bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2024–12–29
thirty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz



  1. Methods Mol Biol. 2025 ;2884 13-23
      Mass spectrometry (MS) enables the high-throughput characterization of thousands of proteins in one single run. Nowadays, advances in the field have allowed for deep analysis of low cell numbers, even reaching the single-cell level. However, standardized protocols are still needed in this regard, especially oriented for non-proteomics specialists. Here we detail a reproducible, easy-to-implement workflow for cell-limited sample preparation, from cell storage to peptide labelling, for further TMT-based relative quantitative MS-based analysis.
    Keywords:  Cell lysis; Cell storage; Mass spectrometry; Protein quantification; Proteomics sample preparation; Quantity-limited sample; SP3 technology; Tandem mass tag
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_2
  2. J Proteome Res. 2024 Dec 23.
      The identification of peptides is a cornerstone of mass spectrometry-based proteomics. Spectral library-based algorithms are well-established methods to enhance the identification efficiency of peptides during database searches in proteomics. However, these algorithms are not specifically tailored for tandem mass tag (TMT)-based proteomics due to the lack of high-quality TMT spectral libraries. Here, we introduce JUMPlib, a computational tool for a TMT-based spectral library search. JUMPlib comprises components for generating spectral libraries, conducting library searches, filtering peptide identifications, and quantifying peptides and proteins. Fragment ion indexing in the libraries increases the search speed and utilizing the experimental retention time of precursor ions improves peptide identification. We found that methionine oxidation is a major factor contributing to large shifts in peptide retention time. To test the JUMPlib program, we curated two comprehensive human libraries for the labeling of TMT6/10/11 and TMT16/18 reagents, with ∼286,000 precursor ions and ∼304,000 precursor ions, respectively. Our analyses demonstrate that JUMPlib, employing the fragment ion index strategy, enhances search speed and exhibits high sensitivity and specificity, achieving approximately a 25% increase in peptide-spectrum matches compared to other search tools. Overall, JUMPlib serves as a streamlined computational platform designed to enhance peptide identification in TMT-based proteomics. Both the JUMPlib source code and libraries are publicly available.
    Keywords:  data-dependent acquisition (DDA); database search; mass spectrometry; peptide identification; proteomics; retention time; spectral library search; tandem mass tag (TMT)
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00410
  3. Anal Chem. 2024 Dec 27.
      Mass spectrometry (MS)-based metabolomics often rely on separation techniques when analyzing complex biological specimens to improve method resolution, metabolome coverage, quantitative performance, and/or unknown identification. However, low sample throughput and complicated data preprocessing procedures remain major barriers to affordable metabolomic studies that are scalable to large populations. Herein, we introduce PeakMeister as a new software tool in the R statistical environment to enable standardized processing of serum metabolomic data acquired by multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS), a high-throughput separation platform (<4 min/sample) which takes advantage of a serial injection format of 13 samples within a single analytical run. We performed a rigorous validation of PeakMeister by analyzing 47 cationic metabolites consistently measured in 5,000 serum and 420 quality control samples from the Brazilian National Survey on Child Nutrition (ENANI-2019) comprising a total of 224,983 metabolite peaks acquired in 40 days across three batches over an eight-month period. A migration time index using a panel of 11 internal standards was introduced to correct for large variations in migration times, which allowed for reliable peak annotation, peak integration, and sample position assignment for serum metabolites having two flanking internal standards or a single comigrating stable-isotope internal standard. PeakMeister accelerated data preprocessing times by 30-fold compared to manual processing of MSI-CE-MS data by an experienced analyst using vendor software, while also achieving excellent peak annotation fidelity (median accuracy >99.9%), acceptable intermediate precision (median CV = 16.0%), consistent metabolite peak integration (mean bias = -2.1%), and good mutual agreement when quantifying 16 plasma metabolites from NIST SRM-1950 (mean bias = -1.3%). Reference ranges are also reported for 40 serum metabolites in a national nutritional survey of Brazilian children under 5 years of age from the ENANI-2019 study. MSI-CE-MS in conjunction with PeakMeister allows for rapid and automated processing of large-scale metabolomic studies that tolerate nonlinear migration time shifts without complicated dynamic time warping or effective mobility scale transformations.
    DOI:  https://doi.org/10.1021/acs.analchem.4c03513
  4. Mol Cell Proteomics. 2024 Dec 24. pii: S1535-9476(24)00189-0. [Epub ahead of print] 100899
      Multiplexed proteomics has become a powerful tool for investigating biological systems. Using balancer-peptide conjugates (e.g., TMTproC complementary ions) in the MS2 spectra for quantification circumvents the ratio distortion problem inherent in multiplexed proteomics. However, TMTproC quantification scans require long Orbitrap transients and extended ion injection times to achieve sufficient ion statistics and spectral resolution. Real-Time Search (RTS) algorithms have demonstrated increased speed and sensitivity by selectively informing precursor peak quantification. Here, we combine complementary ion quantification with Real-Time Search (TMTproC-RTS) to enhance sensitivity while maintaining accuracy and precision in quantitative proteomics at the MS2 level. We demonstrate the utility of this method by quantifying protein dynamics during the embryonic development of Drosophila melanogaster (fly), Ciona robusta (sea squirt), and Xenopus laevis (frog). We quantify 7.8k, 8.6k, and 12.7k proteins in each organism, which is an improvement of 12%, 13%, and 14%, respectively, compared to naive TMTproC analysis. For all three organisms, the newly acquired data outperform previously published datasets and provides a diverse, deep, and accurate database of protein dynamics during embryogenesis which will advance the study of evolutionary comparison in early embryogenesis.
    DOI:  https://doi.org/10.1016/j.mcpro.2024.100899
  5. Mol Cell Proteomics. 2024 Dec 19. pii: S1535-9476(24)00187-7. [Epub ahead of print] 100897
      Histone post-translational modifications (PTMs) regulate gene expression patterns through epigenetic mechanisms. The 5 histone proteins (H1, H2A, H2B, H3, and H4) are extensively modified, with over 75 distinct modification types spanning more than 200 sites. Despite strong advances in mass spectrometry-based approaches, identification and quantification of modified histone peptides remains challenging due to factors such as isobaric peptides, pseudo-isobaric PTMs, and low stoichiometry of certain marks. Here we describe the development of a new high-throughput method to identify and quantify over 150 modified histone peptides by liquid chromatography-mass spectrometry (LC-MS). Fast gradient microflow liquid chromatography and variable window SWATH data-independent acquisition on a new quadrupole time-of-flight platform is compared to a previous method using nanoflow LC-MS on an Orbitrap hybrid. Histones extracted from cells treated with either a histone deacetylase inhibitor (HDACi) or TGF-beta 1 were analyzed by data-independent acquisition (DIA) on two mass spectrometers: an Orbitrap Exploris 240 with a 55-minute nanoflow LC gradient, and the SCIEX ZenoTOF 7600 with a 10-minute microflow gradient. To demonstrate the reproducibility and speed advantage of the method, 100 consecutive injections of one sample were performed in less than 2 days on the QTOF platform. The result is the comprehensive characterization of histone PTMs achieved in less than 20 minutes of total run time using only 200 ng of sample. Results for drug-treated histone samples are comparable to those produced by the previous method and can be achieved using less than one-third of the instrument time.
    DOI:  https://doi.org/10.1016/j.mcpro.2024.100897
  6. Aging Cell. 2024 Dec 27. e14462
      Aging is accompanied by multiple molecular changes that contribute to aging associated pathologies, such as accumulation of cellular damage and mitochondrial dysfunction. Tissue metabolism can also change with age, in part, because mitochondria are central to cellular metabolism. Moreover, the cofactor NAD+, which is reported to decline across multiple tissues during aging, plays a central role in metabolic pathways such as glycolysis, the tricarboxylic acid cycle, and the oxidative synthesis of nucleotides, amino acids, and lipids. To further characterize how tissue metabolism changes with age, we intravenously infused [U-13C]-glucose into young and old C57BL/6J, WSB/EiJ, and diversity outbred mice to trace glucose fate into downstream metabolites within plasma, liver, gastrocnemius muscle, and brain tissues. We found that glucose incorporation into central carbon and amino acid metabolism was robust during healthy aging across these different strains of mice. We also observed that levels of NAD+, NADH, and the NAD+/NADH ratio were unchanged in these tissues with healthy aging. However, aging tissues, particularly brain, exhibited evidence of upregulated fatty acid and sphingolipid metabolism reactions that regenerate NAD+ from NADH. These data suggest that NAD+-generating lipid metabolism reactions may help to maintain the NAD+/NADH ratio during healthy aging.
    Keywords:  NAD; aging; metabolic rate; mice
    DOI:  https://doi.org/10.1111/acel.14462
  7. Anal Chem. 2024 Dec 23.
      In untargeted lipidomics experiments, putative lipid identifications generated by automated analysis software require substantial manual filtering to arrive at usable high-confidence data. However, identification software tools do not make full use of the available data to assess the quality of lipid identifications. Here, we present a machine-learning-based model to provide coherent, holistic quality scores based on multiple lines of evidence. Underutilized metrics such as isotope ratios and chromatographic behavior allow for much higher accuracy of identification confidence. We find that approximately 50% of tandem mass spectrometry-based automated lipid identifications are incorrect but that multidimensional rescoring reduces false discoveries to only 7% while retaining 80% of true positives. Our method works with most chromatography methods and generalizes across a family of MS instruments. LipoCLEAN is available at https://github.com/stavis1/LipoCLEAN.
    DOI:  https://doi.org/10.1021/acs.analchem.4c04040
  8. bioRxiv. 2024 Dec 10. pii: 2024.12.05.627093. [Epub ahead of print]
      Advances in multiplex mass spectrometry-based technologies have enabled high-throughput, quantitative proteome profiling of large cohort. However, certain experimental design configurations can amplify sample variability and introduce systematic biases. To address these challenges, we incorporated two novel features in a recent proteogenomic investigation: (1) the inclusion of two reference samples within each mass spectrometry run to serve as internal standards, and (2) the analysis of each specimen as technical replicates across two distinct mass spectrometry runs. Building on these enhancements, we present ProMix, a flexible analytical framework designed to fully leverage these supplementary experimental components. Using both simulated and real-world datasets, we demonstrate the improved performance of ProMix and highlight the advantages conferred by these refined experimental design strategies.
    DOI:  https://doi.org/10.1101/2024.12.05.627093
  9. Methods Mol Biol. 2025 ;2884 143-155
      Biological fluids are proteinaceous liquids or suspensions released through different body orifices or through penetration of the skin. These fluids are the result of multiple tissues and cell types and contain extensive, highly complex, and dynamic protein populations that reflect both the transcriptional program of the originating cells and a record of the individual's health status. Body fluids are readily accessible to clinicians and researchers, and as such proteomic analyses are an important component of clinical studies, fertility studies, oral health studies, and forensic investigations. Current mass spectrometry (MS) datasets have a dynamic range of up to six orders of magnitude and are as diverse as the originating tissue types. Mass spectrometry has the potential to provide information across a wide range of applications, including basic research into human biology and pathology, biochemical analysis of protein function, biomarker discovery and detection, as well as forensic investigations wherein investigators interpret a protein profile to identify the body site origin of a biological fluid. The method below describes a specimen processing workflow that is flexible in terms of biological fluid type, sample state (e.g., a dried sample extracted from evidence or neat fluid), and level of degradation. The method described here is compatible with both high sensitivity shotgun liquid chromatography-mass spectrometry LC/MS analysis and targeted (qualitative or quantitative) MS-based analysis of biomarker candidates.
    Keywords:  Biological fluid; Bodily fluid; Forensic proteomics; Peptide biomarker; Quantitative proteomics; Saliva; Semen; Serum; Sexual assault evidence; Urine; Vaginal fluid
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_10
  10. bioRxiv. 2024 Dec 14. pii: 2024.12.06.627264. [Epub ahead of print]
      Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to adapt to and exploit their microenvironment for sustained growth. The liver is a common site of metastasis, but the interactions between tumor cells and hepatocytes remain poorly understood. In the context of liver metastasis, these interactions play a crucial role in promoting tumor survival and progression. This study leverages multiomics coverage of the microenvironment via liquid chromatography and high-resolution, high-mass accuracy mass spectrometry-based untargeted metabolomics, 13 C-stable isotope tracing, and RNA sequencing to uncover the metabolic impact of co-localized primary hepatocytes and a colon adenocarcinoma cell line, SW480, using a 2D co-culture model. Metabolic profiling revealed disrupted Warburg metabolism with an 80% decrease in glucose consumption and 94% decrease in lactate production by hepatocyte-SW480 co-cultures relative to SW480 control cultures. Decreased glucose consumption was coupled with alterations in glutamine and ketone body metabolism, suggesting a possible fuel switch upon co-culturing. Further, integrated multiomic analysis indicates that disruptions in metabolic pathways, including nucleoside biosynthesis, amino acids, and TCA cycle, correlate with altered SW480 transcriptional profiles and highlight the importance of redox homeostasis in tumor adaptation. Finally, these findings were replicated in 3-dimensional microtissue organoids. Taken together, these studies support a bioinformatic approach to study metabolic crosstalk and discovery of potential therapeutic targets in preclinical models of the tumor microenvironment.
    DOI:  https://doi.org/10.1101/2024.12.06.627264
  11. Methods Mol Biol. 2025 ;2884 1-12
      Mass spectrometry-based proteomics is widely applied to human blood serum or plasma in the search of biomarkers for various diseases. However, the enormous complexity and dynamic range of protein concentrations in these samples render a significant analytical challenge, particularly for detecting low-abundance candidate biomarkers. As a result, strategies for enriching low-abundance proteins and improving their identification in serum or plasma proteomics are commonly used. Here, we describe an immunodepletion technique that is routinely used in our lab for removing high-abundance proteins from human serum/plasma.
    Keywords:  High-abundance proteins; Immunoaffinity depletion; Plasma; Proteomics; Serum
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_1
  12. Proteomes. 2024 Nov 27. pii: 35. [Epub ahead of print]12(4):
      As the primary innate immune cells of the brain, microglia play a key role in various homeostatic and disease-related processes. To carry out their numerous functions, microglia adopt a wide range of phenotypic states. The proteomic landscape represents a more accurate molecular representation of these phenotypes; however, microglia present unique challenges for proteomic analysis. This study implemented a streamlined liquid- and gas-phase fractionation method with data-dependent acquisition (DDA) and parallel accumulation-serial fragmentation (PASEF) analysis on a TIMS-TOF instrument to compile a comprehensive protein library obtained from adult-derived, immortalized mouse microglia with low starting material (10 µg). The empirical library consisted of 9140 microglial proteins and was utilized to identify an average of 7264 proteins/run from single-shot, data-independent acquisition (DIA)-based analysis microglial cell lysate digest (200 ng). Additionally, a predicted library facilitated the identification of 7519 average proteins/run from the same DIA data, revealing complementary coverage compared with the empirical library and collectively increasing coverage to approximately 8000 proteins. Importantly, several microglia-relevant pathways were uniquely identified with the empirical library approach. Overall, we report a simplified, reproducible approach to address the proteome complexity of microglia using low sample input and show the importance of library optimization for this phenotypically diverse cell type.
    Keywords:  DDA library generation; DIA methods development; DIA-PASEF; deep proteomics; microglia
    DOI:  https://doi.org/10.3390/proteomes12040035
  13. Methods Mol Biol. 2025 ;2884 279-303
      Mass spectrometry-based investigation of the heterogeneous glycoproteome from complex biological specimens is a robust approach to mapping the structure, function, and dynamics of the glycome and proteome. Sampling whole wet tissues often provides a large amount of starting material; however, there is a reasonable variability in tissue handling prior to downstream processing steps, and it is difficult to capture all the different biomolecules from a specific region. The on-slide tissue digestion approach, outlined in this protocol chapter, is a simple and cost-effective method that allows comprehensive mapping of the glycoproteome from a single spot of tissue of 1 mm or greater diameter. It provides a selection of target areas on tissue slides appropriate for tissue volumes of 10 nL or greater, corresponding to a 1 μL droplet of enzyme solution applied to a 1-mm diameter target on a 10-μm-thick tissue slice. Sequential enzymatic digestions and desalting of the biomolecules without any prior derivatization from the surface of fresh frozen or formalin-fixed paraffin-embedded tissue slides enable the simultaneous identification of glycosaminoglycan disaccharides such as hyaluronan, chondroitin sulfate and heparan sulfate, asparagine or N-linked glycans, and intact (glyco)peptides using liquid chromatography-tandem mass spectrometry. The in-depth information obtained from this method including the disaccharide compositions, glycan structures, peptide abundances, and site-specific glycan occupancies provides a detailed profiling of a single spot of tissue which has the potential to be disseminated to biomedical laboratories.
    Keywords:  Formalin-fixed paraffin-embedded tissue; Fresh frozen tissue; Glycomics; Glycoproteomics; Liquid chromatography; Mass spectrometry; On-slide tissue digestion; Proteomics
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_18
  14. Anal Chem. 2024 Dec 27.
      Spatial stable isotope tracing metabolic imaging is a cutting-edge technique designed to investigate tissue-specific metabolic functions and heterogeneity. Traditional matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-MSI) techniques often struggle with low coverage of low-molecular-weight (LMW) metabolites, which are often crucial for spatial metabolic studies. To address this, we developed a high-coverage spatial isotope tracing metabolic method that incorporates optimized matrix selection, sample preparation protocols, and enhanced post-ionization (MALDI2) techniques. We employed this approach to mouse kidney, brain, and breast tumors to visualize the spatial dynamics of metabolic flow. Our results revealed diverse regional distributions of nine labeled intermediates derived from 13C6-glucose across glycolysis, glycogen metabolism, and the tricarboxylic acid (TCA) cycle in kidney tissues. In brain sections, we successfully mapped six intermediates from the TCA cycle and glutamate-glutamine (Glu-Gln) cycle simultaneously in distinct neurological regions. Furthermore, in breast cancer tumor tissues, our approach facilitated the mapping of nine metabolic intermediates in multiple pathways, including glycolysis, the pentose phosphate pathway (PPP), and the TCA cycle, illustrating metabolic heterogeneity within the tumor microenvironment. This methodology enhances metabolite coverage, enabling more comprehensive imaging of isotope-labeled metabolites and opening new avenues for exploring the metabolic landscape in various biological contexts.
    DOI:  https://doi.org/10.1021/acs.analchem.4c04600
  15. Cell Rep Med. 2024 Dec 11. pii: S2666-3791(24)00649-9. [Epub ahead of print] 101878
      Malignant rhabdoid tumor (MRT) is one of the most aggressive childhood cancers for which no effective treatment options are available. Reprogramming of cellular metabolism is an important hallmark of cancer, with various metabolism-based drugs being approved as a cancer treatment. In this study, we use patient-derived tumor organoids (tumoroids) to map the metabolic landscape of several pediatric cancers. Combining gene expression analyses and metabolite profiling using mass spectrometry, we find nucleotide biosynthesis to be a particular vulnerability of MRT. Treatment of MRT tumoroids with de novo nucleotide synthesis inhibitors methotrexate (MTX) and BAY-2402234 lowers nucleotide levels in MRT tumoroids and induces apoptosis. Lastly, we demonstrate in vivo efficacy of MTX in MRT patient-derived xenograft (PDX) mouse models. Our study reveals nucleotide biosynthesis as an MRT-specific metabolic vulnerability, which can ultimately lead to better treatment options for children suffering from this lethal pediatric malignancy.
    Keywords:  DHODH inhibitor; Methotrexate; cancer metabolism; isotope tracing; malignant rhabdoid tumors; metabolomics; nucleotide synthesis; pediatric kidney cancer
    DOI:  https://doi.org/10.1016/j.xcrm.2024.101878
  16. bioRxiv. 2024 Dec 12. pii: 2024.12.07.627347. [Epub ahead of print]
      Capillary zone electrophoresis (CZE) is gaining attention in the field of single-cell proteomics for its ultra-low-flow and high-resolution separation abilities. Even more sample-limited yet rich in biological information are phosphoproteomics experiments, as the phosphoproteome composes only a fraction of the whole cellular proteome. Rapid analysis, high sensitivity, and maximization of sample utilization are paramount for single-cell analysis. Some challenges of coupling CZE analysis with mass spectrometry analysis (MS) of complex mixtures include 1. sensitivity due to volume loading limitations of CZE and 2. incompatibility of MS duty cycles with electropherographic timescales. Here, we address these two challenges as applied to single-cell equivalent phosphoproteomics experiments by interfacing a microchip-based CZE device integrated with a solid-phase-extraction (SPE) bed with the Orbitrap Astral mass spectrometer. Using 225 phosphorylated peptide standards and phosphorylated peptide-enriched mouse brain tissue, we investigate microchip-based SPE-CZE functionality, quantitative performance, and complementarity to nano-LC-MS (nLC-MS) analysis. We highlight unique SPE-CZE separation mechanisms that can empower fit-for-purpose applications in single-cell-equivalent phosphoproteomics.
    DOI:  https://doi.org/10.1101/2024.12.07.627347
  17. Methods Mol Biol. 2025 ;2884 81-98
      Shotgun proteomics can be applied to identify and study insect species in diverse research areas such as agriculture, forensics, biodiversity conservation, and food safety. In this chapter, we have provided a detailed protocol for shotgun proteomics analytical methods involving enzymatic digestion of insect proteins using trypsin, separation using high-performance liquid chromatography, and detection of separated peptides using high-resolution mass spectrometry. The protocol also covers the utilization of bioinformatics software for protein identification and spectral library building, proposing both proteomic database-dependent and independent methods. This chapter provides a valuable foundation for applying insect proteomics by discussing a step-by-step protocol.
    Keywords:  Insects; Protein identification; Shotgun proteomics; Spectral library matching
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_7
  18. Methods Mol Biol. 2025 ;2884 225-239
      Extracellular vesicles (EVs) are small membrane-bound structures that play important roles in intercellular communication and the transfer of biomolecules between cells. EVs have become a topic of interest for research in translational proteomics for disease biomarker discovery due to their ability to reflect changes in the cellular proteome, including diseases affecting the brain. Utilizing the proteome analysis of EVs to its fullest potential requires proper isolation and purity. In this chapter, we describe a detailed method for the isolation and identification of brain tissue EVs for translational proteomics using our in-house chemical affinity magnetic bead-based (non-antibody) method, the EVtrap. We also discuss various methods for quantification, characterization, and functional analysis of isolated brain tissue EVs, including western blotting, and proteomic profiling of post-translational modifications (PTMs) involved in neurodegenerative diseases, such as protein N-terminal acetylation. This protocol provides a valuable resource for studies conducted on brain tissue EVs and their potential as biomarkers and therapeutic targets for neurological diseases.
    Keywords:  Brain tissue; DDA; EVtrap; Exosomes; Extracellular vesicles (EVs); Label-free proteomics; Mass spectrometry; Relative quantitative proteomics; Tissue
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_15
  19. Methods Mol Biol. 2025 ;2884 333-354
      The ability to bring spatial resolution to omics studies enables a deeper understanding of cell populations and interactions in biological tissues. In the case of proteomics, single-cell and spatial approaches have been particularly challenging, due to limitations in sensitivity and throughput relative to other omics fields. Recent developments at the level of sample handling, chromatography, and mass spectrometry have set the stage for proteomics to be established in these new disciplines.
    Keywords:  Amyloidosis; DIA; FFPE; LCM; Spatial proteomics
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_20
  20. Expert Rev Proteomics. 2024 Dec 25. 1-10
       INTRODUCTION: Spatial biology is an emerging interdisciplinary field facilitating biological discoveries through the use of spatial omics technologies. Recent advancements in spatial transcriptomics, spatial genomics (e.g. genetic mutations and epigenetic marks), multiplexed immunofluorescence, and spatial metabolomics/lipidomics have enabled high-resolution spatial profiling of gene expression, genetic variation, protein expression, and metabolites/lipids profiles in tissue. These developments contribute to a deeper understanding of the spatial organization within tissue microenvironments at the molecular level.
    AREAS COVERED: This report provides an overview of the untargeted, bottom-up mass spectrometry (MS)-based spatial proteomics workflow. It highlights recent progress in tissue dissection, sample processing, bioinformatics, and liquid chromatography (LC)-MS technologies that are advancing spatial proteomics toward cellular resolution.
    EXPERT OPINION: The field of untargeted MS-based spatial proteomics is rapidly evolving and holds great promise. To fully realize the potential of spatial proteomics, it is critical to advance data analysis and develop automated and intelligent tissue dissection at the cellular or subcellular level, along with high-throughput LC-MS analyses of thousands of samples. Achieving these goals will necessitate significant advancements in tissue dissection technologies, LC-MS instrumentation, and computational tools.
    Keywords:  Laser-capture microdissection; mass spectrometry; nanoPOTS; proteomics; single-cell proteomics; spatial biology; spatial omics
    DOI:  https://doi.org/10.1080/14789450.2024.2445809
  21. Methods Mol Biol. 2025 ;2884 57-69
      Next-generation shotgun proteomics is one of the most valuable tools for gaining insight into the function of organisms. By providing a list of peptides and abundance information, proteomics enables the identification of proteins, their quantities, posttranslational modifications, and localization. The most refined shotgun proteomics workflow involves protein extraction, trypsin digestion, ultrahigh-performance liquid chromatography coupled to high-resolution tandem mass spectrometry, and confident assignment of resulting spectra to peptide sequences. In this study, we present a versatile, time- and cost-efficient experimental workflow for protein extraction, digestion, and analysis that can be applied to any type of microorganism. Our experimental procedure exhibits superior sensitivity compared to gel-based protocols and can be used for comparative microbial proteomics to highlight key players that explain phenotypic differences between conditions or for proteotyping new microbial isolates for taxonomic purposes.
    Keywords:  Microbial proteomics; Tandem mass spectrometry; Taxonomical proteotyping; Versatile proteomic workflow
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_5
  22. Methods Mol Biol. 2025 ;2892 211-231
      The biochemical makeup of any organism provides insight into key factors regarding its biological functions. These factors can be explored using proteomics, which allows us to obtain a snapshot of the protein content and abundance in an organism, cell type or sub-cellular compartment. Here, we describe proteomic methodologies that can be used to dissect the biochemical mechanism of phytopathogenicity in oomycetes. These methodologies include protein extraction, purification, subsequent processing, mass spectrometry analysis, and qualitative and quantitative data processing of oomycete proteomes for comparative studies. Additionally, the use of mass spectra to assist in gene validation and modelling in unfinished oomycete genomes is also described.
    Keywords:  Mass spectrometry; Oomycete; Proteogenomics; Proteome; Proteomics
    DOI:  https://doi.org/10.1007/978-1-0716-4330-3_15
  23. Methods Mol Biol. 2025 ;2884 43-56
      Adipose tissue (AT) is a complex, multifunctional endocrine organ that plays a significant role in animal evolution and human disease. Profiling of the proteome, or the set of proteins produced by a cell or tissue at a given time, can be used to explore the myriad functions of adipose tissue and understand its role in health and disease. The main challenges of adipose tissue proteomics include the high lipid and low protein content of the tissue and association of many proteins with lipid droplets. Here, we present a protocol for gel-free, label-free, bottom-up, relative quantitative proteomics of adipose tissue based on findings from the literature and our laboratory that yields reproducible protein and peptide identification rates while minimizing cost and processing time. This approach involves tissue homogenization, protein precipitation from homogenates, solubilization and denaturation of proteins in a buffer containing 5% sodium deoxycholate (SDC, an acid-insoluble detergent) and 5 mM tris(2-carboxyethyl)phosphine (TCEP, a reducing agent), alkylation with chloroacetamide, and in-solution tandem digestion with trypsin and Lys-C enzymes in the presence of 1% SDC. Acidification of peptides efficiently removes SDC prior to desalting and mass spectrometry. This method has been used successfully in our laboratory by both experienced researchers and those with limited technical backgrounds, including high school, undergraduate, and graduate students. We have identified >1500 proteins in adipose tissue of non-model mammals (e.g., blubber of marine mammals) spanning a dynamic range of 105 using this approach, including proteins of interest for comparative physiology such as adipokines, metabolic and antioxidant enzymes, lipid droplet proteins, metabolite transporters, and mitochondrial proteins, among others.
    Keywords:  Adipose tissue (AT); Bottom-up proteomics; In-solution digest; Label-free quantitative proteomics; Shotgun proteomics; Sodium deoxycholate (SDC)
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_4
  24. NPJ Precis Oncol. 2024 Dec 23. 8(1): 289
      Eyelid tumors pose diagnostic challenges due to their diverse pathological types and limited biopsy materials. This study aimed to develop an artificial intelligence (AI) diagnostic system for accurate classification of eyelid tumors. Utilizing mass spectrometry-based proteomics, we analyzed proteomic data from eight tissue types and identified eighteen novel biomarkers based on 233 formalin-fixed, paraffin-embedded (FFPE) samples from 150 patients. The 18-protein model, validated by an independent cohort (99 samples from 60 patients), exhibited high accuracy (84.8%), precision (86.2%), and recall (84.8%) in multi-class classification. The model demonstrated distinct clustering of different lesion types, as visualized through UMAP plots. Receiver operator characteristic (ROC) curve analysis revealed strong predictive ability with area under the curve (AUC) values ranging from 0.80 to 1.00. This AI-based diagnostic system holds promise for improving the efficiency and precision of eyelid tumor diagnosis, addressing the limitations of traditional pathological methods.
    DOI:  https://doi.org/10.1038/s41698-024-00767-8
  25. Se Pu. 2025 Jan;43(1): 22-32
      Lipids are indispensable components of living organisms and play pivotal roles in cell-membrane fluidity, energy provision, and neurotransmitter transmission and transport. Lipids can act as potential biomarkers of diseases given their abilities to indicate cell-growth status. For example, the lipid-metabolism processes of cancer cells are distinct from those of normal cells owing to their rapid proliferation and adaptation to ever-changing biological environments. As a result, the ability to rapidly detect, identify, and monitor lipid components is critical for tracking life-related processes and may enhance cancer diagnosis and treatment efficacy. Mass spectrometry (MS) is regarded to be among the most efficient methods for directly obtaining molecular-structural information, and is distinctly advantageous for identifying lipids. Recent years have witnessed the emergence of ambient mass spectrometry (AMS), which enables direct analyte sampling and ionization without the need for sample preprocessing. These characteristics endow AMS with special advantages for identifying and monitoring lipids. Furthermore, the ongoing development of soft ionization technologies has led to the widespread use of AMS for the detection of complex and diverse lipid molecules. Electrospray ionization (ESI) is a gentle ionization method that can be used to detect medium-to-high-polarity compounds and provide detailed chemical information for lipids by producing a fine mist of charged droplets from a liquid sample. Consequently, a series of ESI-based ionization methods have been developed for fabricating different AMS systems capable of rapidly detecting lipids in a simple manner. For example, desorption electrospray ionization (DESI) is among the most extensively employed ambient ionization techniques, and has been used to detect a wide range of samples, including solids, liquids, and gases. DESI involves spraying a charged solvent onto the surface of a sample, after which the solvent is desorbed, the analyte is ionized, and the generated ions are transferred to the detector of the mass spectrometer via a gas plume. DESI can easily and precisely regulate the sampling space, thereby offering a highly effective approach for the in-situ detection of lipids from tissue samples. Additionally, single-cell lipid analysis is limited by small cell volumes, complex cellular matrices, and minimal absolute amounts of analyte. Common detection methods for single cells include flow cytometry and fluorescence microscopy, both of which require fluorescent labeling to detect specific target molecules, which limits detection selectivity and reproducibility to some extent. ESI-based single-cell mass spectrometry has emerged as a more-effective method for detecting cellular lipids owing to advantages that include high sensitivity, low sample consumption, high throughput, and multiple-detection capabilities. Moreover, lipid chemical diversity poses a significant challenge for determining structural details. Therefore, AMS-based lipid detection has been augmented with a series of chemical-treatment methods that provide more-comprehensive structural information for lipids. For example, diverse gas-phase dissociation techniques have been used to discriminate between lipid C=C-bond isomers and their sn-positions. Strategies that involve chemically modifying specific target C=C bonds prior to MS detection have also been employed. For example, the Paternò-Büchi (P-B) photochemical reaction oxidizes C=C bonds in unsaturated lipids to form oxetane structures, C=C bonds can be epoxidized to form the corresponding oxaziridines, the N-H aziridination reaction converts C=C bonds into aziridines, and the 1ΔO2 ene reaction adds an OOH group to a C=C bond. In this review, we discuss various environmental ionization techniques for lipid AMS developed over the past five years, with an emphasis on typical chemical strategies used to analyze lipid fine structures. Obtaining a high-coverage, high-sensitivity lipid-detection platform based on AMS remains challenging and requires further in-depth studies despite significant improvements in lipid MS-based detection techniques.
    Keywords:  ambient mass spectrometry (AMS); ionization techniques; lipid; lipid fine structure; tandem mass spectrometry (MS/MS)
    DOI:  https://doi.org/10.3724/SP.J.1123.2024.06007
  26. Cold Spring Harb Protoc. 2024 Dec 23.
      In cereal crops, seed quality is determined by the composition and levels of protein-bound amino acids, which account for ∼90% of the seed total amino acid content. In maize particularly, seed quality is affected by the low levels of lysine and tryptophan, two amino acids that humans and animals cannot synthesize and must obtain from the diet. The low levels of these two amino acids in seeds is due to the dominance of seed storage proteins, namely zeins, which are deficient in these two amino acids. Many efforts have been deployed to improve the nutritional composition of maize kernels (i.e., seeds). Still, the lack of high-throughput and inexpensive methods for the quantification of amino acids that are found within proteins has limited those efforts, especially when large populations are targeted. In this protocol, we describe a robust, efficient, and high-throughput method for the quantification of all 20 proteogenic (protein-forming) amino acids from a crude protein extract. The method consists of four major parts: first, release of the 20 proteogenic amino acids from the protein backbone through hydrolysis; second, aqueous extraction of the released amino acids; third, separation, detection, and quantification of the released amino acids using a multiple reaction monitoring-based ultraperformance liquid chromatography-tandem mass spectrometry detection; and fourth, data analysis and processing using the MassLynx data analysis software, TargetLynx.
    DOI:  https://doi.org/10.1101/pdb.prot108632
  27. Methods Mol Biol. 2025 ;2884 259-278
      Protein phosphorylation is an important post-translational modification that regulates almost all cellular processes, such as cellular metabolism, growth, differentiation, signal transduction, and gene regulation. Mass spectrometry, which acts as an automated and sensitive method, enables global analysis of protein phosphorylation. However, several technical challenges need to be addressed when analyzing protein phosphorylation in a global manner. Low-abundant phosphopeptides need to be enriched before analysis with LC-MS/MS, so specific enrichment of phosphopeptides is central for a successful analysis of the phosphoproteome. Due to the complexity of phosphoproteome, fractionation of phosphopeptides before LC-MS/MS is essential to increase it coverage. Here, we present a detailed protocol for in-depth analysis of tissue phosphoproteome, including collection of tissue samples, extraction of tissue proteins, proteolytic digestion of proteins into peptides, enrichment of phosphopeptides with TiO2 using lactic acid as non-phosphopeptide excluder, fractionation of phosphopeptides with TEA-based high-pH reversed-phase (HpH-RP) chromatography, and identification of phosphopeptides with LC-MS/MS. We also outline the essential steps for data processing.
    Keywords:  High-pH reversed-phase chromatography (HpH-RP) fractionation; Mass spectrometry (MS); Post-translational modification (PTM); Protein phosphorylation; TiO2 enrichment; Tissue phosphoproteomics
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_17
  28. Anal Chem. 2024 Dec 21.
      An increasing number of spatial multiomic workflows have recently been developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform the region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a new multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. This workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a poly(ethylene naphthalate)-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed the downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 μm × 200 μm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial- and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.
    DOI:  https://doi.org/10.1021/acs.analchem.4c04462
  29. Anal Chem. 2024 Dec 23.
      Rapid identification of asparagine (Asn) deamidation and isoaspartate (isoAsp) in proteins remains a challenging analytical task during the development of biological therapeutics. For this study, 46 therapeutically relevant peptides corresponding to 13 peptide families (13 unmodified peptides and 33 modified peptides) were obtained; modified peptides included Asn deamidation and isoAsp. The peptide families were characterized by three methods: reversed-phase ultrahigh performance liquid chromatography-mass spectrometry (RP-UHPLC-MS); flow injection analysis high-resolution ion mobility-mass spectrometry (FIA-HRIM-MS); and shortened gradient RP-UHPLC-HRIM-MS. UHPLC-MS data acquisition was 2 h per injection, in contrast to high-throughput 1 min data acquisition of the FIA-HRIM-MS technique. A rapid 2D peptide map has been demonstrated by combining shortened gradient RP-UHPLC with HRIM, to optimize the resolution of the Asn-, Asp-, and isoAsp-containing peptides, increasing the likelihood of detecting peptides containing these quality attributes with expedited data acquisition. Additionally, this paper provides an ion mobility calibration data set for therapeutically relevant peptides (unmodified and modified) over an ion-neutral collisional cross-section range of 300-800 Å2.
    DOI:  https://doi.org/10.1021/acs.analchem.4c05246
  30. Methods Mol Biol. 2025 ;2884 25-41
      Identifying proteins from living organisms helps us understand the biological functions of cells, discover new molecular mechanisms, and interrogate known mechanisms for improving our understanding. For a comprehensive understanding of cellular functions, identifying the whole protein content, or proteome, of a cell is desirable but challenging. Here, we describe in detail two methods of proteome fractionation at either the protein (SDS-PAGE) or peptide (high-pH reversed-phase fractionation) level, which can be used to maximize the identification of proteins from complex biological samples. We apply these methods to two different sample types commonly processed in our laboratory to demonstrate the versatility of these protocols. These methods produce many more peptide identifications when compared to conventional single-shot analysis and can also be used in combination to generate larger complementary datasets with greater depth of proteome coverage.
    Keywords:  Filter-aided sample preparation (FASP); High-pH reversed-phase fractionation; In-gel digestion; Proteomics; Sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE)
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_3
  31. Methods Mol Biol. 2025 ;2884 305-332
      Recent work in single-cell imaging has allowed unprecedented insight into single-cell interactions that control disease progression. However, approaches to understanding the combined extracellular and cellular microenvironment are limited. In the current protocol, we describe an approach that allows single-cell type imaging using matrix-assisted laser desorption/ionization immunohistochemistry (MALDI-IHC) of UV (ultraviolet) photocleavable mass tags combined with N-glycomic and ECM-targeted proteomic imaging from the same formalin-fixed paraffin-embedded tissue section. These approaches use the same imaging mass spectrometry platform to provide a comprehensive view of both the cellular and extracellular tissue microenvironment.
    Keywords:  Antibody; Collagen; Collagen peptide imaging; Extracellular matrix; Fibrosis; Histology; Imaging mass spectrometry; MALDI-IHC; N-Glycan; N-Glycosylation; Photocleavable mass tags; Proteomics; Single-cell type; Tissue imaging
    DOI:  https://doi.org/10.1007/978-1-0716-4298-6_19
  32. Proteomes. 2024 Dec 09. pii: 37. [Epub ahead of print]12(4):
      Abdominal aortic aneurysm (AAA) is a life-threatening condition characterized by the weakening and dilation of the abdominal aorta. Few diagnostic biomarkers have been proposed for this condition. We performed mass spectrometry-based proteomics analysis of affinity-enriched plasma from 45 patients with AAA and 45 matched controls to identify changes to the plasma proteome and potential diagnostic biomarkers. Gene ontology analysis revealed a significant upregulation of the proteins involved in inflammation, coagulation, and extracellular matrix in AAA patients, while proteins related to angiogenesis were among those downregulated. Using recursive feature elimination, we identified a subset of 10 significantly regulated proteins that were highly predictive of AAA. A random forest classifier trained on these proteins achieved an area under the curve (AUC) of 0.93 [95% CI: 0.91-0.95] using cross-validation. Further validation in a larger cohort is necessary to confirm these results.
    Keywords:  abdominal aortic aneurysm; affinity enrichment; machine learning; mass spectrometry; plasma biomarkers; proteomics
    DOI:  https://doi.org/10.3390/proteomes12040037