Reference electrodes are essential for meaningful electrochemical measurements but face significant challenges in miniaturization and long-term stable potential. Conventional commercial reference electrodes, which rely on ion migration across porous frits, suffer from electrolyte leakage, potential drift, and limited scalability to micro dimensions. Quasi-reference electrodes lack a thermodynamically defined interface, exhibiting polarization-sensitive potentials that drift with experimental conditions, compromising their reliability for rigorous quantitative analysis. Therefore, a miniaturized reference electrode capable of stable potential output is critical for techniques like scanning electrochemical cell microscopy (SECCM), where accuracy and spatial resolution depend on electrode robustness. Herein, we report a facile and reproducible strategy to fabricate a nanopipette reference electrode (NPRE) leveraging nanoscale ion transport. This architecture replaces conventional glass bodies and fritted junctions with a monolithic pulled capillary structure, integrating a nanoscale terminal pore that inherently minimizes electrolyte leakage. The NPRE demonstrates excellent potential stability (>24 h) in both aqueous and organic media, underscoring its robustness across distinct chemical environments. Successful integration of NPRE with SECCM enables precise nanoscale electrochemical measurements, effectively overcoming the inherent limitations of conventional systems and providing an ideal platform for high-resolution studies in complex physiological or materials environments.

Single-atom nanozymes have been recognized as promising alternatives to natural metalloenzymes, but challenges remain in precise structure engineering for efficient biochemical reactions. Herein, we demonstrate a facile strategy for the efficient production of iron single-atom nanozymes with multidimensional coordination engineering, where atomically dispersed iron sites are modulated by axial F and first-shell B (Fe-N3B1/F). Combined theoretical and experimental studies reveal that the introduction of F and B could effectively break the stringent linear scaling relations of conventional Fe-N4 sites, converting a prohibitive *OH desorption penalty into a spontaneous exergonic process, thereby accelerating H2O2 activation and reactive oxygen species generation. Benefiting from the excellent peroxidase-like activity of Fe-N3B1/F nanozymes, we developed a cross-reactive sensor array. Coupled with machine learning algorithms, the sensor platform could achieve the intelligent pattern recognition of four homologous tetracycline (TC) antibiotics with about 80% classification accuracy. Moreover, integrating this sensing array with a smartphone-based paper analytical device could enable the rapid and instrument-free quantification of TCs in real environmental water samples. The present study highlights the significance of dimensionality engineering and establishes a multifunctional paradigm for environmental monitoring.

RNA guanine quadruplexes (rG4s) are noncanonical nucleic acid structures that contribute to diverse cellular functions and disease mechanisms. Defining the proteins that interact with rG4s (rG4IPs) is essential for elucidating their biological roles. Here, we build on the RNA-protein interaction detection (RaPID) platform to develop G4-RaPID, a tailored chemoproteomic strategy for the unbiased profiling of rG4IPs in living cells. Using G4-RaPID, we identified 105 candidate rG4IPs that were commonly enriched across three distinct rG4 sequences. Biochemical analyses confirmed that recombinant hnRNPA0, CHD4, and IGF2BP1 proteins directly bind rG4 structures in vitro. In addition, CLIP-seq experiments revealed significant enrichment of hnRNPA0 binding at endogenous rG4 loci. Luciferase reporter assays further demonstrated that hnRNPA0 engages the rG4 in the 5' UTR of NRAS mRNA to negatively regulate its translation. Together, these results establish G4-RaPID as a robust approach for mapping rG4-protein interactions in living cells and document hnRNPA0-rG4 recognition as a regulatory mechanism controlling NRAS mRNA translation.

Tire wear particles (TWPs) are generated by mechanical abrasion of tires on road surfaces and represent a significant source of microplastic pollution, contributing an estimated 30-50% of total microplastic emissions in Europe. Due to their persistence and limited biodegradability, TWPs accumulate in terrestrial, aquatic, and atmospheric environments. However, their detection and quantification remain challenging: carbon black hampers FTIR analysis, while pyrolysis-GC-MS yields only bulk mass data without information about particle abundance or size distribution. We present a novel approach to address this gap using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) combined with elemental fingerprinting and machine learning. We apply this method to zebrafish gut tissue to differentiate TWPs from biological tissue, paraffin-embedded material, and other naturally occurring particles. A random forest model trained on multielement signatures enables pixelwise classification of imaging data recorded with 7-μm lateral resolution despite the complexity of both TWP and biological matrices. Our results demonstrate the potential of LA-ICP-MS elemental imaging as a sensitive tool for TWP detection in biological tissue, providing new opportunities for monitoring and ecotoxicological studies.

Aflatoxin B1 (AFB1) is one of the most toxic and carcinogenic mycotoxins, and its trace-level determination in complex food matrices remains a major analytical challenge. Conventional chromatographic methods, while highly accurate, rely on expensive instrumentation and labor-intensive sample pretreatment, whereas most CRISPR/Cas-based biosensors depend on fluorescence intensity turn-on readouts that are vulnerable to matrix autofluorescence, photobleaching, and signal instability, limiting their reliability in real samples. Herein, we propose a rigidity-responsive fluorescence polarization (FP) biosensing strategy that integrates aptamer-based molecular recognition, rolling circle amplification (RCA), and CRISPR/Cas12a trans-cleavage for robust and matrix-tolerant AFB1 detection. In this system, target binding induces the release of a complementary DNA strand from an immobilized aptamer duplex, initiating padlock probe circularization and RCA to generate abundant Cas12a-activating amplicons. A rationally engineered conformation-restricted depolarization reporter (CRD-Reporter), in which the fluorophore is confined within a rigid duplex framework, provides an intrinsically high FP signal. Upon Cas12a activation, collateral cleavage disrupts the rigid architecture, releasing freely rotating fragments and producing a pronounced FP decrease. Unlike intensity-based CRISPR assays, the FP readout effectively suppresses background interference and signal fluctuations. The proposed assay exhibits a wide linear range from 0.003 to 300 ng/mL with a low detection limit of 0.00113 ng/mL, high specificity, and excellent accuracy in grains, peanuts, and tea samples. This work establishes a robust FP-based CRISPR sensing paradigm for reliable mycotoxin monitoring in complex food systems.

Urinary extracellular vesicles (EVs) are promising biomarkers for noninvasive diagnosis of urologic cancers, yet current workflows often require labor-intensive EV preisolation and multistep assays that limit clinical translation. Here we develop AuEIH, a temperature-responsive EV-imprinted hydrogel integrated with a monolayer AuNP array, enabling one-step urinary EV capture and in situ SERS profiling on the same substrate. At 25 °C, the boronic-acid-functionalized imprinted hydrogel selectively captures EVs from urine. Raising the temperature to 37 °C triggers hydrogel contraction, decreases AuNP interparticle gaps, and generates abundant plasmonic hot spots, thereby switching the substrate to a detection state for enhanced SERS acquisition at a physiological temperature. We evaluated AuEIH using 56 clinical urine samples (20 healthy volunteers; 12 bladder cancer, 12 prostate cancer, and 12 renal cancer). The platform achieved 100% accuracy in distinguishing cancer patients from healthy volunteers in this cohort. To further enable robust multiclass tumor typing from label-free spectra, we implemented a CNN-embedded Transformer model, which yielded accuracies of 98% (healthy), 98% (bladder), 94% (prostate), and 94% (renal). This capture-to-detection integrated AuEIH platform, coupled with attention-based spectral learning, provides a practical route toward a high-accuracy, noninvasive urologic cancer diagnosis from urinary EVs.

Real-time visualization of nucleolar dynamics is essential for elucidating its physiological functions and understanding the mechanisms underlying nucleolus-related stress and diseases. However, this task is technically challenging: super-resolution imaging is required to resolve the organelle's intricate architecture and rapidly changing morphology. Two major bottlenecks remain─the scarcity of probes compatible with such nanoscopy and the exclusive focus on proteins, which leaves the complete nucleolar morphology invisible. Here, we report a novel quinoline derivative probe (QVT) that can rapidly "light up" cellular RNA and exhibits superior performance compared to the commercial dye SYTO RNA Select, including red emission, greater RNA selectivity and binding affinity, and enhanced photostability. These optical advantages facilitate super-resolution mapping of nucleolar ultrastructure using structured-illumination microscopy (SIM), particularly in clearly delineating RNA-rich subcompartments such as the dense fibrillar component and fibrillar centers. More importantly, QVT enables SIM to monitor nucleolar RNA dynamics in living cells during ferroptosis in real time and to investigate the previously elusive dynamics of nucleolus and nucleolar RNA in fatty liver disease. This study not only establishes a versatile chemical scaffold for next-generation RNA-targeted probes but also opens new avenues for mechanistic exploration of nucleolar biology in both health and disease.

Achieving selective recognition and in situ monitoring of chiral isomers in complex biological environments remains a major analytical challenge. This study presents a portable sensing platform that combines machine learning with iron-doped carbon dots (FeCDs) for differential dual-mode detection and real-time bioimaging of chiral ascorbic acid (L-AA and D-AA). The FeCDs exhibit distinct chirality-dependent fluorescence responses: L-AA triggers a pronounced "red-to-cyan" fluorescence blue-shift by inducing the reduction of Fe3+ to Fe2+ and its subsequent dissociation from the carbon skeleton surface, thereby blocking ligand-to-metal charge transfer. In contrast, D-AA leads to a "red-to-colorless" fluorescence quenching predominantly via weak interactions governed by steric hindrance, following a photoinduced electron transfer pathway. DFT and IGMH analyses elucidate the chirality-dependent signal transduction mechanisms. A hydrogel-based chip integrated with FeCDs was fabricated and coupled with smartphone imaging and an XGBoost algorithm, enabling extraction of 1019 image features for high-accuracy quantification (R2 > 0.97, Error < 1.5%) and 100% chiral discrimination. The platform was successfully applied to complex real-world samples and enabled in vivo imaging and semiquantitative analysis of ascorbic acid distribution in an Oryzias latipes model. This work provides insight into stereoselective interactions with metal-doped carbon dots and offers an intelligent, portable tool for chiral sensing and physiological tracing.

Tumor heterogeneity is the major driver of cancer drug resistance, but its quantitative in situ characterization remains difficult to probe at the single-cell level. Here, laser tweezers Raman spectroscopy (LTRS) is employed to achieve micrometer-scale, spatially resolved, label-free biochemical imaging of eight representative cancer cell lines (Jurkat, Raji, NCI-H82, HCC827, PC-9, NCI-H1437, DLD-1, and CHO). Two-dimensional Raman spectral snapshots of optically trapped single-cell lines were acquired, from which pixel-resolved Raman spectra were extracted. Characteristic Raman peak ratios associated with lipids, proteins, nucleic acids, and membrane components were mapped to reveal intracellular biochemical distributions. Based on the spatial profiles of these ratios, the spatial full width at half-maximum (fwhm) was introduced as a quantitative spatial heterogeneity marker. Hematological cancer cells (e.g., Jurkat) exhibit multipeaked distributions with narrow spatial fwhm values (∼2.0-5.3 μm), reflecting organelle-level fragmented heterogeneity, whereas solid tumor cells (e.g., NCI-H82) display single-peaked profiles with broader values (∼18.6-35.9 μm), indicating more uniform and continuous molecular organization. Furthermore, a label-free classification model based on characteristic Raman ratios enables accurate tumor phenotype discrimination. This work establishes spatial fwhm-based, spatially resolved LTRS analysis as a robust framework for quantifying single-cell heterogeneity with potential applications in early cancer screening and therapeutic monitoring.

In sensing analysis, strong output signals and interface anti-interference capacity, as well as the combination of multiple analysis modes, can greatly improve the detection sensitivity and accuracy of sensors. In this study, copper-doped carbon dots and Cu-CeO2 nanozymes were chosen as efficient signal probes, which could generate strong electrochemical (EC) and colorimetric (CL) signals through interface electron transfer and catalysis, achieving significant "signal-on". Meanwhile, the triggering of two different signals enabled a dual-mode analysis of the target, effectively reducing false-positive results. Second, a Y-shaped zwitterionic biopeptide was designed for interface modification, which consisted of an anchoring fragment (C), a supporting fragment (PPPP), and an antifouling fragment (EK(KSRE)DER) with a dual-branched structure, resisting nonspecific adsorption of interfering substances. Furthermore, COF-Au NPs with a high electrochemically active surface area were selected as a sensing substrate for loading ferrocene as internal standards, which not only promoted electron transfer but also eliminated background and environmental interferences, thus further improving detection sensitivity and reliability. On this basis, an antifouling ratiometric biosensor was constructed for dual-mode monitoring of human epidermal growth factor receptor 2 (HER-2), a crucial indicator for early clinical diagnosis and prognosis evaluation of breast cancer. Specifically, the linear ranges of EC and CL modes were 100 fg/mL-100 ng/mL and 10 fg/mL-100 ng/mL, with detection limits of 41 fg/mL and 4.3 fg/mL (3σ/S), demonstrating the enormous potential of the designed dual-mode platform for HER-2 detection in serum.