bims-mascan 2023-05-14 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2023‒05‒14
twenty papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

HyperSCP: Combining Isotopic and Isobaric Labeling for Higher Throughput Single-Cell Proteomics.
Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis.
Deciphering Phenotypes from Protein Biomarkers for Translational Research with PIPER.
Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses.
Global xenobiotic profiling of rat plasma using untargeted metabolomics and background subtraction-based approaches: Method evaluation and comparison.
Improved Structural Characterization of Glycerophospholipids and Sphingomyelins with Real-Time Library Searching.
Using stable isotope tracing to unravel the metabolic components of neurodegeneration: Focus on neuron-glia metabolic interactions.
Isolation and Preparation of Extracellular Proteins from Lignocellulose-Degrading Fungi for Comparative Proteomic Studies Using Mass Spectrometry.
Lipid metabolism in dendritic cell biology.
Cell-selective proteomics segregates pancreatic cancer subtypes by extracellular proteins in tumors and circulation.
Optimization of energy production and central carbon metabolism in a non-respiring eukaryote.
Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.
The Edge Effect in High-Throughput Proteomics: A Cautionary Tale.
Lipidomics in food quality and authentication: A comprehensive review of novel trends and applications using chromatographic and spectroscopic techniques.
Diazo probe-based chemical isotope labeling assisted liquid chromatography-tandem mass spectrometry analysis for sensitive determination of amino acids in biofluids.
Localization analysis of metabolites from complex biological samples-recent analytical technique of mass spectrometry imaging.
HyperSpec: Ultrafast Mass Spectra Clustering in Hyperdimensional Space.
Obesity-induced changes in cancer cells and their microenvironment: Mechanisms and therapeutic perspectives to manage dysregulated lipid metabolism.
Lipophagy-mediated cholesterol synthesis inhibition is required for the survival of hepatocellular carcinoma under glutamine deprivation.
Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer.

Anal Chem. 2023 May 11.

HyperSCP: Combining Isotopic and Isobaric Labeling for Higher Throughput Single-Cell Proteomics.

Yiran Liang, Thy Truong, Aubrianna J Saxton, Hannah Boekweg, Samuel H Payne, Pam M Van Ry, Ryan T Kelly.

Recent developments in mass spectrometry-based single-cell proteomics (SCP) have resulted in dramatically improved sensitivity, yet the relatively low measurement throughput remains a limitation. Isobaric and isotopic labeling methods have been separately applied to SCP to increase throughput through multiplexing. Here we combined both forms of labeling to achieve multiplicative scaling for higher throughput. Two-plex stable isotope labeling of amino acids in cell culture (SILAC) and isobaric tandem mass tag (TMT) labeling enabled up to 28 single cells to be analyzed in a single liquid chromatography-mass spectrometry (LC-MS) analysis, in addition to carrier, reference, and negative control channels. A custom nested nanowell chip was used for nanoliter sample processing to minimize sample losses. Using a 145-min total LC-MS cycle time, ∼280 single cells were analyzed per day. This measurement throughput could be increased to ∼700 samples per day with a high-duty-cycle multicolumn LC system producing the same active gradient. The labeling efficiency and achievable proteome coverage were characterized for multiple analysis conditions.

DOI: https://doi.org/10.1021/acs.analchem.3c00906
Anal Chem. 2023 May 08.

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis.

Zhikun Jia, Qiongju Qiu, Ruiping He, Tianyu Zhou, Li Chen.

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

DOI: https://doi.org/10.1021/acs.analchem.3c00804
J Proteome Res. 2023 May 12.

Deciphering Phenotypes from Protein Biomarkers for Translational Research with PIPER.

Sudhir Putty Reddy, Aileen Y Alontaga, Eric A Welsh, Eric B Haura, Theresa A Boyle, Steven A Eschrich, John M Koomen.

  Liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) has widespread clinical use for detection of inborn errors of metabolism, therapeutic drug monitoring, and numerous other applications. This technique detects proteolytic peptides as surrogates for protein biomarker expression, mutation, and post-translational modification in individual clinical assays and in cancer research with highly multiplexed quantitation across biological pathways. LC-MRM for protein biomarkers must be translated from multiplexed research-grade panels to clinical use. LC-MRM panels provide the capability to quantify clinical biomarkers and emerging protein markers to establish the context of tumor phenotypes that provide highly relevant supporting information. An application to visualize and communicate targeted proteomics data will empower translational researchers to move protein biomarker panels from discovery to clinical use. Therefore, we have developed a web-based tool for targeted proteomics that provides pathway-level evaluations of key biological drivers (e.g., EGFR signaling), signature scores (representing phenotypes) (e.g., EMT), and the ability to quantify specific drug targets across a sample cohort. This tool represents a framework for integrating summary information, decision algorithms, and risk scores to support Physician-Interpretable Phenotypic Evaluation in R (PIPER) that can be reused or repurposed by other labs to communicate and interpret their own biomarker panels.

Keywords:  pathway; protein biomarker; targeted proteomics; visualization software

DOI:  https://doi.org/10.1021/acs.jproteome.3c00137
J Am Soc Mass Spectrom. 2023 May 10.

Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses.

Khiry L Patterson, Albert B Arul, Min Ji Choi, Nekesa C Oliver, Marsalas D Whitaker, Angel C Bodrick, Julia B Libby, Shania Hansen, Logan Dumitrescu, Katherine A Gifford, Angela L Jefferson, Timothy J Hohman, Renã A S Robinson.

  Proteomics research has been transformed due to high-throughput liquid chromatography (LC-MS/MS) tandem mass spectrometry instruments combined with highly sophisticated automated sample preparation and multiplexing workflows. However, scaling proteomics experiments to large sample cohorts (hundreds to thousands) requires thoughtful quality control (QC) protocols. Robust QC protocols can help with reproducibility, quantitative accuracy, and provide opportunities for more decisive troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of N = 335 patient samples using tandem mass tag (TMTpro) 16-plex batches. Over the course of a 10-month data acquisition period for this cohort we collected 271 pooled QC LC-MS/MS result files obtained from MS/MS analysis of a patient-derived pooled plasma sample, representative of the entire cohort population. This sample was tagged with TMTzero or TMTpro reagents and used to inform the daily performance of the LC-MS/MS instruments and to allow within and across sample batch normalization. Analytical variability of a number of instrumental and data analysis metrics including protein and peptide identifications, peptide spectral matches (PSMs), number of obtained MS/MS spectra, average peptide abundance, percent of peptides with a Δ m/z between ±0.003 Da, percent of MS/MS spectra obtained at the maximum injection time, and the retention time of selected tracking peptides were evaluated to help inform the design of a robust LC-MS/MS QC workflow for use in future cohort studies. This study also led to general tips for using selected metrics to inform real-time troubleshooting of LC-MS/MS performance issues with daily QC checks.

Keywords:  liquid chromatography; mass spectrometry; plasma; proteins; proteomics; quality control

DOI:  https://doi.org/10.1021/jasms.3c00050
Curr Drug Metab. 2023 May 08.

Global xenobiotic profiling of rat plasma using untargeted metabolomics and background subtraction-based approaches: Method evaluation and comparison.

Xiaojuan Jiang, Simian Chen, Mingshe Zhu, Caisheng Wu.

  BACKGROUND: Global xenobiotic profiling (GXP) is to detect and structurally characterize all xenobiotics in biological samples using mainly liquid chromatography-high resolution mass spectrometry (LC-HRMS) based methods. GXP is highly needed in drug metabolism study, food safety testing, forensic chemical analysis, and exposome research. For detecting known or predictable xenobiotics, targeted LC-HRMS data processing methods based on molecular weights, mass defects and fragmentations of analytes are routinely employed. For profiling unknown xenobiotics, untargeted and LC-HRMS based metabolomics and background subtraction-based approaches are required.OBJECTIVE: This study aimed to evaluate the effectiveness of untargeted metabolomics and the precise and thorough background subtraction (PATBS) in GXP of rat plasma.
METHODS: Rat plasma samples collected from an oral administration of nefazodone (NEF) or Glycyrrhizae Radix et Rhizoma (Gancao, GC) were analyzed by LC-HRMS. NEF metabolites and GC components in rat plasma were thoroughly searched and characterized via processing LC-HRMS datasets using targeted and untargeted methods.
RESULTS: PATBS detected 68 NEF metabolites and 63 GC components, while the metabolomic approach (MS-DIAL) found 67 NEF metabolites and 60 GC components in rat plasma. The two methods found 79 NEF metabolites and 80 GC components with 96% and 91% successful rates, respectively.
CONCLUSION: Metabolomics methods are capable of GXP and measuring alternations of endogenous metabolites in a group of biological samples, while PATBS is more suited for sensitive GXP of a single biological sample. A combination of metabolomics and PATBS approaches can generate better results in the untargeted profiling of unknown xenobiotics.

Keywords:  drug metabolism; mass defect filtering; metabolomics; precise and thorough background subtraction; unpredicted metabolites

DOI:  https://doi.org/10.2174/1389200224666230508122240
Anal Chem. 2023 May 12.

Improved Structural Characterization of Glycerophospholipids and Sphingomyelins with Real-Time Library Searching.

Dain R Brademan, Katherine A Overmyer, Yuchen He, William D Barshop, Jesse D Canterbury, Brandon J Bills, Benton J Anderson, Paul D Hutchins, Seema Sharma, Vlad Zabrouskov, Graeme C McAlister, Joshua J Coon.

In mass spectrometry-based lipidomics, complex lipid mixtures undergo chromatographic separation, are ionized, and are detected using tandem MS (MSn) to simultaneously quantify and structurally characterize eluting species. The reported structural granularity of these identified lipids is strongly reliant on the analytical techniques leveraged in a study. For example, lipid identifications from traditional collisionally activated data-dependent acquisition experiments are often reported at either species level or molecular species level. Structural resolution of reported lipid identifications is routinely enhanced by integrating both positive and negative mode analyses, requiring two separate runs or polarity switching during a single analysis. MS3+ can further elucidate lipid structure, but the lengthened MS duty cycle can negatively impact analysis depth. Recently, functionality has been introduced on several Orbitrap Tribrid mass spectrometry platforms to identify eluting molecular species on-the-fly. These real-time identifications can be leveraged to trigger downstream MSn to improve structural characterization with lessened impacts on analysis depth. Here, we describe a novel lipidomics real-time library search (RTLS) approach, which utilizes the lipid class of real-time identifications to trigger class-targeted MSn and to improve the structural characterization of phosphotidylcholines, phosphotidylethanolamines, phosphotidylinositols, phosphotidylglycerols, phosphotidylserine, and sphingomyelins in the positive ion mode. Our class-based RTLS method demonstrates improved selectivity compared to the current methodology of triggering MSn in the presence of characteristic ions or neutral losses.

DOI: https://doi.org/10.1021/acs.analchem.2c04633
Neurobiol Dis. 2023 May 05. pii: S0969-9961(23)00159-6. [Epub ahead of print] 106145

Using stable isotope tracing to unravel the metabolic components of neurodegeneration: Focus on neuron-glia metabolic interactions.

Emil W Westi, Jens V Andersen, Blanca I Aldana.

  Disrupted brain metabolism is a critical component of several neurodegenerative diseases. Energy metabolism of both neurons and astrocytes is closely connected to neurotransmitter recycling via the glutamate/GABA-glutamine cycle. Neurons and astrocytes hereby work in close metabolic collaboration which is essential to sustain neurotransmission. Elucidating the mechanistic involvement of altered brain metabolism in disease progression has been aided by the advance of techniques to monitor cellular metabolism, in particular by mapping metabolism of substrates containing stable isotopes, a technique known as isotope tracing. Here we review key aspects of isotope tracing including advantages, drawbacks and applications to different cerebral preparations. In addition, we narrate how isotope tracing has facilitated the discovery of central metabolic features in neurodegeneration with a focus on the metabolic cooperation between neurons and astrocytes.

Keywords:  Alzheimer's disease; Amyotrophic lateral sclerosis; Astrocytes; Brain energy metabolism; Huntington's disease; Mass spectrometry; Neurodegenerative disorders; Parkinson's disease

DOI:  https://doi.org/10.1016/j.nbd.2023.106145
Methods Mol Biol. 2023 ;2657 305-313

Isolation and Preparation of Extracellular Proteins from Lignocellulose-Degrading Fungi for Comparative Proteomic Studies Using Mass Spectrometry.

Robert J Gruninger, Adrian Tsang, Tim A McAllister.

  Fungi utilize a unique mechanism of nutrient acquisition involving extracellular digestion. To understand the biology of these microbes, it is important to identify and characterize the function of proteins that are secreted and involved in nutrient acquisition. Mass spectrometry-based proteomics is a powerful tool to study complex mixtures of proteins and understand how the proteins produced by an organism change in response to different conditions. Many fungi are efficient decomposers of plant cell walls, and anaerobic fungi are well recognized for their ability to digest lignocellulose. Here we outline a protocol for the enrichment and isolation of proteins secreted by anaerobic fungi after growth on simple (glucose) and complex (straw and alfalfa hay) carbon sources. We provide detailed instruction on generating protein fragments and preparing these for proteomic analysis using reversed-phase chromatography and mass spectrometry. The interpretation of results and their relevance to a particular biological system is study-dependent and beyond the scope of this protocol.

Keywords:  Carbohydrate-active enzymes; Exo-proteome; Fungi; Lignocellulose; Mass spectrometry; Proteomics

DOI:  https://doi.org/10.1007/978-1-0716-3151-5_21
Immunol Rev. 2023 May 12.

Lipid metabolism in dendritic cell biology.

Zhiyuan You, Hongbo Chi.

  Dendritic cells (DCs) are innate immune cells that detect and process environmental signals and communicate them with T cells to bridge innate and adaptive immunity. Immune signals and microenvironmental cues shape the function of DC subsets in different contexts, which is associated with reprogramming of cellular metabolic pathways. In addition to integrating these extracellular cues to meet bioenergetic and biosynthetic demands, cellular metabolism interplays with immune signaling to shape DC-dependent immune responses. Emerging evidence indicates that lipid metabolism serves as a key regulator of DC responses. Here, we summarize the roles of fatty acid and cholesterol metabolism, as well as selective metabolites, in orchestrating the functions of DCs. Specifically, we highlight how different lipid metabolic programs, including de novo fatty acid synthesis, fatty acid β oxidation, lipid storage, and cholesterol efflux, influence DC function in different contexts. Further, we discuss how dysregulation of lipid metabolism shapes DC intracellular signaling and contributes to the impaired DC function in the tumor microenvironment. Finally, we conclude with a discussion on key future directions for the regulation of DC biology by lipid metabolism. Insights into the connections between lipid metabolism and DC functional specialization may facilitate the development of new therapeutic strategies for human diseases.

Keywords:  cholesterol; dendritic cells; fatty acid; innate immunity; lipid metabolism; lipid metabolites

DOI:  https://doi.org/10.1111/imr.13215
Nat Commun. 2023 May 08. 14(1): 2642

Cell-selective proteomics segregates pancreatic cancer subtypes by extracellular proteins in tumors and circulation.

Jonathan J Swietlik, Stefanie Bärthel, Chiara Falcomatà, Diana Fink, Ankit Sinha, Jingyuan Cheng, Stefan Ebner, Peter Landgraf, Daniela C Dieterich, Henrik Daub, Dieter Saur, Felix Meissner.

Cell-selective proteomics is a powerful emerging concept to study heterocellular processes in tissues. However, its high potential to identify non-cell-autonomous disease mechanisms and biomarkers has been hindered by low proteome coverage. Here, we address this limitation and devise a comprehensive azidonorleucine labeling, click chemistry enrichment, and mass spectrometry-based proteomics and secretomics strategy to dissect aberrant signals in pancreatic ductal adenocarcinoma (PDAC). Our in-depth co-culture and in vivo analyses cover more than 10,000 cancer cell-derived proteins and reveal systematic differences between molecular PDAC subtypes. Secreted proteins, such as chemokines and EMT-promoting matrisome proteins, associated with distinct macrophage polarization and tumor stromal composition, differentiate classical and mesenchymal PDAC. Intriguingly, more than 1,600 cancer cell-derived proteins including cytokines and pre-metastatic niche formation-associated factors in mouse serum reflect tumor activity in circulation. Our findings highlight how cell-selective proteomics can accelerate the discovery of diagnostic markers and therapeutic targets in cancer.

DOI: https://doi.org/10.1038/s41467-023-38171-8
Curr Biol. 2023 Apr 30. pii: S0960-9822(23)00528-6. [Epub ahead of print]

Optimization of energy production and central carbon metabolism in a non-respiring eukaryote.

Sara Alam, Ying Gu, Polina Reichert, Jürg Bähler, Snezhana Oliferenko.

  Most eukaryotes respire oxygen, using it to generate biomass and energy. However, a few organisms have lost the capacity to respire. Understanding how they manage biomass and energy production may illuminate the critical points at which respiration feeds into central carbon metabolism and explain possible routes to its optimization. Here, we use two related fission yeasts, Schizosaccharomyces pombe and Schizosaccharomyces japonicus, as a comparative model system. We show that although S. japonicus does not respire oxygen, unlike S. pombe, it is capable of efficient NADH oxidation, amino acid synthesis, and ATP generation. We probe possible optimization strategies through the use of stable isotope tracing metabolomics, mass isotopologue distribution analysis, genetics, and physiological experiments. S. japonicus appears to have optimized cytosolic NADH oxidation via glycerol-3-phosphate synthesis. It runs a fully bifurcated TCA pathway, sustaining amino acid production. Finally, we propose that it has optimized glycolysis to maintain high ATP/ADP ratio, in part by using the pentose phosphate pathway as a glycolytic shunt, reducing allosteric inhibition of glycolysis and supporting biomass generation. By comparing two related organisms with vastly different metabolic strategies, our work highlights the versatility and plasticity of central carbon metabolism in eukaryotes, illuminating critical adaptations supporting the preferential use of glycolysis over oxidative phosphorylation.

Keywords:  Schizosaccharomyces japonicus; Schizosaccharomyces pombe; TCA Cycle; bifurcated TCA pathway; fermentation; glycolysis; metabolomics; respiration

DOI:  https://doi.org/10.1016/j.cub.2023.04.046
bioRxiv. 2023 Apr 30. pii: 2023.04.28.538731. [Epub ahead of print]

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples.

Hratch Baghdassarian, Daniel Dimitrov, Erick Armingol, Julio Saez-Rodriguez, Nathan E Lewis.

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/ . This protocol typically takes ∼1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of ∼63k cells, 10 cell types, and 12 samples.

DOI: https://doi.org/10.1101/2023.04.28.538731
J Am Soc Mass Spectrom. 2023 May 08.

The Edge Effect in High-Throughput Proteomics: A Cautionary Tale.

Colleen B Maxwell, Jatinderpal K Sandhu, Thong H Cao, Gerry P McCann, Leong L Ng, Donald J L Jones.

In order for mass spectrometry to continue to grow as a platform for high-throughput clinical and translational research, careful consideration must be given to quality control by ensuring that the assay performs reproducibly and accurately and precisely. In particular, the throughput required for large cohort clinical validation in biomarker discovery and diagnostic screening has driven the growth of multiplexed targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) assays paired with sample preparation and analysis in multiwell plates. However, large scale MS-based proteomics studies are often plagued by batch effects: sources of technical variation in the data, which can arise from a diverse array of sources such as sample preparation batches, different reagent lots, or indeed MS signal drift. These batch effects can confound the detection of true signal differences, resulting in incorrect conclusions being drawn about significant biological effects or lack thereof. Here, we present an intraplate batch effect termed the edge effect arising from temperature gradients in multiwell plates, commonly reported in preclinical cell culture studies but not yet reported in a clinical proteomics setting. We present methods herein to ameliorate the phenomenon including proper assessment of heating techniques for multiwell plates and incorporation of surrogate standards, which can normalize for intraplate variation.

DOI: https://doi.org/10.1021/jasms.3c00035
Crit Rev Food Sci Nutr. 2023 May 10. 1-24

Lipidomics in food quality and authentication: A comprehensive review of novel trends and applications using chromatographic and spectroscopic techniques.

Engy Mahrous, Ruoxin Chen, Chao Zhao, Mohamed A Farag.

  Lipid analysis is an integral part of food authentication and quality control which provides consumers with the necessary information to make an informed decision about their lipid intake. Recent advancement in lipid analysis and lipidome scope represents great opportunities for food science. In this review we provide a comprehensive overview of available tools for extraction, analysis and interpretation of data related to dietary fats analyses. Different analytical platforms are discussed including GC, MS, NMR, IR and UV with emphasis on their merits and limitations alongside complementary tools such as chemometric models and lipid-targeted online databases. Applications presented here include quality control, authentication of organic and delicacy food, tracing dietary fat source and investigating the effect of heat/storage on lipids. A multitude of analytical methods with different sensitivity, affordability, reproducibility and ease of operation are now available to comprehensively analyze dietary fats. Application of these methods range from studies which favor the use of large data generating platforms such as MS-based methods, to routine quality control which demands easy to use affordable equipment as TLC and IR. Hence, this review provides a navigation tool for food scientists to help develop an optimal protocol for their future lipid analysis quest.

Keywords:  Dietary fats; NMR spectroscopy; lipid extraction; mass spectrometry; quality control; vibrational spectroscopy

DOI:  https://doi.org/10.1080/10408398.2023.2207659
J Chromatogr B Analyt Technol Biomed Life Sci. 2023 Apr 26. pii: S1570-0232(23)00134-4. [Epub ahead of print]1223 123724

Diazo probe-based chemical isotope labeling assisted liquid chromatography-tandem mass spectrometry analysis for sensitive determination of amino acids in biofluids.

Chun-Yue Han, Li Ge, Chi Zhang, Li-Na Ding, Peng Wang, Fang Yu, Sheng Wang, Lili Zhu, Qunlin Zhang, Qi Liu, Fei-Long Liu.

  Amino acids are important biomolecules and contribute to essential biological processes. Liquid chromatography tandem mass spectrometry (LC-MS) now is a powerful tool for the analysis of amino acid metabolites; however, the structural similarity and polarity of amino acids can lead to the poor chromatographic retention and low detection sensitivities. In this study, we used a pair of light and heavy isotopomers of diazo probes, d0/d5-2-(diazomethyl)-N-methyl-N-phenyl-benzamide (2-DMBA/d5 -2-DMBA) to label amino acids. The paired MS probes of 2-DMBA and d5 -2-DMBA carry diazo groups that can efficiently and specifically react with the carboxyl group on free amino acid metabolites under mild conditions. Benefiting from the transfer of the 2-DMBA/d5 -2-DMBA to carboxyl group on amino acids, the ionization efficiencies of amino acids presented great enhancement during LC-MS analysis. The results suggested that the detection sensitivities of 17 amino acids increased by 9-133-fold upon 2-DMBA labeling, and the obtained limits of detection (LODs) of amino acids on-column ranged from 0.011 fmol-0.057 fmol. With the application of the developed method, we successfully achieved the sensitive and accurate detection of the 17 amino acids in microliter level of serum sample. Moreover, the contents of most amino acids were different in the serum from normal and B16F10-tumour mice, demonstrating that endogenous amino acids may play important roles in the regulation of tumors development. This developed method of chemical labeling of amino acids with diazo probes assisted LC-MS analysis provides a potentially valuable tool to investigate the relationships between amino acids metabolism and diseases.

Keywords:  Amino acids; Chemical isotope labeling; Diazo probe; Liquid chromatography-tandem mass spectrometry

DOI:  https://doi.org/10.1016/j.jchromb.2023.123724
Front Mol Biosci. 2023 ;10 1169449

Localization analysis of metabolites from complex biological samples-recent analytical technique of mass spectrometry imaging.

Qiang Yang, Ying Cai, Sifan Guo, Zhibo Wang, Shi Qiu, Aihua Zhang.



Keywords:  diagnosis; mass spectrometry imaging; metabolite; metabolomics; target

DOI:  https://doi.org/10.3389/fmolb.2023.1169449
J Proteome Res. 2023 May 11.

HyperSpec: Ultrafast Mass Spectra Clustering in Hyperdimensional Space.

Weihong Xu, Jaeyoung Kang, Wout Bittremieux, Niema Moshiri, Tajana Rosing.

  As current shotgun proteomics experiments can produce gigabytes of mass spectrometry data per hour, processing these massive data volumes has become progressively more challenging. Spectral clustering is an effective approach to speed up downstream data processing by merging highly similar spectra to minimize data redundancy. However, because state-of-the-art spectral clustering tools fail to achieve optimal runtimes, this simply moves the processing bottleneck. In this work, we present a fast spectral clustering tool, HyperSpec, based on hyperdimensional computing (HDC). HDC shows promising clustering capability while only requiring lightweight binary operations with high parallelism that can be optimized using low-level hardware architectures, making it possible to run HyperSpec on graphics processing units to achieve extremely efficient spectral clustering performance. Additionally, HyperSpec includes optimized data preprocessing modules to reduce the spectrum preprocessing time, which is a critical bottleneck during spectral clustering. Based on experiments using various mass spectrometry data sets, HyperSpec produces results with comparable clustering quality as state-of-the-art spectral clustering tools while achieving speedups by orders of magnitude, shortening the clustering runtime of over 21 million spectra from 4 h to only 24 min.

Keywords:  hyperdimensional computing; mass spectrometry; peptide identification; runtime optimization; spectral clustering

DOI:  https://doi.org/10.1021/acs.jproteome.2c00612
Semin Cancer Biol. 2023 May 06. pii: S1044-579X(23)00076-7. [Epub ahead of print]93 36-51

Obesity-induced changes in cancer cells and their microenvironment: Mechanisms and therapeutic perspectives to manage dysregulated lipid metabolism.

Miriam Lee-Rueckert, Marina Canyelles, Mireia Tondo, Noemi Rotllan, Petri T Kovanen, Vicenta Llorente-Cortes, Joan Carles Escolà-Gil.

  Obesity has been closely related to cancer progression, recurrence, metastasis, and treatment resistance. We aim to review recent progress in the knowledge on the obese macroenvironment and the generated adipose tumor microenvironment (TME) inducing lipid metabolic dysregulation and their influence on carcinogenic processes. Visceral white adipose tissue expansion during obesity exerts systemic or macroenvironmental effects on tumor initiation, growth, and invasion by promoting inflammation, hyperinsulinemia, growth-factor release, and dyslipidemia. The dynamic relationship between cancer and stromal cells of the obese adipose TME is critical for cancer cell survival and proliferation as well. Experimental evidence shows that secreted paracrine signals from cancer cells can induce lipolysis in cancer-associated adipocytes, causing them to release free fatty acids and acquire a fibroblast-like phenotype. Such adipocyte delipidation and phenotypic change is accompanied by an increased secretion of cytokines by cancer-associated adipocytes and tumor-associated macrophages in the TME. Mechanistically, the availability of adipose TME free fatty acids and tumorigenic cytokines concomitant with the activation of angiogenic processes creates an environment that favors a shift in the cancer cells toward an aggressive phenotype associated with increased invasiveness. We conclude that restoring the aberrant metabolic alterations in the host macroenvironment and in adipose TME of obese subjects would be a therapeutic option to prevent cancer development. Several dietary, lipid-based, and oral antidiabetic pharmacological therapies could potentially prevent tumorigenic processes associated with the dysregulated lipid metabolism closely linked to obesity.

Keywords:  Adipocytes, cancer; Cholesterol; Fibroblasts, fatty acids; Lipids; Macrophages; Obesity; Tumor microenvironment

DOI:  https://doi.org/10.1016/j.semcancer.2023.05.002
Redox Biol. 2023 May 04. pii: S2213-2317(23)00133-7. [Epub ahead of print]63 102732

Lipophagy-mediated cholesterol synthesis inhibition is required for the survival of hepatocellular carcinoma under glutamine deprivation.

Youzi Kong, Mengting Wu, Xiaoyu Wan, Min Sun, Yankun Zhang, Zhuanchang Wu, Chunyang Li, Xiaohong Liang, Lifen Gao, Chunhong Ma, Xuetian Yue.

  Glutamine is critical for tumor progression, and restriction of its availability is emerging as a potential therapeutic strategy. The metabolic plasticity of tumor cells helps them adapting to glutamine restriction. However, the role of cholesterol metabolism in this process is relatively unexplored. Here, we reported that glutamine deprivation inhibited cholesterol synthesis in hepatocellular carcinoma (HCC). Reactivation of cholesterol synthesis enhanced glutamine-deprivation-induced cell death of HCC cells, which is partially duo to augmented NADPH depletion and lipid peroxidation. Mechanistically, glutamine deprivation induced lipophagy to transport cholesterol from lipid droplets (LDs) to endoplasmic reticulum (ER), leading to inhibit SREBF2 maturation and cholesterol synthesis, and maintain redox balance for survival. Glutamine deprivation decreased mTORC1 activity to induce lipophagy. Importantly, administration of U18666A, CQ, or shTSC2 viruses further augmented GPNA-induced inhibition of xenograft tumor growth. Clinical data supported that glutamine utilization positively correlated with cholesterol synthesis, which is associated with poor prognosis of HCC patients. Collectively, our study revealed that cholesterol synthesis inhibition is required for the survival of HCC under glutamine-restricted tumor microenvironment.

Keywords:  Cholesterol metabolism; Glutamine metabolism; Redox balance; SREBF2; mTORC1

DOI:  https://doi.org/10.1016/j.redox.2023.102732
Nat Commun. 2023 May 10. 14(1): 2692

Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer.

Chenglong Sun, Anqiang Wang, Yanhe Zhou, Panpan Chen, Xiangyi Wang, Jianpeng Huang, Jiamin Gao, Xiao Wang, Liebo Shu, Jiawei Lu, Wentao Dai, Zhaode Bu, Jiafu Ji, Jiuming He.

Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated "tumor-normal interface" region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level.

DOI: https://doi.org/10.1038/s41467-023-38360-5