Computational protocol: A novel quantification-driven proteomic strategy identifies an endogenous peptide of pleiotrophin as a new biomarker of Alzheimer’s disease

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Protocol publication

[…] Peptide identification was performed using Proteome Discoverer 1.4 (Thermo Scientific) using Mascot (MatrixScience) for database searching, and PEAKS Studio (BSI). The search settings were: precursor ∆m tolerance = 10 ppm, fragment ∆m tolerance = 20 milli mass units, missed cleavages = 2, fixed modifications = carbamidomethylation, variable modifications = oxidation of methionine), searching the human subset of the UniProtKB Swiss-Prot database (release 13–10) (www.uniprot.org). Percolator (MatrixScience) was used for scoring peptide specific matches, and 1% false discovery rate (FDR) was set as threshold for identification.Automatic de novo sequencing was performed by the auto-de novo function in PEAKS Studio. Peptide sequence tags derived from spectra were searched using MS Blast (http://genetics.bwh.harvard.edu/msblast/). Matching candidate amino acid sequences from BLAST searches to mass spectrometric data was assisted by use of the software programs GPMAW (Lighthouse Data) and mMass. [...] Spectral clustering was performed using MS-Cluster v2, an open source software for TMT dataset clustering. MS-Cluster uses a hierarchical clustering algorithm similar to the Pep-Miner algorithm, but is optimized for analysis of large numbers of mass spectra. The algorithm clusters spectra in the TMT dataset by similarity using the normalized dot-product, which has shown good results in several studies–. The raw MS data was converted to peak lists in the mgf format using the software Proteome Discoverer 1.4 (Thermo Scientific). A Δm/z window of 0.005 was used for TMT reporter ion detection. Prior to clustering, the TMT reporter ions were temporarily deleted from the data, as not to affect clustering. For the purposes of this study, the clustering algorithm was tuned to minimize the risk of generating clusters containing fragment spectra from divergent peptides, at the calculated cost of a higher probability of rendering cases of split clusters, where the same peptide spectra is spread out over several clusters. This was considered preferable as divergent peptide clusters would result in a higher probability of false positives in the search of new possible biomarkers of disease. The following parameters were used for clustering: sqs 0.5, mixture probability 0.025 and fragment tolerance 0.005.Within each TMT set, the reporter ion intensities of each TMT channel were normalized to the median intensity of the reporter ion intensities for that channel over all spectra to reduce the influence of experimental variation that affect individual samples, such as pipetting error. For each LC-MS data set, the intensity ratio of each TMT reporter (126–130 C) to the TMT-131 reporter (representing the common reference sample) was calculated to reduce the influence of run-to-run variations. [...] The performance of each cluster in the discovery set as a distinguishing factor between the controls and the AD patients was calculated using ROC analysis in R (ver 3.1.1). Kruskall-Wallis test with Dunn’s multiple comparisons test was performed using GraphPad Prism version 6.00 for Windows (GraphPad Software, La Jolla California USA, www.graphpad.com). Mann-Whitney test were used to calculate the significance p-values shown in Figs  and . Correlations and group differences were calculated using Spearman’s Rank-Order correlations and Mann-Whitney U analysis in SPSS statistics v.22 (IBM, New York). […]

Pipeline specifications

Software tools Proteome Discoverer, PEAKS X, GPMAW, mMass, MS-Cluster, PEP-Miner, SPSS
Applications Miscellaneous, MS-based untargeted proteomics
Organisms Homo sapiens