What Role Can Top-Down Proteomics Play in Fighting Disease?

By Jenna Philpott, BScReviewed by Bethan Davies

How Top-Down Proteomics Works
Why Proteoforms Matter in Disease
Improving Biomarker Discovery with Proteoform-Level Insight
Enabling More Targeted Drug Development
Current Limitations of Top-Down Proteomics
Where Top-Down Proteomics is Headed
References and Further Reading

Top-down proteomics (TDP) is a mass spectrometry-based technique that analyzes intact proteins rather than breaking them into smaller peptide fragments. By studying whole proteins, researchers can detect distinct protein variants, known as proteoforms, which may influence disease development and how patients respond to therapy.

Proteins carry out most of the functional work in cells, but a single gene can produce multiple versions of the same protein. These variants are known as proteoforms and are generated through processes such as genetic polymorphisms, RNA splice variants, and post-translational modifications. These changes can alter protein structure and function, so they are often closely linked to how diseases develop and progress.

Understanding this diversity is essential for connecting genetic information with disease biology.

TDP helps to address this challenge by analyzing intact proteins using mass spectrometry, allowing researchers to directly identify proteoforms and their modifications. This method provides a more complete picture of protein variation compared with traditional peptide-based approaches, and is increasingly being used to study disease mechanisms, identify potential biomarkers, and support drug development.¹

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How Top-Down Proteomics Works

To see why TDP is so valuable, it helps to look more closely at how the technique works. TDP is built around top-down mass spectrometry, a technique that analyzes intact proteins without breaking them down through enzymatic digestion. In a typical experiment, the mass of the whole protein is measured first. The protein is then fragmented in the gas phase, producing pieces that provide sequence information. By analyzing these fragments, researchers can identify the protein and determine where any modifications are located.¹

This approach differs from bottom-up proteomics, which is currently the most widely used method in proteomics. In bottom-up workflows, proteins are digested into smaller peptides before mass spectrometry analysis.

While this approach is technically well established and easier to implement, it introduces a well-known limitation called protein inference. Because only a portion of the peptides from a protein are typically detected, it can be difficult to reconstruct the full protein sequence or determine which modifications occur together on the same molecule.²

TDP addresses this limitation by analyzing proteins in their intact form. Measuring the entire protein enables identification of the specific combination of sequence variants and post-translational modifications present in a single molecule. This allows researchers to study proteins at the proteoform level, providing a more detailed view of protein structure, function, and variation.¹

Why Proteoforms Matter in Disease

This ability to resolve proteins at the proteoform level is particularly important when studying disease. Many conditions are linked not only to changes in protein abundance but also to changes in the specific forms a protein takes. These proteoforms may differ in sequence or carry distinct chemical modifications that influence how the protein functions in cells.

Because TDP preserves this information within intact proteins, it enables direct investigation of these differences. Researchers can better understand how specific proteoforms contribute to biological processes and disease progression.

A well-known example is Alzheimer’s disease. Different proteoforms of amyloid beta (Aβ) peptides are produced from the amyloid precursor protein, but only certain forms, such as Aβ1-42, are strongly associated with the disease. This highlights why measuring total protein levels is often not enough; identifying specific proteoforms can provide more meaningful insight into disease mechanisms.³

By capturing this level of detail, TDP helps researchers move beyond measuring protein abundance and toward understanding the molecular variations that drive disease.

Improving Biomarker Discovery with Proteoform-Level Insight

Building on this deeper understanding of disease biology, TDP is also strengthening biomarker discovery and diagnostics. Mass spectrometry-based proteomics is widely used to discover biomarkers by comparing protein levels or post-translational modifications between healthy and diseased samples. However, focusing only on overall protein abundance can overlook important differences between specific proteoforms, which may be more closely linked to disease.

By analyzing intact proteins, TDP enables the detection of distinct proteoforms and their modifications. These patterns can serve as molecular fingerprints, improving the precision of biomarker discovery.

For example, studies in heart disease have shown that different proteoforms of cardiac troponin I (cTnI) can be detected directly in human serum using top-down mass spectrometry. Variations in phosphorylation of these proteoforms have been linked to heart failure and hypertrophic cardiomyopathy, demonstrating how proteoform-level analysis can reveal disease-associated signatures that support more precise diagnostics.⁴

Enabling More Targeted Drug Development

The same level of molecular detail also has important implications for drug discovery. Many proteins function as part of larger complexes, and understanding which proteoforms are involved can help refine therapeutic targets.

Using a native TDP approach, researchers have studied protein complexes in breast cancer cells that overexpress epidermal growth factor receptor (EGFR), a model for resistance to some hormone therapies. This work identified over 100 distinct complexoforms (protein complexes formed from specific proteoforms) and showed that EGFR signaling can disrupt assemblies of nuclear transport factor 2 (NUTF2), which helps regulate estrogen receptor activity.⁵

These findings show how proteoform-level analysis can reveal molecular mechanisms linked to cancer growth and drug resistance, providing insights that may support the development of more targeted therapies.

Current Limitations of Top-Down Proteomics

While these applications highlight the strengths of TDP, several technical challenges still limit its broader use. One key limitation is analytical sensitivity. Top-down workflows often require relatively large amounts of protein to generate reliable signals, which can make studies involving small or limited samples, such as clinical specimens, more difficult.

Another challenge is the analysis of larger proteins. High-molecular-weight proteoforms are harder to separate, detect, and fragment, leading to complex spectra that require advanced instrumentation and data processing.

In addition, data analysis and throughput present ongoing barriers. Top-down experiments generate complex datasets that demand time-intensive computational workflows. Although advances in instrumentation and software are helping address these issues, further improvements are needed to support wider adoption.¹

Where Top-Down Proteomics is Headed

Despite these challenges, continued innovation is steadily expanding what TDP can achieve. Improvements in protein separation and mass spectrometry are enabling the analysis of more complex samples and larger proteins.

Progress in bioinformatics is equally important, as accurate interpretation of top-down data depends on specialized analytical tools. Integrating TDP with genomic and transcriptomic data is also helping to build a more complete picture of protein regulation.

Looking ahead, emerging areas such as single-cell and spatial proteomics may further extend the reach of top-down approaches, opening new opportunities to study proteoforms in greater biological detail.¹

References and Further Reading

1. Roberts, D.S., Loo, J.A., Tsybin, Y.O., Liu, X., Wu, S., Chamot-Rooke, J., Agar, J.N., Paš̌a-Tolić, L., Smith, L.M. and Ge, Y. (2024) Top-down proteomics. Nature Reviews Methods Primers, 4(1), p. 38. DOI:10.1038/s43586-024-00318-2, https://doi.org/10.1038/s43586-024-00318-2.
2. Miller, R.M. and Smith, L.M. (2023). Overview and considerations in bottom-up proteomics. The Analyst. DOI:10.1039/d2an01246d, https://doi.org/10.1039/d2an01246d
3. Kaulich, P.T. and Tholey, A. (2025). Top‐Down Proteomics: Why and When? PROTEOMICS, pp.e202400338–e202400338. DOI:10.1002/pmic.202400338, https://doi.org/10.1002/pmic.202400338.
4. Brown, K.A., Melby, J.A., Roberts, D.S. and Ge, Y. (2020). Top-down proteomics: challenges, innovations, and applications in basic and clinical research. Expert Review of Proteomics, 17(10), pp.719–733. DOI:10.1080/14789450.2020.1855982, https://doi.org/10.1080/14789450.2020.1855982.
5. Gomes, F.P., Durbin, K.R., Schauer, K., Nwachukwu, J.C., Kobylski, R.R., Njeri, J.W., Seath, C.P., Saviola, A.J., McClatchy, D.B., Diedrich, J.K., Garrett, P.T., Papa, A.B., Ianis Ciolacu, Kelleher, N.L., Nettles, K.W. and Yates, J.R. (2025). Native top-down proteomics enables discovery in endocrine-resistant breast cancer. Nature Chemical Biology. [online] DOI:10.1038/s41589-025-01866-8, https://doi.org/10.1038/s41589-025-01866-8.

Last Updated: Mar 26, 2026