Abstract Lectins are widely used to detect glycan-associated features on the cell surface; however, their implementation in flow cytometry often requires empirical optimization and methodological considerations that are not always systematically described. Here, we provide a practical framework for implementing lectin-based glycophenotyping in flow cytometry, addressing both the experimental decisions involved in assay optimization and validation and its integration into conventional multiparametric workflows, highlighting its potential to address more complex biological questions. Using illustrative examples from murine splenocytes, we discuss key aspects of lectin titration, fluorochrome selection, negative controls, and specificity validation, and demonstrate the compatibility of lectin staining with commonly used flow cytometry applications, including viability assessment, immunophenotyping, intracellular cytokine detection, and proliferation analysis. By integrating methodological considerations with representative applications, this Technical Note provides a practical resource to guide experimental decision-making and facilitate the incorporation of lectin-based glycophenotyping into multiparametric flow cytometry workflows.
Abstract Post-traumatic osteoarthritis is a common sequela of joint injury in humans and companion animals, yet glycosylation changes associated with osteoarthritis remain poorly characterized. Lectin microarray analysis enables high-throughput profiling of glycan features in complex biofluids such as synovial fluid. Here, we used lectin microarrays to characterize synovial fluid glycosylation patterns in healthy and osteoarthritis-affected joints from horses, dogs, and humans. Comparative lectin-binding profiles within and across species revealed both conserved and species-specific glycomic signatures associated with osteoarthritis. Supervised machine learning distinguished osteoarthritis from control joints within a pooled multi-species framework and identified the lectins and their glycan epitopes contributing most strongly to classification, though cross-species generalization was limited and depended on the composition of each species’ control cohort. Elevated high-mannose emerged as the shared glycomic signature across species. Together, these findings support the feasibility of synovial fluid glycomics, integrated with machine learning, for identifying conserved osteoarthritis-associated glycan signatures; and inform the design of glycan-based diagnostic and translational strategies in both veterinary and human medicine.
Abstract The extracellular matrix (ECM) and cell surface glycocalyx are key components of biology and play crucial roles in development and tissue function, as well as disease. Proteoglycans, and their glycosaminoglycan (GAG) side chains, are critical components of the ECM and the glycocalyx. GAGs can bind to many different proteins, such as chemokines, and form hydrated barriers around cells. Existing and new methods are helping us to uncover more about the roles of GAGs in biology. Here, we build upon these technologies by providing a streamlined, standardised, and extensively documented end-to-end workflow for GAG disaccharide analysis that can be readily implemented in standard analytical facilities for the analysis of diverse biological sample types. Comprehensive step-by-step guidance is provided for sample disruption, GAG disaccharide preparation from biological samples, and HILIC-MS/MS analysis. In addition, we demonstrate the broad applicability of the workflow across a diverse range of low-input biological samples, including cell pellets, FACS-sorted cells, frozen tissue, FFPE sections, serum, and mucus washes. By complementing existing and emerging analytical approaches, this workflow supports wider adoption of GAG disaccharide analysis and facilitates investigation of GAG function across diverse biological systems. This method will sit alongside existing and new techniques to help improve access to GAG analysis, and thereby further the field of understanding GAG function in complex biological contexts.
