Nanopore Signatures of High-Molecular-Weight Polysaccharides with Recurring Structural Motifs

Polysaccharides in nature possess profound biological significance, and their structural complexity renders them of great research value. However, the structural heterogeneity of complex polysaccharides has long posed a great challenge for analysis. Here, a nanopore profiling strategy was developed for polysaccharide characterization. Experimentally, polysaccharides are hydrolyzed into informative fragments, and the distribution of their resulting nanopore events forms a unique profile that serves as a reliable molecular signature. Seven distinct polysaccharides, including xanthan gum, gum arabic, agarose, sodium alginate, gellan gum, heparin, and chondroitin sulfate A, were successfully identified and discriminated. A profile-based machine learning classifier was also developed, which utilizes the overall distribution profile of fragment events as training input, yielding 100% validation accuracy. Further application of the fully constrained least-squares (FCLS) algorithm enabled accurate quantification of polysaccharide mixture composition, allowing for unambiguous analysis of mixtures of polysaccharides in the same assay. Collectively, this work establishes a universal, label-free, and high-resolution framework for decoding the structural complexity of polysaccharides.