Nano Letters

Adhesion of cells is the key factor determining functioning of multicellular organisms. Viscoelastic properties of cells can be studied by multiple methods. However, attractiveness of cells or extracellular matrix without the elastic component (dispersive adhesion) is not accessible. We present an extension of force spectrometry technology: the Multivalent Adhesive Probe Atomic Force Microscopy (MAPA) that delivers dispersive adhesion maps of live cells and biosurfaces, and identifies differences unresolved by viscoelastic probing.

The blood-brain barrier (BBB) constitutes a major bottleneck for the systemic delivery of most therapeutic agents to the central nervous system. Here, we report near-infrared reversible optoacoustic modulation of the BBB permeability (NIR-ROAMBBB), leveraging endothelial tight junction targeting hyperbranched gold nanoconstructs (HBGNCs) to amplify localized optoacoustic transduction under femtosecond laser excitation. We first synthesized HBGNCs with tunable particle sizes (62-150 nm) and consistent branch morphologies via a seed-mediated growth approach, and uncovered a non-monotonic relationship between particle dimension and optoacoustic output, where the 62 nm HBGNCs generated nearly twofold stronger optoacoustic signal than gold nanorods and gold nanostars under matched excitations. Conjugation with BV11 antibodies against junctional adhesion molecule A increased HBGNC endothelial association and cerebral accumulation, enabling focal and fluence-dependent transient BBB opening (3-6 h) under 800 nm femtosecond pulsed laser excitation, as validated by in vitro trans-endothelial electrical resistance measurements, ex vivo Evans blue extravasation staining, and in vivo NIR imaging. Featuring deep tissue penetration of NIR light, robust optoacoustic conversion of HBGNCs, and negligible femtosecond laser-induced photothermal damage, this non-invasive strategy enables precise focal modulation of BBB permeability and potential drug delivery.

Many biological processes rely on mechanical forces, with protein molecules acting as key mediators. Understanding how proteins respond to mechanical stress is essential for conditions including cardiomyopathy and muscular dystrophy. Natural proteins such as dystrophin and utrophin are composed of heterogeneous folding domains with distinct mechanical properties; deciphering domain-level behavior provides insights into disease mechanisms and informs therapeutic strategies. Single-molecule force spectroscopy (SMFS) enables probing the mechanical properties of entire proteins, yet current approaches struggle to identify heterogeneous folding domains, particularly without prior knowledge. Here, we present the first automated framework to identify heterogeneous folding domains in SMFS data, applying both existing clustering methods and a novel physics-aware deep clustering architecture, LatentUnfold. LatentUnfold learns complementary latent representations from force magnitude and the force-extension physical relationship through dual autoencoders, jointly optimized for clustering assignments. We apply our framework to experimental SMFS data collected from a synthetic two-domain protein (ddFLN4-Titin I27) as well as natural protein constructs of dystrophin and utrophin, with Monte Carlo simulated datasets serving as controlled validation. For the synthetic protein, we recover mechanical properties consistent with previously reported values for each domain. For the natural proteins, we uncover two mechanically distinct domain populations - corresponding to the N-terminal domain and spectrin-like repeats - with differences in both unfolding force and contour length increase, and reveal different unfolding order between them for the first time. This work enables domain-level biological inference, overcoming prior limitations that relied on averaging and overlooked heterogeneity, thus advancing the understanding of mechanical behavior in protein unfolding.

Reading both physical and chemical properties of a microenvironment from a single fluorophore remains a challenge. Here we demonstrate that two coexisting molecular equilibria within one near-infrared cyanine, CyC4, encode two mechanistically distinct ratiometric reporting channels. A meso-amino group and a pendant carboxylate form a tunable intramolecular hydrogen bond that toggles the dye between closed (700 nm) and open (780 nm) emissive conformers. Time-dependent density functional theory (TD-DFT) calculations show that the hydrogen bond raises the LUMO and blue-shifts the emission, establishing the 700/780 emission ratio as a local reporter of hydrogen bonding and polarity. Independently, the chromophore self-associates under crowding- and cosolvent-rich conditions into an aggregate with a blue-shifted, H-type absorption signature near 530-540 nm and a distinct emission near 610 nm upon 540 nm excitation. The intensity of this aggregate band relative to the monomer emission (Ra) serves as a ratiometric reporter of crowding and self-association. Because the two channels arise from distinct molecular equilibria (intramolecular hydrogen bonding vs. intermolecular self-association) they are largely decoupled: a glycerol titration series confirms that the self-association channel (Ra) can be moved while the hydrogen-bonding channel stays essentially fixed. Applied to protein-PEG biomolecular condensates, the two ratios move oppositely with increasing salt, showing that the interior's chemical (polarity, hydrogen bonding) and physical (packing, self-association) environments co-vary across the salt series; a single CyC4 measurement thereby maps this coupled microenvironment, providing a general strategy for multiparametric, ratiometric sensing of crowded microenvironments.

Cell motility drives spatial pattern formation across diverse biological systems. Here, we engineer Escherichia coli motility in semi-solid agar to control Voronoi patterns in two and three dimensions, partitioning space into regions closest to their respective inoculation seeds. Consistent with our reaction-diffusion model, we observed that collisions between expansion fronts generate either biomass depletion (''gaps'') or accumulation (''anti-gaps''), governed by the relative diffusion rates of bacteria and nutrients. By engineering strains with distinct expansion rates and tuneable motility, and by integrating these experimental data into a dynamic Voronoi model, we achieved precise control over pattern geometry. This enabled the generation of gaps with varying widths, curved boundaries, asymmetric structures, seedless regions, and complex composite patterns. Together, these findings establish bacterial Voronoi patterns as a programmable platform for engineering multicellular spatial organization, with potential applications in synthetic biology and materials science.

Spatial-omics workflows enable molecular analysis within tissue spatial context. Despite the prognostic value of tissue stiffness, these approaches have not incorporated direct, mechanical measurements. This omission reflects several challenges, including sample requirements, low throughput, specialized equipment, and complex data registration. Here, we introduce mechanoMaST (mechanics mapped to spatial transcriptomics), the first workflow to combine absolute mechanical measurements with spatial-omics. It pairs atomic force microscopy-based nanoindentation stiffness maps with spatial transcriptomics maps from adjacent tissue cryosections. The two modalities are then computationally co-registered to enable direct spatial correlation at 100 um resolution, with mapping accuracy quantified through error propagation, providing ground-truth mechanical data directly linked to spatial gene expression. We demonstrate mechanoMaST in human colorectal cancer liver metastasis, generating a spatial resource from 10 patients and revealing a four-gene stiffness signature. mechanoMaST is readily adaptable to other tissues across development and disease, and extendable to additional spatial-omics modalities in adjacent sections.

Muscle architecture shapes muscle function and changes with age, growth, training and disease, yet quantifying three-dimensional (3D) muscle architecture in vivo remains challenging. We introduce a hybrid fascicle tractography approach for freehand 3D ultrasound data that accurately reconstructs 3D muscle fascicles with respect to an objective, anatomically relevant coordinate system defined by the muscle's central aponeurosis. The hybrid approach combines Hessian-based fascicle detection with wavelet-based refinement to generate volumetric fascicle orientations. In a synthetic dataset with known ground truth, fascicle orientations and lengths were estimated with errors of [≤]2{degrees} and ~1.5%, respectively. In vivo, the approach detected physiologically plausible fascicle lengthening in the human tibialis anterior following a passive plantar flexion rotation, whereas diffusion tensor imaging of the same muscle did not. The proposed method enables anatomically relevant, objective and non-invasive quantification of 3D muscle architecture in vivo, providing a practical framework for applications in clinical and applied muscle physiology.

Photoacoustic (PA) and ultrasound (US) imaging provide complementary molecular, functional, and anatomical contrasts. Here, we present a panoramic PA-US imaging platform that integrates multispectral PA computed tomography (PACT) along with reflection-mode and transmission-mode US imaging through a single shared full-ring ultrasound array. We employ an ultrafast planewave transmission scheme in reflection-mode US for power Doppler (PWD) imaging and ultrasound localization microscopy (ULM). Additionally, we use the transmission-mode US to reconstruct a spatially resolved speed of sound (SoS) map that corrects both PA and US reconstruction. Such correction sharpens the resolution of PACT, suppresses the artifacts of PWD, and improves microbubble localization of ULM. Elevational scanning further enables whole-body volumetric imaging with co-registered PA and US contrasts. The integrated system maps photoswitchable DrBphP1-expressing tumors alongside their blood perfusion and oxygenation environment. Applying the platform to monitor unilateral renal ischemia-reperfusion injury, we report that microvascular perfusion and renal oxygenation recover at different rates. Collectively, we demonstrate that the integrated PA-US imaging platform provides a unified framework for multiparametric study of anatomy, perfusion, microvascular flow, oxygenation, and molecular activities.

Online structural characterization during microfluidic lipid self-assembly is important for understanding and controlling the formation of nonlamellar liquid crystalline nanodispersions. Here, we report a 3D-printed, X-ray-compatible hydrodynamic flow-focusing microfluidic chip with variable channel dimensions, integrated with synchrotron small-angle X-ray scattering (SAXS), for position-resolved SAXS-on-chip monitoring of Ca2+-triggered hexosome formation. Hexosomes were produced under continuous flow by mixing ethanolic solutions of docosahexaenoic acid monoglyceride (MAG-DHA), the negatively charged phosphatidylglycerol DOPG, and -tocopherol with Ca2+-containing PIPES buffer. Online SAXS-on-chip measurements detected three Bragg reflections characteristic of the internal inverse hexagonal (H2) phase on a tens-of-milliseconds residence-time scale, revealing rapid structural evolution during microfluidic mixing. Complementary ex situ SAXS identified the DOPG/Ca2+ molar ratio as a key parameter modulating the direct vesicle-to-hexosome transformation and the compactness of the internal H2 nanostructures. Dynamic light scattering showed that the flow-rate ratio modulated nanoparticle size, yielding hexosomes with mean hydrodynamic diameters in the range of approximately 120-175 nm and polydispersity index values down to 0.14 at a total flow rate of 200 {micro}L min-1. Cryo-TEM revealed coexistence of hexosomes and vesicular nanostructures, highlighting morphological heterogeneity, while Coarse-Grained Molecular Dynamics simulations supported a central role of Ca2+-DOPG association in promoting a direct lamellar-H2 phase transition. Overall, this work shows that 3D-printed SAXS-compatible microfluidics can integrate continuous production with online structural characterization, providing a basis for future formulation and process optimization of drug-loaded cubosomes, hexosomes, and related nonlamellar liquid crystalline nanodispersions.

SynNotch-CAR circuits improve T cell specificity by coupling antigen recognition to inducible CAR expression. However, basal CAR expression without receptor activation, termed here as leakiness, can reduce the separation between killing of intended target cells and sparing of antigen-positive off-target cells, limiting target-cell discrimination. Here, we systematically quantified basal CAR expression for several synNotch-CAR designs and developed a coupled ordinary differential equation model to show that discrimination depends on basal output, CAR potency, and effector-to-target ratio. We introduced C-terminal tags such as fluorescent proteins, degron domains, endocytosis signals, and endoplasmic reticulum retention motifs as a strategy to reduce CAR leakiness. We found that fluorescent proteins and degron-containing tags reduced basal CAR surface expression while preserving antigen-induced CAR expression, improving discrimination of antigen-density sensing and combinatorial circuits in vitro. In xenograft models, fluorescent protein-tagged CARs improved discrimination by reducing activity against off-target cells while retaining activity against high-antigen tumors. Degron-containing constructs reduced basal CAR expression in vitro but showed suboptimal performance in vivo, revealing a trade-off between basal CAR suppression and induced CAR persistence. Together, these findings demonstrate that basal output expression is a key parameter for inducible genetic circuit designs and establish layered transcriptional and post-translational regulation as a strategy to improve the fidelity of inducible T cell circuits.

Methods for installing synthetic functions on living cell surfaces provide powerful approaches for imaging, sensing, and manipulating cell behavior, but many require genetic modification of the target cell or chemical modification of the plasma membrane. Here, we repurpose the glycosylphosphatidylinositol-anchored protein (GPI-AP)-binding toxin aerolysin as a modular chassis for non-genetic cell-surface functionalization. We show that a non-cytotoxic, monomeric aerolysin mutant retains high-affinity and GPI-AP-dependent cell binding when genetically fused to diverse protein cargos. Fluorescent protein-aerolysin fusions robustly label multiple cell types and remain predominantly associated with the cell surface for at least 24 h, in contrast to wheat germ agglutinin, which is extensively internalized. Aerolysin can also be equipped with SpyTag/SpyCatcher to enable modular assembly with independently expressed protein cargos. Importantly, aerolysin supports functional rather than solely optical modification of the cell surface: fusion to the proximity-labeling enzyme APEX2 enables extracellular protein biotinylation, while fusion to HUH endonuclease tags enables covalent attachment of synthetic DNA to living cells. Using this latter architecture, we developed a DNA hairpin sensor that converts cell-surface nuclease activity into a fluorescent signal and distinguishes cells with different levels of extracellular nuclease activity. Together, these results establish non-cytotoxic aerolysin as a genetically encoded, soluble adapter for installing proteins, enzymes, and programmable nucleic acids onto living cells without modification of the target-cell genome.

Small interfering RNA (siRNA) enables precise post-transcriptional gene silencing for refractory diseases, yet its clinical translation remains limited by the lack of safe and efficient delivery vectors. Inspired by the dissymmetric alkyl chain architecture of natural membrane phospholipids, we designed and synthesized 34 novel ionizable lipids with dissymmetric hydrophobic tails and formulated them into lipid nanoparticles (LNPs). Through systematic physicochemical and biological assessments, we established clear structure-activity relationships and identified two lead LNPs (O14-LNP, H18a-LNP) with superior endosomal escape capacity, enhanced in vivo gene silencing potency, and favorable biosafety relative to the clinical benchmark MC3-LNP. In both streptozotocin-induced and spontaneous db/db type 2 diabetes (T2D) mouse models, lead LNPs delivering ferroptosis-related siRNAs effectively ameliorated glucose and lipid metabolic disorders, restored islet function, and alleviated hepatic steatosis. This study not only lays a theoretical foundation for the rational design of novel ionizable lipids, but also validates the therapeutic potential of siRNA therapy targeting ferroptosis, providing a versatile delivery platform and targeted therapeutic strategy for the treatment of T2D.

Despite the well-known health risks of neutron exposures, key gaps remain in understanding neutron-induced molecular responses and identifying reliable biodosimetric markers that distinguish neutrons from photon exposure. We provide the first genome-wide analysis of the human blood transcriptional response to an accelerator-derived fission-like spectrum of neutrons versus photons, evaluating transcriptomic relative biological effectiveness (RBE) and radiation quality-discriminating gene signatures. Whole blood from healthy donors was irradiated ex vivo with X-rays (140 kV, 0-4 Gy, n = 3) or neutrons (0.1-8 MeV, 0-1 Gy, n = 2), incubated for 6 h or 24 h, and processed for RNA sequencing from peripheral blood mononuclear cells (PBMCs). Neutrons were markedly more potent than X-rays at inducing differentially expressed genes (DEGs) at equal doses, showing a peak response 6 h post-irradiation followed by a decline. In contrast, X-rays caused a continuous increase in DEGs up to 24 h (neutrons vs. X-rays at 1 Gy: 1,449 vs. 121 DEGs at 6 h; 996 vs. 621 DEGs at 24 h). A universal p53-centered 34-gene signature, including FDXR, EDA2R, GADD45A, and ZMAT3, showed highly monotonic dose responses (Spearman correlation coefficient {approx} 1) across donors, radiation qualities, and timepoints. Additionally, difference-in-differences analysis identified radiation quality-discriminating genes only at 6 h, with transcriptional convergence observed by 24 h, suggesting a very narrow time window for biodosimetric differentiation. We identified a neutron-specific gene signature driven by cGAS-STING-NF-{kappa}B signaling (RELB, NFKB1, C3, MALAT1) and suppression of B-cell and myeloid identity genes (IGHD, TCL1A, CLEC7A, TLR2), defining a biologically coherent neutron quality index with distinct immunomodulatory effects. For the first time, we assessed neutron RBEs at the gene, pathway, and global transcriptomic levels in a human blood model, reporting a global transcriptomic neutron RBE of 1.30 (95% CI: 1.14-1.49) at 6 h and 1.21 (95% CI: 1.14-1.28) at 24 h, providing a valuable basis for biodosimetry in mixed-field exposure scenarios. Our findings advance the mechanistic understanding of neutron radiation responses and support the development of biodosimetric approaches for mixed-field exposure scenarios.

Label-free Raman imaging of living cells is photon limited: at exposures compatible with cellular dynamics, single-pixel spectra carry about one count per channel on a dominant smooth background. We present an unmixing framework in which the decoder of a physics-constrained autoencoder is restricted to a data-driven spectroscopic dictionary: band centers,widths, and pseudo-Voigt shapes are measured from the dataset and fixed, and the network learns only nonnegative band amplitudes, a smooth B-spline background, and a per-pixel gain.First, on slit-scanning images of HeLa cells (532 nm) the dictionary yields spike-free component spectra that read as band tables, including a resonance-enhanced cytochrome-c-associated component matching literature spectra, and the most stable decomposition against the component number. Second, the dictionary and initialization calibrated at 1 s exposure perline transfer to 100 ms per line (12 s sweeps): cytochrome-c spectral identity survives a single sweep (correlation 0.92) while its map remains photon limited; the dictionary provides spectral physicality, and the transferred initialization prevents a structural collapse that global map correlations miss; in a measurement-derived phantom the dictionary estimator holds thecytochrome-c spectrum to 17-19{degrees} spectral angle at 100 ms, where classical factorizations and free decoders lose it (55-64{degrees}). Estimation on the count-equivalent detector output uses a calibrated shifted-Poisson quasi-likelihood. Third, evaluation must be time matched:correlation against a separately acquired reference saturates through slow specimen drift and acquisition mismatch rather than photon noise, and the self-consistency of learned denoisers is inflated by shared bias; time-matched self-consistency and independent cross-checks areproposed.

Neurodegenerative diseases are increasingly associated with vascular dysfunction beyond progressive neuronal degeneration, yet how vascular pathology contributes to disease progression remains poorly understood, largely due to the lack of a neurodegenerative disease model capable of capturing neuronal pathology alongside associated vascular dysfunction. Here, we developed a microengineered 3D vascularized brain tissue model that integrates neurospheroids with a self-assembled, perfusable vascular network to recapitulate key features of the neurovascular interface. Using this model, we investigated the vascular contribution to Parkinson's disease pathology by introducing -synuclein preformed fibrils into the engineered vasculature. Intravascular -syn fibril exposure induced endothelial barrier disruption, vascular leakage, inflammation, and vascular regression. Notably, this vascular insult was accompanied by intraneuronal -synuclein aggregation within neurospheroids, suggesting that vascular dysfunction may facilitate the exposure of neural tissue to pathogenic -synuclein. Our neurodegenerative disease modeling approach establishes a versatile and tractable platform for investigating vascular contributions to neurodegenerative disease progression.

Deep learning has accelerated drug discovery, yet most existing models are trained using in vitro affinity datasets and consequently remain disconnected from the cellular context in which functional ligand-protein interactions occur. This limitation hinders the ability to reflect the complexity of native interactomes and characterize biological responses to molecular perturbation. Here we introduce C-PLANK (Chemi-Proteome Language Attention NetworK), a deep learning framework trained on fragment-protein interactions profiled directly in living cells using fully functionalized fragment (FFF) chemoproteomics. C-PLANK combines physicochemical embeddings with a bilinear attention network (BAN) to model both global cellular context and local residue-atom interactions, generating interpretable interaction fingerprints. Particularly, C-PLANK incorporates Cellular Interaction State Index (CISI), a systems-level evidential metric that contextualizes the biological plausibility of each predicted interaction against the global cellular interaction landscape. Across 431 ligand interactomes curated from eight independent chemoproteomic studies, C-PLANK consistently outperformed current state-of-the-art interaction prediction frameworks under both random and cold-protein evaluation settings. The inferred interaction fingerprints aligned with orthogonal evidence from structure-based pocket predictions, co-crystal structures, and cellular binding-site annotations. C-PLANK further generalized to unseen ligands. In a cellular target-focused discovery campaign, C-PLANK identified a previously unrecognized ligand that was subsequently advanced into an active chemical probe acting as a SIRT3 agonist in cellular assays. By learning directly from cellular chemoproteomics, C-PLANK moves beyond isolated interaction prediction toward cellular interaction-state modelling, establishing a computational foundation for future digital-twin frameworks in drug discovery.

Cell shape changes during migration, division, or differentiation require the dynamic regulation of actin network mechanics. Actin crosslinkers are central to this regulation, controlling network connectivity and the transmission of contractile forces. A large diversity of crosslinkers exists, differing in length, domain structure, and binding kinetics, yet why cells deploy specific crosslinkers in a physiological context remains unclear. To bridge this gap, we developed a light-controlled actin crosslinker toolbox spanning three physiologically relevant lengths: ~9 nm (fascin-like), ~16 nm (fimbrin-like), and ~56 nm (alpha-actinin-like). Using magnetic pincher experiments and in silico modelling, we show that short and mid-length crosslinkers dynamically tune cortical stiffness and thickness in a density- and myosin-dependent manner, with short crosslinkers also driving pronounced stress-stiffening as the cortex is deformed. Strikingly, minute-scale activation reveals a length-dependent switch in cell behaviour: short crosslinkers cause cortical delamination, while long ones instead drive cell polarization and symmetry breaking. This switch can be overridden by perturbing actin turnover, which unlocks polarization in mid-length crosslinkers that otherwise delaminate. Crosslinker-induced polarization is not merely a local cortical event: it directs subsequent cell spreading, coupling a nanometre-scale molecular choice to a cell-scale decision about movement. Together, these findings establish a versatile optogenetic platform for manipulating actin crosslinking, and show that the cortex can encode a behavioural switch directly in its material architecture.

Tissue mechanics influence diverse biological processes, yet directly linking stiffness measurements to spatially resolved molecular states in intact tissues remains challenging. Here we developed a paired-surface spatial mechanomics approach to map Young's modulus by nanoindentation on a fresh tissue surface and co-register the stiffness grid with 10x Genomics Visium HD spatial transcriptome bins from the immediately adjacent, parallel surface. Applied to the mouse ovary, which has spatially distinct compartments and undergoes extracellular matrix remodeling with cycle and age, the workflow generated >2,900 matched measurements across 21 regions of interest. Nanoindentation at 50-m grid spacing enabled millimeter-scale stiffness maps while balancing acquisition time in fresh tissues, with ~92 4-m transcriptome bins assigned to each stiffness value. Global and compartment-specific analyses associated stiffer regions with lower elastic fiber programs and higher inflammatory signaling, with age-dependent differences. This correlative strategy integrates experimentally measured mechanics with spatial omics in fresh tissues.

UCH-L1 is a monomeric deubiquitinating enzyme whose native structure embeds a shallow $5_2$ knot located near the N-terminus, placing the knotted topology in direct proximity to both the substrate-binding pocket and the catalytic site. While our previous work established that N-terminal integrity is critical for catalytic activity, the energetic cost of unknotting and its structural consequences remained unquantified. Here, we combine steered molecular dynamics with an umbrella sampling scheme to generate topologically modified variants of UCH-L1 and, for the first time, reconstruct the free-energy profile of UCH-L1 unknotting. The potential of mean force reveals a steep energetic barrier to knot disruption, consistent with knotting being a late, rate-limiting folding step that is effectively locked in once the native structure is established. Long unbiased MD simulations of fully unknotted variants in both apo and holo states show that knot removal increases local flexibility at the N-terminus without inducing significant global structural destabilization. Binding energy calculations indicate that the unknotted variant binds to ubiquitin less tightly than the wild-type ($\sim$-62~vs~$\sim$-76~kcal/mol), suggesting that topological integrity contributes to substrate affinity. Together, these results show that the $5_2$ knot in UCH-L1 is not a passive structural feature but a functional element that fine-tunes folding kinetics and contributes to substrate binding efficiency.

Rational design of covalent protein-labeling reagents in complex biological environments requires a molecular-level understanding of how the protein microenvironment governs chemical reactivity; yet, such mechanistic details remain inaccessible to experimental methods alone. In living neurons, Ligand-Directed Acyl Imidazole (LDAI) chemistry has been used to label AMPA receptors as a traceless, affinity-based protein labeling method. Although LDAI labeling reagents have been optimized in the lab, the atomic details of their interactions with the protein and the underlying mechanism remain elusive. In this work, we combined Quantum Mechanical (QM) calculations and molecular dynamics (MD) simulations to propose a detailed reaction mechanism for AMPAR labeling by LDAI reagents and to clarify how the protein microenvironment governs reactivity. Although Lys residues are usually protonated at physiological pH and therefore less nucleophilic in water, our QM results show that Lys labeling is energetically more favorable than competing reactions with Ser or water. MD simulations reveal that PFQX ---the LDAI reagent precursor--- binds dynamically to the GluA2 AMPAR as an antagonist, inducing conformational changes that reshape the local environment of the acyl imidazole (AI) warhead, underscoring that ligand identity strongly affects labeling outcomes. We also identified intra and intermolecular hydrogen bond networks that may contribute to further immobilize and pre-organize the LDAI reagent. Moreover, the probe's chemical nature shapes its interactions with the Ligand Binding Domain (LBD), offering a plausible rationale for the previously experimentally observed ligand-dependent fluorescent response. Taken together, our results establish design principles for exploiting the reagent geometry and binding pocket hydrogen-bonding networks for the rational design of LDAI reagents.