The heart of the device is MspA, a porin from the soil bacterium Mycobacterium smegmatis that has already earned a storied reputation in DNA sequencing. MspA forms a stable, conical pore roughly one nanometre wide at its constriction, a scale at which a single small molecule can obstruct the flow of ions and produce a characteristic electrical signature. When a voltage is applied across a membrane containing the pore, analytes that wander into the aperture transiently block or modulate the ionic current, and the resulting blips carry fingerprints of the molecule’s size, shape, charge and chemistry.

The problem has always been versatility. Nanopores are exquisitely selective, which is a virtue for detecting one target molecule but a liability when the target is unknown. Different chemical classes interact with the pore environment in different ways: saccharides are neutral and hard to trap, amino acids span a dramatic range of charge and hydrophobicity, and nucleotides carry dense negative charges that make them rush through too quickly to be read. Earlier engineered pores succeeded with individual analyte classes—discriminating all twenty proteinogenic amino acids in one design, or distinguishing monosaccharides in another—but no single pore had tackled all of them at once.

The new work solves this by grafting a chemical adapter into the pore. The team modified MspA with a maleimido-C2-FPBA moiety, a benzaboronic acid derivative attached through a short linker to a defined site inside the pore lumen. Boronic acids are famous in chemical biology for forming reversible covalent complexes with cis-diols, the pairing of hydroxyl groups found abundantly on sugars. But the adapter does more than catch carbohydrates. Inspired by iminoboronate chemistry, the reversible interaction between boronic acids and nitrogen-containing functional groups allows the adapter to transiently capture amines as well, slowing the passage of amino acids and peptides so that their signals can be recorded.

The result is a pore that no longer lets any of these small molecules simply tumble through. Instead, each analyte is repeatedly captured, held and released at the adapter site, producing long trains of current fluctuations rather than a single fleeting blip. Amino acids produce residence events whose depths and durations reflect their side chains; nucleoside monophosphates generate distinct blockade patterns shaped by their base and phosphate; saccharides, bound through their diols, yield slow, stuttering signals; and peptides produce composite signatures reflecting both their terminal amines and their residue composition. Crucially, all four classes can be present in the same solution and still be told apart, because their event statistics cluster in separate regions of feature space.

Distinguishing those clusters is where machine learning enters. The researchers extracted features from thousands of individual events—mean and variance of the blockade current, dwell times, recurrence rates and higher-order statistics of the fluctuation patterns—and trained classifiers on labelled mixtures. Using a few-shot learning strategy, which requires only a small number of labelled examples per analyte, the system assigned unseen events to the correct analyte with 98.7% accuracy across the four chemical classes. The approach is notable for its data efficiency: instead of demanding enormous training sets, the engineered pore’s physically distinct binding chemistry produces such characteristic signals that a handful of reference measurements per molecule suffices.

The most striking demonstration involves glycopeptides, molecules that marry a peptide backbone to one or more covalently attached glycans. Glycopeptides are central to biology—most secreted and membrane proteins carry them—and their analysis is a major bottleneck in proteomics, typically requiring enzymatic release of the sugars and elaborate liquid chromatography tandem mass spectrometry workflows. Because the FPBA adapter engages the cis-diols of the glycan while the pore constriction senses the peptide, the sensor reads both parts of the molecule in one event. The team showed that the device could determine the compositional makeup of native, unlabelled glycopeptides, distinguishing variants that differ in their sugar content without any prior chemical derivatization.

Experts in bioanalytical chemistry will recognize how much engineering subtlety underlies this apparent simplicity. The site-specific attachment of the adapter required a genetically introduced handle in the MspA protein, and the linker length and chemistry had to be tuned so that analytes of wildly different sizes—from a single amino acid of around one hundred daltons to glycopeptides several times larger—could all access and interact with the boronic acid. The ionic current readout itself is straightforward, but converting raw fluctuation trains into reliable chemical identities demands rigorous control of pH, salt concentration and voltage, conditions the authors establish and characterize in the study.

The broader significance lies in what a practical four-class nanopore sensor could enable. A benchtop or even portable device that identifies metabolites, nucleotides, sugars and peptide fragments from a crude mixture would be transformative for point-of-care diagnostics, metabolic flux studies, quality control in biopharmaceutical manufacturing and the emerging field of glycomics. It also complements the rapid progress in nanopore peptide sequencing, where related engineered pores have recently been used to read stepwise-shortened peptides, and with nanopore single-molecule chemistry more generally, which now extends far beyond its nucleic-acid origins. A universal small-molecule reader, in other words, no longer seems fanciful.

Challenges remain before the technology migrates from research lab to routine use. Real biological samples contain hundreds of analytes, some present at vanishingly low concentrations, and the classifier’s performance on such dense, unbalanced mixtures will need to be demonstrated. Sensor lifetime, throughput and the standardization of pore fabrication will also matter for adoption. But the conceptual hurdle has been cleared: a single chemically versatile protein pore, read by a modest machine-learning model, can simultaneously name molecules from four different chemical worlds. As nanopore sensing matures, the humble ionic-current trace is quietly becoming one of the most information-dense signals in all of analytical science.

Subject of Research: An engineered MspA nanopore sensor that simultaneously identifies amino acids, nucleoside monophosphates, saccharides and peptides, including native glycopeptides, using machine-learning classification.

Article Title: A versatile nanopore identifies four classes of analytes simultaneously

Article References: A versatile nanopore identifies four classes of analytes simultaneously. (2026). Nature Biotechnology. https://doi.org/10.1038/s41587-026-03322-x

Image Credits: AI Generated

DOI: 10.1038/s41587-026-03322-x

Keywords: nanopore sensing, MspA, biosensor, machine learning, amino acids, saccharides, peptides, nucleoside monophosphates, glycopeptides, single-molecule detection, bioanalytical chemistry, chemical biology

The study, led by Mingqian Zhang, Ziyi Li, Lei Liu and Hai-Chen Wu of the Institute of Chemistry at the Chinese Academy of Sciences together with colleagues, reconstitutes the complete catecholamine biosynthetic pathway in a test tube using purified enzymes and their required cofactors. The researchers then coupled this miniature chemical factory to an engineered protein nanopore — a nanoscale pore embedded in a membrane through which an ionic current flows. Whenever a metabolite from the pathway interacts with the pore, it briefly perturbs that current, producing an electrical fingerprint specific to that molecule. By reading these fingerprints as they accumulate, the team could follow the transformation of each intermediate into the next, second by second.

The technical heart of the work lies in how the researchers achieved specificity. Small molecules are notoriously difficult for nanopores to distinguish because they zip through the pore too quickly and too subtly to leave clean, separable signals. The team solved this by integrating two orthogonal molecular recognition modalities within a single nanopore platform. In essence, different chemical strategies were used to make different members of the pathway register at the pore: some metabolites, such as the catecholamines themselves, generate characteristic blockade patterns as they translocate, while the amino acids phenylalanine and tyrosine were detected through a tailored probe chemistry involving a copper-coordinated molecular adaptor and a cucurbituril host that captures the amino acid and presents it to the pore as a distinct complex.

With this dual recognition scheme in place, the researchers demonstrated highly specific detection of all six key players in the pathway: phenylalanine, tyrosine, levodopa, dopamine, noradrenaline and adrenaline. Extended data traces show characteristic current blockades and dwell-time distributions for each analyte at 100 micromolar concentration, recorded in a high-salt buffer at a transmembrane potential of +100 millivolts. Gaussian fits to the blockade histograms and single-exponential fits to the dwell times confirm that each metabolite produces a statistically distinct signature, which is the prerequisite for unambiguous identification in a mixed sample.

Having validated each molecule individually, the team turned to the full cascade. They initiated the enzymatic sequence and sampled the reaction mixture every five minutes, feeding each sample to the nanopore. The resulting single-channel recordings amount to a time-lapse film of neurotransmitter synthesis: phenylalanine signals fade as tyrosine rises, tyrosine gives way to levodopa, and levodopa is converted onward to dopamine, noradrenaline and finally adrenaline. Because the readout is label-free and single-molecule, nothing was added to the reaction to make it visible, and no amplification step could distort the quantitative picture of pathway dynamics.

This time-resolved view matters because the catecholamine pathway is not a simple conveyor belt. It is a branched network whose flux is governed by enzyme kinetics, cofactor availability and regulatory feedback, and disruption at any node is implicated in conditions ranging from Parkinson’s disease and attention-deficit hyperactivity disorder to orthostatic hypotension and stress-related endocrine disorders. Conventional analytical methods — liquid chromatography, mass spectrometry, electrochemistry, ELISAs — can measure metabolite concentrations at chosen endpoints, but they are generally snapshot techniques that require aliquots, labels or lengthy separation steps, making continuous, coupled monitoring of a multi-enzyme cascade cumbersome.

The platform also opened a window on pathology. A striking application involves iodotyrosine dehalogenase 1, or DEHAL1, an enzyme best known for recycling iodide in the thyroid, where it removes iodine from iodinated tyrosine residues. Defects in the DEHAL1 gene cause hypothyroidism, and 3-iodo-L-tyrosine, the substrate of this enzyme, has long been known to inhibit tyrosine hydroxylase, the rate-limiting enzyme that converts tyrosine to levodopa. The researchers used their nanopore assay to examine what happens to the catecholamine cascade when dehalogenase activity is compromised. They found that 6-methylisothiocyanate, a compound that disrupts the dehalogenase, perturbs catecholamine biosynthesis in a way that offers a mechanistic explanation for how DEHAL1 deficiency may compromise neurotransmitter production — potentially linking an inherited thyroid disorder to impaired catecholamine output.

Figure 5 of the study, titled ‘Effect of thyroiditis-like disorders on catecholamine metabolism,’ presents this perturbation experiment, with extended recordings showing how the cascade changes when monoiodotyrosine is present. The single-molecule records reveal shifts in the relative abundance of the pathway intermediates, illustrating the value of the method: rather than inferring a block from diminished end-product levels, researchers can see exactly where flux stalls and which intermediates accumulate. That level of mechanistic detail is precisely what drug developers and enzymologists need when trying to dissect regulatory nodes in metabolic networks.

Broader implications extend well beyond catecholamines. The authors describe their integrated enzymology-and-nanopore-sensing platform as a general framework for studying complex biochemical pathways with single-molecule resolution. Because protein nanopores can be engineered with different recognition elements, the same design philosophy could in principle be applied to other branched metabolic cascades, to drug-metabolism studies, or to screening enzyme inhibitors in real time. The approach builds on two decades of stochastic sensing, in which engineered pores have been taught to identify analytes as diverse as metal ions, nucleotides, amino acids, peptides, proteins and microRNAs, and it extends that lineage from static identification to dynamic pathway monitoring.

The team has deposited its source data publicly on Zenodo, and the paper — received in December 2025, accepted in August 2026 and published on 1 September 2026 — carries no reported competing interests. What the work ultimately delivers is a new kind of laboratory instrument: a single pore through which the chemistry of a neurotransmitter can be watched being born. If the framework generalizes as its authors hope, metabolic biochemists may soon spend far less time taking snapshots of pathways and far more time watching them move.

Subject of Research: Time-resolved single-molecule monitoring of catecholamine biosynthesis from phenylalanine using an engineered nanopore sensor

Article Title: Nanopore-enabled time-resolved monitoring of catecholamine-related phenylalanine metabolism

Article References: Zhang, M., Li, Z., Hao, W., Yi, Y., Zhou, K., Liu, L., & Wu, H.-C. (2026). Nanopore-enabled time-resolved monitoring of catecholamine-related phenylalanine metabolism. Nature Nanotechnology. https://doi.org/10.1038/s41565-026-02273-3

Image Credits: AI Generated

DOI: 10.1038/s41565-026-02273-3

Keywords: nanopore sensing, catecholamines, dopamine, adrenaline, phenylalanine metabolism, single-molecule detection, neurotransmitter biosynthesis, DEHAL1, iodotyrosine dehalogenase, label-free biosensing, Nature Nanotechnology, enzymology