Increasing Clinical Usability of Protein Identification

Although conventional mass spectrometry (MS) protocols allow for large-scale protein analysis and thorough interrogation of the genome, a full MS scan with a long chromatography gradient comes with inherent constraints on both time and reproducibility. Recently, Ding et al. (2013) developed a fast sequencing workflow that may allow researchers to cut the average scan time by up to 83 percent. The researchers hope this expedited workflow strategy will impact the efficiency with which research teams can offer clinically relevant biomedical information to physicians and patients.

Ding et al. cite two current liquid chromatography–mass spectrometry (LC-MS) approaches to reduce peptide complexity during deep proteome analysis: a single run with a long chromatography gradient and a sequential run with two-phase separation. They point to previous studies, one that identified a relatively large number of proteins (2,761 and 4,500) using a 10-hour chromatography gradient on an LTQ Orbitrap Velos (Thermo Scientific) and another that identified 3,734 proteins using an 8-hour gradient on a 2-meter column. In terms of the two-dimensional approach, previous researchers have achieved deep proteome coverage with very long running times. For example, in excess of 10,000 proteins were previously identified with a two- to three-week running time. The researchers assert that the most contemporary machines can now identify 7,000 to 8,500 proteins (7,000 to 8,000 gene products) over the course of three days; however, even that level of efficiency comes with a price. In addition to time constraints, Ding et al. point to issues with reproducibility that may plague MS approaches that utilize a long column and long gradient.

In order to address the need for a streamlined approach to deep proteome MS, the researchers utilized an innovative strategy of dual-short, two-dimensional reverse-phase LC-MS/MS to identify 8,586 proteins (8,406 gene products) within a 24-hour time frame. Then the researchers turned to three separate instruments for comparison purposes: the LTQ Orbitrap Velos, the QExactive (Thermo Scientific) and a hybrid Q-TOF instrument. Using parallel reactions on these three machines, Ding et al. identified 6,763, 8,154 and 8,145 proteins (6,683, 8,032 and 8,031 gene products, respectively) within a 12-hour run time. The team also utilized fraction pooling to further reduce running time and identify 6,000 gene products within six hours on all three instruments. Overall, the researchers determined that the ideal sequencing time is 12 hours for both protein identification and quantification. Applying stringent standards to a re-analyzed data set on the QExactive, they identified greater than 7,600 proteins, with a less than 1 percent false discovery rate. They also identified 73,680 unique peptides on the QExactive, indicating 21.26 percent median sequence coverage, and slightly lower coverage levels on the hybrid instrument. Additionally, the research team used an optimized fast-sequence quantification cycle for protein identification of human umbilical vein endothelial cells treated with a drug candidate currently in clinical trials. They were able to efficiently quantify greater than 6,700 gene products and 99 upregulated proteins with extremely high confidence.

Ding et al. offer the scientific community a peek into the existing possibilities for pushing current technology, in terms of efficiency and accuracy, to its fullest capacity. The opportunity to utilize a streamlined workflow to achieve efficient deep proteome coverage may help researchers further incorporate proteome-based research into clinically relevant applications and better meet the needs of researchers, physicians and patients.

Reference

Ding, C. et al. (2013) “A Fast Workflow for Identification and Quantification of Proteomes,” Molecular & Cellular Proteomics, doi: 10.1074/mcp.O112.025023.

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