How Mass Spectrometry is Accelerating Quantitative Proteomics

July 20, 2020

Contributed Commentary by Daniel Lopez-Ferrer

July 20, 2020 | Mass spectrometry (MS) has transitioned from being used mostly in routine analytical applications to becoming a powerful biological method for protein quantitation. Protein analysis using MS leverages its high-resolution accurate mass measurements coupled with an expansive database to identify and quantify diverse proteins in a sample. To fully comprehend the pathophysiology of diseases, researchers need a deeper understanding of the proteins involved, their expression levels and how they interact with one another. As projects scale-up in size and require more reproducible and accurate methods, biologists can maximize the potential of their research by including MS in their workflows.

Thinking Beyond Antibodies

When it comes to quantifying proteins, biologists tend to have a deep-rooted preference for antibody-based approaches, namely, Western blots and immunoassays. These methods are easy to perform, require minimal training and are accessible at a lower price point, resulting in widespread use across the biological community.

As the scope of a project expands, demanding faster turnarounds, higher throughput, and larger proteome depth, antibody-based methods quickly become the bottleneck in proteomics. Research projects that dive into the unknown, often involving poorly studied proteins, may need to bypass crucial experiments due to the lack of relevant antibodies. Here are a few of the limitations caused by antibody-based methods on the overall research output:

  • Cross-reactivity: Immunoassay and Western blot results rely heavily on antibody selectivity. A good quality antibody, if unavailable, is often difficult to generate.
  • Labor-intensive assays: With manual incubation and washing steps, each assay can take over 2-3 hours to complete, often requiring frequent monitoring.
  • Poor reproducibility: Subjective steps in the workflow, such as ‘overnight incubation,’ brings high intra- and inter-assay variability, making it challenging to reliably execute long-term projects.

Laboratories that take on large-scale, population-level projects or start using rare clinical samples for analysis will need more reliable methods. In these cases, the capabilities of immunoassays and immunoblots, unfortunately, don’t measure up.

MS-based Protein Quantitation Made Accessible

Protein quantitation with MS brings improved levels of selectivity with even low abundance proteins, expanding research possibilities in biology. The ability to apply automation from start to finish boosts throughput rates for sizable projects. Moreover, MS methods enable thousands of analytes to be measured in a single proteomics study. However, embracing a new analytical technique into biological laboratories isn’t without hesitation.

There is a common assumption that MS-based methods are difficult to implement due to complex workflows and the need for niche expertise. Previously, sample preparation steps in MS may have required detailed optimization when working with incompatible buffers or laboratory staff may not have had the required training to perform data analysis, but MS workflows are now becoming more accessible to biology laboratories. Taking these apprehensions and challenges into account, technology manufacturers have designed better systems to bring MS to biologists.

For instance, modern MS systems now have simplified, built-in settings developed for proteomics research. These turn-key MS workflows are equipped with already optimized steps from sample prep to data analysis, allowing biologists to tap into the immense potential of MS for protein quantitation without going through intensive training.

Finally, the biggest factor holding back laboratories is the high upfront investment on MS technology. Run by experts, core MS laboratories at universities and research institutes, equipped with the latest technologies, provide access to MS systems without the need for individual laboratories to make an investment. Researchers can seek advice from trained staff and perform a range of MS experiments at minimal costs.

The New Era of MS-Based Proteomics

MS-based methods have brought unique advantages to the field of proteomics, enabling scientists to achieve a deeper proteome coverage, study thousands of proteins in one experiment and accelerate biomarker research crucial for clinical applications.

  • Unbiased protein quantitation: Starting with a preconceived idea of which proteins will change in a diseased state can limit the scope of one’s hypothesis, generating biased research outcomes. An unbiased approach using label-free quantitative proteomics helps study complex diseases at a more systems-level, allowing researchers to identify ~10,000 proteins in individual samples. Labeling methods using isobaric tags have also become more routine in protein quantitation. Proteomic analysis of human cortical samples corresponding to different stages of Alzheimer’s disease used isobaric tags to quantify 6,533 proteins.
  • Large-scale protein-protein interaction studies: With unbiased protein screening using MS, coupled with protein enrichment techniques, biologists can examine endogenous protein-protein interactions to map intricate connections. In the Cancer Cell Map Initiative, protein-protein interactions identified in healthy and cancer cells alike were mapped to identify cancer-driving protein networks and pathways.
  • High-throughput biomarker discovery: Multiplexing capabilities in MS offered by tandem mass tags (TMT) enable high-throughput quantitative analysis. Requiring lower sample volumes, a multiplexed MS-based approach to identify biomarkers from clinical samples can speed up translational research. In 2017, a group of researchers at Harvard Medical School used TMT 10-plex to map the global landscape of protein networks in 41 different breast cancer cell lines. They discovered 14,909 protein-protein interactions predicting cancer vulnerabilities between the 6,911 proteins quantified.

MS has enabled researchers to fully explore the proteome at a level that was previously considered impossible. Even with minimal expertise, modern systems designed for intuitive use allow biologists to adopt MS and develop assays with ease. As the technology has continued to evolve to eliminate past pain points and save valuable time for researchers, it has revolutionized the way we now study proteins.

Daniel Lopez-Ferrer is senior manager, Proteomics Marketing, at Thermo Fisher Scientific. In his current role, Dani and his team are focused on the identification and development of new proteomics analytical tools and applications that bring broad benefits to the biosciences community. He has held positions as a Senior Scientist at Caprion Proteomics (CA, USA) and Pacific Northwest National Laboratory (WA, USA) developing technologies for high-throughput, large-scale proteomics projects. Dani has over 35 peer review papers and several patents. He can be reached at