bims-mascan 2025-07-27 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–07–27
twenty-one papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Repurposing proteomic nanoLC-MS platforms for untargeted metabolomics: evaluating DIA and polarity switching performance in human plasma.
LC-QTOF-MSE with MS1-based precursor ion quantification and SiMD-assisted identification enhances human urine metabolite analysis.
Multilaboratory Untargeted Mass Spectrometry Metabolomics Collaboration to Identify Bottlenecks and Comprehensively Annotate A Single Dataset.
Thin-diaPASEF: diaPASEF for maximizing proteome coverage in single-shot proteomics.
DeepSecMS Advances DIA-Based Selenoproteome Profiling Through Cys-to-Sec Proxy Training.
Targeted Metabolomic Serum Analysis of Patients with High and Low Risk of Endometrial Cancer Recurrence and Positive and Negative Lymph Node Status.
High-Throughput Fluorescence-Guided Sequential Single-Cell MALDI-ICC Mass Spectrometry.
Rapid Annotation Strategy for in Vivo Phase II Metabolites of Anabolic-Androgenic Steroids Using Liquid Chromatography-Ion Mobility-Mass Spectrometry.
mineMS2: annotation of spectral libraries with exact fragmentation patterns.
HDL proteome: from its characterization to quantitative measurements in particle subspecies and functional associations.
Accurate MS-based diagnostic amyloid typing using endogenously normalized protein intensities in formalin-fixed paraffin-embedded tissue.
Extracellular Vesicle Metabolomics Holds Promise for Adult Axon Regeneration.
Analytical development and application of a targeted liquid chromatography-tandem mass spectrometry assay for chimeric aducanumab.
Decoding the metabolic dialogue in the tumor microenvironment: from immune suppression to precision cancer therapies.
Metabolic reprogramming and functional crosstalk within the tumor microenvironment (TME) and A Multi-omics anticancer approach.
Assessment of the primary cancer cell secretome using amino acid-analog labeling.
Lipidomic Profiling of Red Blood Cells in the Mitochondrial Fatty Acid β-oxidation Disorder MCADD Reveals Phospholipid and Sphingolipid Dysregulation.
Metabolic adaptations of brain metastasis.
Ether lipids and sphingolipids drive sex-specific human aging dynamics.
A Mass Spectrometry-Based Pipeline for Analyzing RNA Modifications in Cells and Tissues.
15-Lipoxygenase-dependent radiomitigation by NO●-Donors suppresses ferroptosis in intestinal Epithelium: Multiomics MS imaging and LC-MS evidence.

Expert Rev Proteomics. 2025 Jul 24. 1-8

Repurposing proteomic nanoLC-MS platforms for untargeted metabolomics: evaluating DIA and polarity switching performance in human plasma.

Frederico G Pinto, Alexander D Giddey, Nesrin Mohamed, Rauda S B Almarri, Munazza Murtaza, Nasna Nassir, Omer S Alkhnbashi, Mohammed J Uddin, Nelson C Soares.

   BACKGROUND: Many of the advanced MS methods applied in proteomics such as nanoflow LC-MS with data-independent acquisition have yet to be verified and/or optimized on metabolomics applications.
RESEARCH DESIGN AND METHODS: This study evaluates the feasibility of repurposing a proteomics-optimized nanoLC-MS platform for untargeted metabolomics. Using NIST SRM 1950 reference human plasma, we compared the performance of polarity switching and separate polarity modes under DIA conditions, focusing on metabolite coverage, annotation, and response linearity.
RESULTS: We observed, in the separate polarity and switching polarity runs 669 and 353 features in (+) mode and 558 and 446 features in (-) mode, respectively. A total of 233 metabolites were annotated using the (±) separate polarities and 179 using the (±) switching polarity based on MassBank of North America (MoNA) public MS library and filtered with the Human Metabolome Database (HMDB). Both switching and separate polarity methods performed well regarding response linearities which were investigated by spiking some amino acid compounds into plasma matrix.
CONCLUSIONS: The polarity switching DIA approach for metabolomics reduced sample consumption and analysis time, but led to fewer detected features and annotations compared to separate polarity runs. These findings support the use of unified nanoLC-MS platforms for integrated multi-omics analysis.

Keywords:  Untargeted metabolomics; data independent analysis; human plasma; mass spectrometry; switching polarity

DOI:  https://doi.org/10.1080/14789450.2025.2537210
Comput Struct Biotechnol J. 2025 ;27 3079-3089

LC-QTOF-MSE with MS1-based precursor ion quantification and SiMD-assisted identification enhances human urine metabolite analysis.

Alongkorn Kurilung, Suphitcha Limjiasahapong, Kwanjeera Wanichthanarak, Weerawan Manokasemsan, Khwanta Kaewnarin, Kassaporn Duangkumpha, Siriphan Manocheewa, Rossarin Tansawat, Roongruedee Chaiteerakij, Intawat Nookaew, Yongyut Sirivatanauksorn, Sakda Khoomrung.

  This study presents the development and validation of a liquid chromatography-quadrupole-time-of-flight mass spectrometry method with data-independent acquisition (LC-QTOF-MSE) for targeted quantification, post-targeted screening, and untargeted metabolite profiling. Using MS1-based precursor ion quantification, the method demonstrated excellent analytical performance with linearity (R² > 0.99), accuracy (84 %-131 %), and precision (1 %-17 % relative standard deviation (RSD)). Although LC-QTOF‑MSE sensitivity is at least nine-fold lower than LC-triple quadrupole MS with multiple reaction monitoring, it remains adequate for quantifying urinary metabolites, particularly those that fragment poorly or yield low‑intensity product ions. For post‑targeted screening and untargeted profiling, an in‑house reference library (the Siriraj Metabolomics Data Warehouse, SiMD), comprising 174 curated metabolite standards, was integrated into the workflow to enhance metabolite identification confidence. The official website for SiMD can be accessed at https://si-simd.com/. To demonstrate the method's utility, 11 amino and organic acids were quantified in urine samples from 100 healthy individuals. Four compounds-L-methionine, L-histidine, L-tryptophan, and trans-ferulic acid-were significantly higher levels in females (P < 0.05), likely reflecting sex-specific physiological or dietary intake differences. Post‑targeted screening identified 29 additional metabolites and assigned them to level 1 (m/z, RT, isotope pattern, and MS/MS spectra matched to reference standards) based on the Metabolomics Standards Initiative guidelines. Untargeted retrospective profiling revealed level 1 seven metabolites, including ribitol, creatine, glucuronic acid, trans-ferulic acid, succinic acid, dimethylglycine, and 3-hydroxyphenylacetic acid related to sex variation (VIP > 1.5). In summary, the LC-QTOF-MSE method coupled with SiMD provides a robust and comprehensive workflow for metabolomics analysis. It enables reliable target quantification and enhances confidence in metabolite identification while also reducing sample and instrumental demands. These features make it particularly well-suited for clinical metabolomics studies.

Keywords:  Data independent acquisition; Human urine; Mass spectrometry; Metabolomics; Post-targeted screening; Targeted quantification; Untargeted profiling

DOI:  https://doi.org/10.1016/j.csbj.2025.07.009
Anal Chem. 2025 Jul 22.

Multilaboratory Untargeted Mass Spectrometry Metabolomics Collaboration to Identify Bottlenecks and Comprehensively Annotate A Single Dataset.

Joelle Houriet, Preston K Manwill, Armando Alcázar Magaña, Victoria M Anderson, Mehdi A Beniddir, Samuel Bertrand, Jaewoo Choi, Trevor N Clark, Leonard J Foster, Maria Halabalaki, Alan K Jarmusch, Niek F de Jonge, Aswad Khadilkar, John B MacMillan, Claudia S Maier, Luke C Marney, Guillaume Marti, Eleni V Mikropoulou, Damien Olivier-Jimenez, Amélie Perez, Justin J J van der Hooft, Mitja M Zdouc, Roger G Linington, Nadja B Cech.

Annotation is the process of assigning features in mass spectrometry metabolomics data sets to putative chemical structures or "analytes." The purpose of this study was to identify challenges in the annotation of untargeted mass spectrometry metabolomics datasets and suggest strategies to overcome them. Toward this goal, we analyzed an extract of the plant ashwagandha (Withania somnifera) using liquid chromatography-mass spectrometry on two different platforms (an Orbitrap and Q-ToF) with various acquisition modes. The resulting 12 datasets were shared with ten teams that had established expertise in metabolomics data interpretation. Each team annotated at least one positive ion dataset using their own approaches. Eight teams selected the positive ion mode data-dependent acquisition (DDA) data collected on the Orbitrap platform, so the results reported for that dataset were chosen for an in-depth comparison. We compiled and cross-checked the annotations of this dataset from each laboratory to arrive at a "consensus annotation," which included 142 putative analytes, of which 13 were confirmed by comparison with standards. Each team only reported a subset (24 to 57%) of the analytes in the consensus list. Correct assignment of ion species (clusters and fragments) in MS spectra was a major bottleneck. In many cases, in-source redundant features were mistakenly considered to be independent analytes, causing annotation errors and resulting in overestimation of sample complexity. Our results suggest that better tools/approaches are needed to effectively assign feature identity, group related mass features, and query published spectral and taxonomic data when assigning putative analyte structures.

DOI: https://doi.org/10.1021/acs.analchem.4c05577
DNA Res. 2025 Jul 23. pii: dsaf019. [Epub ahead of print]

Thin-diaPASEF: diaPASEF for maximizing proteome coverage in single-shot proteomics.

Ryo Konno, Masaki Ishikawa, Daisuke Nakajima, Kaori Inukai, Osamu Ohara, Yusuke Kawashima.

  Proteomics using mass spectrometry (MS) has significantly advanced, offering deep insights into complex proteomes. The timsTOF MS platform with its parallel accumulation-serial fragmentation (PASEF) technology has achieved high scan speeds and high-quality spectra. Buker's timsTOF HT, which features TIMS-XR technology, offers an improved dynamic range and analysis depth, supporting high sample loadings. Moreover, various improvements to the data-independent acquisition method based on the PASEF technology (diaPASEF) have been reported. Despite these advancements, most high-level deep proteomic reports are based on the Orbitrap Astral and Orbitrap Exploris 480, and analytical systems using timsTOF MS still require improvement. Here, Bruker's timsTOF HT was used to validate and optimize key diaPASEF parameters, leading to the development of a Thin-diaPASEF method. This method provides a high quantitative accuracy and consistency. In our validation, 9,400 proteins were identified in a single shot from HEK cells (strictly controlled Protein false discovery rate < 1%), the highest number analyzed by the timsTOF MS series using standard human cultured cells. Furthermore, by combining Thin-diaPASEF with an improved Lycopersicon esculentum lectin method, over 5,000 proteins were identified in a 24-sample/d analysis from the plasma, and we succeeded in constructing a system with high proteome coverage that can be used for biomarker discovery.

Keywords:  Deep Proteomics; LEL Method; Plasma Proteome; diaPASEF/Thin-diaPASEF; timsTOF HT

DOI:  https://doi.org/10.1093/dnares/dsaf019
Adv Sci (Weinh). 2025 Jul 22. e04109

DeepSecMS Advances DIA-Based Selenoproteome Profiling Through Cys-to-Sec Proxy Training.

Chenfang Si, Yamei Yuan, Yu Zong, Liang Qiao, Wen-Feng Zeng, Yaoyang Zhang.

  Selenoproteins, defined as proteins containing the 21st amino acid, selenocysteine (Sec, U), are functionally important but rare, with only 25 selenoproteins characterized in the entire human proteome to date. To comprehensively analyze selenoproteomes, previously developed selenocysteine-specific mass spectrometry (SecMS) and the selenocysteine insertion sequence (SECIS)-independent selenoprotein database (SIS) have provided effective tools for analyzing the selenoproteome and, more importantly, hold the potential to uncover new selenoproteins. In this study, a deep learning approach is employed to develop the DeepSecMS method. Given the rarity of Sec and its chemical similarity to cysteine (Cys, C), a proxy training strategy is utilized using a large dataset of Cys-containing peptides to generate a large-scale theoretical library of Sec-containing peptides. It is shown that DeepSecMS enables the accurate prediction of critical features of Sec-containing peptides, including MS2, retention time (RT), and ion mobility (IM). By integrating DeepSecMS with data-independent acquisition (DIA) methods, the identification of known selenoproteins is significantly enhanced across diverse cell types and tissues. More importantly, it facilitates the identification of numerous highly scored, potential novel selenoproteins. These findings highlight the powerful potential of DeepSecMS in advancing selenoprotein research. Moreover, the proxy training strategy may be extended to the analysis of other rare post-translational modifications.

Keywords:  SecMS; data independent acquisition; deep learning; selenocysteine; selenoprotein

DOI:  https://doi.org/10.1002/advs.202504109
Metabolites. 2025 Jun 20. pii: 422. [Epub ahead of print]15(7):

Targeted Metabolomic Serum Analysis of Patients with High and Low Risk of Endometrial Cancer Recurrence and Positive and Negative Lymph Node Status.

Dagmara Pietkiewicz, Mikołaj Piotr Zaborowski, Szymon Plewa, Michał Potograbski, Cezary Miedziarek, Tomasz Kluz, Ewa Nowak-Markwitz, Jan Matysiak.

   BACKGROUND: Endometrial cancer is among the most prevalent gynecological malignancies, with increasing mortality primarily due to initially advanced disease with lymph node metastasis or tumor recurrence. Current risk stratification models show limited accuracy, highlighting the need for more accurate biomarkers. This study aimed to identify metabolic compounds that can serve as predictors of recurrence risk and lymph node status in endometrial cancer.
METHODS: Targeted metabolomic profiling of preoperative serum samples from 123 patients with endometrial cancer, stratified into high- or low-risk and lymph node-positive or -negative groups, was conducted using the AbsoluteIDQ p180 Kit and high-performance liquid chromatography-mass spectrometry.
RESULTS: Analysis revealed significant differences in metabolites related to lipid and amino acid metabolism between groups. High-risk and lymph node-positive patients presented significantly lower concentrations of phosphatidylcholines, lysophosphatidylcholines, medium-chain acylcarnitines, and specific amino acids such as alanine, histidine, and tryptophan compared to low-risk and lymph node-negative patients. Receiver operating characteristic curve analyses highlighted the diagnostic potential of these metabolites, particularly alanine and taurine, in distinguishing patient groups.
CONCLUSIONS: The findings indicate complex metabolic reprogramming associated with aggressive endometrial cancer phenotypes, involving enhanced lipid utilization and amino acid metabolism alterations, potentially supporting tumor proliferation and metastatic progression. Thus, targeted metabolomic serum profiling might be a powerful tool for improving risk assessment, enabling more personalized therapeutic approaches and management strategies in endometrial cancer.

Keywords:  amino acids; biomarkers; endometrial cancer; lipids; metabolomics

DOI:  https://doi.org/10.3390/metabo15070422
Anal Chem. 2025 Jul 21.

High-Throughput Fluorescence-Guided Sequential Single-Cell MALDI-ICC Mass Spectrometry.

Marisa Asadian, Seth W Croslow, Timothy J Trinklein, Stanislav S Rubakhin, Fan Lam, Jonathan V Sweedler.

Lipids are a diverse class of biomolecules essential for brain function, yet their cell-type-specific distributions remain underexplored, presenting significant knowledge gaps in the era of single-cell biology. Traditional bulk measurements provide valuable insights into lipid composition across brain regions but lack the resolution to distinguish lipid profiles at the single-cell level. To address this, we introduce fluorescence-guided sequential single-cell mass spectrometry (SSMS), an automated workflow combining untargeted lipid profiling with antibody-targeted protein detection via photocleavable mass tags, enabling neurolipidomic classification of cell types and cell states. We applied this approach to rodent hippocampal cells, analyzing over a thousand single cells and annotating more than a hundred lipid species with complementary liquid chromatography-mass spectrometry (LC-MS/MS) measurements. Our findings show that phosphatidylcholine (PC) species are predominantly enriched in oligodendrocytes and neurons compared to astrocytes, while hexosylceramide (HexCer) species are differentially expressed across these cell types. Furthermore, neuronal state analysis revealed an enrichment of phosphatidylethanolamines (PEs) in presynaptic neurons, while nonpresynaptic neurons exhibited a more diverse lipid composition, including HexCer, PC, sphingomyelin, triacylglycerol, and PE. Our findings provide new insights into brain lipid heterogeneity with cell-type and cell-state specificity and extend-capabilities of next-generation single-cell mass spectrometry to map brain biochemistry.

DOI: https://doi.org/10.1021/acs.analchem.5c02092
J Am Soc Mass Spectrom. 2025 Jul 23.

Rapid Annotation Strategy for in Vivo Phase II Metabolites of Anabolic-Androgenic Steroids Using Liquid Chromatography-Ion Mobility-Mass Spectrometry.

David C Koomen, Katrina L Leaptrot, Jody C May, Bailey S Rose, Kyle E Lira, Julia A Raziel, Andrew D Pumford, Gustavo de A Cavalcanti, Monica C Padilha, Henrique M G Pereira, John A McLean.

  Doping control laboratories are responsible for the precise measurement of anabolic-androgenic steroids (AASs) and determination of athlete usage. Intact phase II AASs are difficult to analyze due to their low abundance in complex biological matrices and their structural similarities that convolute tandem mass spectrometry interpretation. Discovery efforts of unknown phase II metabolites of new-to-the-field steroids have been challenging due to these deficiencies in current analytical techniques. Several methods for determining unknown conjugated AAS compounds have been developed that include deuterium tagging, fractionation, derivatization, and utilization of synthesized standards. Ion mobility (IM), a rapid gas-phase separation, allows for improved molecular differentiation and provides additional information for analyzing intact phase II AASs without sacrificing throughput. Here, candidate metabolites were putatively identified for oxymetholone (OXM) and methyl-1-testosterone (M1T) utilizing liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) and two independent data analysis strategies: a fully untargeted approach using mass defect analysis and collision cross section (CCS) filtering and a pseudotargeted approach using the biologically anticipated isotopic envelope in conjunction with CCS filtering, temporal profiling, and tandem mass spectrometry confirmation. A proof-of-concept time-course study was conducted using the urine from healthy male individuals after steroid administration. The fully untargeted approach reduced the number of original features by >85% while the pseudotargeted approach reduced original features by >99%, yielding 11 possible novel phase II AAS candidates for OXM and 23 for M1T.

Keywords:  CCS regression model; LC-IM-MS; mass−mobility correlation; nontargeted discovery workflow; phase II AAS; untargeted metabolomics

DOI:  https://doi.org/10.1021/jasms.5c00129
J Cheminform. 2025 Jul 24. 17(1): 111

mineMS2: annotation of spectral libraries with exact fragmentation patterns.

Alexis Delabrière, Coline Gianfrotta, Sylvain Dechaumet, Annelaure Damont, Thaïs Hautbergue, Pierrick Roger, Emilien L Jamin, Olivier Puel, Christophe Junot, François Fenaille, Etienne A Thévenot.

  Identification is a major challenge in metabolomics due to the large structural diversity of metabolites. Tandem mass spectrometry is a reference technology for studying the fragmentation of molecules and characterizing their structure. Recent instruments can fragment large amounts of compounds in a single acquisition. The search for similarities within a collection of MS/MS spectra is a powerful approach to facilitate the identification of new metabolites. We propose an innovative de novo strategy for searching for exact fragmentation patterns within collections of MS/MS spectra. This approach is based on (i) a new representation of spectra as graphs of m/z differences, and (ii) an efficient frequent-subgraph mining algorithm. We demonstrate both on a spectral database from standards and on acquisitions in biological matrices that these new fragmentation patterns capture similarities that are not extracted by existing methods, and facilitate the structural interpretation of molecular network components and the elucidation of unknown spectra. The mineMS2 software is publicly available as an R package ( https://github.com/odisce/mineMS2 ). SCIENTIFIC CONTRIBUTION: We present an innovative strategy for structural elucidation, which extracts exact fragmentation patterns of m/z differences within collections of MS/MS spectra. The algorithms are implemented in a software library enabling efficient mining of MS/MS data and coupling to molecular networks. We show on real datasets the specific value of the patterns as fragmentation graphs for structural interpretation and de novo identification, and their complementarity to existing approaches.

Keywords:  Computer-aided structure elucidation; Frequent subgraph mining; Graph theory; Mass spectrometry; Metabolomics

DOI:  https://doi.org/10.1186/s13321-025-01051-y
Expert Rev Proteomics. 2025 Jul;22(7): 273-286

HDL proteome: from its characterization to quantitative measurements in particle subspecies and functional associations.

Tomáš Vaisar, Graziella Eliza Ronsein.

   INTRODUCTION: Recent failures of therapeutic interventions aimed at elevating high-density lipoprotein cholesterol (HDL-C) have renewed the need to reconceptualize HDL. The HDL proteome constitutes over 50% of the HDL mass and it is the richest among lipoproteins. HDL is also the most heterogeneous lipoprotein, and unraveling the determinants of its pleiotropic functions requires new approaches to characterize HDL subspecies as well as harmonization of isolation and quantification methodologies.
AREAS COVERED: We have reviewed studies focusing on HDL proteomes from the past 5 years, with particular focus on recent developments in mass spectrometry-based proteomics applied to HDL, and the studies of HDL heterogeneity and function. Emphasis is also given on the quantification of the proteome of HDL-specific subspecies and on the studies relating the HDL proteome to its function. We further discuss the need for harmonization in HDL subpopulations and subspecies isolation and in proteome quantification.
EXPERT OPINION: The HDL proteome is undisputedly related to the HDL function. The development of new approaches to isolate HDL subspecies, together with the implementation of consistent quantification strategies, is needed to provide new insights into the structure-function relationship of HDL particles, unravel the role of HDL in the pathology of diseases, and provide new metrics of HDL.

Keywords:  HDL; HDL function; data-independent analysis; proteomics; quantification; subspecies; targeted-proteomics

DOI:  https://doi.org/10.1080/14789450.2025.2534397
Mol Cell Proteomics. 2025 Jul 21. pii: S1535-9476(25)00139-2. [Epub ahead of print] 101040

Accurate MS-based diagnostic amyloid typing using endogenously normalized protein intensities in formalin-fixed paraffin-embedded tissue.

Vanessa Hollfoth, Arslan Ali, Eyyub Bag, Philip Riemenschneider, Sven Mattern, Julia Luibrand, Mohamed Ali Jarboui, Kerstin Singer, Benjamin Goeppert, Mirita Franz-Wachtel, Martina Sauter, Shabnam Asadikomeleh, Tobias Feilen, Christian Hentschker, Silvia Ribback, Elke Hammer, Karsten Boldt, Frank Dombrowski, Oliver Schilling, Boris Macek, Marius Ueffing, Karin Klingel, Stephan Singer.

  Amyloidoses are a group of diseases characterized by the pathological deposition of non-degradable misfolded protein fibrils, including those associated with plasma cell neoplasias, chronic inflammatory conditions, and age-related disorders, among others. Precise identification of the fibril-forming and thereby amyloidosis type defining protein is crucial for prognosis and correct therapeutic intervention. While immunohistochemistry (IHC) is widely used for amyloid typing, it requires extensive interpretation expertise and can be limited by inconclusive staining results. Thus, mass spectrometry (MS), if available, has been proposed as the preferred method for amyloid typing by international specialized centers (USA, UK) using primarily spectral counts for quantification. Here, we introduce an alternative method of relative quantification to further enhance the accuracy and reliability of proteomic amyloid typing. We analyzed 62 formalin-fixed, paraffin-embedded (FFPE) tissue samples, primarily endomyocardial biopsies, using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and employed internal normalization of iBAQ values of amyloid-related proteins relative to serum amyloid P component (APCS) for amyloidosis typing. The APCS method demonstrated robust performance across multiple LC-MS/MS platforms and achieved complete concordance with clear cut IHC typed amyloidosis cases. More importantly, it resolved unclear amyloid cases with inconclusive staining results. Additionally, for samples without a distinct fibril-forming protein identified in the standard procedure, de novo sequencing uncovered immunoglobulin light chain components, enabling the diagnosis of rare AL-amyloidosis subtypes. Finally, we established machine learning approach (XGBoost) achieving 94% accuracy by using ∼160 amyloid-related proteins as input variables. In summary, the iBAQ APCS normalization method extended by de novo sequencing allows robust, accurate, and reliable diagnostic amyloid typing, and can be complemented by an AI-based classification. Careful reviewing of each histological sample and the clinical context, nevertheless, remains indispensable for accurate interpretation.

Keywords:  FFPE; amyloidosis; diagnostics; mass spectrometry; proteomics

DOI:  https://doi.org/10.1016/j.mcpro.2025.101040
Metabolites. 2025 Jul 04. pii: 454. [Epub ahead of print]15(7):

Extracellular Vesicle Metabolomics Holds Promise for Adult Axon Regeneration.

Maria D Cabrera Gonzalez, Jackson Watson, Laura Leal, Isabella Moceri, Camille Plummer, Biraj Mahato, Abdelrahman Y Fouda, Sanjoy K Bhattacharya.

  Extracellular vesicles (EVs) are bilayer lipid membrane particles that are released by every cell type. These secretions are further classified as exosomes, ectosomes, and microvesicles. They contain biomolecules (RNAs, proteins, metabolites, and lipids) with the ability to modulate various biological processes and have been shown to play a role in intercellular communication and cellular rejuvenation. Various studies suggest exosomes and/or microvesicles as a potential platform for drug delivery. EVs may deliver lipids and nucleotides directly to an injury site in an axon, promoting growth cone stabilization and membrane expansion as well as repair, thus positively modulating adult axon regeneration. In this review, we will provide a perspective on the metabolite composition of EVs in adult axonal regeneration relevant to the central nervous system (CNS), specifically that pertaining to the optic nerve. We will present an overview of the methods for isolation, enrichment, omics data analysis and quantification of extracellular vesicles with the goal of providing direction for future studies relevant to axon regeneration. We will also include current resources for multi-omics data integration relevant to extracellular vesicles from diverse cell types.

Keywords:  adult axonal regeneration; exosomes; extracellular vesicles; lipids; mass spectrometry; metabolites; metabolomics; microglia; multi-omics; optic nerve; retina ganglion cells

DOI:  https://doi.org/10.3390/metabo15070454
MAbs. 2025 Dec;17(1): 2537118

Analytical development and application of a targeted liquid chromatography-tandem mass spectrometry assay for chimeric aducanumab.

Emma H Doud, Kasi Hansen, Kathryn A Haynes, Kierra Eldridge, Jaison Arrivalagan, Nitin Charbe, Lais Da Silva, Sara K Quinney, Amber L Mosley, Stacey J Sukoff Rizzo, Paul R Territo.

  A sensitive and specific liquid chromatography-tandem mass spectrometry assay was developed for the quantification of chimeric aducanumab (chAdu), a therapeutic antibody targeting pathological amyloid plaques in Alzheimer's disease, in murine biological matrices. This method addresses the challenges of quantifying biotherapeutics in multiple tissue types within preclinical animal models with complex genetic backgrounds, where traditional enzyme-linked immunosorbent assay (ELISA) methods may suffer from interference and limited sensitivity. The assay uses parallel reaction monitoring on a Lumos Tribrid Orbitrap mass spectrometer, coupled with an Evosep LC system, and AssayMap Bravo-based automated sample processing. Key features include protein A enrichment for improved sensitivity, optimized peptide selection based on sequence uniqueness and ionization response, and incorporation of stable isotope-labeled peptides for accurate quantification. Assay performance was evaluated for selectivity, repeatability, and stability. The fit-for-purpose assay was successfully applied to quantify chAdu in both mouse cortex and plasma samples obtained from a pilot pharmacokinetic study of a mouse model of amyloid plaque deposition. This targeted mass spectrometry workflow offers a robust and reproducible alternative to ELISA for preclinical biotherapeutic analysis, particularly when dealing with complex biological samples.

Keywords:  5XFAD; Aducanumab; PRM; biotherapeutic; pharmacokinetics; targeted mass spectrometry

DOI:  https://doi.org/10.1080/19420862.2025.2537118
Exp Hematol Oncol. 2025 Jul 22. 14(1): 99

Decoding the metabolic dialogue in the tumor microenvironment: from immune suppression to precision cancer therapies.

Ruoli Wang, Jincheng Zhuang, Qi Zhang, Wantao Wu, Xinrui Yu, Hao Zhang, Zongyi Xie.

  The tumor microenvironment (TME) represents a metabolic battleground where immune cells and cancer cells vie for essential nutrients, ultimately influencing antitumor immunity and treatment outcomes. Recent advancements have shed light on how the metabolic reprogramming of immune cells, including macrophages, T cells, and DCs, determines their functional polarization, survival, and interactions within the TME. Factors such as hypoxia, acidosis, and nutrient deprivation drive immune cells toward immunosuppressive phenotypes, while metabolic interactions between tumors and stromal cells further entrench therapeutic resistance. This review synthesizes new insights into the metabolic checkpoints that regulate immune cell behavior, focusing on processes like glycolysis, oxidative phosphorylation (OXPHOS), lipid oxidation, and amino acid dependencies. We emphasize how metabolic enzymes (e.g., IDO1, ACLY, CPT1A) and metabolites (e.g., lactate, kynurenine) facilitate immune evasion, and we propose strategies to reverse these pathways. Innovations such as single-cell metabolomics, spatial profiling, and AI-driven drug discovery are transforming our understanding of metabolic heterogeneity and its clinical implications. Furthermore, we discuss cutting-edge therapeutic approaches-from dual-targeting metabolic inhibitors to biomaterial-based delivery systems-that aim to reprogram immune cell metabolism and enhance the effectiveness of immunotherapy. Despite the promise in preclinical studies, challenges persist in translating these findings to clinical applications, including biomarker validation, metabolic plasticity, and interpatient variability. By connecting mechanistic discoveries with translational applications, this review highlights the potential of immunometabolic targeting to overcome resistance and redefine precision oncology.

Keywords:  Immune cells metabolism; Immunotherapy resistance; Metabolic reprogramming; Therapeutic targeting; Tumor microenvironment

DOI:  https://doi.org/10.1186/s40164-025-00689-6
Med Oncol. 2025 Jul 24. 42(9): 373

Metabolic reprogramming and functional crosstalk within the tumor microenvironment (TME) and A Multi-omics anticancer approach.

Rashid Mir, Jamsheed Javid, Mohammad Fahad Ullah, Salma Alrdahe, Ibrahim Abdullah Altedlawi, Syed Khalid Mustafa, Mohammed M Jalal, Malik A Altayar, Aziz Dhaher Albalawi, Muhammed Kamal Abunab, Hanadi Saud Alanazi, Jameel Barnawi, Naseh A Algehainy, Faisal H Altemani, Faris J Tayeb.

  Tumors are characterized by a complex interplay of various cell types, each contributing to the unique metabolic landscape of the tumor microenvironment (TME). The key metabolic interactions explored within the TME include nutrient competition, symbiotic nutrient exchange, and the role of metabolites as signaling messengers. Metabolic flexibility allows cancer cells to survive and proliferate even under harsh conditions, such as hypoxia and nutrient deprivation. Recent advances highlight that tumors possess inherent metabolic heterogeneity, underpinning the intricate web of intra- and extra- tumoral metabolic connections. Harnessing the power of multi-omics approaches offers unprecedented insights into this metabolic diversity, paving the way for innovative therapeutic strategies targeting the metabolic crosstalk within the tumor microenvironment. Multi-omics approaches, integrating genomics, transcriptomics, proteomics, and metabolomics data, provide a comprehensive view of tumor metabolism. This holistic approach allows for the identification of key metabolic pathways and regulatory networks that drive tumor progression, as well as potential vulnerabilities that can be exploited for therapeutic intervention. In this review, we discuss the metabolic symphony within the TME, the intricacies of tumor metabolism through multi-omics methodologies, and the prospects of devising innovative and effective cancer therapeutic strategies.

Keywords:  Cancer; Cancer therapeutic strategies; Metabolic reprogramming; Multi-omics; Tumor metabolism; Tumor microenvironment

DOI:  https://doi.org/10.1007/s12032-025-02945-5
Methods Cell Biol. 2025 ;pii: S0091-679X(24)00239-5. [Epub ahead of print]196 43-65

Assessment of the primary cancer cell secretome using amino acid-analog labeling.

Annemieke C Bouwman, Antoinette van Weverwijk, Onno B Bleijerveld, Liesbeth Hoekman, Bob J Ignacio, Kimberly M Bonger, Karin E de Visser.

  It is well established that reciprocal communication between cancer cells and other cells in the tumor microenvironment plays a crucial role in cancer progression and therapy response. There are multiple ways by which cells communicate, including direct cell-cell contact and the secretion of soluble mediators. The secretome of cancer cells contains valuable information to disentangle the complex conversation that is happening between cancer cells and neighboring or distant cells such as immune cells, fibroblasts and endothelial cells. Here, we provide a workflow of mapping the cancer cell secretome in an unbiased way using amino acid-analog labeling in combination with mass spectrometry. The generation of single cells from fresh tumors, isolation of primary cancer cells from a complex multi-cellular pool, and the detection of newly synthesized proteins that are secreted into the medium is described in detailed protocols. Using this experimental pipeline the secretome of cancer cells across different tumors can be determined, paving the way to unravel cell-cell communication networks in the tumor microenvironment, which may uncover novel therapeutic targets.

Keywords:  Amino acid-labeling; Cancer cell secretome; Cancer-immunity crosstalk; Cell-cell interaction; Mass spectometry; Tumor microenvironment

DOI:  https://doi.org/10.1016/bs.mcb.2024.11.002
J Proteome Res. 2025 Jul 24.

Lipidomic Profiling of Red Blood Cells in the Mitochondrial Fatty Acid β-oxidation Disorder MCADD Reveals Phospholipid and Sphingolipid Dysregulation.

Inês M S Guerra, Helena B Ferreira, Luísa Diogo, Sónia Moreira, Stefano Bonciarelli, Laura Goracci, Tânia Melo, Pedro Domingues, M Rosário Domingues, Ana S P Moreira.

  Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is characterized by the accumulation of medium-chain acylcarnitines. Despite the therapeutic approach, changes in lipid homeostasis have been reported in MCADD plasma samples. Compared to plasma lipidomics, red blood cell (RBC) profiling provides a more stable biomarker that is less influenced by dietary changes and reflects long-term metabolic alterations. In this study, we assessed the plasticity of the lipidomic profile of RBC from children with MCADD and controls using C18 liquid chromatography-mass spectrometry. The results revealed significant alterations in 240 lipid species in MCADD, highlighting an upregulation of sphingolipids (sphingomyelins and ceramides) and lysophospholipid species (lysophosphatidylcholines and lysophosphatidylethanolamines) alongside a downregulation of polyunsaturated and ether-linked phosphatidylcholines (PCs) and phosphatidylethanolamines (PEs). Also, altered PC/PE and (PC + SM)/(PE + PS) ratios could be associated with alterations in RBC membranes properties, e.g., fluidity and asymmetry. The observed changes in the lipidome suggest compromised antioxidant defenses, enhanced oxidative stress, and an inflammatory state, with potential systemic implications in MCADD lipid metabolism and long-term complications in older age. This study underscores the utility of RBC lipidomics as a robust tool for understanding the pathophysiology of MCADD. It may prove to be a useful tool for monitoring disease progression in the near future.

Keywords:  FAOD; erythrocytes; glycerophospholipids; lipidomics; lipids; mass spectrometry; medium-chain acyl-CoA dehydrogenase deficiency; plasmanyl; plasmenyl; sphingolipids

DOI:  https://doi.org/10.1021/acs.jproteome.5c00308
Nat Rev Cancer. 2025 Jul 24.

Metabolic adaptations of brain metastasis.

Pravat Kumar Parida, Srinivas Malladi.

Brain metastases remain a major clinical challenge, characterized by high mortality rates and often limited therapeutic options. The cellular and molecular processes that drive brain metastases are highly intricate, underscored by dynamic metabolic adaptations that enable tumour cells to thrive in the unique microenvironment of the brain. Emerging clinical and preclinical evidence reveals that these metabolic adaptations are not uniform but vary based on the tumour's tissue of origin, oncogenomic landscape and capacity to endure nutrient stress. Notably, proliferative and dormant metastatic cells within the brain exhibit distinct metabolic profiles, highlighting the complexity of targeting these cells. Key metabolic pathways, including glucose, fatty acid and amino acid metabolism, are co-opted not only to sustain cancer cell survival and growth but also to modulate interactions with resident brain cells, reshaping their function to support metastasis. Importantly, this metabolic heterogeneity underscores the inadequacy of a one-size-fits-all therapeutic approach. Here, we review the adaptive metabolic reprogramming that facilitates brain metastases and discuss emerging strategies to tailor interventions aimed at preventing and treating overt brain metastases.

DOI: https://doi.org/10.1038/s41568-025-00848-1
Redox Biol. 2025 Jul 18. pii: S2213-2317(25)00292-7. [Epub ahead of print]85 103779

Ether lipids and sphingolipids drive sex-specific human aging dynamics.

Joaquim Sol, Anna Fernàndez-Bernal, Natalia Mota-Martorell, Meritxell Martín-Garí, Èlia Obis, Alba Juanes, Victoria Ayala, Jordi Mayneris-Perxachs, Rafel Ramos, Víctor Pineda, Josep Garre-Olmo, Manuel Portero-Otín, José Manuel Fernández-Real, Josep Puig, Mariona Jové, Reinald Pamplona.

  Aging is a dynamic process characterized by complex molecular changes, including shifts in lipid metabolism. To systematically define lipidome dynamics with age and identify sex-specific lipidomic signatures, we performed targeted lipidomic profiling of plasma samples from 1030 adults aged 50-98 years, analyzing 543 lipid species across all lipid classes using high-throughput mass spectrometry and assessing the circulating fatty acid composition by gas chromatography. Our results reveal age-related lipidomic shifts, with ceramides and ether-linked phospholipids most affected. We identified three aging crests (55-60, 65-70, 75-80 years), with the 65-70 years crest dominant in men and the 75-80 years crest in women. Lipid enrichment analyses highlight acylcarnitines, sphingolipids and ether-linked phospholipids as key contributors, with functional indices indicating compositional shifts in lipid species. These findings suggest an impairment of lipid functional categories, including loss of dynamic properties, alterations in bioenergetics, antioxidant defense, cellular identity, and signaling platforms. This study underscores the non-linear nature of lipid metabolism in aging and provides a foundation for identifying biomarkers and interventions to promote healthy aging.

Keywords:  Aging crests; Aging dynamics; Ether-linked phospholipids; Lipid metabolism; Metabolic adaptation; Sex-specific lipidomics; Sphingolipids

DOI:  https://doi.org/10.1016/j.redox.2025.103779
Methods Mol Biol. 2025 ;2962 199-213

A Mass Spectrometry-Based Pipeline for Analyzing RNA Modifications in Cells and Tissues.

Aurore Attina, Jérôme Vialaret, Christophe Hirtz, Alexandre David.

  Mass spectrometry has become indispensable in studying post-transcriptional modifications of RNA, a.k.a. epitranscriptomics, which are crucial for regulating RNA metabolism, gene expression, and major biological processes. Over 150 chemical modifications have been identified across all RNA subtypes. Dysregulation of RNA mark deposition can lead to disease development and progression. Therefore, accurately detecting and characterizing these modifications are vital for understanding their functional impact and uncovering potential therapeutic and diagnostic biomarkers. In this chapter, we describe a mass spectrometry-based method for detecting and quantifying nearly 40 different RNA modifications.

Keywords:  Biomarker discovery; Multiple reaction monitoring (MRM); RNA modifications; Targeted epitranscriptomics

DOI:  https://doi.org/10.1007/978-1-0716-4726-4_14
Redox Biol. 2025 Jul 21. pii: S2213-2317(25)00290-3. [Epub ahead of print]85 103777

15-Lipoxygenase-dependent radiomitigation by NO●-Donors suppresses ferroptosis in intestinal Epithelium: Multiomics MS imaging and LC-MS evidence.

Yulia Y Tyurina, Hua Tian, Haider H Dar, Mert Akdogan, Ecem Saritas, Vladimir A Tyurin, Louis J Sparvero, Alexander A Kapralov, Galina Shurin, Renee Fisher, Michael W Epperly, Kunal Singh, Yuri L Bunimovich, Joel S Greenberger, Valerian E Kagan, Hülya Bayir.

  Threats of irradiation (IR) exposure increase the need for radiomitigators. An important contributor to radiation injury is ferroptosis, triggered by the disbalanced redox metabolism. We showed that 15-lipoxygenase (15-LOX) catalyzed peroxidation of arachidonoyl-phosphatidyl-ethanolamine is an essential ferroptotic response of ileum to total body IR (TBI). Given that nitric oxide (NO● ) can suppress ferroptosis by inhibiting 15-LOX and by directly scavenging lipid radicals, we tested NO●-donors with optimized half decay times as radiomitigators. Here, we report that diethylenetriamine-NONOate (DETA-NONOate) (with a half decay-time of 20 hr) acted as an effective radiomitigator when administered 24 hr after exposure to TBI (9.25Gy) and markedly prolonged survival of C57BlJ6 mice by - i) decreasing the levels of pro-ferroptotic HOO-PUFA-PE signals, and ii) decreasing the expression of 15-LOX2 - in the ileum on day 4 after TBI. Redox lipidomics LC-MS and two mass spectrometric imaging (MSI) protocols: i) single-cell multi-omics Dual C60/gas cluster ion beam (GCIB) secondary ion mass spectrometry (SIMS), and ii) matrix-assisted laser desorption ionization (MALDI)-MSI, visualized DETA-NONOate's effectiveness in suppressing TBI-induced HOO-PUFA-PE production and preserving intestinal epithelium structural integrity. In vitro, NO● donors were effective in suppressing PUFA-PE peroxidation and ferroptotic death in human intestinal epithelial cells (FHs 74 Int) exposed to radiation (8Gy) plus enzymatic (15-LOX2) pro-ferroptotic stimulation.

Keywords:  GCIB-multiomics/ SIMS imaging of lipids and metabolites; Lipid peroxidation; NO donors; Radiomitigation; Total body Irradiation

DOI:  https://doi.org/10.1016/j.redox.2025.103777