In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.

Background: The growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool. Results: In order to simplify the development of proteomics tools, we have implemented an open-source support library for computational proteomics, called compomics-utilities. The library contains a broad set of features required for reading, parsing, and analyzing proteomics data. compomics-utilities is already used by a long list of existing software, ensuring library stability and continued support and development. Conclusions: As a user-friendly, well-documented and open-source library, compomics-utilities greatly simplifies the implementation of the basic features needed in most proteomics tools. Implemented in 100% Java, compomics-utilities is fully portable across platforms and architectures. Our library thus allows the developers to focus on the novel aspects of their tools, rather than on the basic functions, which can contribute substantially to faster development, and better tools for proteomics.

Over the last years there has been an increased focus on the importance of knowing the effect of pre-analytical influence on the proteomes under study, particularly in the field of biomarker discovery. We present three proteomics studies examining the effect of blood contamination and the rostro-caudal gradient (RCG) on the cerebrospinal fluid (CSF) proteome, in addition to plasma/CSF protein ratios. The studies showed that the central nervous system (CNS) derived proteins appeared to be unaffected by the RCG, while the plasma-derived proteins showed an increase in concentration towards the lumbar area. This implies that the concentration of the plasma-derived proteins in CSF will vary depending on the volume of CSF that is collected. In the CSF samples spiked with blood, 262 of 814 quantified proteins showed an abundance increase of more than 1.5 fold, while 403 proteins had a fold change of less than 1.2 and appeared to be unaffected by blood contamination. Proteins with a high plasma/CSF ratio appeared to give the largest effect on the CSF proteome upon blood contamination. The results give important background information on how factors like blood contamination, RCG and blood-CNS-barrier influences the CSF proteome. This information is particularly important in the field of biomarker discovery, but also for routine clinical measurements. The data from the blood contamination and RCG discovery studies have been deposited to the ProteomeXchange with identifier PXD000401.

The clinical course of IgA nephropathy (IgAN) is variable and complement activation may predict prognosis. The present study investigated whether glomerular abundance of complement proteins associates with progression to end-stage renal disease (ESRD) in patients for whom prognosis could not be predicted based on clinical variables. Based on data from the Norwegian Kidney Biopsy Registry and the Norwegian Renal Registry, three groups were included: IgAN patients with (n = 9) or without (n = 16) progression to ESRD during 10 years, and controls (n = 15) with a normal kidney biopsy. IgAN patients had eGFR > 45 ml/min/1.73 m2 and non-nephrotic proteinuria at time of biopsy. Using stored formalin-fixed paraffin embedded kidney biopsy tissue, about 100 glomerular cross sections were microdissected for each patient. Samples were analyzed by liquid chromatography–tandem mass spectrometry and relative abundances of complement proteins were compared between groups. Proteomic analyses quantified 2018 proteins, of which 28 proteins belong to the complement system. As compared to IgAN patients without progressive disease, glomeruli from patients with progressive IgAN had significantly higher abundance of components of the classical and the terminal complement pathways, and inhibitory factors such as Factor H and factor H related proteins. Abundance of complement proteins classified progressors from non-progressors with an area under ROC curve of 0.91 (p = 0.001). Clinical and morphological data were similar between the two patient groups and could not predict progressive IgAN. In conclusion, higher glomerular abundance of complement proteins was associated with a progressive clinical course in IgAN and are candidate biomarkers to predict prognosis.

Satisfactory sample preparation for mass spectrometry-based analysis is a critical step in the proteomics workflow. The quality and reproducibility of sample preparation can determine the coverage and confidence of proteomics results. Up to date, several methodologies have been described to produce suitable peptides for mass spectrometry analysis, followed by strategies for enrichment of post-translational modified peptides, if desired. Among them, the filter-aided sample preparation (FASP) has been introduced as a method to allow for removal of denaturants, reductants, alkylators, lipids and nucleic acids prior to trypsin digestion. Despite the high proteolytic digestion and contaminant removal efficiency described for this method, filter failure and consequently complete sample loss can discourage the use of this approach by the proteomic community. As judged by our quality controls, we were able to perform reliable and reproducible FASP for mass spectrometry analysis that allowed the quantification of 2141 proteins and 3694 phosphopeptides from as little as 20 and 320 μg of protein lysate from acute myeloid leukemia (AML) patients, respectively. Using the immobilized metal ion affinity chromatography (IMAC) method resulted in samples specifically enriched in phosphopeptides and allowed the quantification of a high number of both di- and multi-phosphopeptides in addition to the abundant mono-phosphopeptides. The workflows’ high reproducibility from three biological replicates was demonstrated by the similar number of quantified proteins and localized phosphosites, and confirmed by the similar distributions of their molecular functions. We found that the combination of the FASP procedure with StageTip mixed-mode fractionation and IMAC are excellent workflows for the reproducible and deep study of AML proteomes and phosphoproteomes, respectively. The FASP procedure can be carried out without the risk of filter failure by performing a simple test of the filter quality before adding the protein sample. Herein, we demonstrate an efficient and reproducible FASP-based pipeline for the proteomic and phosphoproteomic analysis of AML patient samples which also can be used for the analysis of any other protein samples.