise as candidate biomarkers in parkinsonism. (C) 2017 Published by Elsevier Ltd.
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