Evaluation of LC-MS data for the absolute quantitative analysis of marker proteins

The serum complexity makes the absolute quantitative analysis of medium to low-abundant proteins very challenging. Tens of thousands proteins are present in human serum and dispersed over an extremely wide dynamic range. The reliable identification and quantitation of proteins, which are potential biomarkers of disease, in serum or plasma as matrix still represents one of the most difficult analytical challenges. The difficulties arise from the presence of a few, but highly abundant proteins in serum and from the non-availability of isotope-labeled proteins, which serve to calibrate the method and to account for losses during sample preparation. For the absolute quantitation of serum proteins, we have developed an analytical scheme based on first-dimension separation of the intact proteins by anion-exchange high-performance liquid chromatography (HPLC), followed by proteolytic digestion and second-dimension separation of the tryptic peptides by reversed-phase HPLC in combination with electrospray ionization mass spectrometry (ESI-MS). The potential of mass spectrometric peptide identification in complex mixtures by means of peptide mass fingerprinting (PMF) and peptide fragment fingerprinting (PFF) was evaluated and compared utilizing synthetic mixtures of commercially available proteins and electrospray-ion trap- or electrospray time-of-flight mass spectrometers. While identification of peptides by PFF is fully supported by automated spectrum interpretation and database search routines, reliable identification by PMF still requires substantial efforts of manual calibration and careful data evaluation in order to avoid false positives. Quantitation of the identified peptides, however, is preferentially performed utilizing full-scan mass spectral data typical of PMF. Algorithmic solutions for PMF that incorporate both recalibration and automated feature finding on the basis of peak elution profiles and isotopic patterns are therefore highly desirable in order to speed up the process of data evaluation and calculation of quantitative results. Calibration for quantitative analysis of serum proteins was performed upon addition of known amounts of authentic protein to the serum sample. This was essential for the analysis of human serum samples, for which isotope-labeled protein standards are usually not available. We present the application of multidimensional HPLC-ESI-MS to the absolute quantitative analysis of myoglobin in human serum, a very sensitive biomarker for myocardial infarction. It was possible to determine myoglobin concentrations in human serum down to 100-500 ng/mL. Calibration graphs were linear over at least one order of magnitude and the relative standard deviation of the method ranged from 7-15%

Quantification of vancomycin in human serum by LC-MS/MS

Background: The aim of our work was to develop and validate a reliable LC-MS/MS-based measurement procedure for the quantification of vancomycin in serum, to be applied in the context of efforts to standardize and harmonize therapeutic drug monitoring of this compound using routine assays. Methods: Sample preparation was based on protein precipitation followed by ultrafiltration. In order to minimize differential modulation of ionization by matrix constituents extended chromatographic separation was applied leading to a retention time of 9.8 min for the analyte. Measurement was done by HPLC-ESI-MS/MS. For internal standardization the derivative vancomycin-glycin (ISTD) prepared by chemical synthesis was used, HPLC conditions ensured coelution of ISTD with the analyte. Results: In a bi-center validation total CVs of <4% were observed for quality control material ranging from 5.3 mg/L to 79.4 mg/L; accuracy was ±4%. No relevant ion suppression was observed. Comparative measurement of aliquots from 70 samples at the two validation sites demonstrated close agreement. Conclusions: Employing a closely related homologue molecule for internal standardization and the use of MS/MS following highly efficient sample pre-fractionation by HPLC, the method described here can be considered to offer the highest level of analytical reliability realized so far for the quantification of vancomycin in human serum. Thus, the method is suitable to be used in a comprehensive reference measurement system for vancomycin

CERTIFICATION REPORT: The certification of Amyloid β1-42 in CSF in ERM®-DA480/IFCC, ERM®-DA481/IFCC and ERM®-DA482/IFCC

This report describes the production of ERM®-DA480/IFCC, ERM®-DA481/IFCC and ERM®-DA482/IFCC, which are human cerebrospinal fluid (CSF) materials certified for the mass concentration of amyloid β1-42 peptide (Aβ1-42). These materials were produced by the European Commission, Joint Research Centre (EC-JRC) in collaboration with the International Federation for Clinical Chemistry and Laboratory Medicine (IFCC) following ISO Guide 34:2009 and are certified in accordance with ISO Guide 35:2006. The starting material used to prepare ERM-DA480/IFCC, ERM-DA481/IFCC and ERM-DA482/IFCC was human CSF collected from normal pressure hydrocephalus patients by continuous lumbar drainage. After collection, the CSF was aliquoted and frozen at -80 °C. For the preparation of each certified reference material (CRM) a selected number of CSF donations were thawed, pooled, mixed, filled in microvials and stored at (-70 ± 10) °C immediately thereafter. Between unit-homogeneity was quantified and stability during dispatch and storage were assessed in accordance with ISO Guide 35:2006 [ ]. The material was characterised by an interlaboratory comparison of laboratories of demonstrated competence and adhering to ISO/IEC 17025 [ ]. Technically invalid results were removed but no outlier was eliminated on statistical grounds only. Uncertainties of the certified values were calculated in accordance with the Guide to the Expression of Uncertainty in Measurement (GUM) [ ] and include uncertainties related to possible inhomogeneity, instability and characterisation. The materials are intended for the calibration of methods, quality control and/or the assessment of method performance. As with any reference material, they can be used for establishing control charts or validation studies. The CRMs are available in microvials containing at least 0.5 mL of frozen liquid. The minimum amount of sample to be used is 15 µL.JRC.F.6-Reference Material

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics

<p>Abstract</p> <p>Background</p> <p>High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination with database searching algorithms. A major problem with existing methods lies within the significant number of false positive and false negative annotations. So far, standard algorithms for protein identification do not use the information gained from separation processes usually involved in peptide analysis, such as retention time information, which are readily available from chromatographic separation of the sample. Identification can thus be improved by comparing measured retention times to predicted retention times. Current prediction models are derived from a set of measured test analytes but they usually require large amounts of training data.</p> <p>Results</p> <p>We introduce a new kernel function which can be applied in combination with support vector machines to a wide range of computational proteomics problems. We show the performance of this new approach by applying it to the prediction of peptide adsorption/elution behavior in strong anion-exchange solid-phase extraction (SAX-SPE) and ion-pair reversed-phase high-performance liquid chromatography (IP-RP-HPLC). Furthermore, the predicted retention times are used to improve spectrum identifications by a <it>p</it>-value-based filtering approach. The approach was tested on a number of different datasets and shows excellent performance while requiring only very small training sets (about 40 peptides instead of thousands). Using the retention time predictor in our retention time filter improves the fraction of correctly identified peptide mass spectra significantly.</p> <p>Conclusion</p> <p>The proposed kernel function is well-suited for the prediction of chromatographic separation in computational proteomics and requires only a limited amount of training data. The performance of this new method is demonstrated by applying it to peptide retention time prediction in IP-RP-HPLC and prediction of peptide sample fractionation in SAX-SPE. Finally, we incorporate the predicted chromatographic behavior in a <it>p</it>-value based filter to improve peptide identifications based on liquid chromatography-tandem mass spectrometry.</p

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics-6

<p><b>Copyright information:</b></p><p>Taken from "Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics"</p><p>http://www.biomedcentral.com/1471-2105/8/468</p><p>BMC Bioinformatics 2007;8():468-468.</p><p>Published online 30 Nov 2007</p><p>PMCID:PMC2254445.</p><p></p

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics-5

<p><b>Copyright information:</b></p><p>Taken from "Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics"</p><p>http://www.biomedcentral.com/1471-2105/8/468</p><p>BMC Bioinformatics 2007;8():468-468.</p><p>Published online 30 Nov 2007</p><p>PMCID:PMC2254445.</p><p></p>icance threshold value, b) all predictions of spectra having a score equal or greater than 95% of the significance threshold value, c) all predictions of spectra having a score equal or greater than 60% of the significance threshold value. The model was trained using the dataset and the performance was measured on and . If there was more than one spectrum with the same identification we plotted the mean values of the observed NRTs against the predicted NRT

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics-7

<p><b>Copyright information:</b></p><p>Taken from "Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics"</p><p>http://www.biomedcentral.com/1471-2105/8/468</p><p>BMC Bioinformatics 2007;8():468-468.</p><p>Published online 30 Nov 2007</p><p>PMCID:PMC2254445.</p><p></p

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics-1

<p><b>Copyright information:</b></p><p>Taken from "Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics"</p><p>http://www.biomedcentral.com/1471-2105/8/468</p><p>BMC Bioinformatics 2007;8():468-468.</p><p>Published online 30 Nov 2007</p><p>PMCID:PMC2254445.</p><p></p

Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics-4

<p><b>Copyright information:</b></p><p>Taken from "Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics"</p><p>http://www.biomedcentral.com/1471-2105/8/468</p><p>BMC Bioinformatics 2007;8():468-468.</p><p>Published online 30 Nov 2007</p><p>PMCID:PMC2254445.</p><p></p>ning sample size, we randomly selected the training peptides, and 40 test peptides and repeated this evaluation 100 times. The plot shows the mean squared correlation coefficients of these 100 runs for every training sample size as well as the standard deviation for the and the methods introduced by Klammer [16] using the RBF kernel as well as the models by Petritis [13, 14]. The vertical line corresponds to the minimal number of distinct peptides in one of our verified datasets which was acquired in one run