For decades, mass spectrometry has been the gold standard in proteomics because of its combination of specificity, sensitivity, high target plex, and lack of requirement for affinity reagents. Yet, its promise remains largely unrealized outside of specialized research labs. Nowhere is this more evident than in the analysis of the plasma proteome, where mass spectrometry has had to sit on the sidelines, reluctantly watching other, often inferior technologies (based on menu, proteoform specificity, analytical specificity, and cost per target) advance the field toward the long-sought goal of discovering protein-based diagnostic, prognostic, and therapeutic markers.
Whereas genomics and transcriptomics have flourished1 and integrated seamlessly into clinical and translational workflows, proteomics has struggled to keep pace.2
However, with recent innovations in ion utilization, fragmentation strategies, and data acquisition, mass spectrometry stands on the brink of unleashing a new era of proteomics, one where it reclaims its role as the definitive tool for protein-based biomarker discovery, clinical diagnostics, and beyond.
Mass spectrometry has undergone rapid and remarkable advancements over the past several decades, transforming from a niche analytical tool into a powerful engine for proteomic discovery. The evolution began with innovations like electrospray ionization (ESI)
and matrix-assisted laser desorption/ionization (MALDI), which enabled the efficient introduction of biomolecules into the mass spectrometer. Those techniques laid the foundation for modern proteomics by making it possible to analyze intact proteins and peptides with greater sensitivity and resolution.
The introduction of time-of-flight (TOF) and Orbitrap analyzers in the 1990s and early 2000s further enhanced mass accuracy and resolution, enabling high-confidence identification of a greater number of peptides per sample, driving increased demand for proteome profiling. Coupling these high-resolution analyzers with tandem mass spectrometry techniques provided deeper structural insights, while advancements in data acquisition strategies—such as data-independent acquisition (DIA) and parallel accumulation-serial fragmentation (PASEF)—have increased speed, coverage, and specificity.3
Most recently, ion mobility spectrometry has been integrated with mass spectrometry to add another dimension of separation, improving sensitivity and decreasing spectral complexity (see Figure 1). However, despite these advances, mass spectrometry-based proteomics workflows remain more time-consuming, labor-intensive, and variable (with respect to quantitation) within and across laboratories.
Mass spectrometry’s impact across proteomics
Mass spectrometry continues to drive significant advances in life science research and clinical applications. Notable technological improvements have given rise to exciting new areas of research, including:
• Single-Cell Proteomics: Mass spectrometry has enabled protein analysis at the single-cell level, providing insights into cellular heterogeneity and protein regulation. Techniques like SCoPE24 and scDVP5 have revolutionized our ability to study individual cells and their dynamic proteomes.
• Post-Translational Modifications (PTMs): Mass spectrometry is unmatched in detecting modifications like phosphorylation and glycosylation, crucial for understanding protein function and disease mechanisms.6
• Structural and Functional Proteomics: Beyond identification, mass spectrometry offers insights into protein folding, interactions, and stability—key aspects in drug development and biomolecular research.7
• Tissue and Cellular Proteomics: Mass spectrometry has been instrumental in studying different cellular states and tissue-specific proteomes, fueling advances across the landscape of life science research including infectious diseases, neuroscience, cardiovascular diseases, and oncology.8
Despite decades of refinement, mass spectrometry-based proteomics remains largely confined to research labs due to the complexity of sample preparation, instrument operation, and data interpretation.9 While genomics workflows have become increasingly automated and scalable, mass spectrometry-based proteomics still requires intricate workflows on expensive instrumentation with highly skilled operators, hindering its ability to make the same impact in clinical diagnostics, pharmaceutical development, and real-world applications.
Mass spectrometry also faces challenges in handling the vast dynamic range of abundance exhibited by proteins within biological systems. Without a PCR analog to amplify concentrations, low-abundance proteins, which often play critical roles in disease pathology and cellular function, are easily overshadowed by more abundant species.
Competitor technologies are advancing
Affinity-based approaches, such as aptamer and antibody arrays, have made significant strides in targeted protein detection. These methods offer high sensitivity and scalability, but their reliance on affinity reagent development and validation limits the rate of assay menu expansion. Current offerings for affinity-based approaches range from highly targeted with 10s of protein targets to the 11k SomaScan Assay measuring 10,000 unique human proteins.
Single-molecule detection techniques, such as nanopore and related direct sequencing technologies, promise to push proteomics into new territory, offering an alternative to mass spectrometry by directly sequencing proteins or measuring molecular interactions at an unprecedented scale. While still in their infancy, these approaches highlight the growing demand and interest for more accessible, high-throughput proteomic solutions.
At first glance, affinity-based approaches and mass spectrometry appear to be on equal ground; they yield comparable data quality and output in the context of discovery proteomics. Expansion of affinity-based assay menus (e.g., to 20,000 proteins plus proteoform specificity) will require large capital and time investments in reagent development and validation, but the next breakthroughs in mass spectrometry are already in sight. However, for mass spectrometry to reclaim its place at the forefront of biological discovery, fundamental shifts in its design and application are necessary.
• Expanding Accessibility: Current mass spectrometry platforms require expert users and extensive effort to operate. Future iterations must prioritize automation, streamlined workflows, reduced capital equipment costs, and user-friendly interfaces to broaden accessibility beyond core facilities.
• Boosting Speed and Throughput: Innovations in ion mobility and high-speed data acquisition are pushing mass spectrometry towards real-time proteomic analysis. There has been a noticeable, industry-wide shift toward faster QTOF architectures instead of the traditional, slower Orbitrap-based systems. By improving ion utilization and reducing redundancy, new approaches promise to increase efficiency without sacrificing depth.
• Merging DIA and DDA Advantages: Data-independent acquisition (DIA) offers unbiased broad coverage but generates complex datasets requiring sophisticated deconvolution. In contrast, data-dependent acquisition (DDA) provides high specificity but can miss low-abundance proteins. Integrating the strengths of both approaches into a single, high-performance workflow is critical.
• AI and Computational Advancements: The complexity of mass spectrometry data remains a major barrier to widespread adoption. AI-driven tools are now being developed to automate data analysis, improve confidence in protein identifications, and translate the raw data into biological insights.
Case study for the role of advanced ion utilization strategies
One major factor limiting the performance of conventional mass spectrometry approaches is poor ion utilization efficiency. Traditional fragmentation methods rely on quadrupole mass filters that discard the majority of available ions, dramatically decreasing sensitivity and proteome coverage. New methodologies are emerging to address this bottleneck by leveraging high-resolution ion mobility separation to pre-sort and isolate precursor ions before fragmentation. This approach enables significantly higher duty cycles, decreasing signal loss while maintaining spectral clarity.
One of the most promising advancements in this space integrates parallel accumulation strategies with mobility-aligned fragmentation, allowing for nearly complete ion utilization and enhanced fragmentation efficiency.10 By ensuring that nearly all generated ions contribute to meaningful data, this technique not only improves sensitivity but also increases the depth of proteome coverage.
More importantly, it has the potential to resolve long-standing trade-offs between DIA and DDA, capturing the broad information depth of DIA while preserving the specificity and clarity of DDA spectra (Figure 2).
Furthermore, these approaches also introduce fundamental changes to how data is generated and processed. The ability to fragment multiple precursor ions simultaneously at high speeds requires rethinking both instrument design and data processing workflows. This shift represents an opportunity to break free from legacy constraints and create a new paradigm in mass spectrometry-based proteomics—one that is faster, more efficient, and capable of truly unlocking the complexity of the proteome.
Realizing the promise of proteomics will require extending beyond simple protein expression studies focused on the relative concentration of proteins. Rather, the ultimate goal for maximizing biologically relevant insights is to unravel the true proteome where each canonical protein is a proteoform family on its own that encompasses PTMs, genetic variations, and alternative splicing isoforms for each expressed gene.11
As a result, the field of proteomics will fail to realize its full potential if it fails to fully integrate mass spectrometry into plasma proteomics to go beyond the limitations of affinity-based discovery workflows. Without mass spectrometry, proteomics risks falling behind in:
• Biomarker Discovery: Plasma is one of the richest sources for biomarkers, reflecting systemic and organ-specific disease states. Ignoring this resource would hinder disease prediction and diagnostics.
• Non-Invasive Diagnostics: Plasma sampling is minimally invasive, making it ideal for routine and longitudinal studies. In areas where affinity-based technologies are limited (e.g., PTM and proteoform specificity), increased reliance on more invasive and less practical methods will be required.
• Personalized Medicine: Protein biomarkers derived from plasma play a crucial role in early disease detection and treatment monitoring. Excluding mass spectrometry from this space would stall progress in tailoring therapies to individuals.
• Translational Research: Plasma proteomics is a direct bridge between laboratory research and clinical applications. Neglecting mass spectrometry in this area would slow the development of FDA-approved diagnostic tests and novel therapeutics.
• Systemic Disease Monitoring: Many conditions, such as autoimmune disorders and metabolic syndromes, require plasma analysis for a full understanding of disease progression.
The time is now for mass spectrometry to evolve, adapt, and lead the charge in unlocking the full potential of the proteome to improve human health.
Daniel DeBord, PhD, is CTO and Frederick G. Strathmann, PhD, serves as SVP global business, MOBILion Systems ([email protected]). Philip L. Lorenzi, PhD, is director of the metabolomics core facility and associate professor in the department of bioinformatics and computational biology, the University of Texas MD Anderson Cancer Center.
References
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