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Enregistrement W4415900069 · doi:10.1136/jitc-2025-sitc2025.0064

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 sur 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

Notice bibliographique

RevueRegular and Young Investigator Award Abstracts · 2025
Typearticle
Langue
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGlycosylation and Glycoproteins Research
Établissements canadiensQueen's University
Organismes subventionnairesnon disponible
Mots-clésGlycanImmune systemTumor microenvironmentCell

Résumé

récupéré en direct d'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)

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,008
Tête enseignante GPT0,248
Écart entre enseignants0,241 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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