Abstract
Enzyme-linked immunosorbent assay (ELISA) has been widely used in cancer diagnostics due to its specificity, sensitivity and high throughput. However, conventional ELISA is semiquantitative and has an insufficiently low detection limit for applications requiring ultrahigh sensitivity. In this study, we developed an α-hemolysin-nanopore-based ELISA for detecting cancer biomarkers. After forming the immuno-sandwich complex, peptide probes carrying enzymatic cleavage sites are introduced, where they interact with enzymes conjugated to the detection antibodies within the complex. These probes generate distinct current signatures when translocated through the nanopore after enzymatic cleavage, enabling precise biomarker quantification. This approach offers a low detection limit of up to 0.03 fg ml–1 and the simultaneous detection of six biomarkers, including antigen and antibody biomarkers in blood samples. Overall, the nanopore-based ELISA demonstrates high sensitivity and multiplexing capability, making it suitable for next-generation diagnostic and point-of-care testing applications.
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Data supporting the findings of this study are given in the Article and its Supplementary Information. Source data are provided with this paper and available via Zenodo at https://doi.org/10.5281/zenodo.15043060 (ref. 55).
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The custom machine learning code is shared as ‘probe signal classification’ via figshare at https://figshare.com/s/a741e3ebf50f48a529ed.
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Acknowledgements
This project was funded by the National Key Research and Development Program of China (2022YFC2603900 to H.-C.W.), the National Natural Science Foundation of China (no. 22025407 to H.-C.W.; no. 22374151 to L.L.) and the Institute of Chemistry, Chinese Academy of Sciences. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Authors and Affiliations
Contributions
Y.Y. and Z.L. performed the peptide probe modification, nanopore measurement and machine learning experiments. Y.Y., J.J. and Q.R. performed the data analysis. K.Z. provided the αHL nanopores. P.S. and G.S. performed the chemiluminescence immunoassays of clinical samples. Y.Y., L.L. and H.-C.W. performed the data analysis. Y.Y., L.L. and H.-C.W. conceived the project, designed the experiments and wrote the paper.
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H.-C.W. and Y.Y. have filed a patent describing NELISA in China with the application number 202510363929.9. The other authors declare no competing interests.
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Nature Nanotechnology thanks Xiyun Guan, Caroline Koch and Keisuke Motone for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 NELISA detection of SCCA based on FGpTD8 and alkaline phosphatase.
(a) The enzymatic cleavage of FGpTD8 by alkaline phosphatase and corresponding translocation current signal changes. (b) Quantification of SCCA by NELISA using FGpTD8. Linear equation of the standard working curve: y = -25.26 lgx + 343.31; R2 = 0.996; LOD is 100 pg/mL. (c) Specificity of alkaline phosphatase and FGpTD8 for the detection of SCCA. Different types of protein antigens (1.0 μg/mL SCCA, 1.0 kU/mL CA125, 1.0 μg/mL NSE, 600.0 ng/mL AFP, 1.0 kU/mL CA19-9 and 500.0 ng/mL CEA) are used in the test. All data were acquired in the buffer of 3.6 M KCl, 10.0 mM PBS, pH 5.0 in trans, 1.0 M KCl, 10.0 mM PBS, pH 5.0 in cis, with the transmembrane potential held at +200 mV. Number of individual experiments n = 3. Each data comes from three independently prepared standard solution samples of the same concentration and three independent nanopore translocation experiments. Data are presented as mean ± SD.
Extended Data Fig. 2 NELISA detection of NSE based on PBAP-FGED8 and glucose oxidase.
(a) The enzymatic cleavage interaction between PBAP-FGED8 and H2O2 derived from glucose oxidase and corresponding translocation current signal changes. (b) Quantification of NSE by NELISA using PBAP-FGED8. Linear equation of the standard working curve: y = 39.55 lgx – 29.73; R2 = 0.995; LOD is 100 pg/mL. (c) Specificity of glucose oxidase and PBAP-FGED8 for the detection of NSE. Different types of protein antigens (1.0 μg/mL NSE, 1.0 μg/mL SCCA, 1.0 kU/mL CA125, 600.0 ng/mL AFP, 500.0 ng/mL CEA and 1.0 kU/mL CA19-9) are used in the test. All data were acquired in the buffer of 3.6 M KCl, 10.0 mM PBS, pH 5.0 in trans, 1.0 M KCl, 10.0 mM PBS, pH 5.0 in cis, with the transmembrane potential held at +200 mV. Number of individual experiments n = 3. Each data comes from three independently prepared standard solution samples of the same concentration and three independent nanopore translocation experiments. Data are presented as mean ± SD.
Extended Data Fig. 3 NELISA detection of CA125 based on PBAP-FGFD8 and glucose oxidase.
(a) The enzymatic cleavage interaction between PBAP-FGFD8 and H2O2 derived from glucose oxidase and corresponding translocation current signal changes. (b) Quantification of NSE by NELISA using PBAP-FGFD8. Linear equation of the standard working curve: y = 16.89 lgx + 71.91; R2 = 0.993; LOD is 10 mU/mL. (c) Specificity of glucose oxidase and PBAP-FGFD8 for the detection of CA125. Different types of protein antigens (1.0 μg/mL NSE, 1.0 μg/mL SCCA, 1 kU/mL CA125, 600.0 ng/mL AFP, 500.0 ng/mL CEA and 1.0 kU/mL CA19-9) are used in the test. All data were acquired in the buffer of 3.6 M KCl, 10.0 mM PBS, pH 5.0 in trans, 1.0 M KCl, 10.0 mM PBS, pH 5.0 in cis, with the transmembrane potential held at +200 mV. Number of individual experiments n = 3. Each data comes from three independently prepared standard solution samples of the same concentration and three independent nanopore translocation experiments. Data are presented as mean ± SD.
Extended Data Fig. 4 Simultaneous detection of six biomarkers—CA19-9, CEA, AFP, SCCA, NSE, and CA125—in a single sample.
(a) Working curves for CA19-9: y = -11.86 lgx + 110.37, R2 = 0.989. (b) Working curves for CEA: y = -11.15 lgx + 105.76, R2 = 0.992. (c) Working curves for AFP: y = 10.58 lgx + 6.22, R2 = 0.998. (d) Working curves for SCCA: y = - 15.69 lgx + 137.29, R2 = 0.993. (e) Working curves for NSE: y = 11.08 lgx + 24.40, R2 = 0.998. (f) Working curves for NSE: y = 17.17 lgx – 2.30, R2 = 0.993. All data were acquired in the buffer of 3.6 M KCl, 10.0 mM PBS, pH 5.0 in trans, 1.0 M KCl, 10.0 mM PBS, pH 5.0 in cis, with the transmembrane potential held at +200 mV. Number of individual experiments n = 3. Each data comes from three independently prepared standard solution samples of the same concentration and three independent nanopore translocation experiments. Data are presented as mean ± SD. Experimental procedure: Standard sample solutions of different concentrations were used for the NELISA tests. Sample solutions of gradient concentrations (CA19-9: 50 mU/mL - 500 U/mL; CEA: 50 pg/mL - 500 ng/mL; AFP: 40 pg/mL - 400 ng/mL; SCCA: 100 pg/mL - 1.0 μg/mL; NSE: 1.0 ng/mL - 10.0 μg/mL; CA125: 100 mU/mL - 1.0 kU/mL) were added to 96-well plates pre-coated with specific capture antibodies, forming a sandwich complex with subsequently added enzyme-labeled detection antibodies. Corresponding peptide probes were then added to the wells containing the sandwich complexes and incubated under optimal conditions for 1 h (Supplementary Figs. 9–21). After the enzymatic cutting step, the reaction solutions from the same sample were combined and subjected to nanopore translocation experiments. For these experiments, the ratio of peptide probes is as follows. FGK(Gal)GGD8: PBAP-FGLD8: FGpYD8: PBAP-FGED8: FGpTD8: PBAP-FGFD8 = 125: 30: 25: 25: 25: 30 (nM). Under these experimental conditions, the working curves for various biomarkers were obtained.
Extended Data Fig. 5 The residual plots of sample data between the clinically used chemiluminescence immunoassay (CD) and the NELISA detection (ND).
(a) CA19-9; (b) CEA; (c) AFP. The majority of the plots are evenly distributed around zero, indicating that the detection results of the two methods exhibit high consistency.
Supplementary information
Supplementary Information
Supplementary Schemes 1 and 2, Figs. 1–38, Tables 1–12 and references.
Source Data Supplementary Fig. 1
Mass spectroscopic characterization of the modified peptide probes.
Source Data Supplementary Fig. 3
Translocation of FGpYD8⊂CB[7] through the αHL nanopore.
Source Data Supplementary Fig. 4
Translocation of FGpYD8⊂CB[7] through the αHL nanopore in the presence of alkaline phosphatase.
Source Data Supplementary Fig. 5
Translocation of FGK(Gal)GGD8⊂CB[7] through the αHL nanopore.
Source Data Supplementary Fig. 6
Translocation of FGK(Gal)GGD8⊂CB[7] through the αHL nanopore in the presence of β-galactosidase.
Source Data Supplementary Fig. 8
Translocation of PBAP-FGLD8⊂CB[7] through the αHL nanopore in the presence of glucose oxidase and glucose.
Source Data Supplementary Fig. 10
Effect of reaction time on the alkaline-phosphatase-linked immunosorbent assay for the detection of CA19-9.
Source Data Supplementary Fig. 11
Effect of pH on the alkaline-phosphatase-linked immunosorbent assay for the detection of CA19-9.
Source Data Supplementary Fig. 12
Effect of substrate probe concentration on the alkaline-phosphatase-linked immunosorbent assay for the detection of CA19-9.
Source Data Supplementary Fig. 14
Effect of reaction time on the β-galactosidase-linked immunosorbent assay for the detection of CEA.
Source Data Supplementary Fig. 15
Effect of pH on the β-galactosidase-linked immunosorbent assay for the detection of CEA.
Source Data Supplementary Fig. 16
Effect of peptide probe concentration on the β-galactosidase-linked immunosorbent assay for the detection of CEA.
Source Data Supplementary Fig. 18
Effect of reaction time on the glucose-oxidase-linked immunosorbent assay for the detection of AFP.
Source Data Supplementary Fig. 19
Effect of pH on the glucose-oxidase-linked immunosorbent assay for the detection of AFP.
Source Data Supplementary Fig. 20
Effect of glucose concentration on the glucose-oxidase-linked immunosorbent assay for the detection of AFP.
Source Data Supplementary Fig. 21
Effect of probe concentration on the glucose-oxidase-linked immunosorbent assay of AFP.
Source Data Supplementary Fig. 22
Specificity of alkaline phosphatase and FGpYD8 for the detection of CA19-9.
Source Data Supplementary Fig. 23
Specificity of β-galactosidase and FGK(Gal)GGD8 for the detection of CEA.
Source Data Supplementary Fig. 24
Specificity of glucose oxidase and PBAP-FGLD8 for the detection of AFP.
Source Data Supplementary Fig. 25
Mass spectroscopic characterization of the modified peptide probes.
Source Data Supplementary Fig. 26
Translocation of FGpTD8⊂CB[7] through the αHL nanopore.
Source Data Supplementary Fig. 27
Translocation of FGpTD8⊂CB[7] through the αHL nanopore in the presence of alkaline phosphatase.
Source Data Supplementary Fig. 29
Translocation of PBAP-FGED8⊂CB[7] through the αHL nanopore in the presence of glucose oxidase and glucose.
Source Data Supplementary Fig. 31
Translocation of PBAP-FGFD8⊂CB[7] through the αHL nanopore in the presence of glucose oxidase and glucose.
Source Data Supplementary Fig. 33
Effect of pH conditions on the HRP-linked immunosorbent assay for the detection of anti-HBc.
Source Data Supplementary Fig. 34
Effect of H2O2 concentration on the HRP-linked immunosorbent assay for the detection of anti-HBc.
Source Data Supplementary Fig. 35
Effect of reaction time on the HRP-linked immunosorbent assay for the detection of anti-HBc.
Source Data Supplementary Fig. 36
Specificity of HRP and PBAP-FGLD8 for the detection of anti-HBc.
Source Data Supplementary Fig. 37
Comparison between multiplexed detection and single-plex detection.
Source Data Supplementary Fig. 38
Detection of AFP by NELISA using alkaline phosphatase and FGpYD8.
Supplementary Code
MATLAB code for data analysis and instructions to run it—including example data.
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Yi, Y., Song, P., Li, Z. et al. Nanopore-based enzyme-linked immunosorbent assay for cancer biomarker detection. Nat. Nanotechnol. (2025). https://doi.org/10.1038/s41565-025-01918-z
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DOI: https://doi.org/10.1038/s41565-025-01918-z
