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. 2026 Sep 22. Online ahead of print. doi: 10.1039/d6sc02337a

Single-monosaccharide resolution glycan profiling enabled by the nanopipette

Hui Ma a,b,†,✉, Ya-Xue Li a,†, Zhuojia Xu c,†, Wenjing Ma c,†, Bingqing Xia d,e, Zhaobing Gao d,e,✉, Tiehai Li c,✉, Yi-Lun Ying a,f,✉
PMCID: PMC13595367  PMID: 42775189

Abstract

Glycans encode critical biological information through variations in monosaccharide composition, branching architecture, and chemical modifications. However, decoding these structural features with single-monosaccharide precision at physiological (nM–µM) concentrations remains a major challenge. Herein, we develop a rectification-guided nanopipette strategy that identifies individual monosaccharide differences in heparosan oligosaccharides at picomolar concentrations. This capability arises from an optimized ionic current rectification ratio, which results in a suitable balance between electroosmotic flow (EOF) and electrophoretic force (EPF), thereby enabling enhanced capture efficiency and signal-to-noise ratio. The success rate of single-molecule glycan detection increases from below 20% to over 80%, enabling reliable discrimination of linear and branched oligosaccharides with single-monosaccharide resolution and over 98% accuracy. Combining high sensitivity with single-monosaccharide resolution, this method provides a promising platform for nanopore-based glycomics and sequencing.

A rectification-guided nanopipette enables identification of monosaccharide differences and sulfation patterns in single oligosaccharide molecules at picomolar concentrations.graphic file with name d6sc02337a-ga.webp

Introduction

Glycans, the most intricate biopolymers in nature, govern pivotal biological functions through precise variations in composition,1,2 glycosidic linkages,3 branching patterns,4 and post-synthetic modifications.5 Resolving these features at the single-molecule level is essential for understanding their biological and pathological roles. However, current analytical tools face inherent trade-offs between achieving ultralow detection limits and attaining resolution sufficient to discriminate single-monosaccharide differences.6–9 Detailed structural analysis by mass spectrometry typically requires relatively large sample amounts, because complex glycans exhibit lower ionization efficiency, limiting detection sensitivity and throughput. For example, tandem mass spectrometry (MS/MS and MSn) enables compositional profiling of glycans,10–12 but usually requires micromolar-level sample quantities, restricting its applicability to trace-level glycans.13,14 While glycan microarrays can detect glycans at the pM–nM level,15 they generally provide limited information on fine structural features such as linkage, branching, or sulfation patterns. Nanopore techniques have advanced single-molecule DNA sequencing and moved toward protein sequencing.16–19 In contrast, glycan analysis by nanopores remains at an early stage.20,21 Biological nanopores have demonstrated the ability to identify a select few monosaccharides among 75 specific monosaccharides or class of monosaccharides found in nature,22–24 with reported glycosidic linkage discrimination primarily focused on β-1,3 and β-1,4.25–29 While these pores are effective for certain applications, their relatively narrow diameter (∼1 nm) and the modest driving force present challenges for the analysis of larger, branched glycans. A recent biological nanopore study has taken advantage of electroosmotic flow (EOF) to enhance analyte event frequency, but its sensitivity still remains in the micromolar range.18 Solid-state nanopores featuring larger diameters than biological nanopores can distinguish molecular weight difference greater than ∼50 kDa in polysaccharides30 and resolve heparin and chondroitin sulfate with the same degree of polymerization.31 While this method enables nanomolar-level glycan detection, it cannot resolve fine structural differences at the monosaccharide level. Consequently, nanopore glycan sensing faces not only the challenge of reaching high sensitivity at physiological (nM–µM) concentrations, but also that of reaching monosaccharide-level resolution.

Herein, we develop a rectification-guided nanopipette strategy that achieves sensitive detection of individual monosaccharide differences in heparosan oligosaccharides at picomolar concentrations. By systematically modulating the ionic current rectification ratio (R), we identify a narrow optimal window (Rm = 1.6–2.4) that yields a high signal-to-noise ratio (SNR) and enhanced capture efficiency. By virtue of its high sensitivity, we directly discriminate the site- and degree-specific sulfation patterns in branched glycans at the single-molecule level. To the best of our knowledge, this is the first nanopore method reaching both monosaccharide-level resolution and picomolar sensitivity for glycan profiling, paving the way for nanopore glycomics.

Results and discussion

A rectification-guided nanopipette for single glycan profiling

Nanopipette-based single-molecule sensing mainly relies on electrohydrodynamic forces of electroosmotic flow (EOF) and electrophoretic force (EPF).32,33 EOF, driven by surface charge of the nanopore and ionic composition of the electrolyte, controls the bulk fluid motion through the pore, whereas EPF, related to the net charge on the analyte, dictates the direct electrophoretic motion of charged glycans. In this study, both oligosaccharides and the nanopipette surface carry negative charges at the operational pH. Under a positive bias applied to the inner electrode with the outer electrode grounded, the outward EOF partially counterbalances the inward EPF. These forces collectively influence analyte dwell time and capture efficiency.

Conventional covalent or electrostatic surface modification strategies with charged molecules often induce several-fold changes in R value, leading to pronounced variations in EOF strength and poor reproducibility. As a result, glycans can be driven either too fast to be resolved or too slow to be captured by the nanopore. To address this challenge, we modulate a series of R values of the nanopipette (tip diameters: 12.3 ± 0.3 nm; see SI Methods, Fig. S1). Using linear heparosan and branched ganglioside oligosaccharides as model analytes, the nanopipettes show rectification ratio dependent oligosaccharide detection performance (Fig. 1). The optimal rectification ratio (Rm) between 1.6 and 2.4 achieved success rates from 71% to 100% (Fig. 1c). To further correlate glycan structure with sensing performance, we defined two parameters: number of monosaccharide unit (Nunit) and number of polar functional groups (Npolar) as shown in Fig. 1d. The success rate for each 0.2 rectification ratio interval was calculated as the ratio of experiments achieving the criteria of event frequency (f > 0.1 s−1) and signal-to-noise ratio (SNR > 3) to the total number of experiments. Outside this range, the sensing performance weakened. Specifically, when R < 1.6, the event frequency dropped 1.67-fold, indicating that the outward EOF was insufficient to counterbalance the inward EPF, leading to poor event frequency, while R > 2.4 resulted in pronounced baseline fluctuations, significantly reducing the SNR to a level where single-molecule events became indistinguishable from the background noise. This result confirms that the appropriately opposing EOF partially offsets EPF, resulting in slower analyte speed and improved event resolvability. The rectification ratio, also governed by the surface charge, can be modulated by adjusting the electrolyte concentration through Debye screening (Fig. S2). At lower ionic strengths (<0.2 M), insufficient screening leads to excessive rectification, whereas at higher ionic strengths (>0.2 M), charges are effectively neutralized, driving R toward 1. Both insufficient and excessive ionic screening compromise reliable detection.

Fig. 1. Rectification-guided nanopipettes for glycan profiling. (a) Left: schematic diagram of the experimental setup for single-saccharide detection (not to scale). Right: representative ionic current trace from individual oligosaccharides. (b) Current–voltage (I–V) curve of nanopipettes measured in 0.2 M phosphate buffer (PB, pH 7.4) with R = 2.1. Error bars represent standard deviation from three independent experiments. (c) Top: correlation between event frequency (f = Nevent/Ttot, blue) and signal-to-noise ratio (SNR = ΔI/IN, red) as functions of rectification ratio. Nevent represents the number of events in the total measurement time (Ttot). Inset: raw current traces from nanopipettes with rectification ratios (R = |I−700 mV/I+700 mV|) of 1.3, 2.1, and 3.0. Bottom: rectification ratio-dependent glycan detection efficiency of nanopipettes. R was discretized into 0.2 intervals (bins) and the success rate was determined for each bin. The success rate for each 0.2 rectification ratio interval was calculated as the proportion of experiments achieving both criteria of event frequency (f > 0.1 s−1) and signal-to-noise ratio (SNR > 3) to the total number of experiments. The optimal ionic current rectification ratio (Rm, 1.6–2.4) refers to the rectification range where each bin exhibits a success rate >60% (black dotted line). (d) Glycans with structural diversity were represented using Symbol Nomenclature for Glycans (SNFG), highlighting two key structural features: number of monosaccharide units (Nunit) and number of polar functional groups (Npolar) marked with red. Gray dots represent the reducing-end modification: p-nitrophenyl (pNP) in heparosan or an aminopropyl group in ganglioside oligosaccharides. Saccharide units are color-coded: N-acetylglucosamine (GlcNAc, blue squares), glucuronic acid (GlcA, blue divided diamonds), and d-galactose (Gal, yellow circles) with 3-O-sulfation (3S) modifications indicated where applicable, N-Acetyl-d-Galactosamine (GalNAc, yellow squares) with 6-O-sulfation (6S) modifications indicated where applicable, N-Acetyl-Neuraminic Acid (Neu5Ac, purple diamonds).

Fig. 1

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With a suitable Rm from the balancing between EOF and EPF, the nanopipette achieves both enhanced event frequency and elongated the event durations to meet the bandwidth of the amplifier for nanopore sensing. Its high SNR enables detection of glycans as short as disaccharides, which is a considerable advantage over previous solid-state nanopore reports that are typically applied to detecting long polysaccharides.34,35 As shown in Fig. 2, the nanopipette could detect heparosan disaccharides (Di) over a wide concentration range from pM to nM (Fig. S3 and S4). The event frequency showed a linear increase with analyte concentration down to 1 pM, allowing quantitative analysis. This result represents over a 106-fold improvement in sensitivity compared to conventional biological nanopore methods.18,22,36,37 Importantly, both the normalized current blockade (ΔI/I0 = 0.022 ± 0.001) and dwell time (1.6 ± 0.8 ms) exhibited concentration independence from 1 pM to 750 pM, demonstrating that each event corresponds to an individual glycan molecule. Therefore, the nanopipette acts as a picomolar-sensitive platform for single-glycan analysis.

Fig. 2. Rectification-guided nanopipettes for heparosan disaccharides (Di) identification. (a) Raw current traces at Di concentrations of 1, 500, and 1000 pM. (b) Box plot of ΔI/I0, dwell time and event frequency versus Di concentration, with concentrations of 1, 250, 500, 750, and 1000 pM. All experiments were conducted in 0.2 M PB solution (pH = 7.4) at +700 mV. Each frequency value was obtained from Gaussian fitting to the interval time histogram (Fig. S4).

Fig. 2

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Identification of heparosan oligosaccharides with single-monosaccharide resolution

To demonstrate the capability of the rectification-guided nanopipette in identifying glycans beyond disaccharides, we analyzed enzymatically synthesized heparosan oligosaccharides (Fig. S5–S20; synthesis details in SI Methods). Nanopore measurements were conducted in 0.2 M PB (pH = 7.4) at +700 mV, adding each oligosaccharide into the outside chamber at 500 pM, respectively. Fig. 3a and b shows representative raw traces and scatter plots of dwell time vs. ΔI/I0 for disaccharide (Di), trisaccharide (Tri), and tetrasaccharide (Tetra). These scatter plots exhibit distinct ΔI/I0 characteristics, demonstrating that the rectification-guided nanopipette achieves single-monosaccharide resolution in distinguishing oligosaccharides. A total of 1590 events were obtained to construct the feature matrix (Fig. S21). Random Forest achieved the highest accuracy (99.61%, Fig. S22), identifying Di, Tri and Tetra with a high accuracy >98% (Fig. 3c). In the mixture samples, scatter plots and ΔI/I0 distributions (Fig. 3d and S23) confirmed that each oligosaccharide could be clearly distinguished. The trained model then classified unlabelled events from these mixtures with high accuracy. To further assess robustness, we tested the model on simulated mixtures with varying abundance ratios (4 : 4 : 2, 1 : 5 : 4, 4 : 1 : 5, 5 : 4 : 1). The classifier-maintained accuracy above 99.80% across all tested ratios (Fig. 3e and S24).

Fig. 3. Single-molecule identification of oligosaccharides with different lengths. (a) Current traces of GlcNAc-GlcA (Di, red), GlcA-GlcNAc-GlcA (Tri, blue) and GlcNAc-GlcA-GlcNAc-GlcA (Tetra, orange). (b) Scatter plots and the corresponding ΔI/I0 distributions for Di, Tri, and Tetra. (c) Confusion matrix results from the test set. Detection accuracy was 100% for Di, 98% for Tri, and 100% for Tetra. Bin width of ΔI/I0 is 3 × 10−4. (d) Scatter plots and the corresponding ΔI/I0 distribution (N = 2193) and all experiments were conducted in a 0.2 M PB solution (pH = 7.4). The applied voltage was +700 mV for Di, Tri, and Tetra. Bin width of ΔI/I0 is 2 × 10−4. (e) Prediction results for Di, Tri, and Tetra at different extraction ratios: 4 : 4 : 2, 1 : 5 : 4, 4 : 1 : 5, and 5 : 4 : 1, with a total of 1590 events. Prediction accuracies were 99.80%, 99.80%, 99.90%, and 99.80%, respectively. Gray dots represent the pNP groups.

Fig. 3

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Discrimination of ganglioside oligosaccharides with position- and degree-specific sulfation

With a pore diameter of 12.3 ± 0.3 nm, the rectification-guided nanopipette can readily accommodate these complex branched structures, enabling detection (Fig. 4a). To evaluate this sensing ability, the rectification-guided nanopipette was used to distinguish branched ganglioside oligosaccharides that vary in both the position and degree of sulfation. These ganglioside oligosaccharides with an aminopropyl linker at the reducing end were synthesized according to the method described in our previous work.38 Remarkably, our method distinguished the sulfation isomer at the 3-position of galactose (Gal-3S) and the 6-position of N-acetylgalactosamine (GalNAc-6S). Each oligosaccharide produced characteristic current patterns (Fig. 4b). Gal-3S-modified glycans showed small, uniform blockades (ΔI/I0 = 0.014 ± 0.001) with longer dwell times (Fig. 4c). In contrast, GalNAc-6S-modified glycans generated larger current amplitude with wide distribution (ΔI/I0 = 0.066 ± 0.005) and shorter dwell times. Oligosaccharides with dual 3S/6S sulfation displayed ΔI/I0 amplitudes (0.047 ± 0.001) that fell between Gal-3S and GalNAc-6S glycans. The rectification-guided nanopipette, with an R value in the range of 1.6–2.4, indicates that the nanopipette maintains a partially negative interface. As a result, electrostatic interactions can occur between this negative surface and the negatively charged sulfation groups on the glycans. These interactions enhance the nanopipettes ability to discriminate sulfation site differences through distinct ionic current signatures. Our results provide the first demonstration that a solid-state nanopore can detect branched oligosaccharides and resolve position-specific chemical modifications at the single-molecule level.

Fig. 4. Identification of ganglioside oligosaccharides. Chemical structure (a), current traces (b) and histograms of ΔI/I0 (c) of the ganglioside oligosaccharides with varying sulfation patterns. Oligosaccharides were added outside the nanopore at a final concentration of 500 pM, and all experiments were conducted in a 0.2 M PB solution (pH = 7.4). Bin width of ΔI/I0 is 7 × 10−4. Gray dots represent the aminopropyl groups.

Fig. 4

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Deciphering rectification-guided nanopipette performance via correlation analysis

To further study the applicability of the rectification-guided nanopipette, we systematically evaluated its performance across glycoform structural features of monosaccharide unit number (Nunit) and polar functional group number (Npolar), detection performance characteristics (SNR, f), and nanopipette interface properties (pore conductance, G = ΔI±200 mV/ΔV, and R). Parallel coordinate plots revealed that in successful measurements, R exhibited a narrow distribution, whereas G, SNR, and f exhibited broader distributions (Fig. 5a). This suggests that R is a key parameter associated with effective glycan detection. Nanopipettes with rectification ratios within Rm (R ∈ [Rm]) consistently enabled reliable detection, producing clear, high-SNR signals with event frequencies exceeding 0.1 s−1 in 80% of measurements. In contrast, nanopipettes with R values outside this range showed substantially weak or inconsistent event frequency, signatures, with reduced SNR that compromised reliable event detection.

Fig. 5. Assessment of nanopipette characteristics suited for glycan sensing. (a) Parallel coordinate plots for 37 nanopipettes used for detecting oligosaccharides. The plot includes glycoform structural features (Npolar and Nunit), current signal signatures (f and SNR), and nanopipette physical characteristics (G, R). Among the 15 nanopipettes with rectification ratios within the optimal range (R ∈ [Rm]), 80% successfully detected oligosaccharides, compared to only 29.4% and 20% for nanopipettes with R < 1.6 or R > 2.4, respectively. Effective experiments are defined by the conditions f > 0.1 s−1 and SNR > 3 (orange line), while experiments failing to meet these criteria are considered invalid (gray line). Experiments were conducted using 500 pM oligosaccharides in 0.2 M PB (pH 7.4). (b) Radar plot and Pearson correlation heatmap illustrating correlations among f, SNR, G, R, Npolar, and Nunit for nanopipettes having R ∈ [Rm] and R ∉ [Rm]. Gray dots represent pNP in heparosan or an aminopropyl group in ganglioside oligosaccharides. A perfect positive correlation is represented by a Pearson correlation coefficient (PCC) of 1, a perfect negative correlation by a value of −1, and no correlation by a value of 0. Low positive correlation (PCC between 0.3 and 0.5) indicates a weaker positive relationship. The corresponding p-values were derived from a two-tailed t-test. Significance labeling: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001; no marker indicates p > 0.05.

Fig. 5

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A significant Pearson correlation was observed between sensing performance (current signal signatures), glycoform structural features, and nanopipette physical characteristics. These correlations are illustrated in Fig. 5b, with separate radar plot panels for SNR and f from the rectification-guided nanopipette with R within and outside Rm. Within Rm, SNR correlated moderately with conductivity (PCC = 0.64), but showed only low dependence on R (PCC = 0.13), indicating that ionic conductance plays a more critical role in enhancing signal quality. f correlated more strongly with R (PCC = 0.56), likely due to the balance between EOF and EPF which enhances the analyte event frequency, thereby facilitating capture and detection. Notably, oligosaccharide structural features also significantly influenced detection efficiency. Both f and SNR showed negative correlations with both Nunit and Npolar. This trend is likely due to stronger electrostatic and polar repulsion between larger, more polar glycans and the negatively charged nanopipette surface. Such interactions reduce the probability of successful entry, thereby decreasing event frequency. Moreover, the unstable and strong interactions increase ionic perturbations, thereby reducing SNR.

For nanopipettes with R values outside the Rm range, correlations between detection performance, nanopipette factors, and glycoform structural features were significantly diminished. As shown in Fig. 5b, SNR showed low positive correlations with Nunit and Npolar, and low negative correlations with R and G. Similarly, f showed low correlations with Nunit and Npolar with no significant relationship to R or conductivity. These results suggest that, for the heparosan and ganglioside oligosaccharides, R can serve as a measurable indicator of the electrohydrodynamic balance within the nanopipette, which is critical for single-molecule glycan profiling.

Conclusions

In conclusion, a rectification-guided nanopipette with an optimized R range of 1.6–2.4 was used for glycan profiling. Within this range, the rectification-guided nanopipette successfully identified heparosan and ganglioside oligosaccharides with monosaccharide variations and resolved sulfation isomers. It achieves both high sensitivity with detection limits down to 1 pM, and precise single monosaccharide-level resolution, addressing the limitations of existing glycan nanopore sensing methods. Correlation analyses between event frequency characteristics and glycan features highlight the structural sensitivity of the nanopipette.

Conventional mass spectrometry typically requires micromolar glycan samples due to low ionization efficiency, whereas the rectification-guided nanopipette achieves single-monosaccharide discrimination at picomolar concentrations, markedly minimizing sample needs. By overcoming key limitations in sensitivity and structural resolution, the rectification-guided nanopipette represents a promising tool for glycomics sensing, with applications including glycosaminoglycan (heparosan, chondroitin sulfate) analysis, sulfation pattern mapping, and glycobiomarker discovery. Recent work on nanopipette analysis of hydrolyzed oligosaccharides39 suggests its potential combination with rectification-guided nanopipette sensing for glycan identification. Since our approach relies on well-defined synthetic glycans, enabling a deep understanding of how these structural features govern the nanopipette response. These structure–signal relationships provide a rational basis for extending the method to more complex natural glycans. Overall, this rectification-guided nanopipette establishes a new benchmark for single-molecule glycan analysis, paving the way for comprehensive and precise glycomics studies.

Author contributions

H. M. and Y. L. Y. proposed the idea. H. M., Z. B. G., T. L., and Y. L. Y. conceived and supervised the project. H. M. and Y. L. Y. designed the experiments. Y. X. L. fabricated the rectification-guided nanopipette, performed the nanopore measurements, and conducted data analysis along with machine learning studies. T. L. provided all glycans. Z. J. X. synthesized the ganglioside oligosaccharides and performed their NMR characterization. W. J. M. synthesized the heparosan oligosaccharides and performed their NMR characterization. B. Q. X., T. L., and Y. L. Y. revised the manuscript. All authors reviewed and approved the final version of the manuscript.

Conflicts of interest

There are no conflicts to declare.

Supplementary Material

SC-OLF-D6SC02337A-s001

Acknowledgments

This research was supported by the National Natural Science Foundation of China (32250019, 22377134, 22525403), Shanghai Municipal Science and Technology Major Project (22334006), the Strategic Priority Research Program of Chinese Academy of Sciences (XDB1060000), the Science Foundation of Zhejiang Sci-Tech University under Grant No. 24262198-Y, and He Science Foundation. We acknowledge the use of ChatGPT and DeepSeek for language polishing, while the original manuscript was written by the authors.

Data availability

The data that support the findings of this work have been included in the main text and supplementary information (SI). Supplementary information: methods, synthesis and characterization of glycans, nanopore characterization, single-molecule data analysis and results. See DOI: https://doi.org/10.1039/d6sc02337a.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

SC-OLF-D6SC02337A-s001
SC-OLF-D6SC02337A-s001.pdf (1.8MB, pdf)

Data Availability Statement

The data that support the findings of this work have been included in the main text and supplementary information (SI). Supplementary information: methods, synthesis and characterization of glycans, nanopore characterization, single-molecule data analysis and results. See DOI: https://doi.org/10.1039/d6sc02337a.

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