bims-mascan 2021-12-19 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2021‒12‒19
33 papers selected by
Giovanny Rodriguez Blanco
University of Edinburgh

Thiol profiling in cancer cell lines by HPLC-mass spectrometry.
Spatially resolved sampling of the human oral cavity for metabolic profiling.
Mass Spectrometry Imaging of Metabolites by Nanostructure Initiator Mass Spectrometry with Fluorinated Gold Nanoparticles.
Proteomic Profiling of Cerebrospinal Fluid by 16-Plex TMT-Based Mass Spectrometry.
Advances in Analyzing the Breast Cancer Lipidome and Its Relevance to Disease Progression and Treatment.
Mass Spectrometry-Based Analytical Strategy for Single-Cell Proteomics.
Recent Metabolomics Analysis in Tumor Metabolism Reprogramming.
Gas-Phase Ion-Ion Reactions for Lipid Identification in Biological Tissue Sections.
Defining the landscape of metabolic dysregulations in cancer metastasis.
Single-Cell Metabolomics with Rapid Determination of Chemical Formulas from Isotopic Fine Structures.
Deep learning approaches for data-independent acquisition proteomics.
Development of a Standardized MRM Method for the Quantification of One Carbon Metabolism Enzymes.
Generation of HLA Allele-Specific Spectral Libraries to Identify and Quantify Immunopeptidomes by SWATH/DIA-MS.
Recent advances in mass-spectrometry based proteomics software, tools and databases.
Data-independent acquisition (DIA): An emerging proteomics technology for analysis of drug-metabolizing enzymes and transporters.
Quantitative Mass Spectrometry Imaging with Liquid Microjunction Surface Sampling.
Features of Peptide Fragmentation Spectra in Single-Cell Proteomics.
Purification of HLA Immunopeptidomes from Human Thymus.
Isolation of Normal and Cancer-Associated Fibroblasts.
DECR1 directly activates HSL to promote lipolysis in cervical cancer cells.
LC-MS lipidomics of renal biopsies for the diagnosis of Fabry disease.
Sample Size-Comparable Spectral Library Enhances Data-Independent Acquisition-Based Proteome Coverage of Low-Input Cells.
Data-Independent Acquisition Mass Spectrometry-Based Deep Proteome Analysis for Hydrophobic Proteins from Dried Blood Spots Enriched by Sodium Carbonate Precipitation.
Establishing pteridine metabolism in a progressive isogenic breast cancer cell model.
Mass spectrometry based approaches and strategies in bioanalysis for qualitative and quantitative analysis of pharmaceutically relevant molecules.
Implementation of Clinical Phosphoproteomics and Proteomics for Personalized Medicine.
Lysine Acetylation Stoichiometry Analysis at the Proteome Level.
N-Alkylpyridinium sulfonates for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry-based metabolomics.
Chemical Biology Tools to Study Lipids and their Metabolism with Increased Spatial and Temporal Resolution.
Detection and structural analysis of pyrimidine-derived radicals generated on DNA using a profluorescent nitroxide probe.
Nanosecond Photochemical Reaction (nsPCR) for Enhanced Mass Spectrometric Identification, Quantification, and Visualization of Metabolites and Neuropeptides.
Heterocellular OSM-OSMR signalling reprograms fibroblasts to promote pancreatic cancer growth and metastasis.
pH/Acetonitrile-Gradient Reversed-Phase Fractionation of Enriched Hyper-Citrullinated Library in Combination with LC-MS/MS Analysis for Confident Identification of Citrullinated Peptides.

STAR Protoc. 2021 Dec 17. 2(4): 100977

Thiol profiling in cancer cell lines by HPLC-mass spectrometry.

Xiaoguang Liu, Pranavi Koppula, Kellen Olszewski, Boyi Gan.

  We describe a protocol for identifying cellular thiol metabolites such as cysteine and cystine in adherent cells using high performance liquid chromatography (HPLC) tandem mass spectrometry-based metabolomics. We applied a modified extraction and sample derivatization protocol to accurately quantify the intracellular levels of labile thiol species and to inhibit oxidation prior to analysis. For complete details on the use and execution of this protocol, please refer to Liu et al. (2020) and Koppula et al. (2021).

Keywords:  Cancer; Mass Spectrometry; Metabolism

DOI:  https://doi.org/10.1016/j.xpro.2021.100977
STAR Protoc. 2021 Dec 17. 2(4): 101002

Spatially resolved sampling of the human oral cavity for metabolic profiling.

Alessio Ciurli, Rico J E Derks, Maximilian Liebl, Christine Ammon, Jacques Neefjes, Martin Giera.

  Here, we present a spatially resolved sampling protocol for the oral human cavity aimed at untargeted metabolomics. We describe the spatial collection of salivary biospecimens, their preparation, and subsequent mass-spectrometry-based untargeted metabolomics analysis. Our protocol avoids complex procedures generally required for gland-specific saliva collection. For the human oral cavity, we provide an easy, flexible, and reproducible solution to comprehensively map the highly heterogeneous environment and elucidate the functionality of salivary components. For complete details on the use and execution of this protocol, please refer to Ciurli et al. (2021).

Keywords:  Clinical Protocol; Health Sciences; Mass Spectrometry; Metabolism; Metabolomics

DOI:  https://doi.org/10.1016/j.xpro.2021.101002
Methods Mol Biol. 2022 ;2437 117-125

Mass Spectrometry Imaging of Metabolites by Nanostructure Initiator Mass Spectrometry with Fluorinated Gold Nanoparticles.

Amelia Palermo.

  Nanostructure initiator mass spectrometry (NIMS) with fluorinated gold nanoparticles (f-AuNPs) enables the detection and spatial localization of a breath of polar metabolites and lipids with high spatial resolution and ultrasensitivity. Here we describe the methods and procedures for the synthesis and application of f-AuNPs for NIMS of small molecule metabolites and lipids in biological tissues, encompassing sample preparation, mass spectrometric detection, and data analysis and interpretation.

Keywords:  Gold nanoparticles; Mass spectrometry imaging; Metabolism heterogeneity; Metabolite imaging; Metabolomics; Nanostructure initiator mass spectrometry

DOI:  https://doi.org/10.1007/978-1-0716-2030-4_8
Methods Mol Biol. 2022 ;2420 21-37

Proteomic Profiling of Cerebrospinal Fluid by 16-Plex TMT-Based Mass Spectrometry.

Kaushik Kumar Dey, Huan Sun, Zhen Wang, Mingming Niu, Hong Wang, Yun Jiao, Xiaojun Sun, Yuxin Li, Junmin Peng.

  Mass spectrometry (MS) has become a mainstream platform for comprehensive profiling of proteome, especially with the improvement of multiplexed tandem mass tag labeling coupled with two-dimensional liquid chromatography and tandem mass spectrometry (TMT-LC/LC-MS/MS). Recently, we have established a robust method for direct profiling of undepleted cerebrospinal fluid (CSF) proteome with the 16-plex TMTpro method, in which we optimized parameters in experimental steps of sample preparation, TMT labeling, LC/LC fractionation, tandem mass spectrometry, and computational data processing. The extensive LC fractionation not only enhances proteome coverage of the CSF but also alleviates ratio distortion of TMT quantification. The crucial quality control steps and improvements specific for the TMT16 analysis are highlighted. More than 3000 proteins can be quantified in a single experiment from 16 different CSF samples. This multiplexed method offers a powerful tool for profiling a variety of complex biofluids samples such as CSF, serum/plasma, and other clinical specimens.

Keywords:  Cerebrospinal fluid; Clinical proteomics; Isobaric labeling; Liquid chromatography; Mass spectrometry; Plasma; Proteome; Proteomics; Serum; Tandem mass tag

DOI:  https://doi.org/10.1007/978-1-0716-1936-0_3
J Mammary Gland Biol Neoplasia. 2021 Dec 16.

Advances in Analyzing the Breast Cancer Lipidome and Its Relevance to Disease Progression and Treatment.

Ashley V Ward, Steven M Anderson, Carol A Sartorius.

  Abnormal lipid metabolism is common in breast cancer with the three main subtypes, hormone receptor (HR) positive, human epidermal growth factor 2 (HER2) positive, and triple negative, showing common and distinct lipid dependencies. A growing body of studies identify altered lipid metabolism as impacting breast cancer cell growth and survival, plasticity, drug resistance, and metastasis. Lipids are a class of nonpolar or polar (amphipathic) biomolecules that can be produced in cells via de novo synthesis or acquired from the microenvironment. The three main functions of cellular lipids are as essential components of membranes, signaling molecules, and nutrient storage. The use of mass spectrometry-based lipidomics to analyze the global cellular lipidome has become more prevalent in breast cancer research. In this review, we discuss current lipidomic methodologies, highlight recent breast cancer lipidomic studies and how these findings connect to disease progression and therapeutic development, and the potential use of lipidomics as a diagnostic tool in breast cancer. A better understanding of the breast cancer lipidome and how it changes during drug resistance and tumor progression will allow informed development of diagnostics and novel targeted therapies.

Keywords:  Breast cancer; Fatty acid; Lipid; Lipidomics

DOI:  https://doi.org/10.1007/s10911-021-09505-3
Methods Mol Biol. 2022 ;2420 63-72

Mass Spectrometry-Based Analytical Strategy for Single-Cell Proteomics.

Ákos Végvári.

  Single-cell proteomics is a novel application area of bioanalysis aiming to characterize proteomes of isolated single cells, which in contrast to bulk cell analysis has the potential to reveal a more detailed heterogeneity of cell populations. Although several antibody-based targeted approaches have been readily available for single-cell analysis, so far only the mass spectrometry methodology can offer unbiased proteome profiling. While this strategy has only recently emerged, it has already demonstrated unparalleled analytical power quantifying >1000 proteins in single cells. Several applications of a general isobaric labeling scheme for multiplexed sample preparation and data acquisition have been outlined using various cell types and instrumentation. This chapter provides a typical example of mass spectrometry-based single-cell proteomics workflow with details about the critical steps of analysis and alternative methods useful for optimization purposes.

Keywords:  Clean sample preparation; Isobaric labeling; Mass spectrometry; Single-cell proteomics

DOI:  https://doi.org/10.1007/978-1-0716-1936-0_6
Front Mol Biosci. 2021 ;8 763902

Recent Metabolomics Analysis in Tumor Metabolism Reprogramming.

Jingjing Han, Qian Li, Yu Chen, Yonglin Yang.

  Metabolic reprogramming has been suggested as a hallmark of cancer progression. Metabolomic analysis of various metabolic profiles represents a powerful and technically feasible method to monitor dynamic changes in tumor metabolism and response to treatment over the course of the disease. To date, numerous original studies have highlighted the application of metabolomics to various aspects of tumor metabolic reprogramming research. In this review, we summarize how metabolomics techniques can help understand the effects that changes in the metabolic profile of the tumor microenvironment on the three major metabolic pathways of tumors. Various non-invasive biofluids are available that produce accurate and useful clinical information on tumor metabolism to identify early biomarkers of tumor development. Similarly, metabolomics can predict individual metabolic differences in response to tumor drugs, assess drug efficacy, and monitor drug resistance. On this basis, we also discuss the application of stable isotope tracer technology as a method for the study of tumor metabolism, which enables the tracking of metabolite activity in the body and deep metabolic pathways. We summarize the multifaceted application of metabolomics in cancer metabolic reprogramming to reveal its important role in cancer development and treatment.

Keywords:  biomarkers; drug resistance; metabolic reprogramming; metabolomics; stable isotope resolved metabolomics

DOI:  https://doi.org/10.3389/fmolb.2021.763902
Methods Mol Biol. 2022 ;2437 3-19

Gas-Phase Ion-Ion Reactions for Lipid Identification in Biological Tissue Sections.

Boone M Prentice.

  The unambiguous identification of isobaric (i.e., same nominal mass) and isomeric (i.e., same exact mass) lipids remains a challenging yet vital aspect of imaging mass spectrometry (IMS) workflows. This chapter presents a methodology for the preparation of biological tissue samples and the use of a hybrid mass spectrometer to perform gas-phase charge inversion ion/ion reactions for improved lipid identification. This gas-phase ion/ion reaction method provides lipid structural information beyond what can be obtained via conventional tandem mass spectrometry (MS/MS) experiments. While this procedure is described here for the identification of phosphatidylcholine (PC) analyte cations using 1,4-phenylenedipropionic acid reagent dianions, it can readily be generalized to perform a diverse array of ion/ion reaction chemistries.

Keywords:  Gas-phase; Imaging mass spectrometry; Ion/ion reactions; Lipid identification

DOI:  https://doi.org/10.1007/978-1-0716-2030-4_1
Clin Exp Metastasis. 2021 Dec 18.

Defining the landscape of metabolic dysregulations in cancer metastasis.

Sara Abdul Kader, Shaima Dib, Iman W Achkar, Gaurav Thareja, Karsten Suhre, Arash Rafii, Anna Halama.

  Metastasis is the primary cause of cancer related deaths due to the limited number of efficient druggable targets. Signatures of dysregulated cancer metabolism could serve as a roadmap for the determination of new treatment strategies. However, the metabolic signatures of metastatic cells remain vastly elusive. Our aim was to determine metabolic dysregulations associated with high metastatic potential in breast cancer cell lines. We have selected 5 triple negative breast cancer (TNBC) cell lines including three with high metastatic potential (HMP) (MDA-MB-231, MDA-MB-436, MDA-MB-468) and two with low metastatic potential (LMP) (BT549, HCC1143). The normal epithelial breast cell line (hTERT-HME1) was also investigated. The untargeted metabolic profiling of cells and growth media was conducted and total of 479 metabolites were quantified. First we characterized metabolic features differentiating TNBC cell lines from normal cells as well as identified cell line specific metabolic fingerprints. Next, we determined 92 metabolites in cells and 22 in growth medium that display significant differences between LMP and HMP. The HMP cell lines had elevated level of molecules involved in glycolysis, TCA cycle and lipid metabolism. We identified metabolic advantages of cell lines with HMP beyond enhanced glycolysis by pinpointing the role of branched chain amino acids (BCAA) catabolism as well as molecules supporting coagulation and platelet activation as important contributors to the metastatic cascade. The landscape of metabolic dysregulations, characterized in our study, could serve as a roadmap for the identification of treatment strategies targeting cancer cells with enhanced metastatic potential.

Keywords:  Branch chain amino acid metabolism; Metabolic profiling; Metastasis; Metastatic potential; TCA cycle; Triple negative breast cancer

DOI:  https://doi.org/10.1007/s10585-021-10140-9
Methods Mol Biol. 2022 ;2437 61-75

Single-Cell Metabolomics with Rapid Determination of Chemical Formulas from Isotopic Fine Structures.

Laith Z Samarah, Akos Vertes, Christopher R Anderton.

  Metabolomic measurements can provide functional readouts of cellular states and phenotypes. Here, we present a protocol for single-cell metabolomics that permits direct untargeted detection of a broad number of metabolites under ambient conditions, without the need for sample processing, and with high confidence in the discovery and identification of the molecular formulas for detected metabolites. This protocol describes combining fiber-based laser ablation electrospray ionization (f-LAESI) with a 21 Tesla Fourier transform ion cyclotron resonance mass spectrometer (21T-FTICR-MS) to obtain high confidence molecular formula information about detected metabolites. The f-LAESI source utilizes mid-infrared laser ablation through a sharp optical fiber tip, affording direct ambient analysis of cells without the need for sample processing. Using the 21T-FTICR-MS as a mass analyzer enabled measurement of the isotopic fine structure (IFS) for numerous metabolites simultaneously from single cells, and the IFSs were in turn computationally processed to rapidly determine the corresponding elemental compositions. This metabolomics technique complements other single cell omics measurement methods, helping to resolve complex molecular interactions that take place within cells unattainable from single cell transcriptomic and proteomics methods.

Keywords:  FTICR; FTMS; LAESI; Mass spectrometry; Single-cell analysis; Spatially resolved mass spectrometry; Ultrahigh mass resolution

DOI:  https://doi.org/10.1007/978-1-0716-2030-4_4
Expert Rev Proteomics. 2021 Dec 17.

Deep learning approaches for data-independent acquisition proteomics.

Yi Yang, Ling Lin, Liang Qiao.

  INTRODUCTION: Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.AREAS COVERED: This review discusses and provides an overview of the deep learning methods that are used for DIA data analysis, including spectral library prediction, feature scoring and statistical control in peptide-centric analysis, as well as de novo peptide sequencing. Literature searches were performed for articles, including preprints, up to December 2021 from PubMed, Scopus and Web of Science databases.
EXPERT OPINION: While spectral library prediction has broken through the limitation on proteome coverage of experimental libraries, the statistical burden due to the large query space is the remaining challenge of utilizing proteome-wide predicted libraries. Analysis of post-translational modifications is another promising direction of deep learning-based DIA methods.

Keywords:  data-independent acquisition; de novo sequencing; deep learning; detectability; fragment spectrum; ion mobility; post-translational modifications; retention time; spectral library; statistical control

DOI:  https://doi.org/10.1080/14789450.2021.2020654
Methods Mol Biol. 2022 ;2420 159-175

Development of a Standardized MRM Method for the Quantification of One Carbon Metabolism Enzymes.

Laura Guerrero, Alberto Paradela, Fernando J Corrales.

  One-carbon metabolism (1CM) plays a central role in liver physiology, being the source of essential metabolites such as S-adenosylmethionine, the main alkylating agent in living cells, and glutathione, their most important nonenzymatic antioxidant defense. Impairment of 1CM in hepatocytes is a recognized factor associated to chronic liver disorders and hepatocellular carcinoma. With this in mind, we have proposed the concept of functional biomarker referring to a cellular pathway that can be systematically monitored as indicative of a particular physiological or pathological condition. Here we describe a targeted mass spectrometry (MRM) protocol to simultaneously quantify 13 1CM enzymes in liver tissue specimens.

Keywords:  Hepatocellular carcinoma; Liver diseases; Mass spectrometry; Multiple reaction monitoring; One-carbon metabolism; Targeted proteomics

DOI:  https://doi.org/10.1007/978-1-0716-1936-0_13
Methods Mol Biol. 2022 ;2420 137-147

Generation of HLA Allele-Specific Spectral Libraries to Identify and Quantify Immunopeptidomes by SWATH/DIA-MS.

Kevin Kovalchik, David Hamelin, Etienne Caron.

  Developing a deep and comprehensive understanding of the collection of peptides presented by class I human leukocyte antigens (HLA ), collectively referred to as the immunopeptidome , is conducive to the success of a wide range of immunotherapies. The development of tools that enable the deconvolution of immunopeptidomes in the context of disease can help improve the specificity and effectiveness of therapeutic strategies targeting these peptides, such as adoptive T-cell therapy and vaccines. Here, we describe a computational workflow that facilitates the processing and interpretation of data-independent acquisition mass spectrometry (DIA-MS). We consider a specific variation of DIA-MS known as SWATH-MS. SWATH-MS is a promising technique that can be utilized to reproducibly characterize and quantify immunopeptidomes isolated from a wide range of biological sources. In this workflow, we use an assortment of database search engines and computational tools to build high-quality HLA allele-specific peptide spectral peptide libraries for the analysis of immunopeptidomic datasets acquired by SWATH-MS. Generating and sharing these spectral libraries are essential for the SWATH-MS technology to meet its full potential and to enable the rapid and reproducible quantification of HLA-specific peptides across multiple samples.

Keywords:  Computational proteomics; Immunopeptidome; Mass spectrometry; Spectral library

DOI:  https://doi.org/10.1007/978-1-0716-1936-0_11
Drug Discov Today Technol. 2021 Dec;pii: S1740-6749(21)00016-0. [Epub ahead of print]39 69-79

Recent advances in mass-spectrometry based proteomics software, tools and databases.

Ankit Halder, Ayushi Verma, Deeptarup Biswas, Sanjeeva Srivastava.

The field of proteomics immensely depends on data generation and data analysis which are thoroughly supported by software and databases. There has been a massive advancement in mass spectrometry-based proteomics over the last 10 years which has compelled the scientific community to upgrade or develop algorithms, tools, and repository databases in the field of proteomics. Several standalone software, and comprehensive databases have aided the establishment of integrated omics pipeline and meta-analysis workflow which has contributed to understand the disease pathobiology, biomarker discovery and predicting new therapeutic modalities. For shotgun proteomics where Data Dependent Acquisition is performed, several user-friendly software are developed that can analyse the pre-processed data to provide mechanistic insights of the disease. Likewise, in Data Independent Acquisition, pipelines are emerged which can accomplish the task from building the spectral library to identify the therapeutic targets. Furthermore, in the age of big data analysis the implications of machine learning and cloud computing are appending robustness, rapidness and in-depth proteomics data analysis. The current review talks about the recent advancement, and development of software, tools, and database in the field of mass-spectrometry based proteomics.

DOI: https://doi.org/10.1016/j.ddtec.2021.06.007
Drug Discov Today Technol. 2021 Dec;pii: S1740-6749(21)00015-9. [Epub ahead of print]39 49-56

Data-independent acquisition (DIA): An emerging proteomics technology for analysis of drug-metabolizing enzymes and transporters.

Jiapeng Li, Logan S Smith, Hao-Jie Zhu.

Data-independent acquisition (DIA) proteomics is a recently-developed global mass spectrometry (MS)-based proteomics strategy. In a DIA method, precursor ions are isolated into pre-defined isolation windows and fragmented; all fragmented ions in each window are then analyzed by a high-resolution mass spectrometer. DIA proteomics analysis is characterized by a broad protein coverage, high reproducibility, and accuracy, and its combination with advances in other techniques such as sample preparation and computational data analysis could lead to further improvements in assay performances. DIA technology has been increasingly utilized in various proteomics studies, including quantifying drug-metabolizing enzymes and transporters. Quantitative proteomics study of drug-metabolizing enzymes and transporters could lead to a better understanding of pharmacokinetics and pharmacodynamics and facilitate drug development. This review summarizes the application of DIA technology in proteomic analysis of drug-metabolizing enzymes and transporters.

DOI: https://doi.org/10.1016/j.ddtec.2021.06.006
Methods Mol Biol. 2022 ;2437 181-194

Quantitative Mass Spectrometry Imaging with Liquid Microjunction Surface Sampling.

Qian Wu, Hongmei Lu.

  Mass spectrometry imaging (MSI) could provide chemical spatial distribution within a diverse range of samples, but absolute quantitation with those techniques is still challenging. Recent years, ambient liquid extraction-based MSI techniques, such as liquid microjunction surface sampling (LMJSS), have been largely developed and were found to be favorable to quantitation by directly doping standards in the extraction solvent. Here, we describe the detailed experimental protocols and the data processing methods for quantitative MSI with LMJSS. The new methods could have absolute quantitative MSI of both endogenous lipids and small metabolites from tissue samples.

Keywords:  Calibration methods; Lipidomics; Liquid microjunction surface sampling; Metabolomics; Quantitative mass spectrometry imaging

DOI:  https://doi.org/10.1007/978-1-0716-2030-4_13
J Proteome Res. 2021 Dec 17.

Features of Peptide Fragmentation Spectra in Single-Cell Proteomics.

Hannah Boekweg, Daisha Van Der Watt, Thy Truong, S Madisyn Johnston, Amanda J Guise, Edward D Plowey, Ryan T Kelly, Samuel H Payne.

  The goal of proteomics is to identify and quantify the complete set of proteins in a biological sample. Single-cell proteomics specializes in the identification and quantitation of proteins for individual cells, often used to elucidate cellular heterogeneity. The significant reduction in ions introduced into the mass spectrometer for single-cell samples could impact the features of MS2 fragmentation spectra. As all peptide identification software tools have been developed on spectra from bulk samples and the associated ion-rich spectra, the potential for spectral features to change is of great interest. We characterize the differences between single-cell spectra and bulk spectra by examining three fundamental spectral features that are likely to affect peptide identification performance. All features show significant changes in single-cell spectra, including the loss of annotated fragment ions, blurring signal and background peaks due to diminishing ion intensity, and distinct fragmentation pattern, compared to bulk spectra. As each of these features is a foundational part of peptide identification algorithms, it is critical to adjust algorithms to compensate for these losses.

Keywords:  MS/MS features; algorithms; computational proteomics; peptide identification optimization; single-cell proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.1c00670
Methods Mol Biol. 2022 ;2420 127-136

Purification of HLA Immunopeptidomes from Human Thymus.

Iñaki Alvarez.

  Mass spectrometry has become an essential technique for the analysis of peptide repertoires presented by MHC molecules to T lymphocytes. Years ago, analyses of MHC peptidomes were performed using a great number of cells, and cell lines were chosen as the main peptide source. Mass spectrometry devices have been improved in terms of sensitivity and resolution, making feasible the analysis of samples with relatively small amounts of cells. Thus, analyses of MHC peptide repertoires from different tissue samples are now available. Here, I describe a protocol to process human thymus samples to purify HLA class I- or HLA-DR-associated peptidomes. For that, cells are lysed using a nonionic detergent together with a mechanical cell rupture. Immunopeptidomes are purified by immunoaffinity chromatography. The peptide pool is fractionated by ionic chromatography. Finally, peptide fragmentation and identification are conducted by LC-MS/MS and the use of MASCOT search engine.

Keywords:  Antigen presentation; Human leukocyte antigen (HLA); Immunopeptidome; Mass spectrometry; Thymus

DOI:  https://doi.org/10.1007/978-1-0716-1936-0_10
Methods Mol Biol. 2022 ;2424 155-165

Isolation of Normal and Cancer-Associated Fibroblasts.

Katarzyna Zawieracz, Mark A Eckert.

  Cancer-associated fibroblasts (CAFs) play important roles in regulating tumor progression, metastasis, and response to therapies. Accurately modeling the interplay between cancer cells and the tumor microenvironment (TME) requires the use of primary cells from patient samples. Here we describe methods for the isolation of both primary CAFs and fibroblasts from omental tissue using a combination of mechanical dissociation and enzymatic digestion. Primary cells can be used for functional and mechanistic studies and may be safely cryopreserved.

Keywords:  Cancer; Cancer-associated fibroblasts; Fibroblasts; Isolation; Mesothelial cells; Metastasis; Omentum; Ovarian cancer; Primary cells; Stroma; Tumor microenvironment

DOI:  https://doi.org/10.1007/978-1-0716-1956-8_10
Biochim Biophys Acta Mol Cell Biol Lipids. 2021 Dec 10. pii: S1388-1981(21)00218-3. [Epub ahead of print]1867(3): 159090

DECR1 directly activates HSL to promote lipolysis in cervical cancer cells.

Huijuan Zhou, Jie Zhang, ZhongKang Yan, Min Qu, Gaojian Zhang, Jianxiong Han, Feifei Wang, Kai Sun, Lili Wang, Xingyuan Yang.

  Fatty acids have a high turnover rate in cancer cells to supply energy for tumor growth and proliferation. Lipolysis is particularly important for the regulation of fatty acid homeostasis and in the maintenance of cancer cells. In the current study, we explored how 2,4-Dienoyl-CoA reductase (DECR1), a short-chain dehydrogenase/reductase associated with mitochondrial and cytoplasmic compartments, promotes cancer cell growth. We report that DECR1 overexpression significantly reduced the triglyceride (TAG) content in HeLa cells; conversely, DECR1 silencing increased intracellular TAG content. Subsequently, our experiments demonstrate that DECR1 promotes lipolysis via effects on hormone sensitive lipase (HSL). The direct interaction of DECR1 with HSL increases HSL phosphorylation and activity, facilitating the translocation of HSL to lipid droplets. The ensuing enhancement of lipolysis thus increases the release of free fatty acids. Downstream effects include the promotion of cervical cancer cell migration and growth, associated with the enhanced levels of p62 protein. In summary, high levels of DECR1 serves to enhance lipolysis and the release of fatty acid energy stores to support cervical cancer cell growth.

Keywords:  Fatty acids; Hormone sensitive lipase (HSL); Lipolysis; Triglyceride; p62

DOI:  https://doi.org/10.1016/j.bbalip.2021.159090
J Mass Spectrom Adv Clin Lab. 2021 Nov;22 71-78

LC-MS lipidomics of renal biopsies for the diagnosis of Fabry disease.

Hoda Safari Yazd, Sina Feizbakhsh Bazargani, Christine A Vanbeek, Kelli King-Morris, Coy Heldermon, Mark S Segal, William L Clapp, Timothy J Garrett.

  Introduction: Lipidomics analysis or lipid profiling is a system-based analysis of all lipids in a sample to provide a comprehensive understanding of lipids within a biological system. In the last few years, lipidomics has made it possible to better understand the metabolic processes associated with several rare disorders and proved to be a powerful tool for their clinical investigation. Fabry disease is a rare X-linked lysosomal storage disorder (LSD) caused by a deficiency in α-galactosidase A (α-GAL A). This deficiency results in the progressive accumulation of glycosphingolipids, mostly globotriaosylceramide (Gb3), globotriaosylsphingosine (lyso-Gb3), as well as galabiosylceramide (Ga2) and their isoforms/analogs in the vascular endothelium, nerves, cardiomyocytes, renal glomerular podocytes, and biological fluids.Objectives: The primary objective of this study was to evaluate lipidomic signatures in renal biopsies to help understand variations in Fabry disease markers that could be used in future diagnostic tests.
Methods: Lipidomic analysis was performed by ultra-high pressure liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) on kidney biopsies that were left over after clinical pathology analysis to diagnose Fabry disease.
Results: We employed UHPLC-HRMS lipidomics analysis on the renal biopsy of a patient suspicious for Fabry disease. Our result confirmed α-GAL A enzyme activity declined in this patient since a Ga2-related lipid biomarker was substantially higher in the patient's renal tissue biopsy compared with two controls. This suggests this patient has a type of LSD that could be non-classical Fabry disease.
Conclusion: This study shows that lipidomics analysis is a valuable tool for rare disorder diagnosis, which can be conducted on leftover tissue samples without disrupting normal patient care.

Keywords:  CAN, Acetonitrile; CDH, Cerebrodihexoside; Chcl3, Chloroform; Cnvs, Copy Number Variants; EIC, Extracted Ion Chromatogram; ERT, Enzyme Replacement Therapy; GLA, Glactosidase Alpha; Ga2, Galabiosylceramide; Gb3, Globotriaosylceramide; IPA, 2-Propanol; LC/MS, Liquid Chromatography-Mass Spectrometry; LSD, Lysosomal Storage Disorder; Lyso-Gb3, Globotriaosylsphingosine; MS/MS, Tandem Mass Spectrometry; Meoh, Methanol; ND, Not Detected; OCT, Optimal Cutting Temperature; SECIM, Southeast Center for Integrated Metabolomics; SRM, Selected Reaction Monitoring; UHPLC-HRMS, Ultra-High Pressure Liquid Chromatography-High-Resolution Mass Spectrometry; α-GAL A, α-Galactosidase A

DOI:  https://doi.org/10.1016/j.jmsacl.2021.11.004
Anal Chem. 2021 Dec 14.

Sample Size-Comparable Spectral Library Enhances Data-Independent Acquisition-Based Proteome Coverage of Low-Input Cells.

Asad Ali Siyal, Eric Sheng-Wen Chen, Hsin-Ju Chan, Reta Birhanu Kitata, Jhih-Ci Yang, Hsiung-Lin Tu, Yu-Ju Chen.

Despite advancements of data-independent acquisition mass spectrometry (DIA-MS) to provide comprehensive and reproducible proteome profiling, its utility in very low-input samples is limited. Due to different proteome complexities and corresponding peptide ion abundances, the conventional LC-MS/MS acquisition and widely used large-scale DIA libraries may not be suitable for the micro-nanogram samples. In this study, we report a sample size-comparable library-based DIA approach to enhance the proteome coverage of low-input nanoscale samples (i.e., nanogram cells, ∼5-50 cells). By constructing sample size-comparable libraries, 2380 and 3586 protein groups were identified from as low as 0.75 (∼5 cells) and 1.5 ng (∼10 cells), respectively, highlighting one of the highest proteome coverage with good reproducibility (86%-99% in triplicate results). For the 0.75 ng sample (∼5 cells), significantly superior identification (2380 proteins) was achieved by small-size library-based DIA, compared to 1908, 1749, and 107 proteins identified from medium-size and large-size libraries and a lung cancer resource spectral library, respectively. A similar trend was observed using a different instrument and data analysis pipeline, indicating the generalized conclusion of the approach. Furthermore, the small-size library uniquely identified 518 (22%) proteins in the low-abundant region and spans over a 5-order dynamic range. Spectral similarity analysis revealed that the fragmentation ion pattern in the DIA-MS/MS spectra of the dataset and spectral library play crucial roles for mapping low abundant proteins. With these spectral libraries made freely available, the optimized library-based DIA strategy and DIA digital map will advance quantitative proteomics applications for mass-limited samples.

DOI: https://doi.org/10.1021/acs.analchem.1c03477
Methods Mol Biol. 2022 ;2420 39-52

Data-Independent Acquisition Mass Spectrometry-Based Deep Proteome Analysis for Hydrophobic Proteins from Dried Blood Spots Enriched by Sodium Carbonate Precipitation.

Daisuke Nakajima, Osamu Ohara, Yusuke Kawashima.

  Dried blood spots (DBS) are widely used for screening molecular profiles, including enzymatic activity. However, hydrophilic proteins present in large amounts in blood inhibit detection of other proteins in DBS by liquid chromatography-mass spectrometry (LC-MS/MS) without preenrichment. Sodium carbonate precipitation (SCP) can concentrate hydrophobic proteins from DBS and effectively remove soluble hydrophilic proteins. Furthermore, SCP combination with data-independent acquisition (DIA) for quantitative LC-MS/MS enhanced the proteome analysis sensitivity and quantification limits. In this protocol, we have described in detail a simple preenrichment method using SCP and a deep proteome analysis method for LC-MS/MS data using DIA.

Keywords:  Clinical proteomics; Data-independent acquisition; Dried blood spots; Newborn screening; Sodium carbonate

DOI:  https://doi.org/10.1007/978-1-0716-1936-0_4
Metabolomics. 2021 Dec 17. 18(1): 2

Establishing pteridine metabolism in a progressive isogenic breast cancer cell model.

Lindsey Rasmussen, Zachary Foulks, Casey Burton, Honglan Shi.

  INTRODUCTION: Pteridines include folate-derived metabolites that have been putatively associated with certain cancers in clinical studies. However, their biological significance in cancer metabolism and role in cancer development and progression remains poorly understood.OBJECTIVES: The purpose of this study was to examine the effects of tumorigenicity on pteridine metabolism by studying a panel of 15 pteridine derivatives using a progressive breast cancer cell line model with and without folic acid dosing.
METHODS: The MCF10A progressive breast cancer model, including sequentially derived MCF10A (benign), MCF10AT (premalignant), and MCF10CA1a (malignant) cell lines were dosed with 0, 100, and 250 mg/L folic acid. Pteridines were analyzed in both intracellular and extracellular contexts using an improved high-performance liquid chromatography-tandem mass spectrometry method.
RESULTS: Pteridines were located predominately in the extracellular media. Folic acid dosing increased extracellular levels of pterin, 6-hydroxylumazine, xanthopterin, 6-hydroxymethylpterin, and 6-carboxypterin in a dose-dependent manner. In particular, pterin and 6-hydroxylumazine levels were positively correlated with tumorigenicity upon folate dosing.
CONCLUSIONS: Folic acid is a primary driver for pteridine metabolism in human breast cell. Higher folate levels contribute to increased formation and excretion of pteridine derivatives to the extracellular media. In breast cancer, this metabolic pathway becomes dysregulated, resulting in the excretion of certain pteridine derivatives and providing in vitro evidence for the observation of elevated pteridines in the urine of breast cancer patients. Finally, this study reports a novel use of the MCF10A progressive breast cancer model for metabolomics applications that may readily be applied to other metabolites of interest.

Keywords:  Breast cancer; Folate-derived pteridine metabolism; Folic acid; HPLC-MS/MS; MCF10A cell line; Pterin

DOI:  https://doi.org/10.1007/s11306-021-01861-9
Drug Discov Today Technol. 2021 Dec;pii: S1740-6749(21)00029-9. [Epub ahead of print]40 64-68

Mass spectrometry based approaches and strategies in bioanalysis for qualitative and quantitative analysis of pharmaceutically relevant molecules.

Frank Klont, Gérard Hopfgartner.

Mass spectrometry plays an essential role in qualitative and quantitative analysis of pharmaceutically relevant molecules. The present review summarizes some the most common applications of LC-MS for the characterization of therapeutic low-molecular-weight compounds, peptides and proteins, and oligonucleotides using low-resolution and high-resolution tandem mass spectrometry. In addition, the benefit of multistage MS, differential ion mobility, and data independent acquisition is emphasized. At last, the potential of coupling MS with novel interfaces for high-throughput analysis is discussed.

DOI: https://doi.org/10.1016/j.ddtec.2021.10.004
Methods Mol Biol. 2022 ;2420 87-106

Implementation of Clinical Phosphoproteomics and Proteomics for Personalized Medicine.

Pedro Casado, Maruan Hijazi, Henry Gerdes, Pedro R Cutillas.

  The identification of biomarkers for companion diagnostics is revolutionizing the development of treatments tailored to individual patients in different disease areas including cancer. Precision medicine is most frequently based on the detection of genomic markers that correlate with the efficacy of selected targeted therapies. However, since nongenetic mechanisms also contribute to disease biology, there is a considerable interest of using proteomic techniques as additional source of biomarkers to personalize therapies. In this chapter, we describe label-free mass spectrometry methods for proteomic and phosphoproteomic analysis compatible with routine analysis of clinical samples. We also outline bioinformatic pipelines based on statistical learning that use these proteomics datasets as input to quantify kinase activities and predict drug responses in cancer cells.

Keywords:  Bioinformatics; Cancer; Clinical phosphoproteomics; Clinical proteomics; Kinase activity; Personalized medicine

DOI:  https://doi.org/10.1007/978-1-0716-1936-0_8
Methods Mol Biol. 2022 ;2420 73-86

Lysine Acetylation Stoichiometry Analysis at the Proteome Level.

Jeovanis Gil, Sergio Encarnación-Guevara.

  Lysine acetylation is a widespread posttranslational modification (PTM) in all kingdoms of live. A large number of proteins involved in most of biological pathways are targets of this PTM. The lysine acetylation is a reversible modification controlled by two main groups of enzymes, lysine acetyltransferases responsible for transferring the acetyl group of acetylCoA to the side chain of lysine residues and lysine deacetylases which effectively remove the acetyl tag. Dysregulation of enzymes that control acetylation and/or target proteins have been associated with a growing number of human pathologies. Lysine acetylation is largely a modification that occurs at low stoichiometry at its target sites. Here we describe a method to identify lysine acetylation sites and estimate their site occupancy at the proteome scale. The method relies on a high-resolution mass spectrometry-based proteomics approach, which includes a specific chemical acetylation reaction on unmodified lysine residues that carry heavy isotopes. The procedures described here have been applied to cell line cultures and to clinically relevant samples stored as both snap-frozen and formalin-fixed paraffin-embedded (FFPE) tissues.

Keywords:  Cell lines; FFPE tissue; Frozen tissue; Lysine acetylation; Mass spectrometry-based proteomics; N-acetoxysuccinimide-d3; Stoichiometry

DOI:  https://doi.org/10.1007/978-1-0716-1936-0_7
Anal Bioanal Chem. 2021 Dec 15.

N-Alkylpyridinium sulfonates for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry-based metabolomics.

Rainer Stoffel, Michael A Quilliam, Normand Hardt, Anders Fridstrom, Michael Witting.

  Chromatographic retention time information is valuable, orthogonal information to MS and MS/MS data that can be used in metabolite identification. However, while comparison of MS data between different instruments is possible to a certain degree, retention times (RTs) can vary extensively, even when nominally the same phase system is used. Different factors such as column dead volumes, system extra column volume, and gradient dwell volume can influence absolute retention times. Retention time indexing (RTI), routinely employed in gas chromatography (e.g., Kovats index), allows compensation for deviations in experimental conditions. Different systems have been reported for RTI in liquid chromatography, but none of them have been applied to metabolomics to the same extent as they have with GC. Recently, a more universal RTI system has been reported based on a homologous series of N-alkylpyridinium sulfonates (NAPS). These reference standards ionize in both positive and negative ionization modes and are UV-active. We demonstrate the NAPS can be used for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry (RP-LC-MS)-based metabolomics. Having measured >500 metabolite standards and varying flow rate and column dimension, we show that conversion of RT to retention indices (RI) substantially improves comparability of retention information and enables to use of RI for metabolite annotation and identification. Graphical Abstract.

Keywords:  Metabolite annotation; Metabolomics; Retention time indexing; Reversed-phase

DOI:  https://doi.org/10.1007/s00216-021-03828-0
Chimia (Aarau). 2021 Dec 22. 75(12): 1012-1016

Chemical Biology Tools to Study Lipids and their Metabolism with Increased Spatial and Temporal Resolution.

Clémence Simon, Suihan Feng, Howard Riezman.

Lipids are important cellular components providing many essential functions. To fulfill these various functions evolution has selected for a diverse set of lipids and this diversity is seen at the organismal, cellular and subcellular level. Understanding how cells maintain this complex lipid organization is a very challenging problem, which for lipids, is not easily addressed using biochemical and genetic techniques. Therefore, chemical tools have an important role to play in our quest to understand the complexities of lipid metabolism. Here we discuss new chemical tools to study lipids, their distribution and metabolism with increased spatial and temporal resolution.

DOI: https://doi.org/10.2533/chimia.2021.1012
Chem Commun (Camb). 2021 Dec 13.

Detection and structural analysis of pyrimidine-derived radicals generated on DNA using a profluorescent nitroxide probe.

Kosho Yamauchi, Yuta Matsuoka, Masatomo Takahashi, Yoshihiro Izumi, Hideto Naka, Yosuke Taniguchi, Kazuaki Kawai, Takeshi Bamba, Ken-Ichi Yamada.

The oxidative damage of DNA is associated with aging and the development of various diseases. Although nucleoside-derived radicals play an important role in DNA oxidation, their analysis methods are limited. Herein, we propose a fluorometric detection and structural analysis of radicals on the surface of oxidatively damaged DNA using a profluorescent nitroxide probe combined with liquid chromatography-fluorometry and high-resolution tandem mass spectrometry.

DOI: https://doi.org/10.1039/d1cc04998d
Methods Mol Biol. 2022 ;2437 143-157

Nanosecond Photochemical Reaction (nsPCR) for Enhanced Mass Spectrometric Identification, Quantification, and Visualization of Metabolites and Neuropeptides.

Gongyu Li, Yuan Liu, Lingjun Li.

  Small-molecule (e.g., metabolite) and low-abundance neuropeptide analyses by mass spectrometry (MS) represent important research directions and have witnessed tremendous growth and developments during past decades. With innate advantages of MS and gentle nature of soft ionization techniques including electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI), profiling and visualization of these bioactive metabolites and neuropeptides have undergone technological advancements that can be applied to real biological systems, although numerous challenges still exist. We herein present a rapid and efficient strategy to improve both metabolite and neuropeptide analysis, the nanosecond photochemical reaction (nsPCR)-enabled fast chemical derivatization. Amine-directed chemoselectivity facilitates the rapid tagging on amine-containing metabolites and neuropeptides, resulting in improved detection sensitivity. Additionally, the nsPCR generates a localized pH jump zone and enables localized thermophoresis at nanosecond timescale which benefits on-demand matrix removal during MALDI-MS identification and visualization of low-abundance biomolecules. A step-by-step nsPCR experimental protocol is introduced in detail herein for both spot analysis and imaging analysis, followed by suggestions for data analysis to ensure successful application of the nsPCR strategy.

Keywords:  Amine-containing metabolites; MALDI; Mass spectrometry imaging; On-tissue derivatization; Photochemical reaction; nsPCR

DOI:  https://doi.org/10.1007/978-1-0716-2030-4_10
Nat Commun. 2021 Dec 17. 12(1): 7336

Heterocellular OSM-OSMR signalling reprograms fibroblasts to promote pancreatic cancer growth and metastasis.

Brian Y Lee, Elizabeth K J Hogg, Christopher R Below, Alexander Kononov, Adrian Blanco-Gomez, Felix Heider, Jingshu Xu, Colin Hutton, Xiaohong Zhang, Tamara Scheidt, Kenneth Beattie, Angela Lamarca, Mairéad McNamara, Juan W Valle, Claus Jørgensen.

Pancreatic ductal adenocarcinoma (PDA) is a lethal malignancy with a complex microenvironment. Dichotomous tumour-promoting and -restrictive roles have been ascribed to the tumour microenvironment, however the effects of individual stromal subsets remain incompletely characterised. Here, we describe how heterocellular Oncostatin M (OSM) - Oncostatin M Receptor (OSMR) signalling reprograms fibroblasts, regulates tumour growth and metastasis. Macrophage-secreted OSM stimulates inflammatory gene expression in cancer-associated fibroblasts (CAFs), which in turn induce a pro-tumourigenic environment and engage tumour cell survival and migratory signalling pathways. Tumour cells implanted in Osm-deficient (Osm-/-) mice display an epithelial-dominated morphology, reduced tumour growth and do not metastasise. Moreover, the tumour microenvironment of Osm-/- animals exhibit increased abundance of α smooth muscle actin positive myofibroblasts and a shift in myeloid and T cell phenotypes, consistent with a more immunogenic environment. Taken together, these data demonstrate how OSM-OSMR signalling coordinates heterocellular interactions to drive a pro-tumourigenic environment in PDA.

DOI: https://doi.org/10.1038/s41467-021-27607-8
Methods Mol Biol. 2022 ;2420 107-126

pH/Acetonitrile-Gradient Reversed-Phase Fractionation of Enriched Hyper-Citrullinated Library in Combination with LC-MS/MS Analysis for Confident Identification of Citrullinated Peptides.

Aneta Stachowicz, Niveda Sundararaman, Vidya Venkatraman, Jennifer Van Eyk, Justyna Fert-Bober.

  Citrullination, the Ca2+-driven enzymatic conversion of arginine residues to citrulline, is a posttranslational modification, implicated in several physiological and pathological processes. Several methods to detect citrullinated proteins have been developed, including color development reagent, fluorescence, phenylglyoxal, and antibody-based methods. These methods yet suffer from limitations in sensitivity, specificity, or citrullinated site determination. Mass spectrometry (MS)-based proteomic analysis has emerged as a promising method to resolve these problems. However, due to low abundance of citrullinated proteins and similar MS features to deamidation of asparagine and glutamine, confident identification of citrullinated proteome is challenging. Here, we present a systematic approach to identify a compendium of steps to enhance the number of detected citrullinated residue and implement diagnostic MS feature that allow the confidence of MS-based identifications. Our method is based on the concept of generation of hyper-citrullinated library with high-pH reversed-phase peptide fractionation that allows to enrich in low abundance citrullinated peptides and amplify the effect of charge loss upon citrullination. Application of our approach to complex global citrullino-proteome datasets demonstrates the confident assessment of citrullinated peptides, thereby enhancing the size and functional interpretation of citrullinated proteomes.

Keywords:  Citrullination; Deamidation; Hyper-citrullinated peptide library; Mass spectrometry; Retention time; pH reversed-phase peptide fractionation

DOI:  https://doi.org/10.1007/978-1-0716-1936-0_9