Laser Desorption Ionization Mass Spectrometry Research Articles

Insights on Stability Constants and Structures of Complexes between Coumarin Derivatives and Pb(II) in Aqueous Media.

In the frame of a systematic study on the sequestering ability of natural antioxidants towards metal cations, here the complexation of coumarin-3-carboxilic acid (HCCA) with Pb(II) and the overall stability constants of the resulting complexes, at 37 °C and in 0.16 M NaClO4, are discussed. Reaction of Pb(ClO4)2 with HCCA in an aqueous medium at a pH range from 2 to 6 and various ratios (1:1-1:10) yielded the Pb-CCA complexes, which were characterized spectrometrically by laser desorption ionization mass spectrometry (LD-MS). LD-MS has provided the composition and structure of Pb-CCA species according to the speciation model proposed on the basis of the potentiometric data. The graphic representation of the complex's concentration curves is given by the distribution diagram, which provides a whole depiction of the species present in the solution at the selected pH ranges.

Matrix- and surface-assisted laser desorption/ionization-mass spectrometry analysis of fingermark components for forensic studies: current trends and future prospects.

The chemical analysis of fingermarks (FMs) has attracted considerable attention in the realm of forensic investigations. Techniques based on direct ionization of a sample by laser irradiation, specifically matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS), have provided excellent figures of merit for analyzing high molecular-weight compounds. However, it can be challenging to analyze low molecular-weight compounds using MALDI-MS owing to potential interference produced by the organic matrices in the low molecular-weight region, which can impede the detection of small molecules (m/z < 700Da). Alternately, surface-assisted laser desorption/ionization-mass spectrometry (SALDI-MS) has shown great promise for small molecules analysis owing to the unique properties of the nanostructures used, particularly, minimal chemical background in low m/z region improved theproduction of ions involved in this method. The advancement of MALDI-MS and SALDI-MS has propelled their application in the analysis of FM components, focused on gaining deep insights into individual traits. This review aims to outline the current role of MALDI-MS and SALDI-MS in the chemical analysis of FMs. It also describes the latest achievements in forensic intelligence derived from fingermark analysis using these powerful methods. The accomplishments include the understanding of certain characteristics and lifestyles of donors. The review offers a comprehensive overview of the challenges and demands in this field. It suggests potential enhancements in this rapidly expanding domain to bridge the gap between research and practical police casework.

Distinguishing methicillin-resistant Staphylococcus aureus from methicillin-sensitive strains by combining Fe3O4 magnetic nanoparticle-based affinity mass spectrometry with a machine learning strategy

Pathogenic bacteria, including drug-resistant variants such as methicillin-resistant Staphylococcus aureus (MRSA), can cause severe infections in the human body. Early detection of MRSA is essential for clinical diagnosis and proper treatment, considering the distinct therapeutic strategies for methicillin-sensitive S. aureus (MSSA) and MRSA infections. However, the similarities between MRSA and MSSA properties present a challenge in promptly and accurately distinguishing between them. This work introduces an approach to differentiate MRSA from MSSA utilizing matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in conjunction with a neural network-based classification model. Four distinct strains of S. aureus were utilized, comprising three MSSA strains and one MRSA strain. The classification accuracy of our model ranges from ~ 92 to ~ 97% for each strain. We used deep SHapley Additive exPlanations to reveal the unique feature peaks for each bacterial strain. Furthermore, Fe3O4 MNPs were used as affinity probes for sample enrichment to eliminate the overnight culture and reduce the time in sample preparation. The limit of detection of the MNP-based affinity approach toward S. aureus combined with our machine learning strategy was as low as ~ 8 × 103 CFU mL−1. The feasibility of using the current approach for the identification of S. aureus in juice samples was also demonstrated.Graphical

Multi-spectroscopic characterization of organic salt components in medicinal plant

The characterization of structure of organic salts in complex mixtures has been a difficult problem in analytical chemistry. In the analysis of Scutellariae Radix (SR), the pharmacopoeia of many countries stipulates that the quality control component is baicalin (≥9% by high performance liquid chromatography (HPLC)). The component with highest response in SR was also baicalin detected by liquid chromatography-mass spectrometry (LC-MS). However, in the attenuated total reflection Fourier transform infrared spectroscopy, the carbonyl peak of glucuronic acid of baicalin did not appear in SR. The results of element analysis, time of flight secondary ion mass spectrometry, matrix assisted laser desorption ionization mass spectrometry and solid-state nuclear magnetic resonance all supported the existence of baicalin magnesium salt. Based on this, this study proposes an analysis strategy guided by infrared spectroscopy and combined with multi-spectroscopy techniques to analyze the structure of organic salt components in medicinal plant. It is meaningful for the research of mechanisms, development of new drugs, and quality control.

Molecularly Imprinted Heterostructure-Assisted Laser Desorption Ionization Mass Spectrometry Analysis and Imaging of Quinolones.

Quinolone residues resulting from body metabolism and waste discharge pose a significant threat to the ecological environment and to human health. Therefore, it is essential to monitor quinolone residues in the environment. Herein, an efficient and sensitive matrix-assisted laser desorption/ionization mass spectrometry (MALDI/MS) method was devised by using a novel molecularly imprinted heterojunction (MIP-TNs@GCNs) as the matrix. Molecularly imprinted titanium dioxide nanosheets (MIP-TNs) and graphene-like carbon nitrides (GCNs) were associated at the heterojunction interface, allowing for the specific, rapid, and high-throughput ionization of quinolones. The mechanism of MIP-TNs@GCNs was clarified using their adsorption properties and laser desorption/ionization capability. The prepared oxygen-vacancy-rich MIP-TNs@GCNs heterojunction exhibited higher light absorption and ionization efficiencies than TNs and GCNs. The good linearity (in the quinolone concentration range of 0.5-50 pg/μL, R2 > 0.99), low limit of detection (0.1 pg/μL), good reproducibility (n = 8, relative standard deviation [RSD] < 15%), and high salt and protein resistance for quinolones in groundwater samples were achieved using the established MIP-TNs@GCNs-MALDI/MS method. Moreover, the spatial distributions of endogenous compounds (e.g., amino acids, organic acids, and flavonoids) and xenobiotic quinolones from Rhizoma Phragmitis and Rhizoma Nelumbinis were visualized using the MIP-TNs@GCNs film as the MALDI/MS imaging matrix. Because of its superior advantages, the MIP-TNs@GCNs-MALDI/MS method is promising for the analysis and imaging of quinolones and small molecules.

Application of graphite carbon black assisted-laser desorption ionization-mass spectrometry for soy sauce product discrimination.

In a previous study, we developed a novel analytical method to directly and simultaneously detect taste- and odor-active compounds using graphite carbon black (GCB)-assisted laser desorption ionization mass spectrometry (LDI-MS). In this study, we aimed to evaluate food quality using a variety of soy sauces using the method to discriminate each product. Graphite carbon black-laser desorption ionization-mass spectrometry allowed the provision of hundreds of MS peaks derived from soy sauces in both positive and negative modes without any tedious sample pretreatments. Principal component analysis using the obtained MS peaks clearly distinguished three soy sauce products based on the manufacturing countries (Japan, China, and India). Moreover, this method identified distinct MS peaks for discrimination, which significantly correlated with their quantitative amounts in the products. Thus, GCB-LDI-MS analysis was established as a simple and rapid technique for food analysis, illustrating the chemical patterns of food products.

Multimodal Spectroscopic Studies to Evaluate the Effect of Nod-Factor-Based Fertilizer on the Maize (Zea mays) Stem.

Maize (Zea mays) is one of the most cultivated plants in the world. Due to the large area, the scale of its production, and the demand to increase the yield, there is a need for new environmentally friendly fertilizers. One group of such candidates is bacteria-produced nodulation (or nod) factors. Limited research has explored the impact of nodulation, factors on maize within field conditions, with most studies restricted to greenhouse settings and early developmental stages. Additionally, there is a scarcity of investigations that elucidate the metabolic alterations in the maize stem due to nod-factor exposure. It was therefore the aim of this study. Maize stem's metabolites and fibers were analyzed with various imaging analytical techniques: matrix assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI), Raman spectroscopy, attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR), and diffuse reflectance infrared Fourier transform spectroscopy. Moreover, the biochemical analyses were used to evaluate the proteins and soluble carbohydrates concentration and total phenolic content. These techniques were used to evaluate the influence of nod factor-based biofertilizer on the growth of a non-symbiotic plant, maize. The biofertilizer increased the grain yield and the stem mass. Moreover, the spectroscopic and biochemical investigation proved the appreciable biochemical changes in the stems of the maize in biofertilizer-treated plants. Noticeable changes were found in the spatial distribution and the increase in the concentration of flavonoids such as maysin, quercetin, and rutin. Moreover, the concentration of cell wall components (fibers) increased. Furthermore, it was shown that the use of untargeted analyses (such as Raman and ATR FT-IR, spectroscopic imaging, and MALDI-MSI) is useful for the investigation of the biochemical changes in plants.

Abstract 7088: MALDI-IHC high-plex protein biomarker imaging for fast, non-iterative identification of lung carcinoma subtypes

Abstract Lung cancer is the second most common cancer worldwide, with over 2.2 million new cases in 2020 and is the leading cause of cancer-related deaths worldwide. Most lung cancer (80-85%) is non-small cell (NSCLC), comprising mostly adenocarcinoma and squamous cell carcinoma, while roughly 10-15% of lung cancer is small cell lung cancer (SCLC), also termed neuroendocrine lung cancer. Subtype identification is critical for determining treatment, and with limited samples, it is critical to get the most data possible from each tissue section. NSCLC subtypes can be treated with chemotherapy or targeted drugs, whereas SCLC tends to respond best to a combination of chemotherapy and radiation. While there have been many improvements in the detection and treatment of lung carcinoma, the immuno-histochemical (IHC) confirmation of subtype remains a prolonged and often iterative process. To overcome this limitation, we have developed a new IHC approach which uses antibodies labeled with novel photocleavable mass-tags combined with matrix assisted laser desorption ionization (MALDI) mass spectrometry imaging. The approach, termed MALDI-IHC, provides fast (1 hr/cm2 imaging time) and highly multiplexed IHC (100+-plex) without the need for iterative cycling procedures. Here, we present a 20+-plex panel for the determination of subtype in lung cancer. An antibody panel comprised of established clinical biomarkers was developed to differentiate between the lung carcinoma subtypes and provide potentially prognostic information. This included markers for adenocarcinoma (TTF-1, Napsin A), squamous cell carcinoma (p40, CK5), and small cell lung cancer (CD56, Chromogranin A and Synaptophysin), as well as general carcinoma markers for t cells, b cells, macrophages, and fibroblasts These markers were first validated individually by immunofluorescence and then compared to MALDI-IHC on annotated tissues. Here we present an overview of the antibody validation for the 20+-plex lung carcinoma panel. Biomarker presence and peak signal intensity for the various subtypes was then determined using pathologist classified tissues for normal lung, adenocarcinoma, squamous cell carcinoma and small cell lung cancer. We report application of the MALDI-IHC approach to the simultaneous determination of lung carcinoma subtypes on a 99-core human lung tissue microarray (TMA) comprised of normal, normal adjacent, cancer adjacent, and cancerous tissues. Moreover, because MALDI-IHC is a non-destructive process, it can be followed by conventional H&amp;E staining on the same tissue section for accurate co-registration of traditional pathology annotations with the highly multiplexed MALDI-IHC results. In the future, even larger, 100+-plex antibody panels can be used for precision medicine on a wide range of cancers, in order to determine the molecular subtypes and guide treatment paths. Citation Format: Catherine A. Kita, Kristina Schwamborn, Gargey Yagnik, Kenneth J. Rothschild, Mark J. Lim. MALDI-IHC high-plex protein biomarker imaging for fast, non-iterative identification of lung carcinoma subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7088.

Vacuum promoted on-tissue derivatization strategy: Unravelling spatial distribution of glycerides on tissue

Matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) has shown its capability in visualizing the spatial distribution of various kinds of endogenous metabolites. Nevertheless, high quality mass imaging of low polar metabolites remains challenging. Herein, a platform for sensitive matrix-assisted laser desorption ionization-mass spectrometry imaging of cholesterol and glycerides has been proposed. In the platform, a vacuum promoted on-tissue derivatization strategy was proposed to constantly make the derivatization reaction proceed towards to the direction of products. Compared with traditional on-tissue derivatization procedure, the strategy improved the acquired intensity of derivatized glycerides about 50 %. Additionally, the mass spectrometry image reflecting the signal ratio between 3 classes of glycerides was achieved to exploit the metabolic level of glycerides on tissue slice. Finally, the platform was applied to brain slices of Alzheimer's transgenic mice, type 2 diabetes mice and normal mice. Significant difference was found in mass spectrometry images reflecting the signal ratio of multiple endogenous metabolites. The work constructed a promising platform for mapping of glycerides in tissue by mass spectrometry imaging.

Functional Melanin Nanoparticles-Assisted Laser Desorption Ionization Mass Spectrometry for High-Sensitivity Detection of TBBPA and TBBPS Contaminations in Animal-Derived Foodstuffs.

Tetrabromobisphenol A (TBBPA) and tetrabromobisphenol S (TBBPS) have been widely used as additives in various products; however, their residues damage human health mainly via dietary ingestion. The current detection techniques remain challenging in directly and sensitively identifying TBBPA and TBBPS from food samples. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) has great potential as an alternative tool for the analysis of low-mass environmental pollution. Herein, we successfully screened and optimized COOH-MNP-COOH as a novel MALDI matrix to enhance deprotonation for the analysis of TBBPA and TBBPS from animal-derived food samples in negative-ion mode. Notably, COOH-MNP-COOH was synthesized by a facile self-assembly strategy and characterized by TEM, FT-IR, UV-vis, and zeta potential analysis. Compared with conventional and control matrices, the COOH-MNP-COOH matrix exhibited excellent performance of TBBPA and TBBPS with high chemical stability, favorable reproducibility, remarkable salt and protein tolerance, and high sensitivity owing to abundant active groups, stronger UV-vis absorption at 355 nm, and better hydrophilicity and biocompatibility. TBBPA and TBBPS were detected with the assistance of an internal standard with limits of detection (LODs) of 300 and 200 pg/mL, respectively. Moreover, this method was applied to directly identify the residues of TBBPA and TBBPS in milk products, followed by basa catfish and meat. This research may provide a promising approach for the analysis of environmental pollutants in foodstuffs.

Development of a dual sensitive N,N,N’,N’,N”,N”-hexa-n-octylnitrilotriacetamide (HONTA) based potentiometric sensor for direct thorium(IV) estimation

A dual sensitive, simple, and rapid membrane-based potentiometric sensor was developed to determine thorium (Th) ion in nuclear fuel samples which does not require any prior chemical separation of other matrix elements. Sensor was prepared by incorporating N,N,N’,N’,N”,N”-hexa-n-octylnitrilotriacetamide (HONTA) in a polyvinyl chloride (PVC) matrix in presence of 2-nitrophenyl octyl ether (NPOE) and sodium tetraphenyl borate (NaTPB). The optimum membrane composition was found to be 14.1% HONTA, 3.2% NaTPB, 54.9% NPOE, 27.8% PVC, which showed a dual sensitive linear region in the calibration plot with sub-Nernstian and super-Nernstian slopes of 12.1 ± 0.4 mV/decade and 71.7 ± 3.5 mV/decade, respectively, with the corresponding linear dynamic ranges of 2.0 × 10−5 to 5.3 × 10−4 M and 3.8 ×10−3 to 1.4 × 10−2 M, respectively. The super-Nernstian sensitivity exhibited by the sensor increases with Th(IV) as well as HONTA concentrations in the membrane and can be thought to be due to micelle-mediated selective adsorption (MMSA) sensing of the metal ion. Solution speciation of Th(IV) in acetate buffer was determined by laser desorption/ionization mass spectrometry (LDI-MS) and Medusa software. Pristine and used membranes were characterized by Fourier transform infrared (FTIR), scanning electron microscopy–energy dispersive x–rays (SEM-EDS) and small angle neutron scattering (SAXS). Thorium was measured in an actual (Th–U) mixed solution and in a synthetic advance heavy water reactor (AHWR) fuel sample. The present study describes a new approach for the detection of Th(IV) ion through MMSA in an ion selective electrode (ISE).

Thermally Engineered Ferric Yolk‐Shells for Vaginal Secretion Based Cervical Cancer Screening

AbstractEngineering of materials is crucial for the advanced detection of bio‐molecules, particularly in laser desorption/ionization mass spectrometry (LDI‐MS). However, effective screening with enhanced LDI performance remains challenging due to the lack of engineered substrate materials. Herein, thermally engineered ferric yolk‐shells are designed for enhanced metabolic screening of cervical cancer (CC) using vaginal secretion. A facile thermal engineering strategy is developed to prepare amorphous ferric yolk‐shells with integrated properties. Notably, the amorphous mesoporous yolk‐shells offer signal amplification up to orders of magnitude in LDI‐MS for standard metabolites and diverse bio‐samples. High‐performance CC screening (including identification of human papillomavirus (HPV) infection, high‐grade squamous intraepithelial lesion (HSIL), and CC) is achieved, with the areas under the curve of 0.842–0.963. Finally, a metabolic panel for CC screening, covering the stages of HPV, HSIL, and CC, is established. This work elucidates the LDI‐MS mechanism for enhanced metabolic detection and highlights potential applications in cancer screening.

Lipid-related ion suppression on the herbicide atrazine in earthworm samples in ToF-SIMS and matrix-assisted laser desorption ionization mass spectrometry imaging and the role of gas-phase basicity.

In mass spectrometry imaging (MSI), ion suppression can lead to a misinterpretation of results. Particularly phospholipids, most of which exhibit high gas-phase basicity (GB), are known to suppress the detection of metabolites and drugs. This study was initiated by the observation that the signal of an herbicide, i.e., atrazine, was suppressed in MSI investigations of earthworm tissue sections. Herbicide accumulation in earthworms was investigated by time-of-flight secondary ion mass spectrometry and matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Additionally, earthworm tissue sections without accumulation of atrazine but with a homogeneous spray deposition of the herbicide were analyzed to highlight region-specific ion suppression. Furthermore, the relationship of signal intensity and GB in binary mixtures of lipids, amino acids, and atrazine was investigated in both MSI techniques. The GB of atrazine was determined experimentally through a linear plot of the obtained intensity ratios of the binary amino acid mixtures, as well as theoretically. The GBs values for atrazine of 896 and 906 kJ/mol in ToF-SIMS and 933 and 987 kJ/mol in MALDI-MSI were determined experimentally and that of 913 kJ/mol by quantum mechanical calculations. Compared with the GB of a major lipid component, phosphatidylcholine (GBPC = 1044.7 kJ/mol), atrazine's experimentally and computationally determined GBs in this work are significantly lower, making it prone to ion suppression in biological samples containing polar lipids.

Urine Metabolic Profiling for Rapid Lung Cancer Screening: A Strategy Combining Rh-Doped SrTiO3-Assisted Laser Desorption/Ionization Mass Spectrometry and Machine Learning.

Lung cancer ranks among the cancers with the highest global incidence rates and mortality. Swift and extensive screening is crucial for the early-stage diagnosis of lung cancer. Laser desorption/ionization mass spectrometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample processing, rapid speed, and high-throughput performance. As n-type semiconductors, titanate-based perovskite materials can generate charge carriers under ultraviolet light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO3 (STO/Rh)-assisted LDI-MS combined with machine learning to establish a method for urine-based lung cancer screening. We directly analyzed urine metabolites from lung cancer patients (LCs), pneumonia patients (PNs), and healthy controls (HCs) without employing any pretreatment. Through the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 for LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related pathways through metabolic enrichment analysis. These results suggest the potential of this method for rapidly distinguishing LCs in clinical applications and promoting precision medicine.

A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Imaging.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies. MALDI-2 (laser-induced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies. Mass spectra acquired using MALDI-2 constitute a combination of ions generated by both MALDI and MALDI-2 processes. Until now, no studies have focused on a detailed comparison between the ion images (as opposed to the generated m/z values) produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI) experiments. Herein, we investigated the ion images produced by both MALDI and MALDI-2 on the same tissue section using correlation analysis (to explore similarities in ion images for ions common to both MALDI and MALDI-2) and a deep learning approach. For the latter, we used an analytical workflow based on the Xception convolutional neural network, which was originally trained for human-like natural image classification but which we adapted to elucidate similarities and differences in ion images obtained using the two MSI techniques. Correlation analysis demonstrated that common ions yielded similar spatial distributions with low-correlation species explained by either poor signal intensity in MALDI or the generation of additional unresolved signals using MALDI-2. Using the Xception-based method, we identified many regions in the t-SNE space of spatially similar ion images containing MALDI and MALDI-2-related signals. More notably, the method revealed distinct regions containing only MALDI-2 ion images with unique spatial distributions that were not observed using MALDI. These data explicitly demonstrate the ability of MALDI-2 to reveal molecular features and patterns as well as histological regions of interest that are not visible when using conventional MALDI.

Lipidomics Reveals Myocardial Lipid Composition in a Murine Model of Insulin Resistance Induced by a High-Fat Diet.

Ectopic fat accumulation in non-adipose tissues is closely related to diabetes-related myocardial dysfunction. Nevertheless, the complete picture of the lipid metabolites involved in the metabolic-related myocardial alterations is not fully characterized. The aim of this study was to characterize the specific lipid profile in hearts in an animal model of obesity/insulin resistance induced by a high-fat diet (HFD). The cardiac lipidome profiles were assessed via liquid chromatography-mass spectrometry (LC-MS)/MS-MS and laser desorption/ionization-mass spectrometry (LDI-MS) tissue imaging in hearts from C57BL/6J mice fed with an HFD or standard-diet (STD) for 12 weeks. Targeted lipidome analysis identified a total of 63 lipids (i.e., 48 triacylglycerols (TG), 5 diacylglycerols (DG), 1 sphingomyelin (SM), 3 phosphatidylcholines (PC), 1 DihydroPC, and 5 carnitines) modified in hearts from HFD-fed mice compared to animals fed with STD. Whereas most of the TG were up-regulated in hearts from animals fed with an HFD, most of the carnitines were down-regulated, thereby suggesting a reduction in the mitochondrial β-oxidation. Roughly 30% of the identified metabolites were oxidated, pointing to an increase in lipid peroxidation. Cardiac lipidome was associated with a specific biochemical profile and a specific liver TG pattern. Overall, our study reveals a specific cardiac lipid fingerprint associated with metabolic alterations induced by HFD.

Large-scale atomistic model construction of subbituminous and bituminous coals for solvent extraction simulations with reactive molecular dynamics

Large-scale atomistic models for complex polycyclic aromatic hydrocarbon systems help understand the chemical properties and behaviors of complex feedstocks such as coal or petroleum. However, the development and utilization of large-scale models remain limited due to the difficulty in achieving the varied structural characteristics necessary to capture stochastic nature of these feedstocks. We demonstrate a systematic workflow to construct stochastic molecular systems from a broad analytical suite: high-resolution transmission electron microscopy (HRTEM), carbon-13 nuclear magnetic resonance spectroscopy (13C NMR), laser desorption ionization mass spectroscopy (LDI-MS), and elemental analysis. We present a model construction and analysis utility of a new Python-based module. We selected one subbituminous and three high-volatile bituminous coals to construct large-scale models (∼40,000 atoms). The constructed models were utilized to examine the affinity for solvent extraction (naphthalene or tetralin) and the effect of structural properties (e.g., aromatic cluster size, functional groups, and cross-linking) in reactive molecular dynamics simulations. Complex chemical reactions were monitored with bond order transitions, intermediates formation, and mass distributions. Reactive molecular dynamics simulations suggest a plausible chemical extraction process and products for the complex fossil feedstocks. The results indicated that radical formations with bond breaking of bridging oxygens and carbons were required at high temperatures to facilitate hydrogeneration and extraction of gas molecules from radical-free molecules. We observed that aliphatic chains of tetralin were easily decomposed and combined with radicals to form small size of molecules with aryl bonding, mainly increasing molecules in the 500–1000 Da, while naphthalene had little impact on chemical extraction process.

Characterization of Regolith And Trace Economic Resources (CRATER): An Orbitrap-based laser desorption mass spectrometry instrument for in situ exploration of the Moon.

Characterization of Regolith And Trace Economic Resources (CRATER), an Orbitrap™-based laser desorption mass spectrometry instrument designed to conduct high-precision, spatially resolved analyses of planetary materials, is capable of answering outstanding science questions about the Moon's formation and the subsequent processes that have modified its (sub)surface. Here, we describe the baseline design of the CRATER flight model, which requires <20 000 cm3 volume, <10kg mass, and <60 W peak power. The analytical capabilities and performance metrics of a prototype that meets the full functionality of the flight model are demonstrated. The instrument comprises a high-power, solid-state, pulsed ultraviolet (213 nm) laser source to ablate the surface of the lunar sample, a custom ion optical interface to accelerate and collimate the ions produced at the ablation site, and an Orbitrap mass analyzer capable of discriminating competing isobars via ultrahigh mass resolution and high mass accuracy. The CRATER instrument can measure elemental and isotopic abundances and characterize the organic content of lunar surface samples, as well as identify economically valuable resources for future exploration. An engineering test unit of the flight model is currently in development to serve as a pathfinder for near-term mission opportunities.

Metabolomics unveils the exacerbating role of arachidonic acid metabolism in atherosclerosis.

Atherosclerosis is a complex vascular disorder characterized by the deposition of lipids, inflammatory cascades, and plaque formation in arterial walls. A thorough understanding of its causes and progression is necessary to develop effective diagnostic and therapeutic strategies. Recent breakthroughs in metabolomics have provided valuable insights into the molecular mechanisms and genetic factors involved in atherosclerosis, leading to innovative approaches for preventing and treating the disease. In our study, we analyzed clinical serum samples from both atherosclerosis patients and animal models using laser desorption ionization mass spectrometry. By employing methods such as orthogonal partial least-squares discrimination analysis (OPLS-DA), heatmaps, and volcano plots, we can accurately classify atherosclerosis (AUC = 0.892) and identify key molecules associated with the disease. Specifically, we observed elevated levels of arachidonic acid and its metabolite, leukotriene B4, in atherosclerosis. By inhibiting arachidonic acid and monitoring its downstream metabolites, we discovered the crucial role of this metabolic pathway in regulating atherosclerosis. Metabolomic research provides detailed insights into the metabolic networks involved in atherosclerosis development and reveals the close connection between abnormal metabolism and the disease. These studies offer new possibilities for precise diagnosis, treatment, and monitoring of disease progression, as well as evaluating the effectiveness of therapeutic interventions.

Cutinase Immobilization on a Supramolecular Cage Protein Scaffold

Background: Self-assembly of preformed nanoparticles into larger and more complex materials, termed nanoarchitectonics, is an area of great interest as the resulting higher-order architectures can exhibit advanced supramolecular properties important in sensor design, catalysis, and ferromagnetic properties. Objective: The aim of the current investigation is to explore the application of self-assembling protein networks to serve as molecular scaffolds for immobilization of enzyme catalysts. The use of 12 nm ferritin cage proteins to serve as components of these scaffolds would expand the application of these types of multifunctional proteins to the fabrication of advanced biomaterials. Method: Humicola insolens cutinase was immobilized on a supramolecular protein scaffold using bioconjugation to biotinylate the enzyme of interest. The protein-based scaffold consisted of a ferritin- biotin-avidin system, and the interaction of biotin and avidin was used to suspend the enzyme molecules onto this network. Matrix-assisted laser desorption mass spectrometry, scanning electron microscopy, and energy dispersive X-ray spectroscopy were employed to analyze the supramolecular cage protein scaffold at various stages of fabrication. Results: The activities of these scaffold-bound enzymes towards chromogenic esters and polyethylene terephthalate (PET) were analyzed and found to remain active towards both substrates following biotinylation and immobilization. Conclusion: Biotinylated Humicola insolens cutinase enzymes can be immobilized on nanodimensional protein networks composed of avidin and biotinylated horse spleen ferritin and exhibit catalytic activity toward a small substrate, p-nitrophenylbutyrate, as well as an industrial plastic. Selfassembling protein networks may provide new approaches for biomolecular immobilization.