ABSTRACT
Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases. Recently much emphasis has been placed upon producing highly reliable data for quantitative profiling for which highly reproducible methodologies are indispensable. The main problems that affect experimental reproducibility stem from variations introduced by sample collection, preparation, and storage protocols and LC-MS settings and conditions. On the basis of a formally precise and quantitative definition of similarity between LC-MS experiments, we have developed Chaorder, a fully automatic software tool that can assess experimental reproducibility of sets of large scale LC-MS experiments. By visualizing the similarity relationships within a set of experiments, this tool can form the basis of systematic quality control and thus help assess the comparability of mass spectrometry data over time, across different laboratories, and between instruments. Applying Chaorder to data from multiple laboratories and a range of instruments, experimental protocols, and sample complexities revealed biases introduced by the sample processing steps, experimental protocols, and instrument choices. Moreover we show that reducing bias by correcting for just a few steps, for example randomizing the run order, does not provide much gain in statistical power for biomarker discovery.
Subject(s)
Mass Spectrometry , Proteomics/methods , Research Design , Angiotensin II/pharmacology , Animals , Bias , Biomarkers/metabolism , Cell Cycle/drug effects , Chromatography, Liquid , Disease Models, Animal , Freezing , Humans , Huntington Disease/metabolism , Mice , Reproducibility of Results , Saccharomyces cerevisiae/cytology , Saccharomyces cerevisiae/drug effects , Time FactorsABSTRACT
We describe an integrated suite of algorithms and software for general accurate mass and time (AMT) tagging data analysis of mass spectrometry data. The AMT approach combines identifications from liquid chromatography (LC) tandem mass spectrometry (MS/MS) data with peptide accurate mass and retention time locations from high-resolution LC-MS data. Our workflow includes the traditional AMT approach, in which MS/MS identifications are located in external databases, as well as methods based on more recent hybrid instruments such as the LTQ-FT or Orbitrap, where MS/MS identifications are embedded with the MS data. We demonstrate our AMT workflow's utility for general data synthesis by combining data from two dissimilar biospecimens. Specifically, we demonstrate its use relevant to serum biomarker discovery by identifying which peptides sequenced by MS/MS analysis of tumor tissue may also be present in the plasma of tumor-bearing and control mice. The analysis workflow, referred to as msInspect/AMT, extends and combines existing open-source platforms for LC-MS/MS (CPAS) and LC-MS (msInspect) data analysis and is available in an unrestricted open-source distribution.
Subject(s)
Algorithms , Mass Spectrometry , Peptides/analysis , Software , Animals , Biomarkers/blood , Chromatography, Liquid , Databases, Protein , Electronic Data Processing , Mice , Systems IntegrationABSTRACT
New approaches for identifying biological threat agents in raw milk using spectroscopy were tested using Bacillus anthracis (BA) Sterne strain spores seeded into unpasteurized bulk tank milk. Direct filtration onto Tyvek membranes provided the optimal filtration approach from raw milk, but detection limits were not ideal. When beads coated with anti-BA antibodies were mixed with spores in raw milk, the beads were capable of concentrating the spores that could be later detected and characterized by MALDI spectroscopy based on presence of previously characterized small acid-soluble proteins (SASP's). This approach could provide a very rapid assessment of whether milk or milk products have been purposefully contaminated with BA spores. This work was fundamentally a proof-of-concept study demonstrating feasibility of the approach in milk. Other parameters could be changed to potentially lower detection limits, and additional studies are currently underway to improve the approach.
