1
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La Rocca R, Kune C, Tiquet M, Stuart L, Eppe G, Alexandrov T, De Pauw E, Quinton L. Adaptive Pixel Mass Recalibration for Mass Spectrometry Imaging Based on Locally Endogenous Biological Signals. Anal Chem 2021; 93:4066-4074. [PMID: 33583182 DOI: 10.1021/acs.analchem.0c05071] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful and convenient method for revealing the spatial chemical composition of different biological samples. Molecular annotation of the detected signals is only possible if a high mass accuracy is maintained over the entire image and the m/z range. However, the change in the number of ions from pixel-to-pixel of the biological samples could lead to small fluctuations in the detected m/z-values, called mass shift. The use of internal calibration is known to offer the best solution to avoid, or at least to reduce, mass shifts. Their "a priori" selection for a global MSI acquisition is prone to false positive detection and therefore to poor recalibration. To fill this gap, this work describes an algorithm that recalibrates each spectrum individually by estimating its mass shift with the help of a list of pixel-specific internal calibrating ions, automatically generated in a data-adaptive manner (https://github.com/LaRoccaRaphael/MSI_recalibration). Through a practical example, we applied the methodology to a zebrafish whole-body section acquired at a high mass resolution to demonstrate the impact of mass shift on data analysis and the capability of our algorithm to recalibrate MSI data. In addition, we illustrate the broad applicability of the method by recalibrating 31 different public MSI data sets from METASPACE from various samples and types of MSI and show that our recalibration significantly increases the numbers of METASPACE annotations (gaining from 20 up to 400 additional annotations), particularly the high-confidence annotations with a low false discovery rate.
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Affiliation(s)
- Raphaël La Rocca
- Mass Spectrometry Laboratory, MolSys Research Unit, Department of Chemistry, University of Liège, Allée du Six Août, 11, Quartier Agora, Liège 4000, Belgium
| | - Christopher Kune
- Mass Spectrometry Laboratory, MolSys Research Unit, Department of Chemistry, University of Liège, Allée du Six Août, 11, Quartier Agora, Liège 4000, Belgium
| | - Mathieu Tiquet
- Mass Spectrometry Laboratory, MolSys Research Unit, Department of Chemistry, University of Liège, Allée du Six Août, 11, Quartier Agora, Liège 4000, Belgium
| | - Lachlan Stuart
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Gauthier Eppe
- Mass Spectrometry Laboratory, MolSys Research Unit, Department of Chemistry, University of Liège, Allée du Six Août, 11, Quartier Agora, Liège 4000, Belgium
| | - Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla 92093-0657, California, United States
| | - Edwin De Pauw
- Mass Spectrometry Laboratory, MolSys Research Unit, Department of Chemistry, University of Liège, Allée du Six Août, 11, Quartier Agora, Liège 4000, Belgium
| | - Loïc Quinton
- Mass Spectrometry Laboratory, MolSys Research Unit, Department of Chemistry, University of Liège, Allée du Six Août, 11, Quartier Agora, Liège 4000, Belgium
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2
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Eriksson J, Sánchez Brotons A, Rezeli M, Suits F, Markó-Varga G, Horvatovich P. MSIWarp: A General Approach to Mass Alignment in Mass Spectrometry Imaging. Anal Chem 2020; 92:16138-16148. [PMID: 33317272 PMCID: PMC7745203 DOI: 10.1021/acs.analchem.0c03833] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/16/2020] [Indexed: 02/02/2023]
Abstract
Mass spectrometry imaging (MSI) is a technique that provides comprehensive molecular information with high spatial resolution from tissue. Today, there is a strong push toward sharing data sets through public repositories in many research fields where MSI is commonly applied; yet, there is no standardized protocol for analyzing these data sets in a reproducible manner. Shifts in the mass-to-charge ratio (m/z) of molecular peaks present a major obstacle that can make it impossible to distinguish one compound from another. Here, we present a label-free m/z alignment approach that is compatible with multiple instrument types and makes no assumptions on the sample's molecular composition. Our approach, MSIWarp (https://github.com/horvatovichlab/MSIWarp), finds an m/z recalibration function by maximizing a similarity score that considers both the intensity and m/z position of peaks matched between two spectra. MSIWarp requires only centroid spectra to find the recalibration function and is thereby readily applicable to almost any MSI data set. To deal with particularly misaligned or peak-sparse spectra, we provide an option to detect and exclude spurious peak matches with a tailored random sample consensus (RANSAC) procedure. We evaluate our approach with four publicly available data sets from both time-of-flight (TOF) and Orbitrap instruments and demonstrate up to 88% improvement in m/z alignment.
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Affiliation(s)
| | - Alejandro Sánchez Brotons
- Department
of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands
| | - Melinda Rezeli
- Department
of Biomedical Engineering, Lund University, Lund 221 00, Sweden
| | - Frank Suits
- IBM
Research - Australia, 60 City Road, Southbank, VIC 3006, Australia
| | - György Markó-Varga
- Department
of Biomedical Engineering, Lund University, Lund 221 00, Sweden
| | - Peter Horvatovich
- Department
of Biomedical Engineering, Lund University, Lund 221 00, Sweden
- Department
of Analytical Biochemistry, Groningen Research Institute of Pharmacy, University of Groningen, Antonius Deusinglaan 1, 9713 AV Groningen, The Netherlands
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3
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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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4
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Hypertensive disorders of pregnancy: Strategy to develop clinical peptide biomarkers for more accurate evaluation of the pathophysiological status of this syndrome. Adv Clin Chem 2019; 94:1-30. [PMID: 31952570 DOI: 10.1016/bs.acc.2019.07.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Hypertensive disorders of pregnancy (HDP) is the most common and widely known as serious complication of pregnancy. As this syndrome is a major leading cause of maternal, fetal, and neonatal morbidity/mortality worldwide, many studies have sought to identify candidate molecules as potential disease biomarkers (DBMs) for use in clinical examinations. Accumulating evidence over the past 2 decades that the many proteolytic peptides appear in human humoral fluids, including peripheral blood, in association with an individual's health condition. This review provides the potential utility of peptidomic analysis for monitoring for pathophysiological status in HDP, and presents an overview of current status of peptide quantification technology. Especially, the technical limitations of the methods used for DBM discovery in the blood are discussed.
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5
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Brochu F, Plante PL, Drouin A, Gagnon D, Richard D, Durocher F, Diorio C, Marchand M, Corbeil J, Laviolette F. Mass spectra alignment using virtual lock-masses. Sci Rep 2019; 9:8469. [PMID: 31186508 PMCID: PMC6560045 DOI: 10.1038/s41598-019-44923-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Accepted: 05/08/2019] [Indexed: 12/18/2022] Open
Abstract
Mass spectrometry is a valued method to evaluate the metabolomics content of a biological sample. The recent advent of rapid ionization technologies such as Laser Diode Thermal Desorption (LDTD) and Direct Analysis in Real Time (DART) has rendered high-throughput mass spectrometry possible. It is used for large-scale comparative analysis of populations of samples. In practice, many factors resulting from the environment, the protocol, and even the instrument itself, can lead to minor discrepancies between spectra, rendering automated comparative analysis difficult. In this work, a sequence/pipeline of algorithms to correct variations between spectra is proposed. The algorithms correct multiple spectra by identifying peaks that are common to all and, from those, computes a spectrum-specific correction. We show that these algorithms increase comparability within large datasets of spectra, facilitating comparative analysis, such as machine learning.
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Affiliation(s)
- Francis Brochu
- Big Data Research Center, Université Laval, Québec, Qc, Canada. .,Département d'Informatique et Génie Logiciel, Université Laval, Québec, Qc, Canada.
| | - Pier-Luc Plante
- Centre de Recherche du CHU de Québec, Université Laval, Québec, Qc, Canada
| | - Alexandre Drouin
- Big Data Research Center, Université Laval, Québec, Qc, Canada.,Département d'Informatique et Génie Logiciel, Université Laval, Québec, Qc, Canada
| | - Dominic Gagnon
- Centre de Recherche du CHU de Québec, Université Laval, Québec, Qc, Canada.,Infectious Disease Reasearch Center, Université Laval, Québec, Qc, Canada
| | - Dave Richard
- Centre de Recherche du CHU de Québec, Université Laval, Québec, Qc, Canada.,Infectious Disease Reasearch Center, Université Laval, Québec, Qc, Canada
| | - Francine Durocher
- Centre de Recherche du CHU de Québec, Université Laval, Québec, Qc, Canada.,Department of Molecular Medicine, Université Laval, Québec, Qc, Canada
| | - Caroline Diorio
- Centre de Recherche du CHU de Québec, Université Laval, Québec, Qc, Canada.,Department of Social and Preventative Medicine, Université Laval, Québec, Qc, Canada
| | - Mario Marchand
- Big Data Research Center, Université Laval, Québec, Qc, Canada.,Département d'Informatique et Génie Logiciel, Université Laval, Québec, Qc, Canada
| | - Jacques Corbeil
- Big Data Research Center, Université Laval, Québec, Qc, Canada.,Centre de Recherche du CHU de Québec, Université Laval, Québec, Qc, Canada.,Department of Molecular Medicine, Université Laval, Québec, Qc, Canada
| | - François Laviolette
- Big Data Research Center, Université Laval, Québec, Qc, Canada.,Département d'Informatique et Génie Logiciel, Université Laval, Québec, Qc, Canada
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6
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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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7
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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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8
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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: 46] [Impact Index Per Article: 7.7] [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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9
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Farrell Z, Merz S, Seager J, Dunn C, Egorov S, Green DL. Development of Experiment and Theory to Detect and Predict Ligand Phase Separation on Silver Nanoparticles. Angew Chem Int Ed Engl 2015; 54:6479-82. [DOI: 10.1002/anie.201500906] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 02/22/2015] [Indexed: 11/06/2022]
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10
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Farrell Z, Merz S, Seager J, Dunn C, Egorov S, Green DL. Development of Experiment and Theory to Detect and Predict Ligand Phase Separation on Silver Nanoparticles. Angew Chem Int Ed Engl 2015. [DOI: 10.1002/ange.201500906] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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11
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Cho YT, Su H, Wu WJ, Wu DC, Hou MF, Kuo CH, Shiea J. Biomarker Characterization by MALDI-TOF/MS. Adv Clin Chem 2015; 69:209-54. [PMID: 25934363 DOI: 10.1016/bs.acc.2015.01.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Mass spectrometric techniques frequently used in clinical diagnosis, such as gas chromatography-mass spectrometry, liquid chromatography-mass spectrometry, ambient ionization mass spectrometry, and matrix-assisted laser desorption ionization/time-of-flight mass spectrometry (MALDI-TOF/MS), are discussed. Due to its ability to rapidly detect large biomolecules in trace amounts, MALDI-TOF/MS is an ideal tool for characterizing disease biomarkers in biologic samples. Clinical applications of MS for the identification and characterization of microorganisms, DNA fragments, tissues, and biofluids are introduced. Approaches for using MALDI-TOF/MS to detect various disease biomarkers including peptides, proteins, and lipids in biological fluids are further discussed. Finally, various sample pretreatment methods which improve the detection efficiency of disease biomarkers are introduced.
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Affiliation(s)
- Yi-Tzu Cho
- Department of Cosmetic Applications and Management, Yuh-Ing Junior College of Health Care & Management, Kaohsiung, Taiwan
| | - Hung Su
- Department of Chemistry, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Wen-Jeng Wu
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Deng-Chyang Wu
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Center for Stem Cell Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ming-Feng Hou
- Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Chao-Hung Kuo
- Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Center for Stem Cell Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Jentaie Shiea
- Department of Chemistry, National Sun Yat-sen University, Kaohsiung, Taiwan; Center for Stem Cell Research, Kaohsiung Medical University, Kaohsiung, Taiwan; Cancer Center, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Medicinal and Applied Chemistry, Kaohsiung Medical University, Kaohsiung, Taiwan.
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12
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Padoan A, Basso D, La Malfa M, Zambon CF, Aiyetan P, Zhang H, Di Chiara A, Pavanello G, Bellocco R, Chan DW, Plebani M. Reproducibility in urine peptidome profiling using MALDI-TOF. Proteomics 2015; 15:1476-85. [DOI: 10.1002/pmic.201400253] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2014] [Revised: 10/09/2014] [Accepted: 12/15/2014] [Indexed: 12/18/2022]
Affiliation(s)
- Andrea Padoan
- Department of Medicine-DIMED; University of Padova; Padova Italy
| | - Daniela Basso
- Department of Medicine-DIMED; University of Padova; Padova Italy
| | - Marco La Malfa
- Department of Medicine-DIMED; University of Padova; Padova Italy
| | | | - Paul Aiyetan
- Department of Pathology; Johns Hopkins University School of Medicine; Baltimore MD USA
| | - Hui Zhang
- Department of Pathology; Johns Hopkins University School of Medicine; Baltimore MD USA
| | | | | | - Rino Bellocco
- Department of Statistics and Quantitative Methods; University of Milano-Bicocca; Milano Italy
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute; Stockholm Sweden
| | - Daniel W. Chan
- Department of Pathology; Johns Hopkins University School of Medicine; Baltimore MD USA
| | - Mario Plebani
- Department of Medicine-DIMED; University of Padova; Padova Italy
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13
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González N, Iloro I, Soria J, Duran JA, Santamaría A, Elortza F, Suárez T. Human tear peptide/protein profiling study of ocular surface diseases by SPE-MALDI-TOF mass spectrometry analyses. EUPA OPEN PROTEOMICS 2014. [DOI: 10.1016/j.euprot.2014.02.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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14
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Araki Y, Nonaka D, Hamamura K, Yanagida M, Ishikawa H, Banzai M, Maruyama M, Endo S, Tajima A, Lee LJ, Nojima M, Takamori K, Yoshida K, Takeda S, Tanaka K. Clinical peptidomic analysis by a one-step direct transfer technology: Its potential utility for monitoring of pathophysiological status in female reproductive system disorders. J Obstet Gynaecol Res 2013; 39:1440-8. [DOI: 10.1111/jog.12140] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2013] [Accepted: 04/04/2013] [Indexed: 12/11/2022]
Affiliation(s)
- Yoshihiko Araki
- Institute for Environmental and Gender-specific Medicine; Juntendo University Graduate School of Medicine; Chiba Japan
- Department of Obstetrics and Gynecology; Juntendo University Graduate School of Medicine; Tokyo Japan
| | - Daisuke Nonaka
- Membrane Protein and Ligand Analysis Center; Protosera Inc; Hyogo Japan
| | - Kensuke Hamamura
- Institute for Environmental and Gender-specific Medicine; Juntendo University Graduate School of Medicine; Chiba Japan
- Department of Obstetrics and Gynecology; Juntendo University Graduate School of Medicine; Tokyo Japan
| | - Mitsuaki Yanagida
- Institute for Environmental and Gender-specific Medicine; Juntendo University Graduate School of Medicine; Chiba Japan
| | - Hitoshi Ishikawa
- Department of Health Information Management; Yamagata Saisei Hospital; Yamagata Japan
| | - Michio Banzai
- Department of Obstetrics and Gynecology; Yamagata Saisei Hospital; Yamagata Japan
| | - Mayuko Maruyama
- Department of Obstetrics and Gynecology; Juntendo University Graduate School of Medicine; Tokyo Japan
- Department of Obstetrics and Gynecology; Juntendo University Urayasu Hospital; Chiba Japan
| | - Shuichiro Endo
- Institute for Environmental and Gender-specific Medicine; Juntendo University Graduate School of Medicine; Chiba Japan
- Department of Obstetrics and Gynecology; Juntendo University Graduate School of Medicine; Tokyo Japan
| | - Atsushi Tajima
- Department of Obstetrics and Gynecology; Juntendo University Graduate School of Medicine; Tokyo Japan
- Department of Obstetrics and Gynecology; Juntendo University Urayasu Hospital; Chiba Japan
| | - Lyang-Ja Lee
- Membrane Protein and Ligand Analysis Center; Protosera Inc; Hyogo Japan
| | - Michio Nojima
- Department of Obstetrics and Gynecology; Juntendo University Graduate School of Medicine; Tokyo Japan
- Department of Obstetrics and Gynecology; Juntendo University Urayasu Hospital; Chiba Japan
| | - Kenji Takamori
- Institute for Environmental and Gender-specific Medicine; Juntendo University Graduate School of Medicine; Chiba Japan
| | - Koyo Yoshida
- Department of Obstetrics and Gynecology; Juntendo University Graduate School of Medicine; Tokyo Japan
- Department of Obstetrics and Gynecology; Juntendo University Urayasu Hospital; Chiba Japan
| | - Satoru Takeda
- Department of Obstetrics and Gynecology; Juntendo University Graduate School of Medicine; Tokyo Japan
| | - Kenji Tanaka
- Membrane Protein and Ligand Analysis Center; Protosera Inc; Hyogo Japan
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15
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Jones EA, Deininger SO, Hogendoorn PC, Deelder AM, McDonnell LA. Imaging mass spectrometry statistical analysis. J Proteomics 2012; 75:4962-4989. [DOI: 10.1016/j.jprot.2012.06.014] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2012] [Revised: 06/06/2012] [Accepted: 06/16/2012] [Indexed: 12/22/2022]
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Araki Y, Nonaka D, Tajima A, Maruyama M, Nitto T, Ishikawa H, Yoshitake H, Yoshida E, Kuronaka N, Asada K, Yanagida M, Nojima M, Yoshida K, Takamori K, Hashiguchi T, Maruyama I, Lee LJ, Tanaka K. Quantitative peptidomic analysis by a newly developed one-step direct transfer technology without depletion of major blood proteins: Its potential utility for monitoring of pathophysiological status in pregnancy-induced hypertension. Proteomics 2011; 11:2727-37. [DOI: 10.1002/pmic.201000753] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2010] [Revised: 03/27/2011] [Accepted: 04/13/2011] [Indexed: 01/22/2023]
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Liu X, Wen F, Yang J, Chen L, Wei YQ. A review of current applications of mass spectrometry for neuroproteomics in epilepsy. MASS SPECTROMETRY REVIEWS 2010; 29:197-246. [PMID: 19598206 DOI: 10.1002/mas.20243] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The brain is unquestionably the most fascinating organ, and the hippocampus is crucial in memory storage and retrieval and plays an important role in stress response. In temporal lobe epilepsy (TLE), the seizure origin typically involves the hippocampal formation. Despite tremendous progress, current knowledge falls short of being able to explain its function. An emerging approach toward an improved understanding of the complex molecular mechanisms that underlie functions of the brain and hippocampus is neuroproteomics. Mass spectrometry has been widely used to analyze biological samples, and has evolved into an indispensable tool for proteomics research. In this review, we present a general overview of the application of mass spectrometry in proteomics, summarize neuroproteomics and systems biology-based discovery of protein biomarkers for epilepsy, discuss the methodology needed to explore the epileptic hippocampus proteome, and also focus on applications of ingenuity pathway analysis (IPA) in disease research. This neuroproteomics survey presents a framework for large-scale protein research in epilepsy that can be applied for immediate epileptic biomarker discovery and the far-reaching systems biology understanding of the protein regulatory networks. Ultimately, knowledge attained through neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of epilepsy on society.
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Affiliation(s)
- Xinyu Liu
- National Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China
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Kuschner KW, Malyarenko DI, Cooke WE, Cazares LH, Semmes OJ, Tracy ER. A Bayesian network approach to feature selection in mass spectrometry data. BMC Bioinformatics 2010; 11:177. [PMID: 20377906 PMCID: PMC3098056 DOI: 10.1186/1471-2105-11-177] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2009] [Accepted: 04/08/2010] [Indexed: 11/16/2022] Open
Abstract
Background Time-of-flight mass spectrometry (TOF-MS) has the potential to provide non-invasive, high-throughput screening for cancers and other serious diseases via detection of protein biomarkers in blood or other accessible biologic samples. Unfortunately, this potential has largely been unrealized to date due to the high variability of measurements, uncertainties in the distribution of proteins in a given population, and the difficulty of extracting repeatable diagnostic markers using current statistical tools. With studies consisting of perhaps only dozens of samples, and possibly hundreds of variables, overfitting is a serious complication. To overcome these difficulties, we have developed a Bayesian inductive method which uses model-independent methods of discovering relationships between spectral features. This method appears to efficiently discover network models which not only identify connections between the disease and key features, but also organizes relationships between features--and furthermore creates a stable classifier that categorizes new data at predicted error rates. Results The method was applied to artificial data with known feature relationships and typical TOF-MS variability introduced, and was able to recover those relationships nearly perfectly. It was also applied to blood sera data from a 2004 leukemia study, and showed high stability of selected features under cross-validation. Verification of results using withheld data showed excellent predictive power. The method showed improvement over traditional techniques, and naturally incorporated measurement uncertainties. The relationships discovered between features allowed preliminary identification of a protein biomarker which was consistent with other cancer studies and later verified experimentally. Conclusions This method appears to avoid overfitting in biologic data and produce stable feature sets in a network model. The network structure provides additional information about the relationships among features that is useful to guide further biochemical analysis. In addition, when used to classify new data, these feature sets are far more consistent than those produced by many traditional techniques.
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Affiliation(s)
- Karl W Kuschner
- Department of Physics, The College of William and Mary, Williamsburg, VA, USA.
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Integrated multi-level quality control for proteomic profiling studies using mass spectrometry. BMC Bioinformatics 2008; 9:519. [PMID: 19055809 PMCID: PMC2657802 DOI: 10.1186/1471-2105-9-519] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2008] [Accepted: 12/04/2008] [Indexed: 01/28/2023] Open
Abstract
Background Proteomic profiling using mass spectrometry (MS) is one of the most promising methods for the analysis of complex biological samples such as urine, serum and tissue for biomarker discovery. Such experiments are often conducted using MALDI-TOF (matrix-assisted laser desorption/ionisation time-of-flight) and SELDI-TOF (surface-enhanced laser desorption/ionisation time-of-flight) MS. Using such profiling methods it is possible to identify changes in protein expression that differentiate disease states and individual proteins or patterns that may be useful as potential biomarkers. However, the incorporation of quality control (QC) processes that allow the identification of low quality spectra reliably and hence allow the removal of such data before further analysis is often overlooked. In this paper we describe rigorous methods for the assessment of quality of spectral data. These procedures are presented in a user-friendly, web-based program. The data obtained post-QC is then examined using variance components analysis to quantify the amount of variance due to some of the factors in the experimental design. Results Using data from a SELDI profiling study of serum from patients with different levels of renal function, we show how the algorithms described in this paper may be used to detect systematic variability within and between sample replicates, pooled samples and SELDI chips and spots. Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms. Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array. Conclusion Using the techniques described in this paper it is possible to reliably detect poor quality data within proteomic profiling experiments undertaken by MS. The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.
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