1
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Chung HH, Huang P, Chen CL, Lee C, Hsu CC. Next-generation pathology practices with mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2023; 42:2446-2465. [PMID: 35815718 DOI: 10.1002/mas.21795] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 04/13/2022] [Accepted: 04/22/2022] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful technique that reveals the spatial distribution of various molecules in biological samples, and it is widely used in pathology-related research. In this review, we summarize common MSI techniques, including matrix-assisted laser desorption/ionization and desorption electrospray ionization MSI, and their applications in pathological research, including disease diagnosis, microbiology, and drug discovery. We also describe the improvements of MSI, focusing on the accumulation of imaging data sets, expansion of chemical coverage, and identification of biological significant molecules, that have prompted the evolution of MSI to meet the requirements of pathology practices. Overall, this review details the applications and improvements of MSI techniques, demonstrating the potential of integrating MSI techniques into next-generation pathology practices.
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Affiliation(s)
- Hsin-Hsiang Chung
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Penghsuan Huang
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chih-Lin Chen
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
| | - Chuping Lee
- Department of Chemistry, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Cheng-Chih Hsu
- Department of Chemistry, National Taiwan University, Taipei City, Taiwan
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2
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Deulofeu M, Peña-Méndez EM, Vaňhara P, Havel J, Moráň L, Pečinka L, Bagó-Mas A, Verdú E, Salvadó V, Boadas-Vaello P. Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models. ACS Chem Neurosci 2022; 14:300-311. [PMID: 36584284 PMCID: PMC9853500 DOI: 10.1021/acschemneuro.2c00665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity.
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Affiliation(s)
- Meritxell Deulofeu
- Research
Group of Clinical Anatomy, Embryology and Neuroscience (NEOMA), Department
of Medical Sciences, University of Girona, Girona, Catalonia 17003, Spain,Department
of Chemistry, Faculty of Science, Masaryk
University, Kamenice 5/A14, 625 00 Brno, Czech Republic,Department
of Histology and Embryology, Faculty of Medicine, Masaryk University, 62500 Brno, Czech Republic
| | - Eladia M. Peña-Méndez
- Department
of Chemistry, Analytical Chemistry Division, Faculty of Sciences, University of La Laguna, 38204 San Cristóbal de
La Laguna, Tenerife, Spain
| | - Petr Vaňhara
- Department
of Histology and Embryology, Faculty of Medicine, Masaryk University, 62500 Brno, Czech Republic,International
Clinical Research Center, St. Anne’s
University Hospital, 656
91 Brno, Czech Republic
| | - Josef Havel
- Department
of Chemistry, Faculty of Science, Masaryk
University, Kamenice 5/A14, 625 00 Brno, Czech Republic,International
Clinical Research Center, St. Anne’s
University Hospital, 656
91 Brno, Czech Republic
| | - Lukáš Moráň
- Department
of Histology and Embryology, Faculty of Medicine, Masaryk University, 62500 Brno, Czech Republic,Research
Centre for Applied Molecular Oncology (RECAMO), Masaryk Memorial Cancer Institute, 62500 Brno, Czech Republic
| | - Lukáš Pečinka
- Department
of Chemistry, Faculty of Science, Masaryk
University, Kamenice 5/A14, 625 00 Brno, Czech Republic,International
Clinical Research Center, St. Anne’s
University Hospital, 656
91 Brno, Czech Republic
| | - Anna Bagó-Mas
- Research
Group of Clinical Anatomy, Embryology and Neuroscience (NEOMA), Department
of Medical Sciences, University of Girona, Girona, Catalonia 17003, Spain
| | - Enrique Verdú
- Research
Group of Clinical Anatomy, Embryology and Neuroscience (NEOMA), Department
of Medical Sciences, University of Girona, Girona, Catalonia 17003, Spain
| | - Victoria Salvadó
- Department
of Chemistry, Faculty of Science, University
of Girona, 17071 Girona, Catalonia, Spain,
| | - Pere Boadas-Vaello
- Research
Group of Clinical Anatomy, Embryology and Neuroscience (NEOMA), Department
of Medical Sciences, University of Girona, Girona, Catalonia 17003, Spain,
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3
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A Comprehensive Mass Spectrometry-Based Workflow for Clinical Metabolomics Cohort Studies. Metabolites 2022; 12:metabo12121168. [PMID: 36557207 PMCID: PMC9782571 DOI: 10.3390/metabo12121168] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/14/2022] [Accepted: 11/16/2022] [Indexed: 11/27/2022] Open
Abstract
As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLineTM and UlibMS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.
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4
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Dannhorn A, Swales JG, Hamm G, Strittmatter N, Kudo H, Maglennon G, Goodwin RJA, Takats Z. Evaluation of Formalin-Fixed and FFPE Tissues for Spatially Resolved Metabolomics and Drug Distribution Studies. Pharmaceuticals (Basel) 2022; 15:1307. [PMID: 36355479 PMCID: PMC9697942 DOI: 10.3390/ph15111307] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 08/26/2023] Open
Abstract
Fixation of samples is broadly used prior to the histological evaluation of tissue samples. Though recent reports demonstrated the ability to use fixed tissues for mass spectrometry imaging (MSI) based proteomics, glycomics and tumor classification studies, to date comprehensive evaluation of fixation-related effects for spatially resolved metabolomics and drug disposition studies is still missing. In this study we used matrix assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI) MSI to investigate the effect of formalin-fixation and formalin-fixation combined with paraffin embedding on the detectable metabolome including xenobiotics. Formalin fixation was found to cause significant washout of polar molecular species, including inorganic salts, amino acids, organic acids and carnitine species, oxidation of endogenous lipids and formation of reaction products between lipids and fixative ingredients. The slow fixation kinetics under ambient conditions resulted in increased lipid hydrolysis in the tissue core, correlating with the time-dependent progression of the fixation. Paraffin embedding resulted in subsequent partial removal of structural lipids resulting in the distortion of the elucidated biodistributions.
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Affiliation(s)
- Andreas Dannhorn
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK
| | - John G. Swales
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK
| | - Gregory Hamm
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK
| | - Nicole Strittmatter
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK
| | - Hiromi Kudo
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Gareth Maglennon
- Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK
| | - Richard J. A. Goodwin
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8TA, UK
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
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5
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Nwabufo CK, Aigbogun OP. The Role of Mass Spectrometry Imaging in Pharmacokinetic Studies. Xenobiotica 2022; 52:811-827. [PMID: 36048000 DOI: 10.1080/00498254.2022.2119900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Abstract
Although liquid chromatography-tandem mass spectrometry is the gold standard analytical platform for the quantification of drugs, metabolites, and biomarkers in biological samples, it cannot localize them in target tissues.The localization and quantification of drugs and/or their associated metabolites in target tissues is a more direct measure of bioavailability, biodistribution, efficacy, and regional toxicity compared to the traditional substitute studies using plasma.Therefore, combining high spatial resolution imaging functionality with the superior selectivity and sensitivity of mass spectrometry into one analytical technique will be a valuable tool for targeted localization and quantification of drugs, metabolites, and biomarkers.Mass spectrometry imaging (MSI) is a tagless analytical technique that allows for the direct localization and quantification of drugs, metabolites, and biomarkers in biological tissues, and has been used extensively in pharmaceutical research.The overall goal of this current review is to provide a detailed description of the working principle of MSI and its application in pharmacokinetic studies encompassing absorption, distribution, metabolism, excretion, and toxicity processes, followed by a discussion of the strategies for addressing the challenges associated with the functional utility of MSI in pharmacokinetic studies that support drug development.
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Affiliation(s)
- Chukwunonso K Nwabufo
- Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada
| | - Omozojie P Aigbogun
- Drug Discovery and Development Research Group, College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Canada.,Department of Chemistry, University of Saskatchewan, Saskatoon, Canada
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6
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Baquer G, Sementé L, Mahamdi T, Correig X, Ràfols P, García-Altares M. What are we imaging? Software tools and experimental strategies for annotation and identification of small molecules in mass spectrometry imaging. MASS SPECTROMETRY REVIEWS 2022:e21794. [PMID: 35822576 DOI: 10.1002/mas.21794] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mass spectrometry imaging (MSI) has become a widespread analytical technique to perform nonlabeled spatial molecular identification. The Achilles' heel of MSI is the annotation and identification of molecular species due to intrinsic limitations of the technique (lack of chromatographic separation and the difficulty to apply tandem MS). Successful strategies to perform annotation and identification combine extra analytical steps, like using orthogonal analytical techniques to identify compounds; with algorithms that integrate the spectral and spatial information. In this review, we discuss different experimental strategies and bioinformatics tools to annotate and identify compounds in MSI experiments. We target strategies and tools for small molecule applications, such as lipidomics and metabolomics. First, we explain how sample preparation and the acquisition process influences annotation and identification, from sample preservation to the use of orthogonal techniques. Then, we review twelve software tools for annotation and identification in MSI. Finally, we offer perspectives on two current needs of the MSI community: the adaptation of guidelines for communicating confidence levels in identifications; and the creation of a standard format to store and exchange annotations and identifications in MSI.
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Affiliation(s)
- Gerard Baquer
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Lluc Sementé
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Toufik Mahamdi
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
| | - Xavier Correig
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - Pere Ràfols
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
- Institut D'Investigacio Sanitaria Pere Virgili, Tarragona, Spain
| | - María García-Altares
- Department of Electronic Engineering, University Rovira I Virgili, Tarragona, Spain
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain
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7
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Spruill ML, Maletic-Savatic M, Martin H, Li F, Liu X. Spatial analysis of drug absorption, distribution, metabolism, and toxicology using mass spectrometry imaging. Biochem Pharmacol 2022; 201:115080. [PMID: 35561842 PMCID: PMC9744413 DOI: 10.1016/j.bcp.2022.115080] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 12/14/2022]
Abstract
Mass spectrometry imaging (MSI) is emerging as a powerful analytical tool for detection, quantification, and simultaneous spatial molecular imaging of endogenous and exogenous molecules via in situ mass spectrometry analysis of thin tissue sections without the requirement of chemical labeling. The MSI generates chemically specific and spatially resolved ion distribution information for administered drugs and metabolites, which allows numerous applications for studies involving various stages of drug absorption, distribution, metabolism, excretion, and toxicity (ADMET). MSI-based pharmacokinetic imaging analysis provides a histological context and cellular environment regarding dynamic drug distribution and metabolism processes, and facilitates the understanding of the spatial pharmacokinetics and pharmacodynamic properties of drugs. Herein, we discuss the MSI's current technological developments that offer qualitative, quantitative, and spatial location information of small molecule drugs, antibody, and oligonucleotides macromolecule drugs, and their metabolites in preclinical and clinical tissue specimens. We highlight the macro and micro drug-distribution in the whole-body, brain, lung, liver, kidney, stomach, intestine tissue sections, organoids, and the latest applications of MSI in pharmaceutical ADMET studies.
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Affiliation(s)
- Michelle L Spruill
- Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, TX 77204, USA
| | - Mirjana Maletic-Savatic
- Department of Pediatrics, Baylor College of Medicine, Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA
| | | | - Feng Li
- Center for Drug Discovery and Department of Pathology & Immunology, Baylor College of Medicine, Houston, TX 77030, USA; NMR and Drug Metabolism Core, Advanced Technology Cores, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Xinli Liu
- Department of Pharmacological and Pharmaceutical Sciences, College of Pharmacy, University of Houston, Houston, TX 77204, USA.
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8
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Dannhorn A, Kazanc E, Hamm G, Swales JG, Strittmatter N, Maglennon G, Goodwin RJA, Takats Z. Correlating Mass Spectrometry Imaging and Liquid Chromatography-Tandem Mass Spectrometry for Tissue-Based Pharmacokinetic Studies. Metabolites 2022; 12:metabo12030261. [PMID: 35323705 PMCID: PMC8954739 DOI: 10.3390/metabo12030261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/08/2022] [Accepted: 03/11/2022] [Indexed: 01/12/2023] Open
Abstract
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a standard tool used for absolute quantification of drugs in pharmacokinetic (PK) studies. However, all spatial information is lost during the extraction and elucidation of a drugs biodistribution within the tissue is impossible. In the study presented here we used a sample embedding protocol optimized for mass spectrometry imaging (MSI) to prepare up to 15 rat intestine specimens at once. Desorption electrospray ionization (DESI) and matrix assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) were employed to determine the distributions and relative abundances of four benchmarking compounds in the intestinal segments. High resolution MALDI-MSI experiments performed at 10 µm spatial resolution allowed to determine the drug distribution in the different intestinal histological compartments to determine the absorbed and tissue bound fractions of the drugs. The low tissue bound drug fractions, which were determined to account for 56–66% of the total drug, highlight the importance to understand the spatial distribution of drugs within the histological compartments of a given tissue to rationalize concentration differences found in PK studies. The mean drug abundances of four benchmark compounds determined by MSI were correlated with the absolute drug concentrations. Linear regression resulted in coefficients of determination (R2) ranging from 0.532 to 0.926 for MALDI-MSI and R2 values ranging from 0.585 to 0.945 for DESI-MSI, validating a quantitative relation of the imaging data. The good correlation of the absolute tissue concentrations of the benchmark compounds and the MSI data provides a bases for relative quantification of compounds within and between tissues, without normalization to an isotopically labelled standard, provided that the compared tissues have inherently similar ion suppression effects.
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Affiliation(s)
- Andreas Dannhorn
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK; (A.D.); (E.K.)
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
| | - Emine Kazanc
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK; (A.D.); (E.K.)
| | - Gregory Hamm
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
| | - John G. Swales
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
| | - Nicole Strittmatter
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
| | - Gareth Maglennon
- Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK;
| | - Richard J. A. Goodwin
- Imaging & Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge CB4 0WG, UK; (G.H.); (J.G.S.); (N.S.); (R.J.A.G.)
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8TA, UK
| | - Zoltan Takats
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK; (A.D.); (E.K.)
- Laboratoire PRISM, Inserm U1192, University of Lille, Villeneuve d’Ascq, 59655 Lille, France
- The Rosalind Franklin Institute, Harwell OX11 0QG, UK
- Correspondence:
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9
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Lee PY, Yeoh Y, Omar N, Pung YF, Lim LC, Low TY. Molecular tissue profiling by MALDI imaging: recent progress and applications in cancer research. Crit Rev Clin Lab Sci 2021; 58:513-529. [PMID: 34615421 DOI: 10.1080/10408363.2021.1942781] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Matrix-assisted laser desorption/ionization (MALDI) imaging is an emergent technology that has been increasingly adopted in cancer research. MALDI imaging is capable of providing global molecular mapping of the abundance and spatial information of biomolecules directly in the tissues without labeling. It enables the characterization of a wide spectrum of analytes, including proteins, peptides, glycans, lipids, drugs, and metabolites and is well suited for both discovery and targeted analysis. An advantage of MALDI imaging is that it maintains tissue integrity, which allows correlation with histological features. It has proven to be a valuable tool for probing tumor heterogeneity and has been increasingly applied to interrogate molecular events associated with cancer. It provides unique insights into both the molecular content and spatial details that are not accessible by other techniques, and it has allowed considerable progress in the field of cancer research. In this review, we first provide an overview of the MALDI imaging workflow and approach. We then highlight some useful applications in various niches of cancer research, followed by a discussion of the challenges, recent developments and future prospect of this technique in the field.
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Affiliation(s)
- Pey Yee Lee
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Yeelon Yeoh
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Nursyazwani Omar
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Yuh-Fen Pung
- Division of Biomedical Science, University of Nottingham Malaysia, Selangor, Malaysia
| | - Lay Cheng Lim
- Department of Life Sciences, School of Pharmacy, International Medical University (IMU), Kuala Lumpur, Malaysia
| | - Teck Yew Low
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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10
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Sementé L, Baquer G, García-Altares M, Correig-Blanchar X, Ràfols P. rMSIannotation: A peak annotation tool for mass spectrometry imaging based on the analysis of isotopic intensity ratios. Anal Chim Acta 2021; 1171:338669. [PMID: 34112434 DOI: 10.1016/j.aca.2021.338669] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 05/12/2021] [Accepted: 05/20/2021] [Indexed: 11/15/2022]
Abstract
Mass spectrometry imaging (MSI) consist of spatially located spectra with thousands of peaks. Only a fraction of these peaks corresponds to unique monoisotopic peaks, as mass spectra include isotopes, adducts and fragments of compounds. Current peak annotation solutions depend on matching MS features to compounds libraries. We present rMSIannotation, a peak annotation algorithm to annotate carbon isotopes and adducts in metabolomics and lipidomics imaging mass spectrometry datasets without using supporting libraries. rMSIannotation measures and evaluates the intensity ratio between carbon isotopic peaks and models their distribution across the m/z axis of the compounds in the Human Metabolome Database. Monoisotopic peak selection is based on the isotopic likelihood score (ILS) made of three components: image morphology correlation, validation of isotopic intensity ratios, and peak centroid mass deviation. rMSIannotation proposes pairs of peaks that can be adducts based on three scores: isotopic pattern coherence, image correlation and mass error. We validated rMSIannotation with three MALDI-MSI datasets which were manually annotated by experts, and compared the annotations obtained with rMSIannotation and with the METASPACE annotation platform. rMSIannotation replicated more than 90% of the manual annotation reported in FT-ICR datasets and expanded the list of annotated compounds with additional monoisotopic peaks and neutral masses. Finally, we evaluated isotopic peak annotation as a data reduction method for MSI by comparing the results of PCA and k-means segmentation before and after removing non-monoisotopic peaks. The results show that monoisotopic peaks retain most of the biologic variance in the dataset.
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Affiliation(s)
- Lluc Sementé
- University Rovira I Virgili, Department of Electronic Engineering, Tarragona, Spain
| | - Gerard Baquer
- University Rovira I Virgili, Department of Electronic Engineering, Tarragona, Spain
| | - María García-Altares
- University Rovira I Virgili, Department of Electronic Engineering, Tarragona, Spain; Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029, Madrid, Spain.
| | - Xavier Correig-Blanchar
- University Rovira I Virgili, Department of Electronic Engineering, Tarragona, Spain; Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029, Madrid, Spain; Institut D'Investigació Sanitària Pere Virgili, Tarragona, Spain
| | - Pere Ràfols
- University Rovira I Virgili, Department of Electronic Engineering, Tarragona, Spain; Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029, Madrid, Spain; Institut D'Investigació Sanitària Pere Virgili, Tarragona, Spain
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11
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Janda M, Seah BKB, Jakob D, Beckmann J, Geier B, Liebeke M. Determination of Abundant Metabolite Matrix Adducts Illuminates the Dark Metabolome of MALDI-Mass Spectrometry Imaging Datasets. Anal Chem 2021; 93:8399-8407. [PMID: 34097397 PMCID: PMC8223199 DOI: 10.1021/acs.analchem.0c04720] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
Spatial metabolomics
using mass spectrometry imaging (MSI) is a
powerful tool to map hundreds to thousands of metabolites in biological
systems. One major challenge in MSI is the annotation of m/z values, which is substantially complicated by
background ions introduced throughout the chemicals and equipment
used during experimental procedures. Among many factors, the formation
of adducts with sodium or potassium ions, or in case of matrix-assisted
laser desorption ionization (MALDI)-MSI, the presence of abundant
matrix clusters strongly increases total m/z peak counts. Currently, there is a limitation to identify
the chemistry of the many unknown peaks to interpret their biological
function. We took advantage of the co-localization of adducts with
their parent ions and the accuracy of high mass resolution to estimate
adduct abundance in 20 datasets from different vendors of mass spectrometers.
Metabolites ranging from lipids to amines and amino acids form matrix
adducts with the commonly used 2,5-dihydroxybenzoic acid (DHB) matrix
like [M + (DHB-H2O) + H]+ and [M + DHB + Na]+. Current data analyses neglect those matrix adducts and overestimate
total metabolite numbers, thereby expanding the number of unidentified
peaks. Our study demonstrates that MALDI-MSI data are strongly influenced
by adduct formation across different sample types and vendor platforms
and reveals a major influence of so far unrecognized metabolite–matrix
adducts on total peak counts (up to one third). We developed a software
package, mass2adduct, for the community
for an automated putative assignment and quantification of metabolite–matrix
adducts enabling users to ultimately focus on the biologically relevant
portion of the MSI data.
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Affiliation(s)
- Moritz Janda
- Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359 Bremen, Germany
| | - Brandon K B Seah
- Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359 Bremen, Germany
| | - Dennis Jakob
- Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359 Bremen, Germany
| | - Janine Beckmann
- Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359 Bremen, Germany
| | - Benedikt Geier
- Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359 Bremen, Germany
| | - Manuel Liebeke
- Max Planck Institute for Marine Microbiology, Celsiusstrasse 1, 28359 Bremen, Germany
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12
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Davoli E, Zucchetti M, Matteo C, Ubezio P, D'Incalci M, Morosi L. THE SPACE DIMENSION AT THE MICRO LEVEL: MASS SPECTROMETRY IMAGING OF DRUGS IN TISSUES. MASS SPECTROMETRY REVIEWS 2021; 40:201-214. [PMID: 32501572 DOI: 10.1002/mas.21633] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/24/2020] [Accepted: 04/29/2020] [Indexed: 06/11/2023]
Abstract
Mass spectrometry imaging (MSI) has seen remarkable development in recent years. The possibility of getting quantitative or semiquantitative data, while maintaining the spatial component in the tissues has opened up unique study possibilities. Now with a spatial window of few tens of microns, we can characterize the events occurring in tissue subcompartments in physiological and pathological conditions. For example, in oncology-especially in preclinical models-we can quantitatively measure drug distribution within tumors, correlating it with pharmacological treatments intended to modify it. We can also study the local effects of the drug in the tissue, and their effects in relation to histology. This review focuses on the main results in the field of drug MSI in clinical pharmacology, looking at the literature on the distribution of drugs in human tissues, and also the first preclinical evidence of drug intratissue effects. The main instrumental techniques are discussed, looking at the different instrumentation, sample preparation protocols, and raw data management employed to obtain the sensitivity required for these studies. Finally, we review the applications that describe in situ metabolic events and pathways induced by the drug, in animal models, showing that MSI makes it possible to study effects that go beyond the simple concentration of the drug, maintaining the space dimension. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.
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Affiliation(s)
- Enrico Davoli
- Laboratory of Mass Spectrometry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Massimo Zucchetti
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Cristina Matteo
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Paolo Ubezio
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Maurizio D'Incalci
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Lavinia Morosi
- Laboratory of Antitumoral Pharmacology, Department of Oncology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
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13
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Hollerbach AL, Conant CR, Nagy G, Monroe ME, Gupta K, Donor M, Giberson CM, Garimella SVB, Smith RD, Ibrahim YM. Dynamic Time-Warping Correction for Shifts in Ultrahigh Resolving Power Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:996-1007. [PMID: 33666432 PMCID: PMC8216491 DOI: 10.1021/jasms.1c00005] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Detection of arrival time shifts between ion mobility spectrometry (IMS) separations can limit achievable resolving power (Rp), particularly when multiple separations are summed or averaged, as commonly practiced in IMS. Such variations can be apparent in higher Rp measurements and are particularly evident in long path length traveling wave structures for lossless ion manipulations (SLIM) IMS due to their typically much longer separation times. Here, we explore data processing approaches employing single value alignment (SVA) and nonlinear dynamic time warping (DTW) to correct for variations between IMS separations, such as due to pressure fluctuations, to enable more effective spectrum summation for improving Rp and detection of low-intensity species. For multipass SLIM IMS separations, where narrow mobility range measurements have arrival times that can extend to several seconds, the SVA approach effectively corrected for such variations and significantly improved Rp for summed separations. However, SVA was much less effective for broad mobility range separations, such as obtained with multilevel SLIM IMS. Changes in ions' arrival times were observed to be correlated with small pressure changes, with approximately 0.6% relative arrival time shifts being common, sufficient to result in a loss of Rp for summed separations. Comparison of the approaches showed that DTW alignment performed similarly to SVA when used over a narrow mobility range but was significantly better (providing narrower peaks and higher signal intensities) for wide mobility range data. We found that the DTW approach increased Rp by as much as 115% for measurements in which 50 IMS separations over 2 s were summed. We conclude that DTW is superior to SVA for ultra-high-resolution broad mobility range SLIM IMS separations and leads to a large improvement in effective Rp, correcting for ion arrival time shifts regardless of the cause, as well as improving the detectability of low-abundance species. Our tool is publicly available for use with universal ion mobility format (.UIMF) and text (.txt) files.
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Affiliation(s)
- Adam L Hollerbach
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Christopher R Conant
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Gabe Nagy
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Khushboo Gupta
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Micah Donor
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Cameron M Giberson
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Sandilya V B Garimella
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
| | - Yehia M Ibrahim
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington 99354, United States
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14
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Rozanova S, Barkovits K, Nikolov M, Schmidt C, Urlaub H, Marcus K. Quantitative Mass Spectrometry-Based Proteomics: An Overview. Methods Mol Biol 2021; 2228:85-116. [PMID: 33950486 DOI: 10.1007/978-1-0716-1024-4_8] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics.
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Affiliation(s)
- Svitlana Rozanova
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany.,Medical Proteome Analysis, Center for protein diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
| | - Katalin Barkovits
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany.,Medical Proteome Analysis, Center for protein diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany
| | - Miroslav Nikolov
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany
| | - Carla Schmidt
- Interdisciplinary Research Center HALOmem, Charles Tanford Protein Center, Institute for Biochemistry and Biotechnology, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany.,Bioanalytics Group, Institute of Clinical Chemistry, University Medical Center Goettingen, Goettingen, Germany.,Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University, Frankfurt, Germany
| | - Katrin Marcus
- Medizinisches Proteom-Center, Medical Faculty, Ruhr-University Bochum, Bochum, Germany. .,Medical Proteome Analysis, Center for protein diagnostics (PRODI), Ruhr-University Bochum, Bochum, Germany.
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15
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Tuck M, Blanc L, Touti R, Patterson NH, Van Nuffel S, Villette S, Taveau JC, Römpp A, Brunelle A, Lecomte S, Desbenoit N. Multimodal Imaging Based on Vibrational Spectroscopies and Mass Spectrometry Imaging Applied to Biological Tissue: A Multiscale and Multiomics Review. Anal Chem 2020; 93:445-477. [PMID: 33253546 DOI: 10.1021/acs.analchem.0c04595] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Michael Tuck
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Landry Blanc
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Rita Touti
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232-8575, United States
| | - Sebastiaan Van Nuffel
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Sandrine Villette
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Jean-Christophe Taveau
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Andreas Römpp
- Bioanalytical Sciences and Food Analysis, University of Bayreuth, Universitätsstraße 30, 95440 Bayreuth, Germany
| | - Alain Brunelle
- Laboratoire d'Archéologie Moléculaire et Structurale, LAMS UMR 8220, CNRS, Sorbonne Université, 4 Place Jussieu, 75005 Paris, France
| | - Sophie Lecomte
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nicolas Desbenoit
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
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16
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Hsiao CH, Lai YH, Kuo SY, Cai YH, Lin CH, Wang YS. A Dynamic Data Correction Method for Enhancing Resolving Power of Integrated Spectra in Spectroscopic Analysis. Anal Chem 2020; 92:12763-12768. [PMID: 32897048 DOI: 10.1021/acs.analchem.0c00737] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A dynamic data correction method embedded in the process of data acquisition improves spectral quality. The method minimizes the impact of random errors in spectroscopic measurements by correcting peak positions in every single-scan spectrum. The method is fast enough to facilitate online data correction. The integration of corrected spectra improves resolving power and signal-to-noise ratio. The correction method can apply to most analytical spectra. In mass spectrometry and Raman spectroscopy, observations show that it improved the average resolving power by roughly 40-150% and revealed unresolved spectral features.
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Affiliation(s)
- Chih-Hao Hsiao
- Genomics Research Center, Academia Sinica, No. 128 Academia Road, Section 2, Taipei 11529, Taiwan (ROC)
| | - Yin-Hung Lai
- Genomics Research Center, Academia Sinica, No. 128 Academia Road, Section 2, Taipei 11529, Taiwan (ROC).,Department of Chemical Engineering, National United University, No. 2, Lien Da, Nan Shih Li, Miaoli 36063, Taiwan (ROC)
| | - Shu-Yun Kuo
- Genomics Research Center, Academia Sinica, No. 128 Academia Road, Section 2, Taipei 11529, Taiwan (ROC)
| | - Yi-Hong Cai
- Genomics Research Center, Academia Sinica, No. 128 Academia Road, Section 2, Taipei 11529, Taiwan (ROC).,Department of Chemistry, National Taiwan Normal University, No. 88, Section 4, Ting-Chow Road, Taipei 11677, Taiwan (ROC)
| | - Cheng-Huang Lin
- Department of Chemistry, National Taiwan Normal University, No. 88, Section 4, Ting-Chow Road, Taipei 11677, Taiwan (ROC)
| | - Yi-Sheng Wang
- Genomics Research Center, Academia Sinica, No. 128 Academia Road, Section 2, Taipei 11529, Taiwan (ROC)
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Abstract
Analysis of intact proteins by native mass spectrometry has emerged as a powerful tool for obtaining insight into subunit diversity, post-translational modifications, stoichiometry, structural arrangement, stability, and overall architecture. Typically, such an analysis is performed following protein purification procedures, which are time consuming, costly, and labor intensive. As this technology continues to move forward, advances in sample handling and instrumentation have enabled the investigation of intact proteins in situ and in crude samples, offering rapid analysis and improved conservation of the biological context. This emerging field, which involves various ion source platforms such as matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) for both spatial imaging and solution-based analysis, is expected to impact many scientific fields, including biotechnology, pharmaceuticals, and clinical sciences. In this Perspective, we discuss the information that can be retrieved by such experiments as well as the current advantages and technical challenges associated with the different sampling strategies. Furthermore, we present future directions of these MS-based methods, including current limitations and efforts that should be made to make these approaches more accessible. Considering the vast progress we have witnessed in recent years, we anticipate that the advent of further innovations enabling minimal handling of MS samples will make this field more robust, user friendly, and widespread.
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Affiliation(s)
- Shay Vimer
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Gili Ben-Nissan
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Michal Sharon
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
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18
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Goodwin RJA, Takats Z, Bunch J. A Critical and Concise Review of Mass Spectrometry Applied to Imaging in Drug Discovery. SLAS DISCOVERY 2020; 25:963-976. [PMID: 32713279 DOI: 10.1177/2472555220941843] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
During the past decade, mass spectrometry imaging (MSI) has become a robust and versatile methodology to support modern pharmaceutical research and development. The technologies provide data on the biodistribution, metabolism, and delivery of drugs in tissues, while also providing molecular maps of endogenous metabolites, lipids, and proteins. This allows researchers to make both pharmacokinetic and pharmacodynamic measurements at cellular resolution in tissue sections or clinical biopsies. Despite drug imaging within samples now playing a vital role within research and development (R&D) in leading pharmaceutical companies, however, the challenges in turning compounds into medicines continue to evolve as rapidly as the technologies used to discover them. The increasing cost of development of new and emerging therapeutic modalities, along with the associated risks of late-stage program attrition, means there is still an unmet need in our ability to address an increasing array of challenging bioanalytical questions within drug discovery. We require new capabilities and strategies of integrated imaging to provide context for fundamental disease-related biological questions that can also offer insights into specific project challenges. Integrated molecular imaging and advanced image analysis have the opportunity to provide a world-class capability that can be deployed on projects in which we cannot answer the question with our battery of established assays. Therefore, here we will provide an updated concise review of the use of MSI for drug discovery; we will also critically consider what is required to embed MSI into a wider evolving R&D landscape and allow long-lasting impact in the industry.
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Affiliation(s)
- Richard J A Goodwin
- Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK.,Institute of Infection, Immunity, and Inflammation, College of Medical, Veterinary, and Life Sciences, University of Glasgow, UK
| | - Zoltan Takats
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.,The Rosalind Franklin Institute, Oxfordshire, UK
| | - Josephine Bunch
- Department of Surgery and Cancer, Faculty of Medicine, Imperial College, London, UK.,The Rosalind Franklin Institute, Oxfordshire, UK.,National Physical Laboratory, Teddington, London, UK
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19
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Yu J, Chudinov A, Li L, Liu J, Gao W, Huang Z, Zhou Z, Nikiforov S, Kozlovskiy V. Improvement of m/z accuracy for linear matrix-assisted laser desorption/ionization time-of-flight mass spectrometer. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2020; 34:e8701. [PMID: 31845394 DOI: 10.1002/rcm.8701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 11/09/2019] [Accepted: 12/13/2019] [Indexed: 06/10/2023]
Abstract
RATIONALE Linear matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) is widely used in analytical and biomedical applications. The use of delayed extraction increases the resolution, but the roughness of the matrix crystals and the misalignment of the target plate in the order of a few micrometers cause a substantial spread in the ion TOF values and a decrease in mass accuracy. METHODS The method of mass spectra correction based on the correlation of matrix fragment peaks in MALDI mass spectra was used. Experiments were performed using the MALDI-TOF instrument CMI-1600. SIMION 8.1 and MATLAB were used for ion motion simulations. Data analysis was done using the home-built custom-developed software and MATLAB. RESULTS It was shown that the peak position drift in the MALDI-TOF mass spectra depends linearly on the TOF in a wide mass range. While using the linear correction of the TOF scale, an increase in m/z accuracy of more than 10 times was achieved. The mass accuracy was limited by the resolution of the fast Analog-to-Digital Converter (ADC) used. CONCLUSIONS It is expected that the proposed method will significantly increase the dynamic range, since it becomes possible to sum up corrected individual mass spectra without a significant loss of resolution. Timescale adjusting can be used for both linear TOF instruments and reflector systems of various configurations.
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Affiliation(s)
- Jiajun Yu
- Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou, China
| | - Alexey Chudinov
- Federal Research Centre, Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
| | - Lei Li
- Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou, China
| | - Jinzhao Liu
- Guangzhou Hexin Instrument Co., Ltd, Guangzhou, China
| | - Wei Gao
- Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou, China
| | - Zhengxu Huang
- Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou, China
| | - Zhen Zhou
- Institute of Mass Spectrometer and Atmospheric Environment, Jinan University, Guangzhou, China
| | - Sergey Nikiforov
- Prokhorov General Physics Institute of the Russian Academy of Sciences, Moscow, Russia
| | - Viatcheslav Kozlovskiy
- Federal Research Centre, Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, Chernogolovka, Moscow, Russia
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20
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Alexandrov T. Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence. Annu Rev Biomed Data Sci 2020; 3:61-87. [PMID: 34056560 DOI: 10.1146/annurev-biodatasci-011420-031537] [Citation(s) in RCA: 107] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology-imaging mass spectrometry-generate big hyper-spectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117 Heidelberg, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, USA
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21
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Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) for Investigation of Mycobacterium tuberculosis Complex Outbreaks: a Type Dream? J Clin Microbiol 2020; 58:JCM.02077-19. [PMID: 31941688 DOI: 10.1128/jcm.02077-19] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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22
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Tortorella S, Tiberi P, Bowman AP, Claes BSR, Ščupáková K, Heeren RMA, Ellis SR, Cruciani G. LipostarMSI: Comprehensive, Vendor-Neutral Software for Visualization, Data Analysis, and Automated Molecular Identification in Mass Spectrometry Imaging. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:155-163. [PMID: 32881505 DOI: 10.1021/jasms.9b00034] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Mass Spectrometry Imaging (MSI) is an established and powerful MS technique that enables molecular mapping of tissues and cells finding widespread applications in academic, medical, and pharmaceutical industries. As both the applications and MSI technology have undergone rapid growth and improvement, the challenges associated both with analyzing large datasets and identifying the many detected molecular species have become apparent. The lack of readily available and comprehensive software covering all necessary data analysis steps has further compounded this challenge. To address this issue we developed LipostarMSI, comprehensive and vendor-neutral software for targeted and untargeted MSI data analysis. Through user-friendly implementation of image visualization and co-registration, univariate and multivariate image and spectral analysis, and for the first time, advanced lipid, metabolite, and drug metabolite (MetID) automated identification, LipostarMSI effectively streamlines biochemical interpretation of the data. Here, we introduce LipostarMSI and case studies demonstrating the versatility and many capabilities of the software.
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Affiliation(s)
- Sara Tortorella
- Molecular Horizon Srl, Via Montelino 30, 06084 Bettona, Perugia, Italy
- Consortium for Computational Molecular and Materials Sciences (CMS)2, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Paolo Tiberi
- Molecular Discovery Ltd., Centennial Park, WD6 3FG Borehamwood, Hertfordshire, United Kingdom
| | - Andrew P Bowman
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Britt S R Claes
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Klára Ščupáková
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Ron M A Heeren
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Shane R Ellis
- Maastricht MultiModal Molecular Imaging (M4I) Institute, Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands
| | - Gabriele Cruciani
- Consortium for Computational Molecular and Materials Sciences (CMS)2, Via Elce di Sotto 8, 06123 Perugia, Italy
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
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23
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Holzlechner M, Eugenin E, Prideaux B. Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer. Cancer Rep (Hoboken) 2019; 2:e1229. [PMID: 32729258 PMCID: PMC7941519 DOI: 10.1002/cnr2.1229] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 11/04/2019] [Accepted: 11/07/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Current methods to identify, classify, and predict tumor behavior mostly rely on histology, immunohistochemistry, and molecular determinants. However, better predictive markers are required for tumor diagnosis and evaluation. Due, in part, to recent technological advancements, metabolomics and lipid biomarkers have become a promising area in cancer research. Therefore, there is a necessity for novel and complementary techniques to identify and visualize these molecular markers within tumors and surrounding tissue. RECENT FINDINGS Since its introduction, mass spectrometry imaging (MSI) has proven to be a powerful tool for mapping analytes in biological tissues. By adding the label-free specificity of mass spectrometry to the detailed spatial information of traditional histology, hundreds of lipids can be imaged simultaneously within a tumor. MSI provides highly detailed lipid maps for comparing intra-tumor, tumor margin, and healthy regions to identify biomarkers, patterns of disease, and potential therapeutic targets. In this manuscript, recent advancement in sample preparation and MSI technologies are discussed with special emphasis on cancer lipid research to identify tumor biomarkers. CONCLUSION MSI offers a unique approach for biomolecular characterization of tumor tissues and provides valuable complementary information to histology for lipid biomarker discovery and tumor classification in clinical and research cancer applications.
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Affiliation(s)
- Matthias Holzlechner
- Department of Neuroscience, Cell Biology, and AnatomyThe University of Texas Medical Branch at Galveston (UTMB)GalvestonTexas
| | - Eliseo Eugenin
- Department of Neuroscience, Cell Biology, and AnatomyThe University of Texas Medical Branch at Galveston (UTMB)GalvestonTexas
| | - Brendan Prideaux
- Department of Neuroscience, Cell Biology, and AnatomyThe University of Texas Medical Branch at Galveston (UTMB)GalvestonTexas
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24
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Wehrli PM, Michno W, Blennow K, Zetterberg H, Hanrieder J. Chemometric Strategies for Sensitive Annotation and Validation of Anatomical Regions of Interest in Complex Imaging Mass Spectrometry Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2019; 30:2278-2288. [PMID: 31529404 PMCID: PMC6828630 DOI: 10.1007/s13361-019-02327-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 06/12/2019] [Accepted: 08/10/2019] [Indexed: 05/04/2023]
Abstract
Imaging mass spectrometry (IMS) is a promising new chemical imaging modality that generates a large body of complex imaging data, which in turn can be approached using multivariate analysis approaches for image analysis and segmentation. Processing IMS raw data is critically important for proper data interpretation and has significant effects on the outcome of data analysis, in particular statistical modeling. Commonly, data processing methods are chosen based on rational motivations rather than comparative metrics, though no quantitative measures to assess and compare processing options have been suggested. We here present a data processing and analysis pipeline for IMS data interrogation, processing and ROI annotation, segmentation, and validation. This workflow includes (1) objective evaluation of processing methods for IMS datasets based on multivariate analysis using PCA. This was then followed by (2) ROI annotation and classification through region-based active contours (AC) segmentation based on the PCA component scores matrix. This provided class information for subsequent (3) OPLS-DA modeling to evaluate IMS data processing based on the quality metrics of their respective multivariate models and for robust quantification of ROI-specific signal localization. This workflow provides an unbiased strategy for sensitive annotation of anatomical regions of interest combined with quantitative comparison of processing procedures for multivariate analysis allowing robust ROI annotation and quantification of the associated molecular histology.
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Affiliation(s)
- Patrick M Wehrli
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
| | - Wojciech Michno
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, UK
- Department of Neurodegenerative Disease, Queen Square Instritute of Neurology, University College London, London, UK
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden.
- Department of Neurodegenerative Disease, Queen Square Instritute of Neurology, University College London, London, UK.
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25
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Bredehöft J, Bhandari DR, Pflieger FJ, Schulz S, Kang JX, Layé S, Roth J, Gerstberger R, Mayer K, Spengler B, Rummel C. Visualizing and Profiling Lipids in the OVLT of Fat-1 and Wild Type Mouse Brains during LPS-Induced Systemic Inflammation Using AP-SMALDI MSI. ACS Chem Neurosci 2019; 10:4394-4406. [PMID: 31513369 DOI: 10.1021/acschemneuro.9b00435] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Lipids, including omega-3 polyunsaturated fatty acids (n-3-PUFAs), modulate brain-intrinsic inflammation during systemic inflammation. The vascular organ of the lamina terminalis (OVLT) is a brain structure important for immune-to-brain communication. We, therefore, aimed to profile the distribution of several lipids (e.g., phosphatidyl-choline/ethanolamine, PC/PE), including n-3-PUFA-carrying lipids (esterified in phospholipids), in the OVLT during systemic lipopolysaccharide(LPS)-induced inflammation. We injected wild type and endogenously n-3-PUFA producing fat-1 transgenic mice with LPS (i.p., 2.5 mg/kg) or PBS. Brain samples were analyzed using immunohistochemistry and high-resolution atmospheric-pressure scanning microprobe matrix-assisted laser desorption/ionization orbital trapping mass spectrometry imaging (AP-SMALDI-MSI) for spatial resolution of lipids. Depending on genotype and treatment, several distinct distribution patterns were observed for lipids [e.g., lyso(L)PC (16:0)/(18:0)] proposed to be involved in inflammation. The distribution patterns ranged from being homogeneously disseminated [LPC (18:1)], absent/reduced signaling within the OVLT relative to adjacent preoptic tissue [PE (38:6)], either treatment- and genotype-dependent or independent low signal intensities [LPC (18:0)], treatment- and genotype-dependent [PC 38:6)] or independent accumulation in the OVLT [PC (38:7)], and accumulation in commissures, e.g., nerve fibers like the optic nerve [LPE (18:1)]. Overall, screening of lipid distribution patterns revealed distinct inflammation-induced changes in the OVLT, highlighting the prominent role of lipid metabolism in brain inflammation. Moreover, known and novel candidates for brain inflammation and immune-to-brain communication were detected specifically within this pivotal brain structure, a window between the periphery and the brain. The biological significance of these newly identified lipids abundant in the OVLT and the adjacent preoptic area remains to be further analyzed.
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Affiliation(s)
- Janne Bredehöft
- Institute of Veterinary Physiology and Biochemistry, Justus Liebig University Giessen, Frankfurter Strasse 100, D-35392 Giessen, Germany
| | - Dhaka Ram Bhandari
- Institute of Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Heinrich-Buff-Ring 17, D-35392 Giessen, Germany
| | - Fabian Johannes Pflieger
- Institute of Veterinary Physiology and Biochemistry, Justus Liebig University Giessen, Frankfurter Strasse 100, D-35392 Giessen, Germany
| | - Sabine Schulz
- Institute of Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Heinrich-Buff-Ring 17, D-35392 Giessen, Germany
| | - Jing X. Kang
- Laboratory for Lipid Medicine and Technology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, 149 13th Street, Charlestown, Massachusetts 02129, United States
| | - Sophie Layé
- UMR 1286, NutriNeuro: Laboratoire Nutrition et Neurobiologie Intégrée, Institut National de la Recherche Agronomique, Université de Bordeaux, Bordeaux 33076, France
| | - Joachim Roth
- Institute of Veterinary Physiology and Biochemistry, Justus Liebig University Giessen, Frankfurter Strasse 100, D-35392 Giessen, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg 35032, Germany
| | - Rüdiger Gerstberger
- Institute of Veterinary Physiology and Biochemistry, Justus Liebig University Giessen, Frankfurter Strasse 100, D-35392 Giessen, Germany
| | - Konstantin Mayer
- University of Giessen and Marburg Lung Center (UGMLC), Justus Liebig University Giessen, Klinikstrasse 33, Giessen D-35392, Germany
| | - Bernhard Spengler
- Institute of Inorganic and Analytical Chemistry, Justus Liebig University Giessen, Heinrich-Buff-Ring 17, D-35392 Giessen, Germany
| | - Christoph Rummel
- Institute of Veterinary Physiology and Biochemistry, Justus Liebig University Giessen, Frankfurter Strasse 100, D-35392 Giessen, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Marburg 35032, Germany
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26
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Systematic evaluation of repeatability of IR-MALDESI-MS and normalization strategies for correcting the analytical variation and improving image quality. Anal Bioanal Chem 2019; 411:5729-5743. [PMID: 31240357 DOI: 10.1007/s00216-019-01953-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 05/21/2019] [Accepted: 05/28/2019] [Indexed: 12/20/2022]
Abstract
Mass spectrometry imaging is a powerful tool widely used in biological, clinical, and forensic research, but its often poor repeatability limits its application for quantitative and large-scale analysis. A systematic evaluation of infrared matrix-assisted laser desorption electrospray ionization mass spectrometry (IR-MALDESI-MS) repeatability in absolute ion abundances during short- and long-term experiments was carried out on liver slices from the same rat with minimal biological variability to be expected. Results of median %RSDs ranging from 14 to 45, pooled %RMADs ranging from 11 to 33, and Pearson correlation coefficients ranging from 0.83 to 1.00 demonstrated an acceptable repeatability of IR-MALDESI-MS. Normalization is commonly applied for the purpose of accounting for analytical variability of spectra generated from different runs so as to reveal real biological differences. Nine data normalization strategies were performed on the rat liver data sets to examine their effects on reducing analytical variation, and further on a hen ovary data set containing more morphological features for the investigation of their impact on ion images. Results demonstrated that the majority of normalization approaches benefit data quality to some extent, and local normalization methods significantly outperform their global counterparts, resulting in a reduction of median %RSD up to 22. Local median normalization was found to be promisingly robust for both homogeneous and heterogeneous samples.
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27
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Drzeżdżon J, Jacewicz D, Sielicka A, Chmurzyński L. MALDI-MS for polymer characterization – Recent developments and future prospects. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.04.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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28
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Michno W, Wehrli PM, Blennow K, Zetterberg H, Hanrieder J. Molecular imaging mass spectrometry for probing protein dynamics in neurodegenerative disease pathology. J Neurochem 2018; 151:488-506. [PMID: 30040875 DOI: 10.1111/jnc.14559] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2018] [Revised: 07/03/2018] [Accepted: 07/12/2018] [Indexed: 12/14/2022]
Abstract
Recent advances in the understanding of basic pathological mechanisms in various neurological diseases depend directly on the development of novel bioanalytical technologies that allow sensitive and specific chemical imaging at high resolution in cells and tissues. Mass spectrometry-based molecular imaging (IMS) has gained increasing popularity in biomedical research for mapping the spatial distribution of molecular species in situ. The technology allows for comprehensive, untargeted delineation of in situ distribution profiles of metabolites, lipids, peptides and proteins. A major advantage of IMS over conventional histochemical techniques is its superior molecular specificity. Imaging mass spectrometry has therefore great potential for probing molecular regulations in CNS-derived tissues and cells for understanding neurodegenerative disease mechanism. The goal of this review is to familiarize the reader with the experimental workflow, instrumental developments and methodological challenges as well as to give a concise overview of the major advances and recent developments and applications of IMS-based protein and peptide profiling with particular focus on neurodegenerative diseases. This article is part of the Special Issue "Proteomics".
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Affiliation(s)
- Wojciech Michno
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Patrick M Wehrli
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK.,UK Dementia Research Institute at UCL, London, UK
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK.,Department of Chemistry and Chemical Engineering, Chalmers University of Technology, Gothenburg, Sweden
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29
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Tobias F, Olson MT, Cologna SM. Mass spectrometry imaging of lipids: untargeted consensus spectra reveal spatial distributions in Niemann-Pick disease type C1. J Lipid Res 2018; 59:2446-2455. [PMID: 30266834 DOI: 10.1194/jlr.d086090] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2018] [Revised: 09/24/2018] [Indexed: 12/12/2022] Open
Abstract
Mass spectrometry imaging (MSI) is a tool to rapidly map the spatial location of analytes without the need for tagging or a reporter system. Niemann-Pick disease type C1 (NPC1) is a neurodegenerative, lysosomal storage disorder characterized by accumulation of unesterified cholesterol and sphingolipids in the endo-lysosomal system. Here, we use MSI to visualize lipids including cholesterol in cerebellar brain tissue from the NPC1 symptomatic mouse model and unaffected controls. To complement the imaging studies, a data-processing pipeline was developed to generate consensus mass spectra, thereby using both technical and biological image replicates to assess differences. The consensus spectra are used to determine true differences in lipid relative abundance; lipid distributions can be determined in an unbiased fashion without prior knowledge of location. We show the cerebellar distribution of gangliosides GM1, GM2, and GM3, including variants of lipid chain length. We also performed MALDI-MSI of cholesterol. Further analysis of lobules IV/V and X of the cerebellum gangliosides indicates regional differences. The specificity achieved highlights the power of MSI, and this new workflow demonstrates a universal approach for addressing reproducibility in imaging experiments applied to NPC1.
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Affiliation(s)
- Fernando Tobias
- Department of Chemistry University of Illinois at Chicago, Chicago, IL 60607
| | - Matthew T Olson
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, FL 32224
| | - Stephanie M Cologna
- Department of Chemistry University of Illinois at Chicago, Chicago, IL 60607 .,Laboratory of Integrative Neuroscience, University of Illinois at Chicago, Chicago, IL 60607
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30
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Ràfols P, Castillo ED, Yanes O, Brezmes J, Correig X. Novel automated workflow for spectral alignment and mass calibration in MS imaging using a sputtered Ag nanolayer. Anal Chim Acta 2018; 1022:61-69. [DOI: 10.1016/j.aca.2018.03.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 03/12/2018] [Accepted: 03/20/2018] [Indexed: 02/06/2023]
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31
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Falcetta F, Morosi L, Ubezio P, Giordano S, Decio A, Giavazzi R, Frapolli R, Prasad M, Franceschi P, D'Incalci M, Davoli E. Past-in-the-Future. Peak detection improves targeted mass spectrometry imaging. Anal Chim Acta 2018; 1042:1-10. [PMID: 30428975 DOI: 10.1016/j.aca.2018.06.067] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 06/19/2018] [Accepted: 06/24/2018] [Indexed: 11/30/2022]
Abstract
Mass spectrometry imaging is a valuable tool for visualizing the localization of drugs in tissues, a critical issue especially in cancer pharmacology where treatment failure may depend on poor drug distribution within the tumours. Proper preprocessing procedures are mandatory to obtain quantitative data of drug distribution in tumours, even at low intensity, through reliable ion peak identification and integration. We propose a simple preprocessing and quantification pipeline. This pipeline was designed starting from classical peak integration methods, developed when "microcomputers" became available for chromatography, now applied to MSI. This pre-processing approach is based on a novel method using the fixed mass difference between the analyte and its 5 d derivatives to set up a mass range gate. We demonstrate the use of this pipeline for the evaluating the distribution of the anticancer drug paclitaxel in tumour sections. The procedure takes advantage of a simple peak analysis and allows to quantify the drug concentration in each pixel with a limit of detection below 0.1 pmol mm-2 or 10 μg g-1. Quantitative images of paclitaxel distribution in different tumour models were obtained and average paclitaxel concentrations were compared with HPLC measures in the same specimens, showing <20% difference. The scripts are developed in Python and available through GitHub, at github.com/FrancescaFalcetta/Imaging_of_drugs_distribution_and_quantifications.git.
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Affiliation(s)
- Francesca Falcetta
- Department of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19-20156, Milan, Italy
| | - Lavinia Morosi
- Department of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19-20156, Milan, Italy
| | - Paolo Ubezio
- Department of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19-20156, Milan, Italy
| | - Silvia Giordano
- Mass Spectrometry Laboratory, Department of Environmental Health Sciences, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19-20156, Milan, Italy
| | - Alessandra Decio
- Department of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19-20156, Milan, Italy
| | - Raffaella Giavazzi
- Department of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19-20156, Milan, Italy
| | - Roberta Frapolli
- Department of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19-20156, Milan, Italy
| | - Mridula Prasad
- IMM/Analytical Chemistry, Radboud University, Heyendaalseweg, 6525 AJ, Nijmegen, Netherlands; Biostatistics and Data Management Group, Fondazione Edmund Mach, 38010, San Michele all' Adige, Italy; Nanotechnology in Medicinal Chemistry, Department of Molecular Biotechnology and Health Sciences, Università di Torino, 10126, Torino, Italy
| | - Pietro Franceschi
- Biostatistics and Data Management Group, Fondazione Edmund Mach, 38010, San Michele all' Adige, Italy
| | - Maurizio D'Incalci
- Department of Oncology, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19-20156, Milan, Italy
| | - Enrico Davoli
- Mass Spectrometry Laboratory, Department of Environmental Health Sciences, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa, 19-20156, Milan, Italy.
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32
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Ràfols P, Vilalta D, Brezmes J, Cañellas N, Del Castillo E, Yanes O, Ramírez N, Correig X. Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications. MASS SPECTROMETRY REVIEWS 2018; 37:281-306. [PMID: 27862147 DOI: 10.1002/mas.21527] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2016] [Accepted: 10/11/2016] [Indexed: 06/06/2023]
Abstract
Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach. MSI data processing is challenging due to several factors. The large volume of mass spectra involved in a MSI experiment makes choosing the correct computational strategies critical. Furthermore, pixel to pixel variation inherent in the technique makes choosing the correct preprocessing steps critical. The primary aim of this review was to provide an overview of the data-processing steps and tools that can be applied to an MSI experiment, from preprocessing the raw data to the more advanced strategies for image visualization and segmentation. This review is particularly aimed at researchers performing MSI experiments and who are interested in incorporating new data-processing features, improving their computational strategy, and/or desire access to data-processing tools currently available. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:281-306, 2018.
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Affiliation(s)
- Pere Ràfols
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Dídac Vilalta
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Jesús Brezmes
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Nicolau Cañellas
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Esteban Del Castillo
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Oscar Yanes
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Noelia Ramírez
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
| | - Xavier Correig
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), C/Monforte de Lemos 3-5, Madrid, 28029, Spain
- Department of Electronic Engineering, Institute of Health Research Pere Virgili, Rovira i Virgili University, IISPV, Avinguda Països Catalans 26, Tarragona, 43007, Spain
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33
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Rae Buchberger A, DeLaney K, Johnson J, Li L. Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights. Anal Chem 2018; 90:240-265. [PMID: 29155564 PMCID: PMC5959842 DOI: 10.1021/acs.analchem.7b04733] [Citation(s) in RCA: 556] [Impact Index Per Article: 92.7] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Amanda Rae Buchberger
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Kellen DeLaney
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Jillian Johnson
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, 1101 University Avenue, Madison, Wisconsin 53706, United States
- School of Pharmacy, University of Wisconsin-Madison, 777 Highland Avenue, Madison, Wisconsin 53705, United States
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34
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Matrix-Assisted Laser Desorption/Ionisation Mass Spectrometry Imaging in the Study of Gastric Cancer: A Mini Review. Int J Mol Sci 2017; 18:ijms18122588. [PMID: 29194417 PMCID: PMC5751191 DOI: 10.3390/ijms18122588] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 11/25/2017] [Accepted: 11/28/2017] [Indexed: 02/07/2023] Open
Abstract
Gastric cancer (GC) is one of the leading causes of cancer-related deaths worldwide and the disease outcome commonly depends upon the tumour stage at the time of diagnosis. However, this cancer can often be asymptomatic during the early stages and remain undetected until the later stages of tumour development, having a significant impact on patient prognosis. However, our comprehension of the mechanisms underlying the development of gastric malignancies is still lacking. For these reasons, the search for new diagnostic and prognostic markers for gastric cancer is an ongoing pursuit. Modern mass spectrometry imaging (MSI) techniques, in particular matrix-assisted laser desorption/ionisation (MALDI), have emerged as a plausible tool in clinical pathology as a whole. More specifically, MALDI-MSI is being increasingly employed in the study of gastric cancer and has already elucidated some important disease checkpoints that may help us to better understand the molecular mechanisms underpinning this aggressive cancer. Here we report the state of the art of MALDI-MSI approaches, ranging from sample preparation to statistical analysis, and provide a complete review of the key findings that have been reported in the literature thus far.
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35
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Aboulmagd S, Esteban-Fernández D, Moreno-Gordaliza E, Neumann B, El-Khatib AH, Lázaro A, Tejedor A, Gómez-Gómez MM, Linscheid MW. Dual Internal Standards with Metals and Molecules for MALDI Imaging of Kidney Lipids. Anal Chem 2017; 89:12727-12734. [DOI: 10.1021/acs.analchem.7b02819] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Sarah Aboulmagd
- Department
of Chemistry, Humboldt-Universität zu Berlin, Brook-Taylor Strasse 2, 12489 Berlin, Germany
| | - Diego Esteban-Fernández
- Department
of Chemistry, Humboldt-Universität zu Berlin, Brook-Taylor Strasse 2, 12489 Berlin, Germany
| | - Estefanía Moreno-Gordaliza
- Department
of Analytical Chemistry, Faculty of Chemistry, Universidad Complutense de Madrid, Avda. Complutense s/n, 28040, Madrid, Spain
| | - Boris Neumann
- Proteome Factory, Magnusstraße 11, 12489 Berlin, Germany
- Charité-Universitätmedizin Berlin, Institute of Pharmacology, Hessische Straße 3-4, 10115 Berlin, Germany
| | - A. H. El-Khatib
- Department
of Chemistry, Humboldt-Universität zu Berlin, Brook-Taylor Strasse 2, 12489 Berlin, Germany
- Pharmaceutical
Analytical Chemistry Department, Faculty of Pharmacy, Ain Shams University, 11566 Cairo, Egypt
| | - Alberto Lázaro
- Renal Pathophysiology
Laboratory, Department of Nephrology, Instituto de Investigación
Sanitaria Gregorio Marañón, Hospital General Universitario Gregorio Marañón, C/Dr. Esquerdo 46, 28007, Madrid, Spain
| | - Alberto Tejedor
- Renal Pathophysiology
Laboratory, Department of Nephrology, Instituto de Investigación
Sanitaria Gregorio Marañón, Hospital General Universitario Gregorio Marañón, C/Dr. Esquerdo 46, 28007, Madrid, Spain
| | - M. Milagros Gómez-Gómez
- Department
of Analytical Chemistry, Faculty of Chemistry, Universidad Complutense de Madrid, Avda. Complutense s/n, 28040, Madrid, Spain
| | - Michael W. Linscheid
- Department
of Chemistry, Humboldt-Universität zu Berlin, Brook-Taylor Strasse 2, 12489 Berlin, Germany
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36
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In situ monitoring of molecular changes during cell differentiation processes in marine macroalgae through mass spectrometric imaging. Anal Bioanal Chem 2017; 409:4893-4903. [PMID: 28600691 DOI: 10.1007/s00216-017-0430-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 05/15/2017] [Accepted: 05/24/2017] [Indexed: 10/19/2022]
Abstract
Matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) was employed to discriminate between cell differentiation processes in macroalgae. One of the key developmental processes in the algal life cycle is the production of germ cells (gametes and zoids). The gametogenesis of the marine green macroalga Ulva mutabilis (Chlorophyta) was monitored by metabolomic snapshots of the surface, when blade cells differentiate synchronously into gametangia and giving rise to gametes. To establish MSI for macroalgae, dimethylsulfoniopropionate (DMSP), a known algal osmolyte, was determined. MSI of the surface of U. mutabilis followed by chemometric data analysis revealed dynamic metabolomic changes during cell differentiation. DMSP and a total of 55 specific molecular biomarkers, which could be assigned to important stages of the gametogenesis, were detected. Our research contributes to the understanding of molecular mechanisms underlying macroalgal cell differentiation. Graphical abstract Molecular changes during cell differentiation of the marine macroalga Ulva were visualized by matrix assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI).
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Sui P, Watanabe H, Artemenko K, Sun W, Bakalkin G, Andersson M, Bergquist J. Neuropeptide imaging in rat spinal cord with MALDI-TOF MS: Method development for the application in pain-related disease studies. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2017; 23:105-115. [PMID: 28657437 DOI: 10.1177/1469066717703272] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Spinal cord as a connection between brain and peripheral nervous system is an essential material for studying neural transmission, especially in pain-related research. This study was the first to investigate pain-related neuropeptide distribution in rat spinal cord using a matrix-assisted laser desorption ionization-time of flight imaging mass spectrometry (MALDI TOF MS) approach. The imaging workflow was evaluated and showed that MALDI TOF MS provides efficient resolution and robustness for neuropeptide imaging in rat spinal cord tissue. The imaging result showed that in naive rat spinal cord the molecular distribution of haeme, phosphatidylcholine, substance P and thymosin beta 4 were well in line with histological features. Three groups of pain-related neuropeptides, which are cleaved from prodynorphin, proenkephalin and protachykinin-1 proteins were detected. All these neuropeptides were found predominantly localized in the dorsal spinal cord and each group had unique distribution pattern. This study set the stage for future MALDI TOF MS application to elucidate signalling mechanism of pain-related diseases in small animal models.
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Affiliation(s)
- Ping Sui
- 1 Analytical Chemistry, Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden
| | - Hiroyuki Watanabe
- 2 Molecular Neuropsychopharmacology, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Konstantin Artemenko
- 1 Analytical Chemistry, Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden
| | - Wei Sun
- 2 Molecular Neuropsychopharmacology, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Georgy Bakalkin
- 2 Molecular Neuropsychopharmacology, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Malin Andersson
- 3 Drug Safety and Toxicology, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Jonas Bergquist
- 1 Analytical Chemistry, Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden
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Dilillo M, Ait-Belkacem R, Esteve C, Pellegrini D, Nicolardi S, Costa M, Vannini E, Graaf ELD, Caleo M, McDonnell LA. Ultra-High Mass Resolution MALDI Imaging Mass Spectrometry of Proteins and Metabolites in a Mouse Model of Glioblastoma. Sci Rep 2017; 7:603. [PMID: 28377615 PMCID: PMC5429601 DOI: 10.1038/s41598-017-00703-w] [Citation(s) in RCA: 114] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 03/08/2017] [Indexed: 01/27/2023] Open
Abstract
MALDI mass spectrometry imaging is able to simultaneously determine the spatial distribution of hundreds of molecules directly from tissue sections, without labeling and without prior knowledge. Ultra-high mass resolution measurements based on Fourier-transform mass spectrometry have been utilized to resolve isobaric lipids, metabolites and tryptic peptides. Here we demonstrate the potential of 15T MALDI-FTICR MSI for molecular pathology in a mouse model of high-grade glioma. The high mass accuracy and resolving power of high field FTICR MSI enabled tumor specific proteoforms, and tumor-specific proteins with overlapping and isobaric isotopic distributions to be clearly resolved. The protein ions detected by MALDI MSI were assigned to proteins identified by region-specific microproteomics (0.8 mm2 regions isolated using laser capture microdissection) on the basis of exact mass and isotopic distribution. These label free quantitative experiments also confirmed the protein expression changes observed by MALDI MSI and revealed changes in key metabolic proteins, which were supported by in-situ metabolite MALDI MSI.
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Affiliation(s)
- M Dilillo
- Fondazione Pisana per la Scienza ONLUS - Via Panfilo Castaldi 2, 56121, Pisa, Italy
- Department of Chemistry and Industrial Chemistry - Università di Pisa - Via Giuseppe Moruzzi 13, 56124, Pisa, Italy
| | - R Ait-Belkacem
- Fondazione Pisana per la Scienza ONLUS - Via Panfilo Castaldi 2, 56121, Pisa, Italy
| | - C Esteve
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - D Pellegrini
- Fondazione Pisana per la Scienza ONLUS - Via Panfilo Castaldi 2, 56121, Pisa, Italy
- NEST, Istituto Nanoscienze-National Research Council, 56127, Pisa, Italy
| | - S Nicolardi
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - M Costa
- CNR Neuroscience Institute, Via Moruzzi 1, 56124, Pisa, Italy
| | - E Vannini
- CNR Neuroscience Institute, Via Moruzzi 1, 56124, Pisa, Italy
| | - E L de Graaf
- Fondazione Pisana per la Scienza ONLUS - Via Panfilo Castaldi 2, 56121, Pisa, Italy
| | - M Caleo
- CNR Neuroscience Institute, Via Moruzzi 1, 56124, Pisa, Italy
| | - L A McDonnell
- Fondazione Pisana per la Scienza ONLUS - Via Panfilo Castaldi 2, 56121, Pisa, Italy.
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands.
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.
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Spatial Metabolite Profiling by Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 965:291-321. [DOI: 10.1007/978-3-319-47656-8_12] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Rzagalinski I, Volmer DA. Quantification of low molecular weight compounds by MALDI imaging mass spectrometry - A tutorial review. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1865:726-739. [PMID: 28012871 DOI: 10.1016/j.bbapap.2016.12.011] [Citation(s) in RCA: 65] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 12/01/2016] [Accepted: 12/19/2016] [Indexed: 10/20/2022]
Abstract
Matrix-assisted laser desorption/ionization (MALDI)-mass spectrometry imaging (MSI) permits label-free in situ analysis of chemical compounds directly from the surface of two-dimensional biological tissue slices. It links qualitative molecular information of compounds to their spatial coordinates and distribution within the investigated tissue. MALDI-MSI can also provide the quantitative amounts of target compounds in the tissue, if proper calibration techniques are performed. Obviously, as the target molecules are embedded within the biological tissue environment and analysis must be performed at their precise locations, there is no possibility for extensive sample clean-up routines or chromatographic separations as usually performed with homogenized biological materials; ion suppression phenomena therefore become a critical side effect of MALDI-MSI. Absolute quantification by MALDI-MSI should provide an accurate value of the concentration/amount of the compound of interest in relatively small, well-defined region of interest of the examined tissue, ideally in a single pixel. This goal is extremely challenging and will not only depend on the technical possibilities and limitations of the MSI instrument hardware, but equally on the chosen calibration/standardization strategy. These strategies are the main focus of this article and are discussed and contrasted in detail in this tutorial review. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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Affiliation(s)
- Ignacy Rzagalinski
- Institute of Bioanalytical Chemistry, Saarland University, 66123 Saarbrücken, Germany
| | - Dietrich A Volmer
- Institute of Bioanalytical Chemistry, Saarland University, 66123 Saarbrücken, Germany.
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Karlsson O, Hanrieder J. Imaging mass spectrometry in drug development and toxicology. Arch Toxicol 2016; 91:2283-2294. [PMID: 27933369 PMCID: PMC5429351 DOI: 10.1007/s00204-016-1905-6] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Accepted: 11/24/2016] [Indexed: 11/25/2022]
Abstract
During the last decades, imaging mass spectrometry has gained significant relevance in biomedical research. Recent advances in imaging mass spectrometry have paved the way for in situ studies on drug development, metabolism and toxicology. In contrast to whole-body autoradiography that images the localization of radiolabeled compounds, imaging mass spectrometry provides the possibility to simultaneously determine the discrete tissue distribution of the parent compound and its metabolites. In addition, imaging mass spectrometry features high molecular specificity and allows comprehensive, multiplexed detection and localization of hundreds of proteins, peptides and lipids directly in tissues. Toxicologists traditionally screen for adverse findings by histopathological examination. However, studies of the molecular and cellular processes underpinning toxicological and pathologic findings induced by candidate drugs or toxins are important to reach a mechanistic understanding and an effective risk assessment strategy. One of IMS strengths is the ability to directly overlay the molecular information from the mass spectrometric analysis with the tissue section and allow correlative comparisons of molecular and histologic information. Imaging mass spectrometry could therefore be a powerful tool for omics profiling of pharmacological/toxicological effects of drug candidates and toxicants in discrete tissue regions. The aim of the present review is to provide an overview of imaging mass spectrometry, with particular focus on MALDI imaging mass spectrometry, and its use in drug development and toxicology in general.
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Affiliation(s)
- Oskar Karlsson
- Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institute, 171 76, Stockholm, Sweden.
- Department of Pharmaceutical Biosciences, Drug Safety and Toxicology, Uppsala University, 751 24, Uppsala, Sweden.
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, Mölndal Hospital, House V, 431 80, Mölndal, Sweden
- Department of Molecular Neuroscience, UCL Institute of Neurology, University College London, Queen Square, London, WC1N, UK
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Rocha B, Cillero-Pastor B, Blanco FJ, Ruiz-Romero C. MALDI mass spectrometry imaging in rheumatic diseases. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2016; 1865:784-794. [PMID: 27742553 DOI: 10.1016/j.bbapap.2016.10.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2016] [Revised: 09/29/2016] [Accepted: 10/04/2016] [Indexed: 01/15/2023]
Abstract
Mass spectrometry imaging (MSI) is a technique used to visualize the spatial distribution of biomolecules such as peptides, proteins, lipids or other organic compounds by their molecular masses. Among the different MSI strategies, MALDI-MSI provides a sensitive and label-free approach for imaging of a wide variety of protein or peptide biomarkers from the surface of tissue sections, being currently used in an increasing number of biomedical applications such as biomarker discovery and tissue classification. In the field of rheumatology, MALDI-MSI has been applied to date for the analysis of joint tissues such as synovial membrane or cartilage. This review summarizes the studies and key achievements obtained using MALDI-MSI to increase understanding on rheumatic pathologies and to describe potential diagnostic or prognostic biomarkers of these diseases. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.
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Affiliation(s)
- Beatriz Rocha
- Proteomics Unit-ProteoRed/ISCIII, Rheumatology Group, INIBIC - Hospital Universitario de A Coruña, SERGAS, A Coruña, Spain
| | | | - Francisco J Blanco
- Proteomics Unit-ProteoRed/ISCIII, Rheumatology Group, INIBIC - Hospital Universitario de A Coruña, SERGAS, A Coruña, Spain; RIER-RED de Inflamación y Enfermedades Reumáticas, INIBIC-CHUAC, A Coruña, Spain.
| | - Cristina Ruiz-Romero
- Proteomics Unit-ProteoRed/ISCIII, Rheumatology Group, INIBIC - Hospital Universitario de A Coruña, SERGAS, A Coruña, Spain; CIBER-BBN Instituto de Salud Carlos III, INIBIC-CHUAC, A Coruña, Spain.
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Trim PJ, Snel MF. Small molecule MALDI MS imaging: Current technologies and future challenges. Methods 2016; 104:127-41. [DOI: 10.1016/j.ymeth.2016.01.011] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2015] [Revised: 01/19/2016] [Accepted: 01/21/2016] [Indexed: 11/25/2022] Open
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Kiss A, Hopfgartner G. Laser-based methods for the analysis of low molecular weight compounds in biological matrices. Methods 2016; 104:142-53. [DOI: 10.1016/j.ymeth.2016.04.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 02/28/2016] [Accepted: 04/13/2016] [Indexed: 01/26/2023] Open
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Galli M, Zoppis I, Smith A, Magni F, Mauri G. Machine learning approaches in MALDI-MSI: clinical applications. Expert Rev Proteomics 2016; 13:685-96. [PMID: 27322705 DOI: 10.1080/14789450.2016.1200470] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging. AREAS COVERED This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing. Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.
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Affiliation(s)
- Manuel Galli
- a Department of Medicine and Surgery , University of Milano Bicocca , Monza Brianza , Italy
| | - Italo Zoppis
- b Department of Informatics, Systems and Communication , University of Milano Bicocca , Milano , Italy
| | - Andrew Smith
- a Department of Medicine and Surgery , University of Milano Bicocca , Monza Brianza , Italy
| | - Fulvio Magni
- a Department of Medicine and Surgery , University of Milano Bicocca , Monza Brianza , Italy
| | - Giancarlo Mauri
- b Department of Informatics, Systems and Communication , University of Milano Bicocca , Milano , Italy
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A Support Vector Machine Classification of Thyroid Bioptic Specimens Using MALDI-MSI Data. Adv Bioinformatics 2016; 2016:3791214. [PMID: 27293431 PMCID: PMC4886047 DOI: 10.1155/2016/3791214] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 04/24/2016] [Indexed: 02/03/2023] Open
Abstract
Biomarkers able to characterise and predict multifactorial diseases are still one of the most important targets for all the “omics” investigations. In this context, Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry Imaging (MALDI-MSI) has gained considerable attention in recent years, but it also led to a huge amount of complex data to be elaborated and interpreted. For this reason, computational and machine learning procedures for biomarker discovery are important tools to consider, both to reduce data dimension and to provide predictive markers for specific diseases. For instance, the availability of protein and genetic markers to support thyroid lesion diagnoses would impact deeply on society due to the high presence of undetermined reports (THY3) that are generally treated as malignant patients. In this paper we show how an accurate classification of thyroid bioptic specimens can be obtained through the application of a state-of-the-art machine learning approach (i.e., Support Vector Machines) on MALDI-MSI data, together with a particular wrapper feature selection algorithm (i.e., recursive feature elimination). The model is able to provide an accurate discriminatory capability using only 20 out of 144 features, resulting in an increase of the model performances, reliability, and computational efficiency. Finally, tissue areas rather than average proteomic profiles are classified, highlighting potential discriminating areas of clinical interest.
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Challenges in biomarker discovery with MALDI-TOF MS. Clin Chim Acta 2016; 458:84-98. [PMID: 27134187 DOI: 10.1016/j.cca.2016.04.033] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Revised: 04/21/2016] [Accepted: 04/27/2016] [Indexed: 12/30/2022]
Abstract
MALDI-TOF MS technique is commonly used in system biology and clinical studies to search for new potential markers associated with pathological conditions. Despite numerous concerns regarding a sample preparation or processing of complex data, this strategy is still recognized as a popular tool and its awareness has risen in the proteomic community over the last decade. In this review, we present comprehensive application of MALDI mass spectrometry with special focus on profiling research. We also discuss major advantages and disadvantages of universal sample preparation methods such as micro-SPE columns, immunodepletion or magnetic beads, and we show the potential of nanostructured materials in capturing low molecular weight subproteomes. Furthermore, as the general protocol considerably affects spectra quality and interpretation, an alternative solution for improved ion detection, including hydrophobic constituents, data processing and statistical analysis is being considered in up-to-date profiling pattern. In conclusion, many reports involving MALDI-TOF MS indicated highly abundant proteins as valuable indicators, and at the same time showed the inaccuracy of available methods in the detection of low abundant proteome that is the most interesting from the clinical perspective. Therefore, the analytical aspects of sample preparation methods should be standardized to provide a reproducible, low sample handling and credible procedure.
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Wang S, Chen X, Luan H, Gao D, Lin S, Cai Z, Liu J, Liu H, Jiang Y. Matrix-assisted laser desorption/ionization mass spectrometry imaging of cell cultures for the lipidomic analysis of potential lipid markers in human breast cancer invasion. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2016; 30:533-42. [PMID: 26777684 DOI: 10.1002/rcm.7466] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 11/03/2015] [Accepted: 11/17/2015] [Indexed: 05/15/2023]
Abstract
RATIONALE Breast cancer is the leading cause of cancer death among women worldwide. Identification of lipid targets that play a role in breast cancer invasion may advance our understanding of the rapid progression of cancer and may lead to the development of new biomarkers for the disease. METHODS Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) was applied for the lipidomic profiling of two poorly invasive and two highly invasive breast cancer cell lines to identify the differentially accumulated lipids related to the invasive phenotype. The four cell lines were individually grown on indium tin oxide (ITO)-coated glass slides, analyzed as cell cultures. The raster width and matrix for detection were optimized to improve detection sensitivity. RESULTS Optimized MSI measurements were performed directly on the cell culture with 9-aminoacridine as matrix, resulting in 215 endogenous compounds detected in positive ion mode and 267 endogenous compounds in negative ion mode in all the four cell lines, representing the largest group of analytes that have been analyzed from cells by a single MSI study. In highly invasive cell lines, 31 lipids including phosphatidylglycerol (PG) and phosphatidic acids were found upregulated and eight lipids including sphingomyelin (SM) downregulated in negative ion mode. The products of de novo fatty acid synthesis incorporated into membrane phospholipids, like oleic-acid-containing PG, may be involved in mitochondrial dysfunction and thus affect the invasion of breast cancer cells. The deficiency of SM may be related to the disruption of apoptosis in highly invasive cancer cells. CONCLUSIONS This work uncovered more analytes in cells by MSI than previous reports, providing a better visualization and novel insights to advance our understanding of the relationship between rapid progression of breast cancer and lipid metabolism. The most altered lipids may aid the discovery of diagnostic markers and therapeutic targets of breast cancer.
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Affiliation(s)
- Shujuan Wang
- State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
- Key Laboratory of Metabolomics at Shenzhen, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
| | - Xiaowu Chen
- State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
- Key Laboratory of Metabolomics at Shenzhen, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
| | - Hemi Luan
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Dan Gao
- State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
- Key Laboratory of Metabolomics at Shenzhen, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
| | - Shuhai Lin
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, SAR, China
| | - Jianjun Liu
- Key Laboratory of Modern Toxicology of Shenzhen, Shenzhen Center for Disease Control and Prevention, Shenzhen, 518055, China
| | - Hongxia Liu
- State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
- Key Laboratory of Metabolomics at Shenzhen, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
| | - Yuyang Jiang
- State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, China
- School of Medicine, Tsinghua University, Beijing, 10084, China
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Ludwig MR, Kojima K, Bowersock GJ, Chen D, Jhala NC, Buchsbaum DJ, Grizzle WE, Klug CA, Mobley JA. Surveying the serologic proteome in a tissue-specific kras(G12D) knockin mouse model of pancreatic cancer. Proteomics 2016; 16:516-31. [DOI: 10.1002/pmic.201500133] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2015] [Revised: 09/30/2015] [Accepted: 11/09/2015] [Indexed: 12/21/2022]
Affiliation(s)
| | - Kyoko Kojima
- Comprehensive Cancer Center; University of Alabama at Birmingham; Birmingham AL USA
| | - Gregory J. Bowersock
- Comprehensive Cancer Center; University of Alabama at Birmingham; Birmingham AL USA
| | - Dongquan Chen
- Comprehensive Cancer Center; University of Alabama at Birmingham; Birmingham AL USA
- Departments of Medicine; University of Alabama at Birmingham; Birmingham AL USA
| | - Nirag C. Jhala
- Department of Pathology and Laboratory Medicine; University of Pennsylvania; Philadelphia PA USA
| | - Donald J. Buchsbaum
- Comprehensive Cancer Center; University of Alabama at Birmingham; Birmingham AL USA
- Radiation Oncology; University of Alabama at Birmingham; Birmingham AL USA
| | - William E. Grizzle
- Comprehensive Cancer Center; University of Alabama at Birmingham; Birmingham AL USA
- Pathology; University of Alabama at Birmingham; Birmingham AL USA
| | - Christopher A. Klug
- Comprehensive Cancer Center; University of Alabama at Birmingham; Birmingham AL USA
- Microbiology; University of Alabama at Birmingham; Birmingham AL USA
| | - James A. Mobley
- Comprehensive Cancer Center; University of Alabama at Birmingham; Birmingham AL USA
- Departments of Medicine; University of Alabama at Birmingham; Birmingham AL USA
- Surgery; University of Alabama at Birmingham; Birmingham AL USA
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Bodzon-Kulakowska A, Suder P. Imaging mass spectrometry: Instrumentation, applications, and combination with other visualization techniques. MASS SPECTROMETRY REVIEWS 2016; 35:147-69. [PMID: 25962625 DOI: 10.1002/mas.21468] [Citation(s) in RCA: 123] [Impact Index Per Article: 15.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2014] [Accepted: 01/23/2015] [Indexed: 05/18/2023]
Abstract
Imaging Mass Spectrometry (IMS) is strengthening its position as a valuable analytical tool. It has unique ability to identify structures and to unravel molecular changes that occur in the precisely defined part of the sample. These unique features open new possibilities in the field of various aspects of biological research. In this review we briefly discuss the main imaging mass spectrometry techniques, as well as the nature of biological samples and molecules, which might be analyzed by such methodology. Moreover, a novel approach, where different analytical techniques might be combined with the results of IMS study, is emphasized and discussed. With such a fast development of IMS and related methods, we can foresee the promising future of this technique.
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Affiliation(s)
- Anna Bodzon-Kulakowska
- Department of Biochemistry and Neurobiology, Faculty of Materials Sciences and Ceramics, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Piotr Suder
- Department of Biochemistry and Neurobiology, Faculty of Materials Sciences and Ceramics, AGH University of Science and Technology, 30-059 Krakow, Poland
- Academic Centre for Materials and Nanotechnology (ACMiN), AGH University of Science and Technology, 30-059 Krakow, Poland
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