About the Research Resource
A primary objective of the Resource is to develop and apply an integrated set of biological methods, new analytical technologies, and associated computational and informatics tools for much more rapid, quantitative, sensitive, and comprehensive proteomic measurements than presently possible. Our technological developments also aim at providing new capabilities for the characterization of protein modifications and the quantitation of protein abundances spanning more than six orders of magnitude.
These fundamental developments will be augmented by 1) capabilities for high-throughput isolation of protein complexes needed to obtain better information on protein interactions and to test proteomic hypotheses, and 2) computational and bioinformatics tools needed to effectively extract and visualize data with statistically sound measures of quality to aid in development of new biological understandings.
Our efforts to develop a multifaceted approach for performing global, comprehensive, and high-throughput proteome measurements are described below in three focus areas: automated sample processing; methods, instrumentation and techniques; and data analysis.
Automated Sample Processing
Developments in this area are associated with sample processing - specifically, the development of automated processing for consistent, high-throughput manipulation of very small samples (< = 1 µg), with an emphasis on methods for isolation of protein complexes. Technologies in development include:
- High-throughput methods for rapid generation of single-chain antibodies for isolation and validation of specific protein complexes.
- Methods based upon automated sample processing of proteome samples and affinity-isolated protein complexes to enable much more effective characterization.
Methods, Instrumentation, and Techniques for Proteome Analyses
Research in this area addresses the need to improve the dynamic range, sensitivity, and both quality and reproducibility of proteomic analyses. Advancements enable both high-precision proteome measurements, as well as global absolute abundance measurements. Technologies in development include:
- Improved separation methods to increase the comprehensiveness and throughput of proteome analyses and to address the complexity of mammalian proteomes.
- Sub-attomole sensitivity, a dynamic range of more than six orders of magnitude, and high-throughput broad proteome analyses based on Fourier transform ion cyclotron resonance (FTICR) mass spectrometry, using the accurate mass and time (AMT) tag approach introduced by PNNL.
- Improved methods for both relative quantitation (e.g., using stable-isotope labeling methods) and absolute quantitation of protein abundance with statistical measures of quality for each analysis.
- Sensitive, high-throughput data-directed tandem mass spectrometry for the identification of targeted proteins based on relative (e.g., using stable-isotope labeling) or absolute abundance changes.
- An extension of the AMT tag concept to allow broad proteome studies at the intact protein level using capillary isoelectric-focusing FTICR mass spectrometry, and the capability to combine information from peptide-level analyses to improve identification and quantification of protein modifications.
Tools and Methods for Data Analysis
Developments in the related and equally important area of data quality include efforts to develop an approach that will provide a statistical evaluation of the quality of proteomic data that is comprehensive, and provides a firm foundation for subsequent use of these data. Technologies in development include:
- Approaches and software tools to aid quantitation, validation, and statistical evaluation of proteome analyses.
- New approaches using separations information (e.g., liquid chromatography elution times) to improve protein identifications.
- Development of software tools for the integration of peptide-level and intact protein-level analyses and improved characterization of modified proteins.
- Methods for the analysis and interpretation of proteomic data in conjunction with transcriptome-level expression microarray data.
- Development of an integrated computational infrastructure that enables researchers to access and use large and heterogeneous data sets and analysis tools.
- Integration of bioinformatic tools to aid interpretation, the extraction of biological understanding, and the development of hypotheses from proteomic analyses.