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The Translational Proteomics Workflows Driving Biomarker Research beyond Discovery
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The field of proteomics has advanced considerably as technologies and workflows have steadily matured. Advances in liquid chromatography-mass spectrometry (LC-MS), now capable of high-throughput analysis and deep proteomic coverage, have increased the number of potential protein biomarkers identified in discovery studies. However, a significant translational gap exists between research activity and clinical practice: while the number of candidate biomarkers continues to rise, the number of FDA-approved diagnostic markers remains relatively low.1 Fortunately, the latest translational proteomics workflows and LC-MS technologies are helping to overcome the bottlenecks in the biomarker pipeline.
Label-Free DDA Biomarker Verification
LC-MS workflows are used across the discovery, verification and validation stages of the biomarker pipeline. But while success criteria for discovery efforts often tend towards proteome coverage and measurement sensitivity, these requirements evolve as the analytical focus shifts from identification to robust quantitation. As sample numbers grow to support the larger studies used to assess statistical significance and clinical utility, factors such as throughput, reproducibility, and scalability become increasingly important.
Label-free data-dependent acquisition (DDA) methods are commonplace for larger discovery and targeted verification stage workflows. These workflows, based on tandem MS analysis of the most abundant precursor ions, seek to minimize redundant peptide precursor selection and maximize proteome coverage. However, despite the widespread adoption of these workflows, the precision and reproducibility of conventional DDA methods have been problematic. For large-scale proteomics studies requiring easily standardized and transferable quantitative methods, this presents a significant challenge.
Ongoing advances in LC-MS technologies, such as more precise capillary flow techniques and novel column technologies, are helping to achieve more consistent results. These design improvements are resulting in superior analytical sensitivity and significantly minimized mobile phase dead volumes, leading to more stable peak areas and enhanced measurement reproducibility.
In addition to improving sample separation, advances in high-resolution accurate mass are also increasing the run-to-run reproducibility of biomarker verification workflows. The precision, sensitivity and mass accuracy of the latest generation of Orbitrap™ mass spectrometers are enabling the delivery of more comprehensive peptide coverage and better quantitative data. In turn, these advances are enabling greater inter-run consistency, facilitating easier transfer of standardized methods between instruments. Combining next-generation chromatographic and MS performance, these ‘DDA-plus’ workflows are increasing the quantitative power of analytical runs.
High-Resolution DIA Targeted Quantitation
Offering improved sensitivity over DDA methods, label-free data-independent acquisition (DIA) workflows are gaining traction as an alternative approach for biomarker verification. DIA workflows are based on the analysis of all peptide fragments isolated within consecutive isolation windows. As each MS/MS spectrum records the fragment ions from all co-eluting peptides within the predefined m/z precursor window, DIA methods offer significant multiplexing capacity and proteome-wide quantitation.
Despite the potential of DIA workflows, issues around selectivity and dynamic range have limited their use. The wide isolation windows employed in conventional DIA experiments collect data on multiple co-isolates, resulting in highly complex spectra. Quantification of complex matrixes such as clinical plasma samples is, therefore, challenging due to the peptide diversity and naturally broad dynamic range of plasma proteins.
High-resolution DIA workflows based on hybrid quadrupole-Orbitrap MS technologies are addressing the twin challenges of selectivity and dynamic range and increasing the quality of large-scale proteomics data. The exceptional resolution offered by these instruments means that much narrower acquisition windows can be used, allowing for improved precursor selectivity, quantitative reproducibility, and precision.
PRM Biomarker Validation
At the validation stages of the biomarker pipeline, sensitive and specific protein quantification is required. Conventional MS approaches for biomarker validation have generally been based on selected reaction monitoring (SRM) methods utilizing triple quadrupole instruments. However, due to the need to select the most intense product ions, the development of methods capable of sensitive protein quantitation can be complex.2
Parallel reaction monitoring (PRM) is an alternative approach for biomarker validation that is underpinned by hybrid triple quadrupole-Orbitrap technologies. PRM workflows offer outstanding levels of selectivity, sensitivity, and throughput, but generally, require less extensive assay development than SRM approaches. Moreover, the exceptional resolution offered by Orbitrap analyzers also provides higher specificity, enabling the confident detection of low abundance peptide targets in complex biological matrices.
Conventional PRM methods have been limited by the retention time variation of peptide targets caused by fluctuations in temperature, solvent and column conditions between runs. Although these effects can be minimized using wider retention time windows, improving measurement consistency often means compromising on the number of targets that can be monitored simultaneously.
Direct retention time PRM (dRT-PRM) is a new generation of PRM workflow’s designed to overcome this challenge. By enabling real-time monitoring and adjustment of retention time windows, and using internal calibration peptides to produce easily-recognized standard signals, dRT-PRM workflows allow for recalibration of the retention time during analysis, offering a more reproducible method for targeted protein quantitation.
The latest generation of high-throughput LC-MS workflows is supporting proteomics research at the discovery stage and beyond. By addressing the evolving needs of the translational pipeline, these workflows are helping to accelerate biomarker development and opening up new opportunities for precision medicine.