Advances in proteomic technologies—used to analyze proteins in organisms, tissues, or cells—have the potential to significantly improve clinical diagnostics by revealing deeper insights into disease mechanisms, identifying potential drug targets, and supporting more precise diagnostic and treatment strategies.

Unlike with genomic or transcriptomic analyses, proteomics offers a means to measure activation states of cell signalling via post-translation modifications. This supports early disease detection and personalized therapies, with the potential to reduce overall health care costs and improve patient outcomes in areas such as cancer, neurodegenerative diseases and infectious diseases.

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In this article, we’ll explore the opportunities, challenges, and operational considerations for laboratories looking to integrate proteomics into clinical diagnostic workflows.

Considerations for laboratory diagnostic workflows

Integrating proteomics into clinical settings requires substantial investment in specialized instrumentation. Liquid chromatography (LC) coupled with mass spectrometry (MS) is the most widely used technique, often with two or more analyzers used in tandem.

While MS instruments can be expensive, they offer high sensitivity, specificity and detailed structural information. Analyzers such as time of flight (TOF) and Orbitrap have seen advancements in their capabilities, with improved reproducibility, throughput, simplicity, sensitivity, and cost-effectiveness. This, along with advanced software for assay development and analysis, has helped to accelerate proteomic diagnostics toward the clinic.

For MS-based tests to inform clinical decision-making, they must integrate well into clinical workflows. Instruments should be user-friendly, minimize hands-on time, and deliver results with rapid turnaround. Data outputs must be interpretable for clinicians, ensuring that findings can directly inform patient management decisions.

Managing vast amounts of data

One of the most significant challenges in clinical proteomics is data management, due to the vast amounts of complex data generated. Global repositories such as EMBL-EBI’s Proteome Identifications Database (PRIDE) and PeptideAtlas enable data sharing worldwide; however, there is an increasing amount of “human sensitive proteomics data” that cannot be openly shared due to legal and ethical considerations.

For a more comprehensive view of disease, data analysis can benefit from a multi-omics approach, incorporating data sets from other omics fields, such as genomics, transcriptomics or metabolomics. However, combining multi-omics data sets significantly increases the complexity of data management due to the vast quantities of data and varying data formats. Data from multiple modalities can also be integrated, for example, in spatial proteomics, where imaging and MS data are combined.  

Laboratories must employ highly skilled personnel, including proteomics specialists and bioinformaticians to manage this data analysis and interpretation. Artificial intelligence (AI) and machine learning (ML) play a supporting role and are becoming essential tools to extract meaningful patterns and translating them into actionable clinical information, particularly where imaging and proteomics data are combined.

Certifying your laboratory meets clinical standards

The lack of standardization in data formats, acquisition methods, and experimental designs between laboratories is a major challenge, for example, when comparing in-house data sets to those in publicly available databases. To enable the proteomics field to more closely integrate with clinical applications, robust controls and standardized practices are required, from sample collection and preparation, through to laboratory assays and AI-enabled analysis pipelines.¹

Laboratories must also conform to clinical laboratory standards, such as the Clinical Laboratory Improvement Amendments (CLIA) and The College of American Pathologists (CAP) Accreditation Program. To meet the standards for both, instruments and methods will need to meet defined metrics and conform to regulatory standards. There is also a requirement for laboratory staff to be enrolled in an accredited proficiency testing program, where blinded specimens are used to test that participants deliver accurate results.

Moving toward personalized medicine

Proteomics offers enormous potential for personalized medicine. Rapid screening of samples can enable early detection of disease and indicate severity, allowing for early intervention and improved patient outcomes.

Advancements in single-cell proteomics are enabling the analysis heterogenous samples by producing rich data sets and reducing the complexity of population-based proteomic approaches. This knowledge can inform the development of personalized therapy strategies by giving researchers a window into rare cell populations, such as cancer stem cells.

MS has become widely used for rapid and accurate pathogen identification for patients, with commercially available matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) instruments registered as medical devices for clinical use. This technique has also been extended to detect β-lactamase activity in bacteria—an important class of antimicrobial resistance.

MALDI-TOF MS has been used for the diagnosis and prognosis of ovarian, prostate and liver cancer, as well as osteosarcoma and multiple myeloma. MS has proven to be a great asset for discovery and characterization of biomarkers for clinical research and diagnostics. For Alzheimer’s disease pathologies, targeted MS has been used to characterize and validate a variety of biomarker classes. In breast cancer research, a multiple reaction monitoring-mass spectrometry (MRM-MS) assay has been developed to detect human epidermal growth factor receptor 2 (HER2) in tissue samples, which correlates to worse outcomes. The assay was found to be more accurate than current immunohistochemical methods, demonstrating how powerful the technique can be.

Opportunities in point-of-care diagnostics

The integration of proteomics and point-of-care (POC) testing offers opportunities to develop rapid and accessible diagnostics with the potential to reduce the time to diagnosis and intervene earlier in disease.

POC test devices commonly use lateral flow technology for protein determination, but these devices often only provide qualitative or semi-quantitative results. However, advances in miniaturized MS systems are enabling deeper, quantitative analyses at or near the POC, while AI and biomarker discovery are also helping to drive forward proteomics-based POC testing.

One example is a miniature MS–based POC test for quantification of metformin and sitagliptin in human blood and urine that has been developed to help patients manage type 2 diabetes mellitus.

Other examples include mail-in testing kits that emerged during the COVID-19 pandemic. These FDA-approved tests are based on RT-PCR as an initial amplification platform coupled with MALDI-TOF detection, eliminating the need for expensive fluorescent reagents.

The future of proteomics

Proteomics is helping to reshape clinical diagnostics, offering laboratories powerful new tools to improve patient care through early detection, precise disease characterization, and personalized treatment strategies.

While advances in sample treatment techniques, instrumentation, automation, and AI have contributed to the success of the field, challenges such as data management, standardization and regulatory requirements for clinical validation remain. 

For the full potential of proteomics to be harnessed, standardized protocols are needed to ensure results are robust and reproducible across different health care settings. Regulatory bodies must also adapt to validate new proteomic-based diagnostic tools and AI-driven analysis. Good instrument design can help to overcome these challenges, by ensuring high accuracy, simple operation and data outputs suitable for clinical use. 

Translating biomarker candidates into clinically approved diagnostics is a complex process, and the routine application of proteomics in the clinic is still in its early stages. As proteomics becomes a more central component of the modern laboratory, the field is set not only to advance precision medicine but to fundamentally shift how we approach prevention and treatment and the next generation of diagnostics.

References

1. Albrecht V, Müller-Reif J, Nordmann TM, et al. Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium. Mol. Cell. Proteom. 2024;23(12):100877. doi: 10.1016/j.mcpro.2024.100877.