Computational analysis of quantitative proteomics data using stable isotope labeling

Annotator: postprocessing software for generating function-based signatures from quantitative mass spectrometry

Mass spectrometry is used to investigate global changes in protein abundance in cell lysates. Increasingly powerful methods of data collection have emerged over the past decade, but this has left researchers with the task of sifting through mountains of data for biologically significant results. Often, the end result is a list of proteins with no obvious quantitative relationships to define the larger context of changes in cell behavior. Researchers are often forced to perform a manual analysis from this list or to fall back on a range of disparate tools, which can hinder the communication of results and their reproducibility. To address these methodological problems, we developed Annotator, an application that filters validated mass spectrometry data and applies a battery of standardized heuristic and statistical tests to determine significance. To address systems-level interpretations, we incorporated UniProt and Gene Ontology keywords as statistical units of analysis, yielding quantitative information about changes in abundance for an entire functional category. This provides a consistent and quantitative method for formulating conclusions about cellular behavior, independent of network models or standard enrichment analyses. Annotator allows for "bottom-up" annotations that are based on experimental data and not inferred by comparison to external or hypothetical models. Annotator was developed as an independent postprocessing platform that runs on all common operating systems, thereby providing a useful tool for establishing the inherently dynamic nature of functional annotations, which depend on results from ongoing proteomic experiments. Annotator is available for download at http://people.cs.uchicago.edu/∼tyler/annotator/annotator_desktop_0.1.tar.gz .

Differential Human Plasma Proteomics Based on AniBal Quantification and Peptide-level Off-Gel Isoelectric Focussing

Despite its enormous complexity, human plasma is still one of the most frequently used body fluids for identification and quantification of health and disease biomarkers. We have developed a new workflow for qualitative and quantitative analysis of human plasma proteins. The first step was to remove the seven most abundant plasma proteins (MARS). Moreover, in order to reduce the complexity of the sample and to increase protein and proteome coverage, Off-Gel fractionation was performed at peptide level. Our own stable isotope-based quantitative proteomics approach termed AniBAL was chosen for relative quantification of proteins between conditions. The method was developed with commercial human plasma and resulted in the identification of 85 proteins, of which 68 revealed quantitative information (Mascot database search combined with Peptide-/ProteinProphet validation). The combined methods consisting of MARS, AniBAL, Off-Gel and nano-LC-MS/MS on a Bruker HCT ion trap represent a new and efficient platform to quantify human plasma proteome differences between conditions. The method was also found technically compatible to a pair of human plasma pilot samples from the European FP6 project “DiOGenes”. Many of the identifiable/quantifiable proteins are relevant to obesity, diabetes and inflammation, which form the context of investigation within “DiOGenes”.