Computational protocol: Delivery of Alginate Scaffold Releasing Two Trophic Factors for Spinal Cord Injury Repair

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Protocol publication

[…] Tandem mass spectra were processed with Thermo Scientific Proteome Discoverer software version 1.3. Resultant spectra were searched against the Swiss-Prot® Rattus norvergicus database (version January 2012) using the SEQUEST® algorithm. The search was performed choosing trypsin as the enzyme with two missed cleavages allowed. Precursor mass tolerance was 10 ppm, and fragment mass tolerance was 0.5 Da. N-terminal acetylation, methionine oxidation and arginine deamination were set as variable modifications. Peptide validation was performed with the Percolator algorithm. Peptides were filtered based on a q-Value below 0.01, which corresponds to a false discovery rate (FDR) of 1%. All the MS data were processed with MaxQuant (version 1.5.1.2) using Andromeda search engine. Proteins were identified by searching MS and MS/MS data against Decoy version of the complete proteome for Rattus norvegicus of the UniProt database [UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res. 2012, 40 (Database issue), D71−5.] (Release June 2014, 33675 entries) combined with 262 commonly detected contaminants. Trypsin specificity was used for digestion mode, with N-terminal acetylation and methionine oxidation selected as variable, carbarmidomethylation of cysteines was set as a fixed modification and we allow up to two missed cleavages. For MS spectra an initial mass accuracy of 6 ppm was selected and the MS/MS tolerance was set to 0.5 Th for CID data. For identification, the FDR at the peptide spectrum matches (PSM) and protein level was set to 0.01. Relative, label-free quantification of proteins was done using the MaxLFQ algorithm integrated into MaxQuant with the default parameters.The data sets used for analysis are deposited at the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier.Analysis of the proteins identified was done using Perseus software (http://www.perseus-framework.org/) (version 1.5.0.31). The file containing the information from identification was used and hits to the reverse database, proteins only identified with modified peptides and potential contaminants were removed. Then the LFQ intensity were logarithmized (log2(x)). A normalization was achieved using a Z-score with a matrix access by rows. Data coming from control samples were average as the one coming from the lesion part. Six conditions were then analyzed: control (ctrl), lesion part (lesion), segment R1 or C1 seven days (respectively r1_7D and C1_7D) or ten days (r1_10D and C1_10D) after lesion. Only proteins presenting a valid value of LFQ intensity for these six conditions were used for statistical analysis. A Hierarchical clustering was first performed using a Pearson correlation for distance calculation and average option for linkage in row and column trees using a maximum of 300 clusters. For visualization of the variation of proteins expression depending to the segment/time parameter, the profile plot tool was used with a reference profile and an automatic selection of the 10 or 15 correlated profiles. […]

Pipeline specifications

Software tools Proteome Discoverer, Comet, Percolator, MaxQuant, Andromeda, Perseus
Databases UniProt ProteomeXchange
Application MS-based untargeted proteomics
Organisms Rattus norvegicus
Diseases Spinal Cord Injuries
Chemicals Choline