Biology subjects

Gine, R.

Publications and source records attributed to Gine, R..

5 recordsLinked to original sources

bioRxiv2025

Solid Ionic Matrices applied via Low-Temperature Evaporation enable High-Resolution and Sensitive MALDI Imaging of Metabolites

Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) has become a key technology for spatially resolved molecular analysis. However, imaging of low molecular weight compounds remains challenging due to spectral interferences in the low m/z range caused by conventional organic matrices. Ionic matrices (IMs), in contrast, reduce matrix-related background signals, improve spectral reproducibility, and enhance sensitivity for detecting metabolites and lipids, but their use in MALDI-MSI has so far been limited, primarily focusing on lipid imaging above m/z 500. Here, we present a novel approach combining solid ionic matrices (ISMs) with low-temperature thermal evaporation (LTE) to enhance MALDI-MSI performance for small metabolites. ISMs based on -cyano-4-hydroxycinnamic acid (CHCA) and 2,5-dihydroxybenzoic acid (DHB) provided superior sensitivity, with CHCA + N,N-diethylaniline enabling the detection and annotation of 73 small metabolites (m/z <500) in mouse brain at 20 {micro}m spatial resolution. When applied to the pancreas, this matrix enabled the first specific localization of melatonin and 3-iodotyrosine within the islets of Langerhans, opening new avenues for spatial metabolomics in endocrine research.

bioengineering↗

bioRxiv2025

ChemEmbed: A deep learning framework for metabolite identification using enhanced MS/MS data and multidimensional molecular embeddings

Machine learning tools have become essential for annotating the vast number of unidentified MS/MS spectra in metabolomics, addressing the limitations of current reference spectral libraries. However, these tools often struggle with the high dimensionality and sparsity of MS/MS spectra and metabolite structures. ChemEmbed introduces a novel approach by combining multidimensional and continuous vector representations of chemical structures with enhanced MS/MS spectra. This enhancement is achieved by merging spectra from multiple collision energies and incorporating calculated neutral losses from 38,472 distinct compounds, providing richer input for a convolutional neural network (CNN). ChemEmbed achieves top-ranked candidate annotations in over 42% of cases and identifies the correct compound within the top five in more than 76% of cases in a test dataset. Against external benchmarks such as CASMI 2016 and 2022, ChemEmbed outperforms SIRIUS, the current state-of-the-art in computational metabolomics. In a validation experiment with the Annotated Recurrent Unidentified Spectra (ARUS) dataset-- including over 25,000 spectra from human plasma and 68,000 from urine-- ChemEmbed successfully identified 24 previously unannotated compounds. By aligning with the advanced capabilities of modern mass spectrometry instrumentation, ChemEmbed balances accuracy, computational efficiency, and scalability, making it a powerful solution for high-throughput metabolomics applications.

bioinformatics↗

bioRxiv2025

Improving MALDI Mass Spectrometry Imaging Performance: Low-Temperature Thermal Evaporation for Controlled Matrix Deposition and Improved Image Quality

The deposition of matrix compounds significantly influences the effectiveness of matrix-assisted laser desorption/ionization (MALDI) Mass Spectrometry Imaging (MSI) experiments, impacting sensitivity, spatial resolution, and reproducibility. Dry deposition methods offer advantages by producing homogeneous matrix layers and minimizing analyte delocalization without the use of solvents. However, refining these techniques to precisely control matrix thickness, minimize heating temperatures, and ensure high-purity matrix layers is crucial for optimizing MALDI-MSI performance. Here, we present a novel approach utilizing low-temperature thermal evaporation (LTE) for organic matrix deposition under reduced vacuum pressure. Our method allows for reproducible control of matrix layer thickness, as demonstrated by linear calibration for two organic matrices, 2,5-dihydroxybenzoic acid (DHB), and 1,5-Diaminonaphthalene (DAN). The environmental scanning electron microscopy images reveal a uniform distribution of small-sized matrix crystals, consistently on the submicrometer scale, across tissue slides following LTE deposition. Remarkably, LTE serves as an additional purification step for organic matrices, producing very pure layers irrespective of initial matrix purity. Furthermore, stability assessment of MALDI-MSI data from mouse brain sections coated with LTE-deposited DHB or DAN matrix indicates minimal impact on ionization efficiency, signal intensity, and image quality even after storage at - 80{degrees}C for two weeks, underscoring the robustness of LTE-deposited matrices for MSI applications. Comparative analysis with the spray-coating method highlights several advantages of LTE deposition, including enhanced ionization, reduced analyte diffusion, and improved MSI image quality.

bioengineering↗

bioRxiv2024

SingleFrag: A deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation

Metabolite and small molecule identification via MS/MS involves matching experimental spectra with prerecorded spectra of known compounds. This process is hindered by the current lack of comprehensive reference spectral libraries. To address this gap, we need accurate in silico fragmentation tools for predicting MS/MS spectra of compounds for which empirical spectra do not exist. Here, we present SingleFrag, a novel deep learning tool that predicts individual fragments separately, rather than attempting to predict the entire fragmentation spectrum at once. Our results demonstrate that SingleFrag surpasses state-of-the-art in silico fragmentation tools, providing a powerful method for annotating unknown MS/MS spectra of known compounds. As a proof of concept, we successfully annotate three previously unidentified compounds frequently found in human samples.

bioinformatics↗

bioRxiv2021

HERMES: a molecular formula-oriented method to target the metabolome

Comprehensive metabolome analyses are hampered by low identification rates of metabolites due to suboptimal strategies in MS and MS2 acquisition, and data analysis. Here we present a molecular formula-oriented and peak detection-free method, HERMES, that improves sensitivity and selectivity for metabolite profiling in MS and structural annotation in MS2. An analysis of environmental water, E. coli, and human plasma extracts by HERMES showed increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared to iterative data-dependent acquisition (DDA). HERMES is available as an R package with a user-friendly graphical interface to allow data analysis and interactive tracking of compound annotations.

bioinformatics↗