bims-mascan 2025-10-05 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–10–05
twenty papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

ID-MS-Based Quantitative Analysis of Metabolites in Pichia pastoris: A Step-by-Step Protocol.
mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format.
QuantyFey: An open-source tool for targeted LC-MS quantification with integrated drift correction.
Multicenter evaluation of label-free quantification in human plasma on a high dynamic range benchmark set.
Proteomics and metabolomics in cancer biomarker discovery.
Quantification of Dystrophin in Human Muscle Biopsies by Mass Spectrometry.
Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data.
Plasma-Microdroplet Fusion for Online Post-Column Epoxidation: Toward Deep Lipidomics on Unmodified Mass Spectrometers.
Protein corona formed on lipid nanoparticles compromises delivery efficiency of mRNA cargo.
A new deep field scan liquid chromatography-mass spectrometry method to identify host cell proteins in therapeutic proteins.
High-Throughput Top-Down Proteomic Analysis of Picogram-Level Complex Samples Using Multisegment Spray-Capillary CE-MS.
Demonstrating voxel-by-voxel (V × V) single-point calibration in liver tissue by IR-MALDESI quantitative MSI.
Alternatively Spliced Citrate Synthase Supports Colorectal Cancer.
Lipidomic Profiling: A Promising New Direction in the Diagnosis and Management of Thyroid Cancer?
Rewiring amino acids in cancer.
Development and validation of a streamlined workflow for proteomic analysis of proteins and post-translational modifications from dried blood.
Lipidomic Sample Preparation to Detect Phosphatidylcholine Alterations in Skeletal Muscle.
A robust protocol for proteomic profiling of secreted proteins in conditioned culture medium.
Identifying Oxysterols Associated With Age and Diet in Mice Using Optimized Reversed-phase Liquid Chromatography-Mass Spectrometry (RPLC-MS).
Protein Extraction Protocol for Legumes Compatible with Detergent: Gel Fractionation and Mass Spectrometry.

Methods Mol Biol. 2026 ;2697 137-152

ID-MS-Based Quantitative Analysis of Metabolites in Pichia pastoris: A Step-by-Step Protocol.

Marc Carnicer, S Aljoscha Wahl, Reza Maleki Seifar, Joan Albiol, Walter van Gulik, Pau Ferrer.

  Quantitative metabolomics is based on a set of experimental approaches to accurately quantify intracellular metabolite concentrations. This allows us to characterize the response of a metabolic network (i.e., the metabolic phenotype) to an environmental or genetic perturbation. Here, we describe a four-step protocol adapted to the methylotrophic yeast Komagataella phaffii: (1) separation of the cells from the fermentation broth by cold filtration and addition of 13C-labeled cell extract, (2) a metabolic quenching step based on aqueous cold methanol, (3) a metabolite extraction method based on boiling ethanol, and (4) quantification by isotope dilution mass spectrometry (LC-IDMS/MS and/or GC-IDMS). This method allows us to quantify most metabolites of central carbon metabolism, including glycolytic, tricarboxylic acid cycle, and pentose phosphate pathway intermediates, as well as cofactors and free amino acids. This method has been validated for K. phaffii grown on glucose, as well as on a mixture of carbon substrates such as methanol in combination with glucose or glycerol.

Keywords:  13C-labeled internal standard; Extraction; Intracellular metabolites; Isotope dilution mass spectrometry; Komagataella phaffii; Metabolomics; Microbial metabolism; Pichia pastoris; Quenching

DOI:  https://doi.org/10.1007/978-1-0716-4779-0_8
J Proteome Res. 2025 Oct 02.

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format.

Tim Van Den Bossche, Theodore Alexandrov, Aivett Bilbao, Wout Bittremieux, Federico Ivan Brigante, Matthew Chase Chambers, Joshua Charkow, Eric Deutsch, Andrew W Dowsey, Yasin El Abiead, Ralf Gabriels, Helge Hecht, Steffen Heuckeroth, Joshua A Klein, Michael Knierman, Lennart Martens, Robert L Moritz, Laura-Isobel McCall, Steffen Neumann, Yasset Perez-Riverol, Hannes L Röst, Elliott J Price, Jim Shofstahl, David L Tabb, Julian Uszkoreit, Juan Antonio Vizcaíno, Mingxun Wang, Sander Willems, Dirk Winkelhardt, Oliver Kohlbacher, Samuel P Wein.

  Advances in mass spectrometry (MS) instrumentation, including higher resolution, faster scan speeds, and improved sensitivity, have dramatically increased the data volume and complexity. The adoption of imaging and ion mobility further amplifies these challenges in proteomics, metabolomics, and lipidomics. Current open formats such as mzML and imzML struggle to keep pace due to large file sizes, slow data access, and limited metadata support. Vendor-specific formats offer faster access but lack interoperability and long-term archival guarantees. We here lay the groundwork for mzPeak, a next-generation community data format designed to address these challenges and support high-throughput, multidimensional MS workflows. By adopting a hybrid model that combines efficient binary storage for numerical data and both human- and machine-readable metadata storage, mzPeak will reduce file sizes, accelerate data access, and offer a scalable, adaptable solution for evolving MS technologies. For researchers, mzPeak will support complex workflows and regulatory compliance through faster access, improved metadata, and interoperability. For vendors, it offers a streamlined, open alternative to proprietary formats. mzPeak aims to become a cornerstone of MS data management, enabling sustainable, high-performance solutions for future data types and fostering collaboration across the mass spectrometry community.

Keywords:  data formats; lipidomics; mass spectrometry; metabolomics; proteomics; proteomics standards initiative; standards

DOI:  https://doi.org/10.1021/acs.jproteome.5c00435
Anal Chim Acta. 2025 Nov 15. pii: S0003-2670(25)00965-1. [Epub ahead of print]1375 344571

QuantyFey: An open-source tool for targeted LC-MS quantification with integrated drift correction.

Markus Aigensberger, Christoph Bueschl, Barbara U Metzler-Zebeli, Franz Berthiller, Heidi E Schwartz-Zimmermann.

   BACKGROUND: Signal intensity drift is a well-recognized issue in quantitative LC-MS(MS) analysis, especially during long analytical sequences or when internal standards (IS) are unavailable. While IS correction is widely supported by commercial platforms, other correction strategies, such as quality control (QC)-based drift correction, or quantification bracketing, are unavailable. This limits the ability of analysts to maintain data quality in more complex or resource-limited experimental setups. To address this, we developed QuantyFey, an open-source, vendor-independent tool for external calibration-based quantification, with support for multiple drift correction strategies.
RESULTS: We applied QuantyFey to a targeted LC-MS/MS dataset comprising amino acids, amino acid-related metabolites, and biogenic amines measured in porcine plasma. The dataset was affected by substantial signal drift across the run. A calibration standard was used as a proxy for a QC sample and different drift correction strategies were compared: IS correction, QC-based drift correction, custom bracketing and weighted bracketing. Concentrations of all compounds were calculated using different drift correction strategies and manual tuning of calibration functions, and remaining intensity drift was assessed. Both QC-based and IS-based correction significantly reduced drift effects. Custom- or weighted bracketing methods also improved quantification accuracy but demonstrated variable performance across compounds. This study highlights the importance of evaluating compound-specific behavior when selecting drift correction strategies.
SIGNIFICANCE: QuantyFey offers a transparent and accessible framework for quantitative LC-MS(MS) analysis, especially in situations where drift correction is critical and IS are limited. Its flexible design allows for compound-specific evaluation and quantification, making it a practical tool for handling complex datasets. By supporting tailored drift correction strategies, QuantyFey addresses key challenges in maintaining data quality and reproducibility.

Keywords:  Intensity-drift; LC-MS; R; Shiny; Tandem mass spectrometry; Targeted metabolomics

DOI:  https://doi.org/10.1016/j.aca.2025.344571
Nat Commun. 2025 Oct 02. 16(1): 8774

Multicenter evaluation of label-free quantification in human plasma on a high dynamic range benchmark set.

Ute Distler, Han Byul Yoo, Oliver Kardell, Dana Hein, Malte Sielaff, Marian Scherer, Anna M Jozefowicz, Christian Leps, David Gomez-Zepeda, Christine von Toerne, Juliane Merl-Pham, Teresa K Barth, Johanna Tüshaus, Pieter Giesbertz, Torsten Müller, Georg Kliewer, Karim Aljakouch, Barbara Helm, Henry Unger, Dario L Frey, Dominic Helm, Luisa Schwarzmüller, Oliver Popp, Di Qin, Susanne I Wudy, Ludwig Roman Sinn, Julia Mergner, Christina Ludwig, Axel Imhof, Bernhard Kuster, Stefan F Lichtenthaler, Jeroen Krijgsveld, Ursula Klingmüller, Philipp Mertins, Fabian Coscia, Markus Ralser, Michael Mülleder, Stefanie M Hauck, Stefan Tenzer.

Human plasma is routinely collected during clinical care and constitutes a rich source of biomarkers for diagnostics and patient stratification. Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is a key method for plasma biomarker discovery, but the high dynamic range of plasma proteins poses significant challenges for MS analysis and data processing. To benchmark the quantitative performance of neat plasma analysis, we introduce a multispecies sample set based on a human tryptic plasma digest containing varying low level spike-ins of yeast and E. coli tryptic proteome digests, termed PYE. By analysing the sample set on state-of-the-art LC-MS platforms across twelve different sites in data-dependent (DDA) and data-independent acquisition (DIA) modes, we provide a data resource comprising a total of 1116 individual LC-MS runs. Centralized data analysis shows that DIA methods outperform DDA-based approaches regarding identifications, data completeness, accuracy, and precision. DIA achieves excellent technical reproducibility, as demonstrated by coefficients of variation (CVs) between 3.3% and 9.8% at protein level. Comparative analysis of different setups clearly shows a high overlap in identified proteins and proves that accurate and precise quantitative measurements are feasible across multiple sites, even in a complex matrix such as plasma, using state-of-the-art instrumentation. The collected dataset, including the PYE sample set and strategy presented, serves as a valuable resource for optimizing the accuracy and reproducibility of LC-MS and bioinformatic workflows for clinical plasma proteome analysis.

DOI: https://doi.org/10.1038/s41467-025-64501-z
Methods Cell Biol. 2025 ;pii: S0091-679X(25)00068-8. [Epub ahead of print]198 173-219

Proteomics and metabolomics in cancer biomarker discovery.

Rinki Basoya, Nidhi Bhasin, Preeti Jain, Sadhna Aggarwal.

  Cancer biomarker discovery is crucial for early diagnosis, prognosis, and therapeutic monitoring, and proteomics and metabolomics have emerged as powerful tools in biomarker research. Proteomics, the large-scale study of proteins, provides insights into the complex molecular changes that occur in cancer cells, offering potential biomarkers for tumor identification and monitoring treatment responses. Through techniques such as mass spectrometry and protein microarrays, proteomic analysis can identify differential protein expression, post-translational modifications, and protein-protein interactions that characterize different cancer stages. Similarly, metabolomics, the comprehensive analysis of small molecule metabolites, enables the identification of metabolic alterations associated with cancer. Tumor cells often exhibit reprogrammed metabolic pathways to sustain growth, making metabolites valuable as biomarkers for early cancer detection and treatment stratification. Both omics approaches allow for the identification of cancer-specific signatures, uncovering potential biomarkers with clinical relevance. This chapter describes the different proteomics and metabolomics techniques which are used in cancer biomarker discovery.

Keywords:  Biomarker discovery; Cancer biomarkers; Data analysis; HPLC; Mass-spectrometry; Metabolomics; NMR; Proteomics; Targeted Metabolomics; Tissue Microarray; Untargeted Metabolomics

DOI:  https://doi.org/10.1016/bs.mcb.2025.02.010
Methods Mol Biol. 2026 ;2975 135-150

Quantification of Dystrophin in Human Muscle Biopsies by Mass Spectrometry.

Emily H Canessa, Yetrib Hathout.

  With the increasing number of dystrophin replacement therapy trials, the need for reliable and accurate dystrophin quantification in muscle biopsies is becoming an important outcome measure. Quantification of dystrophin protein by mass spectrometry provides advantages over methods such as western blotting by offering improved precision, reproducibility, specificity, and a large quantifiable dynamic range. This protocol details a targeted mass spectrometry method for quantifying dystrophin in total muscle protein extracts using stable isotope-labeled dystrophin as a spike-in standard.

Keywords:  DMD; Dystrophin; In-gel digestion; Parallel reaction monitoring; Quantification; SDS-PAGE; SILAC; Targeted mass spectrometry

DOI:  https://doi.org/10.1007/978-1-0716-4811-7_9
Nat Commun. 2025 Sep 30. 16(1): 8714

Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data.

Jakub Idkowiak, Jonas Dehairs, Jana Schwarzerová, Dominika Olešová, Jacob X M Truong, Aleš Kvasnička, Marios Eftychiou, Ruben Cools, Xander Spotbeen, Robert Jirásko, Vullnet Veseli, Marco Giampà, Vincent de Laat, Lisa M Butler, Wolfram Weckwerth, David Friedecký, Jonas Demeulemeester, Karel Hron, Johannes V Swinnen, Michal Holčapek.

Mass spectrometry-based lipidomics and metabolomics generate extensive data sets that, along with metadata such as clinical parameters, require specific data exploration skills to identify and visualize statistically significant trends and biologically relevant differences. Besides tailored methods developed by individual labs, a solid core of freely accessible tools exists for exploratory data analysis and visualization, which we have compiled here, including preparation of descriptive statistics, annotated box plots, hypothesis testing, volcano plots, lipid maps and fatty acyl chain plots, unsupervised and supervised dimensionality reduction, dendrograms, and heat maps. This review is intended for those who would like to develop their skills in data analysis and visualization using freely available R or Python solutions. Beginners are guided through a selection of R and Python libraries for producing publication-ready graphics without being overwhelmed by the code complexity. This manuscript, along with associated GitBook code repository containing step-by-step instructions, offers readers a comprehensive guide, encouraging the application of R and Python for robust and reproducible chemometric analysis of omics data.

DOI: https://doi.org/10.1038/s41467-025-63751-1
Anal Chem. 2025 Oct 03.

Plasma-Microdroplet Fusion for Online Post-Column Epoxidation: Toward Deep Lipidomics on Unmodified Mass Spectrometers.

Niraj K Panday, Alexander J Grooms, Santosh R Acharya, Abraham K Badu-Tawiah.

The ability to characterize closely related lipids is clinically important, requiring the development of analytical tools to differentiate species responsible for metabolic disorders from those needed for metabolic homeostasis. Herein, we report a new liquid chromatographic (LC) method that utilizes online microdroplet-based epoxidation reactions during electrospray to enable C═C bond localization on conventional tandem mass spectrometry (MS/MS). Through a coaxial spray mechanism, charged microdroplets derived from the LC column and containing the lipid analyte were fused with nonthermal plasma, which facilitated (i) positive ion mode detection of various lipid classes and (ii) instantaneous C═C bond epoxidation via reaction with reactive oxygen species in the nonthermal plasma. Consequently, conventional low-energy MS/MS based on collision-induced dissociation was effective in characterizing the positional isomers of various lipids. Our ability to modify electrosprayed microdroplets post-column allowed independent optimization of the LC mobile phase, which in turn enabled both polar and nonpolar lipids to be separated on a C12 reverse-phase column. A data-dependent acquisition (DDA) method was created to enable the automated characterization of epoxide products in a 17-component lipid mixture. The DDA method was applied to characterize new triacylglycerol previously not detected in extra virgin olive oil.

DOI: https://doi.org/10.1021/acs.analchem.5c02269
Nat Commun. 2025 Sep 30. 16(1): 8699

Protein corona formed on lipid nanoparticles compromises delivery efficiency of mRNA cargo.

Elizabeth Voke, Mariah L Arral, Henry J Squire, Teng-Jui Lin, Lining Zheng, Roxana Coreas, Alison Lui, Anthony T Iavarone, Rebecca L Pinals, Kathryn A Whitehead, Markita P Landry.

Lipid nanoparticles (LNPs) are the most clinically advanced nonviral RNA-delivery vehicles, though challenges remain in fully understanding how LNPs interact with biological systems. In vivo, proteins form an associated corona on LNPs that redefines their physicochemical properties and influences delivery outcomes. Despite its importance, the LNP protein corona is challenging to study owing to the technical difficulty of selectively recovering soft nanoparticles from biological samples. Herein, we develop a quantitative, label-free mass spectrometry-based proteomics approach to characterize the protein corona on LNPs. Critically, this protein corona isolation workflow avoids artifacts introduced by the presence of endogenous nanoparticles in human biofluids. We apply continuous density gradient ultracentrifugation for protein-LNP complex isolation, with mass spectrometry for protein identification normalized to protein composition in the biofluid alone. With this approach, we quantify proteins consistently enriched in the LNP corona including vitronectin, C-reactive protein, and alpha-2-macroglobulin. We explore the impact of these corona proteins on cell uptake and mRNA expression in HepG2 human liver cells, and find that, surprisingly, increased levels of cell uptake do not correlate with increased mRNA expression in part due to protein corona-induced lysosomal trafficking of LNPs. Our results underscore the need to consider the protein corona in the design of LNP-based therapeutics.

DOI: https://doi.org/10.1038/s41467-025-63726-2
J Pharm Biomed Anal. 2025 Sep 26. pii: S0731-7085(25)00505-9. [Epub ahead of print]267 117164

A new deep field scan liquid chromatography-mass spectrometry method to identify host cell proteins in therapeutic proteins.

Leo Wang, Chris Barton, Yan Wang, Nicole Sessler, Larry Wang.

  Residual host cell proteins (HCPs) in therapeutic proteins pose a persistent challenge to detect due to their low abundance and wide dynamic range relative to the drug substance. To address this, we developed a "deep field scan" liquid chromatography tandem mass spectrometry (LC-MS/MS) method that enhances HCP detection without sample enrichment or clean-up, by leveraging an automated, cumulative target mass exclusion list and iterative data acquisition. Built on the Thermo Orbitrap AcquireX platform, this method optimizes MS efficiency by reducing redundant peptide sampling and improving MS/MS spectral quality, enabling higher-confidence HCP identification. Applying this method to NISTmAb demonstrated superior performance over traditional top10 data-dependent acquisition (DDA), confirming its viability as an alternative to native digest for monoclonal antibodies (mAbs). More importantly, its compatibility with non-antibody (non-mAb) biologics broadens its usage across diverse therapeutic modalities. Additionally, we established a benchmark HCP library from three additional commercial antibody standards, providing a valuable resource for cross-comparison within the HCP research community. By offering an automated and adaptable workflow, this method represents a novel and notable advancement in biologics impurity HCP characterization, supporting more efficient and comprehensive data collection for therapeutic protein development.

Keywords:  AcquireX; Data-dependent acquisition; Host cell proteins; Mass spectrometry; Proteomics; Therapeutic proteins

DOI:  https://doi.org/10.1016/j.jpba.2025.117164
Anal Chem. 2025 Oct 01.

High-Throughput Top-Down Proteomic Analysis of Picogram-Level Complex Samples Using Multisegment Spray-Capillary CE-MS.

Zhitao Zhao, Yanting Guo, Kellye A Cupp-Sutton, Zhige Wang, Xiaowen Liu, Si Wu.

Ultrasensitive top-down proteomics techniques provide valuable insights into PTM-regulated cellular functions in mass-limited samples. Capillary electrophoresis (CE) is a promising separation technique for top-down proteomics due to its high resolution, high sensitivity, and short cycle times compared to traditional liquid chromatography (LC)-based methods. We recently developed the "Spray-Capillary," an ESI-assisted device for quantitative ultralow-volume sampling and online CE-MS analysis, which successfully characterized hundreds of intact proteoforms from picogram-level cell lysate samples and showed promise for quantitative analysis of mass-limited complex biological samples. In this study, we further improved throughput for mass-limited top-down proteomics by integrating multisegment sample injection with our spray-capillary CE-MS analysis platform. By optimizing the spacer between sample plugs, we enabled the injection of multiple samples into the spray-capillary prior to a single CE-MS analysis, achieving baseline separation of identical proteins from different segments. Under optimized conditions, we quantifiedE. colilysate (10-250 pg) using a six-point calibration curve in a single analysis (e.g., ∼60 min run time), yielding a strong linear correlation (R2 > 0.98). Our method supports up to 17 sample segments per run (e.g., ∼90 min run time) while maintaining baseline separation. This optimized multisegment injection platform has the potential to analyze hundreds of mass-limited samples (e.g., single cells) per day, significantly enhancing throughput in top-down proteomics.

DOI: https://doi.org/10.1021/acs.analchem.5c02334
Anal Bioanal Chem. 2025 Sep 29.

Demonstrating voxel-by-voxel (V × V) single-point calibration in liver tissue by IR-MALDESI quantitative MSI.

Emily R Bruce, Russell R Kibbe, Logan J Opperman, David C Muddiman.

  Quantitative mass spectrometry imaging (qMSI) provides information regarding the colocalization, relative abundance, and concentration of a target analyte in a tissue without homogenization. Ionization sources, including IR-MALDESI, commonly utilize an on-tissue spatial calibration curve approach; however, this approach has several limitations including tedious sample preparation, and this approach does not account for local matrix effects. To compensate for these two limitations, we developed voxel-by-voxel (V × V) quantification to provide an internal standard calibration point for every voxel which requires a simple sample preparation and accounts for local matrix effects. In this work, we evaluate the performance of V × V quantification against the spatial calibration curve to assess the quantitative capacity of this newly developed method. Quantification of glutathione (GSH) on a per-voxel basis involves homogenously spraying a known amount of stable isotope-labeled glutathione (SIL-GSH) on a microscope slide. Next, we mount liver sections on top of the coated slides and image them using IR-MALDESI MSI. Statistical analysis demonstrated high precision for V × V quantification over a wide concentration range; however, the method's accuracy is currently limited due to the sprayer's configuration. Results support the feasibility of V × V quantification as evidenced by concentration heatmaps. Additionally, V × V quantification allows for parallel reaction monitoring (PRM) imaging which provides high specificity. Combined with relativity, straightforward sample preparation, and promising initial statistics, the V × V method offers significant advantages over spatial calibration curves.

Keywords:  IR-MALDESI; Quantitative mass spectrometry imaging; Quantitative sampling; Single-point calibration

DOI:  https://doi.org/10.1007/s00216-025-06138-x
Cancer Res. 2025 Oct 01. OF1-OF3

Alternatively Spliced Citrate Synthase Supports Colorectal Cancer.

Désirée Schatton, Christian Frezza.

Metabolic changes are a major hallmark of cancer with the mitochondrial tricarboxylic acid (TCA) cycle playing a central role in this process. Remodeling of the TCA cycle occurs in cancer cells to sustain the increased anabolic and energetic demands required to grow, proliferate, and metastasize. Alternative splicing (AS) is increasingly recognized as a key regulator of cancer metabolism, yet its specific impact on TCA cycle enzymes remains unclear. In this issue of Cancer Research, Cheung and colleagues describe a novel splicing isoform of citrate synthase (CS), termed CS-ΔEx4, which is highly expressed in colorectal cancer. This CS-ΔEx4 isoform forms heterocomplexes with full-length CS, enhancing CS activity and promoting the metabolic reprogramming characteristic of malignancy. Overexpression of CS-ΔEx4 increases mitochondrial respiration and drives glycolytic carbon flux toward TCA intermediates, resulting in elevated levels of the metabolite 2-hydroxyglutarate. Mechanistically, this increase in 2-hydroxyglutarate, facilitated by increased activity of phosphoglycerate dehydrogenase, leads to epigenetic alterations that support oncogenic gene expression and tumor progression. Suppression of CS-ΔEx4 or pharmacologic inhibition of its activity reverts these metabolic and epigenetic changes, reducing cancer cell survival and metastatic potential. These findings establish a direct link between AS of a core metabolic enzyme and the emergence of cancer hallmarks, suggesting that targeting AS-derived variants like CS-ΔEx4 may represent a promising therapeutic strategy for colorectal cancer and potentially other malignancies in which such isoforms are expressed. See related article by Cheung et al., p. XX.

DOI: https://doi.org/10.1158/0008-5472.CAN-25-3356
Cureus. 2025 Aug;17(8): e91345

Lipidomic Profiling: A Promising New Direction in the Diagnosis and Management of Thyroid Cancer?

Valentin Daniealopol, Ruxandra Daniealopol, Tatiana Daniela Sala, Radu Neagoe.

  Thyroid carcinoma (TC) is the most common endocrine malignancy worldwide, with an ongoing rise in its incidence. Despite improved diagnostic and therapeutic methods, distinguishing between benign and malignant nodules and predicting disease aggressiveness remains challenging. Lipidomics is a comprehensive approach to lipid profiling that is able to provide new insights into thyroid cancer biology. We conducted a methodical literature search for studies that looked into lipid metabolism alterations in the biofluids and tissue samples of TC patients and investigated the potential of the lipidomic fingerprint as a diagnostic or therapeutic target. Studies not employing lipidomic techniques, those using animal models, or focusing on other cancer types were excluded. The reviewed studies consistently revealed significant alterations in various lipid classes, including fatty acids (FA), phospholipids (PL), and sphingolipids (SL) across different sample types (serum, plasma, urine, and tissue) from TC patients compared to benign or healthy controls. Clinically, these findings provide a foundation for more accurate, non-invasive diagnostic tools and for classifying disease subtypes based on lipidomic signatures. For instance, modified glycerophospholipid (GPL) and SL species were observed in the plasma of thyroid cancer patients, and enhanced FA metabolism was correlated with tumor aggressiveness and poor prognosis. Lipidomics is a rapidly evolving field with tremendous potential for improving the clinical management of differentiated TC patients, from diagnosis to therapeutic intervention. The consistent identification of modified lipid profiles highlights their essential role in the metabolic reprogramming associated with tumorigenesis and also their importance as reliable clinical biomarkers. In order to implement routine clinical use of lipidomics, further large-scale validation studies are needed, along with standardized lipidomic protocols, to ensure reproducibility.

Keywords:  biomarkers; differentiated thyroid carcinoma; lipid profile; lipidomics; thyroid cancer

DOI:  https://doi.org/10.7759/cureus.91345
Biochim Biophys Acta Rev Cancer. 2025 Sep 26. pii: S0304-419X(25)00207-0. [Epub ahead of print]1880(6): 189465

Rewiring amino acids in cancer.

Yiqing Zhang, Jianxin Lyu, Hezhi Fang.

  Cancer cells often survive in harsh microenvironments. To sustain rapid growth and proliferation, they reprogram metabolic pathways through multiple mechanisms to meet the demands of biosynthesis and energy production. Both essential and non-essential amino acids support cancer cell synthesis of macromolecules such as proteins and nucleotides. They also participate in diverse biological processes, including oxidative stress defense, epigenetic regulation, and signaling pathway modulation. In this review, we summarize the role of amino acid metabolism in cancer initiation and progression, and highlight recent advances in therapies targeting amino acid metabolism. The aim of this review is to stimulate both basic research and translational studies on cancer therapy through targeting amino acid metabolism.

Keywords:  Amino acid; Cancer metabolism; Metabolism reprogramming; Targeted therapy

DOI:  https://doi.org/10.1016/j.bbcan.2025.189465
bioRxiv. 2025 Sep 28. pii: 2025.09.26.678912. [Epub ahead of print]

Development and validation of a streamlined workflow for proteomic analysis of proteins and post-translational modifications from dried blood.

Matthew W Foster, Youwei Chen, Marlene Violette, Michael T Forrester, J Scott Mellors, Brett S Phinney, Robert S Plumb, J Will Thompson, Timothy J McMahon.

It is increasingly recognized that the 'omic analysis of whole blood has applications for precision medicine and disease phenotyping. Despite this realization, whole blood is generally viewed as a challenging analytical matrix in comparison to plasma or serum. Moreover, proteomic analyses of whole blood proteomics have almost exclusively focused on (non)targeted analyses of protein abundances and much less on post-translational modifications (PTMs). Here, we developed a streamlined workflow for processing twenty microliters of venous blood collected by volumetric absorptive microsampling that incorporates serial trypsinization, N-glycopeptide and phosphopeptide enrichment and avoids laborious sample dry-down or cleanup steps. Up to 10,000 analytes (reported as protein groups, glycopeptidoforms and phosphosites) were quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in approximately 2 h of MS acquisition time. Using these methods, we explored the stability of "dried" and "wet" blood proteomes, as well as effects of ex vivo inflammatory stimulus or phosphatase inhibition. Multi-omics factor analysis enabled facile identification of analytes that contributed to inter-individual variability of the blood proteomes, including N-glycopeptides that distinguish immunoglobulin heavy constant alpha 2 allotypes. Collectively, our results help to establish feasibility and best practices for the integrated MS-based quantification of proteins and PTMs from dried blood.

DOI: https://doi.org/10.1101/2025.09.26.678912
Methods Mol Biol. 2026 ;2963 79-89

Lipidomic Sample Preparation to Detect Phosphatidylcholine Alterations in Skeletal Muscle.

William J Valentine, Suzumi M Tokuoka, Yoshihiro Kita, Hideo Shindou.

  Altered phospholipid compositions in skeletal muscle occur in muscular dystrophy and other pathological conditions; however, origins and relationships to the disease course are unclear. Liquid chromatography-mass spectrometry (LC-MS) technologies allow the determination of phospholipid compositions and alteration patterns in disease states. Here, we describe a basic protocol to prepare methanolic extracts from skeletal muscle, which may be analyzed by lipidomic LC-MS in order to detect compositional alterations in phosphatidylcholine, a major phospholipid species of skeletal muscle, as well as other phospholipid classes such as phosphatidylethanolamine. These analyses are useful to determine relative increases or decreases in individual phospholipid species between samples and capture changes in membrane compositions rather than the absolute quantities of lipids. LC-MS lipidomic analyses often yield large datasets containing hundreds of chromatographic peaks for each sample, and a ratiometric analysis strategy to determine the relative abundances of the major PC species is also briefly introduced.

Keywords:  Lipidomic analyses; Muscular dystrophy; Phosphatidylcholine; Phospholipid compositions; Skeletal muscle

DOI:  https://doi.org/10.1007/978-1-0716-4738-7_5
Biol Methods Protoc. 2025 ;10(1): bpaf068

A robust protocol for proteomic profiling of secreted proteins in conditioned culture medium.

Takayoshi Otsuka, Atsushi Hatano, Masaki Matsumoto, Hideaki Matsui.

  Reliable secretome analysis is crucial for understanding cellular communication and developing therapeutic strategies. However, conventional protein quantification methods, such as the bicinchoninic acid (BCA) assay, can overestimate protein concentrations in concentrated culture media, leading to inconsistent protein loading and compromised quantitative accuracy in mass spectrometry-based proteomics. To address this methodological challenge, we developed an improved sample preparation method for secretome analysis. Our approach introduces a concentration rate-based normalization method that adjusts sample volumes according to the ultrafiltration concentration ratio, ensuring more consistent protein loading across samples. This method enabled reliable identification of 3468 secreted proteins with high reproducibility (r > 0.93) in a model system of nuclear DNA (nucDNA)-induced inflammation in HeLa cells. Secretome profiles were distinctly altered by nucDNA transfection, with 89 proteins showing significant differential release between control and nucDNA-transfected wild-type HeLa cells. Furthermore, we identified a subset of proteins, including chaperone and proteasome complexes, that were consistently released across all conditions, suggesting their potential utility as internal controls for secretome analysis. This study presents a practical solution to the methodological challenge in secretome analysis, enabling more reliable and reproducible secretome profiling. This improved methodology represents an important step toward establishing standardized protocols for secretome analysis, ultimately enhancing the quality and comparability of research in this rapidly growing field.

Keywords:  DIA proteomics; conditioned medium; secreted proteins; silver stain

DOI:  https://doi.org/10.1093/biomethods/bpaf068
J Sep Sci. 2025 Oct;48(10): e70274

Identifying Oxysterols Associated With Age and Diet in Mice Using Optimized Reversed-phase Liquid Chromatography-Mass Spectrometry (RPLC-MS).

Indhumathy Subramaniyan, Benjamin Barr, Ninh M La-Beck, Benjamin G Janesko, Lauren Gollahon, Li Li.

Structurally similar oxysterols such as 7α-hydroxycholesterol, 7β-hydroxycholesterol, and 7-ketocholesterol; 5,6α- and 5,6β-epoxycholesterol; and 24(R/S)-hydroxy cholesterol, 25-hydroxy cholesterol, and 27-hydroxycholesterol are traditionally difficult to resolve using reversed-phase liquid chromatography (RPLC). We present a simple yet highly optimized method for the simultaneous quantification of eight oxysterols using RPLC coupled with mass spectrometry (MS) without derivatization. Optimal separation of most oxysterols was achieved at a lower column temperature (25°C), with specific combinations of stationary and mobile phases enhancing resolution, particularly for isomeric pairs such as 7α-/7β-OHC, 5,6α-/5,6β-EC, 24 R/S-OHC, and 25-OHC. Although certain analytes (e.g., 24S-OHC and 27-OHC) remained challenging to separate due to similar retention behavior, they were distinguishable by their unique MRM transitions. We applied this method to investigate oxysterol changes in a longitudinal mouse study comparing a normal diet to a high-fat diet. Liver and brain samples were analyzed, revealing distinct distribution patterns between the two organs. Notably, 24(S)-hydroxycholesterol levels, a signature cholesterol metabolite exclusively produced in the brain, increased with age independent of diet. In contrast, 5,6α-epoxycholesterol production in the liver was influenced by both age and dietary factors. Our method provides a robust tool for studying oxysterol variation and its implications in aging and diet, offering new insights into cholesterol-derived lipid regulation across different physiological conditions.

DOI: https://doi.org/10.1002/jssc.70274
Methods Mol Biol. 2026 ;2977 153-165

Protein Extraction Protocol for Legumes Compatible with Detergent: Gel Fractionation and Mass Spectrometry.

Palak Chaturvedi, Arindam Ghatak, Cristina López-Hidalgo, Wolfram Weckwerth.

  Liquid chromatography coupled with mass spectrometry is a vital tool for proteomic analyses. While significant progress has been made in decoding the genomes of crops in recent decades, the composition of their proteomes-the complete set of expressed proteins in a species-remains largely unknown. The success of this technique heavily relies on efficient and optimized sample preparation, which is one of the most critical steps for maximizing the recovery of reliable information. In this chapter, we present a universal protein extraction protocol originally developed for a gel-based approach combined with an initial extraction step using methanol: chloroform: water (MCW) to remove high concentrations of secondary metabolites, such as pigments, phenolic compounds, lipids, carbohydrates, and terpenes. This enhanced protocol was specifically designed for extracting proteins from the phenolic-rich tissues of legumes. Our workflow allows the use of small amounts (less than 20 mg) of fresh-weight tissue and can identify over 2000 proteins per sample. Additionally, this approach is cost-effective compared to commercial kits, and its broad applicability across various plant tissues makes it particularly effective for challenging leguminous samples.

Keywords:  Crop improvement; Legumes; Liquid chromatography—mass spectrometry (LC-MS); Orbitrap; Proteomics; Systems biology

DOI:  https://doi.org/10.1007/978-1-0716-4820-9_11