Single-molecule super-resolution microscopy allows pinpointing individual molecular positions in cells with nanometer precision. However, achieving molecular resolution through tissues is often difficult because of optical scattering and aberrations. We introduced 4Pi single-molecule nanoscopy for the brain with in-situ point spread function retrieval through opaque tissue (4Pi-BRAINSPOT), integrating 4Pi single-molecule switching nanoscopy with dynamic in-situ coherent PSF modeling, single-molecule compatible tissue clearing, light-sheet illumination, and a quantitative analysis pipeline utilizing the highly accurate 3D molecular coordinates. This approach enables the quantification of protein distribution with sub-15-nm resolution in all three dimensions within complex tissue specimens. We demonstrated 4Pi-BRAINSPOT's capacities in revealing the molecular arrangements in various sub-cellular organelles and resolved the membrane morphology of individual dendritic spines through 50-µm mouse brain slices. This ultra-high-resolution approach allows us to decipher nanoscale organelle architecture and molecular distribution in both isolated cells and native tissue environments with precision down to a few nanometers.

Rational design of nanozymes with enhanced catalytic efficiency remains a central challenge in the development of artificial enzymes. Herein, we report the construction of ultrasmall gold nanocluster-based nanoassemblies (Dp-AuNCs@Fe2+) through the coordination of Fe2+ ions by a dopa-containing peptidomimetic ligand (DpCDp). This nanoarchitecture simultaneously integrates catalytically active gold cores and redox-active Fe2+ centers, bridged by DpCDp to facilitate directional electron transfer. Comprehensive spectroscopic and kinetic analyses reveal that DpCDp promotes efficient charge migration from the Au core to surface-bound Fe2+, significantly enhancing H2O2-mediated peroxidase-like activity. Compared to bare Dp-AuNCs, Dp-AuNCs@Fe2+ display a 4.3-fold improvement in detection sensitivity, a 6.7-fold increase in catalytic efficiency, and markedly stronger hydroxyl radical generation. Mechanistically, this activity stems from a synergistic triad: direct H2O2 oxidation at gold surfaces, radical generation at Fe2+ sites, and DpCDp-facilitated electron shuttling. This work presents a robust strategy for nanozyme enhancement via electronic and structural co-engineering, offering valuable insights for the future design of bioinspired catalytic systems.

Bacterial discrimination is essential for microbiological studies and clinical diagnosis. However, the development of highly selective probes remains challenging. Herein, we present a lipid-mimicking fluorescent probe, namely, TB12NS, for achieving selective bacterial imaging and rapid antibiotic screening. Specifically, TB12NS integrates a donor-π-acceptor structural luminophore, a hydrophobic alkyl chain, and a hydrophilic ammonium sulfonate headgroup, enabling targeted intercalation into phospholipid-rich cytoplasmic membranes. TB12NS exhibits pronounced fluorescence responses toward Gram-positive bacteria, facilitating rapid bacterial discrimination and membrane polarity sensing. Moreover, when incubated with membrane-disrupting antibiotics in the presence of Gram-negative bacteria, TB12NS effectively intercalates into the cytoplasmic membrane, emitted markedly enhanced fluorescence for probing membrane integrity and evaluating antibiotic activity. Overall, TB12NS provides a robust platform for selective bacterial imaging and rapid membrane-disrupting antibiotic screening.

Single-molecule fluorescence blinking reflects reversible transitions between open emissive and closed nonemissive forms of rhodamine dyes. These transitions are strongly influenced by the local chemical environment. Here, we establish fluorescence blinking as a quantitative and interpretable readout of local physicochemical interactions. Hydroxymethyl silicon-rhodamine (HMSiR) was covalently linked to a series of short peptides designed to span defined electrostatic, hydrophobic, and hydrogen-bonding properties. Each peptide created a distinct microenvironment that modulated the spirocyclization equilibrium of the fluorophore. Blinking trajectories recorded under controlled conditions yielded descriptors such as on-state dwell times and state-transition statistics, which served as optical signatures of peptide-fluorophore interactions. Machine learning regression mapped these descriptors onto continuous physicochemical parameters, enabling accurate prediction of peptide net-charge, hydrophobicity, and hydrogen-bonding capacity. This work provides a direct connection between blinking dynamics and local physicochemical interactions, transforming stochastic fluorescence blinking into a mechanism-based chemical readout.

Accurate quantification of molecular dynamics in live cells is critical for elucidating receptor signaling and guiding therapeutic strategies. Yet current deep learning methods for fluorescence imaging often distort intensity and lack robustness under diverse imaging settings, limiting quantitative utility. We present an adaptive deep learning framework that constructs in situ training data sets from ongoing sequences and integrates a self-feedback algorithm to preserve absolute molecular intensities. This approach achieves ≥ 3.9-fold gains in signal-to-noise and 1.6-fold improvements in localization precision across organic dyes, quantum dots, and fluorescent proteins. By maintaining both spatial and intensity fidelity, it enhances super-resolution reconstruction, stepwise photobleaching analysis, and live-cell single-molecule tracking. Applied to programmed death-ligand 1 (PD-L1), it uncovers density-dependent clustering, with small-molecule inhibitors inducing dimerization and reduced mobility, while antibodies increase diffusivity. Combined treatments exert complementary effects on PD-L1 membrane organization. This adaptive and intensity-preserving framework provides a broadly applicable platform for high-precision quantitative bioimaging across modalities and experimental conditions.

A series of manganese terpyridine dicarbonyl derivatives (1-5) covalently attached to a proximal polyamine moiety have been prepared. 1,4-Diazepane promotes CO2 reduction to formic acid with noticeably higher rates.

Despite being a mature technology, the large-scale deployment of amine-based solvents for post-combustion carbon capture is limited by their high energy demand for regeneration, slow absorption-desorption kinetics, and susceptibility to degradation, underscoring the need for more efficient and viable solutions. This study developed an innovative hybrid absorption system that synergistically integrated monoethanolamine (MEA) with imidazolium-based ionic liquids (ILs) and zeolitic imidazolate frameworks (ZIFs) to overcome these limitations. Through systematic optimization of MEA-IL binary blends and the subsequent incorporation of ZIFs, the optimal types and concentrations of ILs and ZIFs were identified, along with key process parameters. The resulting selected formulation, 30 wt% MEA blended with 0.15 g/mL [BMIM][Br] and 0.125 g/mL ZIF-67, delivered substantial performance gains compared to 30 wt% MEA benchmark. The maximum absorption rate and capacity increased by 106.4% and 76.3%, respectively, while the desorption rate and capacity were enhanced by 51.1% and 63.4%. 13C and 1H NMR spectroscopy provided clear evidence of the chemical transformations during absorption, enabling the proposal and confirmation of a novel synergistic mechanism in which ILs and ZIFs complementarily enhance the capture process. Moreover, the developed composite system retained over 80% of its initial capacity over six consecutive cycles and showed reliable performance in scale-up experiments up to 40 & times; the baseline, confirming its robustness for industrial implementation. This work elucidates the synergistic mechanisms underlying the MEA-IL-ZIF system and demonstrates its potential as a scalable, energy-efficient, and durable solution for industrial CO2 capture.

Knotted proteins possess complex topologies that impose unique constraints on folding and unfolding. Whether the knot persists during denaturation and how its conformation reorganizes, however, remain unresolved. Here, we investigated the knotted protein 1O6D using site-specific fluorescence resonance energy transfer (FRET) and time-resolved fluorescence anisotropy (TRFA). The combined data support a staged denaturation process. At low denaturant concentrations, the knotted architecture undergoes progressive loosening accompanied by spatially nonuniform local rearrangement and C-terminal-directed reorganization. TRFA further reveals distinct site-dependent dynamical responses, including nonmonotonic anisotropy changes at W120 and W127, consistent with transient confinement within a motion-restricted intermediate microenvironment. At higher denaturant concentrations, the protein expands substantially, yet the FRET-derived distances and apparent thermodynamic parameters remain more consistent with a topologically constrained denatured ensemble than with a fully extended random coil. These results provide site-resolved evidence for asymmetric relaxation and persistence of a loosened but still tied topological state.

Endometrial cancer (EC) is one of the most common gynecological malignancies worldwide. Although numerous patients are diagnosed at an early stage with favorable outcomes, advanced and metastatic disease remains associated with limited therapeutic options and poor prognosis. Advances in molecular characterization have reshaped the understanding of EC pathogenesis and enabled the development of classification-driven treatment strategies. The present review summarized current standard therapies, including surgery, chemotherapy and radiotherapy, and highlighted the growing role of molecularly targeted treatments. The integration of pathogenetic, histopathological and molecular classifications provides a framework for identifying actionable alterations. Key oncogenic signaling pathways, including PI3K/AKT/mTOR and RAS/RAF/MEK/ERK, were discussed in the context of therapeutic targeting and precision medicine. In addition, emerging strategies, particularly immunotherapy and combination approaches, were addressed. A deeper understanding of molecular heterogeneity may facilitate individualized treatment selection and improve clinical outcomes in patients with EC.

Moesin is a member of the ERM (Ezrin-Radixin-Moesin) protein family that links the plasma membrane to the actin cytoskeleton through a conformational activation process. While autoinhibition is known to be regulated by interactions between the N-terminal FERM domain and the C-terminal domain (CTD), the role of the central α-helical domain (CHD), which connects these regions, remains poorly understood. Here, we combine single-molecule and ensemble Förster resonance energy transfer (FRET) with complementary biophysical approaches, including circular dichroism spectroscopy and isothermal titration calorimetry, to investigate the intrinsic conformation, stability, and ligand responsiveness of the CHD. Single-molecule FRET measurements reveal that the CHD adopts a compact helical bundle that is intrinsically stable and independent of FERM-CTD interactions, a finding further supported by thermal denaturation analysis. Upon binding of phosphatidylinositol 4,5-bisphosphate (PIP2), both single-molecule and ensemble FRET analyses show a pronounced increase in intradomain distances, indicating a transition from a compact bundle to a more extended conformation. Consistently, isothermal titration calorimetry confirms direct binding between PIP2 and the CHD. Together, these results identify the CHD as an active regulatory element, rather than a passive linker, and suggest that its conformational remodeling provides a critical structural basis for Moesin activation. More broadly, this work offers new mechanistic insight into how lipid binding is coupled to large-scale conformational transitions in ERM proteins.

The complex synthesis, purification, and delivery of photosensitizers remain major bottlenecks for clinical photodynamic therapy (PDT). Here, we develop PCB@Ecoli@CaP, an engineered Escherichia coli-based living therapeutic that autonomously biosynthesizes the natural photosensitizer phycocyanobilin (PCB) and is encapsulated within a calcium phosphate (CaP) shell to form a self-contained photodynamic system. Under 660 nm laser irradiation, PCB@Ecoli@CaP efficiently generates reactive oxygen species (ROS), which induce potent oxidative stress to eradicate 4T1 tumor cells and simultaneously trigger bacterial self-killing, thereby establishing a self-limiting therapeutic platform. The CaP coating enhances biocompatibility and stability while modulating light penetration and ROS release, as confirmed by DPBF photobleaching, intracellular ROS imaging, and CCK-8 viability measurements. This dual-function system integrates in situ photosensitizer biosynthesis, on-demand photodynamic activation, and built-in safety through self-elimination into a single, programmable microbial platform, offering a simplified, scalable, and safer strategy for next-generation PDT.

Single-particle tracking (SPT) provides high-resolution spatial-temporal information on biomolecule dynamics. However, localization inaccuracies, limited track lengths, heterogeneous fluorescence backgrounds, and potential molecular motion blur pose significant challenges that hinder the accurate extraction of movement trajectories and their underlying motion behavior. The conventional SPT pipeline struggles to comprehensively address detection, localization, linkage, and motion parameter inference simultaneously, resulting in information loss during sequential processing. To overcome these challenges, we propose SPTnet, an end-to-end deep learning framework that leverages a Transformer-based architecture to optimize trajectory and motion parameter estimations in parallel through a global loss. SPTnet bypasses traditional SPT processes, directly inferring molecular trajectories and motion parameters from fluorescence microscopy videos with a precision approaching the statistical information limit. Our results demonstrate that SPTnet outperforms conventional methods under commonly encountered but challenging conditions such as short trajectories, low signal-to-noise ratio (SNR), heterogeneous backgrounds, motion blur, and especially when molecules exhibit non-Brownian behaviors. ### Competing Interest Statement C.B. and F.H. are inventors on patent application submitted by Purdue University that covers basic principles of SPTnet.

Cryo-correlative light and electron microscopy (cryo-CLEM) facilitates in situ imaging and structural analysis by combining the molecular specificity of fluorescence microscopy with the ultrastructural resolution of cryo-electron microscopy. By further combining single molecule localization with cryo-CLEM, molecular positions of individual emitters can be revealed in the context of the electron density map of a cell, providing unique insights to profound questions in cell biology and virology. However, cryogenic fluorescence light microscopy (cryo-FLM) suffers from severe and spatially heterogeneous optical aberrations that distort the point spread function, limiting the accuracy of molecular localizations as well as downstream cryo-transmission electron microscopy workflows. Here, we present a systematic and quantitative analysis of optical aberrations in a commercial cryo-FLM system, uncovering the sources of significant distortions such as system imperfections, refractive index mismatches, and sample-induced heterogeneities. These system and sample induced aberrations lead to localization errors up to 90 nm laterally and over 300 nm axially, challenging the feasibility of precise molecular positioning within the vitrified specimen. We demonstrate that these errors are partially mitigated by spatially matched or adaptive point spread function models pushing the error rate down to ten nanometers or less, offering practical guidance for aberration-aware cryo-FLM and cryo-CLEM strategies. Our findings highlight the necessity of accurate, in situ point spread function modeling to achieve nanometer-scale localization in cryo-FLM. The experimental pipeline developed in this work establishes a novel tool to assess optical performance in cryo-CLEM and cryogenic focused ion beam milling workflows as the field strives toward accurate and precise molecular localization.