Biomarker testing is undergoing a transformation, fueled by advances in proteomics that enable high-resolution, high-throughput analysis of protein expression and function. Once confined to exploratory research, proteomics now plays a central role in how we detect, classify, and monitor disease, from early diagnostics to tracking therapeutic response. Protein biomarkers, in particular, offer insight into dynamic biological processes that other molecular approaches may miss.
This article examines the evolving role of proteomics in biomarker testing within translational research. It highlights emerging sampling strategies, enabling technologies, data analysis workflows, and the regulatory landscape influencing clinical adoption.
Proteomics: The Analytical Core of Modern Biomarker Testing
Proteomics provides the analytical depth required for modern biomarker testing, enabling researchers to quantify protein biomarkers with high sensitivity, specificity, and throughput. The two primary proteomics strategies used in biomarker testing are targeted and discovery workflows:
- Targeted proteomics uses methods such as multiple or parallel reaction monitoring (MRM/PRM) to precisely quantify predefined protein biomarkers. These approaches are valuable for validation and routine clinical monitoring.
- Discovery proteomics employs liquid chromatography, coupled with tandem mass spectrometry (LC–MS/MS), to support unbiased profiling across thousands of proteins, helping researchers uncover novel biomarkers.
Within discovery proteomics, the mass spectrometer can operate in either data-dependent acquisition (DDA) or data-independent acquisition (DIA) mode:
- DDA selects and fragments the most abundant peptide ions detected in an initial mass scan. While this provides high-resolution spectra, it may overlook low-abundance proteins.
- DIA fragments all peptide ions within predefined mass-to-charge (m/z) windows, enabling comprehensive and reproducible proteome coverage.
A prominent implementation of DIA is sequential windowed acquisition of all theoretical fragment ion spectra (SWATH-MS), which captures fragment ion spectra across the full initial mass scan range, allowing for retrospective data mining. Together, these acquisition strategies underpin modern discovery workflows and complement targeted approaches.
Proteomics is also evolving in line with several technological trends:
- Single-cell and spatial proteomics offer resolution at the cellular level.
- Microfluidics reduces sample and reagent volumes, enabling miniaturized workflows.
- Machine learning enhances spectral prediction, protein quantification, and biomarker discovery.
These innovations are expanding the precision and scope of biomarker testing.
Comparison of Proteomics Workflows in Biomarker Testing
Proteomics Workflow | Use Case | Limitations |
Targeted (MRM/PRM) | Quantifying predefined protein biomarkers | Requires prior knowledge; limited multiplexing |
Discovery (DDA/DIA, SWATH-MS) | Unbiased identification and quantification across proteome | Complex data analysis; may need validation downstream |
Emerging workflows, including single-cell and spatial workflows | Enables analysis of proteins at cellular or subcellular resolution, useful for tumor microenvironment profiling and rare cell detection | Low throughput; still developing standard workflows |
To support these increasingly sophisticated workflows, a robust technological foundation is essential, spanning sample preparation, analytical instrumentation, and data interpretation.
Sampling Strategies in Proteomics-Based Biomarker Testing
Sampling is a foundational element in any proteomics-based biomarker testing workflow. The quality, type, and accessibility of samples directly affect downstream data integrity and clinical relevance. Broadly, sampling strategies in proteomics fall into several categories:
- Tissue biopsies: These invasive procedures provide rich proteomic detail and are often used in oncology and pathology. However, they can be impractical for routine or longitudinal sampling due to patient risk and procedural constraints.
- Liquid biopsy: This minimally invasive method uses fluids, including plasma, urine, saliva, and cerebrospinal fluid, to obtain protein biomarkers. It enables repeat sampling for longitudinal monitoring and extends testing to otherwise inaccessible tissues.
- Archived or formalin-fixed samples: These enable retrospective studies or population-scale biomarker discovery but may present technical challenges due to protein crosslinking or degradation.
- Dried blood spots (DBS): Convenient for remote or low-resource settings, DBS are increasingly used in proteomics but pose challenges related to protein recovery and quantitation.
- Cell culture supernatants and conditioned media: Valuable in discovery workflows or mechanistic studies where secreted proteins and extracellular vesicles are of interest.
- Microdialysates: Offer continuous sampling from interstitial fluid in real-time physiological monitoring, though limited in volume and protein concentration.
Each strategy has implications for sample preparation, detection sensitivity, and reproducibility. Selecting the appropriate sampling approach is essential to ensure data quality, minimize matrix effects, and enable the reliable detection of low-abundance protein biomarkers across diverse clinical and research settings.
Challenges and Clinical Readiness
As proteomics moves from research into clinical diagnostics, it must meet rigorous analytical and regulatory standards while overcoming persistent hurdles in reproducibility, standardization, and data complexity.
- Analytical performance: Assays must be accurate, sensitive, and reproducible, for example, meeting target coefficients of variation (CV%), and validated across a broad dynamic range with defined limits of quantification (LOQs).
- Regulatory compliance: Workflows must meet requirements from agencies such as the U.S. Food and Drug Administration (FDA), Clinical Laboratory Improvement Amendments (CLIA), and the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC-17025).
- Reproducibility and standardization: Variability across populations, platforms, and sample preparation methods remains a concern, highlighting the need for harmonized workflows.
- Regulatory clarity: Evolving guidelines create ambiguity around clinical validation and approval pathways.
- Data integrity and integration: Ensuring traceable, secure workflows is essential, particularly as integrated multi-omics approaches and machine learning introduce greater complexity.
Machine learning adds power to interpretation but increases the demand for robust infrastructure and interdisciplinary collaboration. Addressing these challenges is key to realizing the full potential of proteomics in precision diagnostics.
Conclusion: The Future of Biomarker Testing Lies in Proteomics
As biomarker testing evolves, proteomics is positioned to play a leading role in translating biological insight into clinical action. Its ability to reveal protein-level changes across diverse sample types and its compatibility with high-throughput, multiplexed assays make it a powerful tool for modern diagnostics.
The field’s success, however, will depend on how effectively it navigates the challenges of reproducibility, standardization, and regulatory clarity. With continued innovation, collaboration, and regulatory alignment, proteomics-based biomarker testing will continue to reshape diagnostics and bring precision medicine closer to routine practice.