Analysis Of Small Molecules Research Articles

Enhanced electrochemiluminescence imaging of single cell membrane proteins based on Co3O4 nanozyme catalysis.

The development of enhanced strategies with excellent biocompatibility is critical for electrochemiluminescence (ECL) imaging of single cells. Here, we report an ECL imaging technique for a single cell membrane protein based on a Co3O4 nanozyme catalytic enhancement strategy. Due to the remarkable catalytic performance of Co3O4 nanozymes, H2O2 can be efficiently decomposed into reactive oxygen radicals, and the reaction with L012 was enhanced, resulting in stronger ECL emission. The anti-carcinoembryonic antigen (CEA) was coupled with nanozyme particles to construct a probe that specifically recognized the overexpressed CEA on the MCF-7 cell membrane. According to the locally enhanced visualized luminescence, the rapid ECL imaging of a single cell membrane protein was eventually realized. Accordingly, Co3O4 nanozymes with highly efficient activity will provide new insights into ECL imaging analysis of more biological small molecules and proteins.

Attempts to Detect Lipid Metabolites from a Single Cell Using Proton-Transfer-Reaction Mass Spectrometry Coupled with Micro-Scale Supercritical Fluid Extraction: A Preliminary Study.

Proton-transfer-reaction (PTR) mass spectrometry (MS), a widely used method for detecting trace-levels of volatile organic compounds in gaseous samples, can also be used for the analysis of small non-volatile molecules by using supercritical fluid as a transporter for the molecules. Supercritical fluid extraction (SFE) is a method that permits lipophilic compounds to be rapidly and selectively extracted from complex matrices. The combination of the high sensitivity of PTR MS with the SFE is a potentially novel method for analyzing small molecules in a single cell, particularly for the analysis of lipophilic compounds. We preliminarily evaluated this method for analyzing the components of a single HeLa cell that is fixed on a stainless steel frit and is then directly introduces the SFE extracts into the PTR MS. A total of 200/91 ions were observed in positive/negative ion mode time-of-flight mass spectra, and the masses of 11/10 ions could be matched to chemical formulae obtained from the LipidMaps lipids structure database. Using various authentic lipophilic samples, the method could be used to detect free fatty acids in the sub-femtomole to femtomole order in the negative ion mode, the femtomole to sub-picomole order for fat-soluble vitamins, and the picomole order for poly aromatic hydrocarbons in both the positive and negative ion mode.

Microbiome and Metabolomics in Liver Cancer: Scientific Technology.

Primary liver cancer is a heterogeneous disease. Liver cancer metabolism includes both the reprogramming of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment and fluctuations in regular tissue metabolism. Currently, metabolomics and metabolite profiling in liver cirrhosis, liver cancer, and hepatocellular carcinoma (HCC) have been in the spotlight in terms of cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecules, chemicals, and metabolites. Metabolomics technologies can provide critical information about the liver cancer state. Here, we review how liver cirrhosis, liver cancer, and HCC therapies interact with metabolism at the cellular and systemic levels. An overview of liver metabolomics is provided, with a focus on currently available technologies and how they have been used in clinical and translational research. We also list scalable methods, including chemometrics, followed by pathway processing in liver cancer. We conclude that important drivers of metabolomics science and scientific technologies are novel therapeutic tools and liver cancer biomarker analysis.

Titania Nanosheet as a Matrix for Surface-Assisted Laser Desorption/Ionization Mass Spectrometry Analysis and Imaging.

Surface-assisted laser desorption/ionization (SALDI) acts as a soft desorption/ionization technique, which has been widely recognized in small-molecule analysis owing to eliminating the requirement of the organic matrix. Herein, titania nanosheets (TiO2 NSs) were applied as novel substrates for simultaneous analysis and imaging of low-mass molecules and lipid species. A wide variety of representative analytes containing amino acids, bases, drugs, peptides, endogenous small molecules, and saccharide-spiked urine were examined by the TiO2 NS-assisted LDI mass spectrometry (MS). Compared with conventional organic matrices and substrates [Ag nanoparticles (NPs), Au NPs, carbon nanotubes, carbon NPs, CeO2 microparticles, and P25 TiO2], the TiO2 NS-assisted LDI MS method shows higher sensitivity and less spectral interference. Repeatability was evaluated with batch-to-batch relative standard deviations for 5-hydroxytryptophan, glucose-spiked urine, and glucose with addition of internal standard, which were 17.4, 14.9, and 2.8%, respectively. The TiO2 NS-assisted LDI MS method also allows the determination of blood glucose levels in mouse serum with a linear range of 0.5-10 mM. Owing to the nanoscale size and uniform deposition of the TiO2 NS matrix, spatial distributions of 16 endogenous small molecules and 16 lipid species from the horizontal section of the mouse brain tissue can be visualized at a 50 μm spatial resolution. These successful applications confirm that the TiO2-assisted LDI MS method has promising prospects in the field of life science.

UV-Absorbing Ligand Capped Gold Nanoparticles for the SALDI-MS Analysis of Small Molecules.

We report that modifying the surface of gold nanoparticles (Au NPs) with 2-mercaptopyridine-3-carboxylic acid (MPyCA) enhances surface-assisted laser desorption/ionization (SALDI) performance in the analysis of small molecules. The MPyCA ligand has a strong UV absorbance at the wavelengths of the typical MALDI laser at 337 nm, resulting in efficient thermal/energy transfer from the Au NPs to analytes during pulse-laser irradiation. In addition, the MPyCA ligand contains carboxylic acid and pyridine groups, providing affinity to various analytes through acid-base interactions. Irganox1010, glucose and meropenem were utilized as model analytes to evaluate SALDI performance because these molecules are generally ionized with difficulty by conventional MALDI-MS. Our results demonstrate that the MPyCA-Au NP based SALDI-MS could detect Irganox1010, glucose and meropenem with stronger ion peaks for these molecules compared to MALDI-MS using CHCA. The limit of detection (LOD) for meropenem was much lower in the case of SALDI (LOD=1 ng/mL) compared to MALDI (LOD=10 μg/mL).

Feature Selection Assists BLSTM for the Ultrasensitive Detection of Bioflavonoids in Different Biological Matrices Based on the 3D Fluorescence Spectra of Gold Nanoclusters.

Rapid and on-site qualitative and quantitative analysis of small molecules (including bioflavonoids) in biofluids are of great importance in biomedical applications. Herein, we have developed two deep learning models based on the 3D fluorescence spectra of gold nanoclusters as a single probe for rapid qualitative and quantitative analysis of eight bioflavonoids in serum. The results proved the efficiency and stability of the random forest-bidirectional long short-term memory (RF-BLSTM) model, which was used only with the most important features after deleting the unimportant features that might hinder the performance of the model in identifying the selected bioflavonoids in serum at very low concentrations. The optimized model achieves excellent overall accuracy (98-100%) in the qualitative analysis of the selected bioflavonoids. Next, the optimized model was transferred to quantify the selected bioflavonoids in serum at nanoscale concentrations. The transferred model achieved excellent accuracy, and the overall determination coefficient (R2) value range was 99-100%. Furthermore, the optimized model achieved excellent accuracies in other applications, including multiplex detection in serum and model applicability in urine. Also, LOD in serum at nanoscale concentration was considered. Therefore, this approach opens the window for qualitative and quantitative analysis of small molecules in biofluids at nanoscale concentrations, which may help in the rapid inclusion of sensor arrays in biomedical and other applications.

Ultrahigh-Throughput Intact Protein Analysis with Acoustic Ejection Mass Spectrometry.

The need for high-throughput intact protein analysis has been rising as drug discovery increasingly requires the analysis of large sets of covalent modifiers and protein therapeutics. Liquid chromatography-mass spectrometry (LC-MS) is the primary analytical tool used to date to characterize proteins within the biopharmaceutical industry. However, the speed of LC-MS prevents the analysis of large-scale sample sets (>1000 within a day). Acoustic ejection mass spectrometry (AEMS) has recently been established as an electrospray ionization (ESI)-MS based platform with both fast analytical throughput and high data quality. Since its introduction, this technology has been applied in numerous fields with a primary focus on small-molecule analysis in high-throughput drug discovery and development. Here we explore the application of AEMS to high-throughput intact protein analysis for proteins ranging in molecular weight from 17 to 150 kDa on a prototype high-resolution quadrupole time-of-flight (HR QTOF) based AEMS system. Data quality obtained on this platform is comparable to LC-MS, while the analysis speed is significantly improved to one-second-per-sample. This ultrahigh-throughput intact protein analysis platform has the potential to be used broadly in drug discovery.

Simultaneous metabolite MALDI-MSI, whole exome and transcriptome analysis from formalin-fixed paraffin-embedded tissue sections

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) allows spatial analysis of proteins, metabolites, or small molecules from tissue sections. Here, we present the simultaneous generation and analysis of MALDI-MSI, whole-exome sequencing (WES), and RNA-sequencing data from the same formalin-fixed paraffin-embedded (FFPE) tissue sections. Genomic DNA and total RNA were extracted from (i) untreated, (ii) hematoxylin-eosin (HE) stained, and (iii) MALDI-MSI-analyzed FFPE tissue sections from three head and neck squamous cell carcinomas. MALDI-MSI data were generated by a time-of-flight analyzer prior to preprocessing and visualization. WES data were generated using a low-input protocol followed by detection of single-nucleotide variants (SNVs), tumor mutational burden, and mutational signatures. The transcriptome was determined using 3'-RNA sequencing and was examined for similarities and differences between processing stages. All data met the commonly accepted quality criteria. Besides SNVs commonly identified between differently processed tissues, FFPE-typical artifactual variants were detected. Tumor mutational burden was in the same range for tissues from the same patient and mutational signatures were highly overlapping. Transcriptome profiles showed high levels of correlation. Our data demonstrate that simultaneous molecular profiling of MALDI-MSI-processed FFPE tissue sections at the transcriptome and exome levels is feasible and reliable.The authors present a workflow that allows the simultaneous measurement of the whole exome and the transcriptome by next-generation sequencing from formalin-fixed paraffin-embedded tissue sections that were analyzed by matrix-assisted laser desorption ionization mass spectrometry imaging. The data and analyses demonstrate the feasibility and reproducibility of this approach, which expands the possibilities of multi-omics integration in cancer research.

A DFT study on the size and defect induced properties of 2H-Au nano surface by small molecules adsorption

The size effect and defect-related properties of the Au nanosurface in the hexagonal phase (Au2H) have been investigated by Density Functional Theory (DFT) using the adsorption of small molecules. The OH, H2O, H2O2, C2H5OH, H2 and O2 molecules were considered. The atomic H and O adsorption were also taken into account for comparison. The adsorption distances, energies, frontier molecular orbitals and density of state analysis of small molecules on top, bridge and hollow sites of the Au2H surfaces were computed in conjunction with charge distribution analysis. The size, dimension and phase-dependent adsorption energies were obtained by comparison with previous calculations. The complexes’ stability changes with the sites and interacting groups. Apart from H2O2 and C2H5OH on top site of Au2H, all studied groups’ adsorption shows exothermic character. The adsorption energies reveal the order O > OH > H > H2O> O2. The Au2H surface is most sensitive to H, O, OH and O2. Therefore, the Au2H can serve as a promising sensor to detect and recognize those atoms/molecules in practical applications. The Au2H also has potential applications as a work function-type sensor for H, OH and C2H5OH. The size limit of Au2H was defined.

Analysis of Small-Molecule Mixtures by Super-Resolved 1H NMR Spectroscopy.

Analysis of small molecules is essential to metabolomics, natural products, drug discovery, food technology, and many other areas of interest. Current barriers preclude from identifying the constituent molecules in a mixture as overlapping clusters of NMR lines pose a major challenge in resolving signature frequencies for individual molecules. While homonuclear decoupling techniques produce much simplified pure shift spectra, they often affect sensitivity. Conversion of typical NMR spectra to pure shift spectra by signal processing without a priori knowledge about the coupling patterns is essential for accurate analysis. We developed a super-resolved wavelet packet transform based 1H NMR spectroscopy that can be used in high-throughput studies to reliably decouple individual constituents of small molecule mixtures. We demonstrate the efficacy of the method on the model mixtures of saccharides and amino acids in the presence of significant noise.

High-Throughput Deconvolution of Intact Protein Mass Spectra for the Screening of Covalent Inhibitors.

Deconvolution from intact protein mass-to-charge spectra to mass spectra is essential to generate interpretable data for mass spectrometry (MS) platforms coupled to ionization sources that produce multiply charged species. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) can be used to analyze intact proteins in multiwell microtiter plates with speed matching small molecule analyses (at least 1 Hz). However, the lack of compatible deconvolution software has limited its use in high-throughput screening applications. Most existing automated deconvolution software packages work best for data generated from LC-MS, and to the best of our knowledge, there is no software capable of performing fast plate-based mass spectral deconvolution. Herein we present the use of a new workflow in ProSight Native for the deconvolution of protein spectra from entire well plates that can be completed within 3 s. First, we successfully demonstrated the potential increased throughput benefits produced by the combined IR-MALDESI-MS - ProSight Native platform using protein standards. We then conducted a screen for Bruton's tyrosine kinase (BTK) covalent binders against a well-annotated compound collection consisting of 2232 compounds and applied ProSight Native to deconvolute the protein spectra. Seventeen hits including five known BTK covalent inhibitors in the compound set were identified. By alleviating the data processing bottleneck using ProSight Native, it may be feasible to analyze and report covalent screening results for >200,000 samples in a single day.

Exploring the Chemical Structures and Phototochemical Properties of Graphene Oxide Derivatives by Laser Desorption/Ionization Time-of-Flight Mass Spectrometry to Develop an Efficient Platform for Mass Spectrometric Analysis.

Laser desorption/ionization time-of-flight mass spectrometry (LDI-TOF-MS) analysis is harnessed to investigate the chemical structure and photochemical properties of two distinct graphene oxide (GO) derivatives simultaneously. The GO derivatives are synthesized with modified Brodie's method (BGO) and Hummers' method (HGO) and characterized by LDI-TOF-MS as well as conventional tools. A series of LDI-TOF-MS analyses reveal that BGO provides higher laser energy absorption, photochemical stability, and photothermal conversion property than HGO based on their fragmentation patterns and laser desorption/ionization behavior of a thermometer molecule. Based on these characteristics, BGO exhibits higher efficiency in the LDI-TOF-MS analysis of various small molecules and synthetic polymers than HGO. These different photochemical properties of BGO are derived from its large sp2 carbon domains compared to HGO. Based on our findings, the analytical potential of LDI-TOF-MS for GO derivatives is clearly demonstrated, which can be an efficient and unique characterization tool to explore both chemical structures and photochemical properties of various carbon materials.

Targeted N-Glycan Analysis with Parallel Reaction Monitoring Using a Quadrupole-Orbitrap Hybrid Mass Spectrometer.

Targeted mass spectrometric analysis is widely employed across various omics fields. The approach has been successfully employed for the structural analysis of proteins, glycans, lipids, and small molecules. Selected reaction monitoring and multiple reaction monitoring (MRM) have been a method of choice for targeted structural studies of biomolecules. However, innovations in instrument designs have led to the development of parallel reaction monitoring (PRM). PRM detects all product ions simultaneously rather than optimizing/preselecting the target glycan transitions, simplifying the analytical workflow. By reducing background interference, increasing selectivity/specificity, and improving data quality, PRM allows reliable quantification of target glycans in complex matrices. PRM can also improve sensitivity for detecting low-abundance target glycans and reduce low-level limit of quantification values with an improved S/N ratio. PRM's advantages are attributed to the development of sensitive and highly selective mass analyzers, orbitrap, and time of flight. In this study, we developed a sensitive PRM method for the quantitative analysis of permethylated N-glycans, an important class of disease biomarkers, using a quadrupole-orbitrap hybrid mass spectrometer. Pooled human cerebrospinal fluid was used for the study as a source of permethylated N-glycans. The method illustrates the fragmentation of N-glycans at different collision energies as well as the optimization of collision energy. The method also detects low-abundance N-glycans more efficiently than MRM. This study is the first attempt to develop a sensitive PRM-based method to analyze permethylated N-glycans.

Modeling pyranose ring pucker in carbohydrates using machine learning and semi-empirical quantum chemical methods.

Pyranose ring pucker is a key coordinate governing the structure, interactions and reactivity of carbohydrates. We assess the ability of the machine learning potentials, ANI-1ccx and ANI-2x, and the GFN2-xTB semiempirical quantum chemical method, to model ring pucker conformers of five monosaccharides and oxane in the gas phase. Relative to coupled-cluster quantum mechanical calculations, we find that ANI-1ccx most accurately reproduces the ring pucker energy landscape for these molecules, with a correlation coefficient r2 of 0.83. This correlation in relative energies lowers to values of 0.70 for ANI-2x and 0.60 for GFN2-xTB. The ANI-1ccx also provides the most accurate estimate of the energetics of the 4 C1 -to-1 C4 minimum energy pathway for the six molecules. All three models reproduce chair more accurately than non-chair geometries. Analysis of small model molecules suggests that the ANI-1ccx model favors puckers with equatorial hydrogen bonding substituents; that ANI-2x and GFN2-xTB models overstabilize conformers with axially oriented groups; and that the endo-anomeric effect is overestimated by the machine learning models and underestimated via the GFN2-xTB method. While the pucker conformers considered in this study correspond to a gas phase environment, the accuracy and computational efficiency of the ANI-1ccx approach in modeling ring pucker in vacuo provides a promising basis for future evaluation and application to condensed phase environments.

High-Throughput Intact Protein Analysis for Drug Discovery Using Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry.

Mass spectrometry (MS) is the primary analytical tool used to characterize proteins within the biopharmaceutical industry. Electrospray ionization (ESI) coupled to liquid chromatography (LC) is the current gold standard for intact protein analysis. However, inherent speed limitations of LC/MS prevent analysis of large sample numbers (>1000) in a day. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI-MS), an ambient ionization MS technology, has recently been established as a platform for high-throughput small molecule analysis. Here, we report the applications of such a system for the analysis of intact proteins commonly performed within the drug discovery process. A wide molecular weight range of proteins 10-150 kDa was detected on the system with improved tolerance to salts and buffers compared to ESI. With high concentrations and model proteins, a sample rate of up to 22 Hz was obtained. For proteins at low concentrations and in buffers used in commonly employed assays, robust data at a sample rate of 1.5 Hz were achieved, which is ∼22× faster than current technologies used for high-throughput ESI-MS-based protein assays. In addition, two multiplexed plate-based high-throughput sample cleanup methods were coupled to IR-MALDESI-MS to enable analysis of samples containing excessive amounts of salts and buffers without fully compromising productivity. Example experiments, which leverage the speed of the IR-MALDESI-MS system to monitor NISTmAb reduction, protein autophosphorylation, and compound binding kinetics in near real time, are demonstrated.

Comparability of Steroid Collision Cross Sections Using Three Different IM-HRMS Technologies: An Interplatform Study

Steroids play key roles in various biological processes and are characterized by many isomeric variants, which makes their unambiguous identification challenging. Ion mobility-mass spectrometry (IM-MS) has been proposed as a suitable platform for this application, particularly using collision cross section (CCS) databases obtained from different commercial IM-MS instruments. CCS is seen as an ideal additional identification parameter for steroids as long-term repeatability and interlaboratory reproducibility of this measurand are excellent and matrix effects are negligible. While excellent results were demonstrated for individual IM-MS technologies, a systematic comparison of CCS derived from all major commercial IM-MS technologies has not been performed. To address this gap, a comprehensive interlaboratory comparison of 142 CCS values derived from drift tube (DTIM-MS), traveling wave (TWIM-MS), and trapped ion mobility (TIM-MS) platforms using a set of 87 steroids was undertaken. Besides delivering three instrument-specific CCS databases, systematic comparisons revealed excellent interlaboratory performance for 95% of the ions with CCS biases within ±1% for TIM-MS and within ±2% for TWIM-MS with respect to DTIM-MS values. However, a small fraction of ions (<1.5%) showed larger biases of up to 7% indicating that differences in the ion conformation sampled on different instrument types need to be further investigated. Systematic differences between CCS derived from different IM-MS analyzers and implications on the applicability for nontargeted analysis are critically discussed. To the best of our knowledge, this is the most comprehensive interlaboratory study comparing CCS from three different IM-MS technologies for analysis of steroids and small molecules in general.

Algorithms and Databases: Unlocking Non-Targeted Screening of Small Molecules with Ambient Ionization Mass Spectrometry

Almost all sectors of analytical chemistry are finding applications for ambient ionization mass spectrometry (AI–MS) because of its ease of use, speed of analysis, and sensitivity. Although emphasis has been placed on developing new hardware that can help analyze unique samples across various applications, there has not been much innovation in the functionality of software tools and mass spectral libraries to support applications like non-targeted searching. In this article, we discuss new algorithms and libraries that have enabled non-targeted analysis of small molecules using AI–MS, as well as some of the key considerations and outstanding questions in the field.

Molecular docking, simulation and binding free energy analysis of small molecules as PfHT1 inhibitors.

Antimalarial drug resistance has thrown a spanner in the works of malaria elimination. New drugs are required for ancillary support of existing malaria control efforts. Plasmodium falciparum requires host glucose for survival and proliferation. On this basis, P. falciparum hexose transporter 1 (PfHT1) protein involved in hexose permeation is considered a potential drug target. In this study, we tested the antimalarial activity of some compounds against PfHT1 using computational techniques. We performed high throughput virtual screening of 21,352 small-molecule compounds against PfHT1. The stability of the lead compound complexes was evaluated via molecular dynamics (MD) simulation for 100 nanoseconds. We also investigated the pharmacodynamic, pharmacokinetic and physiological characteristics of the compounds in accordance with Lipinksi rules for drug-likeness to bind and inhibit PfHT1. Molecular docking and free binding energy analyses were carried out using Molecular Mechanics with Generalized Born and Surface Area (MMGBSA) solvation to determine the selectivity of the hit compounds for PfHT1 over the human glucose transporter (hGLUT1) orthologue. Five important PfHT1 inhibitors were identified: Hyperoside (CID5281643); avicularin (CID5490064); sylibin (CID5213); harpagoside (CID5481542) and quercetagetin (CID5281680). The compounds formed intermolecular interaction with the binding pocket of the PfHT1 target via conserved amino acid residues (Val314, Gly183, Thr49, Asn52, Gly183, Ser315, Ser317, and Asn48). The MMGBSA analysis of the complexes yielded high free binding energies. Four (CID5281643, CID5490064, CID5213, and CID5481542) of the identified compounds were found to be stable within the PfHT1 binding pocket throughout the 100 nanoseconds simulation run time. The four compounds demonstrated higher affinity for PfHT1 than the human major glucose transporter (hGLUT1). This investigation demonstrates the inhibition potential of sylibin, hyperoside, harpagoside, and avicularin against PfHT1 receptor. Robust preclinical investigations are required to validate the chemotherapeutic properties of the identified compounds.

Hybrid bismuth oxide-graphine oxide nanomaterials improve the signal-to-noise response of small molecules analyzed by matrix assisted laser desorption ionization-time-of-flight mass spectrometry

Matrix-assisted laser desorption ionization (MALDI) has received increasing attention for the analysis small molecules. Nanomaterials are frequently used as the matrix in LDI, and various inorganic materials have been developed, particularly those based on thermally-driven positive DI mechanisms. However, the unwanted detection of alkali metal ion adducts in the positive ion mode can compromise small molecule identification. Here, we report the synthesis and application of a novel hybrid bismuth oxide-graphene oxide (Bi2O3@GO) semiconductor matrix for the analysis of small molecules by LDI-time-of-flight mass spectrometry (TOF-MS) operating in the negative ion mode. The structure of the semiconductor nanomaterial was characterized using conventional methods and its performance for the detection of small molecules (e.g., amino acids, fatty acids, sugars and other small molecules) was compared with traditional DI matrices (e.g., cyano-4-hydroxycinnamic acid, 2,5-dihydroxybenzoic acid, 9-aminoacridine and GO). The results showed that the negative ion LDI-TOF MS of small molecules on Bi2O3@GO were free of matrix-related interferences, and possessed good signal intensity and repeatability. Application of Bi2O3@GO to the quantitative determination of glucose in human serum and soft drinks confirmed that the hybrid matrix could also be applied to complex samples. Conclusions drawn from the experimental results, computational chemistry calculations, and previous studies, suggesting that interfacial photogenerated thermal electron transfer and capture are key processes in the LDI mechanism.

An Overview of the Latest Metabolomics Studies on Atopic Eczema with New Directions for Study.

Atopic eczema (AE) is an inflammatory skin disorder affecting approximately 20% of children worldwide and early onset can lead to asthma and allergies. Currently, the mechanisms of the disease are not fully understood. Metabolomics, the analysis of small molecules in the skin produced by the host and microbes, opens a window to observe the mechanisms of the disease which then may lead to new drug targets for AE treatment. Here, we review the latest advances in AE metabolomics, highlighting both the lipid and non-lipid molecules, along with reviewing the metabolites currently known to reside in the skin.