Proteomics has become a major topic of discussion at the moment, as it addresses high-throughput, qualitative, and quantitative exploration of the proteome. In the next few minutes, we outline how proteomics can support biomarker discovery, briefly examine different types of biomarkers, and highlight technical solutions.
Molecular biomarkers can be identified at different biological levels. Alterations in the genome at the DNA level, for example, help to resolve heritable diseases and genetic predispositions. RNA biomarkers, on the other hand, provide a clearer picture by revealing which genes are actively expressed, thereby enabling the study of ongoing cellular processes. However, gene expression does not necessarily translate into functional proteins. Relying on genomic or transcriptomic data alone can therefore lead to the prioritization of targets that are not functionally relevant at the protein level, a key risk in drug development and translational research.
Addressing this gap requires approaches that capture functional changes at the protein level. At this point, the proteome provides additional insights into disease prognosis and is more responsive to minor changes during disease progression. Moreover, proteins are the key players that directly influence cellular pathways and processes (figure 1).
Figure 1 | The Potential of Proteomics in Biomarker Discovery.
Given that proteins represent the functionally relevant molecular layer, selecting an appropriate method for biomarker discovery in this layer is the next critical step. However, this task is more complicated than it might seem. The human genome encodes approximately 20,000 proteins, which can vary due to splicing variants and post-translational modifications. Some proteins have short half-lives and exhibit complex structures and compositions, making their characterization and quantification difficult.
In recent decades, various methods have been developed to identify individual protein biomarkers. However, diseases are dynamic processes, and depending on a single biomarker may not be sufficient. Integrating multiple proteins into a comprehensive protein signature could offer more accurate insights. Despite this potential, creating a cohesive approach to understanding complex protein signatures remains challenging. Additionally, disease-associated proteins often account for only a small fraction of total protein mass.
Therefore, methods are needed that can capture a broad range of proteins, be sensitive, and sufficiently specific for low-abundant proteins. To overcome these issues, two approaches are currently feasible: mass spectrometry (MS) and affinity-based technologies.
MS uses fragmentation and mass-to-charge ratios to identify single molecules. The fragmentation pattern and mass-to-charge ratios allow conclusions about the molecules present in a sample and the post-translational modifications they carry.
Affinity-based methods, on the other hand, use antibodies that specifically bind to proteins. An example of this is Olink®’s proximity extension assay (PEA)-technology (figure 2). To enhance specificity, this approach uses two antibodies per protein. Both antibodies are labeled through complementary oligonucleotide sequences. Only when both protein-specific antibodies bind do the oligonucleotides hybridize and are amplified by a DNA polymerase. The amplified sequences are then sequenced by Next-Generation Sequencing (NGS). Each sequence is specific to a protein, enabling sensitive and specific analysis of protein abundance.
The choice between MS and an affinity-based approach for biomarker discovery — and whether combining the two could be beneficial — depends on the specific research question. Both approaches have distinct strengths and weaknesses, as they target different parts of the proteome. Mass spectrometry is particularly valuable in exploratory settings, where identifying novel proteins or post-translational modifications is required. In contrast, affinity-based methods enable highly sensitive and reproducible profiling of predefined protein panels, making them well-suited for studies with larger cohorts or translational and clinical research applications.
Taken together, choosing an appropriate approach is a critical step in biomarker discovery. We would be happy to support you throughout this decision-making process and help you to integrate Olink®’s PEA technology into your biomarker discovery. Discover our Proteomics Service.
Figure 2 | PEA-Technology (Olink)