PRIDE Archive

Title

Human cerebrospinal fluid exosome, LC-MSMS

Description

Quantitative proteomics analysis of exosomes isolated from cerebrospinal fluid of neuromyelitis optica patients detected potential distinctive disease markers capable of differentiating from multiple sclerosis and idiopathic longitudinally extensive transverse myelitis. Exosomes were enriched with high purity using differential centrifugation from pooled cerebrospinal fluids. The sample pooling strategy included pre-classification by cytokine level, which improved target molecule identification by providing enhanced fold change ratios and lower p-values when the subsets with high-cytokines were compared to each other. In addition to the spectral counts and label-free quantification, orthogonal quantitative validation was performed using tandem mass spectrometry analysis via UPLC and Orbitrap or Q-Exactive Hybrid Quadrupole-Orbitrap instrumentation. The proteomic datasets with 442 significant exosomal proteins detected putative disease specific biomarkers, glial fibrillary acidic protein and fibronectin for neuromyelitis optica and multiple sclerosis, respectively. Additional investigation of expressions of glial fibrillary acidic protein and fibronectin revealed their presence in intact exosomes as detected by flow cytometry. ELISA measurement of glial fibrillary acidic protein within individual samples demonstrated diagnostic applicability by appropriately clustering disease groups. Expression levels of target molecules from label-free quantification were in good agreement with those from Western blotting. Finally, MetaCore pathway analysis of identified proteins supported the involvement of these proteins in disease progression via neurological pathway involvement. This comprehensive study suggests that the exosomal proteomics approach of cerebrospinal fluid can be applied to the identification and characterization of inflammatory disorders in central nervous system.

Sample Processing Protocol

Exosome isolations were prepared using a differential centrifugation method reported previously with minor modifications. Briefly, 2.5mL of pooled CSF acquired from 5 individuals was filtered with 0.45µm PVDF membrane followed by centrifugation at 18,000 x g for 30min. Supernatant was transferred to a clean tube and centrifuged at 200,000 x g overnight. Pellets were washed with cold 0.2µm filtered PBS by re-suspension followed by additional centrifugation at 200,000xg for 3hr. The final pellet was dissolved with cold, 0.2µm filtered PBS containing 1x protease inhibitor cocktail to generate the intact exosome suspension. A portion of each exosome/PBS suspension was mixed with same amount of 2x radioimmunoprecipitation (RIPA) buffer for solubilization of exosome proteins, which were then separated by SDS-PAGE, then subjected to in-gel tryptic digestion for proteomics analysis. 30μg of denatured proteins were separated on 10% Bis-Tris NuPAGE gels. Proteins were visualized by Coomassie staining with GelCode Blue staining reagent (Thermo Scientific, Rockford, IL, USA), and each gel lane was cut into 10 slices, which were chopped into small pieces. Gel pieces were destained with 50% (v/v) acetonitrile (ACN) containing 25 mM ammonium bicarbonate several times, and then were dehydrated in 100% ACN. After being dried in a Centrivap (Labconco, Kansas City, MO, USA), gel pieces were rehydrated in 50mM ammonium bicarbonate containing 12.5ng/μL trypsin, and then incubated at 37°C overnight. Peptides were extracted by adding 100 μL 50% (v/v) ACN containing 5% (v/v) formic acid and incubated at room temperature for 30 minutes 3 times. The extracts were dried under vacuum and then were suspended in 5% (v/v) ACN containing 3% (v/v) formic acid to be analyzed by LC-MS/MS. The LC-MS/MS system used consisted of an LTQ/Orbitrap-XL mass spectrometer (Thermo Scientific, Rockford, IL, USA) equipped with Nanoacquity UPLC system (Waters, Milford, MA, USA). Peptides were separated on a reversed phase analytical column (Nanoacquity BEH C18, 1.7μm, 150mm, Waters, Milford, MA, USA) combined with trap column (Nanoacquity, Waters, Milford, MA, USA). Good chromatographic separation was obtained with a 120 min linear gradient consisting of mobile phases solvent A (0.1% formic acid in water) and solvent B (0.1 % formic acid in ACN) where the gradient was from 5% B at 0 min to 40% B at 105 min. MS spectra were acquired by data dependent scans consisting of MS/MS scans of the ten most intense ions from the full MS scan with dynamic exclusion of 30 seconds.

Data Processing Protocol

The annotated human protein database from UniProtKB (20,161 entries, date; 10-1-2014) was used within Proteome Discoverer analytical software (v1.4, Thermo Scientific, Rockford, IL, USA) applying the generic SEQUEST search algorithm. Search parameters were as follows: parent mass tolerance of 20 ppm, fragment mass tolerance of 0.8 Da (monoisotopic), variable modification on methionine of 16 Da (oxidation) and maximum missed cleavage of 2 sites using the digestion enzyme trypsin. The identified peptides were validated employing reversed database (decoy database) search using percolator node in Proteome Discoverer software under the parameters of 0.05 for maximum delta Cn, 0.01 and 0.05 for each strict and relaxed target FDR based on q-value. Alternatively, MASCOT search engine (Matrix Science, Boston, MA, USA) was used for data comparison with the same search parameters. The .msf files generated from Proteome Discoverer were compiled using Scaffold software (v4.0.5, Proteome Software, Portland, OR, USA) which provided spectral counts for data comparison under the following filter criteria: 99.0% of protein/peptide thresholds in combination with SEQUEST score (XCorr, score versus charge state). XCorr scores were greater than 1.5, 2, and 3 for singly, doubly and triply charged peptides and deltaCn scores was greater than 0.10. For MASCOT search, data were filtered by probability score of 99.3% for peptide thresholds and 99.0% for protein threshold. Shared and partial-tryptic peptides were excluded from spectral counts for both search algorithms. Protein probability and redundancy were assigned by the Protein Prophet algorithm. Proteins that contained similar peptides and multiple isoforms, which could not be differentiated based on MS/MS spectra, were grouped into primarily assigned proteins. Only commonly identified proteins from both SEQUEST and MASCOT algorithms were accepted for the finalized data which included single-hit peptide based identifications. Spectral counts from duplicated analyses were compared using the Power Law Global Error Model (PLGEM) in order to identify the significance of the protein changes. MS1 label-free quantification using ion-intensity chromatograms was performed using Proteome Discoverer and Skyline software (MacCoss Lab Software, Seattle, WA, USA). Among the peptides generated by in-silico tryptic digestion of protein FASTA sequence, distinct peptides for each target protein were selected by BLAST search in UniProt website (http://www.uniprot.org/blast/) for relative quantification.

Contact

Jingyun Lee, Carolinas HealthCare System
Sunil Hwang, Cannon Research Center, Mass Spectrometry Core Facility, Carolinas HealthCare System, United States. ( lab head )

Submission Date

02/07/2015

Publication Date

27/09/2016