bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2026–02–08
fourteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Complete Data Analysis Workflow for Quantitative DIA Mass Spectrometry Using Nextflow.
  2. Low-Input Proteomics Protocols for Laser-Captured FFPE Samples with Orbitrap Astral DIA-MS.
  3. A Spatial Proteomics Approach to Isolate and Analyze Amyloid Proteins Using Laser Capture Microdissection and Mass Spectrometry.
  4. Quantifying the ∼75-95% of Peptides in DIA-MS Data Sets that Were Not Previously Quantified.
  5. Metabolomics Analysis of Trypanosomes Using Ion Mobility-Enhanced Mass Spectrometry.
  6. UltraPlex-TMT: Expanding Isobaric Hyperplexing via Orthogonal Protease Cleavage.
  7. Deeper neuronal and glial proteomic insights using an optimized pipeline for proximity labeling proteomics.
  8. Double derivatization-enhanced LC-MS/MS for isomer-resolved identification and quantification of fatty acids.
  9. Immunofluorescence Imaging-Guided Laser Microdissection for Ultralow Input Spatial Tissue Proteomics.
  10. Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories.
  11. MaSpeQC: An easy-to-use Interactive Pipeline to Assess the Performance of LC-MS/MS Instrumentation.
  12. Laser Capture Microdissection for Quantitative Shotgun Lipidomics.
  13. Application of high-resolution mass spectrometry profiling towards the diagnosis and acute management of maple syrup urine disease.
  14. A Highly Sensitive and High-Throughput Quantitative HILIC-MS/MS Method for Systematic Profiling of RNA Modifications.
  1. J Proteome Res. 2026 Feb 06.
    Complete Data Analysis Workflow for Quantitative DIA Mass Spectrometry Using Nextflow.
    Mats Perk, Sami Pietilä, Tommi Välikangas, Balazs Balint, Tomi Suomi, Laura L Elo.
      Data-independent acquisition (DIA) mass spectrometry is a technique used in proteomics to identify and quantify proteins in complex biological samples. While this comprehensive approach yields more complete and reproducible protein profiles than data-independent acquisition (DDA), the resulting data are substantially larger and more complex, presenting significant challenges for data analysis and interpretation. These challenges can be effectively addressed using dedicated workflow managers that support parallel execution of complex analysis pipelines on high-performance computing infrastructure. Nextflow, in particular, is well-suited for streamlining data analysis, as it automates key aspects of workflow management, allowing researchers to efficiently analyze large-scale data sets with minimal manual intervention. Here, we describe glaDIAtor-nf, a Nextflow version of our software package glaDIAtor for untargeted analysis of DIA mass spectrometry proteomics data. We first demonstrate its technical accuracy through rigorous testing on gold standard data sets. Building on this, we then reveal known proteome patterns from public breast cancer data that remained hidden in the processed data of the original study. This illustrates the potential of reanalyzing the accumulating public data, but also highlights the need for convenient tools to facilitate such reanalysis in large-scale.
    Keywords:  data analysis; data-independent acquisition; mass spectrometry; nextflow; quantitative proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00266
  2. Methods Mol Biol. 2026 ;3015 95-108
    Low-Input Proteomics Protocols for Laser-Captured FFPE Samples with Orbitrap Astral DIA-MS.
    Daniele Musiani, Alessandro Cuomo.
      Laser microdissection (LMD) combined with high-resolution mass spectrometry (MS) has become a powerful approach for analyzing targeted regions of fixed tissues, enabling detailed investigation of their heterogeneity. Here, we present two protocols optimized for low-input samples: a solid-phase enhanced preparation method (SP3) and a detergent/organic solvent-based approach (DDM/ACN). Both workflows are designed to minimize sample loss and are suitable for manual processing when automation platforms are not available. We provide practical guidance to reduce input requirements down to 1500 μm2 of 10 μm thick FFPE tissue while maintaining reproducibility and proteome depth. These protocols, when coupled with fast instruments such as the Orbitrap Astral mass spectrometer working in DIA acquisition mode, enable sensitive, high-resolution proteomic analysis of microdissected tissue regions. Together, they establish a robust approach for investigating spatial heterogeneity in clinical samples with high coverage and precision.
    Keywords:  FFPE tissue; Laser microdissection; Low-input sample preparations for LC-MS/MS; Orbitrap Astral
    DOI:  https://doi.org/10.1007/978-1-0716-5154-4_8
  3. Methods Mol Biol. 2026 ;3015 121-134
    A Spatial Proteomics Approach to Isolate and Analyze Amyloid Proteins Using Laser Capture Microdissection and Mass Spectrometry.
    Jennifer Aguilan, Maxwell Horton, Jeffrey E Pessin, Simone Sidoli.
      Amyloidosis comprises a heterogeneous group of disorders marked by the extracellular deposition of insoluble protein fibrils, known as amyloids, which can disrupt normal tissue architecture and lead to organ dysfunction. Accurate identification and subtyping of the amyloidogenic protein are critical for clinical management as treatment strategies vary significantly depending on the underlying protein species. While conventional diagnostic tools such as Congo red staining and immunohistochemistry are commonly used, they suffer from limited specificity and antibody availability, often resulting in misclassification. Mass spectrometry (MS)-based proteomics has become the gold standard for amyloid subtyping, offering unmatched sensitivity and proteome-wide coverage.Here, we describe a spatial proteomics protocol that integrates laser capture microdissection (LMD) with advanced LC-MS/MS acquisition methods, including data-dependent acquisition (DDA), data-independent acquisition (DIA), and High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS). With the integration of these methods, we present the analysis of formalin-fixed paraffin-embedded (FFPE) tissue sections. This comprehensive workflow enables precise excision of amyloid-rich regions and enhances detection of amyloid proteins and co-deposited biomarkers from minimal tissue input. The combination of FAIMS with DIA and DDA not only improves the depth of proteomic coverage but also increases reproducibility and sensitivity, making it particularly suitable for low-abundance samples. This protocol provides a robust and scalable platform for the accurate molecular subtyping of amyloidosis and has the potential to inform personalized therapeutic decisions in clinical pathology.
    Keywords:  Amyloid protein subtyping; Amyloidosis; DDA; DIA; FAIMS; Laser capture microdissection; MALDI-MSI
    DOI:  https://doi.org/10.1007/978-1-0716-5154-4_10
  4. J Proteome Res. 2026 Feb 04.
    Quantifying the ∼75-95% of Peptides in DIA-MS Data Sets that Were Not Previously Quantified.
    Gautam Saxena, Qin Fu, Aleksandra Binek, Jennifer E Van Eyk.
      We have developed a novel algorithm termed GoldenHaystack (GH) that was designed for enhanced peptide quantification of data-independent acquisition liquid-mass spectrometry (DIA-LC-MS) data files regardless of whether the amino acid sequences are subsequently assigned to the quantified peptide. The two central ideas behind GH are: (a) for sufficiently sized projects (e.g., ≥∼30 LC-MS files), pairs of peptides that coelute exactly in one subset of LC-MS files do not necessarily coelute exactly in a different subset of files, and (b) the ion intensity ratios between MS2 ions for any given peptide tend to stay the same across samples, but the ion intensity ratios of MS2 ions between different peptides tend to differ substantially across different samples. GH thus analyzes a project holistically: It uses multi-partite matching to match both MS2 (primarily) and MS1 (secondarily) ions across all samples, separates and regroups the MS ions into unique analyte quantifiable signatures (UAQS), reduces stochastic noise, and then quantifies those UAQS. In this paper, GH is compared to DIA-NN, a common algorithm used in DIA-MS proteomic analysis, and we demonstrate that GH (a) quantifies and identifies with better FDR accuracy known peptides found in FASTA search spaces (∼5-25% of analytes in DIA-MS data sets), (b) quantifies the remaining ∼75-95% of unassigned peptides that would be typically unquantified and unreported, and (c) runs ∼40-200× faster (or ∼1-10× faster than the LC-MS). Specifically, without a FASTA or spectral library, GH can deconvolute and accurately quantify chimeric LC-MS spectra. The use of a FASTA file occurs during an optional peptide identification step and is deployed only after the analytes in the MS files have already been quantified. We provide details of GH performance on several existing proteomics data sets, including plasma, cerebrospinal fluid, and cells.
    Keywords:  and FDR entrapment evaluations; bioinformatics; data-independent acquisition; discovery proteomics; mass spectrometry; weighted multipartite matching
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00326
  5. Methods Mol Biol. 2026 ;3013 299-313
    Metabolomics Analysis of Trypanosomes Using Ion Mobility-Enhanced Mass Spectrometry.
    Andrew MacKenzie, Fraser Massie, Tessa Moses.
      Metabolomics, the comprehensive study of low-molecular-weight metabolites, provides a direct snapshot of an organism's physiological state and complements genomics and proteomics. Ion mobility (IM) enhanced metabolomics adds crucial gas-phase separation, resolving isobaric and isomeric metabolites for deeper biological insights. Here we describe a method coupling rapid liquid chromatography to IM-enhanced mass spectrometry, which allows high-throughput metabolomics analysis. This method was successfully applied to trypanosomes to elucidate the mode of action of a small-molecule inhibitor in Trypanosoma brucei.
    Keywords:  Collision cross-section; Drift time; Global metabolomics; Liquid chromatography; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-5142-1_16
  6. J Proteome Res. 2026 Feb 01.
    UltraPlex-TMT: Expanding Isobaric Hyperplexing via Orthogonal Protease Cleavage.
    Theodoros I Roumeliotis, Fernando J Sialana, Jenny Ho, Jyoti S Choudhary.
      Isobaric labeling is widely used in quantitative proteomics for its multiplexing capabilities, but scaling beyond current limits remains a challenge. Here, we introduce UltraPlex-TMT, a streamlined and scalable workflow that integrates orthogonal protease digestion with hyperplex TMT/TMTpro labeling to effectively double sample throughput. UltraPlex-TMT can be readily implemented without custom chemistry or instrumentation. We benchmarked UltraPlex-TMT using lysine- and arginine-specific protease digests of a two-species proteome labeled with TMT11plex and TMT18plex across four subplexes in a proof-of-concept pseudo-58-plex design. MS2 acquisition quantified ∼6,000-7,000 proteins per subplex and ∼9,000 in total, with ∼50% overlap across all conditions, generating a robust core proteome set with high quantitative reproducibility. RTS-MS3 acquisition showed similar coverage trends, albeit with fewer quantified proteins. Despite reduced depth, MS3 data provided higher quantification accuracy, illustrating a trade-off between proteome depth and precision. Gene set enrichment analysis revealed strong biological concordance between MS2 and MS3 data, and in all conditions tested, the use of orthogonal protease digestion did not introduce systematic quantification bias. UltraPlex-TMT offers a flexible foundation for isobaric labeling-based high-throughput proteomics and is poised to benefit from faster acquisition platforms and extended multiplexing chemistries, supporting future studies exceeding 200-plex scale, potentially equivalent to subminute analysis.
    Keywords:  TMT; high-throughput proteomics; hyperplexing; isobaric labeling; orthogonal protease digestion; quantitative proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.5c01084
  7. bioRxiv. 2026 Jan 22. pii: 2026.01.21.700735. [Epub ahead of print]
    Deeper neuronal and glial proteomic insights using an optimized pipeline for proximity labeling proteomics.
    Wooyoung Eric Jang, Upasna Srivastava, Amanda D Brandelli, Prateek Kumar, Claudia Espinosa-Garcia, Dilpreet Kour, Rashmi Kumari, Srikant Rangaraju.
      Proximity-based proteomics using TurboID has enabled cell-type-specific profiling without the need for cell purification, although major bottlenecks in sample lysis, biotinylated protein enrichment, digestion, and mass spectrometry (MS) parameters have limited depth of proteome coverage. Here, we systematically optimized these variables using TurboID-based labeling of BV2 microglia in vitro and brain astrocytes in vivo to define conditions that maximize proteome coverage. In microglia, the optimized protocol using 8 M urea lysis with on-bead S-Trap digestion and data-independent acquisition MS (DIA-MS) identified 4,016 proteins, double the depth of prior studies, and revealed metabolic, ribosomal, lipid-processing, autophagy, and trafficking signatures. Brain astrocyte proteomes were best recovered using SDS lysis with S-Trap digestion and DIA-MS, yielding a proteome of over 3,600 highly enriched proteins, twice the depth of prior astrocyte-TurboID studies. The expanded astrocyte proteomes captured canonical astrocyte markers as well as membrane-associated, vesicular trafficking, and presynaptic protein signatures, consistent with labeling of astrocyte-neuron interface regions, including proteins involved in receptor signaling, lipid metabolism, and plasticity at tripartite synapses, and several AD risk proteins. The increased peptide recovery following S-Trap digestion allowed the reduction of starting material to 20 µg protein for DIA-MS, and enabled multiplexed tandem mass tag (TMT-MS) proteomics using even smaller samples. When applied to synaptosomes enriched from mouse brains with neuronal TurboID labeling, our pipeline identified a synapse-specific proteome of 2,529 proteins, revealing synaptic, mitochondrial and disease-relevant signatures not detectable in prior studies. By tackling critical bottlenecks from tissue processing to MS, our optimized pipelines enable cell-type and compartment-specific proximity-labeling proteomics to obtain comprehensive biological and disease-relevant insights across various biological fields.
    DOI:  https://doi.org/10.64898/2026.01.21.700735
  8. Anal Chim Acta. 2026 Mar 08. pii: S0003-2670(26)00083-8. [Epub ahead of print]1390 345133
    Double derivatization-enhanced LC-MS/MS for isomer-resolved identification and quantification of fatty acids.
    Xingyu Shi, Xuan Liang, Shuilin Xie, Ting Zhou, Wei Xiong.
       BACKGROUND: Fatty acids (FAs) are essential metabolites involved in energy storage, signaling, and inflammation regulation, and their biological functions are closely linked to the position and geometric configuration of carbon-carbon double bonds (CC). However, the isomer-resolved characterization and highly sensitive detection of FAs in complex biological matrices remain challenging due to their co-elution, similar fragmentation, and low ionization efficiency. Therefore, there remains a critical need for an efficient approach that can simultaneously enhance chromatographic separation, enable unambiguous CC characterization, and improve detection sensitivity for FA isomers in biological samples.
    RESULTS: In this study, we developed a double derivatization strategy combining magnesium monoperoxyphthalate hexahydrate (MMPP) epoxidation with N,N-diethyl-1,2-ethanediamine (DEEA) amidation, coupled with LC-MS/MS, for isomer-resolved FA analysis. The double derivatization enhanced the chromatographic resolution of positional and cis/trans isomers, enabled reliable localization of CC positions via Δ16 Da diagnostic ion pairs, and improved detection sensitivity by 16-32 fold relative to epoxidation alone. Cis-trans configurations were further supported by parallel linear relationships between retention time and CC position under identical LC conditions, providing a standards-sparing criterion for structural assignment. Applied to mouse plasma, the strategy identified 69 FAs, including 55 unsaturated species, representing an increase of 46 over the underivatized approach. Quantitative analyses and differential analyses showed most FAs were elevated in hepatitis B virus (HBV) mice, with altered isomer ratios pointing to disrupted desaturase activity and oxidative stress.
    SIGNIFICANCE: In conclusion, this double derivatization LC-MS/MS platform provided improved structural resolution, higher detection sensitivity, and more reliable configurational assignment of FAs without requiring extensive standards. Its successful application to HBV mouse plasma demonstrated its suitability for complex biological matrices and highlighted its potential for biomarker discovery and mechanistic studies of lipid-related metabolic diseases.
    Keywords:  Biomarker discovery; Double derivatization; Fatty acid isomers; LC-MS/MS; Structural resolution
    DOI:  https://doi.org/10.1016/j.aca.2026.345133
  9. Methods Mol Biol. 2026 ;3015 83-94
    Immunofluorescence Imaging-Guided Laser Microdissection for Ultralow Input Spatial Tissue Proteomics.
    Sonja Fritzsche, Fabian Coscia.
      Laser microdissection (LMD) enables the precise isolation of specific cells or regions of interest from tissue sections, guiding downstream molecular analyses with high spatial resolution. In this protocol, we describe an optimized protocol combining whole-slide immunofluorescence imaging, LMD, and low-input liquid chromatography (LC) mass spectrometry (MS)-based proteomics of FFPE tissue sections.
    Keywords:  Deep visual proteomics; Immunofluorescence staining; Laser microdissection; Tissue proteomics
    DOI:  https://doi.org/10.1007/978-1-0716-5154-4_7
  10. Environ Sci Technol. 2026 Feb 06.
    Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories.
    Jarmo-Charles Kalinski, Bruno Ruiz Brandão da Costa, Tilman Schramm, Lance R Buckett, Laura T Carlson, Nicole R Coffey, Tito Damiani, Elias Dechent, Yasin El Abiead, Steffen Heuckeroth, Elaine K Jennings, Jan Kaesler, Naomi L Stock, Alice M Orme, Ralph R Torres, Sara Trojahn, Helen L Whelton, Yingfei Yan, Allegra T Aron, Rene M Boiteau, Ian D Bull, Pieter C Dorrestein, Duc Huy Dang, Richard P Evershed, Marta Gledhill, Gerd Gleixner, Andreas F Haas, Martin Hansen, Tilmann Harder, Ellen C Hopmans, Anitra E Ingalls, Uwe Karst, William Kew, Melissa Kido Soule, Boris P Koch, Elizabeth B Kujawinski, Oliver J Lechtenfeld, Krista Longnecker, Tomáš Pluskal, Georg Pohnert, Zachary C Redman, Albert Rivas-Ubach, Philippe Schmitt-Kopplin, Gabriel Singer, Jan Tebben, Patrick L Tomco, Nicholas D Ward, Lihini I Aluwihare, Carsten Simon, Jeffrey Hawkes, Daniel Petras.
      Non-targeted liquid chromatography tandem high-resolution mass spectrometry (LC-MS/MS) is increasingly applied for the structure-resolved chemical analysis of dissolved organic matter (DOM). With new developments in MS instrumentation and analysis software, the approach has gained substantial momentum over the past decade. However, achieving high-quality analytical data that is reproducible and comparable across laboratories can be a bottleneck in non-targeted metabolomics and organic matter chemical analysis, especially for data reuse in repository-scale analyses. Understanding the capabilities as well as challenges of comparing LC-MS/MS data from different laboratories is necessary for inferring global trends from public data sets. To illuminate instrumentation factors that drive differences and variability, we used a standardized data analysis pipeline, including classical (CMN) and feature-based molecular networking (FBMN), to analyze data from a ring trial by 24 laboratories on identical sample sets of algal and DOM extracts that were mixed in predefined concentrations and spiked with standards. Our results showed that data sets from similar mass spectrometer types with unified instrument parameters were qualitatively comparable, resolving the same general trends and shared mass spectral features. Interlaboratory comparability was best for high-intensity features, while low-intensity features showed greater detection variability. Our analysis also highlights challenges when comparing data from instruments with different acquisition rates or operating with less standardized methods. Lastly, we provide recommendations for data integration, public data sharing, standardization, and best practices for standardized LC-MS/MS data acquisition, which will be critical for long-term time series and intercomparability of DOM chemical analyses.
    Keywords:  DOM; LC–MS/MS; dissolved organic matter; high resolution tandem mass spectrometry; interlaboratory comparison; non-targeted analysis; non-targeted metabolomics; structure-resolved chemical analysis
    DOI:  https://doi.org/10.1021/acs.est.5c12691
  11. J Proteome Res. 2026 Jan 30.
    MaSpeQC: An easy-to-use Interactive Pipeline to Assess the Performance of LC-MS/MS Instrumentation.
    Simon J Caven, Christopher K Barlow, Robert J A Goode, Scott A Blundell, Hossein Valipour Kahrood, Haijian Zhang, Anup D Shah, Bosco K Ho, Michael Wybrow, Tobias Czauderna, Ralf B Schittenhelm.
      Mass spectrometers and their attached liquid chromatography (LC) systems, often referred to as LC-MS/MS instrumentation, have become an indispensable tool in biomedical research to identify and quantify proteins, metabolites, and other molecules of interest. However, these sophisticated instruments are very susceptible to malfunction or suboptimal performance, and as a result, quality control (QC) samples are typically acquired at regular intervals to assess their performance. Not surprisingly, several QC software packages have been developed in recent years to analyze and interrogate a variety of QC samples. However, existing QC software predominantly supports proteomic QC samples, with limited options for metabolomic and lipidomic QC samples. In addition, pipelines and workflows that can accommodate both types of QC samples are largely missing. To address this unmet demand, we have developed MaSpeQC, which is a free, easy-to-install, interactive and fully customizable web application to track LC-MS/MS performance across proteomic, metabolomic, and/or lipidomic workflows. MaSpeQC is vendor-agnostic and can handle any commercially available or in-house-generated QC sample from which it extracts relevant metrics. Furthermore, MaSpeQC provides an intuitive web interface for performance monitoring and early detection of issues through customizable email alerts.
    Keywords:  lipidomics; mass spectrometry; metabolomics; proteomics; quality control; web application
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00869
  12. Methods Mol Biol. 2026 ;3015 163-173
    Laser Capture Microdissection for Quantitative Shotgun Lipidomics.
    Silvia Radrezza, HongKee Moon, Ivey Sebastian, Gayathri Nadar, Lena Hersemann, Andrej Shevchenko.
      In tissues or organs, lipids are not distributed uniformly. While staining with dyes or antibodies locates histological features at cryosections of tissues, it does not elucidate their molecular composition or infer their biological function. Here, we describe how to determine the molar abundance of hundreds of molecular species of major lipid classes, including membrane and neutral lipids, in the lipidome of spatially confined areas of tissue cryosections using laser capture microdissection coupled with shotgun mass spectrometry.
    Keywords:  Laser capture microdissection; Mass spectrometry; Shotgun lipidomics; Spatial lipidomics
    DOI:  https://doi.org/10.1007/978-1-0716-5154-4_13
  13. Mol Genet Metab Rep. 2025 Dec;45 101250
    Application of high-resolution mass spectrometry profiling towards the diagnosis and acute management of maple syrup urine disease.
    Rafael Garrett, Sara Pickett, Melinda J Peters, Khadija Belhassan, Adam S Ptolemy, Roy W A Peake.
      The current approach for investigating patients with suspected inborn errors of metabolism (IEMs) involves traditional targeted biochemical assays such as amino/organic acid analyses. Although highly effective for confirmatory testing, they are less effective in identifying disorders not included in newborn screening (NBS) panels, and for patients with non-classical clinical presentations. Targeted assays analyze a narrow range of metabolites and are conducted across different analytical platforms, often requiring more than one specimen type. In contrast, comprehensive metabolic profiling using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) provides significantly more information from a single specimen, eliminating the need for multiple and time-consuming analyses across different platforms. We describe the use of LC-HRMS metabolic profiling in two patients with decompensated maple syrup urine disease (MSUD). In the first patient, a previously healthy 3-month-old infant presenting with altered mental status, apnea, and seizures, LC-HRMS analysis of plasma before treatment showed increased levels of branched-chain amino acids and their related 2-keto and hydroxy acids. The diagnosis of MSUD was confirmed by targeted amino acid analysis. Additionally, the treatment course, which included dialysis and nutritional management, was monitored using LC-HRMS. This approach was successfully applied to a second patient, a 1-week-old infant with classical MSUD identified through NBS. In conclusion, comprehensive metabolic profiling by LC-HRMS is a valuable investigative tool for patients with both classic and non-specific neurometabolic clinical phenotypes, providing additional insights into metabolite perturbations during acute management.
    Keywords:  Inborn error of metabolism; MSUD; Mass spectrometry; Metabolomics
    DOI:  https://doi.org/10.1016/j.ymgmr.2025.101250
  14. Anal Chem. 2026 Feb 02.
    A Highly Sensitive and High-Throughput Quantitative HILIC-MS/MS Method for Systematic Profiling of RNA Modifications.
    Cheng Guo, Xiujuan Hong, Yiqiu Hu, Tao Pan, Yunxiang Zhou, Haifen Lei, Yuanjiang Pan.
      Understanding the functions and regulatory mechanisms of the epitranscriptome entails robust and accurate analytical methods to identify and quantify post-transcriptional modifications in RNA. However, there are still various challenges in analyzing multiple modified nucleosides in RNA. Herein, we established a highly sensitive and high-throughput hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS) method, in conjunction with a stable isotope-dilution technique, for accurate quantification of 35 nucleosides. By the use of malic acid as a mobile phase additive, the HILIC-based separation of nucleosides was improved and the MS signal response of nucleosides was enhanced by 2.5- to 20.0-fold. Notably, seven groups of isomeric nucleosides with identical multiple-reaction monitoring ion transitions and six groups of nucleosides with identical or similar molecular weights that were indistinguishable by MS were well resolved by optimal HILIC separation. Thirty-five nucleosides were analyzed simultaneously within 12.5 min, and the limits of detection of these nucleosides ranged from 15.0 amol to 43.5 fmol. With this method, we conducted a comprehensive analysis and evaluation of the alteration in the RNA modification profile in breast cancer and assessed the RNA modification patterns across different breast cancer subtypes. The developed HILIC-MS/MS method has excellent capabilities for sensitive and high-throughput detection of multiple modified nucleosides, thereby providing a valuable analytical tool for deciphering the epitranscriptomic landscape and screening nucleosides as biomarkers in future clinical research.
    DOI:  https://doi.org/10.1021/acs.analchem.5c06973
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