Guangyan Qing

Single right-handed cellulose nanocrystal rods direct the growth of imine-linked COF shells through interfacial confinement, enabling chirality transcription and enantioselective recognition in CNC@COF hybrids.

Glycans present two intrinsic properties that make nanoscale analysis a necessity rather than a refinement: a microheterogeneity that arises from non‐templated biosynthesis, and a structural information density that surpasses that of nucleic acids and proteins by orders of magnitude. Both demand readout at the level of the individual molecule, on scales from the chemical bond to the residue to the spatial register. Five emerging platforms now meet these requirements along complementary dimensions. Tip‐enhanced Raman spectroscopy reads single glycosidic linkages through vibrational fingerprints. Electrospray ion beam deposition coupled to low‐temperature scanning tunneling microscopy resolves connectivity, stereochemistry, and intact glycoconjugate architecture on individual molecules. Engineered nanopores translate single‐molecule translocations into linkage‐ and modification‐specific ionic‐current signatures in solution. High‐resolution cryo‐electron microscopy places glycans within hydrated higher‐order assemblies as explicit atomic models. Ångström‐resolution fluorescence imaging resolves individual glycans on intact cell surfaces. This Review examines each platform from the standpoint of its working principle, the structural variables it resolves, and the methodological boundary it currently defines, with attention to the recurring trade‐offs they negotiate across chemistry, physiology, ensemble, and interpretation. The mature contribution of these platforms will be the conversion of glycan structure from inventory into mechanism.

Efficient and precise bioseparation is essential and increasingly urgent for advancing life sciences and biotechnology. This demand is amplified by precision medicine, which requires isolating, enriching, characterizing and utilizing low-abundance, complex biomolecules, including post-translationally modified peptides, proteins and metabolites. Conventional separation techniques are unable to handle this challenge, which results in significant bottlenecks in the development of targeted drugs, biomarker discovery, and individualized therapeutic monitoring. Porous framework materials offer transformative solutions via structural tunability, ultrahigh surface areas and tailor-made pore chemistries: metal–organic frameworks (MOFs) enable selective protein separation and modified peptide enrichment through hydrophilic/metal coordination interactions, covalent organic frameworks (COFs) minimize protein denaturation with high stability and modifiable surfaces, and hydrogen-bonded organic frameworks (HOFs) achieve gentle, efficient aqueous biomarker enrichment via intrinsic biocompatibility and dynamic hydrogen bonding. This review systematically classifies MOFs, COFs, and HOFs and summarizes their applications in the separation and analysis of small-molecule drugs, phosphopeptides, glycopeptides, and proteins. We also discuss the key challenges, including pre- and post-modification methodologies, the construction of chiral porous materials, and integration with magnetic microspheres. Finally, we highlight their future prospects in advancing bioseparation for precision medicine, diagnostics, and therapeutic development, driving progress across biotechnology and biomedical engineering.

Conspectus: Glycans play pivotal roles in cell–cell communication, immune recognition, signal transduction, and pathogen-host interactions. Their versatile functions stem from extraordinary structural diversity, defined by variations in composition, linkage, configuration, and branching. Such structural complexity poses substantial challenges for comprehensive glycan analysis. Complicating matters further, many glycans exist only in trace amounts in biological systems, and their nontemplate-driven biosynthesis precludes amplification. This dual constraint further intensifies the analytical difficulties. Thus, the ability to isolate and structurally determine glycans with high efficiency has become paramount for advancing glycoscience. Given the structural diversity of glycans, biological systems have evolved a repertoire of glycan-binding proteins to decode these complex structures. Lectins represent a prime example; renowned for their specific binding to glycans, they have been used to profile glycans in the form of affinity reagents. Inspired by lectins, we turned to the rational design of glycan-affinity peptides (GAPs) that mimic lectin function. Over the past decade, our group has been dedicated to the development of GAPs, driving their evolution from simple binders to high-specificity, high-affinity recognition elements. These efforts have advanced GAPs from single amino acids and dipeptides to oligopeptides, accompanied by a remarkable affinity enhancement from millimolar to nanomolar levels. In parallel, the design paradigm has shifted from empirical trial-and-error to directed evolution and now to preliminary computational design. As a result, these GAPs have evolved into versatile precision recognition engines powering two complementary analytical frontiers: first, they underpin robust affinity materials for the precision capture of glycosylated targets, including lipopolysaccharides and circulating tumor cells; second, they enable high-resolution nanopore sensors capable of precision identification of specific glycans and glycosides. Overall, it is the evolution of GAPs into reliable recognition platforms that represents a significant advance in glycan analytical capability, moving from broad-spectrum enrichment toward targeted capture, and from bulk measurements toward single-feature resolution. In this Account, we summarize our efforts in GAP design and applications, presenting a unified narrative of how we engineered these affinity engines to decipher the glycan code with high precision. We begin with an overview of the fundamental principles, design strategies, and future trends underpinning GAP evolution. Following this, we detail our advances in GAP-driven precision glycan analyses. We believe this Account will drive the development of a new generation of GAPs and facilitate their broader implementation across glycoscience.

Nanopore detection technology has broad application prospects in DNA, protein, and glycan sequencing, as well as in the highly sensitive detection and identification of bio‐molecules. However, precise analysis of the structure‐function relationship and targeted optimization of device performance remain key bottlenecks restricting further development. Computational simulations can not only explain and verify experimental phenomena but also potentially guide experimental design, shorten the research cycle, and reduce research costs. Therefore, this review comprehensively summarizes the application of multiscale computational calculations in nanopore systems. By incorporating quantum mechanics calculations, molecular docking, molecular dynamics simulations, and finite element analysis, we aim to provide theoretical support for addressing the aforementioned bottlenecks. Among these methods, atomic‐scale calculations enable the precise characterization of molecular conformations and intermolecular interactions, thereby clarifying the nanopores’ microscale recognition mechanisms. Mesoscale simulations can trace the analyte translocation dynamics across nanopores, revealing inherent regulatory principles. Macroscale models effectively simulate the hydrodynamic behaviors of nanopore devices. In summary, the synergistic application of multiscale computational simulations establishes theoretical foundation for improving nanopore sensor performance. Furthermore, we discuss the challenges and future trends associated with the synergistic integration of computational simulations, aiming to provide valuable insights for researchers active in computational chemistry and nanopore‐based fields.

Nanopore sensing holds transformative potential for revolutionizing protein and glycan sequencing. However, translating this potential into practical, high-fidelity identification is severely bottlenecked by the challenge of processing massive amounts of highly similar nanopore ionic-current data, spurring an urgent need for robust, AI-driven solutions. Prevailing deep learning methods suffer from two limitations: they often fail to capture the fine-grained temporal dynamics essential for distinguishing structurally similar analytes, and their generic training strategies inadequately extract weak discriminative features, thus limiting classification precision. Here, we present SEDA-Former (Signal Enhancement and Dynamic Attention Transformer), a deep temporal learning framework designed for high-resolution nanopore single-molecule identification. SEDA-Former incorporates a multi-window sliding standard-deviation method for feature enhancement, a multi-channel temporal convolutional network to mine weak features in temporal dynamics, and a progressive adaptive attention training strategy that dynamically reweights sample losses based on learning difficulty. Across a diverse set of challenging benchmark datasets, including nanopore signals of 15 glycosides, 24 ginsenosides, 8 DNA molecules, and 17 cholic acid conjugates, spanning varying levels of signal complexity, SEDA-Former consistently achieves substantially higher classification accuracy than state-of-the-art methods and demonstrates robust cross-dataset transferability. SEDA-Former provides a versatile and scalable solution to facilitate single-molecule identification in nanopore sensing.

Biomimetic spider silk achieves remarkable functionalities through hierarchical architectures with highly oriented crystalline domains, offering potential across multiple disciplines. However, achieving uniform alignment and spatial control of nanocrystalline domains remains a critical challenge, limiting the realization of structure-derived optical and mechanical functionalities in bioinspired systems. Here, we develop an ultrastrong, transparent photonic hydrogel composed of cellulose nanocrystals (CNCs), wherein a programmable five-stage stretching-pause process enables precise alignment of CNC domains without sacrificing their intrinsic chirality—unattainable in conventional flexible polymers. This strategy facilitates uniform nanocrystal reorientation (orientation factor = 0.91) and transforms the porous network into aligned nanofibril bundles, yielding optical transparency (>90%) with anisotropic polarization responses, superior mechanical strength (61.6 MPa), toughness (251.8 MJ·m ⁻³ ), and fatigue resistance (226.7 kJ·m ⁻² ). The flexible hydrogel resists creasing and serves as a sustainable scattering polarizer for programmable polarized displays and secure information encryption, providing a versatile platform for advanced optical and electronic applications.

Citrullination, a post-translational modification (PTM) with a sub-dalton mass shift of only + 0.984 Da, plays critical roles in epigenetics and autoimmunity and is a hallmark of several inflammatory diseases, but remains a particularly long-standing challenge to resolve by conventional proteomics. Here we present Physical Interaction Transduction on Nanopores (PIToN), a semi-rational model for relating local pore–analyte interactions to nanopore discrimination. Guided by PIToN, we engineered an aerolysin nanopore carrying the K238E mutation, which together with E258 forms a localized electrostatic constriction that functions as a precision “reading head.” This pore enabled label-free, single-molecule detection of peptide citrullination with 98.3% accuracy and distinguished citrullination from methylation-related histone peptide states. The platform also enabled real-time monitoring of enzymatic citrullination and voltage-dependent selective peptide enrichment with up to 99.7% purity. This word provides a practical route for physico-chemically guided nanopore design and broader applications in PTM analysis and protein characterization.

The precise detection and dynamic modulation of G-quadruplexes (G4s) are essential for elucidating their roles in gene regulation, genome stability, and disease pathogenesis. However, seldom has a photo-reversible small-molecule assembly capable of dynamically responding to G4s been reported. Here, we present a novel self-assembly strategy based on a π-extended compound, S2, which spontaneously forms functionalized helical nanoassemblies with a 51-fold enhanced chiral signal. These nanoassemblies enable S2 to discriminate parallel, antiparallel, and hybrid G4 topologies through ratiometric fluorescence and stereoselective spatial alignment. Remarkably, the S2 nanoassembly also exhibits enhanced sensitivity for RNA G4s over existing probes. Mechanistic studies reveal that this superior performance arises from a cascade response mechanism involving "assembly disruption-monomer release-fluorescence activation". Moreover, the photoisomerization capability of S2 facilitates reversible binding to parallel G4s under alternating UV/Vis irradiation. Finally, S2 is successfully used for in situ imaging of RNA G4s in live cells and for the detection of RNA G4s in clinical blood samples, enabling the differentiation between healthy individuals and lung cancer patients. This study introduces a "nanoassembly-G4 interactome" framework that extends beyond traditional ligand design paradigms, offering a valuable tool for the analysis of G4s in both biological and clinical contexts.