Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Multiplexed food-borne pathogen detection using an argonaute-mediated digital sensor based on a magnetic-bead-assisted imaging transcoding system

Abstract

Accurate, sensitive and multiplexed detection of food-borne pathogens is crucial for assessing food safety risks. Here we present a digital DNA-amplification-free nucleic acid detection assay to achieve multiplexed and ultrasensitive detection of three food-borne pathogens. We used mesophilic Clostridium butyricum argonaute and magnetic beads in a digital carrier system (d-MAGIC). Clostridium butyricum argonaute, with its two-guide accurate cleavage activity, precisely targets and cleaves fluorescence-quencher reporters corresponding to different bacteria through a two-step process. The system uses fluorescence-encoded magnetic beads as programmable multi-probes, allowing the simultaneous detection of multiple pathogens and easy data interpretation via artificial intelligence. The method showed a wide detection range (101 to 107 CFU ml−1) and a low limit of detection of 6 CFU ml−1 for food-borne pathogens without DNA amplification. Digital nucleic acid testing using d-MAGIC can become a next-generation strategy for accurate and convenient pathogen detection.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Scheme of the Ago-protein-mediated digital nucleic acid biosensor based on a programmable MB-assisted imaging transcoding system.
Fig. 2: Characterization of CbAgo and MB–TA–SA conjugates.
Fig. 3: Programmable MB-assisted imaging transcoding system.
Fig. 4: Single food-borne pathogen detection using d-MAGIC.
Fig. 5: Multiplexed detection of three food-borne pathogens.
Fig. 6: Real sample analysis by d-MAGIC (triangle) and qPCR (circle) for detecting food-borne pathogens.

Similar content being viewed by others

Data availability

Source data are provided with this paper.

Code availability

The related code is available via GitHub at https://github.com/Wang-Qinyu/Panda/tree/main.

References

  1. Food Safety (World Health Organization, 2023); https://www.who.int/health-topics/food-safety#tab=tab_1

  2. Vikram, A., Callahan, M. T., Woolston, J. W., Sharma, M. & Sulakvelidze, A. Phage biocontrol for reducing bacterial foodborne pathogens in produce and other foods. Curr. Opin. Biotechnol. 78, 102805 (2022).

    Article  CAS  PubMed  Google Scholar 

  3. Tavelli, R., Callens, M., Grootaert, C., Abdallah, M. F. & Rajkovic, A. Foodborne pathogens in the plastisphere: can microplastics in the food chain threaten microbial food safety? Trends Food Sci. Technol. 129, 1–10 (2022).

    Article  CAS  Google Scholar 

  4. Lee, H.-W. et al. Identification of microbial communities, with a focus on foodborne pathogens, during kimchi manufacturing process using culture-independent and -dependent analyses. LWT Food Sci. Technol. 81, 153–159 (2017).

    Article  CAS  MATH  Google Scholar 

  5. Wei, C. et al. Recent progress on lateral flow immunoassays in foodborne pathogen detection. Food Biosci. 52, 102475 (2023).

    Article  CAS  MATH  Google Scholar 

  6. Rantsiou, K. & Cocolin, L. in Advances in Microbial Food Safety (ed. Sofos, J.) Ch. 10 (Woodhead Publishing, 2013).

  7. Lee, S. Y., Kim, J. H. & Oh, S. W. Combination of filtration and immunomagnetic separation based on real-time PCR to detect foodborne pathogens in fresh-cut apple. J. Microbiol. Methods 201, 106577 (2022).

    Article  CAS  PubMed  MATH  Google Scholar 

  8. Aslanzadeh, J. Preventing PCR amplification carryover contamination in a clinical laboratory. Ann. Clin. Lab. Sci. 34, 389–396 (2004).

    CAS  PubMed  MATH  Google Scholar 

  9. Borst, A., Box, A. T. A. & Fluit, A. C. False-positive results and contamination in nucleic acid amplification assays: suggestions for a prevent and destroy strategy. Eur. J. Clin. Microbiol. Infect. Dis. 23, 289–299 (2004).

    Article  CAS  PubMed  Google Scholar 

  10. Rutty, G. N., Hopwood, A. & Tucker, V. The effectiveness of protective clothing in the reduction of potential DNA contamination of the scene of crime. Int. J. Legal Med. 117, 170–174 (2003).

    Article  CAS  PubMed  MATH  Google Scholar 

  11. Morono, Y. et al. Assessment of capacity to capture DNA aerosols by clean filters for molecular biology experiments. Microbes Environ. 33, 222–226 (2018).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  12. Wu, H. et al. DropCRISPR: a LAMP-Cas12a based digital method for ultrasensitive detection of nucleic acid. Biosens. Bioelectron. 211, 114377 (2022).

    Article  CAS  PubMed  MATH  Google Scholar 

  13. Wu, X. et al. Digital CRISPR-based method for the rapid detection and absolute quantification of nucleic acids. Biomaterials 274, 120876 (2021).

    Article  CAS  PubMed  MATH  Google Scholar 

  14. Ali, Z., Mahas, A. & Mahfouz, M. CRISPR/Cas13 as a tool for RNA interference. Trends Plant Sci. 23, 374–378 (2018).

    Article  CAS  PubMed  Google Scholar 

  15. Kaminski, M. M., Abudayyeh, O. O., Gootenberg, J. S., Zhang, F. & Collins, J. J. CRISPR-based diagnostics. Nat. Med. 24, 702 (2018).

  16. Xing, G. et al. Multiplexed detection of foodborne pathogens using one-pot CRISPR/Cas12a combined with recombinase aided amplification on a finger-actuated microfluidic biosensor. Biosens. Bioelectron. 220, 114885 (2023).

    Article  CAS  PubMed  Google Scholar 

  17. Leung, R. K. et al. CRISPR–Cas12-based nucleic acids detection systems. Methods 203, 276–281 (2022).

    Article  CAS  PubMed  Google Scholar 

  18. Chen, L., Ding, J., Yuan, H., Chen, C. & Li, Z. Deep-dLAMP: deep learning-enabled polydisperse emulsion-based digital loop-mediated isothermal amplification. Adv. Sci. 9, e2105450 (2022).

    Article  Google Scholar 

  19. Xun, G. et al. Argonaute with stepwise endonuclease activity promotes specific and multiplex nucleic acid detection. Bioresour. Bioprocess 8, 46 (2021).

    Article  PubMed  PubMed Central  MATH  Google Scholar 

  20. Kuzmenko, A. et al. DNA targeting and interference by a bacterial Argonaute nuclease. Nature 587, 632–637 (2020).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  21. Yang, L. et al. Pyrococcus furiosus Argonaute combined with recombinase polymerase amplification for rapid and sensitive detection of Enterocytozoon hepatopenaei. J. Agric. Food Chem. 71, 944–951 (2023).

    Article  CAS  PubMed  MATH  Google Scholar 

  22. Liu, Q. et al. Argonaute integrated single-tube PCR system enables supersensitive detection of rare mutations. Nucleic Acids Res. 49, e75 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  23. Ye, X. et al. Argonaute-integrated isothermal amplification for rapid, portable, multiplex detection of SARS-CoV-2 and influenza viruses. Biosens. Bioelectron. 207, 114169 (2022).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  24. Song, J. et al. Highly specific enrichment of rare nucleic acid fractions using Thermus thermophilus Argonaute with applications in cancer diagnostics. Nucleic Acids Res. 48, e19 (2020).

    Article  PubMed  Google Scholar 

  25. Wang, Y. et al. In vitro Argonaute cleavage-mediated quantitative PCR facilitates versatile CRISPR/Cas-induced mutant analysis. Sens. Actuators B 374, 132781 (2023).

    Article  CAS  Google Scholar 

  26. Kuzmenko, A., Yudin, D., Ryazansky, S., Kulbachinskiy, A. & Aravin, A. A. Programmable DNA cleavage by Ago nucleases from mesophilic bacteria Clostridium butyricum and Limnothrix rosea. Nucleic Acids Res. 47, 5822–5836 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Vaiskunaite, R., Vainauskas, J., Morris, J. J. L., Potapov, V. & Bitinaite, J. Programmable cleavage of linear double-stranded DNA by combined action of Argonaute CbAgo from Clostridium butyricum and nuclease deficient RecBC helicase from E. coli. Nucleic Acids Res. 50, 4616–4629 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hegge, J. W. et al. DNA-guided DNA cleavage at moderate temperatures by Clostridium butyricum Argonaute. Nucleic Acids Res. 47, 5809–5821 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang, Z. et al. Mesophilic Argonaute-mediated polydisperse droplet biosensor for amplification-free, one-pot, and multiplexed nucleic acid detection using deep learning. Anal. Chem. 96, 2068–2077 (2024).

    Article  CAS  PubMed  MATH  Google Scholar 

  30. Hafiz, M. et al. Magnetic nanoparticles draw solution for forward osmosis: current status and future challenges in wastewater treatment. J. Environ. Chem. Eng. 10, 108955 (2022).

    Article  CAS  MATH  Google Scholar 

  31. Chavan, N., Dharmaraj, D., Sarap, S. & Surve, C. Magnetic nanoparticles—a new era innanotechnology. J. Drug Deliv. Sci. Technol. 77, 103899 (2022).

    Article  CAS  Google Scholar 

  32. Binandeh, M. Performance of unique magnetic nanoparticles in biomedicine. Eur. J. Med. Chem. Rep. 6, 100072 (2022).

    CAS  MATH  Google Scholar 

  33. Fu, C. et al. Horseradish peroxidase-repeat assay based on tyramine signal amplification for highly sensitive H2O2 detection by surface-enhanced Raman scattering. Analyst 146, 7320–7326 (2021).

    Article  ADS  CAS  PubMed  MATH  Google Scholar 

  34. Chen, Q. et al. A highly-sensitive colorimetric assay method for antibody array based on an tyramide signal amplification system. Anal. Lett. 45, 219–226 (2012).

    Article  CAS  MATH  Google Scholar 

  35. Gombolay, G. Y. et al. Review of machine learning and artificial intelligence (ML/AI) for the pediatric neurologist. Pediatr. Neurol. 141, 42–51 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Moon, G. et al. Machine learning-based design of meta-plasmonic biosensors with negative index metamaterials. Biosens. Bioelectron. 164, 112335 (2020).

    Article  CAS  PubMed  MATH  Google Scholar 

  37. Wang, S. K. et al. Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases. Cell Genom. 2, 100164 (2022).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  38. Long, E. et al. The case for increasing diversity in tissue-based functional genomics datasets to understand human disease susceptibility. Nat. Commun. 13, 2907 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  39. Shameer, K., Tripathi, L. P., Kalari, K. R., Dudley, J. T. & Sowdhamini, R. Interpreting functional effects of coding variants: challenges in proteome-scale prediction, annotation and assessment. Brief Bioinform. 17, 841–862 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Zander, A. et al. Guide-independent DNA cleavage by archaeal Argonaute from Methanocaldococcus jannaschii. Nat. Microbiol. 2, 17034 (2017).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  41. Swarts, D. C. et al. Autonomous generation and loading of DNA guides by bacterial Argonaute. Mol. Cell 65, 985–998.e6 (2017).

    Article  CAS  PubMed  PubMed Central  MATH  Google Scholar 

  42. Zhao, J. et al. A machine vision-assisted Argonaute-mediated fluorescence biosensor for the detection of viable Salmonella in food without convoluted DNA extraction and amplification procedures. J. Hazard. Mater. 466, 133648 (2024).

    Article  CAS  PubMed  MATH  Google Scholar 

  43. Hou, Y., Chen, S., Zheng, Y., Zheng, X. & Lin, J.-M. Droplet-based digital PCR (ddPCR) and its applications. Trac. Trends Anal. Chem. 158, 116897 (2023).

    Article  CAS  MATH  Google Scholar 

  44. Cretich, M., Daaboul, G. G., Sola, L., Unlu, M. S. & Chiari, M. Digital detection of biomarkers assisted by nanoparticles: application to diagnostics. Trends Biotechnol. 33, 343–351 (2015).

    Article  CAS  PubMed  Google Scholar 

  45. Xiang, X., Shang, Y., Zhang, J., Ding, Y. & Wu, Q. Advances in improvement strategies of digital nucleic acid amplification for pathogen detection. Trac. Trends Anal. Chem. 149, 116568 (2022).

    Article  CAS  Google Scholar 

  46. von Wasielewski, R. et al. Tyramine amplification technique in routine immunohistochemistry. J. Histochem. Cytochem. 45, 1455–1459 (1997).

    Article  MATH  Google Scholar 

  47. Stack, E. C., Wang, C., Roman, K. A. & Hoyt, C. C. Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70, 46–58 (2014).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank the National Natural Science Foundation of China (32172293), the National Key Research and Development Program of China (2022YFF0607900) and the Open Project of Key Laboratory of Detection Technology of Focus Chemical Hazards in Animal-Derived Food for State Market Regulation (number KF-202201) for financial support.

Author information

Authors and Affiliations

Authors

Contributions

Z.W. and Y.C. conceptualized the research. Z.W. and X.C. planned and performed the research. Z.W. assisted in testing the code. Z.W. analysed the data. Z.W. wrote the original draft. A.M., F.J. and Y.C. reviewed and edited the paper.

Corresponding author

Correspondence to Yiping Chen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Food thanks Seyed Mohammad Taghi Gharibzahedi, Sang-Soon Kim and Fengge Song for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–9, Tables 1–7, Methods and information for the Results section.

Reporting Summary

Source data

Source Data Fig. 2

Unprocessed gels (Fig. 2c) and statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Cheng, X., Ma, A. et al. Multiplexed food-borne pathogen detection using an argonaute-mediated digital sensor based on a magnetic-bead-assisted imaging transcoding system. Nat Food 6, 170–181 (2025). https://doi.org/10.1038/s43016-024-01082-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s43016-024-01082-y

This article is cited by

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research