Living cell surface N-glycans act as a "glycan code" that mediates cell–cell communication, immune recognition, and disease progression. Unlike genetic or chemical methods, enzyme-based glycan editing using glycosidases and glycosyltransferases allows dynamic, site-specific remodeling of N-glycans on living cells without compromising cell function.
A review in Glycoscience & Therapy categorizes current strategies into three modes: erasing (e.g., sialidase, galactosidase, mannosidase), replacing (e.g., endo-β-N-acetylglucosaminidase), and creating (e.g., fucosyltransferase, sialyltransferase) N-glycan signals. Representative applications include targeted desialylation for cancer immunotherapy, glycan remodeling to enhance viral detection, and dual enzymatic modification of NK cells for improved lymphoma therapy.
Novelty: Unlike prior reviews that broadly cover glycoscience, the current study specifically focuses on selective editing of N-glycan signals on living cells using enzyme-based tools. It provides a unified framework categorizing editing strategies into erasing, replacing, and creating, and highlights recent breakthroughs such as antibody–sialidase conjugates for targeted tumor therapy, chemoenzymatic remodeling of red blood cells for blood type conversion, and dual-transferase engineering of immune cells. The review also introduces the "biological quick recognition" (BioQR) concept, framing glycan signals as rapid, context-dependent markers beyond traditional pathway analyses.
Significance: The ability to selectively edit N-glycan signals on living cells in real time represents a paradigm shift in glycobiology, moving from static analysis to dynamic functional manipulation. The review consolidates cutting-edge enzymatic tools that enable precise control over N-glycan structures, offering new avenues for dissecting glycan-mediated mechanisms in immunity, cancer, and development. Translational applications include enhanced CAR-T cell targeting, universal blood cell production, and sialidase-based immunotherapies now in preclinical development.
By outlining current limitations and future directions—including AI-driven enzyme design and organoid-based models—this new review serves as a foundational resource for researchers in chemical biology, immunology, and translational medicine.
