Magnus Berle

Haukeland University Hospital, Bergen, Hordaland, Norway

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Publications (10)18.82 Total impact

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    ABSTRACT: 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, while 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.
    Molecular & cellular proteomics : MCP. 07/2014;
  • Tidsskrift for den Norske laegeforening 11/2013; 133(21):2271-5.
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    ABSTRACT: BACKGROUND: There is little knowledge concerning the content and the mechanisms of filling of arachnoid cysts. The aim of this study was to compare the protein content of arachnoid cysts and cerebrospinal fluid by quantitative proteomics to increase the understanding of arachnoid cysts. METHODS: Arachnoid cyst fluid and cerebrospinal fluid from five patients were analyzed by quantitative proteomics in two separate experiments. In a label-free experiment arachnoid cyst fluid and cerebrospinal fluid samples from individual patients were trypsin digested and analyzed by Orbitrap mass spectrometry in a label-free manner followed by data analysis using the Progenesis software. In the second proteomics experiment, a patient sample pooling strategy was followed by MARS-14 immunodepletion of high abundant proteins, trypsin digestion, iTRAQ labelling, and peptide separation by mix-phase chromatography followed by Orbitrap mass spectrometry analysis. The results from these analyzes were compared to previously published mRNA microarray data obtained from arachnoid membranes. RESULTS: We quantified 348 proteins by the label-free individual patient approach and 1425 proteins in the iTRAQ experiment using a pool from five patients of arachnoid cyst fluid and cerebrospinal fluid. This is by far the largest number of arachnoid cyst fluid proteins ever identified, and the first large-scale quantitative comparison between the protein content of arachnoid cyst fluid and cerebrospinal fluid from the same patients at the same time. Consistently in both experiment, we found 22 proteins with significantly increased abundance in arachnoid cysts compared to cerebrospinal fluid and 24 proteins with significantly decreased abundance. We did not observe any molecular weight gradient over the arachnoid cyst membrane. Of the 46 proteins we identified as differentially abundant in our study, 45 were also detected from the mRNA expression level study. None of them were previously reported as differentially expressed. We did not quantify any of the proteins corresponding to gene products from the ten genes previously reported as differentially abundant between arachnoid cysts and control arachnoid membranes. CONCLUSIONS: From our experiments, the protein content of arachnoid cyst fluid and cerebrospinal fluid appears to be similar. There were, however, proteins that were significantly differentially abundant between arachnoid cyst fluid and cerebrospinal fluid. This could reflect the possibility that these proteins are affected by the filling mechanism of arachnoid cysts or are shed from the membranes into arachnoid cyst fluid. Our results do not support the proposed filling mechanisms of oncotic pressure or valves.
    Fluids and barriers of the CNS. 04/2013; 10(1):17.
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    ABSTRACT: The mechanisms behind formation and filling of intracranial arachnoid cysts (AC) are poorly understood. The aim of this study was to evaluate AC fluid by proteomics to gain further knowledge about ACs. Two goals were set: 1) Comparison of AC fluid from individual patients to determine whether or not temporal AC is a homogenous condition; and 2) Evaluate the protein content of a pool of AC fluid from several patients and qualitatively compare this with published protein lists of cerebrospinal fluid (CSF) and plasma. AC fluid from 15 patients with temporal AC was included in this study. In the AC protein comparison experiment, AC fluid from 14 patients was digested, analyzed by LC-MS/MS using a semi-quantitative label-free approach and the data were compared by principal component analysis (PCA) to gain knowledge of protein homogeneity of AC. In the AC proteome evaluation experiment, AC fluid from 11 patients was pooled, digested, and fractionated by SCX chromatography prior to analysis by LC-MS/MS. Proteins identified were compared to published databases of proteins identified from CSF and plasma. AC fluid proteins not found in these two databases were experimentally searched for in lumbar CSF taken from neurologically-normal patients, by a targeted protein identification approach called MIDAS (Multiple Reaction Monitoring (MRM) initiated detection and sequence analysis). We did not identify systematic trends or grouping of data in the AC protein comparison experiment, implying low variability between individual proteomic profiles of AC.In the AC proteome evaluation experiment, we identified 199 proteins. When compared to previously published lists of proteins identified from CSF and plasma, 15 of the AC proteins had not been reported in either of these datasets. By a targeted protein identification approach, we identified 11 of these 15 proteins in pooled CSF from neurologically-normal patients, demonstrating that the majority of abundant proteins in AC fluid also can be found in CSF. Compared to plasma, as many as 104 proteins in AC were not found in the list of 3017 plasma proteins. Based on the protein content of AC fluid, our data indicate that temporal AC is a homogenous condition, pointing towards a similar AC filling mechanism for the 14 patients examined. Most of the proteins identified in AC fluid have been identified in CSF, indicating high similarity in the qualitative protein content of AC to CSF, whereas this was not the case between AC and plasma. This indicates that AC is filled with a liquid similar to CSF. As far as we know, this is the first proteomics study that explores the AC fluid proteome.
    Fluids and barriers of the CNS. 01/2011; 8:19.
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    ABSTRACT: Arachnoid cyst (AC) fluid has not previously been compared with cerebrospinal fluid (CSF) from the same patient. ACs are commonly referred to as containing "CSF-like fluid". The objective of this study was to characterize AC fluid by clinical chemistry and to compare AC fluid to CSF drawn from the same patient. Such comparative analysis can shed further light on the mechanisms for filling and sustaining of ACs. Cyst fluid from 15 adult patients with unilateral temporal AC (9 female, 6 male, age 22-77y) was compared with CSF from the same patients by clinical chemical analysis. AC fluid and CSF had the same osmolarity. There were no significant differences in the concentrations of sodium, potassium, chloride, calcium, magnesium or glucose. We found significant elevated concentration of phosphate in AC fluid (0.39 versus 0.35 mmol/L in CSF; p = 0.02), and significantly reduced concentrations of total protein (0.30 versus 0.41 g/L; p = 0.004), of ferritin (7.8 versus 25.5 ug/L; p = 0.001) and of lactate dehydrogenase (17.9 versus 35.6 U/L; p = 0.002) in AC fluid relative to CSF. AC fluid is not identical to CSF. The differential composition of AC fluid relative to CSF supports secretion or active transport as the mechanism underlying cyst filling. Oncotic pressure gradients or slit-valves as mechanisms for generating fluid in temporal ACs are not supported by these results.
    Cerebrospinal Fluid Research 01/2010; 7:8. · 1.81 Impact Factor
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    ABSTRACT: The discriminating variable (DIVA) test and the selectivity ratio (SR) plot are developed as quantitative tools for revealing the variables in spectral or chromatographic profiles discriminating best between two groups of samples. The SR plot is visually similar to a spectrum or a chromatogram, but with the most intense regions corresponding to the most discriminating variables. Thus, the variables with highest SR represent the variables most important for interpretation of differences between groups. Regions with variables that are positively or negatively correlated to each other are displayed as corresponding negative and positive regions in the SR plot. The nonparametric DIVA test is designed for connecting SR to discriminatory ability of a variable quantified as probability for correct classification. A mean probability for a certain SR range is calculated as the mean correct classification rate (MCCR) for all variables in the same SR interval. The MCCR is thus similar to a mean sensitivity in each SR interval. In addition to the ranking of all variables according to their discriminatory ability provided by the SR plot, the DIVA test connects a probability measure to each SR interval. Thus, the DIVA test makes it possible to objectively define thresholds corresponding to mean probability levels in the SR plot and provides a quantitative means to select discriminating variables. In order to validate the approach, samples of untreated cerebrospinal fluid (CSF) and samples spiked with a multicomponent peptide standard were analyzed by matrix-assisted laser desorption ionization (MALDI) mass spectrometry. The differences in the multivariate spectral profiles of the two groups were revealed using partial least-squares discriminant analysis (PLS-DA) followed by target projection (TP). The most discriminating mass-to-charge (m/z) regions were revealed by calculating the ratio of explained to unexplained variance for each m/z number on the target-projected component and displaying this measure in SR plots with quantitative boundaries determined from the DIVA test. The results are compared to some established methods for variable selection.
    Analytical Chemistry 03/2009; 81(7):2581-90. · 5.70 Impact Factor
  • European Journal of Pharmaceutical Sciences - EUR J PHARM SCI. 01/2008; 34(1).
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    ABSTRACT: Mass spectral profiles are influenced by several factors that have no relation to compositional differences between samples: baseline effects, shifts in mass-to-charge ratio (m/z) (synchronization/alignment problem), structured noise (heteroscedasticity), and, differences in signal intensities (normalization problem). Different procedures for pretreatment of whole mass spectral profiles described by almost 50,000 m/z values are investigated in order to find optimal approaches with respect to revealing the information content in the data. In order to quantitatively assess the impact of different procedures for pretreatment of mass spectral profiles, we use factorial designs with the ratio between intergroup and intragroup (replicate) variance as response. We have examined the influence of smoothing, binning, alignment/synchronization, noise pattern, and normalization on data interpretation. Our analysis shows that the spectral profiles have to be corrected for heteroscedastic noise prior to normalization. An nth root transform, where n is a small, positive integer, is used to create a homoscedastic noise structure without destroying the linear correlation structures describing individual components when using whole mass spectral profiles. The choice of n is decided by a simple graphic procedure using replicate information. Log transform is shown to change the heteroscedastic noise structure from being dominant in high-intensity regions, to produce the largest noise in the low-intensity regions. In addition, log transform has a negative effect on the collinearity in the profiles. Factorial designs reveal strong interactions between several of the pretreatment steps, e.g., noise structure and normalization. This underlines the limited usability of looking at the different pretreatment steps in isolation. Binning turns out to be able to substitute smoothing of spectra by, for example, moving average or Savitsky-Golay, while, at the same time, reducing the data point description of the profiles by 1 order of magnitude. Thus, if the sampling density is high, binning seems to be an attractive option for data reduction without the risk of losing information accompanying the integration of profiles into peaks. In the absence of smoothing, binning should be executed prior to alignment. If binning is not performed, the order of pretreatment should be smoothing, alignment, nth root transform, and normalization.
    Analytical Chemistry 10/2007; 79(18):7014-26. · 5.70 Impact Factor
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    ABSTRACT: Cerebrospinal fluid (CSF) is a perfect source to search for new biomarkers to improve early diagnosis of neurological diseases. Standardization of pre-analytical handling of the sample is, however, important to obtain acceptable analytical quality. In the present study, MALDI-TOF MS was used to examine the influence of pre-analytical sample procedures on the low molecular weight (MW) CSF proteome. Different storage conditions like temperature and duration or the addition of as little as 0.2 µL blood/mL neat CSF caused significant changes in the mass spectra. The performance of different types of MW cut-off spin cartridges from different suppliers used to enrich the low MW CSF proteome showed great variance in cut-off accuracy, stability and reproducibility. The described analytical method achieved a polypeptide discriminating limit of approximately 800 pM, two to three orders of magnitude lower than reported for plasma. Based on this study, we recommend that CSF is centrifuged immediately after sampling, prior to storage at -80ºC without addition of protease inhibitors. Guanidinium hydrochloride is preferred to break protein-protein interactions. A spin cartridge with cut-off limit above the intended analytical mass range is recommended. Our study contributes to the important task of developing standardized pre-analytical protocols for the proteomic study of CSF.
    Proteomics. Clinical applications 07/2007; 1(7):699-711. · 2.93 Impact Factor
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    ABSTRACT: Discovery of disease specific biomarkers in human body fluids has become an important challenge in clinical proteomics. Facing the increasing threat of degenerative and disabling diseases like cancer, cardiovascular, neurological and inflammatory diseases in large parts of the world's population, there is an urgent need to improve early diagnostics. In this review we discuss possibilities and limitations connected to using mass spectrometry based proteomics in the search for novel biomarkers, with focus on multiple sclerosis as a typical representative for the large group of non-curable degenerative and disabling disease with the lack of specific tests for early diagnosis. Careful control of the pre-analytical phase including sampling, storage and fractionation of samples, in addition to a thoroughly considered patient selection, is important in order to avoid false biomarkers to appear in the resulting mass spectra. Furthermore, advanced computational tools are needed in order to discover potential biomarkers from the enormous data amounts generated by the mass spectrometers. The development of such computer tools is a research field currently in the start phase and could prove to be a bottle neck in the biomarker discovery the next years. Therefore, a rather detailed review of the most used computational and pre-analytical methods is given in this review. Mass spectrometry based biomarker discovery is undoubtedly still in its early infancy. However, in light of the potential of this technology to provide deep coverage of the body fluid proteomes, it will certainly consolidate its role in developing molecular medicine into clinical practice.
    Current Pharmaceutical Biotechnology 07/2006; 7(3):147-58. · 2.69 Impact Factor