Supramolecular self-assembly represents a spontaneous and reversible process that bridges discrete molecular building blocks with nanoscale architecture through non-covalent interactions. By rationally tuning these interactions, diverse nanostructures can be precisely constructed, each exhibiting distinct physicochemical and functional properties. The dynamic and multivalent nature of supramolecular assemblies endows them with structural adaptability and cooperative binding, enabling responsiveness to environmental cues and amplification of weak molecular interactions. Nature provides abundant paradigms for such self-organization, in which organized supramolecular interfaces mediate complex biological functions. Inspired by these natural principles, artificial self-assembly systems have been engineered to emulate the hierarchical organization and functional adaptability of living systems. In this Review, we summarize recent advances in nature-inspired supramolecular assemblies, focusing on peptide-based systems that exploit the chemical diversity of amino acids to modulate biomacromolecular interactions and cellular signaling. Understanding these biomimetic design principles offers a foundation for developing next-generation functional materials that bridge molecular precision with biological functionality.
Organic supramolecular hosts can selectively bond to small functional molecules with chromophores, inhibiting molecular motion and isolating quenching factors through spatial confinement, thereby enhancing fluorescence and phosphorescence emission and expanding their applications in chemistry, biology, and materials science. This review mainly focuses on supramolecular luminescent systems constructed from cucurbit[n]urils, cyclodextrins, and other macromolecules. It is very important that cucurbit[n]urils and cyclodextrins both possess rigid hydrophobic cavities, in which cucurbit[n]urils can bond to positively charged guest molecules due to the high negative potential of the carbonyl group at the portal, while cyclodextrin tends to bond to negatively charged guest molecules and has abundant hydroxyl groups, providing numerous modification sites, and both can be further assembled with biomacromolecules through bonding or modification. Biomacromolecules such as hyaluronic acid and chitosan can multivalently bind to guest molecules through electrostatic interactions and hydrogen bonds. Supramolecular luminescent systems formed by these organic macrocyclic hosts or macromolecules and guest molecules have been widely used to construct intelligent supramolecular assemblies and have been successfully applied to near-infrared cell imaging, in situ photodynamic therapy, anti-counterfeiting, information encryption, logic gates, and other fields. With the continuous emergence of novel luminescent groups and new macrocycles, photoluminescence confined within the assembly of organic supramolecular systems will undoubtedly boost their further development in fields such as constructing chiral transfer amplification systems, novel organic light-emitting diodes, and in vivo imaging diagnostics.
With the continuous development of the global economy, environmental pollution has emerged as a significant challenge impeding sustainable economic development. Consequently, the advancement of highly sensitive pollutant detection technologies plays a critical role in analyzing and mitigating environmental issues. Single-molecule junctions (SMJs), by constructing electrode-molecule-electrode junctions, directly convert specific recognition events of individual pollutant molecules into measurable electrical conductance signals, fundamentally enabling ultra-high sensitivity capable of reaching the single-molecule detection limit, along with real-time and label-free detection capabilities. This work begins by outlining the core technological platforms, primarily including two categories of single-molecule junction fabrication strategies, namely, dynamic methods (e.g., scanning tunneling microscopy break junction and mechanically controllable break junction) and static methods (e.g., electromigration break junction and carbon-based molecular junction), and analyzes their respective characteristics. Subsequently, it focuses on reviewing the specific applications of this technology in detecting inorganic pollutants (heavy metal ions, inorganic anions, and acidity/alkalinity), organic pollutants (explosives, pesticides, and dye molecules), and microbial-related pollutants (biomacromolecules). These applications demonstrate its exceptional detection sensitivity, ranging from femtomole to attomole levels, and excellent selectivity, with some techniques already validated in real environmental samples and clinical specimens. This work also identifies challenges in practical applications, such as mass transport limitations, interfacial stability, interference from complex matrices, and data analysis issues. Finally, it outlines future development directions, including the development of parallel array sensing, intelligent responsive probes, integration of artificial intelligence for data analysis, and promoting technological standardization and interdisciplinary collaboration, thereby providing guidance and reference for the subsequent development of single-molecule detection sensors.
Although phase correction is one of the most routine steps in NMR data processing, even the best available automated approaches often require manual adjustments by human experts. A deep learning-based phase correction algorithm is presented as a tandem vision transformer artificial neural network. It has been trained on a large set of synthetic solution-NMR like spectra and determines the zeroth- and first-order phase correction based on the entire input spectrum and achieves very high phasing accuracy for a broad range of experimental spectra without requiring any further manual adjustments. The new method, called DEEP Phaser, is demonstrated for a variety of different real-world solution 1H 1D NMR spectra, from small molecules and their complex mixtures to biomacromolecules, and is available as free software and as a public web server.
Sulfur quantum dots (SQDs), as an emerging member of the fluorescent quantum dot family, hold great promise for application in biological analysis and environmental health. However, their intrinsic resistance to fouling in complex matrix pollution is rarely reported. Herein, we report the remarkable antifouling properties of polyethylene glycol (PEG)-modified SQDs, which exhibit 100% fluorescence retention under harsh conditions, including high salinity (2 M), high temperature (90 °C), and high protein concentration (150 mg/mL). Through fluorescence quenching experiments and density functional theory (DFT) calculations, we elucidate the mechanistic origin of the differential antifouling behavior between PEG-SQDs and carboxymethyl cellulose (CMC)-modified SQDs (CMC-SQDs). DFT calculations reveal that CMC strongly polarizes H2O (dO-H = 1.026 Å), making it more prone to protein adsorption. In contrast, PEG induces substantially weaker polarization of H2O (dO-H = 0.980 Å), effectively inhibiting the adsorption of various hydrophilic biomacromolecules. Leveraging the excellent antifouling performance of PEG-SQDs, we developed a strategy for detecting tyrosinase (TYR) activity. This strategy is based on TYR-catalyzed oxidation of l-tyrosine to melanin-like products, which efficiently quench the fluorescence of PEG-SQDs. Ultimately, a highly sensitive, selective, simple, and environmentally friendly method for TYR detection has been constructed. This method exhibits a linear response range for TYR from 0.5 to 20 U/mL, with a detection limit of 0.08 U/mL (S/N = 3). Furthermore, it enables accurate quantification of TYR activity in human serum samples, underscoring the excellent antifouling capability of PEG-SQDs in complex biological matrices. Overall, this work not only provides a novel tool for the sensitive detection of TYR but also offers new insights into the antifouling mechanism of SQDs.
Immune checkpoint inhibitors (ICIs) have brought revolutionary therapeutic opportunities to advanced cancers. However, the insufficient accuracy of ICIs necessitates urgent advancements in precise targeting. Herein, leveraging the high compatibility of chiral configurations with chiral biomolecules, we constructed a pair of metal-centered chiral iridium(III) nanoparticles Δ-IrBMS8-NPs and Λ-IrBMS8-NPs featuring the BMS-8 moiety to enhance the chiral adaptability with immune checkpoint biomacromolecules while avoiding the use of nanocarriers. The resulting chiral nanoparticles demonstrate markedly distinct chirality-dependent biological activities. Specifically, Δ-IrBMS8-NPs achieve a 129-fold enhancement in tumor targeting, driven by the stereoselective recognition and binding of Δ-IrBMS8-NPs to tumor-specific PD-L1. This elevated targeting accuracy of Δ-IrBMS8-NPs with PD-L1 subsequently results in significantly enhanced anticancer immune responses in vivo, especially in activating the response to cytokine pathways in dendritic cells (DCs) and the recruitment of effector CD8+ T cells to remodel the tumor microenvironment. This work pioneers the discovery of nanoscale metal-centered chirality in precisely targeting immune checkpoints and highlights the pivotal role of delta configurations in enhancing anticancer immune responses, paving the way for the future rational design of effective metal-based immunotherapies.
Free energy calculations are at the heart of physics-based analyses of biochemical processes. They allow us to quantify molecular recognition mechanisms, which determine a wide range of biological phenomena, from how cells send and receive signals to how pharmaceutical compounds can be used to treat diseases. Quantitative and predictive free energy calculations require computational models that accurately capture both the varied and intricate electronic interactions between molecules as well as the entropic contributions from the motions of these molecules and their aqueous environment. However, accurate quantum-mechanical energies and forces can be obtained only for small atomistic models and not for large biomacromolecules. Here, we demonstrate how to consistently link accurate quantum-mechanical data obtained for substructures to the overall potential energy of biomolecular complexes using machine learning in an integrated algorithm. We do so using a two-fold quantum embedding strategy where the innermost quantum cores are treated at a very high level of accuracy. We demonstrate the viability of this approach for the molecular recognition of a ruthenium-based anticancer drug by its protein target by applying traditional quantum chemical methods. As such methods scale unfavorably with system size, we analyze the requirements for quantum computers to provide highly accurate energies that affect the resulting free energies. Once the requirements are met, our computational pipeline, FreeQuantum, is able to make efficient use of the quantum-computed energies, thereby enabling quantum computing-enhanced modeling of biochemical processes. This approach combines the exponential speedups of quantum computers for simulating interacting electrons with modern classical simulation techniques that incorporate machine learning to model large molecules.
Neonatal brain injury, such as hypoxic-ischemic encephalopathy (HIE), is a leading cause of infant mortality and long-term neurodevelopmental disabilities. Current clinical therapeutic strategies are limited by the blood-brain barrier (BBB), the complexity of the injury cascade, and the narrow therapeutic window. Nanomedicine has shown potential in preclinical studies for overcoming these barriers by leveraging its unique nanoscale characteristics and engineerability design to load, stabilize, and deliver vulnerable biomacromolecules across the compromised BBB to the lesion site. This review presents the first systematic horizontal comparison and critical evaluation of the major nanoplatforms employed in neonatal brain injury therapy. Based on data derived primarily from animal models, we analyze the heterogeneity across studies in model systems, administration routes, and efficacy endpoints, revealing common challenges in the field regarding long-term safety, manufacturability, and reproducibility. This review aims to provide guidance for selecting appropriate nanoplatforms to facilitate the translational advancement of this field toward clinical applications.
Dynamic control over polymer sequences remains a major challenge in synthetic macromolecules, limiting their adaptability relative to biomacromolecules. Herein, we introduce an intra-macromolecular sequence inversion in triblock copolymers and terpolymers using a furan-maleimide-anthracene Diels-Alder system, enabling thermal reconfiguration at 120°C without altering composition or molecular weight. Bifunctional initiators bearing reversible Diels-Alder linkages allow the synthesis of symmetric (ABA) and asymmetric (ABC) triblock co/terpolymers. Upon heating, retro-Diels-Alder dissociation followed by anthracene-maleimide (Anth-Mal) cycloaddition induces sequence inversion, yielding BAB or BAC architectures. Comprehensive experimental characterization confirms preservation of chain integrity, while revealing altered diffusion coefficients and distinct microphase-separated morphologies, highlighting the pronounced influence of sequence on polymer properties. This strategy establishes a paradigm for programmable polymer metamorphosis, opening new avenues toward stimuli-responsive materials with tunable self-assembly, crystallinity, and mechanical properties.
Brain metastasis remains a devastating clinical problem. A major challenge in brain metastasis research is the lack of high-quality models that accurately recapitulate the metastatic process, thereby enabling mechanistic insights into how cancer cells colonize in the brain. Traditional intracarotid artery injection models of brain metastasis often require permanent ligation of the common carotid artery (CCA), which alters cerebral hemodynamics and compromises the integrity of the blood-brain barrier (BBB). The protocol presents a refined method for establishing a high-fidelity mouse model of brain metastasis. The core innovation involves the Interlock Pulsatile Injection (IPI) technique for tumor cell delivery, followed by microsurgical arterial reconstruction at the puncture site to restore physiological blood flow in the CCA. Compared with the conventional CCA ligation model, the IPI-microsurgical repair approach significantly reduced perioperative mortality (2.86% vs. 25.71%) and increased the rate of brain metastasis establishment (94.12% vs. 65.38%). The IPI technique utilizes a tandem syringe configuration to minimize cell regurgitation during intracarotid injection. After tumor cell infusion, the CCA is meticulously repaired using microsurgical sutures under a stereomicroscope, thereby avoiding permanent occlusion. This preserves the native cerebral hemodynamics and the integrity of the BBB at the time of tumor cell entry, while significantly improving surgical success rates and reducing mortality. The metastatic intracranial lesions are validated by serial bioluminescence imaging and histopathology. The method provides a superior platform for studying the pathophysiology of brain metastasis and for preclinical therapeutic evaluation, thereby recapitulating the metastatic process.
Supramolecular Polymers (SPs) can undergo reversible self-assembly in response to internal or external stimuli. Design strategies based on the benzene-1,3,5-tricarboxamide (BTA) core are gaining increasing attention. In this study, we describe a BTA amphiphile that self-assembles into supramolecular fibers in water and is able to respond to both light and enzymatic activity. An azobenzene moiety (AZB) and Gly-Phe-Leu-Gly amino acid sequence (GFLG) were incorporated into the BTA monomer skeleton to build three identical wedges that respond to light and Cathepsin B activity. The synergistic application of two orthogonal stimuli enabled modulation of the assembly of BTA-AZB-GFLG fibers through the cooperative response to stimuli. These findings provide new insights into the use of SPs for future drug delivery applications.
Carbohydrate antigen 15-3 (CA15-3) is an important biomarker for early breast cancer diagnosis and therapeutic monitoring. This work presents an ultrasensitive fluorescent probe based on a dual-ligand peptides/UiO-66-NH2@AYG composite for CA15-3 detection. Dual-ligand peptides (GTTFSNYW and MHYLEYPF) target distinct CA15-3 epitopes, enhancing the binding affinity and specificity. The peptides are immobilized on UiO-66-NH2, whose high surface area supports efficient loading of the fluorescent reporter Acridine Yellow G (AYG). This probe demonstrates exceptional performance with a linear detection range of 0.05-1.5 pg/mL (R2 = 0.9981) and an ultralow detection limit of 0.004147 pg/mL. Practical validation in spiked mouse serum shows recovery rates of 91.57-103.7%, while clinical testing with serum samples from 10 cancer patients reveals a strong correlation with ELISA results (r = 0.9944, P < 0.001). This robust platform offers significant potential for cost-effective, early cancer screening in primary healthcare settings.
Understanding the plant cell wall architecture is essential for elucidating its biological function and mechanical properties. This study employs a synthetic approach using spherical core-shell capsules with shells composed of deuterated bacterial cellulose (d-BC) and pectin. The shell structure was created via a bottom-up layer-by-layer assembly onto CaCO3 templates, followed by characterization through microscopy and scattering techniques. Small-angle X-ray scattering (SAXS) and confocal laser scanning microscopy revealed increased pore sizes in hydrated d-BC/pectin shells compared to those of hydrated wood-derived cellulose nanofiber (CNF)-based shells from a previous study. Using small-angle neutron scattering (SANS) with contrast variation, structural changes of individual wall components under varying salinities (0 or 10 mM NaCl) were analyzed. The presence of NaCl selectively influenced the pectin phase, while the d-BC network retained structural stability, highlighting its robustness as a wall component. This platform provides a useful tool for testing hypotheses and advancing our understanding of cell wall porosity and composition-dependent permeability.
DNA methylation is vital for development and diseases, yet no spatial DNA methylation profiling technology has been reported. Here we developed spatial 5mC-seq (SmC-seq), a microfluidic-based method providing unbiased genome-wide methylome at near single-cell resolution. Applying SmC-seq to mouse post-implantation development revealed a clear spatial heterogenous pattern of DNA methylation in inner cell mass-derived tissues. We identified a two-layer organization in the E8.5 ectoplacental cone with distinct methylation and proliferation states. Unexpectedly, a portion of maternal tissue with low DNA methylation level, enriched for nutrient-supplier progenitors, is observed in the middle region of decidua post-implantation. The hypomethylated regions in the nutrient-supplier progenitor cluster are associated with cell proliferation. Notably, the genes associated with hypomethylated regions in mature nutrient-supplier cluster are enriched in exocytosis and nutrient synthesis, linked to nutrient provision for embryogenesis before placental function. In summary, SmC-seq enables spatial DNA methylation mapping, advancing our understanding of biological events.
Mitochondrial transplantation has gathered much attention as therapeutics to improve multiple mitochondrial functions simultaneously. While the administration of naked mitochondria into the target tissue has demonstrated therapeutic outcomes sufficient to advance to clinical trials, there remain many limitations, including a low cellular uptake efficiency in the target tissue and dysfunction of the isolated mitochondria. To address these issues, engineering approaches have been developed to functionalize the isolated mitochondria. In this review, we focus on the three critical topics for efficient mitochondrial transplantation and outline emerging design rules and their limitations for each purpose: (i) tissue targeting, (ii) protection of mitochondria from external stresses, and (iii) improvement of cellular uptake efficiency. From these achievements, we also discuss the current limitations of mitochondrial transplantation and propose the future direction of the attractive therapeutic methodology.
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Clostridioides difficile infection (CDI) remains a major healthcare challenge due to recurrent disease, spore persistence, and biofilm-associated tolerance, while conventional antibiotics often disrupt gut microbiota. Here, we report a star-shaped poly(l-lysine) dendrimer (G3-PLL9) formulated into hyaluronic acid-based microgels for targeted oral delivery to the inflamed colon. G3-PLL9 exhibited potent antimicrobial activity, including rapid bactericidal effects, superior spore inhibition compared with vancomycin, and robust biofilm disruption at subinhibitory concentrations. In a murine CDI model, rectal administration of G3-PLL9 alleviated clinical symptoms, reduced tissue damage, and lowered recurrence risk. To enable oral therapy, G3-PLL9 was incorporated into hyaluronic acid microgels, achieving site-specific release through hyaluronidase-mediated degradation in the inflamed colon. Importantly, treatment preserved commensal gut microbiota more effectively than vancomycin. Collectively, these findings highlight G3-PLL9 microgels as a microbiota-sparing therapeutic that targets multiple stages of CDI pathogenesis─including spores and biofilms─and demonstrate their potential for clinical translation.
The mycobacterial outer membrane (MOM) constitutes an asymmetric permeability barrier that influences lipid organization and transport in Mycobacterium tuberculosis. In this study, we have developed Martini 3 coarse-grained (CG) lipid models of the MOM, incorporating α-mycolic acids, 5 different trehalose-based lipids, and PDIM (phthiocerol dimycocerosate). The CG models were parametrized and validated using all-atom simulations of symmetric inner- and outer-leaflet membranes, as well as fully asymmetric MOM models. Bonded parameters were optimized through an iterative refinement procedure targeting atomistic bonded distributions. The CG simulations show good agreement with the all-atom simulation data and available experimental measurements in terms of membrane thickness, solvent accessible surface area, lipid density profiles, and outer-leaflet-induced lipid disorder in α-mycolic acids at the inner leaflet. The model also reproduces the temperature-dependent phase behavior of all-atom α-mycolic acid membranes. Using this model, we demonstrate that PDIM localization, diffusion, and aggregation are strongly modulated by membrane fluidity and lipid composition with enhanced translocation and clustering in liquid disordered environments. Our CG MOM lipid models provide a validated platform for large-scale simulations of mycobacterial membranes and enable mechanistic studies of lipid organization, membrane dynamics, and protein-membrane and membrane-drug interactions.
Hydrogels are used for a wide range of biomedical applications. While mechanical characterization of hydrogels is frequently performed in isotonic saline, the chemical identity of these solutions may vary widely from the ionic environments encountered during their use. To explore this idea, we test the mechanical properties of a hydrogel cross-linked with dynamic covalent chemistry (DCC) in several physiologically relevant ionic solutions that mimic different biological conditions. Specifically, we evaluate rheological properties of a hydrazone-cross-linked hydrogel composed of recombinant, chemically modified hyaluronan and elastin-like protein (ELP). Our results show that the shear moduli and stress relaxation properties of DCC hydrogels can vary significantly in different ionic environments. We identify the thermoresponsive nature of ELP and changes in hydrazone bond kinetics as the primary reasons for the observed differences in mechanical properties. Taken together, this work elucidates mechanisms underpinning changes in hydrogel mechanics in different physiological solutions.
Persicaria chinensis, a well-known traditional Chinese medicinal herb that is both edible and medicinal, has been widely acknowledged for its therapeutic effects, such as anti-inflammatory, antioxidant, and antitumor activities. However, the role of miRNAs from this plant in the cross-kingdom regulation of human diseases has not been investigated. In this study, we analyze the miRNA expression profile of P. chinensis using high-throughput sequencing and identify a total of 673 miRNAs, including 422 novel miRNAs that are unique to this plant and 251 conserved miRNAs. Among the conserved miRNAs, pch-miR319a is found to be the most abundant. Moreover, food-oriented pch-miR319a accumulates in the uterus and tumors and exhibits a rich repertoire of target genes within cancer-related pathways, demonstrating significant cross-kingdom regulatory potential. Utilizing the dual-luciferase reporter gene assay, we demonstrate that pch-miR319a from P. chinensis targets the Itga3 gene, which is associated with cervical cancer progression. Overexpression of pch-miR319a significantly decreases the viability, migration, and induces apoptosis of HeLa cervical cancer cells in vitro. Moreover, in a syngeneic mouse tumor model of cervical cancer, treatment with pch-miR319a effectively inhibits tumor growth and downregulates the expressions of ITGA3 and the proliferation marker Ki-67. Our study highlights the potential of pch-miR319a from P. chinensis as a novel therapeutic agent for cervical cancer by targeting ITGA3 and provides new insights into the cross-kingdom regulatory mechanisms of plant miRNAs in human diseases.