Spatial Proximity Research Articles

Spatial multiplexed immunofluorescence analysis reveals coordinated cellular networks associated with overall survival in metastatic osteosarcoma

Patients diagnosed with advanced osteosarcoma, often in the form of lung metastases, have abysmal five-year overall survival rates. The complexity of the osteosarcoma immune tumor microenvironment has been implicated in clinical trial failures of various immunotherapies. The purpose of this exploratory study was to spatially characterize the immune tumor microenvironment of metastatic osteosarcoma lung specimens. Knowledge of the coordinating cellular networks within these tissues could then lead to improved outcomes when utilizing immunotherapy for treatment of this disease. Importantly, various cell types, interactions, and cellular neighborhoods were associated with five-year survival status. Of note, increases in cellular interactions between T lymphocytes, positive for programmed cell death protein 1, and myeloid-derived suppressor cells were observed in the 5-year deceased cohort. Additionally, cellular neighborhood analysis identified an Immune-Cold Parenchyma cellular neighborhood, also associated with worse 5-year survival. Finally, the Osteosarcoma Spatial Score, which approximates effector immune activity in the immune tumor microenvironment through the spatial proximity of immune and tumor cells, was increased within 5-year survivors, suggesting improved effector signaling in this patient cohort. Ultimately, these data represent a robust spatial multiplexed immunofluorescence analysis of the metastatic osteosarcoma immune tumor microenvironment. Various communication networks, and their association with survival, were described. In the future, identification of these networks may suggest the use of specific, combinatory immunotherapeutic strategies for improved anti-tumor immune responses and outcomes in osteosarcoma.

Brilliant tragedy? Electoral effects of environmental protest cycle in autocracy

ABSTRACT When and how does an environmental protest cycle affect election outcomes under electoral authoritarianism? Drawing on the case of the Bashkortostan republic, a Russian ethnic region, I leverage the spatial proximity to the protest site to identify its effects on parliamentary elections. The environmental protest cycle peaked in Bashkortostan around the regional government’s decisions to extract minerals from shikhans – mountains composed of limestone. Employing a difference-in-differences (DiD) design, I show that precincts exposed to the environmental protest cycle experienced a significant drop in United Russia vote share, while voting for systemic opposition increased in the affected precincts. To explain the dynamics, I propose treating non-political protests, such as environmental ones, as an information revelation mechanism. The mechanism identified through a causal mediation analysis indicates that the environmental protest cycle conveys information on regional malperformance to voters. Their updated preferences, in turn, heavily undermine the electoral mobilizing capacity of local elites.

Embodied/Encoded: A Study of Presence in Digital Music

Presence in Embodied/Encoded is concerned with the physical and embodied dimensions of musicking in relation to digital-informational representations of such phenomena. I explore presence through practices of recording, creative coding, audio production, and spatial electronic music composition. Of particular interest is the link between presence and the sensual/corporeal aspects of sound production and listening. Research in embodied music cognition and extended reality provide a foundation for this type of meaning formation. In the context of immersive electronic music, I suggest that physical presence – a sense of ‘being somewhere’—emerges not just from images/representations of place, but from peripheral aspects of the (embodied) acoustic experience such as spatial proximity and distance, diffuseness, resonance and reverberation, noise floor, etc. Consequently, musical meaning in “Embodied/Encoded” moves away from the symbolic dimension of electronic sounds toward meaning as an outcome of embodied interaction with the environment. The concept of presence can also apply to ‘mixed’ music, or combinations of acoustic and electronic sources, in which virtual presence is complicated by real bodies and spaces. Digital technology renders sound a flexible, malleable entity—a ‘flickering’ signifier to borrow from Hayles—capable of reconfiguring presence in creative ways. The dance between encoded and embodied dimensions inspires and informs this artistic research. Through a body of immersive music and multimedia documentation, I unpack connections between presence and its digital abstractions.

Update on a brain-penetrant cardiac glycoside that can lower cellular prion protein levels in human and guinea pig paradigms.

Lowering the levels of the cellular prion protein (PrPC) is widely considered a promising strategy for the treatment of prion diseases. Building on work that established immediate spatial proximity of PrPC and Na+, K+-ATPases (NKAs) in the brain, we recently showed that PrPC levels can be reduced by targeting NKAs with their natural cardiac glycoside (CG) inhibitors. We then introduced C4'-dehydro-oleandrin as a CG with improved pharmacological properties for this indication, showing that it reduced PrPC levels by 84% in immortalized human cells that had been differentiated to acquire neural or astrocytic characteristics. Here we report that our lead compound caused cell surface PrPC levels to drop also in other human cell models, even when the analyses of whole cell lysates suggested otherwise. Because mice are refractory to CGs, we explored guinea pigs as an alternative rodent model for the preclinical evaluation of C4'-dehydro-oleandrin. We found that guinea pig cell lines, primary cells, and brain slices were responsive to our lead compound, albeit it at 30-fold higher concentrations than human cells. Of potential significance for other PrPC lowering approaches, we observed that cells attempted to compensate for the loss of cell surface PrPC levels by increasing the expression of the prion gene, requiring daily administration of C4'-dehydro-oleandrin for a sustained PrPC lowering effect. Regrettably, when administered systemically in vivo, the levels of C4'-dehydro-oleandrin that reached the guinea pig brain remained insufficient for the PrPC lowering effect to manifest. A more suitable preclinical model is still needed to determine if C4'-dehydro-oleandrin can offer a cost-effective complementary strategy for pushing PrPC levels below a threshold required for long-term prion disease survival.

The impact of gunfire on residential property values

ABSTRACT Hedonic studies of housing unit prices routinely attribute lower prices to perceived undesirable attributes and or amenities of the property sold. A steady stream of literature finds that criminal behaviour is negatively capitalized into housing unit prices. This study focuses on the effects of geocoded gunfire incidents on housing unit prices while controlling for other attributes and amenities at the census-block group level. The primary finding is that spatial and temporal proximity of gunfire incidents reduces housing unit prices in an analysis of 3,844 gunfire incidents, involving over 15,000 rounds of gunfire and 2,053 housing unit sales in Savannah, Georgia, from 2015 to 2020. The effect of gunfire is greater when it occurs in the immediate vicinity of the property. Heterogeneous effects on sale prices were found to be present across time and space when intensity was measured by the number of days with gunfire incidents and to a lesser extent when intensity was measured by the number of shots fired in each incident.

HoLens: A visual analytics design for higher-order movement modeling and visualization

Higher-order patterns reveal sequential multistep state transitions, which are usually superior to origin-destination analyses that depict only firstorder geospatial movement patterns. Conventional methods for higher-order movement modeling first construct a directed acyclic graph (DAG) of movements and then extract higher-order patterns from the DAG. However, DAG-based methods rely heavily on identifying movement keypoints, which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban environments. To overcome these limitations, we propose HoLens, a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban environment. HoLens mainly makes twofold contributions: First, we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity, contextual information, and temporal variability. Second, we developed an interactive visual analytics interface comprising well-established visualization techniques, including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state transitions. Two real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using HoLens. We also demonstrate the feasibility, usability, and effectiveness of our approach through expert interviews with three domain experts.

Cross-domain visual prompting with spatial proximity knowledge distillation for histological image classification

Objective:Histological classification is a challenging task due to the diverse appearances, unpredictable variations, and blurry edges of histological tissues. Recently, many approaches based on large networks have achieved satisfactory performance. However, most of these methods rely heavily on substantial computational resources and large high-quality datasets, limiting their practical application. Knowledge Distillation (KD) offers a promising solution by enabling smaller networks to achieve performance comparable to that of larger networks. Nonetheless, KD is hindered by the problem of high-dimensional characteristics, which makes it difficult to capture tiny scattered features and often leads to the loss of edge feature relationships. Methods:A novel cross-domain visual prompting distillation approach is proposed, compelling the teacher network to facilitate the extraction of significant high-dimensional features into low-dimensional feature maps, thereby aiding the student network in achieving superior performance. Additionally, a dynamic learnable temperature module based on novel vector-based spatial proximity is introduced to further encourage the student to imitate the teacher. Results:Experiments conducted on widely accepted histological datasets, NCT-CRC-HE-100K and LC25000, demonstrate the effectiveness of the proposed method and validate its robustness on the popular dermoscopic dataset ISIC-2019. Compared to state-of-the-art knowledge distillation methods, the proposed method achieves better performance and greater robustness with optimal domain adaptation. Conclusion:A novel distillation architecture, termed VPSP, tailored for histological classification, is proposed. This architecture achieves superior performance with optimal domain adaptation, enhancing the clinical application of histological classification. The source code will be released at https://github.com/xiaohongji/VPSP.

Network versus spatial proximity and firm innovation: The case of the R&D service sector

AbstractThe paper analyzes the relationship between different types of proximities—network and spatial—in relation to innovation in the context of the R&D service industry. In doing so, it contributes to the recent debate in the literature on the effects of network connectivity versus geographical colocation. The paper uses original data from a survey of 145 R&D service establishments in Montreal (Canada) and their interactions with both local and nonlocal organizations. The findings of the paper indicate that collaborative networks (both local and nonlocal) have a stronger association with R&D service innovation than spatial proximity to R&D service organizations and other collaborators. However, when these two dimensions are interacted, they are shown to function as substitutes. The paper also demonstrates that the relationship between spatial proximity and networking varies across three dimensions: local versus nonlocal networking, the type of relationship (client, supplier, competitor, and research institutes and university), and the type of network connectivity—brokerage versus closure.

Network-level optimisation approach for bridge interventions scheduling

This paper introduces a novel extension of the multi-system optimisation method, known as the 3C concept, tailored for optimising budget allocation for bridge interventions at the network level. This extended methodology accounts for the interdependencies among bridges due to their spatial proximity within the network. It incorporates direct and user costs, bridge performance indicators, and a bridge deterioration model. A real-world case study involving a portfolio of 555 bridges demonstrates the practicality of the methodology, efficiently determining the optimal intervention sequence. Over an 18-year analysis period, the proposed methodology achieved a 23% reduction in total costs by combining repairs for bridges with high to severe damage and maintenance for the others. This represents a significant improvement compared to the traditional approach, used by bridge management agencies, which relies exclusively on maintenance. The optimised procedure outperforms human intuition in managing complex bridge networks, particularly over extended periods. This methodology can assist transportation agencies in implementing and exploring various scenarios by adjusting the time between consecutive interventions and budget constraints, supporting comprehensive analysis and informed decision-making.

From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications.

Gliomas are aggressive brain tumors associated with a poor prognosis. Cancer stem cells (CSCs) play a significant role in tumor recurrence and resistance to therapy. This study aimed to identify and characterize glioma stem cells (GSCs), analyze their interactions with various cell types, and develop a prognostic signature. Single-cell RNA sequencing data from 44 primary glioma samples were analyzed to identify GSC populations. Spatial transcriptomics and gene regulatory network analyses were performed to investigate GSC localization and transcription factor activity. CellChat analysis was conducted to infer cell-cell communication patterns. A GSC signature (GSCS) was developed using machine learning algorithms applied to bulk RNA sequencing data from multiple cohorts. In vitro and in vivo experiments were conducted to validate the role of TUBA1C, a key gene within the signature. A distinct GSC population was identified, characterized by high proliferative potential and an enrichment of E2F1, E2F2, E2F7, and BRCA1 regulons. GSCs exhibited spatial proximity to myeloid-derived suppressor cells (MDSCs). CellChat analysis revealed an active MIF signaling pathway between GSCs and MDSCs. A 26-gene GSCS demonstrated superior performance compared to existing prognostic models. Knockdown of TUBA1C significantly inhibited glioma cell migration, and invasion in vitro, and reduced tumor growth in vivo. This study offers a comprehensive characterization of GSCs and their interactions with MDSCs, while presenting a robust GSCS. The findings offer new insights into glioma biology and identify potential therapeutic targets, particularly TUBA1C, aimed at improving patient outcomes.

Abstract A040: Multiplex fluorescent immunohistochemistry reveals immunosuppressive tumor microenvironment in COMPASS-like complex gene mutated pancreatic cancer

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is highly resistant to chemoradiation and immune therapies. There is limited biomarker to identify subsets of PDAC that responds to immunotherapy or targeted therapy. We have identified a subset of PDACs (approximately 25%) carrying COMPASS-like complex (CLC) gene mutations. Analysis of the clinicopathologic and molecular features of this PDAC subset revealed that these tumors are an aggressive group of PDACs with poor or squamous histologic differentiation, increased rates of TP53 mutations, and poor patient survival compared to control PDACs. Our preliminary data showed that PDACs carrying mutations in two members of the CLC genes (KDM6A and KMT2D) have immunosuppressive tumor microenvironment (TME), suggesting that CLC genes are candidate biomarkers for immunotherapy. However, a comprehensive analysis of the immune composition, immune checkpoint expression, and the relationship between tumor cells and immune cells in CLC gene mutated PDACs compared to controls has not been performed. In this study, we characterized the immune composition in CLC gene mutated PDACs compared to paired control PDACs using tyramide signal amplification multiplex fluorescent immunohistochemistry (mfIHC). Methods: Human PDAC tissue slides were stained with 2 mfIHC panels and cell types were defined by different markers: panel 1 (PanCK, CD163, PD-L1, CD3, CD8, FoxP3) and panel 2 (PanCK, CD163, CD3, CD8, TIGIT, TIM3). Random image sections were selected in each sample (5∼8 sections per sample) and divided into wild-type (WT) group (n=57) and mutant group (n=41) and analyzed using InForm Cell Analysis software. Tissue segmentation training was performed to separate stromal and epithelial elements. Cell percentage, cell density, and nearest neighbor distance of each cell type were calculated using R program. Results: The CLC gene mutation is associated with elevated epithelial cells (ECs) and PDL1+ ECs and increased infiltration of exhausted cytotoxic T cells (TIGIT+/TIM3+ CD8 T cells). CLC gene mutated PDACs are characterized by spatial proximity between exhausted T cells (TIGIT+) and epithelial cells/antigen presenting cells (APCs). Higher number of epithelial cells is associated with reduced infiltration of helper T cells in the mutant PDAC tissue. We also observed that high numbers of APC in proximity to CD4 T cells were positively correlated with the proximity of EC and CD8 cytotoxic T cells as well as the proximity of CD4 T cells and cytotoxic T cells in the WT group but not in the CLC gene mutant group. Conclusion: Our results provide insight into the effects of CLC gene mutations on shaping the immune microenvironment of PDAC and suggest that CLC gene mutated PDACs have an immunosuppressive immune microenvironment than other PDACs and may respond differently to immunotherapy. Citation Format: Shungang Zhang, Elaina Daniels, Jake McGue, Hong Sun Kim, Dafydd Thomas, Timothy Frankel, Jiaqi Shi. Multiplex fluorescent immunohistochemistry reveals immunosuppressive tumor microenvironment in COMPASS-like complex gene mutated pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A040.

MultiMatch: geometry-informed colocalization in multi-color super-resolution microscopy

With recent advances in multi-color super-resolution light microscopy, it is possible to simultaneously visualize multiple subunits within biological structures at nanometer resolution. To optimally evaluate and interpret spatial proximity of stainings on such an image, colocalization analysis tools have to be able to integrate prior knowledge on the local geometry of the recorded biological complex. We present MultiMatch to analyze the abundance and location of chain-like particle arrangements in multi-color microscopy based on multi-marginal optimal unbalanced transport methodology. Our object-based colocalization model statistically addresses the effect of incomplete labeling efficiencies enabling inference on existent, but not fully observable particle chains. We showcase that MultiMatch is able to consistently recover existing chain structures in three-color STED images of DNA origami nanorulers and outperforms geometry-uninformed triplet colocalization methods in this task. MultiMatch generalizes to an arbitrary number of color channels and is provided as a user-friendly Python package comprising colocalization visualizations.

Performance evaluation of a 4D similarity filter for dynamic CT angiography imaging of the liver.

Dynamic computed tomography (CT) angiography of the abdomen provides perfusion information and characteristics of the tissues present in the abdomen. This information could potentially help characterize liver metastases. However, radiation dose has to be relatively low for the patient, causing the images to have very high noise content. Denoising methods are needed to increase image quality. The purpose of this study was to investigate the performance, limitations, and behavior of a new 4D filtering method, called the 4D Similarity Filter (4DSF), to reduce image noise in temporal CT data. The 4DSF averages voxels with similar time-intensity curves (TICs). Each phase is filtered individually using the information of all phases except for the one being filtered. This approach minimizes the bias toward the noise initially present in this phase. Since the 4DSF does not base similarity on spatial proximity, loss of spatial resolution is avoided. The 4DSF was evaluated on a 12-phase liver dynamic CT angiography acquisition of 52 digital anthropomorphic phantoms, each containing one hypervascular 1cm lesion with a small necrotic core. The metrics used for evaluation were noise reduction, lesion contrast-to-noise ratio (CNR), CT number accuracy using peak-time and peak-intensity of the TICs, and resolution loss. The results were compared to those obtained by the time-intensity profile similarity (TIPS) filter, which uses the whole TIC for determining similarity, and the combination 4DSF followed by TIPS filter (4DSF + TIPS). The 4DSF alone resulted in a median noise reduction by a factor of 6.8, which is lower than that obtained by the TIPS filter at 8.1, and 4DSF + TIPS at 12.2. The 4DSF increased the median CNR from 0. 44 to 1.85, which is less than the TIPS filter at 2.59 and 4DSF + TIPS at 3.12. However, the peak-intensity accuracy in the TICs was superior for the 4DSF, with a median intensity decrease of -34 HU compared to -88 and -50 HU for the hepatic artery when using the TIPS filter and 4DSF + TIPS, respectively. The median peak-time accuracy was inferior for the 4DSF filter and 4DSF + TIPS, with a time shift of -1 phases for the portal vein TIC compared to no shift in time when using the TIPS. The analysis of the full-width-at-half-maximum (FWHM) of a small artery showed significantly less spatial resolution loss for the 4DSF at 3.2 pixels, compared to the TIPS filter at 4.3 pixels, and 3.4 pixels for the 4DSF + TIPS. The 4DSF can reduce noise with almost no resolution loss, making the filter very suitable for denoising 4D CT data for detection tasks, even in very low dose, i.e., very high noise level, situations. In combination with the TIPS filter, the noise reduction can be increased even further.

Spatial correlation assessment of multiple earthquake intensity measures using physics-based simulated ground motions

Predictive models for spatial correlation play an effective role in the assessment of seismic risk associated with distributed infrastructure and building portfolios. However, existing models often rely on simplified approaches, assuming isotropy and stationarity. This paper verifies these assumptions by presenting a comprehensive study using a database of 3D physics-based simulated broadband ground motions for Istanbul, generated by the SPEED software. The results reveal significant event-to-event variability and nonstationary and anisotropic characteristics of spatial correlation influenced by source, path, and site effects. The development of nonstationary correlation models requires exploring influential metrics beyond spatial proximity and gaining a deep understanding of their impact, which is the focus of this study. Analysis of the spatial correlations of peak ground displacement, peak ground velocity, peak ground acceleration, and response spectral accelerations at different periods, employing both stationary and nonstationary correlation modelling methods and considering the finite fault model, indicates that the slip distribution pattern, direction and distance of station pairs relative to earthquake rupture, soil softness, and homogeneity of soil properties significantly influence the spatial correlations of near-field earthquake ground motions. Implementation of the introduced parameters in predictive spatial correlation models enhances the precision of regional seismic hazard assessments.

Enhancing Mass-Cytometry Imaging Analysis

Mass cytometry imaging is a technique that utilizes mass spectrometry instead of fluorescent flow cytometry to detect metal-conjugated antibodies binding to single cell antigens. This method generates multi-dimensional images, a complex data type with significant computational and interpretation challenges due to the absence of a standardized analysis workflow. To address this, our project evaluated the quality and efficacy of a previous study’s proposed workflow to advance the interpretability and accuracy of mass cytometry data analysis. The approach evaluated whether the previously proposed workflow could effectively analyze alternative samples classified by previous studies as “mixed” immune structure. The methods included an in-depth examination of cell labeling based on spatial proximity, which emerged as a secondary analytical outcome. This research advances spatial labeling by enhancing the spatial information related to physical connections between interacting cells and incorporating biological interactions, such as protein involvement. By integrating these factors, this research proposes a method for more accurate labeling, contributing to a more robust analysis workflow for this complex data type. Unstructured “mixed” samples remained distinguishable, though less effectively than structured “compartmentalized” samples. Future applications involve exploring additional datasets to further develop means of distinguishing between contact and non-contact interactions among cancer and immune cells.

Inference of human contact networks based on epidemic data

A key challenge in epidemic modeling is the lack of adequate data on population interactions (such as traffic flow or mobility patterns) which result in the spread of infectious diseases. Knowledge of social contact patterns is crucial for public health professionals to devise effective non-pharmaceutical interventions to control epidemics. This paper focuses on inferring social contact rates from reported infection counts during the spread of an infectious disease, addressing the increased difficulty that arises when dealing with “sparse” contact networks where only a small subset of the edges have non-zero weights. Specifically, a new geographically constrained lasso approach for network reconstruction for non-homogeneous mixing Susceptible-Infected-Removed (SIR) disease spread models is presented. The new network reconstruction method can explicitly account for the spatial proximity of network nodes in estimating the disease transmission rates and predicting the future evolution of the epidemic dynamics. Extensive numerical experiments are presented to show the proposed method outperforms existing approaches in terms of accuracy of contact identification under various graph topologies. A case study based on real data from the COVID-19 pandemic is presented to demonstrate the application of the approach for inferring contact structures and a counterfactual scenario analysis to assess effectiveness of containment strategies.

Enhancer activation from transposable elements in extrachromosomal DNA.

Extrachromosomal DNA (ecDNA) is a hallmark of aggressive cancer, contributing to both oncogene amplification and tumor heterogeneity. Here, we used Hi-C, super-resolution imaging, and long-read sequencing to explore the nuclear architecture of MYC-amplified ecDNA in colorectal cancer cells. Intriguingly, we observed frequent spatial proximity between ecDNA and 68 repetitive elements which we called ecDNA-interacting elements or EIEs. To characterize a potential regulatory role of EIEs, we focused on a fragment of the L1M4a1#LINE/L1 which we found to be co-amplified with MYC on ecDNA, gaining enhancer-associated chromatin marks in contrast to its normally silenced state. This EIE, in particular, existed as a naturally occurring structural variant upstream of MYC, gaining oncogenic potential in the transcriptionally permissive ecDNA environment. This EIE sequence is sufficient to enhance MYC expression and is required for cancer cell fitness. These findings suggest that silent repetitive genomic elements can be reactivated on ecDNA, leading to functional cooption and amplification. Repeat element activation on ecDNA represents a mechanism of accelerated evolution and tumor heterogeneity and may have diagnostic and therapeutic potential.

Light‐Induced Unlocking Reactivity of Fragments for Fast Target‐Guided Synthesis of Carbonic Anhydrase Inhibitors

AbstractWe showcase the successful combination of photochemistry and kinetic target‐guided synthesis (KTGS) for rapidly pinpointing enzyme inhibitors. KTGS is a fragment‐based drug discovery (FBDD) methodology in which the biological target (BT) orchestrates the construction of its own ligand from fragments featuring complementary reactive functionalities. Notably, fragments interacting with the protein binding sites leverage their spatial proximity, facilitating a preferential reaction. Consequently, the resulting bivalent ligand exhibits heightened affinity. Within the realm of KTGS strategies, in situ click chemistry stands out as the most widely used to identify potent protein binders. This approach requires significant protein contributions, such as binding interactions and appropriate orientations of fragments, to overcome high activation barriers. This leads to prolonged incubation times and the potential for generating false negatives, thereby limiting this strategy to proteins that are stable enough in buffer. We herein unveil the possibility to integrate photochemistry into the realm of KTGS, accelerating the ligation reaction between fragments to a time scale of minutes. This approach should significantly expand the narrow reactivity window of traditional KTGS reactions, paving the way for the exploration and development of novel photo‐KTGS reactions.

Light-Induced Unlocking Reactivity of Fragments for Fast Target-Guided Synthesis of Carbonic Anhydrase Inhibitors.

We showcase the successful combination of photochemistry and kinetic target-guided synthesis (KTGS) for rapidly pinpointing enzyme inhibitors. KTGS is a fragment-based drug discovery (FBDD) methodology in which the biological target (BT) orchestrates the construction of its own ligand from fragments featuring complementary reactive functionalities. Notably, fragments interacting with the protein binding sites leverage their spatial proximity, facilitating a preferential reaction. Consequently, the resulting bivalent ligand exhibits heightened affinity. Within the realm of KTGS strategies, in situ click chemistry stands out as the most widely used to identify potent protein binders. This approach requires significant protein contributions, such as binding interactions and appropriate orientations of fragments, to overcome high activation barriers. This leads to prolonged incubation times and the potential for generating false negatives, thereby limiting this strategy to proteins that are stable enough in buffer. We herein unveil the possibility to integrate photochemistry into the realm of KTGS, accelerating the ligation reaction between fragments to a time scale of minutes. This approach should significantly expand the narrow reactivity window of traditional KTGS reactions, paving the way for the exploration and development of novel photo-KTGS reactions.

Triggering Heteroatom Ensemble Effect over RuFe Alloy to Promote Nitrogen Chemisorption for Efficient Ammonia Electrosynthesis at Ambient Conditions.

Ammonia (NH3) electrosynthesis from nitrogen (N2) provides a promising strategy for carbon neutrality, circumventing the energy-intensive and carbon-emitting Haber-Bosch process. However, the current system still presents unsatisfactory performance, and the bottleneck lies in the rational synthesis of catalytic centers with efficient N2 chemisorption ability. Herein, a heteroatom ensemble effect is deliberately triggered over RuFe alloy with spatial proximity of metal sites to promote electrocatalytic nitrogen reduction. The heteronuclear RuFe ensemble with increased surface polarization and modulated electronic structure offers the feasibility to optimize the adsorption configuration of electroactive substances and facilitate chemical bond scission. The promotion of N2 chemisorption and the following hydrogenation are demonstrated by the in situ Fourier transform infrared spectroscopy characterizations. The catalyst thus permits significantly enhanced conversion of N2 to NH3 in a 0.1 M HCl environment, with a maximum ammonia yield rate of 75.45 μg h-1 mg-1 and a high Faradaic efficiency of 35.49%.