1
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Tian X, Permentier HP, Bischoff R. Chemical isotope labeling for quantitative proteomics. MASS SPECTROMETRY REVIEWS 2023; 42:546-576. [PMID: 34091937 PMCID: PMC10078755 DOI: 10.1002/mas.21709] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/22/2021] [Accepted: 05/17/2021] [Indexed: 05/05/2023]
Abstract
Advancements in liquid chromatography and mass spectrometry over the last decades have led to a significant development in mass spectrometry-based proteome quantification approaches. An increasingly attractive strategy is multiplex isotope labeling, which significantly improves the accuracy, precision and throughput of quantitative proteomics in the data-dependent acquisition mode. Isotope labeling-based approaches can be classified into MS1-based and MS2-based quantification. In this review, we give an overview of approaches based on chemical isotope labeling and discuss their principles, benefits, and limitations with the goal to give insights into fundamental questions and provide a useful reference for choosing a method for quantitative proteomics. As a perspective, we discuss the current possibilities and limitations of multiplex, isotope labeling approaches for the data-independent acquisition mode, which is increasing in popularity.
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Affiliation(s)
- Xiaobo Tian
- Department of Analytical Biochemistry and Interfaculty Mass Spectrometry Center, Groningen Research Institute of PharmacyUniversity of GroningenGroningenThe Netherlands
| | - Hjalmar P. Permentier
- Department of Analytical Biochemistry and Interfaculty Mass Spectrometry Center, Groningen Research Institute of PharmacyUniversity of GroningenGroningenThe Netherlands
| | - Rainer Bischoff
- Department of Analytical Biochemistry and Interfaculty Mass Spectrometry Center, Groningen Research Institute of PharmacyUniversity of GroningenGroningenThe Netherlands
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2
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Charkow J, Röst HL. Trapped Ion Mobility Spectrometry Reduces Spectral Complexity in Mass Spectrometry-Based Proteomics. Anal Chem 2021; 93:16751-16758. [PMID: 34881875 DOI: 10.1021/acs.analchem.1c01399] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
In bottom-up mass spectrometry-based proteomics, deep proteome coverage is limited by high cofragmentation rates. Cofragmentation occurs when more than one analyte is isolated by the quadrupole and the subsequent fragmentation event produces fragment ions of heterogeneous origin. One strategy to reduce cofragmentation rates is through effective peptide separation techniques such as chromatographic separation and, the more recently popularized, ion mobility (IM) spectrometry, which separates peptides by their collisional cross section. Here, we use a computational model to investigate the capability of the trapped IM spectrometry (TIMS) device at effectively separating peptide ions and quantify the separation power of the TIMS device in the context of a parallel accumulation-serial fragmentation (PASEF) workflow. We found that TIMS separation increases the number of interference-free MS1 peptide features 9.2-fold, while decreasing the average peptide density in precursor spectra 6.5-fold. In a data-dependent acquisition PASEF workflow, IM separation increases the number of spectra without cofragmentation by a factor of 4.1 and the number of high-quality spectra 17-fold. Using a categorical model, we estimate that this observed decrease in spectral complexity results in an increased likelihood for peptide spectral matches, which may improve peptide identification rates. In the context of a data-independent acquisition workflow, the reduction in spectral complexity resulting from IM separation is estimated to be equivalent to a 4-fold decrease in the isolation window width (from 25 to 6.5 Da). Our study demonstrates that TIMS separation decreases spectral complexity by reducing cofragmentation rates, suggesting that TIMS separation may contribute toward the high identification rates observed in PASEF workflows.
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Affiliation(s)
- Joshua Charkow
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Hannes L Röst
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada.,Department of Computer Science, University of Toronto, Toronto, Ontario M5T 3A1, Canada
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3
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Chen X, Sun Y, Zhang T, Shu L, Roepstorff P, Yang F. Quantitative Proteomics Using Isobaric Labeling: A Practical Guide. GENOMICS, PROTEOMICS & BIOINFORMATICS 2021; 19:689-706. [PMID: 35007772 PMCID: PMC9170757 DOI: 10.1016/j.gpb.2021.08.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 05/19/2021] [Accepted: 09/27/2021] [Indexed: 01/09/2023]
Abstract
In the past decade, relative proteomic quantification using isobaric labeling technology has developed into a key tool for comparing the expression of proteins in biological samples. Although its multiplexing capacity and flexibility make this a valuable technology for addressing various biological questions, its quantitative accuracy and precision still pose significant challenges to the reliability of its quantification results. Here, we give a detailed overview of the different kinds of isobaric mass tags and the advantages and disadvantages of the isobaric labeling method. We also discuss which precautions should be taken at each step of the isobaric labeling workflow, to obtain reliable quantification results in large-scale quantitative proteomics experiments. In the last section, we discuss the broad applications of the isobaric labeling technology in biological and clinical studies, with an emphasis on thermal proteome profiling and proteogenomics.
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Affiliation(s)
- Xiulan Chen
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China.
| | - Yaping Sun
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China
| | - Tingting Zhang
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China
| | - Lian Shu
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China
| | - Peter Roepstorff
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark
| | - Fuquan Yang
- Key Laboratory of Protein and Peptide Pharmaceuticals & Laboratory of Proteomics, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100149, China.
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4
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Chen CT, Wang JH, Cheng CW, Hsu WC, Ko CL, Choong WK, Sung TY. Multi-Q 2 software facilitates isobaric labeling quantitation analysis with improved accuracy and coverage. Sci Rep 2021; 11:2233. [PMID: 33500498 PMCID: PMC7838301 DOI: 10.1038/s41598-021-81740-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 01/06/2021] [Indexed: 12/12/2022] Open
Abstract
Mass spectrometry-based proteomics using isobaric labeling for multiplex quantitation has become a popular approach for proteomic studies. We present Multi-Q 2, an isobaric-labeling quantitation tool which can yield the largest quantitation coverage and improved quantitation accuracy compared to three state-of-the-art methods. Multi-Q 2 supports identification results from several popular proteomic data analysis platforms for quantitation, offering up to 12% improvement in quantitation coverage for accepting identification results from multiple search engines when compared with MaxQuant and PatternLab. It is equipped with various quantitation algorithms, including a ratio compression correction algorithm, and results in up to 336 algorithmic combinations. Systematic evaluation shows different algorithmic combinations have different strengths and are suitable for different situations. We also demonstrate that the flexibility of Multi-Q 2 in customizing algorithmic combination can lead to improved quantitation accuracy over existing tools. Moreover, the use of complementary algorithmic combinations can be an effective strategy to enhance sensitivity when searching for biomarkers from differentially expressed proteins in proteomic experiments. Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. It also supports a heatmap module, enabling users to cluster proteins based on their abundance ratios and to visualize the clustering results. Multi-Q 2 executable files, sample data sets, and user manual are freely available at http://ms.iis.sinica.edu.tw/COmics/Software_Multi-Q2.html.
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Affiliation(s)
- Ching-Tai Chen
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.
| | - Jen-Hung Wang
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.,Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, 115, Taiwan.,Institute of Biomedical Informatics, National Yang-Ming University, Taipei, 112, Taiwan
| | - Cheng-Wei Cheng
- Genomics Research Center, Academia Sinica, Taipei, 115, Taiwan
| | - Wei-Che Hsu
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan
| | - Chu-Ling Ko
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Wai-Kok Choong
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.
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5
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Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis. Int J Mol Sci 2020; 21:ijms21082873. [PMID: 32326049 PMCID: PMC7216093 DOI: 10.3390/ijms21082873] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/16/2020] [Accepted: 04/18/2020] [Indexed: 01/15/2023] Open
Abstract
Recent advances in mass spectrometry (MS)-based proteomics have enabled tremendous progress in the understanding of cellular mechanisms, disease progression, and the relationship between genotype and phenotype. Though many popular bioinformatics methods in proteomics are derived from other omics studies, novel analysis strategies are required to deal with the unique characteristics of proteomics data. In this review, we discuss the current developments in the bioinformatics methods used in proteomics and how they facilitate the mechanistic understanding of biological processes. We first introduce bioinformatics software and tools designed for mass spectrometry-based protein identification and quantification, and then we review the different statistical and machine learning methods that have been developed to perform comprehensive analysis in proteomics studies. We conclude with a discussion of how quantitative protein data can be used to reconstruct protein interactions and signaling networks.
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6
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Advances and applications of stable isotope labeling-based methods for proteome relative quantitation. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2020.115815] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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7
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Mun DG, Nam D, Kim H, Pandey A, Lee SW. Accurate Precursor Mass Assignment Improves Peptide Identification in Data-Independent Acquisition Mass Spectrometry. Anal Chem 2019; 91:8453-8460. [DOI: 10.1021/acs.analchem.9b01474] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Dong-Gi Mun
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Dowoon Nam
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55902, United States
- Manipal Academy of Higher Education (MAHE), Manipal, 576104 Karnataka, India
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
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8
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Iwasaki M, Tabata T, Kawahara Y, Ishihama Y, Nakagawa M. Removal of Interference MS/MS Spectra for Accurate Quantification in Isobaric Tag-Based Proteomics. J Proteome Res 2019; 18:2535-2544. [PMID: 31039306 DOI: 10.1021/acs.jproteome.9b00078] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Rapid progress in mass spectrometry (MS) has made comprehensive analyses of the proteome possible, but accurate quantification remains challenging. Isobaric tags for relative and absolute quantification (iTRAQ) is widely used as a tool to quantify proteins expressed in different cell types and various cellular conditions. The quantification precision of iTRAQ is quite high, but the accuracy dramatically decreases in the presence of interference peptides that are coeluted and coisolated with the target peptide. Here, we developed "removal of interference mixture MS/MS spectra (RiMS)" to improve the quantification accuracy of isobaric tag approaches. The presence of spectrum interference is judged by examining the overlap in the elution time of all scanned precursor ions. Removal of this interference decreased protein identification (11% loss) but improved quantification accuracy. Further, RiMS does not require any specialized equipment, such as MS3 instruments or an additional ion separation mode. Finally, we demonstrated that RiMS can be used to quantitatively compare human-induced pluripotent stem cells and human dermal fibroblasts, as it revealed differential protein expressions that reflect the biological characteristics of the cells.
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Affiliation(s)
- Mio Iwasaki
- Center for iPS Cell Research and Application , Kyoto University , Kyoto 606-8507 , Japan
| | - Tsuyoshi Tabata
- Center for iPS Cell Research and Application , Kyoto University , Kyoto 606-8507 , Japan.,Graduate school of Pharmaceutical Sciences , Kyoto University , Kyoto 606-8501 , Japan
| | - Yuka Kawahara
- Center for iPS Cell Research and Application , Kyoto University , Kyoto 606-8507 , Japan
| | - Yasushi Ishihama
- Graduate school of Pharmaceutical Sciences , Kyoto University , Kyoto 606-8501 , Japan
| | - Masato Nakagawa
- Center for iPS Cell Research and Application , Kyoto University , Kyoto 606-8507 , Japan
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9
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Zakirova Z, Reed J, Crynen G, Horne L, Hassan S, Mathura V, Mullan M, Crawford F, Ait-Ghezala G. Complementary proteomic approaches reveal mitochondrial dysfunction, immune and inflammatory dysregulation in a mouse model of Gulf War Illness. Proteomics Clin Appl 2017; 11. [PMID: 28371386 PMCID: PMC5637931 DOI: 10.1002/prca.201600190] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2016] [Revised: 02/20/2017] [Accepted: 03/16/2017] [Indexed: 12/30/2022]
Abstract
Purpose Long‐term consequences of combined pyridostigmine bromide (PB) and permethrin (PER) exposure in C57BL6/J mice using a well‐characterized mouse model of exposure to these Gulf War (GW) agents were explored at the protein level. Experimental design We used orthogonal proteomic approaches to identify pathways that are chronically impacted in the mouse CNS due to semiacute GW agent exposure early in life. These analyses were performed on soluble and membrane‐bound protein fractions from brain samples using two orthogonal isotopic labeling LC‐MS/MS proteomic approaches—stable isotope dimethyl labeling and iTRAQ. Results The use of these approaches allowed for greater coverage of proteins than was possible by either one alone and revealed both distinct and overlapping datasets. This combined analysis identified changes in several mitochondrial, as well as immune and inflammatory pathways after GW agent exposure. Conclusions and clinical relevance The work discussed here provides insight into GW agent exposure dependent mechanisms that adversely affect mitochondrial function and immune and inflammatory regulation. Collectively, our work identified key pathways which were chronically impacted in the mouse CNS following acute GW agent exposure, this may lead to the identification of potential targets for therapeutic intervention in the future. Long‐term consequences of combined PB and PER exposure in C57BL6/J mice using a well‐characterized mouse model of exposure to these GW agents were explored at the protein level. Expanding on earlier work, we used orthogonal proteomic approaches to identify pathways that are chronically impacted in the mouse CNS due to semiacute GW agent exposure early in life. These analyses were performed on soluble and membrane‐bound protein fractions from brain samples using two orthogonal isotopic labeling LC‐MS/MS proteomic approaches—stable isotope dimethyl labeling and iTRAQ. The use of these approaches allowed for greater coverage of proteins than was possible by either one alone and revealed both distinct and overlapping datasets. This combined analysis identified changes in several mitochondrial, as well as immune and inflammatory pathways after GW agent exposure. The work discussed here provides insight into GW agent exposure dependent mechanisms that adversely affect mitochondrial function and immune and inflammatory regulation at 5 months postexposure to PB + PER.
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Affiliation(s)
- Zuchra Zakirova
- Department of Genomics, The Roskamp Institute, Sarasota, FL, USA.,Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jon Reed
- Department of Genomics, The Roskamp Institute, Sarasota, FL, USA.,Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT, USA
| | - Gogce Crynen
- Department of Genomics, The Roskamp Institute, Sarasota, FL, USA
| | - Lauren Horne
- Department of Genomics, The Roskamp Institute, Sarasota, FL, USA
| | - Samira Hassan
- Department of Genomics, The Roskamp Institute, Sarasota, FL, USA.,University of Central Florida College of Medicine, Orlando, FL, USA
| | | | - Michael Mullan
- Department of Genomics, The Roskamp Institute, Sarasota, FL, USA
| | - Fiona Crawford
- Department of Genomics, The Roskamp Institute, Sarasota, FL, USA.,James A. Haley Veterans Hospital, Tampa, FL, USA
| | - Ghania Ait-Ghezala
- Department of Genomics, The Roskamp Institute, Sarasota, FL, USA.,James A. Haley Veterans Hospital, Tampa, FL, USA
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10
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Madar IH, Ko SI, Kim H, Mun DG, Kim S, Smith RD, Lee SW. Multiplexed Post-Experimental Monoisotopic Mass Refinement (mPE-MMR) to Increase Sensitivity and Accuracy in Peptide Identifications from Tandem Mass Spectra of Cofragmentation. Anal Chem 2017; 89:1244-1253. [PMID: 27966901 PMCID: PMC5627999 DOI: 10.1021/acs.analchem.6b03874] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Mass spectrometry (MS)-based proteomics, which uses high-resolution hybrid mass spectrometers such as the quadrupole-orbitrap mass spectrometer, can yield tens of thousands of tandem mass (MS/MS) spectra of high resolution during a routine bottom-up experiment. Despite being a fundamental and key step in MS-based proteomics, the accurate determination and assignment of precursor monoisotopic masses to the MS/MS spectra remains difficult. The difficulties stem from imperfect isotopic envelopes of precursor ions, inaccurate charge states for precursor ions, and cofragmentation. We describe a composite method of utilizing MS data to assign accurate monoisotopic masses to MS/MS spectra, including those subject to cofragmentation. The method, "multiplexed post-experiment monoisotopic mass refinement" (mPE-MMR), consists of the following: multiplexing of precursor masses to assign multiple monoisotopic masses of cofragmented peptides to the corresponding multiplexed MS/MS spectra, multiplexing of charge states to assign correct charges to the precursor ions of MS/MS spectra with no charge information, and mass correction for inaccurate monoisotopic peak picking. When combined with MS-GF+, a database search algorithm based on fragment mass difference, mPE-MMR effectively increases both sensitivity and accuracy in peptide identification from complex high-throughput proteomics data compared to conventional methods.
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Affiliation(s)
- Inamul Hasan Madar
- Laboratory of Gaseous Ion Chemistry, Department of Chemistry, Research Institute for Natural Sciences, Korea University, Seoul 136-701, South Korea
| | - Seung-Ik Ko
- Laboratory of Gaseous Ion Chemistry, Department of Chemistry, Research Institute for Natural Sciences, Korea University, Seoul 136-701, South Korea
| | - Hokeun Kim
- Laboratory of Gaseous Ion Chemistry, Department of Chemistry, Research Institute for Natural Sciences, Korea University, Seoul 136-701, South Korea
| | - Dong-Gi Mun
- Laboratory of Gaseous Ion Chemistry, Department of Chemistry, Research Institute for Natural Sciences, Korea University, Seoul 136-701, South Korea
| | - Sangtae Kim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Richard D. Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, United States
| | - Sang-Won Lee
- Laboratory of Gaseous Ion Chemistry, Department of Chemistry, Research Institute for Natural Sciences, Korea University, Seoul 136-701, South Korea
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11
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Cha M, Kwon Y, Ahn H, Jeong H, Lee YY, Moon M, Baik SH, Kim DK, Song H, Yi EC, Hwang D, Kim H, Mook‐Jung I. Protein-Induced Pluripotent Stem Cells Ameliorate Cognitive Dysfunction and Reduce Aβ Deposition in a Mouse Model of Alzheimer's Disease. Stem Cells Transl Med 2016; 6:293-305. [PMID: 28170178 PMCID: PMC5442740 DOI: 10.5966/sctm.2016-0081] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Accepted: 06/13/2016] [Indexed: 12/16/2022] Open
Abstract
Transplantation of stem cells into the brain attenuates functional deficits in the central nervous system via cell replacement, the release of specific neurotransmitters, and the production of neurotrophic factors. To identify patient‐specific and safe stem cells for treating Alzheimer's disease (AD), we generated induced pluripotent stem cells (iPSCs) derived from mouse skin fibroblasts by treating protein extracts of embryonic stem cells. These reprogrammed cells were pluripotent but nontumorigenic. Here, we report that protein‐iPSCs differentiated into glial cells and decreased plaque depositions in the 5XFAD transgenic AD mouse model. We also found that transplanted protein‐iPSCs mitigated the cognitive dysfunction observed in these mice. Proteomic analysis revealed that oligodendrocyte‐related genes were upregulated in brains injected with protein‐iPSCs, providing new insights into the potential function of protein‐iPSCs. Taken together, our data indicate that protein‐iPSCs might be a promising therapeutic approach for AD. Stem Cells Translational Medicine2017;6:293–305
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Affiliation(s)
- Moon‐Yong Cha
- Department of Biochemistry and Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Yoo‐Wook Kwon
- Innovative Research Institute for Cell Therapy, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyo‐Suk Ahn
- National Research Laboratory for Stem Cell Niche, Seoul National University, Seoul, Republic of Korea
| | - Hyobin Jeong
- Department of New Biology and Center for Plant and Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Yong Yook Lee
- The Korean Ginseng Research Institute, Daejeon, Republic of Korea
| | - Minho Moon
- Department of Biochemistry, College of Medicine, Konyang University, Daejeon, Republic of Korea
| | - Sung Hoon Baik
- Department of Biochemistry and Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Dong Kyu Kim
- Department of Biochemistry and Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyundong Song
- Department of Biochemistry and Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Eugene C. Yi
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, School of Medicine and School of Pharmacy, Seoul National University, Seoul, Republic of Korea
| | - Daehee Hwang
- Department of New Biology and Center for Plant and Aging Research, Institute for Basic Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Hyo‐Soo Kim
- Innovative Research Institute for Cell Therapy, Seoul National University Hospital, Seoul, Republic of Korea
- National Research Laboratory for Stem Cell Niche, Seoul National University, Seoul, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, School of Medicine and School of Pharmacy, Seoul National University, Seoul, Republic of Korea
| | - Inhee Mook‐Jung
- Department of Biochemistry and Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
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12
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Bischoff R, Permentier H, Guryev V, Horvatovich P. Genomic variability and protein species — Improving sequence coverage for proteogenomics. J Proteomics 2016; 134:25-36. [DOI: 10.1016/j.jprot.2015.09.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Revised: 09/06/2015] [Accepted: 09/14/2015] [Indexed: 12/30/2022]
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13
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He L, Diedrich J, Chu YY, Yates JR. Extracting Accurate Precursor Information for Tandem Mass Spectra by RawConverter. Anal Chem 2015; 87:11361-7. [PMID: 26499134 PMCID: PMC4777630 DOI: 10.1021/acs.analchem.5b02721] [Citation(s) in RCA: 195] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Extraction of data from the proprietary RAW files generated by Thermo Fisher mass spectrometers is the primary step for subsequent data analysis. High resolution and high mass accuracy data obtained by state-of-the-art mass spectrometers (e.g., Orbitraps) can significantly improve both peptide/protein identification and quantification. We developed RawConverter, a stand-alone software tool, to improve data extraction on RAW files from high-resolution Thermo Fisher mass spectrometers. RawConverter extracts full scan and MS(n) data from RAW files like its predecessor RawXtract; most importantly, it associates the accurate precursor mass-to-charge (m/z) value with the tandem mass spectrum. RawConverter accepts RAW data generated by either data-dependent acquisition (DDA) or data-independent acquisition (DIA). It generates output into MS1/MS2/MS3, MGF, or mzXML file formats, which fulfills the format requirements for most data identification and quantification tools. Using the tandem mass spectra extracted by RawConverter with corrected m/z values, 32.8%, 27.1%, and 84.1%, peptide spectra matches (PSMs) produce 17.4% (13.0%), 14.4% (11.5%), and 45.7% (36.2%) more peptide (protein) identifications than ProteoWizard, pXtract, and RawXtract, respectively. RawConverter is implemented in C# and is freely accessible at http://fields.scripps.edu/rawconv.
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Affiliation(s)
- Lin He
- Department of Chemical Physiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, USA
| | - Jolene Diedrich
- Department of Chemical Physiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, USA
| | - Yen-Yin Chu
- Department of Communication Engineering, National Central University, 300 Jung-da Road, Jung-li City, Taoyuan, Taiwan
| | - John R. Yates
- Department of Chemical Physiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, USA
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