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Lectin-based detection and expression profiling of native glycoRNAs
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  • Open access
  • Published: 14 February 2026

Lectin-based detection and expression profiling of native glycoRNAs

  • Yong Li1,2,
  • Yisong Qian3,
  • Xiang Li1,
  • Tianhua Lei1,
  • Hillary McGraw4,
  • Paula Monaghan-Nichols1 &
  • …
  • Mingui Fu1 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Biochemistry
  • Molecular biology

Abstract

Current methods for detecting glycoRNAs include metabolic labeling in living cells or animals and RNA-optimized periodate oxidation and aldehyde labeling (rPAL), each of which offers distinct advantages and limitations. Here, we report a relatively simple and rapid approach for detecting native glycoRNAs using direct lectin hybridization. This method involves several straightforward steps, including total RNA isolation, northern blotting, and lectin hybridization. Its advantages include high sensitivity, procedural simplicity, and broad applicability. Using this approach, we profiled glycoRNA expressions in RNA samples derived from human and murine tissues and cell lines and compared the results with those obtained using two established detection methods. We also examined differences in glycoRNA expression under physiological and pathological conditions. Notably, we report for the first time the detection of free glycoRNAs in various human biofluids, including plasma, urine, and amniotic fluid. Overall, our findings demonstrate that this method is reliable and reproducible, providing an alternative tool for studying glycoRNA biology and potentially offering utility for future clinical diagnostics.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank Drs. Tony Wang, Daping Fan, Kun Chen, Jianming Qiu, Shizhen Wang and Cuthbert Simpkins for kindly providing cell lines and/or RNA samples. This project was supported by the National Institutes of Health (R15AI138116, to M.F), UMKC Funding for Excellence (2023, to M.F), SPiRE (2024, to M.F) and UMKC SOM Bridge Fund (2025, to M.F).

Funding

This work was supported by the National Institutes of Health (R15AI138116 to M.F.), UMKC Funding for Excellence (2023 to M.F.), SPiRE (2024 to M.F.), and the UMKC School of Medicine Bridge Fund (2025 to M.F.).

Author information

Authors and Affiliations

  1. Department of Biomedical Science, School of Medicine, University of Missouri Kansas City, Kansas City, MO, 64108, USA

    Yong Li, Xiang Li, Tianhua Lei, Paula Monaghan-Nichols & Mingui Fu

  2. Department of Anesthesiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, PR China

    Yong Li

  3. Key Lab for Arteriosclerology of Hunan Province, International Joint Laboratory for Arteriosclerotic Disease Research of Hunan Province, Institute of Cardiovascular Disease, University of South China, Hengyang, 421001, PR China

    Yisong Qian

  4. Division of Biological and Biomedical Systems, School of Science and Engineering, University of Missouri Kansas City, Kansas City, MO, 64110, USA

    Hillary McGraw

Authors
  1. Yong Li
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  2. Yisong Qian
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  3. Xiang Li
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  4. Tianhua Lei
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  5. Hillary McGraw
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  6. Paula Monaghan-Nichols
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  7. Mingui Fu
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Contributions

M.F. conceived, designed and supervised the overall study. Y.L., X.L. and M.F. performed most of the experiments. Y.Q. performed metabolic labeling experiments for mice. T.L. and P.N. help to perform mouse tissue expression experiments, and M.F. wrote the manuscript. M.F., H.M. and P.N. revised the manuscript.

Corresponding author

Correspondence to Mingui Fu.

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The authors declare no competing interests.

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Cite this article

Li, Y., Qian, Y., Li, X. et al. Lectin-based detection and expression profiling of native glycoRNAs. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40291-2

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  • Received: 28 May 2025

  • Accepted: 11 February 2026

  • Published: 14 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-40291-2

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Keywords

  • GlycoRNAs
  • rPAL
  • Lectin
  • Expression profiling
  • Detection
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