Using Mass Spectrometry for Clinical Proteomics and Biomarker Development

By Vijay Kumar MalesuReviewed by Lauren Hardaker

Introduction
Clinical Proteomics Technologies
Recent Advances Accelerating Clinical Translation 
Study Design and Data Analysis 
Reproducibility and Regulatory Expectations 
Clinical Applications
Future Directions 
References

Clinical proteomics enables large-scale protein measurement to identify and validate biomarkers that reflect dynamic disease biology and improve diagnosis, prognosis, and treatment monitoring. It integrates advanced mass spectrometry, computational analysis, and rigorous study design to translate proteomic data into clinically actionable insights.

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Introduction

Proteins are the functional products of gene expression and more closely reflect cell physiology, pathophysiology, and pharmacological effects than genes or ribonucleic acid (RNA). Proteins also mediate cellular processes through complex interaction networks that regulate signaling pathways and disease mechanisms. Changes in protein abundance and protein–protein interactions capture dynamic biological states that are not evident from genomic data alone. Advances in mass spectrometry-based proteomics now enable large-scale, quantitative analyses of complex proteomes, directly connecting clinical proteomics with both discovery biology and diagnostic development.^1,2

This article examines how advances in clinical proteomics enable the discovery and use of biomarkers, including the technologies and regulatory challenges involved.

Clinical Proteomics Technologies

Clinical proteomics primarily uses mass spectrometry and affinity-based platforms to discover, validate, and translate protein biomarkers.

Mass spectrometry can rapidly identify and quantify large numbers of proteins across diverse biological matrices, making it central to both discovery and validation. Typically, proteins are enzymatically digested into peptides, separated by liquid chromatography, and analyzed by tandem mass spectrometry (LC-MS/MS), where peptide mass-to-charge ratios and fragmentation spectra enable protein identification and quantification.² New targeted mass spectrometry methods now allow accurate, reproducible quantification of predefined proteins during validation.

Targeted proteomics techniques such as multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) provide highly specific and reproducible quantification of selected proteins, making them essential for biomarker verification and validation.²

Affinity-based approaches (for example, antibody- or aptamer-based assays) enable high-throughput protein quantification and are particularly suitable for large clinical cohorts. Using mass spectrometry and affinity-based platforms in complementary ways at different stages of the biomarker pipeline improves analytical accuracy and translational efficiency, facilitating a smoother transition from discovery to clinical validation.²

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Recent Advances Accelerating Clinical Translation 

Recent innovations in mass spectrometry have enabled more effective approaches to developing protein biomarkers for clinical use.

The integration of ion mobility spectrometry with improved fragmentation techniques has increased sensitivity and proteome coverage, facilitating the identification of target proteins in complex clinical samples. Technologies such as trapped ion mobility spectrometry (TIMS) and parallel accumulation–serial fragmentation (PASEF) enable high-speed, high-sensitivity peptide sequencing. Data-independent acquisition strategies have also improved the reproducibility and statistical robustness of quantitative data across large patient cohorts.²

In addition to data-independent acquisition (DIA), traditional data-dependent acquisition (DDA) approaches are widely used in discovery proteomics, although DIA offers improved reproducibility and reduced sampling bias in large-scale studies.²

These analytical improvements are supported by advances in computational tools and software that enhance data quality, biomarker prioritization, and throughput for large-scale studies. Standardized, automated sample preparation protocols for biospecimens (for example, biofluids and formalin-fixed tissues) are making it increasingly feasible to implement proteomic analyses routinely in clinical laboratories.²

Study Design and Data Analysis 

Appropriate study design and data analysis are critical for turning proteomic measurements into clinically credible biomarkers. Larger, longitudinal cohorts with detailed clinical annotation increase statistical power, reduce noise, and enable tracking of molecular changes within individuals. Longitudinal study designs help distinguish transient fluctuations from disease-related alterations. Robust clinical proteomics requires strict protocols and quality control to manage variability from sample collection, storage, handling, and batch effects.³ 

Longitudinal omics data are characterized by repeated measurements over time, requiring statistical models that account for within-subject correlation and temporal dynamics. High-dimensional proteomic datasets require specialized statistical approaches. Mixed-effect models can account for repeated measures and subject-level variability, while regularization methods help stabilize model estimates. Machine learning is frequently used to select biomarker panels and build classification models, but it must be paired with appropriate cross-validation and independent test sets to limit overfitting. Control of false discovery rates through rigorous design and sensitivity analyses is essential to ensure the clinical reliability of proteomics-derived biomarkers.³

Reproducibility and Regulatory Expectations 

The routine clinical use of proteomic biomarkers is limited by insufficient standardization across the analytical workflow. To reach clinical testing standards, clinical proteomics must adopt robust quality control and assurance strategies, including the sustained use of internal standards, assay performance benchmarking, and transparent reporting of analytical characteristics. The transition from research-grade assays to clinical tests typically involves moving from discovery workflows to multiple, highly targeted methods. Although mass spectrometry-based assays offer high analytical specificity, they must demonstrate validated performance, reproducibility, and clinical utility.^1,2

The biomarker development pipeline typically progresses through discovery, candidate prioritization, verification, validation, and clinical implementation, each requiring increasing levels of analytical rigor and cohort size. Only a limited number of proteomics-based biomarkers are currently approved for clinical practice, underlining the need for further methodological harmonization and alignment with regulatory expectations.^1,2

Clinical Applications

Clinical proteomics directly measures specific proteins at high volume and helps treat diseases. Proteomics is useful for early cancer detection and risk tracking, and for predicting outcomes and monitoring therapy response. Protein signatures linked to tumor subtype, metastatic potential, drug resistance and treatment response have been identified by large scale proteomic profiling to supplement genomic and transcriptomic studies, which seem to be incapable of representing functional disease states.²

In cardiometabolic disorders, proteomics has improved risk prediction beyond traditional clinical markers by identifying proteins linked to metabolic dysregulation, inflammation, and cardiovascular pathology, including applications in newborn screening and inherited metabolic diseases. Proteomic methods are also useful in neurological and inflammatory diseases, especially for analyzing cerebral fluid and blood-based protein profiles to classify the disease, evaluate its progression, and assess therapies. Mass spectrometry-based proteomics is already applied in clinical microbiology, toxicology, metabolic screening, and therapeutic drug monitoring. It is also noted that few proteomics-based biomarkers are used in clinical practice due to challenges with validation, standardization, and regulation. To succeed, these biomarkers need well-designed studies, consistent methods, and large-scale validation.²

Future Directions 

Broader adoption of clinical proteomics is currently constrained by technology costs and limited standardization and automation across workflows. Future efforts aim to increase access by simplifying and miniaturizing platforms, developing user-friendly and uniform protocols, and integrating proteomics with other biological and clinical data sources. The application of advanced analytical approaches in longitudinal and multi-omic studies is expected to improve the precision of biomarker evaluation and interpretation.^1,2,3

Although mass spectrometry-based proteomics has already been applied to clinical models in some instances, most proteomics-based biomarkers are still in the validation phase. Adopting these new clinical methods need time and it depends on fixing methods, following regulations and proving clear value in precision medicine.^1,2,3

References

1. Chiou, S. H., & Wu, C. Y. (2011). Clinical proteomics: current status, challenges, and future perspectives. The Kaohsiung journal of medical sciences. 27(1). 1-14. DOI:10.1016/j.kjms.2010.12.001, https://www.sciencedirect.com/science/article/pii/S1607551X10002695

2. Birhanu, A. G. (2023). Mass spectrometry-based proteomics as an emerging tool in clinical laboratories. Clinical proteomics. 20(1). DOI:10.1186/s12014-023-09424-x, https://link.springer.com/article/10.1186/s12014-023-09424-x

3. Taheriyoun, A. R., Ross, A., Safikhani, A., Soudbakhsh, D., & Rahnavard, A. (2026). Longitudinal omics data analysis: approaches and applications. Computational and Structural Biotechnology Journal. 31. 302-315. DOI:10.1016/j.csbj.2026.01.001, https://www.sciencedirect.com/science/article/pii/S2001037026000012

Last Updated: Mar 19, 2026