Integrating Mass Spectrometry and Affinity-Based Proteomics
The new era of plasma proteomics
Plasma proteomics holds immense promise for clinical applications of biomarkers, offering a minimally invasive way to identify and measure biomarkers. However, its full potential has been limited by the complexity of the plasma proteome, which spans over 10 orders of magnitude in concentration.
That is changing. Many are calling 2025 the “Year of Proteomics,” fueled by breakthroughs like the UK Biobank Pharma Proteomics Project (UKB-PPP), the largest proteomics study to date.
Mass spectrometry remains a cornerstone of proteomics, offering deep understanding of proteoforms, while affinity-based methods are growing in interest for their high sensitivity and throughput in complex biofluids like plasma and serum..
In a recent expert roundtable, leaders in proteomics explored how mass spectrometry and affinity-based methods can work together, weighing their strengths and limitations.
What are the advantages of combining mass spectrometry and affinity-based proteomics approaches?
Each method offers distinct advantages, and researchers are increasingly looking at synergistic approaches to maximize insights from proteomics studies.
Mass spectrometry provides detailed insights into protein forms, including post-translational modifications, isoforms, and degradation products. Affinity-based methods like Olink target pre-defined proteins with specific antibodies, capturing even low-abundance proteins in their native conformation as they circulate in the body. Differences in sensitivity, specificity, and detection contribute to the unique biological insights each method provides.
Combining the two approaches enhances biological insights by leveraging the depth of protein characterization from mass spectrometry and the sensitivity of affinity-based methods to low-abundance proteins, ultimately increasing actionable information.
Mass spec is great! It gives you a lot of information, but then there are certain areas of the plasma proteome where we can’t really do much with mass spec, such as cytokines, chemokines, and that’s where we turn to affinity-based platforms like Olink.
Why is there often a limited overlap in proteins identified between mass spectrometry and affinity-based methods?
The limited overlap between proteins identified by mass spectrometry and affinity-based methods stems from fundamental differences in their detection principles and the inherent biological complexities of the proteome. Each technique has distinct strengths that influence the subset of proteins it detects.
Mass spectrometry analyzes fragmented peptides, with detection influenced by protein abundance, digestion efficiency, and ionization properties. While highly abundant proteins can overshadow low-abundance targets, mass spectrometry excels at characterizing post-translational modifications and proteoforms.
Affinity-based methods, such as Olink’s PEA, detect proteins in their native conformation using specific binding reagents. This approach excels in detecting low-abundance proteins in complex biofluids like plasma but is inherently limited by the library size..
Since these technologies operate on different principles, they often identify non-overlapping sets of proteins. However, rather than being a limitation, this is a strength, as it provides a more complete and biologically relevant view of the plasma proteome.
I think it's actually a very interesting fact that there is a limiting overlap between these two platforms. You really have complementary data, not redundant data. You can expand your horizon of the plasma proteome. The reason why there is such a low overlap is that these are really two different methods.
The non overlapping nature is great because at the end of the day we end up with more data that we can query to identify the best panel of features to, for instance, differentiate disease from non disease.
What are the biggest challenges in proteomics today?
The key challenge for the field is to demonstrate the value of proteomics to broader scientific communities and integrate proteomics data with other omics for a more comprehensive understanding of biological systems. Additionally, a crucial shift is needed from simple “protein list” comparisons to more biologically meaningful analyses. Technical challenges also remain, including standardizing workflows for reproducible quantitative data, enhancing data analysis pipelines to handle large datasets, and improving accessibility to proteomics technologies.
Any sample that we have should be used for omics - transcriptomics, metabolomics. I think this is a great opportunity to work more closely together with the genomics and genetics core labs and to get as much as possible out of samples.
I think it boils down in a way to a standardization. How reliable, repeatable, transferable are these quantitative values. What standards are we using to do these comparisons and quantification.
Conclusion
The mass spectrometry community and core proteomics labs are increasingly looking to combine affinity-based proteomics with mass spectrometry to maximize biological insights. The key takeaway is that there is no single best method, each offers unique advantages. Mass spectrometry provides unmatched depth in protein characterization, while affinity-based proteomics delivers high sensitivity with uncompromised throughput. The future of proteomics lies in synergistic strategies that integrate multiple technologies, advanced bioinformatics, and multi-omics approaches to accelerate biomarker discovery, clinical applications, and translational research.
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