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Publication Detail
Serum peptide profiling using MALDI mass spectrometry: avoiding the pitfalls of coated magnetic beads using well-established ZipTip technology
Abstract
Blood represents a convenient diagnostic specimen for clinical analysis. Serum metabolites and peptides have the potential to serve as reliable indicators of progression from a normal to a diseased state. However, the presence of salts, lipids and high concentrations of protein in blood serum can adversely affect its mass spectrometric profiling, as all of these moieties can hinder the ionisation and detection of diagnostic biomarkers. Several pre-fractionation strategies using chromatographic adsorbents have been employed to desalt samples and remove abundant proteins such as albumin and immunoglobulin. As an alternative to multi-stage fractionation chromatography, we describe in this practical review the adaptation and validation of a simple and fast solid-phase extraction technique using ZipTips. The protocol allows for the purification and concentration of peptides in a few, mostly automated steps, prior to spectral biomarker pattern diagnostics using MALDI MS and MS/MS. It has the added advantages of being suitable for use in a high-throughput and potentially clinical environment, only requiring a few microlitres of serum. We have evaluated, optimised, and standardised a number of analytical parameters ranging from serum storage and handling to automated peptide extraction, crystallisation, spectral acquisition, and signal processing. Using standardised protocols the average CV of serum profiles within- and between-run replicates has been evaluated and found to be around 10 % based on the variability of all detected peaks (more than 100 peaks per profile). The results show that this easy and fast method of using ZipTips, tested over a whole year, is more reproducible and more suitable for serum profile screening than magnetic bead-based methodologies which show higher variability and higher rates of rejected, low-quality spectra upon applying strict data quality control filters.
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MRC Clinical Trials Unit at UCL
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Div of Biosciences
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