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64 A novel application of selective exo-enzymatic glycan labeling to co-quantify cell-surface glycans and protein-based biomarkers of the tumour immune microenvironment

2025· article· W4415900069 on OpenAlexaff
Katherine C. Brewer, Fabiola V. De León González, A. Uriarte, Eman R. Radwan, Xiaojing J Gou, Andrew D McLellan, Jonathan L. Babulic, Tricia R. Cottrell, Chantelle J. Capicciotti

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2025
Typearticle
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsGlycanImmune systemTumor microenvironmentCell

Abstract

fetched live from OpenAlex

Background Complex carbohydrates called glycans regulate cellular communication in the tumour immune microenvironment (TIME), with abundant N- and O-linked glycans decorating receptors, proteins, and cell surfaces. 1–3 Glycans influence protein folding, localization, and receptor binding2 4–6 Altered glycosylation, such as hyper-sialylation, is associated with cancer progression and resistance to immunotherapy.7 8 A lack of robust detection methods has limited the study of glycans in human tumor tissues.9–11 Selective exo-enzymatic labeling (SEEL) uses glycosyltransferases for highly specific labelling of glycans using nucleotide sugar derivatives functionalized with detectable probes.9 For example, recombinant human sialyltransferases ST6Gal1 and ST3Gal1 install biotinylated CMP-sialic acid (CMP-Neu5Biotin) onto N- and O-glycans, respectively (figure 1A). Streptavidin-fluorophore conjugates bind the biotinylated probe to allow subclass-specific glycan detection.9 12–14 Previous applications of SEEL lacked the spatial resolution and multiplexing depth needed to capture single cell glycan-protein co-expression in the TIME.12 15 16 The current study integrates SEEL with antibody-based immunofluorescent tissue staining to enable high-resolution co-detection of protein and glycan biomarkers on a single tissue slide.Methods To assess SEEL compatibility with clinical tissue processing, SK-BR-3 breast cancer cells were fixed with and without paraffin embedding (FF and FFPE), rehydrated, labeled (ST6Gal1+ CMP-Neu5Biotin+Strep-Alexafluor), and analyzed by flow cytometry. For tissue-level application, sequential FFPE breast cancer sections were deparaffinized, rehydrated, and labeled with two sialyltransferases (ST6Gal1 and ST3Gal1). To test multiplexing, slides underwent Opal-TSA immunofluorescence for pan-cytokeratin (AE1/AE3) and DAPI, followed by SEEL. Imaging was performed using Vectra Polaris, with spectral unmixing and autofluorescence reduction in InForm (v2.4).Results All potential sialylation acceptor sites were detected by removal of existing sialic acid residues using sialidase treatment concurrently with SEEL ( figure 1). Unoccupied acceptor sites were detected with SEEL in the absence of sialidase. Native sialylation is quantified as the difference in SEEL signal between the sialidase(-) and sialidase(+) detection (figure 1B). SEEL was fully compatible with FFPE; biotin intensity did not significantly differ across unfixed, FF, and FFPE SK-BR-3 cells (p > 0.05) (figure 1C-E). In FFPE tissue, N- and O-glycans showed strong, membrane-localized biotin signal (figure 2), including the expected pattern of increased signal with sialidase treatment. Concurrent immunofluorescence with SEEL successfully labeled both glycans and cytokeratin.Conclusions This dual-modality approach enables spatial co-detection of glycans and proteins in FFPE tissue. Future studies will leverage this multimodal mapping technique to characterize the significance of altered glycosylation patterns in association with immunoregulation within the TIME.References Pinho SS, Reis CA. Glycosylation in cancer: mechanisms and clinical implications. Nat Rev Cancer. 2015 Sep;15(9):540–55.Granica M, Laskowski G, Link-Lenczowski P, Graczyk-Jarzynka A. Modulation of N-glycosylation in the PD-1: PD-L1 axis as a strategy to enhance cancer immunotherapies. Biochim Biophys Acta BBA - Rev Cancer. 2025 Apr 1;1880(2):189274.Reily C, Stewart TJ, Renfrow MB, Novak J. Glycosylation in health and disease. Nat Rev Nephrol. 2019 Jun;15(6):346–66.Zheng L, Yang Q, Li F, Zhu M, Yang H, Tan T, et al. The glycosylation of immune checkpoints and their applications in oncology. Pharmaceuticals. 2022 Nov 23;15(12):1451.Lee HH, Wang YN, Xia W, Chen CH, Rau KM, Ye L, et al. Removal of N-linked glycosylation enhances PD-L1 detection and predicts anti-PD-1/PD-L1 therapeutic efficacy. Cancer Cell. 2019 Aug 12;36(2):168-178.e4.Feng H, Feng J, Han X, Ying Y, Lou W, Liu L, et al. The potential of siglecs and sialic acids as biomarkers and therapeutic targets in tumor immunotherapy. [cited 2024 Oct 19]; Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC10813689/Song K, Herzog BH, Fu J, Sheng M, Bergstrom K, McDaniel JM, et al. Loss of core 1-derived O-Glycans decreases breast cancer development in mice. J Biol Chem. 2015 Aug 14;290(33):20159–66.Zhu W, Zhou Y, Guo L, Feng S. Biological function of sialic acid and sialylation in human health and disease. Cell Death Discov. 2024 Sep 30;10(1):1–15.Kofsky JM, Babulic JL, Boddington ME, De León González FV, Capicciotti CJ. Glycosyltransferases as versatile tools to study the biology of glycans. Glycobiology. 2023 Nov 1;33(11):888–910.Khilji SK, Goerdeler F, Frensemeier K, Warschkau D, Lühle J, Fandi Z, et al. Generation of glycan-specific nanobodies. Cell Chem Biol. 2022 Aug;29(8):1353-1361.e6.Sharon N, Lis H. History of lectins: from hemagglutinins to biological recognition molecules. Glycobiology. 2004 Nov 1;14(11):53R-62R.Lopez Aguilar A, Meng L, Hou X, Li W, Moremen KW, Wu P. Sialyltransferase-based chemoenzymatic histology for the detection of N- and O-Glycans. Bioconjug Chem. 2018 Apr 18;29(4):1231–9.Noel M, Gilormini P, Cogez V, Yamakawa N, Vicogne D, Lion C, et al. Probing the CMP-sialic acid donor specificity of two human β-d-galactoside sialyltransferases (ST3Gal I and ST6Gal I) selectively acting on O- and N-glycosylproteins. Chembiochem. 2017 Jul 4;18(13):1251–9.Sun T, Yu SH, Zhao P, Meng L, Moremen KW, Wells L, et al. One-step selective exoenzymatic labeling (SEEL) strategy for the biotinylation and identification of glycoproteins of living cells. J Am Chem Soc. 2016 Sep 14;138(36):11575–82.Kappler K, Hennet T. Emergence and significance of carbohydrate-specific antibodies. Genes Immun. 2020 Aug;21(4):224–39.Berry S, Giraldo NA, Green BF, Cottrell TR, Stein JE, Engle EL, et al. Analysis of multispectral imaging with the AstroPath platform informs efficacy of PD-1 blockade. Science. 2021 Jun 11;372(6547):eaba2609.Ethics Approval This study was approved by Queen’s University Human Subjects Research Ethics Board (HSREB# ONGY-600-21).Abstract 64 Figure 1Determining tolerance of selective exo-enzymatic glycan labeling of SK-BR-3 cell surface glycans to formalin fixation and paraffin embedding preservation. SEEL of N-glycans by ST6Gal1 (1a,b) on SK-BR-3 breast cancer cells subject to FFPE (1c) remains robust and comparable to live cell controls as determined by flow cytometry (1d,e)Abstract 64 Figure 2Preliminary selective exo-enzymatic labeling of N- and O-glycans on FFPE breast tissue with and without the use of sialidase. FFPE Breast tissue specimens (2a) were successfully stained by immunofluorescence for cytokeratin (red), DAPI (blue), and each N- and O-glycans by SEEL (cyan), with and without the use of sialidase to identify sialylation profiling (2b,c)

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.248
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2025
Admission routes1
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