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.

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. Molecular & Cellular Proteomics 6:1741-1748, 2007.Mass spectrometry has exhibited tremendous promise in probing complex biological samples globally at the protein level (1-4). This capability is of key importance for the identification of diagnostic biomarkers for developing early detection methodologies for many human diseases, including cancers, and for unbiased, global measurements of cellular processes that are a key component of systems biology approaches (5).Although mass spectrometers are capable of both selective and sensitive measurements, mass analyzers are limited in their dynamic range. The consequence of this is a limited capability to detect very low abundance analytes in biological samples with large dynamic range. Concomitantly mass spectrometer duty cycles limit the number of CID events per unit of time and often lead to a significant undersampling of more complex proteomes (6). Furthermore the subset of peptides being sampled for CID can vary from one experiment to the next, hindering both interpretation and confidence in quantification.Many approaches therefore go beyond the straightforward use of CID for large scale protein identification. These range from the accurate mass and time tag approach (7), clustering (8), complete workflow solutions for LC-MS data sets (9 -12), alignment algorithms for LC-MS (13, 14), and feature detection approaches for SELDI platforms (15-17). These approaches rely heavily on high quality LC-MS profiles for peak alignment, peptide identification, and quantitation and thus require a high degree of reproducibility in the sample collection, processing, and analytical run con...

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.

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