bims-mascan 2026-10-04 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–10–04
fourteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Scalable clinical proteomics from DNA/RNA extraction flowthroughs enables integrated proteogenomic profiling from a single cancer biopsy.
Emerging Roles of Mass Spectrometry-Based Proteomics in Drug Discovery.
Sub-3 μm porous graphitic carbon for highly polar metabolomics: orthogonal selectivity and comparison with HILIC and reversed-phase chromatography.
Integrating Narrow-Window DIA with AI-Powered Search Enables Deep and Reliable Functional Proteomics.
SpotOn: A Tool to Enhance Echo Acoustic Standard Deposition for Quantitative Mass Spectrometry Imaging.
A phosphoproteome atlas of human cell lines reveals the landscape of kinase activity.
MS-FGNet: An Attention-Guided Deep Learning Framework for Fine-Grained Pathological Subtype Prediction from DIA Proteomics Imaging.
Efferocytosis triggered proteomic reprogramming of macrophages reveals CD97 as a novel apoptotic receptor.
A DOE-optimized supercritical fluid chromatography-mass spectrometry workflow complementing liquid chromatography for comprehensive profiling of Trichoderma reesei specialized metabolism.
X-Align: annotation-independent cross-platform alignment of untargeted metabolomics features.
Metabolic reprogramming in adenomyosis: a systematic review of metabolomics studies.
Glutamine Metabolism in Health and Diseases.
Protein digest extraction strategies improve the identification of lipidated peptides in bottom-up proteomics.
Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times.

Clin Proteomics. 2026 Sep 20. pii: 45. [Epub ahead of print]23(1):

Scalable clinical proteomics from DNA/RNA extraction flowthroughs enables integrated proteogenomic profiling from a single cancer biopsy.

Filip Mundt, Annelaura Bach Nielsen, Juanjuan Wang, Josephine Kerzel Duel, Christina Westmose Yde, Martina Amnitzbøll Eriksen, Ulrik Lassen, Finn Cilius Nielsen, Kristoffer Rohrberg, Matthias Mann.

BACKGROUND: Precision oncology has largely relied on genomic and transcriptomic sequencing to guide therapy selection in patients with advanced cancer. However, these molecular layers do not fully capture the functional state of tumors, as proteins largely represent the active effectors of cellular processes and signaling pathways. Integrating proteomic and phosphoproteomic measurements with genomic data has therefore emerged as an important objective in translational oncology. In routine clinical workflows, tumor biopsies are frequently processed using DNA/RNA extraction kits such as the Qiagen AllPrep system, which generates a protein-rich flowthrough that is typically discarded.
METHODS: Here we systematically evaluated whether the residual protein fraction generated during Qiagen AllPrep DNA/RNA extraction can serve as a reliable source for mass spectrometry-based proteomics in clinical tumor biopsies. Within the Copenhagen Prospective Personalized Oncology (CoPPO) precision oncology trial, residual protein fractions from metastatic cancer biopsies were recovered, precipitated, and processed using optimized workflows for liquid chromatography-mass spectrometry. We established procedures for protein recovery, digestion, and peptide cleanup, and evaluated multiple acquisition strategies including data-dependent acquisition (DDA) and data-independent acquisition (DIA). We further assessed sample stability during long-term storage, analytical reproducibility, scalability, and compatibility with phosphoproteomics.
RESULTS: Optimized single-shot workflows consistently generated deep proteomes from AllPrep-derived biopsy material, quantifying more than 10,000 protein groups in DIA analyses. Protein integrity was preserved in samples stored at - 80 °C for up to five years. The resulting proteomes retained tissue-associated biological signatures and quantified protein products corresponding to approximately 71% of genes represented on the FoundationOne® CDx panel. Phosphoproteomic analyses identified more than 10,000 phosphorylation sites associated with key oncogenic signaling pathways including AKT1, BRAF, and EGFR. Implementation of short liquid chromatography gradients enabled high-throughput analysis of up to 60 proteomes per day without compromising data quality.
CONCLUSIONS: Residual protein fractions from Qiagen AllPrep DNA/RNA extraction provide a scalable resource for deep proteomic and phosphoproteomic profiling from the same biopsy used for genomic and transcriptomic analyses. This workflow enables integrated proteogenomic characterization from a single clinical sample and provides a framework for integrated proteogenomic studies in precision oncology.

DOI: https://doi.org/10.1186/s12014-026-09632-1
Annu Rev Pharmacol Toxicol. 2026 Oct 01.

Emerging Roles of Mass Spectrometry-Based Proteomics in Drug Discovery.

Ka Yang, Weiping Tang, Taosheng Chen, Junmin Peng.

Mass spectrometry (MS)-based proteomics has emerged as a versatile platform in modern drug discovery, enabling system-wide interrogation of protein abundance, posttranslational modifications (PTMs), turnover, structural stability, and interactions with small molecules in native biological contexts. In this review, we introduce recent advances in MS and summarize how scalable, high-throughput technologies support target identification and mechanism-of-action studies. We discuss global profiling of the proteome and PTMs under drug perturbation, as well as proteome-wide stability assays such as thermal proteome profiling and limited proteolysis-MS. We further discuss activity-based protein profiling, fully functionalized and photoaffinity probes, and affinity-matrix approaches that expand covalent and noncovalent ligand discovery. Finally, we highlight multiple proteomics-driven strategies in targeted protein degradation. Together, these integrated MS-based approaches in drug discovery, often termed chemoproteomics, provide a coherent framework for linking chemical perturbations to functional proteome remodeling, expanding the druggable proteome and accelerating therapeutic development.

DOI: https://doi.org/10.1146/annurev-pharmtox-060325-040513
Anal Bioanal Chem. 2026 Oct 02.

Sub-3 μm porous graphitic carbon for highly polar metabolomics: orthogonal selectivity and comparison with HILIC and reversed-phase chromatography.

Danila La Gioia, Emanuela Salviati, Fabrizio Merciai, Vicky Caponigro, Vincenzo Vestuto, Carlo Crescenzi, Pietro Campiglia, Eduardo Maria Sommella.

  The analysis of the polar metabolome remains a major challenge in LC-MS. Beyond established hydrophilic interaction liquid chromatography (HILIC) and reversed-phase (RP) workflows, alternative and orthogonal chromatographic approaches are required to improve the detection and quantification of compounds with low logP values and structurally related isomers. In this study, we evaluated the performance of sub-3 µm porous graphitic carbon (PGC) stationary phase and benchmarked it against state-of-the-art HILIC and RP approaches. Flow rate, column temperature, organic modifier, mobile-phase composition, and pH were optimized using a representative panel of highly polar endogenous metabolites. The three chromatographic modes exhibited markedly complementary selectivity profiles. HILIC provided the broadest coverage of nucleotides and related metabolites, whereas PGC showed enhanced performance for sugar phosphates, polyamines, and, particularly, tricarboxylic acid (TCA) cycle intermediates. Focused optimization for TCA cycle metabolites improved peak shape and signal-to-noise ratio and enabled the separation of citrate and isocitrate, outperforming both HILIC and reversed-phase chromatography for the detection of this metabolite class under the investigated conditions. In mouse liver extracts, PGC detected a complementary subset of metabolites not observed using HILIC or reversed-phase chromatography, confirming its distinctive selectivity in a complex biological matrix. As a proof of concept, the optimized PGC method was applied to lipopolysaccharide-stimulated macrophages using a hybrid workflow combining multiplexed parallel reaction monitoring for the absolute quantification of TCA cycle intermediates with data-dependent acquisition for global metabolic profiling. The workflow captured the characteristic metabolic reprogramming associated with macrophage inflammatory activation. Overall, sub-3 µm PGC represents a valuable orthogonal chromatographic platform for extending analytical coverage and improving the characterization of highly polar metabolites.

Keywords:  HILIC; Metabolomics; Polar metabolites; Porous graphitic carbon; Tricarboxylic acids

DOI:  https://doi.org/10.1007/s00216-026-06846-y
bioRxiv. 2026 Sep 10. pii: 2026.09.04.749476. [Epub ahead of print]

Integrating Narrow-Window DIA with AI-Powered Search Enables Deep and Reliable Functional Proteomics.

Yun Xiong, Dejan Stepec, Huimin Zhang, Lin Tan, Bo Wei, Maximilien Burq, John N Weinstein, Peter Cimermancic, Philip L Lorenzi.

Narrow-window data-independent acquisition (nDIA) is emerging as a powerful technique for bottom-up proteomics. Here, we systematically benchmarked nDIA, wide-window DIA (wDIA), narrow-window data-dependent acquisition (nDDA), and wide-window DDA (wDDA) for rapid, single-shot proteomic analysis. For data processing, we introduced Tesorai Search, a new search engine leveraging a large pre-trained model and compared it with DIA-NN and FragPipe across both DIA and DDA datasets. Among 12 acquisition-analysis pipelines evaluated, nDIA combined with DIA-NN and Tesorai Search delivered the highest proteome coverage, identifying 10,255 and 10,766 protein groups from benchmark samples, respectively. Both search engines maintained rigorous false-discovery rate (FDR) control. While nDIA generally outperformed nDDA in sensitivity, FragPipe-DDA+ approach proved to be the most sensitive within the nDDA pipelines. However, entrapment analyses indicate that this sensitivity comes at the cost of less robust FDR control compared to Tesorai Search. As a proof of concept, we applied nDIA-MS to 17 cancer cell lines harboring DNA damage response (DDR) gene knockouts, successfully detecting significant downregulation of all targeted proteins and uncovering 81 DDR-related proteins modulated in at least one cell line. These results underscore nDIA-MS, together with DIA-NN and Tesorai Search, as a robust and scalable platform for high-throughput functional proteomic screening.

DOI: https://doi.org/10.64898/2026.09.04.749476
Chem Biomed Imaging. 2026 Sep 28. 4(9): 2119-2125

SpotOn: A Tool to Enhance Echo Acoustic Standard Deposition for Quantitative Mass Spectrometry Imaging.

Emily R Sekera, Nathaniel Twarog, Rebekah DeVries, P Jake Slavish, John J Bowling, Anang A Shelat.

  The quantitative evaluation of therapeutics in the drug discovery pipeline is essential for elucidating their pharmacokinetic and pharmacodynamic profiles within complex biological systems. Traditionally, autoradiography and liquid chromatography-mass spectrometry (LC-MS) have been employed for such analyses. However, matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is emerging as a promising alternative, offering a more rapid untargeted workflow. Quantitative MALDI-MSI (qMSI) relies on accurate calibration to achieve robust and reproducible quantification of analytes. Here, we present an open-source software tool, SpotOn, designed to facilitate the discrete deposition of low-volume calibration standards onto glass slides or tissue sections. By enabling precise placement and significantly reducing the volume of standards required, our tool affords the generation of more calibration points across a wider dynamic range, thereby improving the robustness and accuracy of quantitative analyses. Software was validated using JQ1 on both glass slides and murine liver tissue. The resulting calibration curves demonstrated strong linearity, with coefficients of determination (R 2) exceeding 0.92. This tool streamlines the qMSI workflow, conserves valuable reagents, and enhances analytical reliability, making it a helpful resource.

Keywords:  MALDI; acoustic deposition; image analysis; mass spectrometry imaging; quantitation; software

DOI:  https://doi.org/10.1021/cbmi.6c00010
Nat Struct Mol Biol. 2026 Sep 30.

A phosphoproteome atlas of human cell lines reveals the landscape of kinase activity.

Claire Koenig, Hayoung Cho, Kristina B Emdal, Ilaria Piga, Pierre Sabatier, Samuel Lozano-Juárez, Ana Martinez-Val, Jesper V Olsen.

Protein phosphorylation orchestrates cellular signaling and controls most biological processes, with its dysregulation driving diseases, notably cancer. Comprehensive, high-throughput phosphoproteomics remains limited by detection sensitivity, data completeness and computational bottlenecks, especially in low-input settings. Here we present a comprehensive empirical human phosphoproteome resource, regrouping over 200,000 class I phosphosites across 33 diverse human cell lines. We demonstrate that this spectral library dramatically improves single-shot phosphoproteomics with 30-fold faster data processing compared with library-free approaches and enhances confidence in phosphosite localization even from minimal sample input. Integrating proteome and phosphoproteome data, we develop a combined kinase activity score (Cscore), revealing cell line- and cancer-specific signaling vulnerabilities, many correlating with drug sensitivity. This resource accelerates deep and reproducible phosphoproteomics, enables the systematic mapping of cellular signaling networks and may empower precision oncology by highlighting actionable kinase targets in diverse cell states.

DOI: https://doi.org/10.1038/s41594-026-01877-6
J Imaging Inform Med. 2026 Sep 29.

MS-FGNet: An Attention-Guided Deep Learning Framework for Fine-Grained Pathological Subtype Prediction from DIA Proteomics Imaging.

Yarong Ji, Jinze Huang, Shuo Sun, Dong An, Xiang Fang, Yang Zhao.

  Proteomics plays a vital role in precision medicine, enabling biomarker discovery and pathological subtype prediction of complex diseases. Among analytical platforms, data-independent acquisition mass spectrometry (DIA-MS) offers high reproducibility and comprehensive proteomic coverage for rapid disease diagnosis. However, it is challenged by redundant signals and high similarity between different subtypes, making data interpretation more complicated. To address these challenges, we propose MS-FGNet, an attention-guided deep learning framework for fine-grained pathological subtype prediction based on DIA-MS pseudo imaging. Built upon the state-of-the-art DINOv3-pretrained ConvNeXt backbone, MS-FGNet introduces two innovations: (i) an attention-guided recursive feature encoding (ARE) strategy that refines DINOv3-derived representations to progressively locate disease-related spectral regions from noisy DIA-MS images, and capture multiscale feature representation through a global-to-local attention mechanism, (ii) a multibranch bilinear fusion (MBF) strategy to aggregate multibranch features and construct high-order, fine-grained fusion representations, thereby addressing the challenge of high inter-subtype similarity and enabling accurate pathological subtype prediction. When applied to 2006 human formalin-fixed paraffin-embedded samples, including follicular adenoma, multinodular goiter, follicular thyroid cancer, papillary thyroid cancer, and healthy control tissues, MS-FGNet accurately classified thyroid nodules into different pathological subtypes, achieving a mean accuracy of 90.01 ± 0.81% and a macro-averaged F1 score of 89.60 ± 0.46%. Importantly, identified spectral regions by the model effectively avoid redundant signals and exhibit variability among different phenotypes, enhancing clinical interpretability. Experimental results confirm that MS-FGNet not only performs well on thyroid DIA data but also generalizes effectively to kidney DDA data, demonstrating its potential adaptability across different data acquisition strategies.

Keywords:  Attention-guided recursive feature encoding; Bilinear fusion; DIA mass spectrometry; Deep learning; Fine-grained prediction; Pathological subtype prediction

DOI:  https://doi.org/10.1007/s10278-026-02266-7
Cell Death Differ. 2026 Sep 29.

Efferocytosis triggered proteomic reprogramming of macrophages reveals CD97 as a novel apoptotic receptor.

Benjamin Bernard Armando Raymond, Jasmin Novenstern, Abeer Dannoura, Meihan Meng, Andrew Michael Frey, Tamara Sneperger, Luigi Zechini, Alessandro Scopelliti, Will Wood, Matthias Trost.

During the daily process of healthy cellular turnover in the human body, billions of cells undergo apoptosis. These cells are removed by phagocytic cells, namely macrophages through a process known as efferocytosis, which triggers a cascade of reprogramming events in the cell, with a shift towards a pro-resolving or 'wound healing' phenotype. To date, no study has attempted to investigate these phenotypic changes from a proteomic perspective. Here, we present a novel and robust workflow for the investigation of proteome and secretome changes in bone marrow-derived macrophages (BMDMs) and alveolar macrophages following efferocytosis using stable isotope labelling by amino acids in cell culture (SILAC) combined with data-independent acquisition (DIA) mass spectrometry. Using this workflow, we dissected the mixed proteomes of BMDMs and apoptotic cells to map the reprogramming events occurring in macrophages in the later stages of efferocytosis. Specifically, we identified the adhesion G protein-coupled receptor Adgre5/CD97 as a novel efferocytosis-associated protein and showed that this evolutionary conserved protein plays a role in the uptake of apoptotic cells (ACs) by macrophages from mouse, human and fly. Additionally, we show that CD97 may act as a co-receptor for ACs with other efferocytic receptors such as MerTK. Our results provide an unprecedented view of the efferocytic landscape of macrophages and will aid in further understanding this important immunological process in the larger context of immune homeostasis and inflammatory disorders.

DOI: https://doi.org/10.1038/s41418-026-01882-8
J Chromatogr A. 2026 Sep 17. pii: S0021-9673(26)00775-2. [Epub ahead of print]1788 467449

A DOE-optimized supercritical fluid chromatography-mass spectrometry workflow complementing liquid chromatography for comprehensive profiling of Trichoderma reesei specialized metabolism.

Ilias El Ouar, Agnès Le Masle, Frédérique Bidard, Alexandra Berlioz-Barbier, David Touboul.

  Fungal secretomes are prolific sources of structurally diverse bioactive natural products, including peptaibols, lipopeptides, and polar metabolites. Their chemical complexity makes comprehensive untargeted profiling a persistent analytical challenge, particularly for highly polar compounds that are poorly retained in conventional reversed-phase liquid chromatography (RPLC). This study presents the development and optimization of a supercritical fluid chromatography (SFC) method coupled to high-resolution tandem mass spectrometry (HRMS/MS) for the untargeted metabolite profiling of a Trichoderma reesei secretome extract. The SFC method was systematically compared to an established RPLCHRMS/MS workflow to evaluate their respective analytical performances and chemical space coverage. RPLC demonstrated superior overall metabolite detectability, with 3726 features detected compared to 2301 in SFC, and excelled at resolving structurally related isomers, particularly within the peptaibol class where up to six isomers were baseline-separated. SFC, by contrast, provided complementary class-specific retention behavior and uniquely revealed highly polar amino sugar-like compounds alongside a series of 14-residue harzianine-like peptaibols that remained undetected under RPLC conditions. Molecular networking analysis confirmed that 43% of SFC-detected features were absent from RPLC datasets, demonstrating substantial orthogonality between the two separation modes. Neither approach alone was sufficient to capture the full chemical diversity of the secretome. These findings establish that integrating SFC and RPLC workflows substantially expands the accessible chemical space in fungal metabolomics and provides a robust framework for the discovery of bioactive natural products.

Keywords:  DOE; High-resolution mass spectrometry; Molecular network; Natural products; Reversed-phase liquid chromatography; Supercritical fluid chromatography

DOI:  https://doi.org/10.1016/j.chroma.2026.467449
Chem Sci. 2026 Sep 22.

X-Align: annotation-independent cross-platform alignment of untargeted metabolomics features.

Han Bao, Zengqi Yan, Bin Wang, Xinxin Wang, Xinjie Zhao, Chunxia Zhao, Wangshu Qin, Wenzhao Wang, Chang Liu, Xin Lu, Guowang Xu.

Massive untargeted metabolomics datasets are rapidly accumulating, but cross-study and cross-platform reuse remain limited by poor comparability. This limitation is exacerbated by the low rate of confident structural annotation, which restricts structure-based alignment to only a small fraction of detected metabolic features. Here, X-Align is presented, which is an annotation-independent framework for cross-platform alignment of untargeted metabolomics features. Its core component, Align-ID, is a residual attention-based deep regression model that learns reference chromatographic behavior directly from MS/MS (tandem mass spectrometry) spectrum embeddings and maps heterogeneous spectra onto a common reference chromatographic scale without prior structural identification. Align-ID is trained on 804 150 high-resolution MS/MS spectra associated with reference retention values in the METLIN SMRT chromatographic system. X-Align achieved 99.6% accuracy at 52.3% coverage in the controlled benchmark and maintained high accuracy in independent cross-platform and external library validations. X-Align produces a cross-center feature repository containing 1402 aligned features and 60 688 MS/MS spectra from public plasma datasets and supports more consistent biological interpretation across independent experiments in a cross-center high-fat diet case study. This work establishes MS/MS-derived reference retention behavior as a practical basis for annotation-independent comparison and integration of cross-platform untargeted metabolomics data.

DOI: https://doi.org/10.1039/d6sc04644d
Eur J Obstet Gynecol Reprod Biol. 2026 Sep 28. pii: S0301-2115(26)00533-6. [Epub ahead of print]327 115465

Metabolic reprogramming in adenomyosis: a systematic review of metabolomics studies.

Stefano Di Michele, Chiara Camoglio, Luigi Nappi, Raffaele Tinelli, Angelos Daniilidis, Grigoris Grimbizis, Stefano Angioni.

   PURPOSE: To synthesize current evidence on metabolomic alterations in women with adenomyosis and identify recurrent metabolic patterns that may clarify disease pathophysiology and support biomarker research.
METHODS: PubMed/MEDLINE, Scopus, Web of Science, and Embase were searched from inception to December 22, 2025. Eligible studies were peer-reviewed observational studies reporting targeted or untargeted metabolomics analyses, including lipidomics, in adult women with adenomyosis diagnosed by imaging or histopathology. Proteomics-only studies, animal studies, and in vitro studies were excluded. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Newcastle-Ottawa Scale or, where appropriate, the Joanna Briggs Institute Critical Appraisal Checklist. Metabolomics-specific analytical and reporting features were evaluated using a complementary predefined checklist, in which analytical robustness was classified as high, moderate, or low according to key criteria specific to targeted and untargeted metabolomics studies; the complete scoring rules and thresholds are reported in Online Resource 2. Owing to heterogeneity in biological matrices, analytical platforms, and reporting, findings were synthesized qualitatively by biochemical category.
RESULTS: Eleven studies comprising a total of 846 participants were included, most conducted in China and using mass spectrometry across different biological samples. Overall, findings indicate widespread metabolic alterations, particularly in lipid pathways, including glycerophospholipids, lysophospholipids, acylcarnitines, and sphingolipids. Alterations were also observed in amino acid metabolism, nucleotide-related compounds, and central energy pathways such as glycolysis and the tricarboxylic acid cycle. These patterns suggest enhanced lipid signaling, altered mitochondrial function and energy metabolism, inflammation, oxidative stress, and increased cellular proliferation. Several studies proposed discriminative metabolite panels, but none underwent external validation.
CONCLUSION: Adenomyosis is associated with broad metabolic reprogramming involving lipid, amino acid, nucleotide, and energy metabolism. These patterns may improve understanding of disease mechanisms and support future research on diagnostic biomarkers and therapeutic targets.
PROSPERO REGISTRATION: Registration date: January 8, 2026 Registration number: CRD420251271258 URL: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251271258.

Keywords:  Adenomyosis; Biomarkers; Lipidomics; Metabolites; Metabolomics

DOI:  https://doi.org/10.1016/j.ejogrb.2026.115465
MedComm (2020). 2026 Oct;7(10): e71035

Glutamine Metabolism in Health and Diseases.

Yi Ding, Fan Tong, Jing Zeng, Chun-Ping Cui.

  Glutamine, the most abundant nonessential amino acid in the blood and tissues, plays essential roles in cellular proliferation, immune regulation, acid-base homeostasis, and metabolic balance. Although traditionally classified as a nonessential amino acid, glutamine becomes conditionally essential under pathological conditions and physiological stress due to its critical role in supporting cellular adaptation. Under diverse pathological states, glutamine metabolism undergoes extensive reprogramming, and cancer cells exhibit a particularly high dependence on glutamine to sustain proliferation, redox balance, and biosynthetic demands. In this review, we summarize the multifaceted functions of glutamine metabolism in physiological and pathological processes. We first discuss the fundamental pathways of glutamine synthesis, transport, and utilization, followed by an overview of its regulatory roles in cellular responses to oxidative, nutritional, thermal, mechanical, DNA damage, and osmotic stresses. We further highlight the involvement of glutamine metabolism in cancer progression, immune regulation, metabolic plasticity, and therapeutic resistance. Finally, we summarize emerging glutamine-targeted therapeutic strategies, including metabolic inhibitors, combination therapies, and advanced technologies for metabolic imaging and single-cell analysis. This review provides a comprehensive perspective on glutamine metabolism and highlights its potential as a therapeutic target for cancer and other metabolic disorders.

Keywords:  cell metabolism; cellular homeostasis; glutamine; pathological conditions; therapeutic target

DOI:  https://doi.org/10.1002/mco2.71035
Talanta. 2026 Sep 25. pii: S0039-9140(26)01266-X. [Epub ahead of print]313(Pt C): 130610

Protein digest extraction strategies improve the identification of lipidated peptides in bottom-up proteomics.

Adéla Pravdová, Jana Březinová, Marta Vlková, Štěpán Strnad, Kamila Clarová, Josef Cvačka, Martin Hubálek.

  Alteration of proteins by attaching lipid moieties is a type of post-translational modification that affects the properties of proteins. It plays a role in various physiological processes, mostly due to direct interaction with cell membranes. Proteomic identification of lipoproteins typically depends on detecting their unmodified regions. However, lipopeptides, due to their distinct structural and chemical properties, display chromatographic and mass-spectrometric behavior which differs substantially from that of unmodified peptides. The altered chromatographic and mass-spectrometric behavior of lipid-modified peptides makes their detection and identification challenging, largely because of the increased hydrophobicity introduced by lipid moieties. In order to detect lipopeptides by means of LC-MS, the whole analytical approach, including sample preparation, LC-MS analysis and data evaluation usually has to be modified. We propose an optimized strategy that combines an extraction step enriching lipopeptides prior to LC-MS analysis, adjustments to chromatographic gradients and buffers, and a modified data analysis workflow. Solid-phase and liquid-liquid extraction procedures effectively enrich lipopeptides from complex digests, enabling the detection of low-abundance lipidated peptides. Current search algorithms have difficulties in detecting lipopeptides reliably. The application of a simple script to search for diagnostic fragments in MS/MS data revealed that approximately 50% of N-myristoylated peptides were not detected by MaxQuant. De novo sequencing confirmed 37 N-myristoylated peptide sequences, of which 27 were assignable to known proteins. These results demonstrate that optimized sample preparation combined with diagnostic fragment-based searches improves the detection of N-terminal myristoylation while also highlighting the limited reliability of lipopeptide identification using commonly employed database search algorithms.

Keywords:  LC-MS; Lipidation; Liquid-liquid extraction; N-myristoylation; Sample preparation; Solid-phase extraction

DOI:  https://doi.org/10.1016/j.talanta.2026.130610
Nat Methods. 2026 Oct 01.

Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times.

Fleming Kretschmer, Eva-Maria Harrieder, Michael Witting, Sebastian Böcker.

Liquid chromatography is a predominant technology for the separation of small molecules. Hundreds of retention time prediction models have been published over the past decades, yet truly transferable prediction (requiring no training data from the target chromatographic system) remains an open challenge. Unfortunately, retention times may change massively, even for nominally identical chromatographic conditions. Retention order is considerably more conserved; but even retention order may change if chromatographic conditions vary. Here we present 2-step, a two-step method for the prediction of retention times in reversed-phase chromatography. In the first step, a machine-learning model predicts a retention order index, taking into account chromatographic conditions. In the second step, we map predicted indices to absolute retention times. Disentangling these two tasks finally enables transferable retention time prediction across chromatographic conditions and compound classes, without requiring any target-system training data. Our 2-step method outperforms existing methods that were trained on the target dataset. Finally, we systematically study what chromatographic conditions result in notable changes of retention order.

DOI: https://doi.org/10.1038/s41592-026-03243-2