bims-mascan 2025-07-20 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–07–20
twenty papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Unlocking the Neuropeptidome using a Novel Endogenous Peptidomics Framework.
Dissecting Branch-Specific Unfolded Protein Response Activation in Drug-Tolerant BRAF-Mutant Melanoma using Data-Independent Acquisition Mass Spectrometry.
Trimethylaminoethyl ester derivatization and stable isotope derivatization for enhanced analysis of fatty acids in biological samples by electrospray ionization tandem mass spectrometry.
Novel Equivalent Carbon Number Strategy for Large-Scale Lipidomics Data Analysis via Ultrahigh-Performance Liquid Chromatography-Orbitrap Astral Mass Spectrometry.
An Integrated Strategy Using Predicted Spectral Library and Tandem Enrichment for Large-Scale Identification of Low-Abundance Proteins and Intact N-Glycopeptides in Human Plasma.
Cancer cells differentially modulate mitochondrial respiration to alter redox state and enable biomass synthesis in nutrient-limited environments.
LipidCruncher: An open-source web application for processing, visualizing, and analyzing lipidomic data.
EndoGenius: Enabling comprehensive identification and quantitation of endogenous peptides.
Lipids: Driving Forces in the Underlying Biology of Carcinogenesis.
Protease Specificity Profiling Using Synthetic Combinatorial Peptide Libraries and Mass Spectrometry.
Proximity Labeling of NIMA Kinase Complex Components in C. elegans.
An evaluation methodology for machine learning-based tandem mass spectra similarity prediction.
Targeted metabolomics for cardiovascular disease: Validation of a high-throughput HPLC-MS/MS assay.
Constructing a consensus serum metabolome.
Evaluation of the False Discovery Rate in Library-Free Search by DIA-NN Using In Vitro Human Proteome.
PeakClimber: A software tool for the accurate quantification of complex HPLC chromatograms.
Deep and Quantitative Proteomic Profiling of Low Volume Mouse Serum Across the Lifespan.
Ketone body driven lipid remodeling supports thermogenic adaptation to fasting.
Structural and Quantitative Analysis of Polyfluoroalkyl Substances (PFASs) and Para-Phenylenediamines (PPDs) by Direct Analysis in Real Time Ion Mobility Mass Spectrometry (DART-IM-MS).
Mass Spectrometry Imaging.

bioRxiv. 2025 Jun 15. pii: 2025.06.12.659356. [Epub ahead of print]

Unlocking the Neuropeptidome using a Novel Endogenous Peptidomics Framework.

Lauren Fields, Wenxin Wu, Tina C Dang, Angel E Ibarra, Mitchell Gray, Lingjun Li.

Endogenous peptides have garnered increasing attention over the past decade driven by the development of advanced analytical methods. However, large-scale investigations of peptides as potential disease biomarkers or drug candidates are still hindered by their challenging biochemical properties and the scarcity of specialized analytical tools. Among these, neuropeptides are particularly challenging to study due to their low in vivo concentration, rapid turnover rate, and high structural variability. Data-independent acquisition (DIA) mass spectrometry (MS) has shown great ability in profiling low-abundance ions. Nevertheless, most available DIA analytical tools are designed for proteomics studies and are not suitable for endogenous peptides, as there is no set enzymatic cleavage for these peptides. Here, we introduce the novel EndoGenius platform, paired with DIA-NN, to achieve high-confidence neuropeptide identification using an updated spectral library for DIA MS analysis. By employing orthogonal offline fractionation, ion mobility instrumentation, and an optimized database searching algorithm specifically for neuropeptides, we have constructed the largest crustacean neuropeptide spectral library to date. With this library, in combination with neural networking technology, we report a 100-fold increase in the number of neuropeptides identified in all Cancer borealis tissues analyzed. We also cross-validated these findings with transcriptomics data to enhance identification confidence. This workflow presents a novel analytical framework for DIA peptidomics analysis, offering a robust approach to studying neuropeptides and other endogenous peptides.
TOC:
Synopsis: We present a framework that capitalizes on robust analytical innovations and an optimized bioinformatics pipeline to provide the most comprehensive snapshot of the crustacean neuropeptidome to-date.

DOI: https://doi.org/10.1101/2025.06.12.659356
Mol Cell Proteomics. 2025 Jul 14. pii: S1535-9476(25)00135-5. [Epub ahead of print] 101036

Dissecting Branch-Specific Unfolded Protein Response Activation in Drug-Tolerant BRAF-Mutant Melanoma using Data-Independent Acquisition Mass Spectrometry.

Lea A Barny, Jake N Hermanson, Sarah K Garcia, Philip E Stauffer, Lars Plate.

  Cells rely on the Unfolded Protein Response (UPR) to maintain ER protein homeostasis (proteostasis) when faced with elevated levels of misfolded and aggregated proteins. The UPR is comprised of three main branches-ATF6, IRE1, and PERK-that coordinate the synthesis of proteins involved in folding, trafficking, and degradation of nascent proteins to restore ER function. Dysregulation of the UPR is linked to numerous diseases, including neurodegenerative disorders, cancer, and diabetes. Despite its importance, identifying UPR targets has been challenging due to their heterogeneous induction, which varies by cell type and tissue. Additionally, defining the magnitude and range of UPR-regulated genes is difficult because of intricate temporal regulation, feedback between UPR branches, and extensive cross-talk with other stress-signaling pathways. To comprehensively identify UPR-regulated proteins and determine their branch specificity, we developed a data-independent acquisition (DIA) liquid-chromatography mass spectrometry (LC-MS) pipeline. Our optimized workflow improved identifications of low-abundant UPR proteins and leveraged an automated SP3-based protocol on the Biomek i5 liquid handler for label-free peptide preparation. Using engineered stable cell lines that enable selective pharmacological activation of each UPR branch without triggering global UPR activation, we identified branch-specific UPR proteomic targets. These targets were subsequently applied to investigate proteomic changes in multiple BRAF-mutant melanoma cell lines treated with a BRAF inhibitor (PLX4720, i.e., vemurafenib). Our findings revealed differential regulation of the XBP1s branch of the UPR in the BRAF-mutant melanoma cell lines after PLX4720 treatment, likely due to calcium activation, suggesting that the UPR plays a role as a non-genetic mechanism of drug tolerance in melanoma. In conclusion, the validated branch-specific UPR proteomic targets identified in this study provide a robust framework for investigating this pathway across different cell types, drug treatments, and disease conditions in a high-throughput manner.

Keywords:  Activating Transcription Factor 6; Data-Independent Acquisition (DIA); Inositol requiring enzyme 1; Protein Kinase R-like ER Kinase; Proteomics automation; Unfolded Protein Response

DOI:  https://doi.org/10.1016/j.mcpro.2025.101036
J Chromatogr B Analyt Technol Biomed Life Sci. 2025 Jul 10. pii: S1570-0232(25)00287-9. [Epub ahead of print]1264 124733

Trimethylaminoethyl ester derivatization and stable isotope derivatization for enhanced analysis of fatty acids in biological samples by electrospray ionization tandem mass spectrometry.

Liu Yang, Qingchun Wang, Weiwei Chen, Ji Yang, Yu Lin, David M Lubman.

   BACKGROUND: Abnormal fluctuations in free fatty acids (FFAs) are associated with cardiovascular diseases. However, conducting a thorough analysis of individual FFAs via mass spectrometry has historically posed challenges due to their low ionization efficiency and the absence of distinctive fragment ions.
RESULTS: In this study, we introduce a method utilizing paired stable isotope derivatization coupled with liquid chromatography-triple quadrupole mass spectrometry (ID-LC-QQQ-MS) for thorough identification and relative quantification of fatty acids in serum samples. This method involves the derivatization of the carboxyl groups of FFAs using a pair of isotope reagents, resulting in the formation of FA trimethylaminoethyl ester (FA-TMAE-h3/d3), which can yield two distinct neutral fragments with masses of 59 and 62 Da during collision-induced dissociation (CID). Consequently, a quadruple neutral loss scan (QNLS) approach was utilized for the non-targeted profiling of FFAs in serum samples. The derivative peak pairs displaying matching retention times and distinct mass differences were extracted from the two QNLS spectra and recognized as potential FFAs. Subsequently, a multiple reaction monitoring (MRM) detection protocol was established for the relative quantification of fatty acids in the serum of Syrian Golden Hamsters subjected to various treatments, utilizing a pooled sample labeled with a heavy isotope as an internal standard. Partial least squares discriminant analysis (PLS-DA) revealed notable variations in these 23 fatty acids across the four groups.
SIGNIFICANCE: The current stable isotope derivatization (ID) method, in conjunction with tandem mass spectrometry (MS/MS) analysis, stands out as a promising approach for identifying and quantifying FFAs in real samples.

Keywords:  ESI-MRM-MS; ESI-QNLS-MS; Free fatty acids quantification; Serum samples; Stable isotope derivatization

DOI:  https://doi.org/10.1016/j.jchromb.2025.124733
Anal Chem. 2025 Jul 17.

Novel Equivalent Carbon Number Strategy for Large-Scale Lipidomics Data Analysis via Ultrahigh-Performance Liquid Chromatography-Orbitrap Astral Mass Spectrometry.

Yao Chen, Xinxin Wang, Rui Zhong, Zixuan Wang, Ziquan Fan, Tian Liu, Qi Li, Yaorui Ye, Chunxiu Hu, Guowang Xu.

Lipidomics enables studying lipid alterations in physiological or pathological states. The new Orbitrap mass spectrometry (MS), equipped with an Astral analyzer, significantly increases MS/MS acquisition rate. However, simultaneous analysis of lipid retention behavior and structural annotation from this data remains challenging. In this study, we propose a comprehensive strategy using the Equivalent Carbon Number (ECN) model to integrate the advantages of MS-DIAL (providing precise lipid retention behavior) and LipidSearch (offering accurate MS/MS spectral information). By investigating 34 lipid standards spiked into the NIST SRM 1950 plasma sample, the ECN strategy demonstrated high accuracy in retention time prediction with relative standard deviations below ± 5% for 90.0% of lipids in positive ion mode and 100% in negative ion mode. False-positive data from LipidSearch 5.1 were also significantly reduced; for example, in yeast, 68.8% and 80.1% of false positives were removed in the positive and negative ion modes, respectively. A total of 1933, 1539, 1969, 985, and 2786 lipids were annotated with the ECN strategy in HeLa cells, NIST plasma, mouse liver tissues, Saccharomyces cerevisiae yeast, and their pooled sample and were analyzed by Astral-MS, respectively. It was also found that the numbers of annotated lipids from Astral-MS data preprocessed with LipidSearch and MS-DIAL were 3-5 and 2-4 times higher than those from QE-MS data, respectively. This strategy enables efficient and accurate lipid identification with precise retention times and reliable MS/MS annotation, advancing large-scale lipidomics research and offering broad application in diverse biological contexts.

DOI: https://doi.org/10.1021/acs.analchem.5c01863
Anal Chem. 2025 Jul 17.

An Integrated Strategy Using Predicted Spectral Library and Tandem Enrichment for Large-Scale Identification of Low-Abundance Proteins and Intact N-Glycopeptides in Human Plasma.

Xinyi Yang, Zhuokun Du, Yuxin Jiao, Sijie Chen, Juanjuan Ning, Haotian Hong, Bin Fu, Jiayu Liu, Wanjun Zhang, Weijie Qin.

Plasma represents a highly valuable clinical sample for protein biomarker discovery, offering a comprehensive source of physiological and pathological information. N-glycosylation plays key roles in various biological processes and enhances the sensitivity of plasma protein biomarkers for disease diagnosis. Consequently, large-scale characterization of the plasma proteome and N-glycosylation patterns by mass spectrometry (MS) is crucial for identifying biomarkers but remains highly challenging due to three major difficulties. First, plasma protein detection is limited as high-abundance proteins dominate the majority of the MS scans. Second, while plasma proteome coverage can be improved by constructing large-scale empirical spectral libraries and coupling them with DIA-MS, the labor- and time-consuming nature of experimental library generation imposes constraints on its wide adoption in clinical studies. Third, the low concentration and poor ionization of N-glycopeptides make their signals more susceptible to suppression in MS analysis. To address these issues, we developed an integrated workflow applying magnetic graphene-oxide (mGO) nanomaterial enrichment and an in silico predicted spectral library for low-abundance plasma proteome identification, along with tandem enrichment using hydrophilic interaction liquid chromatography (HILIC) for sensitive plasma N-glycoproteome profiling. In this way, 4538 plasma proteins were obtained in a single DIA-MS analysis using a QE-HF mass spectrometer, 10 times more than those obtained from direct analysis of neat plasma. Further HILIC enrichment of the mGO products enabled the identification of 7986 intact N-glycopeptides from 626 proteins with concentrations as low as the nanogram per liter range. Notably, 58.34% of these N-glycopeptides were undetectable by direct HILIC enrichment from neat plasma, highlighting the advantage of applying tandem enrichment.

DOI: https://doi.org/10.1021/acs.analchem.5c02002
bioRxiv. 2025 May 10. pii: 2025.05.09.653205. [Epub ahead of print]

Cancer cells differentially modulate mitochondrial respiration to alter redox state and enable biomass synthesis in nutrient-limited environments.

Sarah M Chang, Muhammad Bin Munim, Sonia E Trojan, Anna Shevzov-Zebrun, Keene L Abbott, Matthew G Vander Heiden.

The cell NAD+/NADH ratio can constrain biomass synthesis and influence proliferation in nutrient-limited environments. However, which cell processes regulate the NAD+/NADH ratio is not known. Here, we find that some cancer cells elevate the NAD+/NADH ratio in response to serine deprivation by increasing mitochondrial respiration. Cancer cells that elevate mitochondrial respiration have higher serine production and proliferation in serine limiting conditions than cells with no mitochondrial respiration response, independent of serine synthesis enzyme expression. Increases in mitochondrial respiration and the NAD+/NADH ratio promote serine synthesis regardless of whether serine is environmentally limiting. Lipid deprivation can also increase the NAD+/NADH ratio via mitochondrial respiration in some cells, including cells that do not increase respiration following serine deprivation. Thus, in cancer cells where lipid depletion raises the NAD+/NADH ratio, proliferation in serine depleted environments improves when lipids are also depleted. Taken together, these data suggest that changes in mitochondrial respiration in response to nutrient deprivation can influence the NAD+/NADH ratio in a cell-specific manner to impact oxidative biomass synthesis and proliferation. Given the complexity of tumor microenvironments, this work provides a metabolic framework for understanding how levels of more than one environmental nutrient affects cancer cell proliferation.

DOI: https://doi.org/10.1101/2025.05.09.653205
bioRxiv. 2025 May 01. pii: 2025.04.28.650893. [Epub ahead of print]

LipidCruncher: An open-source web application for processing, visualizing, and analyzing lipidomic data.

Abdi Hamed, Yohannes A Ambaw, Chandramohan Chitraju, Shubham Singh, Zon Weng Lai, Robert V Farese, Tobias C Walther.

Advances in mass spectrometry (MS)-based lipidomics have led to a surge in data volume, underscoring a need for robust tools to evaluate and visualize these data comprehensively. Current workflows are often hampered by manual spreadsheet handling and insufficient assessment of data quality prior to analysis. Here, we introduce LipidCruncher , an open-source, web-based platform designed to process, visualize, and analyze lipidomic data with high efficiency and rigor. LipidCruncher consolidates key steps of the workflow, including data standardization, normalization, and stringent quality control to identify anomalies. The platform also provides advanced visualization and analysis tools, such as volcano plots, lipid saturation profiles, pathway mapping, and lipid heatmaps, that enable detailed and holistic data exploration. To demonstrate LipidCruncher 's utility, we analyzed lipidomic data from adipose tissue of mice lacking the triacylglycerol synthesis enzymes DGAT1 and DGAT2. We anticipate that LipidCruncher will be a valuable and user-friendly tool for standardizing and analyzing lipidomics data.

DOI: https://doi.org/10.1101/2025.04.28.650893
bioRxiv. 2025 Jun 13. pii: 2025.06.12.659347. [Epub ahead of print]

EndoGenius: Enabling comprehensive identification and quantitation of endogenous peptides.

Lauren Fields, Tina C Dang, Mitchell Gray, Satirtha S Protya, Lingjun Li.

Summary: The investigation of endogenous peptides, specifically with respect to neuropeptides, from mass spectrometry data is rife with bioinformatics bottlenecks, stemming from the low in vivo abundance of these analytes, increased susceptibility to degradation, and an immense search space of possible peptides. To address this, we present EndoGenius in its expanded form, strategically designed to optimize the searching for these endogenous peptides complemented with a pipeline designed for tasks including quantitation, spectral library building, motif extraction, and usage with data-independent acquisition workflows.
Availability and Implementation: EndoGenius is released as an open-source software package under an MIT License. The EndoGenius package with a user interface can be installed from https://www.lilabs.org/resources . The source code for EndoGenius can be accessed at https://github.com/lingjunli-research/EndoGenius-v2.0 .
Contact: Lingjun.Li@wisc.edu.

DOI: https://doi.org/10.1101/2025.06.12.659347
ACS Pharmacol Transl Sci. 2025 Jul 11. 8(7): 1891-1918

Lipids: Driving Forces in the Underlying Biology of Carcinogenesis.

Sohini Chakraborty, Anudarshini Sathish Kumar, Satarupa Banerjee.

  Lipids, mainly composed of cholesterol, phospholipids, sphingolipids, triacylglycerides, and fatty acids, have vital functions within cells. Some lipids function as signaling molecules or secondary messengers and are cellular membranes' energy sources and structural elements. More research is being conducted on metabolic reprogramming as a hallmark of cancer. However, compared with the metabolism of glucose or glutamine, lipid metabolism in cancer has received less attention. There is increasing evidence that certain parts of the lipid metabolism are altered in cancer cells. The alterations could influence the quantity of lipids involved in signaling functions, affect the synthesis and breakdown of lipids necessary for maintaining energy homeostasis, and modify the availability of structural lipids critical for membrane formation. The term "lipid metabolic reprogramming" refers to modifications in the lipid metabolism that can impact cellular processes such as cell division, growth, proliferation, and the cell cycle, ultimately resulting in cancer. Furthermore, interactions between cancer cells and nearby immune cells via an altered lipid metabolism promote the development and spread of tumors. The most recent studies on the involvement of lipid metabolism in different cancers and associated hallmarks and lipids in various aspects of cancer therapeutics, which affect multiple facets of tumorigenesis, are described in this review.

Keywords:  and; cancer hallmarks; lipid droplets and rafts; lipid reprogramming; lipogenic factors; therapeutics; treatment resistance

DOI:  https://doi.org/10.1021/acsptsci.5c00170
Methods Mol Biol. 2025 ;2934 105-124

Protease Specificity Profiling Using Synthetic Combinatorial Peptide Libraries and Mass Spectrometry.

Bart Claushuis, Robert A Cordfunke, Arnoud H de Ru, Peter A van Veelen, Jeroen Corver, Jan W Drijfhout, Paul J Hensbergen.

  The substrate specificity of proteases largely determines their function and biological role. Methods that can profile a protease's specificity with high detail are valuable tools for protease research. Synthetic combinatorial peptide libraries offer a large array of potential substrates that can be tested in a single experiment. Here, we describe the design and synthesis of a combinatorial peptide library using the one-bead-one-compound approach, followed by treatment with a protease, i.e., Pro-Pro endopeptidase (PPEP), and subsequent LC-MS/MS and data analysis.

Keywords:  Bacterial adhesion; LC-MS/MS; Metalloprotease; PPEP; Pro-Pro endopeptidase; Protease

DOI:  https://doi.org/10.1007/978-1-0716-4578-9_8
bioRxiv. 2025 Jun 17. pii: 2025.06.16.659960. [Epub ahead of print]

Proximity Labeling of NIMA Kinase Complex Components in C. elegans.

David S Fay, Boopathi Balasubramaniam, Sean M Harrington, Philip T Eden.

Proximity labeling has emerged as a powerful approach for identifying protein-protein interactions within living systems, particularly those involving weak or transient associations. Here, we present a comprehensive proximity labeling study of five conserved Caenorhabditis elegans proteins-NEKL-2, NEKL-3, MLT-2, MLT-3, and MLT-4-that form two NEKL-MLT kinase-scaffold subcomplexes involved in membrane trafficking and actin regulation. Using endogenously expressed TurboID fusions and a data-independent acquisition (DIA) mass spectrometry (MS) pipeline, we profiled NEKL-MLT interactomes across 23 experiments, including several methodological variations, applying stringent controls and filtering strategies. By analyzing and comparing experimental outcomes, we develop a set of intuitive quantitative metrics to assess experimental outcomes and quality. We demonstrate that DIA-based workflows produce sensitive physiologically relevant findings, even in the presence of experimental noise and variability across biological replicates. Our approach is validated through the identification of known NEKL-MLT binding partners and conserved genetic suppressors of nekl-mlt mutant phenotypes. Gene ontology enrichment further supports the involvement of newly identified NEKL-MLT interactors in processes including membrane trafficking, cytoskeletal regulation, and cell adhesion. Additionally, several novel proximate interactors were functionally validated using genetic assays. Our findings underscore the utility of DIA-MS in proximity labeling applications and highlight the value of incorporating internal controls, quantitative metrics, and biological validation to enhance confidence in candidate interactors. Overall, this study provides a scalable, organismal-level strategy for probing endogenous protein networks and offers practical guidelines for proximity labeling in multicellular systems.

DOI: https://doi.org/10.1101/2025.06.16.659960
BMC Bioinformatics. 2025 Jul 11. 26(1): 174

An evaluation methodology for machine learning-based tandem mass spectra similarity prediction.

Michael Strobel, Alberto Gil-de-la-Fuente, Mohammad Reza Zare Shahneh, Yasin El Abiead, Roman Bushuiev, Anton Bushuiev, Tomáš Pluskal, Mingxun Wang.

   BACKGROUND: Untargeted tandem mass spectrometry serves as a scalable solution for the organization of small molecules. One of the most prevalent techniques for analyzing the acquired tandem mass spectrometry data (MS/MS) - called molecular networking - organizes and visualizes putatively structurally related compounds. However, a key bottleneck of this approach is the comparison of MS/MS spectra used to identify nearby structural neighbors. Machine learning (ML) approaches have emerged as a promising technique to predict structural similarity from MS/MS that may surpass the current state-of-the-art algorithmic methods. However, the comparison between these different ML methods remains a challenge because there is a lack of standardization to benchmark, evaluate, and compare MS/MS similarity methods, and there are no methods that address data leakage between training and test data in order to analyze model generalizability.
RESULT: In this work, we present the creation of a new evaluation methodology using a train/test split that allows for the evaluation of machine learning models at varying degrees of structural similarity between training and test sets. We also introduce a training and evaluation framework that measures prediction accuracy on domain-inspired annotation and retrieval metrics designed to mirror real-world applications. We further show how two alternative training methods that leverage MS specific insights (e.g., similar instrumentation, collision energy, adduct) affect method performance and demonstrate the orthogonality of the proposed metrics. We especially highlight the role that collision energy plays in prediction errors. Finally, we release a continually updated version of our dataset online along with our data cleaning and splitting pipelines for community use.
CONCLUSION: It is our hope that this benchmark will serve as the basis of development for future machine learning approaches in MS/MS similarity and facilitate comparison between models. We anticipate that the introduced set of evaluation metrics allows for a better reflection of practical performance.

Keywords:  Benchmark; Machine learning; Mass spectrometry; Metabolomics; Spectral similarity measure

DOI:  https://doi.org/10.1186/s12859-025-06194-1
J Chromatogr B Analyt Technol Biomed Life Sci. 2025 Jul 09. pii: S1570-0232(25)00286-7. [Epub ahead of print]1264 124732

Targeted metabolomics for cardiovascular disease: Validation of a high-throughput HPLC-MS/MS assay.

Sabina N Baskhanova, Natalia E Moskaleva, Ksenia M Shestakova, Maria V Kozhevnikova, Ekaterina O Korobkova, Victor M Samoylov, Svetlana A Appolonova.

  Cardiovascular diseases (CVD) remain a leading cause of mortality worldwide, necessitating innovative diagnostic tools to improve early detection and management. This study represents the optimization and validation of a high-throughput HPLC-MS/MS method for simultaneous quantification of 98 metabolites in human plasma, including amino acids and its derivatives (n = 29), tryptophan pathway metabolites (n = 17), nucleosides (n = 4), water-soluble vitamins (n = 3), acylcarnitines (n = 39), and others (n = 6). The method utilizes chemical derivatization to enhance retention and sensitivity of polar metabolites providing accurate analysis across diverse physicochemical properties. The presented method was validated in accordance with EMA guidelines and included assessment of linearity, accuracy, precision, matrix effects, recovery, and stability. Parallelism testing confirmed the suitability of a surrogate matrix for calibration. The method was applied for the analysis of plasma samples from 399 patients with cardiovascular diseases and 75 healthy controls, revealing significant metabolic alterations in pathways associated with inflammation, nitric oxide metabolism, and mitochondrial function. The presented comprehensive approach may serve as a rapid screening method for the identification of selective CVD biomarkers using targeted metabolomic profiling.

Keywords:  Cardiovascular diseases; Liquid chromatography - tandem mass spectrometry (LC-MS/MS); Metabolomics; Validation

DOI:  https://doi.org/10.1016/j.jchromb.2025.124732
bioRxiv. 2025 May 11. pii: 2025.05.07.652782. [Epub ahead of print]

Constructing a consensus serum metabolome.

Yuanye Chi, Joshua M Mitchell, Maheshwor Thapa, Shujian Zheng, Zackary Frohock, Yi Li, Aleksandr Smirnov, Xiuxia Du, Shuzhao Li.

Blood analysis is the most common in biomedical applications and a reference metabolome will be critical for effective annotation and for guiding scientific investigations. However, compiling such a reference is hindered by many technical challenges, despite the availability of large amount of metabolomics data today. Based on a new set of data structures and tools, we have assembled a consensus serum metabolome (CSM) from over 100,000 mass spectrometry acquisitions of more than 200 million spectra. This provides a comprehensive survey of human blood chemistry, revealing the frequency dependent nature of metabolome and exposome. Major gaps are found between CSM and the current databases. The CSM enables community-level data alignment and significantly improves annotation quality of LC-MS metabolomics.
Highlights: A reference of human biochemistry linked to observation frequencyMajor gaps revealed in current databases and experimental methodsEnabling cross-laboratory, cross-platform data alignmentAccelerated and cumulative metabolite annotation.

DOI: https://doi.org/10.1101/2025.05.07.652782
J Proteome Res. 2025 Jul 18.

Evaluation of the False Discovery Rate in Library-Free Search by DIA-NN Using In Vitro Human Proteome.

Kongxin Gu, Masanaga Kenko, Koji Ogawa, Naoki Goshima, Takeshi Masuda, Shingo Ito, Sumio Ohtsuki.

  Recently, deep-learning-based in silico spectral libraries have gained increasing attention. Several data-independent acquisition (DIA) software tools have integrated this feature, known as a library-free search, thereby making DIA analysis more accessible. However, controlling the false discovery rate (FDR) is challenging owing to the vast amount of peptide information in in silico libraries. In this study, we introduced a stringent method to evaluate FDR control using DIA software. Recombinant proteins were synthesized from full-length human cDNA libraries and analyzed by using liquid chromatography-mass spectrometry and DIA software. The results were compared with known protein sequences to calculate the FDR. Notably, we compared the identification performance of DIA-NN versions 1.8.1, 1.9.2, and 2.1.0. Versions 1.9.2 and 2.10 identified more peptides than version 1.8.1, and versions 1.9.2 and 2.1.0 used a more conservative identification approach, thus significantly improving the FDR control. Across the synthesized recombinant protein mixtures, the average FDR at the precursor level was 0.538% for version 1.8.1, 0.389% for version 1.9.2, and 0.385% for version 2.1.0; at the protein level, the FDRs were 2.85%, 1.81%, and 1.81%, respectively. Collectively, our data set provides valuable insights for comparing FDR controls across DIA software and aiding bioinformaticians in enhancing their tools.

Keywords:  DIA-NN; data-independent acquisition; false discovery rate; in vitro human proteome; software comparison; synthesized recombinant protein mixture

DOI:  https://doi.org/10.1021/acs.jproteome.5c00036
J Chromatogr B Analyt Technol Biomed Life Sci. 2025 Jul 05. pii: S1570-0232(25)00275-2. [Epub ahead of print]1264 124721

PeakClimber: A software tool for the accurate quantification of complex HPLC chromatograms.

Joshua T Derrick, Pragney Deme, Norman J Haughey, Steven A Farber, William B Ludington.

  High-performance liquid chromatography (HPLC) is a common medium-throughput technique to quantify the components of complex mixtures like those typically obtained from biological tissue extracts. However, analysis of HPLC data from multianalyte samples is hampered by a lack of tools to accurately determine the precise analyte quantities on a level of precision equivalent to mass spectrometry approaches. To address this problem, we developed a tool we call PeakClimber that uses a sum of bidirectional exponentially modified Gaussian (BEMG) functions to accurately deconvolve overlapping, multianalyte peaks in HPLC traces. Here we show that HPLC peaks are well-fit by a BEMG function, that PeakClimber more accurately quantifies known peak areas than standard industry software and other open-source software packages for HPLC, and that PeakClimber accurately quantifies differences in triglyceride abundances between colonized and germ-free fruit flies.

Keywords:  Algorithm; Bidirectional; Exponentially modified Gaussian; Fatty acids; HPLC; Lipids; Software; drosophila

DOI:  https://doi.org/10.1016/j.jchromb.2025.124721
bioRxiv. 2025 Jul 07. pii: 2025.06.24.661372. [Epub ahead of print]

Deep and Quantitative Proteomic Profiling of Low Volume Mouse Serum Across the Lifespan.

SLAM investigators

Assessing and validating circulating biomarkers is essential for the development of pre-clinical biomarkers that predict biological aging and aging-phenotypes in mice. However, comprehensive proteomics of serum, especially in longitudinal mouse studies, are limited by low volumes of samples. In this study, we develop a workflow for comprehensive and quantitative proteomic analysis of low volume mouse serum and demonstrate its utility and performance in identifying and evaluating key associations with aging phenotypes. Notably, a nanoparticle (NP)-based serum processing workflow coupled to mass spectrometry (MS) increases proteomic coverage by 3 to 6-fold across a range of volumes and provides a quantitative and reproducible (CV < 10%) pipeline for NP-based studies. In a study of 30 mice (aged 12, 24, and 30 months), we uncovered 3992 protein groups across all samples (2235 on average) in 20 µL of serum and highlight novel insights into aging-associated changes in serum and associations with glucose and body composition. With 1 µL additional serum, a 48-cytokine assay quantified 39 additional proteins not identified by MS. This study establishes a powerful workflow that enables deep quantitative proteomics of biologically relevant proteins in volumes feasibly obtained from mice (21 µL of serum) and presents fundamental insights into the aging serum proteome.

DOI: https://doi.org/10.1101/2025.06.24.661372
bioRxiv. 2025 Jun 17. pii: 2025.06.13.659540. [Epub ahead of print]

Ketone body driven lipid remodeling supports thermogenic adaptation to fasting.

Zahra Moazzami, Eric D Queathem, Jiawen Ma, Qian Chen, Rini Arianti, Ifrah Aden, Anneliese Brown, Zamzam Abdulahi, Chenxin Gu, Xuechun Chang, Yin Li, Haidong Yu, Alessandro Bartolomucci, Satchidananda Panda, Douglas G Mashek, Endre Kristof, Patrycja Puchalska, Lisa S Chow, Zhihao Jia, Peter A Crawford, Hai-Bin Ruan.

Metabolic adaptation to fasting may have conferred survival advantage to early humans and predicts weight gain caused by overnutrition in modern societies. Fasting suppresses brown adipose tissue (BAT) thermogenesis; however, it is unclear how BAT rewires cellular metabolism to balance between energy conservation and heat generation. Here, we report that BAT in mice under fasting and cold challenge consumed ketone bodies, specifically acetoacetate (AcAc). Ablating liver ketogenesis decreased, while enhancing hepatic AcAc output defended, body temperature in mice facing the dual challenge. Using stable isotope tracing in brown adipocytes in vitro combined with quantitative analysis of metabolic fluxes and lipidomics in BAT from genetic mouse models, we disentangled the two metabolic fates of AcAc - terminal oxidation in the mitochondria and lipid biosynthesis in the cytosol. Notably, AcAc-sourced carbon preferentially supported polyunsaturated fatty acid synthesis in BAT, linking to the positive impact of intermittent fasting on lipid profiles in both mice and humans. Therefore, ketone body utilization by thermogenic adipocytes contributes to metabolic resilience of mammals and can be targeted to optimize benefits of dietary regimens.

DOI: https://doi.org/10.1101/2025.06.13.659540
Molecules. 2025 Jun 30. pii: 2828. [Epub ahead of print]30(13):

Structural and Quantitative Analysis of Polyfluoroalkyl Substances (PFASs) and Para-Phenylenediamines (PPDs) by Direct Analysis in Real Time Ion Mobility Mass Spectrometry (DART-IM-MS).

Calum Bochenek, Jack Edwards, Zhibo Liu, Chrys Wesdemiotis.

  Polyfluoroalkyl substances (PFASs) and para-phenylenediamines (PPDs) are emerging classes of anthropogenic contaminants that are environmentally persistent (most often found in ground and surface water sources), bioaccumulative, and harmful to human health. These chemicals are currently regulated in the US by the Environmental Protection Agency (EPA), the Food and Drug Administration (FDA), and the Occupational Safety and Health Administration (OSHA). Analysis of these contaminants is currently spearheaded by mass spectrometry (MS) coupled to liquid chromatography (LC) because of their high sensitivity and separation capabilities. Although effective, a major flaw in LC-MS analysis is its large consumption of solvents and the amount of time required for each experiment. Direct analysis in real time mass spectrometry (DART-MS) is a new technique that offers high sensitivity and permits rapid analysis with little to no sample preparation. Herein, we present the qualitative and quantitative analysis of PFASs and PPDs by high-resolution DART-MS, interfaced with ion mobility (IM) and tandem mass spectrometry (MS/MS) characterization, demonstrating the utility of this multidimensional approach for the fast separation and detection of environmental contaminants.

Keywords:  DART ionization; PFAS; PPD; ion mobility; mass spectrometry

DOI:  https://doi.org/10.3390/molecules30132828
Anal Chem. 2025 Jul 17.

Mass Spectrometry Imaging.

Aljoscha Körber, Ian G M Anthony, Ron M A Heeren.

Mass spectrometry imaging (MSI) maps the spatial distributions of chemicals on chemically complex surfaces. MSI offers unrivaled sensitivity and information density with each pixel comprising a mass spectrum. Over the past three decades, numerous technological developments have enabled MSI to evolve into a mainstream technique for untargeted molecular and elemental imaging with wide-spread applications ranging from material analysis to life sciences and clinical diagnostics. Here, we review the field of MSI with a focus on key technological advancements. We examine different image acquisition modes and the most popular ionization methods in MSI, including matrix-assisted laser desorption/ionization (MALDI), laser ablation inductively coupled plasma (LA-ICP), laser ablation electrospray ionization (LAESI), secondary ion mass spectrometry (SIMS), and desorption electrospray ionization (DESI). For each method, we discuss figures of merit, such as spatial resolving power and sensitivity, the ionization mechanism, sample preparation, advantages, and disadvantages, including ways to overcome them wherever applicable. We subsequently discuss more aspects of MSI instrumentation, such as commonly used mass analyzers, tandem mass spectrometry, ion mobility, and advancements in imaging throughput. Based on these technological developments, targeted MSI strategies are explained, including imaging mass cytometry (IMC), multiplexed ion beam imaging (MIBI), and stable isotope labeling (SIL), as well as approaches for multimodal imaging. Last, we present selected application examples of MSI in cancer research, single cell analysis, and drug distribution studies. We target this review to provide researchers with an interest in recent developments in MSI with a concise technological understanding of the different main approaches to MSI.

DOI: https://doi.org/10.1021/acs.analchem.4c05249