bims-mascan 2025-09-28 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–09–28
seventeen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Hunter-Dia: An updated protocol for enrichment and mass spectrometry-based identification of protein N-terminal peptides.
Library-Free Multiplexed Targeted Proteomics Enables Quantitative Protein-Level Genotyping.
A Flexible End-to-End Automated Sample Preparation Workflow Enables Standardized and Scalable Bottom-up Proteomics.
A simplified perchloric acid workflow with neutralization (PCA N) for democratizing deep plasma proteomics at population scale.
UCL-MetIsoLib: A Public High-Resolution Tandem Mass Spectrometry Library for HILIC-Based Isomer-Resolved Profiling of Glycolysis, Central Carbon Metabolism, and Beyond in Urine, Plasma, Tissues, Cells, and Patient-Derived Organoids.
Development and Clinical Evaluation of a Multiplexed Health Surveillance Panel Using Ultra High-Throughput PRM-MS in an Inflammatory Bowel Disease Cohort.
Protein cleaver: an interactive web interface for in silico prediction and systematic annotation of protein digestion-derived peptides.
Next-Generation Multiplexed Targeted Proteomics Quantifies Post-Translational Modifications, Compound-Protein Interactions, and Disease Biomarkers with High Throughput.
Profiling of C-terminal prenylated proteins using tandem mass tagging.
Facilitating analysis and dissemination of proteomics data through metadata integration in MaxQuant.
High-throughput proteome integral solubility alteration assay for low cell input using One-Tip.
N-Nitrosamine Quantitation in Pharmaceuticals by Tandem Mass Spectrometry.
DigDig: A Software for In-Depth Analysis and Comparison of Proteolytic Digestion.
TermineR: Bioinformatic processing of shotgun proteomics data for the annotation and quantitation of protein termini.
A standardized framework for circulating blood proteomics.
The role of succinylation-mediated metabolic reprogramming in tumor progression.
Single-Cell Raman Spectroscopy Combined with Metabolomics Reveals Distinct Metabolic Profiles in Medulloblastoma Subtypes.

Methods Enzymol. 2025 ;pii: S0076-6879(25)00233-2. [Epub ahead of print]719 25-42

Hunter-Dia: An updated protocol for enrichment and mass spectrometry-based identification of protein N-terminal peptides.

Raja Tamilselvan, Paula Dewes, Miguel Cosenza-Contreras, Pitter F Huesgen.

  Protein N-termini encode essential biological information, reflecting not only the identity of the translation start site but also a range of co- and post-translational modifications (PTMs), including N-terminal acetylation, myristoylation, and proteolytic processing events. These modifications are critical for regulating protein stability, localization, and function. However, standard bottom-up proteomics workflows typically focus on internal tryptic peptides and often fail to comprehensively capture N-terminal peptides, leading to an underrepresentation of the N-terminome in global proteomic datasets. To address this gap, N-terminal peptides can be enriched by negative selection, which modifies free primary amines before proteome digestion to enable simultaneous enrichment endogenously modified and protease-generated N-terminal peptides by depletion of internal and C-terminal peptides. Here we present an updated, 2-day step-by-step protocol for High-efficiency Undecanal-based Enrichment of N-termini (HUNTER) combined with Data-Independent Acquisition (DIA) mass spectrometry for deep and reproducible N-terminome profiling. To support broad adoption, we provide pre-configured FragPipe and DIA-NN search templates optimized for N-terminomics data, as well as an open-source R/Quarto pipeline for automated downstream analysis. This includes annotation of cleavage sites and PTMs, classification of native and protease-generated neo-N-termini, and statistical analysis of differential abundance across conditions. HUNTER-DIA consistently achieves high N-terminal labeling and internal peptide depletion efficiencies, enabling sensitive detection of endogenous processing events and dynamic N-terminal PTMs across diverse sample types. This platform opens new opportunities for studying protease biology, N-terminal post-translational modifications, and their context-specific regulation in health and disease across virtually any organism.

Keywords:  Degradomics; Mass spectrometry; N-terminal modifications; N-terminome; Peptide quantification; Positional annotation; Protein N-termini; Proteolytic processing

DOI:  https://doi.org/10.1016/bs.mie.2025.06.025
bioRxiv. 2025 Sep 16. pii: 2025.09.10.675407. [Epub ahead of print]

Library-Free Multiplexed Targeted Proteomics Enables Quantitative Protein-Level Genotyping.

Steven R Shuken, Steven P Gygi.

Existing targeted mass spectrometry methods require synthetic standard peptides, manual scan scheduling, or data libraries to quantify target proteins. Here we present GoDig-LiF, which uses spectra and retention times predicted by the Prosit-TMT model in place of data libraries, enabling targeted proteomics with only a tandem mass tag-labeled sample, target list, and mass spectrometer. We applied GoDig-LiF to the quantification of mutated proteins in cancer cell lines, including KRAS G13D.

DOI: https://doi.org/10.1101/2025.09.10.675407
Anal Chem. 2025 Sep 26.

A Flexible End-to-End Automated Sample Preparation Workflow Enables Standardized and Scalable Bottom-up Proteomics.

Sandra Schär, Luca Räss, Liliana Malinovska, Simonas Savickas, Francesca Cavallo, Christopher Below, Marco Tognetti, Polina Shichkova, Benoit Gourdet, Gonzalo Robles, Leo Iu, Jakob Vowinkel, Yuehan Feng, Roland Hjerpe, Roland Bruderer, Lukas Reiter.

Bottom-up proteomics holds significant promise for clinical applications due to its high sensitivity and precision but is limited by labor-intensive, low-throughput sample preparation methods. Advanced automation is essential to enhance throughput, reproducibility, and accuracy and to allow standardization to make bottom-up proteomics amenable to large-scale studies. We developed a fully integrated, automated sample preparation platform that covers the entire process from biological sample input to mass-spectrometry-ready peptide output and can be applied to a multitude of biological samples. With this end-to-end solution, we achieved high intra- and interplate reproducibility, as well as longitudinal consistency, resulting in precise and reproducible workflows. We showed that our automated workflow surpasses established manual and semiautomated workflows, while improving time efficiency. Finally, we demonstrated the suitability of our automated sample preparation platform for drug development by performing high-content compound characterization for targeted protein degradation, where high throughput and quantitative accuracy are indispensable. For this, we coupled application-specific workflows to perform proteome profiling and confirm target degradation by precise protein quantification. Overall, our results highlight the selective degradation of specific proteins of interest for ten selected compounds across two cell lines. Thus, the automated sample preparation platform facilitates rapid adaptation to emerging developments in proteomics sample preparation, combining standardization, flexibility, and high-throughput capabilities to drive significant advancements in clinical assays and proteomics research.

DOI: https://doi.org/10.1021/acs.analchem.5c03829
Mol Cell Proteomics. 2025 Sep 19. pii: S1535-9476(25)00170-7. [Epub ahead of print] 101071

A simplified perchloric acid workflow with neutralization (PCA N) for democratizing deep plasma proteomics at population scale.

Vincent Albrecht, Johannes B Müller-Reif, Vincenth Brennsteiner, Matthias Mann.

Large scale plasma proteomics studies offer tremendous potential for biomarker discovery but face significant challenges in balancing analytical depth, throughput and cost-effectiveness. We present an optimized perchloric acid-based workflow with neutralization - PCA-N - that addresses these limitations. By introducing a neutralization step following protein precipitation, PCA-N enables direct enzymatic digestion without additional purification steps, reducing sample volume requirements to only 5 μL of plasma while maintaining deep plasma proteome coverage. The streamlined protocol allows preparation of over 10,000 samples per day using 384-well formats at costs comparable to undepleted plasma analysis (NEAT). Rigorous validation according to the recently introduced CLSI C64 guideline demonstrated that despite somewhat higher technical variability compared to NEAT, PCA-N maintained excellent biological resolution and reproducibility. We confirmed the workflow's exceptional stability through analysis of over 1,700 quality control samples systematically interspersed among more than 40,000 plasma samples measured continuously over 353 days. Technical performance remained consistent across multiple instruments, sample preparation batches and nearly a year of measurements. Compared to NEAT plasma proteomics, PCA-N doubled the proteomic depth while maintaining comparable reagent costs and throughput. The minimal sample requirements, operational simplicity while using only common laboratory chemicals and exceptional scalability positions PCA-N as an attractive approach for population-level plasma proteomics, democratizing access to deep plasma proteomics analysis.

DOI: https://doi.org/10.1016/j.mcpro.2025.101071
Anal Chem. 2025 Sep 22.

UCL-MetIsoLib: A Public High-Resolution Tandem Mass Spectrometry Library for HILIC-Based Isomer-Resolved Profiling of Glycolysis, Central Carbon Metabolism, and Beyond in Urine, Plasma, Tissues, Cells, and Patient-Derived Organoids.

Kristian Serafimov, Maria Virginia Giolito, Sébastien Ibanez, Ophélie Renoult, Sarah Srhir, Paula Varon Rugeles, Florine Laloux-Morris, Cyril Corbet, Julien Pierrard, Marc Van den Eynde, Michael Lämmerhofer, Olivier Feron.

We present UCL-MetIsoLib, a publicly accessible high-resolution tandem mass spectrometry (HRMS/MS) library developed for HILIC-based, ion-pairing-free, isomer-resolved metabolomics using a bioinert UHPLC system and the Acquity Premier BEH Amide column. The platform integrates two complementary methods operating under distinct chromatographic conditions (pH 3.5, ESI+; pH 11.0, ESI-), enabling broad metabolic coverage. A total of 334 metabolites are annotated in the library structure, with thiol derivatization incorporated into the extraction protocol to mitigate redox-driven artifacts. Metabolite identification is supported by 245 authentic reference standards and curated according to MSI Level 1 and Level 2 criteria. Validation followed FDA guidelines for bioanalytical method validation across five biological matrices─urine, plasma, tissues, cells, and patient-derived colorectal organoids. The method demonstrated high precision (<15% RSD intra/inter-day) and recovery (85-115% across all QC levels). To demonstrate biological applicability, UCL-MetIsoLib was applied to a case study comparing healthy and colorectal cancer-derived organoids. The method enabled confident annotation of metabolite isomers, including key glycolytic intermediates such as DHAP and GA3P, as well as sugar phosphates from the glycolysis and pentose phosphate pathways. Metabolic alterations were observed in tumor organoids, including accumulation of nucleotide derivatives and shifts in central carbon metabolism. The library is constantly under expansion and is freely available in its latest version at: https://github.com/kserafimov10/UCLMetIsoLib.

DOI: https://doi.org/10.1021/acs.analchem.5c03390
Angew Chem Int Ed Engl. 2025 Sep 22. e202507610

Development and Clinical Evaluation of a Multiplexed Health Surveillance Panel Using Ultra High-Throughput PRM-MS in an Inflammatory Bowel Disease Cohort.

Qin Fu, Philip M Remes, Jihyeon Lee, Cristina Jacob, Dalin Li, Manasa Vegesna, Koen Raedschelders, Ali Haghani, Emebet Mengesha, Philip Debbas, Esthelle Hoedt, Sandy Joung, Susan Cheng, Scott Peterman, Justyna Fert-Bober, Gil Y Melmed, Dermot P B McGovern, Christopher I Murray, Jennifer E Van Eyk.

  Despite advances in clinical proteomics, translating protein biomarker discoveries into clinical use remains challenging due to the technical complexity of the validation process. Targeted MS-based proteomic approaches such as parallel reaction monitoring (PRM) offer sensitive and specific assays for biomarker translation. In this study, we developed a multiplex PRM assay using the Stellar mass spectrometry platform to quantify 57 plasma proteins, including 24 FDA-approved biomarkers. Loading curves (11 points) were performed at 4 sample throughputs (100, 144, 180, and 300 samples per day) using independently optimized and scheduled PRM methods. Following optimization, an inflammatory bowel disease (IBD) cohort of plasma samples (493 IBD, 509 matched controls) was analyzed at a throughput of 180 samples per day. To monitor system performance, the study also included over 1000 additional injections for system suitability tests, low-, middle-, and high-quality controls, washes, and blanks. Using this approach, we observed high quantifiability (linearity, sensitivity, and reproducibility) in the PRM assay and consistent data acquisition across a large cohort. We also validated the candidate IBD markers, C-reactive protein and orosomucoid protein, identified in a recent discovery experiment.

Keywords:  Clinical biomarker translation; Inflammatory bowel disease; Targeted peptides; Validation proteomics

DOI:  https://doi.org/10.1002/anie.202507610
Front Bioinform. 2025 ;5 1576317

Protein cleaver: an interactive web interface for in silico prediction and systematic annotation of protein digestion-derived peptides.

Grigorios Koulouras, Yingrong Xu.

  Proteolytic digestion is an essential process in mass spectrometry-based proteomics for converting proteins into peptides, hence crucial for protein identification and quantification. In a typical proteomics experiment, digestion reagents are selected without prior evaluation of their optimality for detecting proteins or peptides of interest, partly due to the lack of comprehensive and user-friendly predictive tools. In this work, we introduce Protein Cleaver, a web-based application that systematically assesses regions of proteins that are likely or unlikely to be identified, along with extensive sequence and structure annotation and visualization features. We showcase practical examples of Protein Cleaver's usability in drug discovery and highlight proteins that are typically difficult to detect using the most common proteolytic enzymes. We evaluate trypsin and chymotrypsin for identifying G-protein-coupled receptors and discover that chymotrypsin produces significantly more identifiable peptides than trypsin. We perform a bulk digestion analysis and assess 36 proteolytic enzymes for their ability to detect most of cysteine-containing peptides in the human proteome. We anticipate Protein Cleaver to be a valuable auxiliary tool for proteomics scientists.

Keywords:  mass spectrometry; peptide annotation; peptide identification; proteolytic digestion; proteomics

DOI:  https://doi.org/10.3389/fbinf.2025.1576317
bioRxiv. 2025 Sep 16. pii: 2025.09.10.675380. [Epub ahead of print]

Next-Generation Multiplexed Targeted Proteomics Quantifies Post-Translational Modifications, Compound-Protein Interactions, and Disease Biomarkers with High Throughput.

Steven R Shuken, Geordon A Frere, Charlotte R Beard, Jesse D Canterbury, Nathan R Zuniga, Brandon M Gassaway, Shane L Dawson, Kean Hean Ooi, João A Paulo, M Windy McNerney, Steven P Gygi, Qing Yu.

The GoDig platform enables sensitive, multiplexed targeted pathway proteomics without manual scheduling or synthetic standards. Here we present GoDig 2.0, which increases sample multiplexing to 35-fold, improves time efficiency and reduces scan delays for higher success rates, and allows flexible spectral and elution library generation from different mass spectrometry data types. GoDig 2.0 measures 2.4× more targets than GoDig 1.0, quantifying >99% of 800 peptides in a single run. We compiled a library of 23,989 human phosphorylation sites from a phosphoproteomic dataset and used it to profile kinase signaling differences across cell lines. In human brain tissue, we established a hyperphosphorylated tau assay including pTau127, revealing potential biomarkers for Alzheimer's disease. We also quantified diglycyl-lysine peptides to assess polyubiquitin branching. Finally, we built a library of 20,946 reactive cysteines and profiled covalent compound-protein interactions spanning diverse pathways. GoDig 2.0 enables high-throughput analyses of site-specific protein modifications across many biological contexts.

DOI: https://doi.org/10.1101/2025.09.10.675380
Methods Enzymol. 2025 ;pii: S0076-6879(25)00216-2. [Epub ahead of print]719 317-345

Profiling of C-terminal prenylated proteins using tandem mass tagging.

Harinarayanan Kottala, Yuanzhe Chen, Mark D Distefano.

  Protein prenylation is a crucial post translational modification that involves the attachment of one or two isoprenoid groups at the C-terminus of a protein, facilitating membrane localization and regulating protein function. Consequently, prenylation is linked to numerous diseases. Identification of prenylated proteins and their quantification is crucial to defining the role of prenylation in these diseases and therapy development. Here, a method for profiling and quantifying prenylated proteins using a bio-orthogonal probe is described. The workflow consists of metabolic incorporation of the probe and click chemistry-mediated biotinylation followed by streptavidin purification and LC-MS3 based proteomic analysis of the enriched proteins. In this chapter, we provide a comprehensive protocol to accomplish this using tandem mass tag (TMT) labels in mammalian cell culture. This includes sample preparation from adherent and suspension cell lines, in-gel fluorescence analysis to verify probe incorporation, click reaction with a biotin handle, streptavidin enrichment of prenylated proteins, multiplexing using TMT labels, LCMS3 data acquisition and data analysis.

Keywords:  Farnesylation; Geranylgeranylation; Post-translational modification; Prenylation; Tandem mass tagging

DOI:  https://doi.org/10.1016/bs.mie.2025.06.008
Nat Commun. 2025 Sep 25. 16(1): 8421

Facilitating analysis and dissemination of proteomics data through metadata integration in MaxQuant.

Walter Viegener, Shamil Urazbakhtin, Daniela Ferretti, Jürgen Cox, Jinqiu Xiao.

Metadata plays an essential role in the analysis and dissemination of proteomics data. It annotates sample information for output tables from library searches and displays sample information from data files in public repositories. However, integrating metadata into data analysis can be time-consuming and is not well standardized. Inconsistent metadata formats in public repositories hinder other researchers' ability to reproduce and reuse these public datasets. Here we present the metadata integration in MaxQuant, which provides a user-friendly way to export metadata as SDRF, the standard format that maps sample properties to proteomics data files. We also implemented the annotation of output tables with the SDRF file, enabling users to perform seamless downstream data analysis with annotated output tables. These features provide a simple and standardized approach to creating and leveraging standardized metadata, thereby facilitating data analysis and improving the reusability and reproducibility of public proteomics datasets.

DOI: https://doi.org/10.1038/s41467-025-64089-4
Commun Chem. 2025 Sep 26. 8(1): 282

High-throughput proteome integral solubility alteration assay for low cell input using One-Tip.

Maico Lechner, Pierre Sabatier, Jesper V Olsen.

Mass spectrometry-based versions of the cellular thermal shift assay (CETSA), like proteome integral solubility alteration (PISA), enable simultaneous monitoring of thousands of proteins for drug-target engagement. These methods are constrained in throughput and scalability, while the sample requirement limits the applicability to widely available material. Here, we combine PISA with the One-Tip method to simplify and streamline sample preparation. Using the mass spectrometry-compatible n-Dodecyl-β-D-Maltoside (DDM) non-ionic detergent for cell lysis in PISA sample preparation enables direct transfer to One-Tip with decreasing cell requirements down to 200 cells per µL. One-Tip provides similar depth and higher reproducibility, with lower material and solvent usage and a faster proteolytic digestion compared to a conventional sample cleaning and digestion protocol, making it a cost-effective, fast, and user-friendly option. To demonstrate its scalability, we applied One-Tip-PISA in a 96-well plate format, profiling a kinase inhibitor panel, allowing cell treatment to injection within 12 h, enhancing workflow efficiency and accessibility for a wide range of laboratories.

DOI: https://doi.org/10.1038/s42004-025-01670-4
Anal Chem. 2025 Sep 22.

N-Nitrosamine Quantitation in Pharmaceuticals by Tandem Mass Spectrometry.

Mousumi Saha, Lingqi Qiu, Stefan Kraft, Daniel E Lee, Andreas Kaerner, Christopher J Welch, Yumin Dai, Hao Li, Scott A Miller, R Graham Cooks.

The mutagenic potential of N-nitrosamines has heightened regulatory scrutiny and prompted numerous drug recalls due to their formation as impurities during pharmaceutical manufacturing. However, the simultaneous and rapid analysis of multiple N-nitrosamines remains challenging. In this study, we show a rapid (<1 min) and robust multiple reaction monitoring (MRM) approach for the trace-level quantitation of N-nitrosamines across a wide concentration range (10 ng/mg to 500 μg/mg) in diverse pharmaceutical matrices. Lithium cation (Li+) adduct formation during ionization is utilized to enable diagnostic fragmentation in tandem mass spectrometry (MS/MS), yielding a limit of detection of 5 ng/mg with high quantitative accuracy and precision. This method is also applied to trace analysis of N-nitrosated drug impurities within their parent pharmaceutical matrices. With minimal sample preparation, rapid analysis times, and broad applicability to complex pharmaceutical formulations, this MRM method offers a practical and powerful tool for the identification and quantitation of N-nitrosamines and its potential adoption in pharmaceutical quality control and industrial settings.

DOI: https://doi.org/10.1021/acs.analchem.5c03641
Anal Chem. 2025 Sep 25.

DigDig: A Software for In-Depth Analysis and Comparison of Proteolytic Digestion.

Zuzana Kalaninová, Jasmína Mária Portašiková, Daniel Kavan, Petr Novák, Petr Man.

Proteolysis is a crucial step in both bottom-up and structural proteomics workflows, directly influencing peptide identification and sequence coverage in mass spectrometry-based analyses. While classical proteomics typically relies on highly specific enzymes with well-defined cleavage patterns, structural MS approaches such as hydrogen/deuterium exchange mass spectrometry (HDX-MS) often employ nonspecific or semispecific proteases, producing complex peptide mixtures that require more detailed digestion analysis. To address these needs and streamline the entire process, we developed DigDig, a standalone, Java-based software tool for evaluating and comparing proteolytic digestion across diverse experimental conditions. DigDig processes output files from common search engines and provides customizable visualizations of key digestion metrics, including sequence coverage, reproducibility, peptide redundancy, cleavage site preferences, and peptide length distributions. A distinguishing feature is its ability to detect and report repetitive peptide sequences, which are frequently missed by standard tools. We demonstrate its capabilities using data sets from both specific and nonspecific digestions, highlighting its utility in digestion quality control, protease characterization, and method development, particularly in HDX-MS workflows. DigDig is freely available at https://peterslab.org/DigDig/.

DOI: https://doi.org/10.1021/acs.analchem.5c04217
Methods Enzymol. 2025 ;pii: S0076-6879(25)00212-5. [Epub ahead of print]719 43-66

TermineR: Bioinformatic processing of shotgun proteomics data for the annotation and quantitation of protein termini.

Miguel Cosenza-Contreras, Pitter F Huesgen, Oliver Schilling.

  Proteolytic processing shapes protein function through irreversible cleavage events that are often overlooked in standard shotgun proteomics workflows. In this methods chapter, we describe a comprehensive strategy for the identification, annotation, and quantitation of proteolytic products and terminal peptides using semi-specific database searches. We present practical guidelines for spectral data processing using the FragPipe software suite, leveraging modern rescoring tools such as Percolator and MSBooster to improve sensitivity. Building upon this, we introduce TermineR, an R-based framework for downstream annotation and statistical evaluation of proteolytic termini. Together, these tools enable the extraction of biologically meaningful insights into proteolytic activity from complex proteomics datasets, without the need for biochemical enrichment.

Keywords:  Bioinformatics; Data Processing; Degradomics; N-terminomics; Post-translational modifications; Proteolysis; Proteomics; Terminomics

DOI:  https://doi.org/10.1016/bs.mie.2025.06.004
Nat Genet. 2025 Sep 23.

A standardized framework for circulating blood proteomics.

Xue Cai, Philipp E Geyer, Yasset Perez-Riverol, Gilbert S Omenn, Lianhua Dong, Robert Winkler, Sara Ahadi, Philip Lössl, Xiaobo Yu, Cheng Chang, Markus Ralser, Connie R Jimenez, Yang Zhao, Yu-Ju Chen, Terence C W Poon, Nicolai Bache, Leming Shi, Xinhua Dai, Ziyue Wang, Yi Zhu, Xiang Fang, Jochen M Schwenk, Jennifer E Van Eyk, Uwe Völker, Tiannan Guo.

The circulating blood proteome holds immense potential for biomarker discovery and understanding disease mechanisms. Notable advances in mass spectrometry and affinity-based technologies have been made, but data integration across studies and platforms is hindered by the absence of unified analytical standards. This limitation impedes comprehensive exploration of human biology across diverse phenotypes and cohorts as well as the translation of findings into clinical applications. The disparities between datasets, stemming from a combination of factors related to differences in sample collection, pre-analytical handling, measurement methods and instrumentation, further complicate data integration. In this Perspective, we outline key challenges in blood-based proteomics and propose actionable strategies. Central to our recommendations are high-quality, technology-agnostic reference samples, which can bridge disparate datasets and enable robust cross-study comparisons. By fostering interconnected investigations across proteomic technologies, blood sample collections, clinical phenotypes and different populations, these references will accelerate the field and its translation.

DOI: https://doi.org/10.1038/s41588-025-02319-7
Mol Biol Rep. 2025 Sep 26. 52(1): 954

The role of succinylation-mediated metabolic reprogramming in tumor progression.

Siyi He, Chunduo Wang, Ranran Li, Kexin Xu, Wuzhiyi Zhang, Binbin Feng, Shengtao Cheng, Jihua Ren, Juan Chen.

  Metabolic reprogramming is a hallmark of tumors, whereby cancer cells remodel their own metabolism to meet the biosynthetic, energetic, and signaling demands required for rapid proliferation and malignant transformation. Posttranslational modifications (PTMs) serve as dynamic molecular switches that fine-tune cellular metabolic networks by precisely modulating the activity, stability, and subcellular localization of metabolic enzymes. This regulatory plasticity drives context-dependent metabolic reprogramming in tumor cells, enabling them to adapt to fluctuating physiological demands or pathological stressors while establishing tumor-specific metabolic signatures critical for survival and progression. Among PTMs, lysine succinylation-a recently identified modification catalyzed by succinyl-CoA-has emerged as a critical regulator of cancer metabolism. This unique modification involves the transfer of a negatively charged four-carbon succinyl group to lysine residues, inducing conformational and functional changes in target proteins. Notably, succinylation is evolutionarily conserved across eukaryotes and prokaryotes and has a broad influence on central metabolic pathways, including the tricarboxylic acid (TCA) cycle, amino acid metabolism, and lipid homeostasis. Mounting evidence highlights its dual roles in both sustaining tumorigenic metabolism and directly activating oncogenic signaling cascades. This review summarizes current insights into how succinylation rewires tumor metabolism and delineates its mechanistic contributions to cancer progression.

Keywords:  Cancer; Lysine succinylation; Metabolic reprogramming; Posttranslational modification; Succinyl-CoA

DOI:  https://doi.org/10.1007/s11033-025-11061-6
Anal Chem. 2025 Sep 26.

Single-Cell Raman Spectroscopy Combined with Metabolomics Reveals Distinct Metabolic Profiles in Medulloblastoma Subtypes.

Yijia Peng, Minghui Li, Yujie Liu, Ren Zhang, Baohong Liu, Kun Zhang.

Tumor heterogeneity poses a major challenge to the precision treatment of medulloblastoma (MB). Rapid and accurate subtyping tools are urgently needed for informed clinical decision-making. Herein, we demonstrate the utility of Raman spectroscopy (RS) as a label-free, noninvasive approach for molecular characterization and subtype discrimination of MB at the single-cell level. Five representative MB cell lines from group 3 and sonic hedgehog (SHH) subtypes, along with microglial controls, were profiled by RS and validated using mass spectrometry-based metabolomics. RS-derived single-cell metabolic fingerprints revealed subtype-specific variations in nucleic acids, amino acids, lipids, and proteins. Group 3 cells, exemplified by metastatic D283, exhibited elevated levels of unsaturated lipids compared with most SHH cells. Notably, Daoy cells from the SHH subtype displayed unsaturation levels comparable to D283, reflecting intragroup heterogeneity and membrane remodeling in MB. Machine learning classifiers achieved high diagnostic performance, with an average area under the curve of 0.994 and a Matthews correlation coefficient of 0.935. Furthermore, cisplatin-treated D283 cells showed increased unsaturated lipid content relative to Daoy cells, revealing subtype-dependent metabolic shifts in drug response. These findings underscore the metabolic diversity of MB subtypes and the role of lipid metabolism in tumor progression and therapy. Collectively, our results exemplify the power of RS combined with metabolomics for molecule-resolved, label-free MB subtyping and therapeutic stratification.

DOI: https://doi.org/10.1021/acs.analchem.5c02816