bims-mascan 2024-10-27 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–10–27
nineteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Lipid availability influences ferroptosis sensitivity in cancer cells by regulating polyunsaturated fatty acid trafficking.
One-shot 13C15N-metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux.
Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.
High-throughput and high-sensitivity biomarker monitoring in body fluid by fast LC SureQuant™ IS targeted quantitation.
METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning.
A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data-Independent Acquisition Mass Spectrometry (DIA-MS) Discovery.
Validation and quantification of peptide antigens presented on MHCs using SureQuant.
Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective.
Development of microflow ultra high performance liquid chromatography-mass spectrometry metabolomic assays for analysis of mammalian biofluids.
COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields.
Combining Quantitative Proteomics and Interactomics for a Deeper Insight into Molecular Differences between Human Cell Lines.
Cell calcification reverses the chemoresistance of cancer cells via the conversion of glycolipid metabolism.
Eleven shades of PASEF.
Biomarker Discovery via N-Glycoproteomics.
Quantification of guanidine in environmental samples using benzoin derivatization and LC-MS analysis.
Advances in intelligent mass spectrometry data processing technology for in vivo analysis of natural medicines.
Deciphering Cancer Complexity: Integrative Proteogenomics and Proteomics Approaches for Biomarker Discovery.
Comprehensive Coverage of Glycolysis and Pentose Phosphate Metabolic Pathways by Isomer-Selective Accurate Targeted Hydrophilic Interaction Liquid Chromatography-Tandem Mass Spectrometry Assay.
Leveraging the potential of 1.0-mm i.d. columns in UHPLC-HRMS-based untargeted metabolomics.

Cell Chem Biol. 2024 Oct 14. pii: S2451-9456(24)00404-5. [Epub ahead of print]

Lipid availability influences ferroptosis sensitivity in cancer cells by regulating polyunsaturated fatty acid trafficking.

Kelly H Sokol, Cameron J Lee, Thomas J Rogers, Althea Waldhart, Abigail E Ellis, Sahithi Madireddy, Samuel R Daniels, Rachel Rae J House, Xinyu Ye, Mary Olesnavich, Amy Johnson, Benjamin R Furness, Ryan D Sheldon, Evan C Lien.

  Ferroptosis is a form of cell death caused by lipid peroxidation that is emerging as a target for cancer therapy, highlighting the need to identify factors that govern ferroptosis susceptibility. Lipid peroxidation occurs primarily on phospholipids containing polyunsaturated fatty acids (PUFAs). Here, we show that even though extracellular lipid limitation reduces cellular PUFA levels, lipid-starved cancer cells are paradoxically more sensitive to ferroptosis. Using mass spectrometry-based lipidomics with stable isotope fatty acid labeling, we show that lipid limitation induces a fatty acid trafficking pathway in which PUFAs are liberated from triglycerides to synthesize highly unsaturated PUFAs such as arachidonic and adrenic acid. These PUFAs then accumulate in phospholipids, including ether phospholipids, to promote ferroptosis sensitivity. Therefore, PUFA levels within cancer cells do not necessarily correlate with ferroptosis susceptibility. Rather, how cancer cells respond to extracellular lipid levels by trafficking PUFAs into proper phospholipid pools contributes to their sensitivity to ferroptosis.

Keywords:  cancer; ferroptosis; lipid metabolism; phospholipids; polyunsaturated fatty acids; triglycerides

DOI:  https://doi.org/10.1016/j.chembiol.2024.09.008
Mol Syst Biol. 2023 Jan 27. 19(3): MSB202211099

One-shot 13C15N-metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux.

Khushboo Borah Slater, Martin Beyß, Ye Xu, Jim Barber, Catia Costa, Jane Newcombe, Axel Theorell, Melanie J Bailey, Dany J V Beste, Johnjoe McFadden, Katharina Nöh.

  Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the tuberculosis pathogen, this is particularly important in informing the development of effective drugs targeting the pathogen's metabolism. Here we performed 13C15N dual isotopic labeling of Mycobacterium bovis BCG steady state cultures, quantified intracellular carbon and nitrogen fluxes and inferred reaction bidirectionalities. This was achieved by model scope extension and refinement, implemented in a multi-atom transition model, within the statistical framework of Bayesian model averaging (BMA). Using BMA-based 13C15N-metabolic flux analysis, we jointly resolve carbon and nitrogen fluxes quantitatively. We provide the first nitrogen flux distributions for amino acid and nucleotide biosynthesis in mycobacteria and establish glutamate as the central node for nitrogen metabolism. We improved resolution of the notoriously elusive anaplerotic node in central carbon metabolism and revealed possible operation modes. Our study provides a powerful and statistically rigorous platform to simultaneously infer carbon and nitrogen metabolism in any biological system.

Keywords:  Bayesian metabolic flux analysis; Mycobacterium tuberculosis ; carbon metabolism; isotope labeling; nitrogen metabolism

DOI:  https://doi.org/10.15252/msb.202211099
Anal Chem. 2024 Oct 25.

Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.

Wasim Sandhu, Ira J Gray, Sarah Lin, Joshua E Elias, Brian C DeFelice.

Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high-throughput workflows, helping researchers recognize low-integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck─in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source Python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semiautonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Initially developed for integration with Thermo Orbitrap workflows, Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS.

DOI: https://doi.org/10.1021/acs.analchem.4c00786
Mol Cell Proteomics. 2024 Oct 21. pii: S1535-9476(24)00158-0. [Epub ahead of print] 100868

High-throughput and high-sensitivity biomarker monitoring in body fluid by fast LC SureQuant™ IS targeted quantitation.

Konstantinos Kalogeropoulos, Simonas Savickas, Aleksander M Haack, Cathrine A Larsen, Jacek Mikosiński, Erwin M Schoof, Hans Smola, Louise Bundgaard, Ulrich Auf dem Keller.

Targeted proteomics methods have been greatly improved and refined over the last decade and are becoming increasingly the method of choice in protein and peptide quantitative assays. Despite the tremendous progress, targeted proteomics assays still suffer from inadequate sensitivity for lower abundant proteins and throughput, especially in complex biological samples. These attributes are essential for establishing targeted proteomics methods at the forefront of clinical use. Here, we report an assay utilizing the SureQuantTM internal standard triggered targeted method on a latest generation mass spectrometer coupled with an EvoSep One liquid chromatography platform, which displays high sensitivity and a high throughput of 100 samples per day (SPD). We demonstrate the robustness of this method by quantifying proteins spanning six orders of magnitude in human wound fluid exudates, a biological fluid that exhibits sample complexity and composition similar to plasma. Among the targets quantified were low-abundance proteins such at tumor necrosis factor A (TNFA) and interleukin 1-β (IL1B), highlighting the value of this method in the quantification of trace amounts of invaluable biomarkers that were until recently hardly accessible by targeted proteomics methods. Taken together, this method extends the toolkit of targeted proteomics assays and will help to drive forward mass spectrometry-based proteomics biomarker quantification.

DOI: https://doi.org/10.1016/j.mcpro.2024.100868
Nat Commun. 2024 Oct 22. 15(1): 9110

METASPACE-ML: Context-specific metabolite annotation for imaging mass spectrometry using machine learning.

Bishoy Wadie, Lachlan Stuart, Christopher M Rath, Bernhard Drotleff, Sergii Mamedov, Theodore Alexandrov.

Imaging mass spectrometry is a powerful technology enabling spatial metabolomics, yet metabolites can be assigned only to a fraction of the data generated. METASPACE-ML is a machine learning-based approach addressing this challenge which incorporates new scores and computationally-efficient False Discovery Rate estimation. For training and evaluation, we use a comprehensive set of 1710 datasets from 159 researchers from 47 labs encompassing both animal and plant-based datasets representing multiple spatial metabolomics contexts derived from the METASPACE knowledge base. Here we show that, METASPACE-ML outperforms its rule-based predecessor, exhibiting higher precision, increased throughput, and enhanced capability in identifying low-intensity and biologically-relevant metabolites.

DOI: https://doi.org/10.1038/s41467-024-52213-9
Angew Chem Int Ed Engl. 2024 Oct 21. e202409446

A Proteomics Pipeline for Generating Clinical Grade Biomarker Candidates from Data-Independent Acquisition Mass Spectrometry (DIA-MS) Discovery.

Qin Fu, Manasa Vegesna, Niveda Sundararaman, Eugen Damoc, Tabiwang N Arrey, Anna Pashkova, Emebet Mengesha, Philip Debbas, Sandy Joung, Dalin Li, Susan Cheng, Jonathan Braun, Dermot Govern, Christopher Murray, Yue Xuan, Jennifer E Van Eyk.

  Clinical biomarker development has been stymied by inaccurate protein quantification from mass spectrometry (MS) discovery data and a prolonged validation process. To mitigate these issues, we created the Targeted Extraction Assessment of Quantification (TEAQ) software package that uses data-independent acquisition analysis from a discovery cohort to select precursors, peptides, and proteins that adhere to analytical criteria required for established targeted assays. TEAQ was applied to DIA-MS data from plasma samples acquired on a new high resolution accurate mass (HRAM) mass spectrometry platform where precursors were evaluated for linearity, specificity, repeatability, reproducibility, and intra-protein correlation based on 8- or 11-point loading curves at three throughputs. This data can be used as a general resource for developing other targeted assays. TEAQ analysis of data from a case and control cohort for inflammatory bowel disease (n=492) identified 1110 signature peptides for 326 quantifiable proteins from the 1179 identified proteins. Applying TEAQ analysis to discovery data will streamline targeted assay development and the transition to validation and clinical studies.

Keywords:  Clinical biomarker translation * peptide selection algorithm * discovery proteomics * targeted peptides * inflammatory bowel disease

DOI:  https://doi.org/10.1002/anie.202409446
Nat Protoc. 2024 Oct 22.

Validation and quantification of peptide antigens presented on MHCs using SureQuant.

Owen Leddy, Yufei Cui, Ryuhjin Ahn, Lauren Stopfer, Elizabeth Choe, Do Hun Kim, Malte Roerden, Stefani Spranger, Bryan D Bryson, Forest M White.

Vaccines and immunotherapies that target peptide-major histocompatibility complexes (peptide-MHCs) have the potential to address multiple unmet medical needs in cancer and infectious disease. Designing vaccines and immunotherapies to target peptide-MHCs requires accurate identification of target peptides in infected or cancerous cells or tissue, and may require absolute or relative quantification to identify abundant targets and measure changes in presentation under different treatment conditions. Internal standard parallel reaction monitoring (also known as 'SureQuant') can be used to validate and/or quantify MHC peptides previously identified by using untargeted methods such as data-dependent acquisition. SureQuant MHC has three main use cases: (i) conclusive confirmation of the identities of putative MHC peptides via comparison with an internal synthetic stable isotope labeled (SIL) peptide standard; (ii) accurate relative quantification by using pre-formed heavy isotope-labeled peptide-MHC complexes (hipMHCs) containing SIL peptides as internal controls for technical variation; and (iii) absolute quantification of each target peptide by using different amounts of hipMHCs loaded with synthetic peptides containing one, two or three SIL amino acids to provide an internal standard curve. Absolute quantification can help determine whether the abundance of a peptide-MHC is sufficient for certain therapeutic modalities. SureQuant MHC therefore provides unique advantages for immunologists seeking to confidently validate antigenic targets and understand the dynamics of the MHC repertoire. After synthetic standards are ordered (3-4 weeks), this protocol can be carried out in 3-4 days and is suitable for individuals with mass spectrometry experience who are comfortable with customizing instrument methods.

DOI: https://doi.org/10.1038/s41596-024-01076-x
J Proteome Res. 2024 Oct 22.

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective.

Boryana Petrova, Arzu Tugce Guler.

  Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Keywords:  cellular heterogeneity; mass spectrometry; metabolic imaging; metabolomics; multiomics; single-cell

DOI:  https://doi.org/10.1021/acs.jproteome.4c00646
Metabolomics. 2024 Oct 25. 20(6): 120

Development of microflow ultra high performance liquid chromatography-mass spectrometry metabolomic assays for analysis of mammalian biofluids.

Annie J Harwood-Stamper, Caroline A Rowland, Warwick B Dunn.

   INTRODUCTION AND OBJECTIVES: The application of untargeted metabolomics assays using ultra high performance liquid chromatography-mass spectrometry (UHPLC-MS) to study metabolism in biological systems including humans is rapidly increasing. In some of these studies there is a requirement to collect and analyse low sample volumes of biofluids (e.g. tear fluid) or low cell and tissue mass samples (e.g. tissue needle biopsies). The application of microflow, capillary or nano liquid chromatography (≤ 1.0 mm column internal diameter (i.d.)) theoretically should accomplish a higher assay sensitivity compared to analytical liquid chromatography (2.1-5.0 mm column internal diameter). To date, there has been limited research into microflow UHPLC-MS assays that can be applied to study samples of low volume or mass.
METHODS: This paper presents three complementary UHPLC-MS assays (aqueous C18 reversed-phase, lipidomics C18 reversed-phase and Hydrophilic Interaction Liquid Chromatography (HILIC)) applying 1.0 mm internal diameter columns for untargeted metabolomics. Human plasma and urine samples were applied for the method development, with porcine plasma, urine and tear fluid used for method assessment. Data were collected and compared for columns of the same length, stationary phase and stationary phase particle size but with two different column internal diameters (2.1 mm and 1.0 mm).
RESULTS AND CONCLUSIONS: All three assays showed an increase in peak areas and peak widths when applying the 1.0 mm i.d. assays. HILIC assays provide an advantage at lower sample dilutions whereas for reversed phase (RP) assays there was no benefit added. This can be seen in the validation study where a much higher number of compounds were detected in the HILIC assay. RP assays were still appropriate for small volume samples with hundreds of compounds being detected. In summary, the 1.0 mm i.d. column assays are applicable for small volume samples where dilution is required during sample preparation.

Keywords:  Metabolomics; Microflow liquid chromatography; Plasma; Tears; UHPLC-MS; Urine

DOI:  https://doi.org/10.1007/s11306-024-02187-y
Anal Chem. 2024 Oct 20.

COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields.

Da-Wei Li, Rodrigo Cabrera Allpas, Munki Choo, Lei Bruschweiler-Li, Alexandar L Hansen, Rafael Brüschweiler.

The field of metabolomics, which is quintessential in today's omics research, involves the large-scale detection, identification, and quantification of small-molecule metabolites in a wide range of biological samples. Nuclear magnetic resonance spectroscopy (NMR) has emerged as a powerful tool for metabolomics due to its high resolution, reproducibility, and exceptional quantitative nature. One of the key bottlenecks of metabolomics studies, however, remains the accurate and automated analysis of the resulting NMR spectra with good accuracy and minimal human intervention. Here, we present the COLMAR1d platform, consisting of a public web server and an optimized database, for one-dimensional (1D) NMR-based metabolomics analysis to address these challenges. The COLMAR1d database comprises more than 480 metabolites from GISSMO enabling a database query of spectra measured at arbitrary magnetic field strengths, as is demonstrated for spectra acquired between 1H resonance frequencies of 80 MHz and 1.2 GHz of mouse serum, DMEM cell growth medium, and wine. COLMAR1d combines the GISSMO metabolomics database concept with the latest tools for automated processing, spectral deconvolution, database querying, and globally optimized mixture analysis for improved accuracy and efficiency. By leveraging advanced computational algorithms, COLMAR1d offers a user-friendly, automated platform for quantitative 1D NMR-based metabolomics analysis allowing a wide range of applications, including biomarker discovery, metabolic pathway elucidation, and integration with multiomics strategies.

DOI: https://doi.org/10.1021/acs.analchem.4c02688
J Proteome Res. 2024 Oct 25.

Combining Quantitative Proteomics and Interactomics for a Deeper Insight into Molecular Differences between Human Cell Lines.

Anna A Bakhtina, Helisa H Wippel, Juan D Chavez, James E Bruce.

  In modern biomedical research, cultivable cell lines are an indispensable tool, and the selection of cell lines that exhibit specific functional profiles is often critical to success. Cellular functional pathways have evolved through the selection of protein intra- and intermolecular interactions collectively referred to as the interactome. In the present work, quantitative in vivo protein cross-linking and mass spectrometry were used to probe large-scale protein interactome differences among three commonly employed human cell lines, namely, HEK293, MCF-7, and HeLa cells. These data illustrated highly reproducible quantitative interactome levels with R2 values larger than 0.8 for all biological replicates. Proteome abundance levels were also measured using data-independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteome information allowed the visualization of cell type-specific interactome changes mediated by proteome level adaptations and independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the largest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study system-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight into cellular functional landscapes.

Keywords:  cellular quantitative interactome; human cell line; mass spectrometry; quantitative cross-linking

DOI:  https://doi.org/10.1021/acs.jproteome.4c00503
Biomaterials. 2024 Oct 10. pii: S0142-9612(24)00420-4. [Epub ahead of print]314 122886

Cell calcification reverses the chemoresistance of cancer cells via the conversion of glycolipid metabolism.

Lihong Zhang, Yandi Sun, Yindan Lin, Hanhui Li, Yuqiao Huang, Ning Tang, Xueyun Zhang, Yin Lu, Vassili A Kovalev, Eduard V Snezhko, Yan Luo, Ben Wang.

  Drug resistance is an inherent challenge during cancer chemotherapy. Cancer cells favor fatty acid metabolism through metabolic reprogramming to achieve therapeutic resistance. However, an effective approach to overcoming the switch from glycolysis-dependent to fatty acid beta-oxidation-dependent anabolic and energy metabolism remains elusive. Here, we developed a macromolecular drug (folate-polySia, FpSA) to induce the extracellular microcalcification of cervical cancer cells with cisplatin resistance. Microcalcification attenuated the uptake of fatty acids and the beta-oxidation of fatty acids by mitochondrial dysfunction but boosted the glycolysis pathway. Consequently, cotreatment with Pt and FpSA inhibited cisplatin-resistant tumor growth and improved tumor-bearing mice's survival rates, indicating that FpSA switched fatty acid metabolism to glycolysis to sensitize cisplatin-resistant cells further. Taken together, cancer cell calcification induced by FpSA provides a reprogramming metabolic strategy for the treatment of chemotherapy-resistant tumors.

Keywords:  Cancer calcification; Cervical cancer; Chemoresistant; Chemotherapy adjuvant; Glycolipid metabolism

DOI:  https://doi.org/10.1016/j.biomaterials.2024.122886
Expert Rev Proteomics. 2024 Oct 24. 1-10

Eleven shades of PASEF.

Marta L Mendes, Klara F Borrmann, Gunnar Dittmar.

   INTRODUCTION: The introduction of trapped ion mobility spectrometry (TIMS) in combination with fast high-resolution time-of-flight (TOF) mass spectrometry to the proteomics field led to a jump in protein identifications and quantifications, as well as a lowering of the limit of detection for proteins from biological samples. Parallel Accumulation-Serial Fragmentation (PASEF) is a driving force for this development and has been adapted to discovery as well as targeted proteomics.
AREAS COVERED: Over the last decade, the PASEF concept has been optimized and led to the implementation of eleven new measurement techniques. In this review, we describe all currently described PASEF measurement techniques and their application to clinical proteomics. Literature was searched using PubMed and Google Scholar search engines.
EXPERT OPINION: The use of a dual TIMS tunnel has revolutionized the depth and the speed of proteomics measurements. Currently, we witness how this technique is pushing clinical proteomics forward.

Keywords:  DIA; PASEF; Parallel Accumulation-Serial Fragmentation; Proteomics; TIMS-TOF; ion mobility; targeted proteomics

DOI:  https://doi.org/10.1080/14789450.2024.2413092
Methods Mol Biol. 2025 ;2859 239-251

Biomarker Discovery via N-Glycoproteomics.

Rajesh Kumar, Abhishek Kumar.

  Posttranslational modifications (PTMs) of proteins regulate several biological processes, and investigating their diversity is crucial for understanding the mechanisms of cell regulation. Glycosylation is one of the most complex posttranslational modifications that control fundamental cellular processes such as protein folding, protein trafficking, host-pathogen interactions, cell adhesion, and cytokine receptor signaling networks. N-linked glycosylation denotes the attachment of glycans (oligosaccharides) to a nitrogen atom of asparagine (N) residues in the consensus motif Asn-X-Ser/Thr (NXS/T), where X is any amino acid except proline. Therefore, mutations in this posttranslational modification (i.e., N-glycosylation) site cause many human genetic diseases, including cancer. In the past decade, high-throughput quantitative proteome profiling tools have significantly renewed our interest in discovering novel cancer diagnostic or prognostic biomarkers through the simultaneous examination of the enormous amount of high-quality data of thousands of proteins and genes in complex biological systems. In this chapter, we describe how aberrant N-linked glycopeptides could be selectively identified as novel single tumor markers through the use of mass spectrometry (MS)-based proteomics, also known as Solid-phase extraction of N-glycopeptides (SPEG), and reasonable hypotheses that have the potential capacity to revolutionize biomarker discovery and bring those markers to the clinic as early as possible.

Keywords:  Mass spectrometry-based proteomics, SPEG, N-linked glycopeptide, LC-MS/MS, Cancer diagnosis, Cancer biomarker discovery

DOI:  https://doi.org/10.1007/978-1-0716-4152-1_13
MethodsX. 2024 Dec;13 102972

Quantification of guanidine in environmental samples using benzoin derivatization and LC-MS analysis.

Richard Gruseck, Marton Palatinszky, Michael Wagner, Thilo Hofmann, Michael Zumstein.

  The recent discovery of guanidine-dependent riboswitches in many microbes raised interest in the biological function and metabolism of this nitrogen-rich compound. However, very little is known about the concentrations of guanidine in the environment. Several methods have been published for quantifying guanidine and guanidino compounds in human urine and blood, often relying on derivatization followed by fluorescence detection. We adapted this analytical approach using benzoin as the derivatization agent to sensitively and selectively quantify guanidine in environmental samples, thereby facilitating future research on the biological and environmental roles of guanidine. This adapted method was applied to human urine, raw wastewater, and biological growth media as relevant matrices. Our liquid chromatography-tandem mass spectrometry analyses of the derivatized solutions identified a different major derivatization product than previously reported. This product was consistently observed across various substrates (guanidine, methylguanidine, and arginine) and derivatization agents (benzoin and anisoin). We observed a constant background signal, restricting our analyses to a lower limit of quantification of 50 nM. Despite this limitation, our method allowed for the quantification of guanidine concentrations significantly lower than those reported in previous derivatization-based studies.•Selective and sensitive detection of guanidine by LC-MS.•Method development and validation for robust detection of guanidine in environmental samples.•Reduction of sample preparation steps and reduced usage of toxic chemicals compared to previous methods.

Keywords:  Environmental and biological matrices; Guanidino compounds; Liquid chromatography coupled to mass spectrometry; Quantification of pre-column derivatized guanidine with LC-MS/MS; Urine

DOI:  https://doi.org/10.1016/j.mex.2024.102972
Chin J Nat Med. 2024 Oct;pii: S1875-5364(24)60687-4. [Epub ahead of print]22(10): 900-913

Advances in intelligent mass spectrometry data processing technology for in vivo analysis of natural medicines.

Simian Chen, Binxin Dai, Dandan Zhang, Yuexin Yang, Hairong Zhang, Junyu Zhang, Di Lu, Caisheng Wu.

  Natural medicines (NMs) are crucial for treating human diseases. Efficiently characterizing their bioactive components in vivo has been a key focus and challenge in NM research. High-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS) systems offer high sensitivity, resolution, and precision for conducting in vivo analysis of NMs. However, due to the complexity of NMs, conventional data acquisition, mining, and processing techniques often fail to meet the practical needs of in vivo NM analysis. Over the past two decades, intelligent spectral data-processing techniques based on various principles and algorithms have been developed and applied for in vivo NM analysis. Consequently, improvements have been achieved in the overall analytical performance by relying on these techniques without the need to change the instrument hardware. These improvements include enhanced instrument analysis sensitivity, expanded compound analysis coverage, intelligent identification, and characterization of nontargeted in vivo compounds, providing powerful technical means for studying the in vivo metabolism of NMs and screening for pharmacologically active components. This review summarizes the research progress on in vivo analysis strategies for NMs using intelligent MS data processing techniques reported over the past two decades. It discusses differences in compound structures, variations among biological samples, and the application of artificial intelligence (AI) neural network algorithms. Additionally, the review offers insights into the potential of in vivo tracking of NMs, including the screening of bioactive components and the identification of pharmacokinetic markers. The aim is to provide a reference for the integration and development of new technologies and strategies for future in vivo analysis of NMs.

Keywords:  Artificial Intelligence; Data-acquisition; Data-processing; High-performance liquid chromatography–High-resolution mass spectrometry; Metabolomics

DOI:  https://doi.org/10.1016/S1875-5364(24)60687-4
Methods Mol Biol. 2025 ;2859 211-237

Deciphering Cancer Complexity: Integrative Proteogenomics and Proteomics Approaches for Biomarker Discovery.

Rashmi Rao, Mohd Gulfishan, Min-Sik Kim, Manoj Kumar Kashyap.

  Proteomics has revolutionized the field of cancer biology because the use of a large number of in vivo (SILAC), in vitro (iTRAQ, ICAT, TMT, stable-isotope Dimethyl, and 18O) labeling techniques or label-free methods (spectral counting or peak intensities) coupled with mass spectrometry enables us to profile and identify dysregulated proteins in diseases such as cancer. These proteome and genome studies have led to many challenges, such as the lack of consistency or correlation between copy numbers, RNA, and protein-level data. This review covers solely mass spectrometry-based approaches used for cancer biomarker discovery. It also touches on the emerging role of oncoproteogenomics or proteogenomics in cancer biomarker discovery and how this new area is attracting the integration of genomics and proteomics areas to address some of the important questions to help impinge on the biology and pathophysiology of different malignancies to make these mass spectrometry-based studies more realistic and relevant to clinical settings.

Keywords:  18O labeling; Biomarker; Carcinoma; ICAT; Mass spectrometry; Proteogenomics; SILAC; Secretome; iTRAQ

DOI:  https://doi.org/10.1007/978-1-0716-4152-1_12
Anal Chem. 2024 Oct 19.

Comprehensive Coverage of Glycolysis and Pentose Phosphate Metabolic Pathways by Isomer-Selective Accurate Targeted Hydrophilic Interaction Liquid Chromatography-Tandem Mass Spectrometry Assay.

Kristian Serafimov, Michael Lämmerhofer.

The accurate liquid chromatography-tandem mass spectrometry analysis of phosphorylated isomers from glycolysis and pentose phosphate pathways is a challenging analytical problem in metabolomics due to extraction problems from the biological matrix, adherence to stainless steel surfaces leading to tailing in LC, and incomplete separation of hexose and pentose phosphate isomers. In this study, we present a targeted HILIC-ESI-MS/MS method based on a BEH amide fully porous 1.7 μm particle column with an inert surface coating of column hardware and multiple reaction monitoring (MRM) acquisition fully covering the glycolysis and pentose phosphate pathway metabolites. To minimize contact of the phosphorylated analytes with stainless steel surfaces, a μ-ESI-MS probe with a hybrid electrode made of PEEKsil was employed. Optimized HILIC gradient elution conditions with 100 mM ammonium formate (pH 11) provided the separation of hexose monophosphate and pentose phosphate isomers. To ensure good retention time repeatability in HILIC, perfluoroalkoxy alkane bottles were used for the mobile phase (with sd over 60 runs between 0.01 and 0.02 min). For the quantitative assay, the U-13C-labeled cell extract was spiked prior to extraction by metal oxide-based affinity chromatography (MOAC) with TiO2 beads. The concentrations of the 24 targets were quantified in HeLa and human embryonic kidney (HEK293) cells. Erastin-induced ferroptosis in HEK293 cells was accompanied by enhanced levels of fructose-1,6-bis-phosphate, 2- and 3-phosphoglycerate, and 2,3-bis-phosphoglycerate.

DOI: https://doi.org/10.1021/acs.analchem.4c03490
Anal Bioanal Chem. 2024 Oct 24.

Leveraging the potential of 1.0-mm i.d. columns in UHPLC-HRMS-based untargeted metabolomics.

Danila La Gioia, Emanuela Salviati, Manuela Giovanna Basilicata, Claudia Felici, Oronza A Botrugno, Giovanni Tonon, Eduardo Sommella, Pietro Campiglia.

  Untargeted metabolomics UHPLC-HRMS workflows typically employ narrowbore 2.1-mm inner diameter (i.d.) columns. However, the wide concentration range of the metabolome and the need to often analyze small sample amounts poses challenges to these approaches. Reducing the column diameter could be a potential solution. Herein, we evaluated the performance of a microbore 1.0-mm i.d. setup compared to the 2.1-mm i.d. benchmark for untargeted metabolomics. The 1.0-mm i.d. setup was implemented on a micro-UHPLC system, while the 2.1-mm i.d. on a standard UHPLC, both coupled to quadrupole-orbitrap HRMS. On polar standard metabolites, a sensitivity gain with an average 3.8-fold increase over the 2.1-mm i.d., along with lower LOD (LODavg 1.48 ng/mL vs. 6.18 ng/mL) and LOQ (LOQavg 4.94 ng/mL vs. 20.60 ng/mL), was observed. The microbore method detected and quantified all metabolites at LLOQ with respect to 2.1, also demonstrating good repeatability with lower CV% for retention times (0.29% vs. 0.63%) and peak areas (4.65% vs. 7.27%). The analysis of various samples, in both RP and HILIC modes, including different plasma volumes, dried blood spots (DBS), and colorectal cancer (CRC) patient-derived organoids (PDOs), in full scan-data dependent mode (FS-DDA) reported a significant increase in MS1 and MS2 features, as well as MS/MS spectral matches by 38.95%, 39.26%, and 18.23%, respectively. These findings demonstrate that 1.0-mm i.d. columns in UHPLC-HRMS could be a potential strategy to enhance coverage for low-amount samples while maintaining the same analytical throughput and robustness of 2.1-mm i.d. formats, with reduced solvent consumption.

Keywords:  Mass spectrometry; Metabolomics; Microbore; UHPLC; Untargeted

DOI:  https://doi.org/10.1007/s00216-024-05588-z