bims-mascan 2021-07-11 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2021–07–11
thirty papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh

MaxDIA enables library-based and library-free data-independent acquisition proteomics.
Local production of lactate, ribose phosphate, and amino acids within human triple-negative breast cancer.
Data Processing and Analysis for DIA-Based Phosphoproteomics Using Spectronaut.
Quantification of Changes in Protein Expression Using SWATH Proteomics.
Non-enzymatic lipid peroxidation initiated by photodynamic therapy drives a distinct ferroptosis-like cell death pathway.
Software Options for the Analysis of MS-Proteomic Data.
Analysis of Label-Based Quantitative Proteomics Data Using IsoProt.
DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution.
Lipidomics As Tools For Finding Biomarkers Of Intestinal Pathology: From Irritable Bowel Syndrome To Colorectal Cancer.
High-throughput screening of salivary polyamine markers for discrimination of colorectal cancer by multisegment injection capillary electrophoresis tandem mass spectrometry.
Ultrafast and Reproducible Proteomics from Small Amounts of Heart Tissue Enabled by Azo and timsTOF Pro.
Oncogenic KRAS creates an aspartate metabolism signature in colorectal cancer cells.
Targeting Immunometabolism in Glioblastoma.
Polyamine metabolism is a central determinant of helper T cell lineage fidelity.
Measuring NF-κB Phosphorylation and Acetylation.
Mass Spectrometry-based Biomarkers for Knee Osteoarthritis: A Systematic Review.
Imaging lipids in biological samples with surface-assisted laser desorption/ionization mass spectrometry: A concise review of the last decade.
The intracellular metabolome of starving cells.
Targeted Lipidomics of Mycobacterial Lipids and Glycolipids.
High-Throughput Analysis of Tissue-Embedded Single Cells by Mass Spectrometry with Bimodal Imaging and Object Recognition.
Adaptations Supporting Plasma Methionine on a Limited-Methionine, High-Cystine Diet Alter the Canine Plasma Metabolome Consistent with Interventions that Extend Life Span in Other Species.
SpaceM reveals metabolic states of single cells.
Triple extraction method enables high quality mass spectrometry-based proteomics and phospho-proteomics for eventual multi-omics integration studies.
Features Selection and Extraction in Statistical Analysis of Proteomics Datasets.
Global Microbiota-Dependent Histone Acetylation Patterns are Irreversible and Independent of Short Chain Fatty Acids.
Integrated tissue proteome and metabolome reveal key elements and regulatory pathways in cutaneous squamous cell carcinoma.
In-Cell Labeling and Mass Spectrometry for Systems-Level Structural Biology.
Online-2D NanoLC-MS for Crude Serum Proteome Profiling: Assessing Sample Preparation Impact on Proteome Composition.
Protein Secretion Prediction Tools and Extracellular Vesicles Databases.
Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices.

Nat Biotechnol. 2021 Jul 08.

MaxDIA enables library-based and library-free data-independent acquisition proteomics.

Pavel Sinitcyn, Hamid Hamzeiy, Favio Salinas Soto, Daniel Itzhak, Frank McCarthy, Christoph Wichmann, Martin Steger, Uli Ohmayer, Ute Distler, Stephanie Kaspar-Schoenefeld, Nikita Prianichnikov, Şule Yılmaz, Jan Daniel Rudolph, Stefan Tenzer, Yasset Perez-Riverol, Nagarjuna Nagaraj, Sean J Humphrey, Jürgen Cox.

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification.

DOI: https://doi.org/10.1038/s41587-021-00968-7
Med (N Y). 2021 Jun 11. 2(6): 736-754

Local production of lactate, ribose phosphate, and amino acids within human triple-negative breast cancer.

Jonathan M Ghergurovich, Jessica D Lang, Maren K Levin, Natalia Briones, Salvatore J Facista, Claudius Mueller, Alexis J Cowan, Matthew J McBride, Esther San Roman Rodriguez, Aaron Killian, Tuoc Dao, Jeffrey Lamont, Alison Barron, Xiaoyang Su, William P D Hendricks, Virginia Espina, Daniel D Von Hoff, Joyce O'Shaughnessy, Joshua D Rabinowitz.

Background: Upregulated glucose metabolism is a common feature of tumors. Glucose can be broken down by either glycolysis or the oxidative pentose phosphate pathway (oxPPP). The relative usage within tumors of these catabolic pathways remains unclear. Similarly, the extent to which tumors make biomass precursors from glucose, versus take them up from the circulation, is incompletely defined.
Methods: We explore human triple negative breast cancer (TNBC) metabolism by isotope tracing with [1,2-13C]glucose, a tracer that differentiates glycolytic versus oxPPP catabolism and reveals glucose-driven anabolism. Patients enrolled in clinical trial NCT03457779 and received IV infusion of [1,2-13C]glucose during core biopsy of their primary TNBC. Tumor samples were analyzed for metabolite labeling by liquid chromatography-mass spectrometry (LC-MS). Genomic and proteomic analyses were performed and related to observed metabolic fluxes.
Findings: TNBC ferments glucose to lactate, with glycolysis dominant over the oxPPP. Most ribose phosphate is nevertheless produced by oxPPP. Glucose also feeds amino acid synthesis, including of serine, glycine, aspartate, glutamate, proline and glutamine (but not asparagine). Downstream in glycolysis, tumor pyruvate and lactate labeling exceeds that found in serum, indicating that lactate exchange via monocarboxylic transporters is less prevalent in human TNBC compared with most normal tissues or non-small cell lung cancer.
Conclusions: Glucose directly feeds ribose phosphate, amino acid synthesis, lactate, and the TCA cycle locally within human breast tumors.

DOI: https://doi.org/10.1016/j.medj.2021.03.009
Methods Mol Biol. 2021 ;2361 95-107

Data Processing and Analysis for DIA-Based Phosphoproteomics Using Spectronaut.

Ana Martinez-Val, Dorte Breinholdt Bekker-Jensen, Alexander Hogrebe, Jesper Velgaard Olsen.

  Data-independent acquisition (DIA) for liquid chromatography tandem mass spectrometry (LC-MS/MS) can improve the depth and reproducibility of the acquired proteomics datasets. DIA solves some limitations of the conventional data-dependent acquisition (DDA) strategy, for example, bias in intensity-dependent precursor selection and limited dynamic range. These advantages, together with the recent developments in speed, sensitivity, and resolution in MS technology, position DIA as a great alternative to DDA. Recently, we demonstrated that the benefits of DIA are extendable to phosphoproteomics workflows, enabling increased depth, sensitivity, and reproducibility of our analysis of phosphopeptide-enriched samples. However, computational data analysis of phospho-DIA samples have some specific challenges and requirements to the software and downstream processing workflows. A step-by-step guide to analyze phospho-DIA raw data using either spectral libraries or directDIA in Spectronaut is presented here. Furthermore, a straightforward protocol to perform differential phosphorylation site analysis using the output results from Spectronaut is described.

Keywords:  Data-independent acquisition; Phosphoproteomics; Spectronaut; directDIA

DOI:  https://doi.org/10.1007/978-1-0716-1641-3_6
Methods Mol Biol. 2021 ;2361 75-94

Quantification of Changes in Protein Expression Using SWATH Proteomics.

Clarissa Braccia, Nara Liessi, Andrea Armirotti.

  Sequential Window Acquisition of all THeoretical fragment ion spectra (SWATH) is a data independent acquisition mode used to accurately quantify thousands of proteins in a biological sample in a single run. It exploits fast scanning hybrid mass spectrometers to combine accuracy, reproducibility and sensitivity. This method requires the use of ion libraries, a sort of databases of spectral and chromatographic information about the proteins to be quantified. In this chapter, a typical workflow of SWATH experiment is described, from the sample preparation to the analysis of proteomics data.

Keywords:  High-resolution LC-MS/MS; Ion library; Multivariate data analysis; Quantitative proteomics; SWATH

DOI:  https://doi.org/10.1007/978-1-0716-1641-3_5
Redox Biol. 2021 Jun 23. pii: S2213-2317(21)00215-9. [Epub ahead of print]45 102056

Non-enzymatic lipid peroxidation initiated by photodynamic therapy drives a distinct ferroptosis-like cell death pathway.

Sufang Shui, Zenglu Zhao, Hao Wang, Marcus Conrad, Guoquan Liu.

  Ferroptosis is primarily triggered by a failure of the glutathione (GSH)-glutathione peroxidase 4 (GPX4) reductive system and associated overwhelming lipid peroxidation, in which enzymes regulating polyunsaturated fatty acid (PUFA) metabolism, and in particular acyl-CoA synthetase long chain family member 4 (ACSL4), are central. Here, we found that exogenous oxygen radicals generated by photodynamic therapy (PDT) can directly peroxidize PUFAs and initiate lipid autoxidation, coinciding with cellular GSH depletion. Different from canonical ferroptosis induced by RSL3 or erastin, PDT-initiated lipid peroxidation and ferroptotis-like cell death is independent of lipoxygenase (ALOXs) and ACSL4. Especially, this form of cell death modality can be triggered in malignant cells insensitive to or acquired resistance to canonical ferroptosis inducers. We also observed a distinct iron metabolism pathway in this PDT-triggered cell death modality, in which cytosolic labile iron is decreased probably due to its relocation to mitochondria. Inhibition of the mitochondrial Ca2+ and Fe2+ uniporter (MCU) effectively prevented PDT-triggered lipid peroxidation and subsequent cell death. Therefore, we tentatively term this distinct ferroptosis-like cell death as liperoptosis. Moreover, using the clinically approved photosensitizer Verteporfin, PDT inhibited tumor growth through inducing prevailing ferroptosis-like cell death in a mouse xenograft model. With its site-specific advantages, these findings highlight the value of using PDT to trigger lipid peroxidation and ferroptosis-like cell death in vivo, and will benefit exploring the exact molecular mechanism of immunological effects of PDT in cancer treatment.

Keywords:  Cell death; Ferroptosis; Liperoptosis; Lipid peroxidation; PDT

DOI:  https://doi.org/10.1016/j.redox.2021.102056
Methods Mol Biol. 2021 ;2361 35-59

Software Options for the Analysis of MS-Proteomic Data.

Avinash Yadav, Federica Marini, Alessandro Cuomo, Tiziana Bonaldi.

  Mass spectrometry (MS)-based proteomics is currently the most successful approach to measure and compare peptides and proteins in a large variety of biological samples. Modern mass spectrometers, equipped with high-resolution analyzers, provide large amounts of data output. This is the case of shotgun/bottom-up proteomics, which consists in the enzymatic digestion of protein into peptides that are then measured by MS-instruments through a data dependent acquisition (DDA) mode. Dedicated bioinformatic tools and platforms have been developed to face the increasing size and complexity of raw MS data that need to be processed and interpreted for large-scale protein identification and quantification. This chapter illustrates the most popular bioinformatics solution for the analysis of shotgun MS-proteomics data. A general description will be provided on the data preprocessing options and the different search engines available, including practical suggestions on how to optimize the parameters for peptide search, based on hands-on experience.

Keywords:  Algorithms; Databases; Mass spectrometry; Protein identification; Protein quantification; Shotgun proteomics; Software

DOI:  https://doi.org/10.1007/978-1-0716-1641-3_3
Methods Mol Biol. 2021 ;2361 61-73

Analysis of Label-Based Quantitative Proteomics Data Using IsoProt.

Johannes Griss, Veit Schwämmle.

  Isobaric labeling has become an essential method for quantitative mass spectrometry based experiments. This technique allows high-throughput proteomics while providing reasonable coverage of protein measurements across multiple samples. Here, the analysis of isobarically labeled mass spectrometry data with a special focus on quality control and potential pitfalls is discussed. The protocol is based on our fully integrated IsoProt workflow. The concepts discussed are nevertheless applicable to the analysis of any isobarically labeled experiment using alternative computational tools and algorithms.

Keywords:  Bioinformatics; IsoProt; Isobaric labeling; Mass spectrometry; Quantitative proteomics; TMT; iTRAQ

DOI:  https://doi.org/10.1007/978-1-0716-1641-3_4
Nat Methods. 2021 Jul;18(7): 779-787

DecoID improves identification rates in metabolomics through database-assisted MS/MS deconvolution.

Ethan Stancliffe, Michaela Schwaiger-Haber, Miriam Sindelar, Gary J Patti.

Chimeric MS/MS spectra contain fragments from multiple precursor ions and therefore hinder compound identification in metabolomics. Historically, deconvolution of these chimeric spectra has been challenging and relied on specific experimental methods that introduce variation in the ratios of precursor ions between multiple tandem mass spectrometry (MS/MS) scans. DecoID provides a complementary, method-independent approach where database spectra are computationally mixed to match an experimentally acquired spectrum by using LASSO regression. We validated that DecoID increases the number of identified metabolites in MS/MS datasets from both data-independent and data-dependent acquisition without increasing the false discovery rate. We applied DecoID to publicly available data from the MetaboLights repository and to data from human plasma, where DecoID increased the number of identified metabolites from data-dependent acquisition data by over 30% compared to direct spectral matching. DecoID is compatible with any user-defined MS/MS database and provides automated searching for some of the largest MS/MS databases currently available.

DOI: https://doi.org/10.1038/s41592-021-01195-3
Curr Drug Targets. 2021 Jul 07.

Lipidomics As Tools For Finding Biomarkers Of Intestinal Pathology: From Irritable Bowel Syndrome To Colorectal Cancer.

Lorena Ortega Moreno, Pilar Navarro Sánchez, Raquel Abalo.

  Lipidomics is an emerging and promising branch that analyses the different lipid mole-cules in a biological sample. It is considered a branch of metabolomics, which is defined as the comprehensive analysis of metabolites in a biological specimen. Nonetheless, in recent years lipidomics is becoming a distinct discipline in the biomedicine field. Lipids play important roles in many biological pathways and can work as biomarkers of disease or therapeutic targets for the treatment of diseases. The major lipidomics strategies are shotgun lipidomics and liquid chromatography coupled with mass spectrometry. Gastro-intestinal diseases, such as irritable bowel syndrome or inflammatory bowel disease, are chronic diseases that need non-invasive biomarkers for prognosis and diagnosis. Even more, patients with inflammatory bowel disease are at significantly increased risk of colorectal cancer, principally resulting from the pro-neoplastic effects of chronic intesti-nal inflammation. Current screening methods utilized globally include sigmoidoscopy or standard colonoscopy, but it is important to develop non-invasive and accurate screen-ing tools to facilitate early detection and precise staging of colorectal cancer. Disease progression and response to treatment may also benefit from the application of these potential new tools. This review focuses on studies that use lipidomics approaches to discover potential biomarkers for monitoring the mentioned intestinal diseases and, par-ticularly, tumor progression.

Keywords:  Lipidomics; biomarkers; colorectal cancer; inflammatory bowel disease; irritable bowel syndrome; omic

DOI:  https://doi.org/10.2174/1389450122666210707122151
J Chromatogr A. 2021 Jun 20. pii: S0021-9673(21)00479-9. [Epub ahead of print]1652 462355

High-throughput screening of salivary polyamine markers for discrimination of colorectal cancer by multisegment injection capillary electrophoresis tandem mass spectrometry.

Kaori Igarashi, Sana Ota, Miku Kaneko, Akiyoshi Hirayama, Masanobu Enomoto, Kenji Katumata, Masahiro Sugimoto, Tomoyoshi Soga.

  Polyamine metabolites provide pathophysiological information on disease or therapeutic efficacy, yet rapid screening methods for these biomarkers are lacking. Here, we developed high-throughput polyamine metabolite profiling based on multisegment injection capillary electrophoresis triple quadrupole tandem mass spectrometry (MSI-CE-MS/MS), which allows sequential 40-sample injection followed by electrophoretic separation and specific mass detection. To achieve consecutive analysis of polyamine samples, 1 M formic acid was used as the background electrolyte (BGE). The BGE spacer volume had an apparent effect on peak resolution among samples, and 20 nL was selected as the optimal volume. The use of polyamine isotopomers as the internal standard enabled the correction of matrix effects in MS detection. This method is sensitive, selective and quantitative, and its utility was demonstrated by screening polyamines in 359 salivary samples within 360 min, resulting in discrimination of colorectal cancer patients from noncancer controls.

Keywords:  Biomarker; Colorectal cancer; Mass spectrometry; Multisegment injection; Polyamine; Saliva; capillary electrophoresis

DOI:  https://doi.org/10.1016/j.chroma.2021.462355
J Proteome Res. 2021 Jul 08.

Ultrafast and Reproducible Proteomics from Small Amounts of Heart Tissue Enabled by Azo and timsTOF Pro.

Timothy J Aballo, David S Roberts, Jake A Melby, Kevin M Buck, Kyle A Brown, Ying Ge.

  Global bottom-up mass spectrometry (MS)-based proteomics is widely used for protein identification and quantification to achieve a comprehensive understanding of the composition, structure, and function of the proteome. However, traditional sample preparation methods are time-consuming, typically including overnight tryptic digestion, extensive sample cleanup to remove MS-incompatible surfactants, and offline sample fractionation to reduce proteome complexity prior to online liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Thus, there is a need for a fast, robust, and reproducible method for protein identification and quantification from complex proteomes. Herein, we developed an ultrafast bottom-up proteomics method enabled by Azo, a photocleavable, MS-compatible surfactant that effectively solubilizes proteins and promotes rapid tryptic digestion, combined with the Bruker timsTOF Pro, which enables deeper proteome coverage through trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF) of peptides. We applied this method to analyze the complex human cardiac proteome and identified nearly 4000 protein groups from as little as 1 mg of human heart tissue in a single one-dimensional LC-TIMS-MS/MS run with high reproducibility. Overall, we anticipate this ultrafast, robust, and reproducible bottom-up method empowered by both Azo and the timsTOF Pro will be generally applicable and greatly accelerate the throughput of large-scale quantitative proteomic studies. Raw data are available via the MassIVE repository with identifier MSV000087476.

Keywords:  bottom-up proteomics; human heart proteomics; photocleavable surfactant; quantitative proteomics; sample preparation

DOI:  https://doi.org/10.1021/acs.jproteome.1c00446
FEBS J. 2021 Jul 06.

Oncogenic KRAS creates an aspartate metabolism signature in colorectal cancer cells.

Peter F Doubleday, Luca Fornelli, Ioanna Ntai, Neil L Kelleher.

  Oncogenic mutations in the KRAS gene are found in 30-50% of colorectal cancers (CRC) and recent findings have demonstrated independent and non-redundant roles for wild-type and mutant KRAS alleles in governing signaling and metabolism. Here, we quantify proteomic changes manifested by KRAS mutation and KRAS allele loss in isogenic cell lines. We show that expression of KRASG13D upregulates aspartate metabolizing proteins including PCK1, PCK2, ASNS and ASS1. Furthermore, differential expression analyses of transcript-level data from CRC tumors identified the upregulation of urea cycle enzymes in CRC. We find that expression of ASS1, supports colorectal cancer cell proliferation and promotes tumor formation in vitro. We show that loss of ASS1 can be rescued with high levels of several metabolites.

Keywords:  Quantitative proteomics; aspartate; colorectal cancer; metabolomics; mutant KRAS; urea cycle

DOI:  https://doi.org/10.1111/febs.16111
Front Oncol. 2021 ;11 696402

Targeting Immunometabolism in Glioblastoma.

Aditya A Mohan, William H Tomaszewski, Aden P Haskell-Mendoza, Kelly M Hotchkiss, Kirit Singh, Jessica L Reedy, Peter E Fecci, John H Sampson, Mustafa Khasraw.

  We have only recently begun to understand how cancer metabolism affects antitumor responses and immunotherapy outcomes. Certain immunometabolic targets have been actively pursued in other tumor types, however, glioblastoma research has been slow to exploit the therapeutic vulnerabilities of immunometabolism. In this review, we highlight the pathways that are most relevant to glioblastoma and focus on how these immunometabolic pathways influence tumor growth and immune suppression. We discuss hypoxia, glycolysis, tryptophan metabolism, arginine metabolism, 2-Hydroxyglutarate (2HG) metabolism, adenosine metabolism, and altered phospholipid metabolism, in order to provide an analysis and overview of the field of glioblastoma immunometabolism.

Keywords:  2HG; adenosine; arginine; glioblastoma; immunometabolism; immunotherapy; metabolism; tryptophan

DOI:  https://doi.org/10.3389/fonc.2021.696402
Cell. 2021 Jun 25. pii: S0092-8674(21)00708-X. [Epub ahead of print]

Polyamine metabolism is a central determinant of helper T cell lineage fidelity.

Daniel J Puleston, Francesc Baixauli, David E Sanin, Joy Edwards-Hicks, Matteo Villa, Agnieszka M Kabat, Marcin M Kamiński, Michal Stanckzak, Hauke J Weiss, Katarzyna M Grzes, Klara Piletic, Cameron S Field, Mauro Corrado, Fabian Haessler, Chao Wang, Yaarub Musa, Lena Schimmelpfennig, Lea Flachsmann, Gerhard Mittler, Nir Yosef, Vijay K Kuchroo, Joerg M Buescher, Stefan Balabanov, Edward J Pearce, Douglas R Green, Erika L Pearce.

  Polyamine synthesis represents one of the most profound metabolic changes during T cell activation, but the biological implications of this are scarcely known. Here, we show that polyamine metabolism is a fundamental process governing the ability of CD4+ helper T cells (TH) to polarize into different functional fates. Deficiency in ornithine decarboxylase, a crucial enzyme for polyamine synthesis, results in a severe failure of CD4+ T cells to adopt correct subset specification, underscored by ectopic expression of multiple cytokines and lineage-defining transcription factors across TH cell subsets. Polyamines control TH differentiation by providing substrates for deoxyhypusine synthase, which synthesizes the amino acid hypusine, and mice in which T cells are deficient for hypusine develop severe intestinal inflammatory disease. Polyamine-hypusine deficiency caused widespread epigenetic remodeling driven by alterations in histone acetylation and a re-wired tricarboxylic acid (TCA) cycle. Thus, polyamine metabolism is critical for maintaining the epigenome to focus TH cell subset fidelity.

Keywords:  T cells; eIF5A; hypusine; immunity; immunometabolism; metabolism; polyamines

DOI:  https://doi.org/10.1016/j.cell.2021.06.007
Methods Mol Biol. 2021 ;2366 3-17

Measuring NF-κB Phosphorylation and Acetylation.

Nikita Tushar Modi, Lin-Feng Chen.

  Posttranslational modifications of NF-κB, including phosphorylation, acetylation, and methylation, have emerged as important regulatory mechanisms to control the transcriptional outcomes of this important transcription factor. These modifications work independently, sequentially or in combination to modulate the diverse biological functions of NF-κB in cancer and inflammatory response. Here, we describe some experimental methods to detect the in vitro and in vivo phosphorylation and acetylation of NF-κB, specifically focusing on the RelA subunit of NF-κB. These methods include labeling the phospho- or acetyl- groups with radioisotopes in vitro and immunoblotting with site-specific anti-phospho-serine or acetyl-lysine antibodies in culture cells and tissue samples.

Keywords:  Acetylation; NF-κB; Phosphorylation

DOI:  https://doi.org/10.1007/978-1-0716-1669-7_1
Expert Rev Proteomics. 2021 Jul 06.

Mass Spectrometry-based Biomarkers for Knee Osteoarthritis: A Systematic Review.

Mirella J J Haartmans, Kaj S Emanuel, Gabrielle J M Tuijthof, Ron M A Heeren, Pieter J Emans, Berta Cillero-Pastor.

  Introduction: Knee osteoarthritis (OA) is a joint disease, affecting multiple tissues in the joint. Early detection and intervention may delay OA development and avoid total knee arthroplasty. Specific biomarker profiles for early detection and guiding clinical decision-making of OA have not yet been identified. One technique that can contribute to the finding of this "OA biomarker" is mass spectrometry (MS), which offers the possibility to analyze different molecules in tissues or fluids. Several proteomic, lipidomic, metabolomic and other -omic approaches aim to identify these molecular profiles; however, variation in methods and techniques complicate the finding of promising candidate biomarkers.Areas covered: In this systematic review, we aim to provide an overview of molecules in OA knee patients. Possible biomarkers in several tissue types of OA and non-OA patients, as well as current limitations and possible future suggestions will be discussed.Expert opinion: According to this review, we do not believe one specific biomarker will function as predictive molecule for OA. Likely, a group of molecules will give insight in OA development and possible therapeutic targets. For clinical implementation of MS-analysis in clinical decision-making, standardized procedures, large cohort studies and sharing protocols and data is necessary.

Keywords:  Biomarker; Hoffa’s fat pad; Infrapatellar fat pad; Lipidomics; Mass spectrometry; Metabolomics; Osteoarthritis; Proteomics; Total knee arthroplasty; Total knee replacement

DOI:  https://doi.org/10.1080/14789450.2021.1952868
Prog Lipid Res. 2021 Jul 02. pii: S0163-7827(21)00030-8. [Epub ahead of print]83 101114

Imaging lipids in biological samples with surface-assisted laser desorption/ionization mass spectrometry: A concise review of the last decade.

Wendy H Müller, Edwin De Pauw, Johann Far, Cedric Malherbe, Gauthier Eppe.

  Knowing the spatial location of the lipid species present in biological samples is of paramount importance for the elucidation of pathological and physiological processes. In this context, mass spectrometry imaging (MSI) has emerged as a powerful technology allowing the visualization of the spatial distributions of biomolecules, including lipids, in complex biological samples. Among the different ionization methods available, the emerging surface-assisted laser desorption/ionization (SALDI) MSI offers unique capabilities for the study of lipids. This review describes the specific advantages of SALDI-MSI for lipid analysis, including the ability to perform analyses in both ionization modes with the same nanosubstrate, the detection of lipids characterized by low ionization efficiency in MALDI-MS, and the possibilities of surface modification to improve the detection of lipids. The complementarity of SALDI and MALDI-MSI is also discussed. Finally, this review presents data processing strategies applied in SALDI-MSI of lipids, as well as examples of applications of SALDI-MSI in biomedical lipidomics.

Keywords:  Biological tissues; Lipidomics; Lipids; Mass spectrometry imaging; SALDI

DOI:  https://doi.org/10.1016/j.plipres.2021.101114
Methods Cell Biol. 2021 ;pii: S0091-679X(21)00043-1. [Epub ahead of print]164 137-156

The intracellular metabolome of starving cells.

Sylvère Durand, Claudia Grajeda-Iglesias, Fanny Aprahamian, Nitharsshini Nirmalathasan, Oliver Kepp, Guido Kroemer.

  Fasting induces vast metabolic adaptations on the cellular level and leads to an organism-wide induction of autophagy. Autophagic degradation subserves resource recycling and facilitates the maintenance of energetic homeostasis. Mass spectrometry offers the possibility to assess changes in the metabolome that occur in conditions of nutrient deprivation and to profile such adaptations. Here we describe a detailed workflow for the targeted quantitation and untargeted profiling of metabolites that can be used to assess the intracellular metabolome of starving cells. Moreover, we outline a workflow for the use of non-radioactive isotope labeled metabolites. Altogether, we show that mass spectrometry is a powerful tool for monitoring metabolic changes in conditions of fasting.

Keywords:  Autophagy; Chromatography; Mass spectrometry; Metabolites

DOI:  https://doi.org/10.1016/bs.mcb.2021.04.001
Methods Mol Biol. 2021 ;2314 549-577

Targeted Lipidomics of Mycobacterial Lipids and Glycolipids.

Emilie Layre.

  Decades of study have highlighted the richness and uniqueness of the repertoire of lipid and glycolipid families produced by mycobacteria. Many of these families potently regulate host immune responses, in stimulatory or suppressive ways. Thus, the global study of this repertoire in different genetic backgrounds or under model conditions of infection is gaining interest. Despite the difficulties associated with the specificities of this repertoire, the field of mass spectrometry-based lipidomics of mycobacteria has recently made considerable progress, particularly at the analytical level. There is still considerable scope for further progress, especially with regard to the development of an efficient bioinfomatics pipeline for the analysis of the large datasets generated. This chapter describes an HPLC-MS methodology allowing the simultaneous screening of more than 20 of the lipid families produced by mycobacteria and provides recommendations to analyze the generated data given the state-of-the-art.

Keywords:  HPLC-MS; Lipidomics; Mass spectrometry; Mycobacterium

DOI:  https://doi.org/10.1007/978-1-0716-1460-0_24
Anal Chem. 2021 Jul 08.

High-Throughput Analysis of Tissue-Embedded Single Cells by Mass Spectrometry with Bimodal Imaging and Object Recognition.

Sylwia A Stopka, Ellen A Wood, Rikkita Khattar, Beverly J Agtuca, Walid M Abdelmoula, Nathalie Y R Agar, Gary Stacey, Akos Vertes.

In biological tissues, cell-to-cell variations stem from the stochastic and modulated expression of genes and the varying abundances of corresponding proteins. These variations are then propagated to downstream metabolite products and result in cellular heterogeneity. Mass spectrometry imaging (MSI) is a promising tool to simultaneously provide spatial distributions for hundreds of biomolecules without the need for labels or stains. Technological advances in MSI instrumentation for the direct analysis of tissue-embedded single cells are dominated by improvements in sensitivity, sample pretreatment, and increased spatial resolution but are limited by low throughput. Herein, we introduce a bimodal microscopy imaging system combined with fiber-based laser ablation electrospray ionization (f-LAESI) MSI with improved throughput ambient analysis of tissue-embedded single cells (n > 1000) to provide insight into cellular heterogeneity. Based on automated image analysis, accurate single-cell sampling is achieved by f-LAESI leading to the discovery of cellular phenotypes characterized by differing metabolite levels.

DOI: https://doi.org/10.1021/acs.analchem.1c00569
J Nutr. 2021 Jul 05. pii: nxab204. [Epub ahead of print]

Adaptations Supporting Plasma Methionine on a Limited-Methionine, High-Cystine Diet Alter the Canine Plasma Metabolome Consistent with Interventions that Extend Life Span in Other Species.

David Allaway, Matthew Harrison, Richard Haydock, Phillip Watson.

   BACKGROUND: Using indicator amino acid oxidation methodology, the mean dietary requirement of adult dogs for methionine (Met) was estimated to be ∼66% of the current recommended allowance. Dogs fed a diet formulated to provide the estimated mean Met requirement for 32 wk maintained plasma Met, seemingly supported by betaine oxidation.
OBJECTIVE: To gain a better understanding of the metabolic changes that were associated with supporting plasma Met when dogs were fed a limited Met diet over 32 wk, we analyzed plasma samples taken from that study using a data-driven metabolomics approach.
METHODS: Labrador retrievers (20 females/13 males; mean age: 4.9 y; range: 2.0-7.9 y) were fed semi-purified, nutritionally complete diets. After 4 wk of feeding a control diet (DL-Met; 1.37 g/1000 kcal), 17 dogs remained on this diet and 16 were transitioned to a test diet formulated to the estimated mean Met requirement (0.55 g/1000 kcal), with dietary total sulfur amino acid maintained with additional l-cystine (Cys). Dogs were individually fed diets to maintain a stable body weight at an ideal body condition score for 32 wk. Plasma samples from fasted blood collected at baseline and 8 and 32 wk were analyzed using untargeted metabolic profiling.
RESULTS: Analysis of metabolites (n = 593) confirmed our primary findings (increased Met, betaine, and dimethylglycine). Metabolite changes consistent with repartitioning choline to support Met cycling included reduced pools of lipids derived via phosphatidylethanolamine N-methyltransferase and enhanced fatty acid oxidation. Some changes were consistent with metabolomics studies reported in other species that used interventions known to extend life span (caloric- and Met-restricted diets or feeding strategy).
CONCLUSIONS: Changes in the plasma metabolome were consistent with reported adaptations to support Met-dependent activities. We propose that feeding a limited-Met, high-Cys diet using the estimated mean Met requirement in adult Labrador retrievers alters regulation of the Met cycle, thereby altering metabolism, similar to interventions that extend life span.

Keywords:  canine; life span; metabolomics; methionine; nutritional requirement

DOI:  https://doi.org/10.1093/jn/nxab204
Nat Methods. 2021 Jul;18(7): 799-805

SpaceM reveals metabolic states of single cells.

Luca Rappez, Mira Stadler, Sergio Triana, Rose Muthoni Gathungu, Katja Ovchinnikova, Prasad Phapale, Mathias Heikenwalder, Theodore Alexandrov.

A growing appreciation of the importance of cellular metabolism and revelations concerning the extent of cell-cell heterogeneity demand metabolic characterization of individual cells. We present SpaceM, an open-source method for in situ single-cell metabolomics that detects >100 metabolites from >1,000 individual cells per hour, together with a fluorescence-based readout and retention of morpho-spatial features. We validated SpaceM by predicting the cell types of cocultured human epithelial cells and mouse fibroblasts. We used SpaceM to show that stimulating human hepatocytes with fatty acids leads to the emergence of two coexisting subpopulations outlined by distinct cellular metabolic states. Inducing inflammation with the cytokine interleukin-17A perturbs the balance of these states in a process dependent on NF-κB signaling. The metabolic state markers were reproduced in a murine model of nonalcoholic steatohepatitis. We anticipate SpaceM to be broadly applicable for investigations of diverse cellular models and to democratize single-cell metabolomics.

DOI: https://doi.org/10.1038/s41592-021-01198-0
Proteomics. 2021 Jul 08. e2000303

Triple extraction method enables high quality mass spectrometry-based proteomics and phospho-proteomics for eventual multi-omics integration studies.

Virginia Sanchez-Quiles, Ming-Jun Shi, Florent Dingli, Damarys Loew, Isabelle Bernard-Pierrot, François Radvanyi.

  Large-scale multi-omic analysis allows a thorough understanding of different physiological or pathological conditions, particularly cancer. Here, an extraction method simultaneously yielding DNA, RNA and protein (thereby referred to as "triple extraction", TEx) was tested for its suitability to unbiased, system-wide proteomic investigation. Largely proven efficient for transcriptomic and genomic studies, we aimed at exploring TEx compatibility with mass spectrometry-based proteomics and phospho-proteomics, as compared to a standard urea extraction. TEx is suitable for the shotgun investigation of proteomes, providing similar results as urea-based protocol both at the qualitative and quantitative levels. TEx is likewise compatible with the exploration of phosphorylation events, actually providing a higher number of correctly localized sites than urea, although the nature of extracted modifications appears somewhat distinct between both techniques. These results highlight that the presented protocol is well suited for the examination of the proteome and modified proteome of this bladder cancer cell model, as efficiently as other more widely used workflows for mass spectrometry-based analysis. Potentially applicable to other mammalian cell types and tissues, TEx represents an advantageous strategy for multi-omics on scarce and/or heterogenous samples. This article is protected by copyright. All rights reserved.

Keywords:  phospho-proteomics; shotgun proteomics; triple extraction; urea extraction

DOI:  https://doi.org/10.1002/pmic.202000303
Methods Mol Biol. 2021 ;2361 143-159

Features Selection and Extraction in Statistical Analysis of Proteomics Datasets.

Marta Lualdi, Mauro Fasano.

  "Omics" techniques (e.g., proteomics, genomics, metabolomics), from which huge datasets can nowadays be obtained, require a different way of thinking about data analysis that can be summarized with the idea that, when data are enough, they can speak for themselves. Indeed, managing huge amounts of data imposes the replacement of the classical deductive approach (hypothesis-driven) with a data-driven hypothesis-generating inductive approach, so to generate mechanistical hypotheses from data.Data reduction is a crucial step in proteomics data analysis, because of the sparsity of significant features in big datasets. Thus, feature selection/extraction methods are applied to obtain a set of features based on which a proteomics signature can be drawn, with a functional significance (e.g., classification, diagnosis, prognosis). Despite big data generated almost daily by proteomics studies, a well-established statistical workflow for data analysis in proteomics is still lacking, opening up to misleading and incorrect data analysis and interpretation. This chapter will give an overview of the methods available for feature selection/extraction in proteomics datasets and how to choose the most appropriate one based on the type of dataset.

Keywords:  Cross-validation; Discriminant analysis; Features extraction; Features selection; Principal component analysis; Proteomics; Signature; Sparsity; Supervised/unsupervised methods; Univariate/multivariate methods

DOI:  https://doi.org/10.1007/978-1-0716-1641-3_9
Hepatology. 2021 Jul 07.

Global Microbiota-Dependent Histone Acetylation Patterns are Irreversible and Independent of Short Chain Fatty Acids.

Yedidya Saiman, Ting-Chin David Shen, Peder J Lund, Victoria M Gershuni, Cholsoon Jang, Shivali Patel, Sunhee Jung, Emma E Furth, Elliot S Friedman, Lillian Chau, Benjamin A Garcia, Gary D Wu.

  Although germ-free mice are an indispensable tool in studying the gut microbiome and its effects on host physiology, they are phenotypically different than their conventional counterparts. While antibiotic-mediated microbiota depletion in conventional mice leads to physiologic alterations that often mimic the germ-free state, the degree to which the effects of microbial colonization on the host are reversible is unclear. The gut microbiota produces abundant short chain fatty acids (SCFAs) and previous studies have demonstrated a link between microbial derived SCFAs and global hepatic histone acetylation in germ-free mice. We demonstrate that global hepatic histone acetylation states measured by mass spectrometry remained largely unchanged despite loss of luminal and portal vein SCFAs after antibiotic-mediated microbiota depletion. In contrast to stable hepatic histone acetylation states, we see robust hepatic transcriptomic alterations after microbiota depletion. Additionally, neither dietary supplementation with supraphysiologic levels of SCFA nor the induction of hepatocyte proliferation in the absence of microbiota-derived SCFAs led to alterations in global hepatic histone acetylation. In conclusion, these results suggest that microbiota-dependent landscaping of the hepatic epigenome via global histone acetylation is static in nature while the hepatic transcriptome is responsive to alterations in the gut microbiota.

Keywords:  Antibiotic-treated mice; Epigenetics; Germ free mice; Liver disease; Microbiome

DOI:  https://doi.org/10.1002/hep.32043
J Proteomics. 2021 Jul 05. pii: S1874-3919(21)00219-0. [Epub ahead of print] 104320

Integrated tissue proteome and metabolome reveal key elements and regulatory pathways in cutaneous squamous cell carcinoma.

Wen Chen, Jun Rao, Zhiwen Liu, Xiaolong You, Fenqian Yuan, Fei Le, Mu Tang, Meng Zhou, Ting Xie.

  Cutaneous squamous cell carcinoma (CSCC) is a widespread malignancy but has a very low long-term survival rate for patients at the metastatic stage. Therefore, it is urgent to identify prognostic biomarkers for CSCC and improve our understanding of disease progression. Here we took advantage of a data-independent acquisition (DIA)-based nano liquid chromatography equipped with an orbitrap mass spectrometry (nLC-MS/MS) and ultraperformance LC coupled to a time-of-flight tandem MS (UPLC-TOF-MS/MS) technique to analyze cancer and corresponding noncancerous tissues from 20 CSCC patients for integrated proteomic and metabolomic analyses. Overall, 6241 tissue proteins were detected, while 136 proteins were significantly expressed in CSCC tissues. Further functional analysis revealed that various biological processes were highly enriched and participated in the pathogenesis of CSCC, especially DNA damage responses. Moreover, 641 named metabolites in total were identified, among which 181 were significantly changed in CSCC tissues. A total of 101 pathways were significantly enriched including apoptosis, autophagy, PI3K-Akt and mTOR signaling pathways. Interestingly, two pathways, protein digestion & absorption and platelet activation were both enriched in proteomic and metabolomic studies involving 5 proteins and 11 metabolites. Accordingly, a four-metabolite panel consisting of arachidonate, glutamine, glutamic acid, and proline (all area under the curve (AUC) values more than 0.9) was developed with a high accuracy (0.971) to distinguish the 20 detected cancer tissues from their noncancerous tissues. Collectively, our work highlighted the key elements and regulatory pathways involved in the pathogenesis of CSCC. More importantly, the present study not only provided potential biomarkers for the early diagnosis of CSCC, but also expanded our knowledge of the physiopathology of the disease. SIGNIFICANCE: CSCC is the second most common human cancer but has few treatment options and few sensitive biomarkers for diagnosis. Here we comprehensively revealed its molecular characteristics by performing integrated tissue proteomic and metabolomic analyses. Significantly distinct profiles and certain enriched pathways including DNA damage responses were identified as associated with CSCC. Moreover, protein digestion & absorption and platelet activation were both enriched in the proteome and metabolome. These identified molecular changes probably play significant roles in CSCC development. Finally, we developed a four-metabolite panel to distinguish CSCC with high accuracy. Overall, our data not only provided potential diagnostic biomarkers, but also extended knowledge on the pathogenesis of CSCC.

Keywords:  Biomarker; Cutaneous squamous cell carcinoma (CSCC); DIA-MS/MS; Metabolomics; Proteomics

DOI:  https://doi.org/10.1016/j.jprot.2021.104320
Chem Rev. 2021 Jul 07.

In-Cell Labeling and Mass Spectrometry for Systems-Level Structural Biology.

Juan D Chavez, Helisa H Wippel, Xiaoting Tang, Andrew Keller, James E Bruce.

Biological systems have evolved to utilize proteins to accomplish nearly all functional roles needed to sustain life. A majority of biological functions occur within the crowded environment inside cells and subcellular compartments where proteins exist in a densely packed complex network of protein-protein interactions. The structural biology field has experienced a renaissance with recent advances in crystallography, NMR, and CryoEM that now produce stunning models of large and complex structures previously unimaginable. Nevertheless, measurements of such structural detail within cellular environments remain elusive. This review will highlight how advances in mass spectrometry, chemical labeling, and informatics capabilities are merging to provide structural insights on proteins, complexes, and networks that exist inside cells. Because of the molecular detection specificity provided by mass spectrometry and proteomics, these approaches provide systems-level information that not only benefits from conventional structural analysis, but also is highly complementary. Although far from comprehensive in their current form, these approaches are currently providing systems structural biology information that can uniquely reveal how conformations and interactions involving many proteins change inside cells with perturbations such as disease, drug treatment, or phenotypic differences. With continued advancements and more widespread adaptation, systems structural biology based on in-cell labeling and mass spectrometry will provide an even greater wealth of structural knowledge.

DOI: https://doi.org/10.1021/acs.chemrev.1c00223
Anal Chem. 2021 Jul 08.

Online-2D NanoLC-MS for Crude Serum Proteome Profiling: Assessing Sample Preparation Impact on Proteome Composition.

Runsheng Zheng, Natalia Govorukhina, Tabiwang N Arrey, Christopher Pynn, Ate van der Zee, György Marko-Varga, Rainer Bischoff, Alexander Boychenko.

Although current LC-MS technology permits scientists to efficiently screen clinical samples in translational research, e.g., steroids, biogenic amines, and even plasma or serum proteomes, in a daily routine, maintaining the balance between throughput and analytical depth is still a limiting factor. A typical approach to enhance the proteome depth is employing offline two-dimensional (2D) fractionation techniques before reversed-phase nanoLC-MS/MS analysis (1D-nanoLC-MS). These additional sample preparation steps usually require extensive sample manipulation, which could result in sample alteration and sample loss. Here, we present and compare 1D-nanoLC-MS with an automated online-2D high-pH RP × low pH RP separation method for deep proteome profiling using a nanoLC system coupled to a high-resolution accurate-mass mass spectrometer. The proof-of-principle study permitted the identification of ca. 500 proteins with ∼10,000 peptides in 15 enzymatically digested crude serum samples collected from healthy donors in 3 laboratories across Europe. The developed method identified 60% more peptides in comparison with conventional 1D nanoLC-MS/MS analysis with ca. 4 times lower throughput while retaining the quantitative information. Serum sample preparation related changes were revealed by applying unsupervised classification techniques and, therefore, must be taken into account while planning multicentric biomarker discovery and validation studies. Overall, this novel method reduces sample complexity and boosts the number of peptide and protein identifications without the need for extra sample handling procedures for samples equivalent to less than 1 μL of blood, which expands the space for potential biomarker discovery by looking deeper into the composition of biofluids.

DOI: https://doi.org/10.1021/acs.analchem.1c01291
Methods Mol Biol. 2021 ;2361 213-227

Protein Secretion Prediction Tools and Extracellular Vesicles Databases.

Daniela Cecconi, Claudia Di Carlo, Jessica Brandi.

  Secreted proteins play important roles in several biological processes such as growth, proliferation differentiation, cell-cell communication, migration, and apoptosis; moreover, these extracellular molecules mediate homeostasis by influencing the cross-talking within the surrounding tissues. Currently, the research area of cell secretome has become of great interest since the profiling of secreted proteins could be essential for the biomarker discovery and for the identification of new therapeutic strategies. Several bioinformatic platforms have been implemented for the in silico characterization of secreted proteins: this chapter describes a typical workflow for the analysis of proteins secreted by cultured cells through bioinformatic approaches. Central issue is related to discrimination between proteins secreted by classical and non-classical pathways. Therefore, specific prediction tools for the classification of candidate secreted proteins are here presented.

Keywords:  Bioinformatic tools; Classical and non-classical secretion; Secretome

DOI:  https://doi.org/10.1007/978-1-0716-1641-3_13
Nat Methods. 2021 Jul;18(7): 747-756

Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices.

Saleh Alseekh, Asaph Aharoni, Yariv Brotman, Kévin Contrepois, John D'Auria, Jan Ewald, Jennifer C Ewald, Paul D Fraser, Patrick Giavalisco, Robert D Hall, Matthias Heinemann, Hannes Link, Jie Luo, Steffen Neumann, Jens Nielsen, Leonardo Perez de Souza, Kazuki Saito, Uwe Sauer, Frank C Schroeder, Stefan Schuster, Gary Siuzdak, Aleksandra Skirycz, Lloyd W Sumner, Michael P Snyder, Huiru Tang, Takayuki Tohge, Yulan Wang, Weiwei Wen, Si Wu, Guowang Xu, Nicola Zamboni, Alisdair R Fernie.

Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.

DOI: https://doi.org/10.1038/s41592-021-01197-1