Ion sources for molecular mass spectrometry are usually driven by direct current power supplies with no user control over the total charges generated. Here, we show that the output of triboelectric ...nanogenerators (TENGs) can quantitatively control the total ionization charges in mass spectrometry. The high output voltage of TENGs can generate single- or alternating-polarity ion pulses, and is ideal for inducing nanoelectrospray ionization (nanoESI) and plasma discharge ionization. For a given nanoESI emitter, accurately controlled ion pulses ranging from 1.0 to 5.5 nC were delivered with an onset charge of 1.0 nC. Spray pulses can be generated at a high frequency of 17 Hz (60 ms in period) and the pulse duration is adjustable on-demand between 60 ms and 5.5 s. Highly sensitive (∼0.6 zeptomole) mass spectrometry analysis using minimal sample (18 pl per pulse) was achieved with a 10 pg ml
cocaine sample. We also show that native protein conformation is conserved in TENG-ESI, and that patterned ion deposition on conductive and insulating surfaces is possible.
Recent advances in direct open air surface ionization in mass spectrometry are detailed. Among the techniques discusses are paperspray ionization and fused droplet extractive electrospray ionization.
Mass spectrometry (MS) has become a central technique in cancer research. The ability to analyze various types of biomolecules in complex biological matrices makes it well suited for understanding ...biochemical alterations associated with disease progression. Different biological samples, including serum, urine, saliva, and tissues have been successfully analyzed using mass spectrometry. In particular, spatial metabolomics using MS imaging (MSI) allows the direct visualization of metabolite distributions in tissues, thus enabling in‐depth understanding of cancer‐associated biochemical changes within specific structures. In recent years, MSI studies have been increasingly used to uncover metabolic reprogramming associated with cancer development, enabling the discovery of key biomarkers with potential for cancer diagnostics. In this review, we aim to cover the basic principles of MSI experiments for the nonspecialists, including fundamentals, the sample preparation process, the evolution of the mass spectrometry techniques used, and data analysis strategies. We also review MSI advances associated with cancer research in the last 5 years, including spatial lipidomics and glycomics, the adoption of three‐dimensional and multimodal imaging MSI approaches, and the implementation of artificial intelligence/machine learning in MSI‐based cancer studies. The adoption of MSI in clinical research and for single‐cell metabolomics is also discussed. Spatially resolved studies on other small molecule metabolites such as amino acids, polyamines, and nucleotides/nucleosides will not be discussed in the context.
Although it is generally accepted that amino acids were present on the prebiotic Earth, the mechanism by which α‐amino acids were condensed into polypeptides before the emergence of enzymes remains ...unsolved. Here, we demonstrate a prebiotically plausible mechanism for peptide (amide) bond formation that is enabled by α‐hydroxy acids, which were likely present along with amino acids on the early Earth. Together, α‐hydroxy acids and α‐amino acids form depsipeptides—oligomers with a combination of ester and amide linkages—in model prebiotic reactions that are driven by wet–cool/dry–hot cycles. Through a combination of ester–amide bond exchange and ester bond hydrolysis, depsipeptides are enriched with amino acids over time. These results support a long‐standing hypothesis that peptides might have arisen from ester‐based precursors.
Amino acids form peptide bonds when subjected to day–night cycles (wet–dry cycles) in the presence of hydroxy acids. Such a reaction could have occurred on the prebiotic Earth.
Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass ...spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 M – H−, 155 M + H+, and 138 M + Na+ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a ΔCCS threshold of 2.8% and a mass error of 10 ppm.
Metabolomics generates complex data necessitating advanced computational methods for generating biological insight. While machine learning (ML) is promising, the challenges of selecting the best ...algorithms and tuning hyperparameters, particularly for nonexperts, remain. Automated machine learning (AutoML) can streamline this process; however, the issue of interpretability could persist. This research introduces a unified pipeline that combines AutoML with explainable AI (XAI) techniques to optimize metabolomics analysis. We tested our approach on two data sets: renal cell carcinoma (RCC) urine metabolomics and ovarian cancer (OC) serum metabolomics. AutoML, using Auto-sklearn, surpassed standalone ML algorithms like SVM and k-Nearest Neighbors in differentiating between RCC and healthy controls, as well as OC patients and those with other gynecological cancers. The effectiveness of Auto-sklearn is highlighted by its AUC scores of 0.97 for RCC and 0.85 for OC, obtained from the unseen test sets. Importantly, on most of the metrics considered, Auto-sklearn demonstrated a better classification performance, leveraging a mix of algorithms and ensemble techniques. Shapley Additive Explanations (SHAP) provided a global ranking of feature importance, identifying dibutylamine and ganglioside GM(d34:1) as the top discriminative metabolites for RCC and OC, respectively. Waterfall plots offered local explanations by illustrating the influence of each metabolite on individual predictions. Dependence plots spotlighted metabolite interactions, such as the connection between hippuric acid and one of its derivatives in RCC, and between GM3(d34:1) and GM3(18:1_16:0) in OC, hinting at potential mechanistic relationships. Through decision plots, a detailed error analysis was conducted, contrasting feature importance for correctly versus incorrectly classified samples. In essence, our pipeline emphasizes the importance of harmonizing AutoML and XAI, facilitating both simplified ML application and improved interpretability in metabolomics data science.
The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other ...endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS–MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)–IMS–MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.
Lipids play a critical role in cell membrane integrity, signaling, and energy storage. However, in-depth structural characterization of lipids is still challenging and not routinely possible in ...lipidomics experiments. Techniques such as collision-induced dissociation (CID) tandem mass spectrometry (MS/MS), ion mobility (IM) spectrometry, and ultrahigh-performance liquid chromatography are not yet capable of fully characterizing double-bond and sn-chain position of lipids in a high-throughput manner. Herein, we report on the ability to structurally characterize lipids using large-area triboelectric nanogenerators (TENG) coupled with time-aligned parallel (TAP) fragmentation IM-MS analysis. Gas-phase lipid epoxidation during TENG ionization, coupled to mobility-resolved MS3 via TAP IM-MS, enabled the acquisition of detailed information on the presence and position of lipid CC double bonds, the fatty acyl sn-chain position and composition, and the cis/trans geometrical CC isomerism. The proposed methodology proved useful for the shotgun lipidomics analysis of lipid extracts from biological samples, enabling the detailed annotation of numerous lipid isobars.
In cancer plasmonic photothermal therapy (PPTT), plasmonic nanoparticles are used to convert light into localized heat, leading to cancer cell death. Among plasmonic nanoparticles, gold nanorods ...(AuNRs) with specific dimensions enabling them to absorb near-infrared laser light have been widely used. The detailed mechanism of PPTT therapy, however, still remains poorly understood. Typically, surface-enhanced Raman spectroscopy (SERS) has been used to detect time-dependent changes in the intensity of the vibration frequencies of molecules that appear or disappear during different cellular processes. A complete proven assignment of the molecular identity of these vibrations and their biological importance has not yet been accomplished. Mass spectrometry (MS) is a powerful technique that is able to accurately identify molecules in chemical mixtures by observing their m/z values and fragmentation patterns. Here, we complemented the study of changes in SERS spectra with MS-based metabolomics and proteomics to identify the chemical species responsible for the observed changes in SERS band intensities during PPTT. We observed an increase in intensity of the bands at around 1000, 1207, and 1580 cm–1, which were assigned in the literature to phenylalanine, albeit with dispute. Our metabolomics results showed increased levels of phenylalanine, its derivatives, and phenylalanine-containing peptides, providing evidence for more confidence in the SERS peak assignments. To better understand the mechanism of phenylalanine increase upon PPTT, we combined metabolomics and proteomics results through network analysis, which proved that phenylalanine metabolism was perturbed. Furthermore, several apoptosis pathways were activated via key proteins (e.g., HADHA and ACAT1), consistent with the proposed role of altered phenylalanine metabolism in inducing apoptosis. Our study shows that the integration of the SERS with MS-based metabolomics and proteomics can assist the assignment of signals in SERS spectra and further characterize the related molecular mechanisms of the cellular processes involved in PPTT.
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