Small Molecules In Complex Samples Research Articles

Improving performance of SALDI-MS by regulating density of plasmonic nanoparticles

Surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) emerges as an effective method for analyzing small molecules. At the current stage, its application is still limited by its detection sensitivity. According to the mechanism of SALDI-MS, the sensitivity highly depends on the laser desorption/ionization (LDI) efficiency. In this study, we adjust the localized surface plasmon resonance (LSPR) between particles by changing the gap, thereby improving the efficiency of LDI. The gaps of nanoparticles can be adjusted by the ionic strength in the solution. With the decrease of the gaps, the thermal effect of the nanoparticles is gradually enhanced and the number of excited electrons is also increased, which improves the sensitivity of SALDI-MS. The prepared substrate with optimized gaps has wide applicability for the detection of small biomolecules and dye molecules. Moreover, the substrate can successfully detect trace small molecules in complex samples. The analysis of sulfonamide in lake water shows the commendable performance of the substrate. The relative standard deviations (RSD) of spot-to-spot and batch-to-batch are 3.73% and 7.22% respectively, confirming the high reproducibility and the detection limit is as low as 100 pM, indicating the high sensitivity of the substrate. The results demonstrate that adjusting the LSPR of metal nanoparticles is a promising method for improving the performance of SALDI-MS.

An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

Coding recognition of the dose-effect interdependence of small biomolecules encrypted on paired chromatographic-based microassay arrays.

The discovery of small biomolecules has suffered from the lack of a comprehensive framework to express the intrinsic correlation between bioactivity and the contribution from small molecules in complex samples with molecular and bioactivity diversity. Here, by mapping a sample’s 2D-HPTLC fingerprint to microplates, paired chromatographic-based microassay arrays are created, which can be used as quasi-chips to characterize multiple attributes of chromatographic components; as the array differential expression of the bioactivity and molecular attributes of irregular chromatographic spots for dose–effect interdependent encoding; and also as the automatic-collimated array mosaics of the multi-attributes of each component itself encrypted by its chromatographic fingerprint. Based on this homologous framework, we propose a correlating recognition strategy for small biomolecules through their self-consistent chromatographic behavior characteristics. In the approach, the small biomolecule recognition in diverse compounds is transformed into a constraint satisfaction problem, which is addressed through examining the dose–effect interdependence of the homologous 2D code pairs by an array matching algorithm, instead of preparing diverse compound monomers of complex test samples for identification item-by-item. Furthermore, considering the dose–effect interdependent 2D code pairs as links and the digital-specific quasimolecular ions as nodes, an extendable self-consistent framework that correlates mammalian cell phenotypic and target-based bioassays with small biomolecules is established. Therefore, the small molecule contributions and the correlations of bioactivities, as well as their pathways, can be comprehensively revealed, so as to improve the reliability and efficiency of screening. This strategy was successfully applied to galangal, and demonstrated the high-throughput digital preliminary screening of small biomolecules in a natural product.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s00216-022-04162-9.

6-Glycosylaminoquinoline-assisted LDI MS for detection and imaging of small molecules with enhanced detection selectivity and sensitivity

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is a powerful tool in the analysis and imaging of small molecules. However, MALDI MS analysis is easily subjected to poor signal reproducibility and selectivity, especially for complex samples. In this study, a matrix glycosylation strategy was proposed to synthesize glycosylated matrices with excellent performances by enhancing the interaction of the matrix with small molecules. A series of glycosylated matrices including 3-glycosylaminoquinoline (3-GAQ), 6-glycosylaminoquinoline (6-GAQ), and 1-amino-5-glycosylaminoquinoline (GDAN) were synthesized by connecting glucose with the existing amine matrices. Compared with their parent matrices and the existing matrix (1,5-diaminonaphathelene, 1,5-DAN), the glycosylated matrices exhibited remarkably-improved sensitivity, higher signal reproducibility (RSD < 9%) in detecting metabolites, demonstrating the effectiveness of the glycosylation strategy. Among them, 6-GAQ exhibited the best performance. Using 6-GAQ, the detection limit of citric acid reached the low fmol range, and the calibration curve of citric acid had ideal linearity (R2 > 0.99), proving that 6-GAQ was capable of accurate quantitative analysis of metabolites. Furthermore, 6-GAQ was used for the imaging of metabolites in the mouse kidney section, showing higher sensitivity and lower background noise than the commonly-used matrices, 9-aminoquinoline (9-AA), and 1,5-DAN. More importantly, 6-GAQ can selectively detect the hydrophilic metabolites, especially the hydrophilic lipids in the mouse kidney. Overall, 6-GAQ is an ideal matrix potentially applied in the imaging and quantitative analysis of hydrophilic small molecules in complex samples.

Oxygen Isotope Exchange Reaction for Untargeted LC-MS Analysis.

LC-MS is a key technique for the identification of small molecules in complex samples. Accurate mass, retention time, and fragmentation spectra from LC-MS experiments are compared to reference values for pure chemical standards. However, this information is often unavailable or insufficient, leading to an assignment to a list of candidates instead of a single hit; therefore, additional features are desired to filter candidates. One such promising feature is the number of specific functional groups of a molecule that can be counted via derivatization or isotope-exchange techniques. Hydrogen/deuterium exchange (HDX) is the most widespread implementation of isotope exchange for mass spectrometry, while oxygen 16O/18O exchange is not applied as frequently as HDX. Nevertheless, it is known that some functional groups may be selectively exchanged in 18O enriched media. Here, we propose an implementation of 16O/18O isotope exchange to highlight various functional groups. We evaluated the possibility of using the number of exchanged oxygen atoms as a descriptor to filter database candidates in untargeted LC-MS-based workflows. It was shown that 16O/18O exchange provides 62% (median, n = 45) search space reduction for a panel of drug molecules. Additionally, it was demonstrated that studying the fragmentation spectra after 16O/18O can aid in eliminating false positives and, in some cases, help to annotate fragments formed with water traces in the collisional cell.

Quantum Chemistry Calculations for Metabolomics.

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Aptamer-Integrated Multianalyte-Detecting Paper Electrochemical Device.

On-site detection of multiple small-molecule analytes in complex sample matrixes would be highly valuable for diverse biosensing applications. Paper electrochemical devices (PEDs) offer an especially appealing sensing platform for such applications due to their low cost, portability, and ease of use. Using oligonucleotide-based aptamers as biorecognition elements, we here for the first time have developed a simple, inexpensive procedure for the fabrication of aptamer-modified multiplex PEDs (mPEDs), which can robustly and specifically detect multiple small molecules in complex samples. These devices are prepared via an ambient vacuum filtration technique using carbon and metal nanomaterials that yields precisely patterned sensing architecture featuring a silver pseudo-reference electrode, a gold counter electrode, and three gold working electrodes. The devices are user-friendly, and the fabrication procedure is highly reproducible. Each working electrode can be readily modified with different aptamers for sensitive and accurate detection of multiple small-molecule analytes in a single sample within seconds. We further demonstrate that the addition of a PDMS chamber allows us to achieve detection in microliter volumes of biological samples. We believe this approach should be highly generalizable, and given the rapid development of small-molecule aptamers, we envision that facile on-site multi-analyte detection of diverse targets in a drop of sample should be readily achievable in the near future.

Deep Learning to Generate in Silico Chemical Property Libraries and Candidate Molecules for Small Molecule Identification in Complex Samples.

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g., mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e., without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large data set of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller data sets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Evaluation of In Silico Multifeature Libraries for Providing Evidence for the Presence of Small Molecules in Synthetic Blinded Samples.

The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples. The process is time consuming and costly, and the majority of molecules are not yet represented by standards. Thus, there is a need to assemble evidence for the presence of small molecules in complex samples through the use of libraries containing calculated chemical properties. To address this need, we developed a Multi-Attribute Matching Engine (MAME) and a library derived in part from our in silico chemical library engine (ISiCLE). Here, we describe an initial evaluation of these methods in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT, Phase 1). For molecules in all mixtures, the initial blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high evidence scores, the FDR was 35%. After unblinding of the sample compositions, we optimized the scoring parameters to better exploit the available evidence and increased the accuracy for molecules suspected as present. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high evidence scores, the FDR was 10%. This study demonstrates that multiattribute matching methods in conjunction with in silico libraries may one day enable reduced reliance on experimentally derived libraries for building evidence for the presence of molecules in complex samples.

Nanoparticles-Enabled Surface-Enhanced Imaging Ellipsometry for Amplified Biosensing.

The main issues of imaging ellipsometry-based biosensing for small molecules are the low sensitivity and narrow detection range due to the low molecular weight of small molecules that results in a negligible signal. To meet this challenge, we theoretically investigated the deciding factors of the ellipsometry signal and further applied the theory to guide the design of ellipsometry-based biosensor using metal nanoparticles that have a high dielectric constant. Significant signal amplification effects can be achieved by using nanoparticle labels including magnetic nanoparticles and gold nanoparticles. Guided by the theory, we have developed a sensitive surface-enhanced imaging ellipsometry (SEIE)-biosensor for detecting chloramphenicol in real milk sample with high sensitivity (with a limit of detection of 6 pg/mL) and broaden detection range. This nanoparticles-enabled SEIE not only greatly improves the sensitivity of conventional imaging ellipsometry-based biosensors but also retains the advantages of conventional methods in terms of automated and convenient operation, providing an effective strategy for detection of trace small molecules in complex samples that holds great promise in scientific research, clinical diagnosis, and food safety.

Ion-Mobility-Derived Collision Cross Section as an Additional Identification Point for Multiresidue Screening of Pesticides in Fish Feed.

Ion mobility spectrometry allows for the measurement of the collision cross section (CCS), which provides information about the shape of an ionic molecule in the gas phase. Although the hyphenation of traveling-wave ion mobility spectrometry (TWIMS) with high-resolution quadrupole time-of-flight mass spectrometry (QTOFMS) has been mainly used for structural elucidation purposes, its potential for fast screening of small molecules in complex samples has not yet been thoroughly evaluated. The current work explores the capabilities of ultrahigh-performance liquid chromatography (UHPLC) coupled to a new design TWIMS-QTOFMS for the screening and identification of a large set of pesticides in complex salmon feed matrices. A database containing TWIMS-derived CCS values for more than 200 pesticides is hereby presented. CCS measurements showed high intra- and interday repeatability (RSD < 1%), and they were not affected by the complexity of the investigated matrices (ΔCCS ≤ 1.8%). The use of TWIMS in combination with QTOFMS was demonstrated to provide an extra-dimension, which resulted in increased peak capacity and selectivity in real samples. Thus, many false-positive detections could be straightforwardly discarded just by applying a maximum ΔCCS tolerance of ±2%. CCS was proposed as a valuable additional identification point in the pesticides screening workflow. Several commercial fish feed samples were finally analyzed to demonstrate the applicability of the proposed approach. Ethoxyquin and pirimiphos-methyl were identified in most of the analyzed samples, whereas tebuconazole and piperonil butoxide were identified for the first time in fish feed samples.

Humic acids as both matrix for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and adsorbent for magnetic solid phase extraction

In the present study, humic acids (HAs) were applied as both a matrix for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) and an adsorbent of magnetic solid phase extraction (MSPE) for the first time. As natural macromolecule compounds, HAs are inherently highly functionalized and contain laser energy absorbing–transferring aromatic structures. This special molecular structure made HAs a good candidate for use as a MALDI matrix in small molecule analysis. At the same time, due to its good adsorption ability, HAs was prepared as MSPE adsorbent via a simple co-mixing method, in which the commercially available HAs were directly mixed with Fe3O4 magnetic nanoparticles (MNPs) in a mortar and grinded evenly and completely. In this process, MNPs were physically wrapped and adhered to tiny HAs leading to the formation of magnetic HAs (MHAs). To verify the bi-function of the MHAs, Rhodamine B (RdB) was chosen as model compound. Our results show that the combination of MHAs-based MSPE and MALDI-TOF-MS can provide a rapid and sensitive method for the determination of RdB in chili oil. The whole analytical procedure could be completed within 30 min for simultaneous determination of more than 20 samples, and the limit of quantitation for RdB was found to be 0.02 μg/g. The recoveries in chili oil were in the range 73.8–81.5% with the RSDs less than 21.3% (intraday) and 20.3% (interday). The proposed strategy has potential applications for high-throughput analysis of small molecules in complex samples.

A tandem liquid chromatography–mass spectrometry (LC–MS) method for profiling small molecules in complex samples

Liquid chromatography–mass spectrometry (LC–MS) methods using either aqueous normal phase (ANP) or reversed phase (RP) columns are routinely used in small molecule or metabolomic analyses. These stationary phases enable chromatographic fractionation of polar and non-polar compounds, respectively. The application of a single chromatographic stationary phase to a complex biological extract results in a significant proportion of compounds which elute in the non-retained fraction, where they are poorly detected because of a combination of ion suppression and the co-elution of isomeric compounds. Thus coverage of both polar and non-polar components of the metabolome generally involves multiple analyses of the same sample, increasing the analysis time and complexity. In this study we describe a novel tandem in-line LC–MS method, in which compounds from one injection are sequentially separated in a single run on both ANP and RP LC-columns. This method is simple, robust, and enables the use of independent gradients customized for both RP and ANP columns. The MS signal is acquired in a single chromatogram which reduces instrument time and operator and data analysis errors. This method has been used to analyze a range of biological extracts, from plant and animal tissues, human serum and urine, microbial cell and culture supernatants. Optimized sample preparation protocols are described for this method as well as a library containing the retention times and accurate masses of 127 compounds.

Non-linear effects in quantitative 2D NMR of polysaccharides: Pitfalls and how to avoid them

Quantitative 2D NMR is a powerful analytical tool which is widely used to determine the concentration of small molecules in complex samples. Due to the site-specific response of the 2D NMR signal, the determination of absolute concentrations requires the use of a calibration or standard addition approach, where the analyte acts as its own reference. Standard addition methods, where the targeted sample is gradually spiked with known amounts of the targeted analyte, are particularly well-suited for quantitative 2D NMR of small molecules. This paper explores the potential of such quantitative 2D NMR approaches for the quantitative analysis of a high molecular weight polysaccharide. The results highlight that the standard addition method leads to a strong under-estimation of the target concentration, whatever the 2D NMR pulse sequence. Diffusion measurements show that a change in the macromolecular organization of the studied polysaccharide is the most probable hypothesis to explain the non-linear evolution of the 2D NMR signal with concentration. In spite of this non-linearity – the detailed explanation of which is out of the scope of this paper – we demonstrate that accurate quantitative results can still be obtained provided that an external calibration is performed with a wide range of concentrations surrounding the target value. This study opens the way to a number of studies where 2D NMR is needed for the quantitative analysis of macromolecules.

Progress in sample preparation and analytical methods for trace polar small molecules in complex samples

Small polar molecules such as nucleosides, amines, amino acids are important analytes in biological, food, environmental, and other fields. It is necessary to develop efficient sample preparation and sensitive analytical methods for rapid analysis of these polar small molecules in complex matrices. Some typical materials in sample preparation, including silica, polymer, carbon, boric acid and so on, are introduced in this paper. Meanwhile, the applications and developments of analytical methods of polar small molecules, such as reversed-phase liquid chromatography, hydrophilic interaction chromatography, etc., are also reviewed.

Nanostructure-initiator mass spectrometry (NIMS) for molecular mapping of animal tissues.

Nanostructure-initiator mass spectrometry (NIMS) is an established method for sensitive detection of small molecules in complex samples. It is based on the optimal combination of a porous Si substrate and a carefully selected polymer coating to allow certain analytes of interest to be concentrated on the substrate for effective ionization with minimal background interference from conventional organic matrices. The previous chapter has detailed the history and current state of the art of the technique in small-molecule profiling and imaging applications. We describe here a simple step-by-step protocol for substrate fabrication and sample preparation that provides a starting point for the technique to be adapted and optimized for 2-D biological imaging applications.

A new combination MALDI matrix for small molecule analysis: application to imaging mass spectrometry for drugs and metabolites

Since the development of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, this procedure has been specifically used for analyzing proteins or high molecular weight compounds because of the interference of matrix signals in the regions of the low mass range. Recently, scientists have been using a wide range of chemical compounds as matrices that ionize small molecules in a mass spectrometer and overcome the limitations of MALDI mass spectrometry. In this study, we developed a new combination matrix of 3-hydroxycoumarin (3-HC) and 6-aza-2-thiothymine (ATT), which is capable of ionizing small molecules, including drugs and single amino acids. In addition to ionization of small molecules, the combination matrix by itself gives less signals in the low mass region and can be used for performing imaging mass spectrometry (IMS) experiments on tissues, which confirms the vacuum stability of the matrix inside a MALDI chamber. The drug donepezil was mapped in the intact tissue slices of mice simultaneously with a spatial resolution of 150 μm during IMS. IMS analysis clearly showed that intact donepezil was concentrated in the cortical region of the brain at 60 min after oral administration. Our observations and results indicate that the new combination matrix can be used for analyzing small molecules in complex samples using MALDI mass spectrometry.