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Molecular Application of Mass Spectrometry and Chromatography in Biomedicine: 2nd Edition

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 3028

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Dr. Giovanni Ventura

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Dipartimento di Chimica, Centro Interdipartimentale SMART, Università degli Studi di Bari, Via Orabona, 4-I-70126 Bari, Italy
Interests: mass spectrometry; liquid chromatography; lipidomics; proteomics; biomarker discovery
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Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of our previous one, entitled “Molecular Application of Mass Spectrometry and Chromatography in Biomedicine” (https://www.mdpi.com/journal/ijms/special_issues/8BW7G1A515).

Mass spectrometry and chromatography are revolutionizing medical research and diagnostics, delving deep into the intricate molecular landscapes of biological systems and uncovering crucial insights into the mechanisms of disease, the identification of biomarkers, drug metabolism, and personalized medicine.

For this Special Issue, considering the field of biomedicine, we aim to collate papers addressing the application of mass spectrometric techniques, whether used alone or in combination with chromatography, for the identification, quantification, or structural elucidation of biomolecules, including proteins, lipids, metabolites, and pharmaceutical compounds; this will enable the study of disease biomarkers, advance drug discovery, unveil potential health benefits, and enhance clinical diagnostics.

This Special Issue is supervised by Dr. Giovanni Ventura and assisted by our Topical Advisory Panel Member Dr. Mariachiara Bianco. We invite you to contribute your original articles or reviews related to approaches involving mass spectrometry and chromatography for biomedical applications to this Special Issue.

Dr. Giovanni Ventura
Guest Editor

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

mass spectrometry
chromatography
LC-MS
GC-MS
biomarker discovery
pharmaceuticals
metabolomics
proteomics
lipidomics

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Published Papers (4 papers)

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Research

19 pages, 2403 KB

Open AccessArticle

LC-MS/MS Method for Therapeutic Drug Monitoring of Abiraterone, Darolutamide, Apalutamide, Enzalutamide, and Metabolites in Prostate Cancer Patients

by Bianca Posocco, Diletta Pasin, Nicoletta De Cesaro, Alice Pivetta, Sara Gagno, Giovanni Canil, Eleonora Cecchin, Riccardo Cecchin, Sara Speziani, Arianna Dri, Giorgia Bortolus, Michele Spina, Sandra Santarossa, Fabio Puglisi, Lucia Fratino and Erika Cecchin

Int. J. Mol. Sci. 2026, 27(7), 3017; https://doi.org/10.3390/ijms27073017 - 26 Mar 2026

Viewed by 484

Abstract

Accurate measurement of androgen receptor pathway inhibitors (ARPIs) and their active metabolites is essential for pharmacokinetic studies and therapeutic drug monitoring (TDM) in patients with prostate cancer (PC). However, their simultaneous determination in human plasma is analytically challenging because of the wide concentration ranges. This study aimed to develop and validate a sensitive and robust LC–MS/MS method for the quantification of abiraterone, Δ4-abiraterone, enzalutamide, N-desmethyl enzalutamide, darolutamide, keto-darolutamide, apalutamide, and N-desmethyl apalutamide in human plasma. Sample preparation was performed by protein precipitation, followed by chromatographic separation and detection using multiple reaction monitoring with isotopically labeled internal standards (total run time 6.5 min). The method was validated in accordance with regulatory guidelines by assessing selectivity, linearity, sensitivity, accuracy, precision, recovery, matrix effects, and stability. The assay demonstrated good linearity (≥0.997) across clinically relevant concentration ranges, with lower limits of quantification between 0.1 and 40 ng/mL, depending on the analyte. Intra- and inter-day precision and accuracy were within acceptable limits, and recovery and matrix effects were consistent across different plasma matrices. Stability experiments conducted in plasma and whole blood provided practical guidance for sample handling. The method was successfully applied to 79 plasma samples from 61 patients with metastatic PC. Measured concentrations were generally consistent with published pharmacokinetic data, while unexpectedly high ABI levels were observed. Sample collection occurred between 1 and 28 h after the last drug intake, enabling assessment of the analytical method across the entire pharmacokinetic profile. Full article

(This article belongs to the Special Issue Molecular Application of Mass Spectrometry and Chromatography in Biomedicine: 2nd Edition)

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16 pages, 2082 KB

Open AccessArticle

MFF-AE: Enhanced Quality Control for Proteomics Mass Spectrometry Data via Multi-Scale Feature Fusion

by Guangkui Fan, Xinyu Ji, Hunyue Liao, Bo Meng, Duotao Pan, Jinze Huang and Yang Zhao

Int. J. Mol. Sci. 2026, 27(5), 2121; https://doi.org/10.3390/ijms27052121 - 25 Feb 2026

Viewed by 350

Abstract

Mass spectrometry (MS) is a core analytical tool in proteomics, and the quality of the generated data directly determines the effectiveness of downstream analyses and the reliability of final research conclusions. While MS is also widely used in other omics applications, this study focuses on label-free quantitative proteomics, where samples are represented as protein-abundance matrices derived from MaxQuant. However, MS data are typically characterized by high dimensionality and substantial noise, posing serious challenges for quality control (QC). Existing QC methods have limited feature extraction capabilities and struggled to capture the key information embedded in the data, resulting in poor performance in identifying anomalous samples. Here, we propose the Multi-Scale Feature Fusion-based Autoencoder (MFF-AE). This deep learning-based anomaly detection model achieves precise identification of anomalous samples by integrating both global and local data features. The model consists of three modules: an autoencoder-based backbone network that efficiently embeds raw data into a low-dimensional semantic space, a local feature extraction and fusion module designed to capture and integrate multi-scale features within MS data, and a sample identification module that enhances discriminative representations to enable accurate anomaly detection. To evaluate the effectiveness of the proposed model, we conduct extensive experiments on a benchmark dataset with synthesized anomalies. Quantitative results on the benchmark dataset show that, compared with 15 baseline models from statistical learning, deep learning, and ensemble learning, our model consistently achieves the best performance across key metrics. Furthermore, through linear relationship analysis on real-world clinical datasets, the exclusion of outlier samples significantly increased the statistical significance and fold change in the identified differential proteins. Overall, the proposed model establishes a solid data foundation, paving the way for downstream mechanistic studies and target discovery. Full article

(This article belongs to the Special Issue Molecular Application of Mass Spectrometry and Chromatography in Biomedicine: 2nd Edition)

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18 pages, 3083 KB

Open AccessArticle

LC-QTOF-MS as a Tool for Quantitative and Qualitative Analysis of Isoniazid and Its Metabolites in Dog Liver Samples

by Julia Horla, Paweł Jajor, Tetiana Holumbiiovska, Mykola Zhyla, Nataliia Vretsona, Galyna Kotsyumbas and Błażej Poźniak

Int. J. Mol. Sci. 2026, 27(4), 1818; https://doi.org/10.3390/ijms27041818 - 13 Feb 2026

Viewed by 737

Abstract

Isoniazid (INH) is an antitubercular drug that exhibits high toxicity in dogs due to the absence of N-acetyltransferase activity in this species. Consequently, it has been implicated in both accidental and intentional poisonings in dogs. The aim of this study was to develop and validate an analytical method for the quantification of INH in canine liver samples and to apply it in the forensic investigation of seven suspected poisoning cases. The method, based on liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-QTOF-MS), enabled both accurate INH measurement and analysis of the molecular pattern of its metabolite formation. In addition, histopathological examination of the stomach, pancreas, liver, and brain was performed. Liver INH concentrations ranged from 11.822 to 30.484 μg/g and were associated with extensive necrotic lesions across all examined tissues. A strong signal for isonicotinic acid was observed in all samples, whereas the acetylated metabolite was negligible. The developed method allows precise quantification of INH in canine liver and facilitates identification of the characteristic molecular profile of its metabolites. Full article

(This article belongs to the Special Issue Molecular Application of Mass Spectrometry and Chromatography in Biomedicine: 2nd Edition)

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22 pages, 2591 KB

Open AccessArticle

Overexpression of GM3 and Ganglioside Pattern Remodeling in Lung Adenocarcinoma Brain Metastases Identified by Ion Mobility Mass Spectrometry

by Mirela Sarbu, Raluca Ica, Željka Vukelić, David E. Clemmer and Alina D. Zamfir

Int. J. Mol. Sci. 2025, 26(24), 12029; https://doi.org/10.3390/ijms262412029 - 14 Dec 2025

Viewed by 672

Abstract

Lung adenocarcinoma (LUAD), the most prevalent subtype of non-small cell lung carcinoma (NSCLC), commonly metastasizes to the brain, particularly in advanced stages. Since brain metastases (BMs) are a leading cause of morbidity and mortality in LUAD patients, their early detection is critical, necessitating the identification of reliable biomarkers. Gangliosides (GGs), a class of bioactive glycosphingolipids involved in cell signaling, adhesion, and immune regulation, have emerged as promising candidates for diagnostic and therapeutic targeting in LUAD-associated brain metastases (BMLA). In this context, ion mobility spectrometry mass spectrometry (IMS-MS) was employed here to analyze GG alterations in BMLA tissues compared to healthy cerebellar control. The results revealed marked differences, including a reduction in the total number of species, altered sialylation profiles, and variations in fatty acid chain length and sphingoid base hydroxylation. GM3, a monosialodihexosylganglioside, was significantly overexpressed in BMLA, supporting its role in tumor progression via immune evasion and oncogenic signaling. Elevated levels of the brain-specific GT1 ganglioside further point to its possible role as a metastasis-associated biomarker, while the presence of asialogangliosides, absent in normal brain, suggests adaptation to the brain microenvironment. Structural modifications such as O-acetylation, fucosylation, and CH₃COO⁻ were more frequent in BMLA, being associated with aggressive tumor phenotypes. Ceramide profiles revealed increased levels of proliferative C16- and C24-ceramides and decreased pro-apoptotic C18-ceramide. Additionally, GM3(d18:1/22:0) and GD3(d18:1/16:0), identified as potential BMLA biomarkers, were structurally characterized using (−) nanoelectrospray ionization (nanoESI) IMS collision-induced dissociation tandem MS (CID MS/MS). Collectively, these findings highlight the clinical potential of GGs for early diagnosis and targeted therapy in BMLA. Full article

(This article belongs to the Special Issue Molecular Application of Mass Spectrometry and Chromatography in Biomedicine: 2nd Edition)

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