A landmark review published in the Annual Review of Analytical Chemistry synthesizes 137 scientific papers to assess the current state and future potential of proteomics. Researchers at the University of Washington, Seattle, conclude that emerging technologies are reshaping the field — moving it beyond bulk measurements toward high-resolution, single-molecule analysis.
What Is Proteomics and Why Does It Matter?
For decades, biological research focused primarily on the genome. DNA was the main subject of study, largely because scientists could amplify and sequence it with relative ease. However, that genomic focus has an important limitation: DNA does not fully capture how a living cell actually functions.
The proteome — a complete snapshot of all proteins expressed in a cell — offers a far more direct window into biological function. Proteins carry out nearly every process in the body. They drive metabolism, signal between cells, and regulate disease. Consequently, understanding protein diversity is critical to advancing medicine.
Proteins are structurally complex. Unlike DNA, which consists of just four building blocks, proteins are made from 20 chemically diverse amino acids. Furthermore, proteins cannot be amplified the way DNA can, making their analysis significantly harder. These challenges have long limited the depth and accuracy of proteomic research.
Why Mass Spectrometry Falls Short
Mass spectrometry (MS) remains the gold standard in proteomics today. It identifies molecules based on their mass-to-charge ratio. Despite its widespread use, MS struggles in one critical area: capturing the full diversity of proteoforms.
A proteoform is any distinct version of a protein that can arise from a single gene. These variations emerge through processes such as:
- Alternative splicing — different combinations of gene segments during protein production
- Proteolysis — enzymatic cleavage of a protein after it is made
- Posttranslational modifications (PTMs) — chemical changes to a protein after synthesis, such as phosphorylation
Research over the past two decades confirms that a single gene can produce countless biologically relevant proteoforms. Understanding these variations is essential for studying diseases like Alzheimer’s and cancer. Yet traditional MS tools often miss low-abundance proteoforms or fail to provide full sequence coverage. These gaps drive the urgent need for next-generation protein sequencing at the single-molecule level.
The Rise of Next-Generation Proteomic Platforms
The review focuses on two major technology groups that have recently reached or are nearing commercialization. Both offer a far more sensitive look at the molecular machinery of life than MS currently provides.
Fluorescence-Based Methods
Fluorescence-based proteomics uses light-emitting tags to improve amino acid identification. Several platforms stand out in this space.
Erisyon’s fluorosequencing labels specific amino acid residues, then removes them one by one through a process called Edman degradation. As each residue is stripped away, a fluorescent signal identifies it. Notably, modeling studies show that labeling just four amino acids — lysine, aspartate/glutamate, tyrosine, and tryptophan — could uniquely identify approximately 95% of proteins in the human proteome. Additionally, proof-of-concept experiments have already shown the ability to identify proteins from zeptomole-scale mixtures, an extraordinarily small quantity.
Quantum-Si uses a semiconductor chip to monitor how “recognizer” molecules bind to the end of a protein. Their “Platinum” system, released in 2022, was the first commercial entry into this space. By late 2025, it could recognize approximately 14 amino acids, enabling coverage of around 83.5% of the human proteome.
Nautilus Biotechnology’s IMaP (Iterative Mapping of Proteoforms) assay uses iterative binding patterns to quantify proteoforms at the single-molecule level. Impressively, it has already identified 130 distinct proteoform groups of tau — a protein linked to neurodegenerative disease — revealing complex phosphorylation patterns that bulk-sample methods would struggle to detect.
Nanopore-Based Methods
Nanopore proteomics threads a linearized protein through a nanometer-sized hole. As the protein passes through, it disrupts an electric current in a sequence-specific manner. Scientists use either inorganic “solid-state” pores or biological “pore proteins” embedded in a membrane to achieve this.
Some methods incorporate molecular motors to regulate the speed at which the protein passes through the pore. For example, the unfoldase enzyme ClpX pulls the protein at a controlled rate. This matters because accuracy is strongly linked to read speed.
Recent “rereading” advances illustrate how significantly accuracy improves with multiple passes. In one study, a single pass through a nanopore correctly classified amino acids just 28% of the time. However, 10 rereads of the same molecule raised accuracy to 61%. Furthermore, functionalized pores using nickel ions (Ni²⁺) have demonstrated the sensitivity needed to distinguish all 20 proteinogenic amino acids — a major technical milestone.
Key Milestones from Emerging Technologies
Together, these platforms represent a significant leap forward. Below are the highlights from the review’s findings.
- Quantum-Si Platinum (2022): First commercial single-molecule protein sequencing platform; now covers ~83.5% of the human proteome.
- Erisyon fluorosequencing: Four-amino-acid labeling strategy theoretically sufficient to identify ~95% of human proteome proteins.
- Nautilus IMaP: Identified 130 tau proteoform groups, revealing phosphorylation complexity invisible to bulk MS.
- Nanopore rereading: Ten-pass rereading improves amino acid classification from 28% to 61% accuracy.
- Ni²⁺ functionalized pores: Achieved sensitivity to distinguish all 20 amino acids.
The Future of Single-Molecule Proteomics
The review concludes that next-generation single-molecule proteomics approaches are nearing commercial maturity. Moreover, they are not replacements for mass spectrometry — they are powerful complements to it. Together, these technologies offer a richer, more complete picture of the proteome.
One practical challenge remains: applying specialized protein-movement-facilitating tags at scale in large-scale proteomics workflows. Nevertheless, the field is advancing rapidly. These tools are poised to become standard instruments in biological research, offering a high-definition view of the molecular variations that drive human health and disease.
In short, next-generation proteomics is not just improving protein analysis — it is rewriting the rules of what biological discovery can look like.
