Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments

<p>Abstract</p> <p>Background</p> <p>Quality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important.</p> <p>Results</p> <p>We propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis.</p> <p>Conclusion</p> <p>We evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies.</p

Automated multi-dimensional liquid chromatography: sample preparation and identification of peptides from human blood filtrate

A comprehensive on-line sample clean-up with an integrated two-dimensional HPLC system was developed for the analysis of natural peptides. Samples comprised of endogenous peptides with molecular weights up to 20 kDa were generated from human hemofiltrate (HF) obtained from patients with chronic renal failure. The (poly-)peptides were separated using novel silica-based restricted access materials with strong cation-exchange functionalities (SCX-RAM). The size-selective sample fractionation step is followed by cation-exchange chromatography as the first dimension. The subsequent second dimension of separation is based on hydrophobic interaction using four parallel short reversed-phase (RP) columns implemented via a fully automated column switching technique. More than 1000 peaks were resolved within the total analysis time of 96 min. Substances of selected peaks were sampled to analyse their molecular weights by off-line MALDI-TOF mass spectrometry and to determine their amino acid sequence by Edman degradation. The potential for comprehensive peptide mapping and identification is demonstrated