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Feature Articles : May 1, 2008 ( )
Quantification & Identification in Proteomics
UPLC and Electrospray MS Drive Advances !--h2>
Charles Cooney, Ph.D., Robert T. Haslam professor of chemical engineering at Massachusetts Institute of Technology (MIT), opened the recent “Label-Free Quantification and Identification for Proteomics” symposium by defining the “proteomics gameplan,” which is determining how a cell responds to its environment and developing strategies to alter cell physiology by modifying metabolic pathways.
Dr. Cooney told the participants at the meeting sponsored by Waters that in addition to relative protein quantification, “absolute quantification is now possible.” But the field of proteomics, which represents a convergence of multiple technologies, is still in its infancy, as existing technologies are being refined and novel methods for protein identification and quantification continue to emerge.
A series of academic researchers presented various platforms and approaches for protein characterization, differential protein expression analysis, and biomarker discovery. John R. Engen, Ph.D., associate professor at Northeastern University, led off the discussion by describing the use of hydrogen/deuterium (H/D) exchange and ultrahigh pressure liquid chromatography (UPLC) to study protein conformation, protein folding pathways, and protein structure and dynamics.
This method is particularly useful for studying proteins that are difficult to purify or crystallize and are too big for nuclear magnetic resonance analysis. Dr. Engen’s group first labeled proteins in solution with deuterium and then enzymatically cut the proteins into pieces. After UPLC, electrospray mass spectrometry (MSE) is used to determine where the deuterium exchanges into the protein.
Conformation experiments begin with the protein in its native state in a physiologic buffer, followed by dilution in D2O at the same pH and temperature. Aliquots are then moved to a quench buffer at various time points to stop the labeling reaction. HPLC exposes the sample to H2O, causing some of the label to be lost. This can be minimized by reducing the pH of the buffer from 7 to 2.5 and submersing the chromatography column in an ice bath to bring the temperature down from 25 to 0ºC.
The goal is to do the HPLC as quickly as possible to minimize the amount of label that is lost. These low temperature conditions prohibit the use of trypsin, but other enzymes such as pepsin can be used.
The final step involves MSE to link ions with peptide fragments. The mass of ions will change depending on the length of time of deuterium incorporation. The different conformations a protein may be present in—active or inactive, for example—can be identified by determining which residues are protected from hydrogen/deuterium exchange. Determining where the deuterium exchanges occur is done by identifying the peptides produced during the digestion step and monitoring exchange in those peptides.
The key to improving H/D MS protein analysis is to minimize the time it takes to do the chromatographic separation. “UPLC could be the answer,” said Dr. Engen, whose group collaborated with Waters on the development of the HD-Exchange nanoAcquity system. Analyses that previously took more than five days using HPLC and tandem MS now require only 30 minutes with UPLC and MSE, according to Dr. Engen.
PEPPeR, an acronym for platform for experimental proteomic pattern recognition, was developed by D.R. Mani, Ph.D., together with Jake Jaffe, Ph.D., in Steven Carr’s group at The Broad Institute of MIT and Harvard University. This technique is useful for discovering proteins or peptides associated with disease and can provide results that extend beyond traditional tandem MS-based protein identification by analyzing MS peaks not subject to MS/MS.
Dr. Mani, senior computation biologist at the Institute, described the “LC-MS/MS bottleneck, in which a limit of how many fragments can be identified in a run makes it difficult to get to the low-abundance peptides.”
In pattern-based biomarker discovery, the LC-MS peaks represent “features” that enable “the rescue of lost information” and make it possible to derive data from the full MS spectra. The pattern of features, based on relative (comparative) quantification, signifies a biomarker fingerprint. For maximum effectiveness, this technique requires instrumentation capable of high resolution and mass accuracy.
In PEPPeR, MS/MS is used to identify a few peptides, and these then serve as “landmarks” to guide peak alignment and matching across samples based on relative elution order, accurate mass, and retention time. The landmarks calibrate clustering tolerances for m/z and retention time, which are then used to cluster unidentified MS peaks. Machine-learning algorithms identify differentially expressed patterns in disease versus normal samples. Peaks represented in these patterns can be sequenced using accurate mass-based, targeted MS/MS protein identification.
The goal of the platform, says Dr. Mani, is to “cast your net wide in the beginning and then go back and quickly identify proteins of interest.” An advantage of this approach is speed. Identity-based biomarker discovery requires extensive sample fractionation—an estimated 280 hours of instrument time per sample pair (about 64 fractions per sample, 130 minutes of LC per fraction). PEPPeR analysis of unfractionated samples, however, requires about 15–25 hours of instrument time per sample pair (150 minutes of LC, 3–5 replicates per sample). Minimal or no fractionation contributes to higher throughput and enables analysis with no pooling of samples.
Proteomic research in plants faces several challenges including incomplete genomic and protein databases and the added complexity of unique cell components required for photosynthesis and carbon fixation. Protein characterization in plants, as in other organisms, must overcome the challenges associated with proteome coverage and dynamic range.
Switching the focus to human disease, Richard Sprenger, Ph.D., from the clinical proteomics group at the University of Amsterdam, spoke on the “Analysis and Quantification of Diagnostic Plasma Markers and Protein Signatures for Gaucher’s Disease.”
Differential Protein Expression
To date, strategies for determining differential protein expression in healthy and disease human cell lines have relied on relative quantification. Label-free detection analyses using MSE make possible absolute quantification, enhancing the ability to compare individual study results.
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