bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2023‒01‒29
25 papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh
  1. Lipid Spectrum Generator: A Simple Script for the Generation of Accurate In Silico Lipid Fragmentation Spectra.
  2. Simulation of mass spectrometry-based proteomics data with Synthedia.
  3. The role of serine metabolism in lung cancer: From oncogenesis to tumor treatment.
  4. MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search.
  5. Site-specific quantitative cysteine profiling with data-independent acquisition-based mass spectrometry.
  6. ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics.
  7. Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils.
  8. Computational mass spectrometry accelerates C = C position-resolved untargeted lipidomics using oxygen attachment dissociation.
  9. Liquid chromatography-mass spectrometry metabolite library for metabolomics: Evaluating column suitability using a scoring approach.
  10. Metabolomic analysis of circulating tumor cells derived liver metastasis of colorectal cancer.
  11. Determination of endogenous sphingolipid content in stroke rats and HT22 cells subjected to oxygen-glucose deprivation by LC‒MS/MS.
  12. The role of metabolic reprogramming in pancreatic cancer chemoresistance.
  13. AlacatDesigner─Computational Design of Peptide Concatamers for Protein Quantitation.
  14. BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.
  15. Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry.
  16. Scribe: Next Generation Library Searching for DDA Experiments.
  17. MS-RIDD paves the way toward routine double bond localization in mass spectrometry-based lipidomics.
  18. Applications of spatially resolved omics in the field of endocrine tumors.
  19. Single-Cell Mass Spectrometry of Metabolites and Proteins for Systems and Functional Biology.
  20. MS1Connect: a mass spectrometry run similarity measure.
  21. Research advances in the understanding of how exosomes regulate ferroptosis in cancer.
  22. When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective.
  23. Development of a quantitative proteomics approach for cyclooxygenases and lipoxygenases in parallel to quantitative oxylipin analysis allowing the comprehensive investigation of the arachidonic acid cascade.
  24. PeptideRanger: An R Package to Optimize Synthetic Peptide Selection for Mass Spectrometry Applications.
  25. One-shot 13 C15 N-metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux.
  1. Anal Chem. 2023 Jan 24.
    Lipid Spectrum Generator: A Simple Script for the Generation of Accurate In Silico Lipid Fragmentation Spectra.
    David S Gertner, Jake P Violi, David P Bishop, Matthew P Padula.
      Due to the complexity of lipids in nature, the use of in silico generated spectral libraries to identify lipid species from mass spectral data has become an integral part of many lipidomic workflows. However, many in silico libraries are either limited in usability or their capacity to represent lipid species. Here, we introduce Lipid Spectrum Generator, an open-source in silico spectral library generator specifically designed to aid in the identification of lipids in liquid chromatography-tandem mass spectrometry analysis.
    DOI:  https://doi.org/10.1021/acs.analchem.2c04518
  2. Bioinform Adv. 2023 ;3(1): vbac096
    Simulation of mass spectrometry-based proteomics data with Synthedia.
    Michael G Leeming, Ching-Seng Ang, Shuai Nie, Swati Varshney, Nicholas A Williamson.
      Motivation: A large number of experimental and bioinformatic parameters must be set to identify and quantify peptides in mass spectrometry experiments and each of these will impact the results. An ability to simulate raw data with known contents would allow researchers to rapidly explore the effects of varying experimental parameters and systematically investigate downstream processing software. A range of data simulators are available for established data-dependent acquisition methodologies, but these do not extend to the rapidly developing field of data-independent acquisition (DIA) strategies.Results: Here, we present Synthedia-a software package to simulate DIA liquid chromatography-mass spectrometry for bottom-up proteomics experiments. Synthedia can generate datasets with known peptide precursor ions and fragments and allows for the customization of a wide variety of chromatographic and mass spectrometry parameters.
    Availability and implementation: Synthedia is freely available via the internet and can be used through a graphical website (https://synthedia.org/) or locally via the command line (https://github.com/mgleeming/synthedia/).
    Supplementary information: Supplementary data are available at Bioinformatics Advances online.
    DOI:  https://doi.org/10.1093/bioadv/vbac096
  3. Front Genet. 2022 ;13 1084609
    The role of serine metabolism in lung cancer: From oncogenesis to tumor treatment.
    Xijia Zhou, Chang Tian, Yingshu Cao, Min Zhao, Ke Wang.
      Metabolic reprogramming is an important hallmark of malignant tumors. Serine is a non-essential amino acid involved in cell proliferation. Serine metabolism, especially the de novo serine synthesis pathway, forms a metabolic network with glycolysis, folate cycle, and one-carbon metabolism, which is essential for rapidly proliferating cells. Owing to the rapid development in metabolomics, abnormal serine metabolism may serve as a biomarker for the early diagnosis and pathological typing of tumors. Targeting serine metabolism also plays an essential role in precision and personalized cancer therapy. This article is a systematic review of de novo serine biosynthesis and the link between serine and folate metabolism in tumorigenesis, particularly in lung cancer. In addition, we discuss the potential of serine metabolism to improve tumor treatment.
    Keywords:  PHGDH; PSAT1; PSPH; lung cancer; serine metabolism
    DOI:  https://doi.org/10.3389/fgene.2022.1084609
  4. J Proteome Res. 2023 Jan 23.
    MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search.
    Sebastian Dorl, Stephan Winkler, Karl Mechtler, Viktoria Dorfer.
      Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.
    Keywords:  bioinformatics; peptide identification; proteomics; spectral library search; tandem mass spectrometry
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00658
  5. Methods Enzymol. 2023 ;pii: S0076-6879(22)00301-9. [Epub ahead of print]679 295-322
    Site-specific quantitative cysteine profiling with data-independent acquisition-based mass spectrometry.
    Fan Yang, Chu Wang.
      Chemical proteomics methods, such as activity-based protein profiling, have emerged as powerful and versatile tools to annotate the protein functions and targets of bioactive small molecules in complex biological systems. Incorporated with mass spectrometry (MS)-based quantitative proteomics method, changes of protein activities could be captured and investigated with site-specific precision. However, the semi-stochastic nature of data-dependent acquisition and high cost of the isotopic-labeled reagents make it challenging for chemical biology research to systematically and reproducibly analyze a large number of samples in multidimensional analysis and high-throughput screening. In this chapter, we describe an efficient quantitative chemical proteomic strategy, termed DIA-ABPP, with good reproducibility and high quantification accuracy. Cysteinome profiling was used as a proof-of-concept example with the detailed protocol to demonstrate the workflow of the DIA-ABPP method, including dose-dependent analysis of cysteines that are sensitive to modification by a reactive metabolite, screening of a cysteine-reactive fragment library, and profiling of circadian cysteinome fluctuation. This quantitative chemoproteomic strategy would provide an opportunity for in-depth multi-dimensional chemical proteomic profiling and illuminate the function of bioactive small molecules and proteins in complex biological systems.
    Keywords:  Activity-based protein profiling; Chemoproteomics; Circadian clock; Cysteine; Data-independent acquisition
    DOI:  https://doi.org/10.1016/bs.mie.2022.07.037
  6. J Proteome Res. 2023 Jan 24.
    ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics.
    Tobias G Rehfeldt, Ralf Gabriels, Robbin Bouwmeester, Siegfried Gessulat, Benjamin A Neely, Magnus Palmblad, Yasset Perez-Riverol, Tobias Schmidt, Juan Antonio Vizcaíno, Eric W Deutsch.
      Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based data sets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides data sets that are useful for comparing state-of-the-art machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available at https://www.proteomicsml.org/, and we welcome the entire proteomics community to contribute to the project at https://github.com/ProteomicsML/ProteomicsML.
    Keywords:  bioinformatics; community platform; deep learning; educational platform; machine learning; proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00629
  7. J Proteome Res. 2023 Jan 23.
    Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils.
    Wout Bittremieux, Lev Levitsky, Matteo Pilz, Timo Sachsenberg, Florian Huber, Mingxun Wang, Pieter C Dorrestein.
      spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.
    Keywords:  Python; mass spectrometry; metabolomics; open source; proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00632
  8. Commun Chem. 2022 Dec 19. 5(1): 162
    Computational mass spectrometry accelerates C = C position-resolved untargeted lipidomics using oxygen attachment dissociation.
    Haruki Uchino, Hiroshi Tsugawa, Hidenori Takahashi, Makoto Arita.
      Mass spectrometry-based untargeted lipidomics has revealed the lipidome atlas of living organisms at the molecular species level. Despite the double bond (C = C) position being a crucial factor in biological system, the C = C defined structures have not yet been characterized comprehensively. Here, we present an approach for C = C position-resolved untargeted lipidomics using a combination of oxygen attachment dissociation and computational mass spectrometry to increase the annotation rate. We validated the accuracy of our platform as per the authentic standards of 85 lipids and the biogenic standards of 52 molecules containing polyunsaturated fatty acids (PUFAs) from the cultured cells fed with various fatty acid-enriched media. By analyzing human and mice-derived samples, we characterized 648 unique lipids with the C = C position-resolved level encompassing 24 lipid subclasses defined by LIPIDMAPS. Our platform also illuminated the unique profiles of tissue-specific lipids containing n-3 and/or n-6 very long-chain PUFAs (carbon [Formula: see text] 28 and double bonds [Formula: see text] 4) in the eye, testis, and brain of the mouse.
    DOI:  https://doi.org/10.1038/s42004-022-00778-1
  9. J Chromatogr A. 2023 Jan 11. pii: S0021-9673(23)00007-9. [Epub ahead of print]1690 463779
    Liquid chromatography-mass spectrometry metabolite library for metabolomics: Evaluating column suitability using a scoring approach.
    Dimitra Diamantidou, Ioannis Sampsonidis, Theodoros Liapikos, Helen Gika, Georgios Theodoridis.
      Untargeted metabolomic studies require an extensive set of analyte (metabolic) information to be obtained from each analyzed sample. Thus, highly selective, and efficient analytical methodologies together with reversed-phase (RP) or hydrophilic interaction liquid chromatography (HILIC) are usually applied in these approaches. Here, we present a performance comparison of five different chromatographic columns (C18, C8, RP Amide, zicHILIC, OH5 HILIC phases) to evaluate their sufficiency of analysis for a large analyte library, consisting of 817 authentic standards. By taking into account experimental chromatographic parameters (i.e. retention time, peak tailing and asymmetry, FWHM, signal-to-noise ratio and peak area and intensity), the proposed column scoring approach provides a simple criterion that may assist analysis in the select of a stationary phase for those metabolites of interest. RPLC methods offered better results regarding metabolic library coverage, while the zicHILIC stationary phase delivered a bigger number of properly eluted compounds. This study demonstrates the importance of choosing the most suitable configuration for the analysis of different metabolic classes.
    Keywords:  Column comparison; Hydrophilic interaction liquid chromatography; MSMLS; Mass spectrometry; Reversed-phase chromatography
    DOI:  https://doi.org/10.1016/j.chroma.2023.463779
  10. Heliyon. 2023 Jan;9(1): e12515
    Metabolomic analysis of circulating tumor cells derived liver metastasis of colorectal cancer.
    Meng Li, Shengming Wu, Chengle Zhuang, Chenzhang Shi, Lei Gu, Peng Wang, Fangfang Guo, Yilong Wang, Zhongchen Liu.
      Metabolic reprogramming is one of the essential features of tumor that may dramatically contribute to metastasis and collapse. The metabolic profiling is investigated on the patient derived tissue and cancer cell line derived mouse metastasis xenograft. As well-recognized "seeds" for remote metastasis of tumor, role of circulating tumor cells (CTCs) in the study of metabolic reprogramming feature of tumor is yet to be elucidated. More specifically, whether there is difference of metabolic features of liver metastasis in colorectal cancer (CRC) derived from either CTCs or cancer cell line is still unknown. In this study, comprehensive untargeted metabolomics was performed using high performance liquid chromatography-mass spectrometry (HPLC-MS) in liver metastasis tissues from CT26 cells and CTCs derived mouse models. We identified 288 differential metabolites associated with the pathways such as one carbon pool by folate, folate biosynthesis and histidine metabolism through bioinformation analysis. Multiple gene expression was upregulated in the CTCs derived liver metastasis, specifically some specific enzymes. These results indicated that the metabolite phenotype and corresponding gene expression in the CTCs derived liver metastasis tissues was different from the parental CT26 cells, displaying a specific up-regulation of mRNAs involved in the above metabolism-related pathways. The metabolic profile of CTCs was characterized on the liver metastatic process in colorectal cancer. The invasion ability and chemo drug tolerance of the CTCs derived tumor and metastasis was found to be overwhelming higher than cell line derived counterpart. Identification of the differential metabolites will lead to a better understanding of the hallmarks of the cancer progression and metastasis, which may suggest potential attractive target for treating metastatic CRC.
    Keywords:  Circulating tumor cell; Colorectal cancer; Liver metastasis; Metabolomics; Mouse models
    DOI:  https://doi.org/10.1016/j.heliyon.2022.e12515
  11. Lipids Health Dis. 2023 Jan 25. 22(1): 13
    Determination of endogenous sphingolipid content in stroke rats and HT22 cells subjected to oxygen-glucose deprivation by LC‒MS/MS.
    Keqi Zeng, Xin Zhou, Wanyi Liu, Cong Nie, Yingfeng Zhang.
      BACKGROUND: Stroke is the leading cause of death in humans worldwide, and its incidence increases every year. It is well documented that lipids are closely related to stroke. Analyzing the changes in lipid content in the stroke model after absolute quantification and investigating whether changes in lipid content can predict stroke severity provides a basis for the combination of clinical stroke and quantitative lipid indicators.METHODS: This paper establishes a rapid, sensitive, and reliable LC‒MS/MS analytical method for the detection of endogenous sphingolipids in rat serum and brain tissue and HT22 cells and quantifies the changes in sphingolipid content in the serum and brain tissue of rats from the normal and pMCAO groups and in cells from the normal and OGD/R groups. Using sphingosine (d17:1) as the internal standard, a chloroform: methanol (9:1) mixed system was used for protein precipitation and lipid extraction, followed by analysis by reversed-phase liquid chromatography coupled to triple quadrupole mass spectrometry.
    RESULTS: Based on absolute quantitative analysis of lipids in multiple biological samples, our results show that compared with those in the normal group, the contents of sphinganine (d16:0), sphinganine (d18:0), and phytosphingosine were significantly increased in the model group, except sphingosine-1-phosphate, which was decreased in various biological samples. The levels of each sphingolipid component in serum fluctuate with time.
    CONCLUSION: This isotope-free and derivatization-free LC‒MS/MS method can achieve absolute quantification of sphingolipids in biological samples, which may also help identify lipid biomarkers of cerebral ischemia.
    Keywords:  Content determination; Endogenous; Ischemic stroke; Liquid chromatography; Mass spectrometry; OGD/R-Induced; Sphingolipids
    DOI:  https://doi.org/10.1186/s12944-022-01762-3
  12. Front Pharmacol. 2022 ;13 1108776
    The role of metabolic reprogramming in pancreatic cancer chemoresistance.
    Chang Liu, Changfeng Li, Yuanda Liu.
      Pancreatic cancer is characterized by hidden onset, high malignancy, and early metastasis. Although a few cases meet the surgical indications, chemotherapy remains the primary treatment, and the resulting chemoresistance has become an urgent clinical problem that needs to be solved. In recent years, the importance of metabolic reprogramming as one of the hallmarks of cancers in tumorigenesis has been validated. Metabolic reprogramming involves glucose, lipid, and amino acid metabolism and interacts with oncogenes to affect the expression of key enzymes and signaling pathways, modifying the tumor microenvironment and contributing to the occurrence of drug tolerance. Meanwhile, the mitochondria are hubs of the three major nutrients and energy metabolisms, which are also involved in the development of drug resistance. In this review, we summarized the characteristic changes in metabolism during the progression of pancreatic cancer and their impact on chemoresistance, outlined the role of the mitochondria, and summarized current studies on metabolic inhibitors.
    Keywords:  chemoresistance; fatty acid synthesis; glutamine metabolism; glycolysis; metabolic reprogramming; pancreatic cancer
    DOI:  https://doi.org/10.3389/fphar.2022.1108776
  13. J Proteome Res. 2023 Jan 23.
    AlacatDesigner─Computational Design of Peptide Concatamers for Protein Quantitation.
    Martin Rusilowicz, David W Newman, Declan R Creamer, James Johnson, Kareena Adair, Victoria M Harman, Chris M Grant, Robert J Beynon, Simon J Hubbard.
      Protein quantitation via mass spectrometry relies on peptide proxies for the parent protein from which abundances are estimated. Owing to the variability in signal from individual peptides, accurate absolute quantitation usually relies on the addition of an external standard. Typically, this involves stable isotope-labeled peptides, delivered singly or as a concatenated recombinant protein. Consequently, the selection of the most appropriate surrogate peptides and the attendant design in recombinant proteins termed QconCATs are challenges for proteome science. QconCATs can now be built in a "a-la-carte" assembly method using synthetic biology: ALACATs. To assist their design, we present "AlacatDesigner", a tool that supports the peptide selection for recombinant protein standards based on the user's target protein. The user-customizable tool considers existing databases, occurrence in the literature, potential post-translational modifications, predicted miscleavage, predicted divergence of the peptide and protein quantifications, and ionization potential within the mass spectrometer. We show that peptide selections are enriched for good proteotypic and quantotypic candidates compared to empirical data. The software is freely available to use either via a web interface AlacatDesigner, downloaded as a Desktop application or imported as a Python package for the command line interface or in scripts.
    Keywords:  QconCATs; absolute quantitation; bioinformatics; peptide surrogates; protein standards; proteomics; proteotypic; quantotypic
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00608
  14. J Proteome Res. 2023 Jan 25.
    BIRCH: An Automated Workflow for Evaluation, Correction, and Visualization of Batch Effect in Bottom-Up Mass Spectrometry-Based Proteomics Data.
    Niveda Sundararaman, Archana Bhat, Vidya Venkatraman, Aleksandra Binek, Zachary Dwight, Nethika R Ariyasinghe, Sean Escopete, Sandy Y Joung, Susan Cheng, Sarah J Parker, Justyna Fert-Bober, Jennifer E Van Eyk.
      Recent surges in large-scale mass spectrometry (MS)-based proteomics studies demand a concurrent rise in methods to facilitate reliable and reproducible data analysis. Quantification of proteins in MS analysis can be affected by variations in technical factors such as sample preparation and data acquisition conditions leading to batch effects, which adds to noise in the data set. This may in turn affect the effectiveness of any biological conclusions derived from the data. Here we present Batch-effect Identification, Representation, and Correction of Heterogeneous data (BIRCH), a workflow for analysis and correction of batch effect through an automated, versatile, and easy to use web-based tool with the goal of eliminating technical variation. BIRCH also supports diagnosis of the data to check for the presence of batch effects, feasibility of batch correction, and imputation to deal with missing values in the data set. To illustrate the relevance of the tool, we explore two case studies, including an iPSC-derived cell study and a Covid vaccine study to show different context-specific use cases. Ultimately this tool can be used as an extremely powerful approach for eliminating technical bias while retaining biological bias, toward understanding disease mechanisms and potential therapeutics.
    Keywords:  batch correction; imputation; mass spectrometry; proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00671
  15. J Proteome Res. 2023 Jan 25.
    Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry.
    Yuxuan Richard Xie, Varsha K Chari, Daniel C Castro, Romans Grant, Stanislav S Rubakhin, Jonathan V Sweedler.
      Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.
    Keywords:  data-driven analysis; machine learning; mass spectrometry; single-cell analysis
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00714
  16. J Proteome Res. 2023 Jan 25.
    Scribe: Next Generation Library Searching for DDA Experiments.
    Brian C Searle, Ariana E Shannon, Damien Beau Wilburn.
      Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.
    Keywords:  data dependent acquisition; database searching; mass spectrometry; peptide identification; spectral library
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00672
  17. Commun Chem. 2022 Dec 29. 5(1): 181
    MS-RIDD paves the way toward routine double bond localization in mass spectrometry-based lipidomics.
    Leonida Marion Lamp, Jürgen Hartler.
      
    DOI:  https://doi.org/10.1038/s42004-022-00805-1
  18. Front Endocrinol (Lausanne). 2022 ;13 993081
    Applications of spatially resolved omics in the field of endocrine tumors.
    Yinuo Hou, Yan Gao, Shudi Guo, Zhibin Zhang, Ruibing Chen, Xiangyang Zhang.
      Endocrine tumors derive from endocrine cells with high heterogeneity in function, structure and embryology, and are characteristic of a marked diversity and tissue heterogeneity. There are still challenges in analyzing the molecular alternations within the heterogeneous microenvironment for endocrine tumors. Recently, several proteomic, lipidomic and metabolomic platforms have been applied to the analysis of endocrine tumors to explore the cellular and molecular mechanisms of tumor genesis, progression and metastasis. In this review, we provide a comprehensive overview of spatially resolved proteomics, lipidomics and metabolomics guided by mass spectrometry imaging and spatially resolved microproteomics directed by microextraction and tandem mass spectrometry. In this regard, we will discuss different mass spectrometry imaging techniques, including secondary ion mass spectrometry, matrix-assisted laser desorption/ionization and desorption electrospray ionization. Additionally, we will highlight microextraction approaches such as laser capture microdissection and liquid microjunction extraction. With these methods, proteins can be extracted precisely from specific regions of the endocrine tumor. Finally, we compare applications of proteomic, lipidomic and metabolomic platforms in the field of endocrine tumors and outline their potentials in elucidating cellular and molecular processes involved in endocrine tumors.
    Keywords:  endocrine tumors; liquid chromatography-mass spectrometry; mass spectrometry imaging; microextraction; multi-omics; spatially resolved microproteomics
    DOI:  https://doi.org/10.3389/fendo.2022.993081
  19. Neuromethods. 2022 ;184 87-114
    Single-Cell Mass Spectrometry of Metabolites and Proteins for Systems and Functional Biology.
    Erika P Portero, Leena Pade, Jie Li, Sam B Choi, Peter Nemes.
      Molecular composition is intricately intertwined with cellular function, and elucidation of this relationship is essential for understanding life processes and developing next-generational therapeutics. Technological innovations in capillary electrophoresis (CE) and liquid chromatography (LC) mass spectrometry (MS) provide previously unavailable insights into cellular biochemistry by allowing for the unbiased detection and quantification of molecules with high specificity. This chapter presents our validated protocols integrating ultrasensitive MS with classical tools of cell, developmental, and neurobiology to assess the biological function of important biomolecules. We use CE and LC MS to measure hundreds of metabolites and thousands of proteins in single cells or limited populations of tissues in chordate embryos and mammalian neurons, revealing molecular heterogeneity between identified cells. By pairing microinjection and optical microscopy, we demonstrate cell lineage tracing and testing the roles the dysregulated molecules play in the formation and maintenance of cell heterogeneity and tissue specification in frog embryos (Xenopus laevis). Electrophysiology extends our workflows to characterizing neuronal activity in sections of mammalian brain tissues. The information obtained from these studies mutually strengthen chemistry and biology and highlight the importance of interdisciplinary research to advance basic knowledge and translational applications forward.
    Keywords:  Single cell; Xenopus laevis; cell and developmental biology; functional biology; mass spectrometry; metabolomics; mouse; neurobiology; proteomics; zebrafish
    DOI:  https://doi.org/10.1007/978-1-0716-2525-5_5
  20. Bioinformatics. 2023 Jan 25. pii: btad058. [Epub ahead of print]
    MS1Connect: a mass spectrometry run similarity measure.
    Andy Lin, Brooke L Deatherage Kaiser, Janine R Hutchison, Jeffrey A Bilmes, William Stafford Noble.
      MOTIVATION: Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repository, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires computing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols.RESULTS: We propose a method, MS1Connect, that calculates the similarity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though this data is not used by MS1Connect.
    AVAILABILITY: The MS1Connect software will be made available upon acceptance at https://github.com/bmx8177/MS1Connect.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btad058
  21. Clin Transl Oncol. 2023 Jan 27.
    Research advances in the understanding of how exosomes regulate ferroptosis in cancer.
    Jiaxuan Liu.
      Exosomes are extracellular vesicles that can release different bioactive substances to affect tumor cells and cell death pathways. As an important mediator of cell communication, exosomes participate in the occurrence and development of a variety of diseases. Ferroptosis, one of the newly defined forms of regulated cell death, is characterized by massive accumulation of iron ions and lipid peroxidation. An increasing number of studies have shown that ferroptosis plays an important role in malignant tumors. Moreover, exosomes have been recognized for their potential in cancer therapy based on ferroptosis. To further describe how could exosomes regulate ferroptosis in cancer and provide better understanding of the mechanisms involved, this paper reviews the definition as well as the underlying molecular mechanisms of ferroptosis, including iron metabolism, amino acid metabolism, lipid metabolism and so on. Then, we illustrated how could exosomes regulate the ferroptosis pathway and suggested their promising potential as a novel tumor therapy for cancer patients. Finally, we described the perspectives of ferroptosis by exosomes in tumor treatment. Therefore, exosomes have the potential to regulate ferroptosis in clinical cancer treatment.
    Keywords:  Cancer; Exosome; Ferroptosis
    DOI:  https://doi.org/10.1007/s12094-023-03089-6
  22. Front Oncol. 2022 ;12 1054233
    When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective.
    Zhiqiang Zhang, Chaohui Bao, Lu Jiang, Shan Wang, Kankan Wang, Chang Lu, Hai Fang.
      Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.
    Keywords:  cancer drug resistance; metabolic reprogramming; metabolomics; single-cell metabolomics; spatial metabolomics
    DOI:  https://doi.org/10.3389/fonc.2022.1054233
  23. Anal Bioanal Chem. 2023 Feb;415(5): 913-933
    Development of a quantitative proteomics approach for cyclooxygenases and lipoxygenases in parallel to quantitative oxylipin analysis allowing the comprehensive investigation of the arachidonic acid cascade.
    Nicole M Hartung, Malwina Mainka, Rebecca Pfaff, Michael Kuhn, Sebastian Biernacki, Lilli Zinnert, Nils Helge Schebb.
      Oxylipins derived from the cyclooxygenase (COX) and lipoxygenase (LOX) pathways of the arachidonic acid (ARA) cascade are essential for the regulation of the inflammatory response and many other physiological functions. Comprehensive analytical methods comprised of oxylipin and protein abundance analysis are required to fully understand mechanisms leading to changes within these pathways. Here, we describe the development of a quantitative multi-omics approach combining liquid chromatography tandem mass spectrometry-based targeted oxylipin metabolomics and proteomics. As the first targeted proteomics method to cover these pathways, it enables the quantitative analysis of all human COX (COX-1 and COX-2) and relevant LOX pathway enzymes (5-LOX, 12-LOX, 15-LOX, 15-LOX-2, and FLAP) in parallel to the analysis of 239 oxylipins with our targeted oxylipin metabolomics method from a single sample. The detailed comparison between MRM3 and classical MRM-based detection in proteomics showed increased selectivity for MRM3, while MRM performed better in terms of sensitivity (LLOQ, 16-122 pM vs. 75-840 pM for the same peptides), linear range (up to 1.5-7.4 μM vs. 4-368 nM), and multiplexing capacities. Thus, the MRM mode was more favorable for this pathway analysis. With this sensitive multi-omics approach, we comprehensively characterized oxylipin and protein patterns in the human monocytic cell line THP-1 and differently polarized primary macrophages. Finally, the quantification of changes in protein and oxylipin levels induced by lipopolysaccharide stimulation and pharmaceutical treatment demonstrates its usefulness to study molecular modes of action involved in the modulation of the ARA cascade.
    Keywords:  Arachidonic acid cascade; Human macrophages; Liquid chromatography tandem mass spectrometry; Multiple reaction monitoring cubed; Targeted oxylipin metabolomics; Targeted proteomics
    DOI:  https://doi.org/10.1007/s00216-022-04489-3
  24. J Proteome Res. 2023 Jan 26.
    PeptideRanger: An R Package to Optimize Synthetic Peptide Selection for Mass Spectrometry Applications.
    Ryan M Riley, Sandra E Spencer Miko, Ryan D Morin, Gregg B Morin, Gian Luca Negri.
      Targeted and semitargeted mass spectrometry-based approaches are reliable methods to consistently detect and quantify low abundance proteins including proteins of clinical significance. Despite their potential, the development of targeted and semitargeted assays is time-consuming and often requires the purchase of costly libraries of synthetic peptides. To improve the efficiency of this rate-limiting step, we developed PeptideRanger, a tool to identify peptides from protein of interest with physiochemical properties that make them more likely to be suitable for mass spectrometry analysis. PeptideRanger is a flexible, extensively annotated, and intuitive R package that uses a random forest model trained on a diverse data set of thousands of MS experiments spanning a variety of sample types profiled with different chromatography setups and instruments. To support a variety of applications and to leverage rapidly growing public MS databases, PeptideRanger can readily be retrained with experiment-specific data sets and customized to prioritize and filter peptides based on selected properties.
    Keywords:  machine learning; mass spectrometry; peptide detectability; proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.2c00538
  25. Mol Syst Biol. 2023 Jan 27. e11099
    One-shot 13 C15 N-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 13 C15 N 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 13 C15 N-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
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