Decoding Galectin-Glycan Recognition with ¹⁹F-Tagged Lectins: from Simple Glycans to the Cellular Glycocalyx

Glycan-lectin interactions at cell surfaces regulate numerous biological processes but remain challenging to characterize at the molecular level. Glycosylation heterogeneity results in lectin-binding targets─from purified glycoproteins to the cell-surface glycocalyx─presenting multiple glycan epitopes simultaneously. Concurrently, distinct lectins often exhibit overlapping glycan binding selectivity, with similar affinities for widely distributed epitopes. Consequently, how lectins compete and achieve selective recognition at glycoprotein and cell-surface levels remains poorly understood. Here, we introduce ¹⁹F lectin tagging, as an NMR-based approach to probe these complex glycan-mediated binding processes. Incorporation of ¹⁹F probes into lectins yields simple, background-free spectra, enabling binding studies in complex biological environments, including the cell surface. Importantly, this approach also allows the analysis of lectin mixtures with overlapping glycan specificities while individually resolving their binding behavior. We focus on galectins, a family of multifunctional and N-acetyllactosamine (LacNAc)-binding lectins that regulate diverse processes at the cell surface, to dissect their competitive binding behavior across targets of increasing complexity, from small carbohydrates to glycoproteins and the cell-surface glycocalyx. Our results provide insight into the mechanisms underlying the recognition of the immune checkpoint glycoprotein TIM-3 by galectins and reveal competitive binding between some galectin family members at the cell surface. Collectively, these findings reveal that galectin specificity is not dictated solely by LacNAc recognition but instead arises from the molecular context in which glycans are presented, including multivalency and competition for shared glycan ligands. More broadly, they highlight the potential of ¹⁹F lectin tagging to investigate binding events in biologically relevant systems.