MetFlexo: An Automated Simulation of Realistic H1-NMR Spectra

MetAssimulo 2.0: a web app for simulating realistic 1D and 2D metabolomic 1H NMR spectra

MOTIVATION

Metabolomics extensively utilizes nuclear magnetic resonance (NMR) spectroscopy due to its excellent reproducibility and high throughput. Both 1D and 2D NMR spectra provide crucial information for metabolite annotation and quantification, yet present complex overlapping patterns which may require sophisticated machine learning algorithms to decipher. Unfortunately, the limited availability of labeled spectra can hamper application of machine learning, especially deep learning algorithms which require large amounts of labeled data. In this context, simulation of spectral data becomes a tractable solution for algorithm development.

RESULTS

Here, we introduce MetAssimulo 2.0, a comprehensive upgrade of the MetAssimulo 1.b metabolomic 1H NMR simulation tool, reimplemented as a Python-based web application. Where MetAssimulo 1.0 only simulated 1D 1H spectra of human urine, MetAssimulo 2.0 expands functionality to urine, blood, and cerebral spinal fluid, enhancing the realism of blood spectra by incorporating a broad protein background. This enhancement enables a closer approximation to real blood spectra, achieving a Pearson correlation of approximately 0.82. Moreover, this tool now includes simulation capabilities for 2D J-resolved (J-Res) and Correlation Spectroscopy spectra, significantly broadening its utility in complex mixture analysis. MetAssimulo 2.0 simulates both single, and groups, of spectra with both discrete (case-control, e.g. heart transplant versus healthy) and continuous (e.g. body mass index) outcomes and includes inter-metabolite correlations. It thus supports a range of experimental designs and demonstrating associations between metabolite profiles and biomedical responses.By enhancing NMR spectral simulations, MetAssimulo 2.0 is well positioned to support and enhance research at the intersection of deep learning and metabolomics.

AVAILABILITY AND IMPLEMENTATION

The code and the detailed instruction/tutorial for MetAssimulo 2.0 is available at https://github.com/yanyan5420/MetAssimulo_2.git. The relevant NMR spectra for metabolites are deposited in MetaboLights with accession number MTBLS12081.

Workflow4Metabolomics (W4M): A User-Friendly Metabolomics Platform for Analysis of Mass Spectrometry and Nuclear Magnetic Resonance Data

Various spectrometric methods can be used to conduct metabolomics studies. Nuclear magnetic resonance (NMR) or mass spectrometry (MS) coupled with separation methods, such as liquid or gas chromatography (LC and GC, respectively), are the most commonly used techniques. Once the raw data have been obtained, the real challenge lies in the bioinformatics required to conduct: (i) data processing (including preprocessing, normalization, and quality control); (ii) statistical analysis for comparative studies (such as univariate and multivariate analyses, including PCA or PLS-DA/OPLS-DA); (iii) annotation of the metabolites of interest; and (iv) interpretation of the relationships between key metabolites and the relevant phenotypes or scientific questions to be addressed. Here, we will introduce and detail a stepwise protocol for use of the Workflow4Metabolomics platform (W4M), which provides user-friendly access to workflows for processing of LC-MS, GC-MS, and NMR data. Those modular and extensible workflows are composed of existing standalone components (e.g., XCMS and CAMERA packages) as well as a suite of complementary W4M-implemented modules. This tool suite is accessible worldwide through a web interface and is hosted on UseGalaxy France. The extensible Virtual Research Environment (VRE) provided offers pre-configured workflows for metabolomics communities (platforms, end users, etc.), as well as possibilities for sharing among users. By providing a consistent ecosystem of tools and workflows through Galaxy, W4M makes it possible to process MS and NMR data from hundreds of samples using an ordinary personal computer, after step-by-step workflow optimization. © 2025 Wiley Periodicals LLC. Basic Protocol 1: W4M account creation, working history preparation, and data upload Support Protocol 1: How to prepare an NMR zip file Support Protocol 2: How to convert MS data from proprietary format to open format Support Protocol 3: How to get help with W4M (IFB forum) and how to report a problem on the GitHub repository Basic Protocol 2: LC-MS data processing Alternate Protocol 1: GC-MS data processing Alternate Protocol 2: NMR data processing Basic Protocol 3: Statistical analysis Basic Protocol 4: Annotation of metabolites from LC-MS data Alternate Protocol 3: Annotation of metabolites from NMR data.

Computational simulation of 1 H NMR profiles of complex biofluid analyte mixtures at differential operating frequencies: Applications to low-field benchtop spectra

Estimations of accurate and reliable NMR chemical shift values, coupling patterns and constants within a reasonable timeframe remain significantly challenging, and the unavailability of reliable software strategies for the prediction of low-field (e.g., 60 MHz) spectra from those acquired at higher operating frequencies hampers their direct comparison. Hence, this study explored the applications of accessible software options for predicting these parameters in the ¹ H NMR profiles of analytes as a function of magnetic field strength; this was performed for individual analytes and also for complex biofluid matrices featured in metabolomics investigations. For this purpose, results from the very first successful experimental acquisition and simulation of the ¹ H NMR profiles of intact human salivary supernatant samples on a 60 MHz benchtop spectrometer were evaluated. Using salivary metabolite concentrations determined at 400 MHz, it was demonstrated that simulation of the low-field spectra of five biomolecules with the most prominent ¹ H resonances detectable allowed multiple component fits to be applied to experimental spectra. Hence, these salivary ¹ H NMR profiles could be successfully predicted throughout the 45-600 MHz operating frequency range. With the exception of propionate resonance multiplets, which revealed more complex coupling patterns at low field and required more astute computational and fitting options, valuable quantitative metabolomics data on salivary acetate, formate, methanol and glycine could be attained from low-field spectrometres. These studies are both timely and pertinent in view of the recent advancement of low-field benchtop NMR facilities for diagnostically significant biomarker tracking in biofluids. Experiments performed with added ammonium chloride to facilitate the release of salivary metabolites from biopolymer binding sites provided evidence that a small but nevertheless significant proportion of propionate, but not lactate, was bound to such sites, an observation of much relevance to biomolecule quantification in salivary metabolomics investigations.

Smart Methylcellulose Hydrogels for pH-Triggered Delivery of Silver Nanoparticles

Infection is a severe complication in chronic wounds, often leading to morbidity or mortality. Current treatments rely on dressings, which frequently contain silver as a broad-spectrum antibacterial agent, although improper dosing can result in severe side effects. This work proposes a novel methylcellulose (MC)-based hydrogel designed for the topical release of silver nanoparticles (AgNPs) via an intelligent mechanism activated by the pH variations in infected wounds. A preliminary optimization of the physicochemical and rheological properties of MC hydrogels allowed defining the optimal processing conditions in terms of crosslinker (citric acid) concentration, crosslinking time, and temperature. MC/AgNPs nanocomposite hydrogels were obtained via an in situ synthesis process, exploiting MC both as a capping and reducing agent. AgNPs with a 12.2 ± 2.8 nm diameter were obtained. MC hydrogels showed a dependence of the swelling and degradation behavior on both pH and temperature and a noteworthy pH-triggered release of AgNPs (release ~10 times higher at pH 12 than pH 4). 1H-NMR analysis revealed the role of alkaline hydrolysis of the ester bonds (i.e., crosslinks) in governing the pH-responsive behavior. Overall, MC/AgNPs hydrogels represent an innovative platform for the pH-triggered release of AgNPs in an alkaline milieu.