Project PXD000764

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Biomedical Dataset
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Discovery of new cerebrospinal fluid biomarkers for meningitis in children C4PR_LIV


Bacterial meningitis is usually fatal without treatment and prompt and accurate diagnosis coupled with the timely administration of parenteral antibiotics, are necessary in order to save lives. The diagnosis can sometimes be delayed whilst samples are analysed in a laboratory using traditional methods of microscopy and antigen testing. The objective of our project is to define specific protein signatures in cerebrospinal fluid associated with Streptococcus pneumoniae infection which could lead to the development of assays or point-of-care devices to improve the speed and accuracy of diagnosis, and guide the clinicians in the treatment and prognosis of children with bacterial meningitis. The associated research paper is in preparation.

Sample Processing Protocol

CSF samples were centrifuge within 2 h of collection and the supernatant fraction was frozen within 4 h of collection, and stored at -80 °C until analysis. Samples (20 L in 200 L total volume) were subjected to in-solution digestion and 0.5 L was analyzed using the ‘Top20’ protocol on DDA mode in an LTQ Orbitrap Velos mass spectrometer (Thermo Fisher Scientific).

Data Processing Protocol

The raw data acquired were converted into a single *.mgf format file containing the peaklist by Proteome Discoverer 1.1 (Thermo Fisher Scientific) using default parameters. Independent *.mgf files for each sample were searched against a merged database composed of reviewed entries of Human Uniprot database (version 20120711; 20,225 entries) and Streptococcus pneumoniae reference strain ATCC BAA-255/R6 (version 20120711; 2,029 entries) with MASCOT search engine (version 2.4.0, Matrix Science), using trypsin as the enzyme, carbamidomethylation of cysteine as fixed modification, allowing methionine oxidation as variable modification and one trypsin missed cleavage, a mass tolerance of 10 ppm for precursors and 0.6 Da for fragment ions. The false discovery rate (FDR) was calculated using the decoy database tool in MASCOT. The outputs from MASCOT were saved in mzIdentML format. The peptides and proteins were quantified using Progenesis LCMS software (version 4.0; Nonlinear Dynamics). Software default thresholds were used. Samples were grouped as control (n=4) and infected (n=8). Features with positive charge states between +2 and +5, and three or more isotopic peaks were taken to further identification. A merged peak list generated by Progenesis LCMS was searched against the composite database, using MASCOT search engine (version 2.4.0, Matrix Science). Trypsin was selected as the enzyme, carbamidomethylation of cysteine as fixed modification, allowing methionine oxidation as variable modification and one trypsin missed cleavage, mass tolerance of 10 ppm for precursors and 0.6 Da for fragment ions. MASCOT results were imported into Progenesis LCMS and a cut off score of 20 was applied after manually evaluating the quality of the lowest score peptides. Similar proteins were grouped and only non-conflicting features were used for quantification. The output Progenesis peptide and protein CSV files were imported to Progenesis Post-Processor version 1.0.4-beta ( for conversion to mzQuantML file.


Da Qi, University of Liverpool
Rob Beynon, Institute of Integrative Biology, University of Liverpool ( lab head )

Submission Date


Publication Date



    Ternent T, Csordas A, Qi D, Gómez-Baena G, Beynon RJ, Jones AR, Hermjakob H, Vizcaíno JA. Standardization and Guidelines: How to submit MS proteomics data to ProteomeXchange via the PRIDE database. Proteomics. 2014 Jul 21 PubMed: 25047258


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# Accession Title Proteins Peptides Unique Peptides Spectra Identified Spectra View in Reactome
1 34937 no assay title provided (mzIdentML) 307 9495 2551 15738 7488
2 34938 no assay title provided (mzIdentML) 30 432 199 13327 430
3 34939 no assay title provided (mzIdentML) 257 7789 1758 12872 6445
4 34946 no assay title provided (mzIdentML) 188 5058 1151 10426 4348
5 34947 no assay title provided (mzIdentML) 406 11003 3768 19566 8245
6 34944 no assay title provided (mzIdentML) 379 11701 4185 21771 9091
7 34945 no assay title provided (mzIdentML) 547 12291 4845 17057 8785
8 34936 no assay title provided (mzIdentML) 472 11395 4370 18729 8586
9 34942 no assay title provided (mzIdentML) 250 9736 2309 17806 7075
10 34943 no assay title provided (mzIdentML) 419 10909 3569 19491 8530