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Minimalistic sample processing

ABSTRACT: Proteomic sample preparation typically involves multi-step workflows that can potentially perturb the biological system under analysis. These datasets were used to demonstrate that the entire processing pipeline, from cell lysis through elution of purified peptides, can be performed in a single, enclosed volume. This in-StageTip sample preparation method largely eliminates contamination or losses and is extremely robust and scalable. Peptides can be eluted in several fractions or in one step for single-run proteomics. In one day of gradient time we obtained the largest proteomes for budding and fission yeast to date. Applying the in-StageTip method to quadruplicate measurements of a human cell line yielded copy number estimates for more than 9000 human proteins. Data Analysis: MS raw files were analyzed by MaxQuant software (version 1.3.10.12) and peak lists were searched either against the human Uniprot FASTA database version 2/25/2012 (81213 entries), against the S. cerevisiae Uniprot FASTA database version 2/25/2012 (6649 entries) or the SGD based S. cerevisiae Fasta database orf_trans.20100105 (5904 entries), or against the S. pombe Uniprot FASTA database version 4/2/2013 (5096 entries) or pompep FASTA database version 4/2/2013 (5031 entries) and a common contaminants database (247 entries) by Andromeda search engine with cysteine carbamidomethylation as a fixed modification and N-terminal acetylation and methionine oxidation as variable modifications. False discovery rate (FDR) was set to 0.01 for proteins and peptides (minimum length of 7 amino acids) and was determined by searching a reverse database. Enzyme specificity was set as C-terminal to Arg and Lys, and a maximum of 2 miscleavages allowed. Peptide identification was performed with an allowed initial precursor mass deviation up to 7 ppm and an allowed fragment mass deviation 20 ppm. Quantification of SILAC pairs was carried out by MaxQuant with standard settings and without the re-quantification option.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human) Saccharomyces Cerevisiae (baker's Yeast) Schizosaccharomyces Pombe

SUBMITTER: Mario Oroshi

LAB HEAD: Mario Oroshi

PROVIDER: PXD000269 | Pride | 2014-01-31

REPOSITORIES: Pride

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			Action	DRS
	20120212_EXQ4_NiKu_SA_Yeast_ClarifiedK0_NonClarifiedK8.raw	Raw
	20120212_EXQ4_NiKu_SA_Yeast_ClarifiedK8_NonClarifiedK0.raw	Raw
	20120508_EXQ4_NaNa_SA_12cellines_cellline01.raw	Raw
	20120508_EXQ4_NaNa_SA_12cellines_cellline02.raw	Raw
	20120508_EXQ4_NaNa_SA_12cellines_cellline03.raw	Raw

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Project description:Here we present the data obtained from a label-free quantitative proteomics analysis of soluble spinal cord extract derived from a mouse model of multiple sclerosis (EAE) and sham-induced mice. Samples were prepared offline using the FASP approach and then submitted for nano-LC-MS/MS analysis on an Orbitrap Velos instrument. After statistical evaluation of the data, 431 differentially expressed proteins (KS-test, p < 0.05) out of a total of ~1400 unique proteins were identified in the comparative spinal cord analysis (peptide FDR=0.55%).Database search and protein identification: Tandem mass spectra were extracted from .RAW files and searched using the SEQUEST-PVM v.27 (rev.9) (Eng et al., 1994) database program against a Mouse protein database downloaded as FASTA-formatted sequences from EBI-IPI (database version 3.72) which contains 56957 entries (with priority given to UniProt identifiers) as well as reverse decoy sequences to empirically assess the false identification rate. This search program was executed on a cluster computer to match the MS/MS spectra to the corresponding most highly correlated peptide sequences Mass tolerances for precursor (MS) and product ions (MS/MS) were set to 3 and 0 m/z, respectively. Searches were performed with the enzyme selectivity set to trypsin with one missed cleavage allowed and protein modifications included fixed carbamidomethylation of cysteines (57 Da). Match likelihoods were assigned a statistical confidence score using the STATQUEST probabilistic model (Kislinger et al., 2003) and candidate peptide identifications were filtered using an estimated peptide confidence score of ≥95%. A 10 ppm high accuracy mass filter accounting for isotopic shifts in the spectra was applied post-SEQUEST analysis thus improving the fidelity of protein identifications. Protein quantitation: To estimate relative protein levels, spectral counts were transformed into normalised spectral abundance factors (NSAF) as previously described (Mosley et al., 2009). Briefly, this involves dividing the spectral count (SC) of a protein by its length (Mw) and finally normalises this value to the sum of all SC/Mw.

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Minimalistic sample processing

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