James Bollinger (Presenter)
University of Washington
Bio: I have primarily focused my research efforts on the development and application of novel analytical techniques for the profiling of enzymes of clinical relevance. Specifically, I have utilized the triple-quadrupole ESI-MS/MS analytical platform to design reagents and assays that enable the sensitive and specific analysis of individual proteins as well as metabolite profiles derived from their enzymatic activity. In collaboration with multiple other labs, I have applied these assays in efforts to characterize relative abundance and kinetic profiles of several proteins with the greater intent of investigating their in vivo roles in cellular homeostasis and disease progression.
Authorship: James G. Bollinger (1), Clark M. Henderson (1), Jarrett D. Egertson (1), Vagisha Sharma (1), Brian C. Searle (1,2), Brendan X. MacLean (1), Andrew N. Hoofnagle (1), Michael J. MacCoss (1)
(1) University of Washington School of Medicine, Seattle, Washington, USA. (2) Proteome Software Inc., Portland, Oregon, USA
| Short Abstract Liquid chromatography used in-line with tandem mass spectrometry (LC-MS/MS) has emerged as the dominant analytical platform for bottom-up proteomics. Described herein is a workflow that utilizes a stable isotope-labeled protein and accompanying set of differentially stable isotope-labeled peptides for the purpose of controlling the variation imparted during sample preparation and MS/MS analysis while simultaneously benchmarking tryptic digestion status and incorporating the indexed retention time (iRT) concept. |
Long Abstract
Introduction
Tandem mass spectrometry coupled with liquid chromatography (LC-MS/MS) has emerged as the dominant analytical platform for characterizing the protein fraction from complex biological matrices (1). Most classical mass spectrometry-based proteomic workflows take a “bottom-up” approach in which the protein analytes are initially digested with an endoprotease. Peptide mixtures are then typically separated by nano-flow liquid chromatography and eluted directly into a mass spectrometer. The coupling of chromatography systems to mass spectrometers gives rise to a time dimension commonly referred to as retention time (RT). This RT dimension permits for unique considerations in both discovery-based and targeted proteomics. In discovery proteomics, RT is applied to align chromatographic peaks across experiments (2,3) and to increase confidence in peptide identifications (4,5). In targeted proteomics, RT is used to schedule instrument data acquisition such that a maximum number of peptide targets can be measured in a single LC-MS/MS injection (6). For these reasons and more, accurately characterizing and predicting RT for proteolytic peptides has evolved into an important consideration for bottom-up proteomic experiments.
Two primary approaches have emerged for peptide RT characterization, in silico prediction and direct empirical measurement. The former uses algorithms trained on either previous LC-MS/MS data (7,8) or on synthetic peptides (9) to predict RT for peptides with no previous empirical reference data. Direct measurement, on the other hand, requires exhaustive characterization of all target peptides for an individual study prior to analysis. The drawback of this approach is that the derived RTs are specific to the experimental conditions in which they were measured and must be measured anew every time any experimental parameter is altered. To address the shortcomings of these approaches, Escher and collegues (10) recently suggested a strategy where retention times for target peptides are standardized against a set of calibration peptides. The approach, coined indexed retention time (iRT), combines the portability and experimental ease of the in silico approach while maintaining most of the accuracy and precision of the empirical approach. While iRT approach has had a definitive impact on proteomics, it is important to note that the concept itself is generalizable to any set of reliably measured peptides that span an appropriate RT range. Thus, one could presumably derive a single set of standards to control for a variety of experimental variables in addition to retention time calibration. Much like any peptide standard, however, the iRT peptides are not subject to proteolysis and therefore limited in their capacity to control variation introduced during sample digestion. One way to address this limitation is via the use standard proteins in place of standard peptides. Unfortunately, using this approach for every target protein is cost-prohibitive and often not even possible due to the limited commercial availability of protein isotopomers. With all these factors in mind, we endeavored to develop a single set of standards (a recombinant 15N isotopomer protein with corresponding Lys/Arg labeled peptides) capable of incorporating the iRT concept, benchmarking digestion progress, and controlling for the variation imparted during sample preparation and MS/MS analysis.
Methods
Preparation and Proteolytic Digestion of Human Plasma:
Human plasma samples were subjected to a standard bottom-up proteomics workflow. Briefly, each plasma sample was diluted 100-fold by combining a 10 µL aliquot with 990 µL of 50 mM Ammonium Bicarbonate (pH~ 7.8). For each digestion reaction, a 25 µL aliquot of diluted plasma was combined with a 25 µL aliquot of a 0.2% solution of PPS Silent Surfactant dissolved in 50 mM Ammonium Bicarbonate containing an equimolar mixture of 15N labeled Apolipoprotein A1 protein and a set of corresponding 13C/15N labeled lysine/arginine peptides. Samples were boiled at 95C for 5 minutes, reduced via 5mM DTT at 60C for 30 minutes, and alkylated via 15 mM Iodoacetamide at 25oC for 30 minutes in the dark. Alkylation was quenched by bringing the final DTT concentration to 7.5 mM. Proteolysis was initiated via the addition of a 1.75 µg (10:1 protein to protease ratio) of MS grade Porcine and incubation at 37C/1400 RPM for 18 hours. Digestion progress was quenched via the addition of a 2.5 µL aliquot of 5N HCl and reactions were incubated for one hour at room temperature to facilitate hydrolysis of the surfactant. Insoluble material was pelleted by centrifuging digests at 20000 RCF for 5 minutes at room temperature.
Time-course analysis of Proteolytic Digestion:
For digestion time-course analysis, a 250 µL aliquot of 100-fold diluted plasma pool was combined with 250 µL of a 0.2% solution of PPS Silent Surfactant dissolved in 50 mM Ammonium Bicarbonate containing an equimolar mixture of 15N labeled Apolipoprotein A1 protein and a set of corresponding 13C/15N labeled lysine/arginine peptides. All reagents for plasma protein denaturation, reduction, alkylation, and digestion described above were scaled to account for the larger reaction volume. A 25 µL aliquot was removed at each corresponding time point and processed for analysis as above.
Results
We demonstrate that peptides produced from tryptic digestion of recombinant APOA1 are a suitable set of iRT peptides by comparing their relative RT’s to those derived from three of the commonly used commercially available sets of iRT peptides. We then demonstrate how a subset of the tryptic peptides from recombinant 15N APOA1 can be used to benchmark digestion progress when used in conjunction with an equimolar mixture of their differentially stable isotope labeled peptide analogs. We then evaluate the performance characteristics of our LC-MS/MS assay for a set of 5 commonly measured plasma proteins. For each protein, we either use stable isotope-labeled analogs of each peptide for normalization (ISpep) or use a stable isotope-labeled protein as a global standard for all peptides (ISprot).
References & Acknowledgements:
1. Aebersold, R., Mann, M. (2003) Mass spectrometry-based proteomics. Nature, 422, 198-207.
2. Petritis, K., Kangas, L.J., Ferguson, P.L., Anderson, G.A., Pasa-Tolić, L., Lipton, M.S., Auberry, K.J., Strittmatter, E.F., Shen, Y., Zhao, R., Smith, R.D. (2003) Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses. Anal. Chem. 75(5), 1039-1048.
3. Shinoda, K., Tomita, M., Ishihama, Y. (2008). Aligning LC peaks by converting gradient retention times to retention index of peptides in proteomic experiments. Bioinformatics, 24(14), 1590-1595.
4. Palmblad, M., Ramström, M., Markides, K.E., Håkansson, P., Bergquist, J. (2002) Prediction of chromatographic retention and protein identification in liquid chromatography/mass spectrometry. Anal. Chem. 74, 5826-5830.
5. Strittmatter, E.F., Ferguson, P.L., Tang K, Smith, R.D. (2003) Proteome analyses using accurate mass and elution time peptide tags with capillary LC time-of-flight mass spectrometry. J. Am. Soc. Mass Spectr. 14, 980-91.
6. Stahl-Zeng, J., Lange, V., Ossola, R., Eckhardt, K., Krek, W., Aebersold, R., Domon, B. (2007) High sensitivity detection of plasma proteins by multiple reaction monitoring of N-glycosites. Mol. Cell Proteomics, 6(10), 1809-1817.
7. Norbeck, A.D., Monroe, M.E., Adkins, J.N., Anderson, K.K., Daly, D.S., Smith, R.D. (2005) The utility of accurate mass and LC elution time information in the analysis of complex proteomes. J. Am. Soc. Mass. Spectrom. 16(8), 1239-1249.
8. Krokhin, O.V. (2006) Sequence-specific retention calculator. Algorithm for peptide retention prediction in ion-pair RP-HPLC: application to 300- and 100-A pore size C18 sorbents. Anal. Chem. 78(22), 7785-7795.
9. Houghten, R.A., DeGraw, S.T. (1987) Effect of positional environmental domains on the variation of high-performance liquid chromatographic peptide retention coefficients. J. Chromatogr. 386, 223-228.
10. Escher, C., Reiter, L., MacLean, B., Ossola, R., Herzog, F., Chilton, J., MacCoss, M.J., Rinner, O. (2012) Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12, 1111-1121.
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