Over the last two decades, mass spectrometry-based workflows have emerged as the preferred approaches for proteomic analysis. However, there are some concerns regarding their quantitative capabilities in discovery proteomics, due to “missing values” across data sets.
Scientists are converging on a new way to process and score signals from MS1 data-dependent acquisition (DDA) to resolve the “missing value” problem. Compared with traditional workflows for DDA, these new software scoring techniques achieve deeper proteome coverage, fewer missing values, and lower quantification variance. The method also enables flexible and robust proteome characterization based on covariation of peptide abundances.1
Label-free quantification (LFQ) is one of the most efficient approaches to quantifying proteome differences using mass spectrometry. It is cost effective, requires less time, and is less process-intensive than labelled methods. DDA and data independent acquisition (DIA) are the two methods of acquiring information in proteomics using LFQ-dependent mass spectrometry. DDA has been the preferred method for years, but is plagued by the stochastic nature of precursor selection and low sampling efficiency due to the limited speed of mass spectrometers. These challenges result in missing individual peptide identification across LC-MS/MS runs within a larger dataset—even after measurements are replicated.
Scientists created DIA methods to overcome the limitations of DDA and the missing values, however DDA remains the gold-standard due to its ability to detect a wider dynamic range of proteins in a complex matrix. DDA would undoubtedly be preferred if the problem of the missing values could be solved.
Scientific teams from around the world are harnessing increased computing power and data storage to introduce a new layer of digital quality testing in DDA workflows. At the Karolinska Institute in Sweden, Professor Roman Zubarev, proposed a quantification-centric approach to DDA, improving several features of the traditional identification-centric approach to signal processing. 1 The team reasons that missing values are not intrinsic to DDA approaches since the signal from the molecular ion is usually present among the MS1 spectra. The signal information simply needs to be processed differently, with less focus on identification prior to quantification. This new analytical workflow recovers missing values using a protein scoring scheme for quality control.
In Germany, Dr. Kirti Sharma and her team at the Max Planck Institute of Biochemistry investigated the mouse brain proteome—by cell type and brain region—using a similar DDA method, and combined it with deep sequencing–based transcriptome analysis to fully map transcripts and protein expression. The result is a profound proteomic profile of the mouse brain that can serve as a rich resource for brain development and functional analyses on a systems level.2
In the United States, scientists led by Dr. Daniel Ferrer-Lopez at Thermo Fisher Scientific (San Jose, California) have demonstrated the value of DDA in LFQ in combination with Thermo Scientific Proteome Discoverer software. The next version of Proteome Discoverer includes a novel processing step that recalibrates retention times and mass deviations, searching additionally for features in MS1 scans. Peptide abundance is linked to corresponding identifications across runs, resulting in an extremely precise proteomic profile. The team’s data demonstrate that this method results in fewer than 5% missing values. Also, median abundance variation is lower than in matching DIA datasets, while at the same time the process quantifies 25% more proteins. These results confirm that MS1-DDA is an optimal technology for LFQ in quantitative proteomics.
“DDA remains the gold-standard technology for deep and accurate proteomic investigations,” said Lopez-Ferrer. “Together the Thermo Scientific Q-Exactive HF Mass Spectrometer, DDA and Proteome Discoverer create a robust solution for a broad spectrum of applications in quantitative proteomics.”
Scientists from each of the three teams will be presenting at ASMS 2016 in a workshop titled Digital Proteome Maps: Label free Protein Quantification and DDA.
1. Zhang, B., et al. (2016) “DeMix-Q: Quantification-centered data processing workflow,” Molecular & Cellular Proteomics, 15(10) (pp. 1467–1478). mcp.O115.055475
2. Sharma, K. (2015) “Cell type—and brain region—resolved mouse brain proteome,” Nature Neuroscience, 18(12) (pp. 1819–1831).
Post Author: Heather Drugge. Heather has 20 years of experience writing about products and services for both the private and public sectors, including more than 15 years with high-tech and biotech companies. She specializes in science-based writing for B2B technology-based companies.