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Measuring carbohydrate recognition profile of lectins on live cells using liquid glycan array (LiGA)

Nature Protocols volume 20, pages 989–1019 (2025)Cite this article

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Abstract

Glycans constitute a significant fraction of biomolecular diversity on cellular surfaces across all kingdoms of life. As the structure of glycans is not directly encoded by the organism’s DNA, it is impossible to use high-throughput DNA technologies to study the role of cellular glycosylation or to understand how glycocalyx is recognized by glycan-binding proteins (GBPs). To address this gap, we recently described a liquid glycan array (LiGA) platform that allows profiling of glycan–GBP interactions on the surface of live cells in vitro and in vivo using next-generation sequencing. LiGA is a library of DNA-barcoded bacteriophages, where each clonal bacteriophage displays 5–1,500 copies of a glycan and the distinct DNA barcode inside each bacteriophage clone encodes the structure and density of the displayed glycans. Deep sequencing of the glycophages associated with live cells yields a glycan-binding profile of GBPs expressed on the surface of cells. This protocol provides detailed instructions for how to use LiGA to probe cell surface receptors and includes information on the preparation of glycophages, analysis by MALDI–TOF mass spectrometry, the assembly of a LiGA library and its deep sequencing. Using this protocol, we measure glycan-binding profiles of the immunomodulatory sialic acid-binding immunoglobulin-like lectins‑1, -2, -6, -7 and -9 expressed on the surface of different cell types. Compared with existing methods that require complex specialist equipment, this method allows users with basic molecular biology expertise to measure the precise glycan-binding profile of GBPs on the surface of any cell type expressing exogenous GBP within 2–3 d.

Key points

  • This protocol describes the preparation of a liquid glycan array (LiGA) platform, a library of DNA-barcoded bacteriophages displaying 5–1,500 copies of a glycan. Deep sequencing of the glycophages associated with live cells yields a glycan-binding profile of GBPs displayed on the surface of cells.

  • The development of this technology enables testing of the biological role of multivalent glycan–lectin interactions in a multiplexed fashion, which was not previously possible.

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Fig. 1: Workflow for cell-surface binding assay using LiGA.
Fig. 2: The workflow for PCR, deep-seq and bioinformatic analysis.
Fig. 3: Siglec-7 enrichment profile on different cell milieu.
Fig. 4: LiGA measures the binding specificity of Siglec-7, -9 and -2 on distinct cell surfaces.
Fig. 5: LiGA profiling of Siglec-1 displayed on CHO cells.

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Data availability

All data relating to differential enrichment analysis are submitted as source data. All raw deep-sequencing data are publicly available in a searchable format on https://48hd.cloud/; for example, search ‘EF Sig9 jurkat’ to obtain raw sequencing data of LiGA EF post panning against Siglec-9+ Jurkat cells. Additionally, raw deep-sequencing data can also be requested from the corresponding author. DNA sequences of the three LiGA phage constructs with the reporter genes LacZ, mNeonGreen and mCherry have been deposited to GenBank (MN865131, MN865132, MN872303). Source data are provided with this paper.

Code availability

MATLAB and R scripts used to generate MALDI–TOF spectra and differential enrichment analysis, respectively, have been deposited to https://github.com/derdalab/liga.

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Acknowledgements

We thank the staff at the University of Alberta mass spectrometry facility (chemistry department) for help with MALDI analysis and S. Dang at the molecular biology service unit for assistance with Illumina sequencing. We acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) (RGPIN-2018-03815 to M.S.M. and RGPIN-2016-402511 to R.D.), the NSERC Accelerator Supplement (to R.D.), NSERC (RGPIN-2022-04484 to R.D.), Canadian Institutes of Health Research (CIHR) (no. 180445 to R.D.), GlycoNet (CR-29 and TP−22 to R.D.), and the Alberta Innovates Strategic Research Project to R.D. Infrastructure support was provided by the Canada Foundation for Innovation New Leader Opportunity (to R.D. and M.S.M.). Many compounds were prepared by the Consortium for Functional Glycomics, supported by National Institutes of Health (NIH) GM061126. G.M.L. acknowledges funding from Alberta Innovates Graduate Student Scholarship.

Author information

Authors and Affiliations

  1. Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada

    Mirat Sojitra, Edward N. Schmidt, Guilherme M. Lima, Eric J. Carpenter, Kelli A. McCord, Alexey Atrazhev, Matthew S. Macauley & Ratmir Derda

  2. Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, Alberta, Canada

    Matthew S. Macauley

  3. Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada

    Matthew S. Macauley

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Contributions

M.S. performed modifications of phages by glycans, MALDI analysis and cell binding assays. G.M.L. and A.A. optimized the cell binding assay. E.N.S., K.A.M. and M.S.M. generated the Siglec expressing cell lines. M.S., E.J.C. and R.D. performed statistical analysis and wrote software in R and MATLAB. R.D. and M.S. wrote the manuscript. R.D. and M.S.M. edited the final manuscript and contributed intellectual and strategic input. All authors approved the final manuscript.

Corresponding author

Correspondence to Ratmir Derda.

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Competing interests

R.D. is a shareholder of the start-up company 48Hour Discovery Inc. that licensed the patent application (WO2018141058A1) describing LiGA technology. The other authors declare no competing interests.

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Nature Protocols thanks Zheng Li, Ruijun Tian and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Sojitra, M. et al. Nat. Chem. Biol. 17, 806–816 (2021): https://doi.org/10.1038/s41589-021-00788-5

Lin, C.-L. et al. Nat. Commun. 14, 5237 (2023): https://doi.org/10.1038/s41467-023-40900-y

Schmidt, E. N. et al. Nat. Commun. 14, 2327 (2023): https://doi.org/10.1038/s41467-023-38030-6

Extended data

Extended Data Fig. 1 Describes the workflow for generating the encoding glycan using SDB M13 phage.

a) The M13KE SDB SVEK library is a combination of two degenerate codon regions in the phage DNA: SB1 and SB2. The combination of SB1 and SB2 yields a total of 1.3 × 1010 DNA possible barcoded phages that are phenotypically identical. b) The M13KE SDB SVEK library was plated at ~100 plaques per plate. Each plaque (clone) was isolated, individually amplified, and purified with Triton X-100 and PEG to remove lipopolysaccharides. c) Schematic showing unmodified phage, DBCO-modified phage, and phage with azido-glycan. d) Workflow for modification of clonal phage with a distinct barcode. First, the phage is reacted with DBCO-NHS and verified by MALDI-TOF. Azide glycan is ligated with the DBCO on the phage. e) Typical MALDI-TOF spectra of unmodified phage, phage modified with DBCO, and after cycloaddition of azide glycan.

Source data

Extended Data Fig. 2 Optimizing LiGA binding, washing, elution, and PCR.

a) Incubation at 37 °C consistently showed higher amounts of eluted phage particles, n=3. b) Titer results describe the binding of a specific reporter phage decorated with DC-SIGN binding glycan (αMan-green) over multiple washes, n=3. Also shown are lactose (red), blank phage (white) and a pool of glyco-phages (LiGA YY-blue). c) RNase I treatment to degrade cellular RNA. d) The amount of phage eluted was consistently higher with Proteinase K treatment compared to boiling the cell pellet. e) Gel describes a one-step and two-step PCR product. Lanes 1 and 16 are DNA 50 bp ladder. Lanes 2-9 are PCR of phage sample in decreasing concentration of phage from 108 to 101 PFU using NF10 and -96 primers. Lanes 10-18 are PCR of phage sample in decreasing concentration of phage from 108 to 101 PFU using F1 and R1 primers. f) PCR using no-overhang primers annealing to regions outside of the SB1 and SB2 regions. g) Second PCR appends the Illumina adapters and multiplexing barcodes to the PCR product from the first step (f). h) Sequence of the final DNA fragment. All respective n values are independent biological replicates.

Extended Data Fig. 3 LiGA profiling of hSiglec-9 on the surface of U937 and CHO cells.

a) Visual representation of the glycans in the LiGA. b) Siglec-9 expressing U937 cells with LiGA-100 enriched broad range of sialylated glycans, n = 5 test and n = 5 control samples. c) Siglec-9 expressing CHO cells showed a higher level of enrichment of the sialylated glycans and minor preference for a2-6 linked and poly-sialylated glycans, n = 5 test and n = 10 control samples. All respective n values are independent biological replicates. In b,c, the FC was calculated by Bioconductor edgeR DE analysis using the negative binomial model, TMM normalization, and BH correction for FDR. Error bars represent s.d. propagated from the variance of the TMM-normalized sequencing data. Red bars indicate glycans with non-background responses in the CHO cells but not in the U937 cells.

Source data

Extended Data Fig. 4 Arginine mutant of Siglec -7 -9 and hCD22.

Data mirrored from Fig. 3for better visualization. a) Visual representation of glycans in the library. b) Siglec-7 R124A showed a lower enrichment compared to the wild-type Siglec on CHO cells binding, n = 10 test and n = 15 control samples. c) Siglec-9 R120A binding to LiGA, n = 10 test and n = 15 control samples. d) hCD22 R120A mutant showed a lack of binding, n = 5 test and n = 15 control samples. All respective n values are independent biological replicates. In b–d, the FC was calculated by Bioconductor edgeR DE analysis using the negative binomial model, TMM normalization, and BH correction for FDR (n= 5 for each cell type). Error bars represent s.d. propagated from the variance of the TMM-normalized sequencing data.

Source data

Extended Data Fig. 5 LiGA binding to purified Siglec-6 and Siglec-6+ CHO cells.

a) Visual representation of the glycans in the LiGA. b) The binding pattern of LiGA on Siglec-6 expressing CHO cells showed no observable binding pattern, n = 5 test and n = 4 control samples. c) Binding of LiGA-100 purified Siglec 6 coated in a well, n = 5 test and n = 11 control samples. d) Purified Siglec-6 R122A mutant binding with LiGA-100. e) Differential enrichment of Siglec-6 compared to R122A mutant, n = 6 test and n = 11 control samples. The AzOH enrichment shows non-specific interaction among LiGA-100. All respective n values are independent biological replicates. In b–e, the FC was calculated by Bioconductor edgeR DE analysis using the negative binomial model, TMM normalization, and BH correction for FDR. Error bars represent s.d. propagated from the variance of the TMM-normalized sequencing data.

Source data

Supplementary information

Supplementary Information

Supplementary Note 1, Method 1, Tables 1–9 and Figs. 1 and 2.

Reporting Summary

Supplementary Data

1. Folder ‘Comparison Campaigns’. Comparison campaigns in csv format. 2. Folder ‘Dictionaries’. Contains excel files of LIGA mixtures, which provide the 1:1 correspondence between DNA sequence and glycan for glycan–phages. Each file represents a library of glycan–phage conjugates. 3. Folder ‘Differential Enrichment files’. Contains files of enrichment analysis between GBP+ and GBP−. 4. Folder ‘Differential-testing_Scripts’. Contains files and folders related to the differential enrichment pipeline. The analysis software is written in R programming language. 5. Folder ‘Maldi’. Contains information about each glycan–phage conjugate used in this manuscript. MATLAB script to analyze MALDI–TOF spectra. MALDI.pdf, which contains spectra for all the glycophages used in the manuscript. 6. Supplementary Data for Supplementary Table 1.

Source data

Source Data Fig. 3

Differential enrichment analysis of sequencing data. Each panel’s source data is tab-separated in the excel file.

Source Data Fig. 4

Differential enrichment analysis of sequencing data. Each panel’s source data is tab-separated in the excel file.

Source Data Fig. 5

Differential enrichment analysis of sequencing data. Each panel’s source data is tab-separated in the excel file.

Source Data Extended Data Fig. 1

Unprocessed gel for panel e.

Source Data Extended Data Fig. 1

Plaque count used to generate data for panels a,b.

Source Data Extended Data Fig. 3

Differential enrichment analysis of sequencing data. Each panel’s source data is tab-separated in the excel file.

Source Data Extended Data Fig. 4

Differential enrichment analysis of sequencing data. Each panel’s source data is tab-separated in the excel file.

Source Data Extended Data Fig. 5

Differential enrichment analysis of sequencing data. Each panel’s source data is tab-separated in the excel file.

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Sojitra, M., Schmidt, E.N., Lima, G.M. et al. Measuring carbohydrate recognition profile of lectins on live cells using liquid glycan array (LiGA). Nat Protoc 20, 989–1019 (2025). https://doi.org/10.1038/s41596-024-01070-3

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