101
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Garrett-Bakelman FE, Darshi M, Green SJ, Gur RC, Lin L, Macias BR, McKenna MJ, Meydan C, Mishra T, Nasrini J, Piening BD, Rizzardi LF, Sharma K, Siamwala JH, Taylor L, Vitaterna MH, Afkarian M, Afshinnekoo E, Ahadi S, Ambati A, Arya M, Bezdan D, Callahan CM, Chen S, Choi AMK, Chlipala GE, Contrepois K, Covington M, Crucian BE, De Vivo I, Dinges DF, Ebert DJ, Feinberg JI, Gandara JA, George KA, Goutsias J, Grills GS, Hargens AR, Heer M, Hillary RP, Hoofnagle AN, Hook VYH, Jenkinson G, Jiang P, Keshavarzian A, Laurie SS, Lee-McMullen B, Lumpkins SB, MacKay M, Maienschein-Cline MG, Melnick AM, Moore TM, Nakahira K, Patel HH, Pietrzyk R, Rao V, Saito R, Salins DN, Schilling JM, Sears DD, Sheridan CK, Stenger MB, Tryggvadottir R, Urban AE, Vaisar T, Van Espen B, Zhang J, Ziegler MG, Zwart SR, Charles JB, Kundrot CE, Scott GBI, Bailey SM, Basner M, Feinberg AP, Lee SMC, Mason CE, Mignot E, Rana BK, Smith SM, Snyder MP, Turek FW. The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight. Science 2019; 364:364/6436/eaau8650. [PMID: 30975860 DOI: 10.1126/science.aau8650] [Citation(s) in RCA: 461] [Impact Index Per Article: 92.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 02/28/2019] [Indexed: 12/11/2022]
Abstract
To understand the health impact of long-duration spaceflight, one identical twin astronaut was monitored before, during, and after a 1-year mission onboard the International Space Station; his twin served as a genetically matched ground control. Longitudinal assessments identified spaceflight-specific changes, including decreased body mass, telomere elongation, genome instability, carotid artery distension and increased intima-media thickness, altered ocular structure, transcriptional and metabolic changes, DNA methylation changes in immune and oxidative stress-related pathways, gastrointestinal microbiota alterations, and some cognitive decline postflight. Although average telomere length, global gene expression, and microbiome changes returned to near preflight levels within 6 months after return to Earth, increased numbers of short telomeres were observed and expression of some genes was still disrupted. These multiomic, molecular, physiological, and behavioral datasets provide a valuable roadmap of the putative health risks for future human spaceflight.
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Affiliation(s)
- Francine E Garrett-Bakelman
- Weill Cornell Medicine, New York, NY, USA.,University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Manjula Darshi
- Center for Renal Precision Medicine, University of Texas Health, San Antonio, TX, USA
| | | | - Ruben C Gur
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ling Lin
- Stanford University School of Medicine, Palo Alto, CA, USA
| | | | | | - Cem Meydan
- Weill Cornell Medicine, New York, NY, USA.,The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | | | - Jad Nasrini
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | | | - Kumar Sharma
- Center for Renal Precision Medicine, University of Texas Health, San Antonio, TX, USA
| | | | - Lynn Taylor
- Colorado State University, Fort Collins, CO, USA
| | | | | | - Ebrahim Afshinnekoo
- Weill Cornell Medicine, New York, NY, USA.,The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | - Sara Ahadi
- Stanford University School of Medicine, Palo Alto, CA, USA
| | - Aditya Ambati
- Stanford University School of Medicine, Palo Alto, CA, USA
| | | | - Daniela Bezdan
- Weill Cornell Medicine, New York, NY, USA.,The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA
| | | | - Songjie Chen
- Stanford University School of Medicine, Palo Alto, CA, USA
| | | | | | | | - Marisa Covington
- National Aeronautics and Space Administration (NASA), Houston, TX, USA
| | - Brian E Crucian
- National Aeronautics and Space Administration (NASA), Houston, TX, USA
| | | | - David F Dinges
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | | | | | | | | | | | | | | | - Ryan P Hillary
- Stanford University School of Medicine, Palo Alto, CA, USA
| | | | | | | | - Peng Jiang
- Northwestern University, Evanston, IL, USA
| | | | | | | | | | | | | | | | - Tyler M Moore
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Hemal H Patel
- University of California, San Diego, La Jolla, CA, USA
| | | | - Varsha Rao
- Stanford University School of Medicine, Palo Alto, CA, USA
| | - Rintaro Saito
- University of California, San Diego, La Jolla, CA, USA
| | - Denis N Salins
- Stanford University School of Medicine, Palo Alto, CA, USA
| | | | | | | | - Michael B Stenger
- National Aeronautics and Space Administration (NASA), Houston, TX, USA
| | | | | | | | | | - Jing Zhang
- Stanford University School of Medicine, Palo Alto, CA, USA
| | | | - Sara R Zwart
- University of Texas Medical Branch, Galveston, TX, USA
| | - John B Charles
- National Aeronautics and Space Administration (NASA), Houston, TX, USA.
| | - Craig E Kundrot
- Space Life and Physical Sciences Division, NASA Headquarters, Washington, DC, USA.
| | - Graham B I Scott
- National Space Biomedical Research Institute, Baylor College of Medicine, Houston, TX, USA.
| | | | - Mathias Basner
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | | | | | - Christopher E Mason
- Weill Cornell Medicine, New York, NY, USA. .,The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA.,The Feil Family Brain and Mind Research Institute, New York, NY, USA.,The WorldQuant Initiative for Quantitative Prediction, New York, NY, USA
| | | | - Brinda K Rana
- University of California, San Diego, La Jolla, CA, USA.
| | - Scott M Smith
- National Aeronautics and Space Administration (NASA), Houston, TX, USA.
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102
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Nunes J, Charneira C, Morello J, Rodrigues J, Pereira SA, Antunes AMM. Mass Spectrometry-Based Methodologies for Targeted and Untargeted Identification of Protein Covalent Adducts (Adductomics): Current Status and Challenges. High Throughput 2019; 8:ht8020009. [PMID: 31018479 PMCID: PMC6631461 DOI: 10.3390/ht8020009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2019] [Revised: 04/18/2019] [Accepted: 04/20/2019] [Indexed: 12/12/2022] Open
Abstract
Protein covalent adducts formed upon exposure to reactive (mainly electrophilic) chemicals may lead to the development of a wide range of deleterious health outcomes. Therefore, the identification of protein covalent adducts constitutes a huge opportunity for a better understanding of events underlying diseases and for the development of biomarkers which may constitute effective tools for disease diagnosis/prognosis, for the application of personalized medicine approaches and for accurately assessing human exposure to chemical toxicants. The currently available mass spectrometry (MS)-based methodologies, are clearly the most suitable for the analysis of protein covalent modifications, providing accuracy, sensitivity, unbiased identification of the modified residue and conjugates along with quantitative information. However, despite the huge technological advances in MS instrumentation and bioinformatics tools, the identification of low abundant protein covalent adducts is still challenging. This review is aimed at summarizing the MS-based methodologies currently used for the identification of protein covalent adducts and the strategies developed to overcome the analytical challenges, involving not only sample pre-treatment procedures but also distinct MS and data analysis approaches.
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Affiliation(s)
- João Nunes
- Centro de Química Estrutural, Instituto Superior Técnico, ULisboa, 1049-001 Lisboa, Portugal.
| | - Catarina Charneira
- Centro de Química Estrutural, Instituto Superior Técnico, ULisboa, 1049-001 Lisboa, Portugal.
| | - Judit Morello
- Centro de Química Estrutural, Instituto Superior Técnico, ULisboa, 1049-001 Lisboa, Portugal.
| | - João Rodrigues
- Clarify Analytical, Rua dos Mercadores 128A, 7000-872 Évora, Portugal.
| | - Sofia A Pereira
- CEDOC, Chronic Diseases Research Centre, NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, 1169-006 Lisboa, Portugal.
| | - Alexandra M M Antunes
- Centro de Química Estrutural, Instituto Superior Técnico, ULisboa, 1049-001 Lisboa, Portugal.
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103
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DeLaney K, Li L. Data Independent Acquisition Mass Spectrometry Method for Improved Neuropeptidomic Coverage in Crustacean Neural Tissue Extracts. Anal Chem 2019; 91:5150-5158. [PMID: 30888792 PMCID: PMC6481171 DOI: 10.1021/acs.analchem.8b05734] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Neuropeptides are an important class of signaling molecules in the nervous and neuroendocrine system, but they are challenging to study due to their low concentration in vivo in the presence of numerous interfering artifacts. Often the limitation of mass spectrometry analyses of neuropeptides in complex tissue extracts is not due to neuropeptides being below the detection limit but due to ions not being selected for tandem mass spectrometry during the liquid chromatography elution time and therefore not being identified. In this study, a data independent acquisition (DIA) method was developed to improve the coverage of neuropeptides in neural tissue from the model organism C. borealis. The optimal mass-to-charge ratio range and isolation window were determined and subsequently used to detect more neuropeptides in extracts from the brain and pericardial organs than the conventional data dependent acquisition method. The DIA method led to the detection of almost twice as many neuropeptides in the brain and approximately 1.5-fold more neuropeptides in the pericardial organs. The technical and biological reproducibility were also explored and found to be improved over the original method, with 56% of neuropeptides detected in 3 out of 3 replicate injections and 62% in 3 out of 3 biological replicates. Furthermore, 68 putative novel neuropeptides were detected and identified with de novo sequencing. The quantitative accuracy of the method was also explored. The developed method is anticipated to be useful for gaining a deeper profiling of neuropeptides, especially those in low abundance, in a variety of sample types.
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Affiliation(s)
- Kellen DeLaney
- Department of Chemistry, University of Wisconsin–Madison, 1101 University Avenue, Madison, Wisconsin 53706-1322, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin–Madison, 1101 University Avenue, Madison, Wisconsin 53706-1322, United States
- School of Pharmacy, University of Wisconsin–Madison, 5125 Rennebohm Hall, 777 Highland Avenue, Madison, Wisconsin 53705-2222, United States
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104
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Campbell MD, Duan J, Samuelson AT, Gaffrey MJ, Merrihew GE, Egertson JD, Wang L, Bammler TK, Moore RJ, White CC, Kavanagh TJ, Voss JG, Szeto HH, Rabinovitch PS, MacCoss MJ, Qian WJ, Marcinek DJ. Improving mitochondrial function with SS-31 reverses age-related redox stress and improves exercise tolerance in aged mice. Free Radic Biol Med 2019; 134:268-281. [PMID: 30597195 PMCID: PMC6588449 DOI: 10.1016/j.freeradbiomed.2018.12.031] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 12/20/2018] [Accepted: 12/26/2018] [Indexed: 12/11/2022]
Abstract
Sarcopenia and exercise intolerance are major contributors to reduced quality of life in the elderly for which there are few effective treatments. We tested whether enhancing mitochondrial function and reducing mitochondrial oxidant production with SS-31 (elamipretide) could restore redox balance and improve skeletal muscle function in aged mice. Young (5 mo) and aged (26 mo) female C57BL/6Nia mice were treated for 8-weeks with 3 mg/kg/day SS-31. Mitochondrial function was assessed in vivo using 31P and optical spectroscopy. SS-31 reversed age-related decline in maximum mitochondrial ATP production (ATPmax) and coupling of oxidative phosphorylation (P/O). Despite the increased in vivo mitochondrial capacity, mitochondrial protein expression was either unchanged or reduced in the treated aged mice and respiration in permeabilized gastrocnemius (GAS) fibers was not different between the aged and aged+SS-31 mice. Treatment with SS-31 also restored redox homeostasis in the aged skeletal muscle. The glutathione redox status was more reduced and thiol redox proteomics indicated a robust reversal of cysteine S-glutathionylation post-translational modifications across the skeletal muscle proteome. The gastrocnemius in the age+SS-31 mice was more fatigue resistant with significantly greater mass compared to aged controls. This contributed to a significant increase in treadmill endurance compared to both pretreatment and untreated control values. These results demonstrate that the shift of redox homeostasis due to mitochondrial oxidant production in aged muscle is a key factor in energetic defects and exercise intolerance. Treatment with SS-31 restores redox homeostasis, improves mitochondrial quality, and increases exercise tolerance without an increase in mitochondrial content. Since elamipretide is currently in clinical trials these results indicate it may have direct translational value for improving exercise tolerance and quality of life in the elderly.
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Affiliation(s)
| | - Jicheng Duan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
| | | | - Matthew J Gaffrey
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
| | | | - Jarrett D Egertson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Lu Wang
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Theo K Bammler
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
| | - Collin C White
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Terrance J Kavanagh
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
| | - Joachim G Voss
- School of Nursing, University of Washington, Seattle, WA, USA.
| | | | | | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Wei-Jun Qian
- School of Nursing, University of Washington, Seattle, WA, USA.
| | - David J Marcinek
- Department of Radiology, University of Washington, Seattle, WA, USA.
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105
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Generation of a zebrafish SWATH-MS spectral library to quantify 10,000 proteins. Sci Data 2019; 6:190011. [PMID: 30747917 PMCID: PMC6371892 DOI: 10.1038/sdata.2019.11] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 12/17/2018] [Indexed: 12/12/2022] Open
Abstract
Sequential window acquisition of all theoretical mass spectra (SWATH-MS) requires a spectral library to extract quantitative measurements from the mass spectrometry data acquired in data-independent acquisition mode (DIA). Large combined spectral libraries containing SWATH assays have been generated for humans and several other organisms, but so far no publicly available library exists for measuring the proteome of zebrafish, a rapidly emerging model system in biomedical research. Here, we present a large zebrafish SWATH spectral library to measure the abundance of 104,185 proteotypic peptides from 10,405 proteins. The library includes proteins expressed in 9 different zebrafish tissues (brain, eye, heart, intestine, liver, muscle, ovary, spleen, and testis) and provides an important new resource to quantify 40% of the protein-coding zebrafish genes. We employ this resource to quantify the proteome across brain, muscle, and liver and characterize divergent expression levels of paralogous proteins in different tissues. Data are available via ProteomeXchange (PXD010876, PXD010869) and SWATHAtlas (PASS01237).
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106
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Peters S, Hains PG, Lucas N, Robinson PJ, Tully B. A Case Study and Methodology for OpenSWATH Parameter Optimization Using the ProCan90 Data Set and 45 810 Computational Analysis Runs. J Proteome Res 2019; 18:1019-1031. [DOI: 10.1021/acs.jproteome.8b00709] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Sean Peters
- ProCan, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Peter G. Hains
- ProCan, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Cell Signalling Unit, Children’s Medical Research Institute, The University of Sydney, Westmead, NSW 2145, Australia
| | - Natasha Lucas
- ProCan, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Phillip J. Robinson
- ProCan, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
| | - Brett Tully
- ProCan, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
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107
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Hu A, Lu YY, Bilmes J, Noble WS. Joint Precursor Elution Profile Inference via Regression for Peptide Detection in Data-Independent Acquisition Mass Spectra. J Proteome Res 2019; 18:86-94. [PMID: 30362768 PMCID: PMC6465123 DOI: 10.1021/acs.jproteome.8b00365] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
In data independent acquisition (DIA) mass spectrometry, precursor scans are interleaved with wide-window fragmentation scans, resulting in complex fragmentation spectra containing multiple coeluting peptide species. In this setting, detecting the isotope distribution profiles of intact peptides in the precursor scans can be a critical initial step in accurate peptide detection and quantification. This peak detection step is particularly challenging when the isotope peaks associated with two different peptide species overlap-or interfere-with one another. We propose a regression model, called Siren, to detect isotopic peaks in precursor DIA data that can explicitly account for interference. We validate Siren's peak-calling performance on a variety of data sets by counting how many of the peaks Siren identifies are associated with confidently detected peptides. In particular, we demonstrate that substituting the Siren regression model in place of the existing peak-calling step in DIA-Umpire leads to improved overall rates of peptide detection.
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108
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Wu X, Xing X, Dowlut D, Zeng Y, Liu J, Liu X. Integrating phosphoproteomics into kinase-targeted cancer therapies in precision medicine. J Proteomics 2019; 191:68-79. [DOI: 10.1016/j.jprot.2018.03.033] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 03/20/2018] [Accepted: 03/31/2018] [Indexed: 12/12/2022]
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109
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Smith BJ, Martins-de-Souza D, Fioramonte M. A Guide to Mass Spectrometry-Based Quantitative Proteomics. Methods Mol Biol 2019; 1916:3-39. [PMID: 30535679 DOI: 10.1007/978-1-4939-8994-2_1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Proteomics has become an attractive science in the postgenomic era, given its capacity to identify up to thousands of molecules in a single, complex sample and quantify them in an absolute and/or relative manner. The use of these techniques enables understanding of cellular and molecular mechanisms of diseases and other biological conditions, as well as identification and screening of protein biomarkers. Here we provide a straightforward, up-to-date compilation and comparison of the main quantitation techniques used in comparative proteomics such as in vitro and in vivo stable isotope labeling and label-free techniques. Additionally, this chapter includes common methods for data acquisition in proteomics and some appropriate methods for data processing. This compilation can serve as a reference for scientists who are new to, or already familiar with, quantitative proteomics.
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Affiliation(s)
- Bradley J Smith
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
| | - Daniel Martins-de-Souza
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
- Center for Neurobiology, University of Campinas (UNICAMP), Campinas, Brazil
- Instituto Nacional de Biomarcadores em Neuropsiquiatria (INBION), Conselho Nacional de Desenvolvimento Cientifico e Tecnologico, Sao Paulo, Brazil
| | - Mariana Fioramonte
- Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil.
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110
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Muntel J, Gandhi T, Verbeke L, Bernhardt OM, Treiber T, Bruderer R, Reiter L. Surpassing 10 000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and data analysis strategy. Mol Omics 2019; 15:348-360. [DOI: 10.1039/c9mo00082h] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Optimization of chromatography and data analysis resulted in more than 10 000 proteins in a single shot at a validated FDR of 1% (two-species test) and revealed deep insights into the testis cancer physiology.
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111
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Tran NH, Qiao R, Xin L, Chen X, Liu C, Zhang X, Shan B, Ghodsi A, Li M. Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry. Nat Methods 2018; 16:63-66. [PMID: 30573815 DOI: 10.1038/s41592-018-0260-3] [Citation(s) in RCA: 196] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 11/09/2018] [Indexed: 12/30/2022]
Abstract
We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data. We use neural networks to capture precursor and fragment ions across m/z, retention-time, and intensity dimensions. They are then further integrated with peptide sequence patterns to address the problem of highly multiplexed spectra. DIA coupled with de novo sequencing allowed us to identify novel peptides in human antibodies and antigens.
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Affiliation(s)
- Ngoc Hieu Tran
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada
| | - Rui Qiao
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Lei Xin
- Bioinformatics Solutions Inc., Waterloo, ON, Canada
| | - Xin Chen
- Bioinformatics Solutions Inc., Waterloo, ON, Canada
| | - Chuyi Liu
- Department of Electrical & Computer Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Xianglilan Zhang
- State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China
| | - Baozhen Shan
- Bioinformatics Solutions Inc., Waterloo, ON, Canada
| | - Ali Ghodsi
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Ming Li
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada.
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112
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Deutsch EW, Perez-Riverol Y, Chalkley RJ, Wilhelm M, Tate S, Sachsenberg T, Walzer M, Käll L, Delanghe B, Böcker S, Schymanski EL, Wilmes P, Dorfer V, Kuster B, Volders PJ, Jehmlich N, Vissers JP, Wolan DW, Wang AY, Mendoza L, Shofstahl J, Dowsey AW, Griss J, Salek RM, Neumann S, Binz PA, Lam H, Vizcaíno JA, Bandeira N, Röst H. Expanding the Use of Spectral Libraries in Proteomics. J Proteome Res 2018; 17:4051-4060. [PMID: 30270626 PMCID: PMC6443480 DOI: 10.1021/acs.jproteome.8b00485] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.
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Affiliation(s)
- Eric W. Deutsch
- Institute for Systems Biology, Seattle, Washington, 98109, United States
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Robert J. Chalkley
- University of California San Francisco, San Francisco, 94158, California, United States
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, 85354, Germany
| | | | - Timo Sachsenberg
- Department of Computer Science, Center for Bioinformatics, University of Tübingen, Sand 14, Tübingen, 72076, Germany
| | - Mathias Walzer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Lukas Käll
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH − Royal Institute of Technology, Stockholm 114 28, Sweden
| | - Bernard Delanghe
- Thermo Fisher Scientific Bremen, Hanna-Kunath Str. 11, 28199 Bremen, Germany
| | - Sebastian Böcker
- Chair for Bioinformatics, Friedrich-Schiller-University Jena, 07743 Jena, Germany
| | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Paul Wilmes
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Viktoria Dorfer
- University of Applied Sciences Upper Austria, Bioinformatics Research Group, Hagenberg, 4232, Austria
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, 85354, Germany
- Bavarian Biomolecular Mass Spectrometry Center (BayBioMS), Technical University of Munich, Freising, 85354, Germany
| | | | - Nico Jehmlich
- Helmholtz-Centre for Environmental Research - UFZ, Leipzig, Germany
| | | | - Dennis W. Wolan
- Department of Molecular Medicine, The Scripps Research Institute, 92037, La Jolla, California, United States
| | - Ana Y. Wang
- Department of Molecular Medicine, The Scripps Research Institute, 92037, La Jolla, California, United States
| | - Luis Mendoza
- Institute for Systems Biology, Seattle, Washington, 98109, United States
| | - Jim Shofstahl
- Thermo Fisher Scientific, 355 River Oaks Parkway San Jose, CA 95134
| | - Andrew W. Dowsey
- Department of Population Health Sciences and Bristol Veterinary School, Faculty of Health Sciences, University of Bristol, Bristol BS9 1BN, UK
| | - Johannes Griss
- Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, Währinger Gürtel 18-20, Vienna 1090, Austria
| | - Reza M. Salek
- The International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon CEDEX 08, France
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Department of Stress and Developmental Biology, 06120 Halle, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, 04103 Leipzig, Germany
| | - Pierre-Alain Binz
- Clinical Chemistry Service, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland
| | - Henry Lam
- Department of Chemical and Biological Engineering, the Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry, Department of Computer Science and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 92093-0404, USA
| | - Hannes Röst
- The Donnelly Centre, University of Toronto, 160 College St., Toronto, ON, M5S 3E1, Canada
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113
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Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nat Commun 2018; 9:5128. [PMID: 30510204 PMCID: PMC6277451 DOI: 10.1038/s41467-018-07454-w] [Citation(s) in RCA: 299] [Impact Index Per Article: 49.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 10/16/2018] [Indexed: 01/27/2023] Open
Abstract
Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of proteomics studies. Here, we introduce an experimental workflow that uses this technique to construct chromatogram libraries that capture fragment ion chromatographic peak shape and retention time for every detectable peptide in a proteomics experiment. These coordinates calibrate protein databases or spectrum libraries to a specific mass spectrometer and chromatography setup, facilitating DIA-only pipelines and the reuse of global resource libraries. We also present EncyclopeDIA, a software tool for generating and searching chromatogram libraries, and demonstrate the performance of our workflow by quantifying proteins in human and yeast cells. We find that by exploiting calibrated retention time and fragmentation specificity in chromatogram libraries, EncyclopeDIA can detect 20–25% more peptides from DIA experiments than with data dependent acquisition-based spectrum libraries alone. Data-independent acquisition (DIA)-based proteomics often relies on mass spectrum libraries from data-dependent acquisition experiments. Here, the authors present a method to generate DIA-based chromatogram libraries, enabling DIA-only workflows and detecting more peptides than with spectrum libraries alone.
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114
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Wilson EA, Anderson KS. Lost in the crowd: identifying targetable MHC class I neoepitopes for cancer immunotherapy. Expert Rev Proteomics 2018; 15:1065-1077. [PMID: 30408427 DOI: 10.1080/14789450.2018.1545578] [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] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The recent development of checkpoint blockade immunotherapy for cancer has led to impressive clinical results across multiple tumor types. There is mounting evidence that immune recognition of tumor derived MHC class I (MHC-I) restricted epitopes bearing cancer specific mutations and alterations is a crucial mechanism in successfully triggering immune-mediated tumor rejection. Therapeutic targeting of these cancer specific epitopes (neoepitopes) is emerging as a promising opportunity for the generation of personalized cancer vaccines and adoptive T cell therapies. However, one major obstacle limiting the broader application of neoepitope based therapies is the difficulty of selecting highly immunogenic neoepitopes among the wide array of presented non-immunogenic HLA ligands derived from self-proteins. Areas covered: In this review, we present an overview of the MHC-I processing and presentation pathway, as well as highlight key areas that contribute to the complexity of the associated MHC-I peptidome. We cover recent technological advances that simplify and optimize the identification of targetable neoepitopes for cancer immunotherapeutic applications. Expert commentary: Recent advances in computational modeling, bioinformatics, and mass spectrometry are unlocking the underlying mechanisms governing antigen processing and presentation of tumor-derived neoepitopes.
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Affiliation(s)
- Eric A Wilson
- a Center for Personalized Diagnostics, Biodesign Institute , Arizona State University , Tempe , AZ , USA
| | - Karen S Anderson
- a Center for Personalized Diagnostics, Biodesign Institute , Arizona State University , Tempe , AZ , USA.,b Department of Medical Oncology , Mayo Clinic Arizona , Scottsdale , AZ , USA
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115
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Banerjee SL, Dionne U, Lambert JP, Bisson N. Targeted proteomics analyses of phosphorylation-dependent signalling networks. J Proteomics 2018; 189:39-47. [DOI: 10.1016/j.jprot.2018.02.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 01/19/2018] [Accepted: 02/01/2018] [Indexed: 01/18/2023]
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116
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Carlyle BC, Trombetta BA, Arnold SE. Proteomic Approaches for the Discovery of Biofluid Biomarkers of Neurodegenerative Dementias. Proteomes 2018; 6:32. [PMID: 30200280 PMCID: PMC6161166 DOI: 10.3390/proteomes6030032] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2018] [Revised: 08/22/2018] [Accepted: 08/29/2018] [Indexed: 12/11/2022] Open
Abstract
Neurodegenerative dementias are highly complex disorders driven by vicious cycles of intersecting pathophysiologies. While most can be definitively diagnosed by the presence of disease-specific pathology in the brain at postmortem examination, clinical disease presentations often involve substantially overlapping cognitive, behavioral, and functional impairment profiles that hamper accurate diagnosis of the specific disease. As global demographics shift towards an aging population in developed countries, clinicians need more sensitive and specific diagnostic tools to appropriately diagnose, monitor, and treat neurodegenerative conditions. This review is intended as an overview of how modern proteomic techniques (liquid chromatography mass spectrometry (LC-MS/MS) and advanced capture-based technologies) may contribute to the discovery and establishment of better biofluid biomarkers for neurodegenerative disease, and the limitations of these techniques. The review highlights some of the more interesting technical innovations and common themes in the field but is not intended to be an exhaustive systematic review of studies to date. Finally, we discuss clear reporting principles that should be integrated into all studies going forward to ensure data is presented in sufficient detail to allow meaningful comparisons across studies.
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Affiliation(s)
- Becky C Carlyle
- Massachusetts General Hospital Department of Neurology, Charlestown, MA 02129, USA.
| | - Bianca A Trombetta
- Massachusetts General Hospital Department of Neurology, Charlestown, MA 02129, USA.
| | - Steven E Arnold
- Massachusetts General Hospital Department of Neurology, Charlestown, MA 02129, USA.
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117
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Ma C, Ren Y, Yang J, Ren Z, Yang H, Liu S. Improved Peptide Retention Time Prediction in Liquid Chromatography through Deep Learning. Anal Chem 2018; 90:10881-10888. [PMID: 30114359 DOI: 10.1021/acs.analchem.8b02386] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The accuracy of peptide retention time (RT) prediction model in liquid chromatography (LC) is still not sufficient for wider implementation in proteomics practice. Herein, we propose deep learning as an ideal tool to considerably improve this prediction. A new peptide RT prediction tool, DeepRT, was designed using a capsule network model, and the public data sets containing peptides separated by reverse-phase liquid chromatography were used to evaluate the DeepRT performance. Compared with other prevailing RT predictors, DeepRT attained overall improvement in the prediction of peptide RTs with an R2 of ∼0.994. Moreover, DeepRT was able to accommodate to the peptides that were separated by different types of LC, such as strong cation exchange (SCX) and hydrophilic interaction liquid chromatography (HILIC) and to reach the RT prediction with R2 values of ∼0.996 for SCX and ∼0.993 for HILIC, respectively. If a large peptide data set is available for one type of LC, DeepRT can be promoted to DeepRT(+) using transfer learning. Based on a large peptide data set gained from SWATH, DeepRT(+) further elevated the accuracy of RT prediction for peptides in a small data set and enabled a satisfactory prediction upon limited peptides approximating hundreds. Further, DeepRT automatically learns retention-related properties of amino acids under different separation mechanisms, which are well consistent with retention coefficients (Rc) of the amino acids. DeepRT was thus proven to be an improved RT predictor with high flexibility and efficiency. DeepRT is available at https://github.com/horsepurve/DeepRTplus .
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Affiliation(s)
- Chunwei Ma
- BGI-Shenzhen , Beishan Industrial Zone 11th Building, Yantian District, Shenzhen , Guangdong 518083 , China.,China National GeneBank , BGI-Shenzhen , Shenzhen 518120 , China
| | - Yan Ren
- BGI-Shenzhen , Beishan Industrial Zone 11th Building, Yantian District, Shenzhen , Guangdong 518083 , China.,China National GeneBank , BGI-Shenzhen , Shenzhen 518120 , China
| | - Jiarui Yang
- BGI-Shenzhen , Beishan Industrial Zone 11th Building, Yantian District, Shenzhen , Guangdong 518083 , China.,China National GeneBank , BGI-Shenzhen , Shenzhen 518120 , China
| | - Zhe Ren
- BGI-Shenzhen , Beishan Industrial Zone 11th Building, Yantian District, Shenzhen , Guangdong 518083 , China.,China National GeneBank , BGI-Shenzhen , Shenzhen 518120 , China
| | - Huanming Yang
- BGI-Shenzhen , Beishan Industrial Zone 11th Building, Yantian District, Shenzhen , Guangdong 518083 , China.,James D. Watson Institute of Genome Sciences , Hangzhou 310008 , China
| | - Siqi Liu
- BGI-Shenzhen , Beishan Industrial Zone 11th Building, Yantian District, Shenzhen , Guangdong 518083 , China.,China National GeneBank , BGI-Shenzhen , Shenzhen 518120 , China
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118
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Ludwig C, Gillet L, Rosenberger G, Amon S, Collins BC, Aebersold R. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 2018; 14:e8126. [PMID: 30104418 PMCID: PMC6088389 DOI: 10.15252/msb.20178126] [Citation(s) in RCA: 625] [Impact Index Per Article: 104.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 05/11/2018] [Accepted: 05/15/2018] [Indexed: 01/16/2023] Open
Abstract
Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.
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Affiliation(s)
- Christina Ludwig
- Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technical University of Munich (TUM), Freising, Germany
| | - Ludovic Gillet
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Sabine Amon
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Faculty of Science, University of Zurich, Zurich, Switzerland
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119
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Heaven MR, Cobbs AL, Nei YW, Gutierrez DB, Herren AW, Gunawardena HP, Caprioli RM, Norris JL. Micro-Data-Independent Acquisition for High-Throughput Proteomics and Sensitive Peptide Mass Spectrum Identification. Anal Chem 2018; 90:8905-8911. [DOI: 10.1021/acs.analchem.8b01026] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | | | - Yuan-Wei Nei
- Mass Spectrometry
Research Center, Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37240, United States
| | - Danielle B. Gutierrez
- Mass Spectrometry
Research Center, Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37240, United States
| | - Anthony W. Herren
- University of California at Davis Proteomics Core, Davis, California 95616, United States
| | - Harsha P. Gunawardena
- Janssen Research and Development, The Janssen Pharmaceutical Companies of Johnson & Johnson, Spring House, Pennsylvania 19002, United States
| | - Richard M. Caprioli
- Mass Spectrometry
Research Center, Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37240, United States
| | - Jeremy L. Norris
- Mass Spectrometry
Research Center, Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37240, United States
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120
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Shen S, Wang X, Orsburn BC, Qu J. How could IonStar challenge the current status quo of quantitative proteomics in large sample cohorts? Expert Rev Proteomics 2018; 15:541-543. [PMID: 29911452 DOI: 10.1080/14789450.2018.1490646] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Shichen Shen
- a Department of Pharmaceutical Sciences , School of Pharmacy and Pharmaceutical Sciences, University at Buffalo , Buffalo , NY , USA.,b New York State Center of Excellence in Bioinformatics & Life Sciences , Buffalo , NY , USA
| | - Xue Wang
- b New York State Center of Excellence in Bioinformatics & Life Sciences , Buffalo , NY , USA.,c Department of Cell Stress Biology , Roswell Park Cancer Institute , Buffalo , NY , USA
| | - Benjamin C Orsburn
- d Cancer Research Technology Program, Frederick National Laboratory for Cancer Research , Frederick , Maryland , USA
| | - Jun Qu
- a Department of Pharmaceutical Sciences , School of Pharmacy and Pharmaceutical Sciences, University at Buffalo , Buffalo , NY , USA.,b New York State Center of Excellence in Bioinformatics & Life Sciences , Buffalo , NY , USA
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121
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Timmins-Schiffman E, Mikan MP, Ting YS, Harvey HR, Nunn BL. MS analysis of a dilution series of bacteria:phytoplankton to improve detection of low abundance bacterial peptides. Sci Rep 2018; 8:9276. [PMID: 29915279 PMCID: PMC6006377 DOI: 10.1038/s41598-018-27650-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 06/06/2018] [Indexed: 11/17/2022] Open
Abstract
Assigning links between microbial activity and biogeochemical cycles in the ocean is a primary objective for ecologists and oceanographers. Bacteria represent a small ecosystem component by mass, but act as the nexus for both nutrient transformation and organic matter recycling. There are limited methods to explore the full suite of active bacterial proteins largely responsible for degradation. Mass spectrometry (MS)-based proteomics now has the potential to document bacterial physiology within these complex systems. Global proteome profiling using MS, known as data dependent acquisition (DDA), is limited by the stochastic nature of ion selection, decreasing the detection of low abundance peptides. The suitability of MS-based proteomics methods in revealing bacterial signatures outnumbered by phytoplankton proteins was explored using a dilution series of pure bacteria (Ruegeria pomeroyi) and diatoms (Thalassiosira pseudonana). Two common acquisition strategies were utilized: DDA and selected reaction monitoring (SRM). SRM improved detection of bacterial peptides at low bacterial cellular abundance that were undetectable with DDA from a wide range of physiological processes (e.g. amino acid synthesis, lipid metabolism, and transport). We demonstrate the benefits and drawbacks of two different proteomic approaches for investigating species-specific physiological processes across relative abundances of bacteria that vary by orders of magnitude.
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Affiliation(s)
| | - Molly P Mikan
- Old Dominion University, Department of Ocean, Earth, and Atmospheric Sciences, Norfolk, VA, 23529, USA
| | - Ying Sonia Ting
- University of Washington, Department of Genome Sciences, Seattle, WA, 98195, USA
- Neon Therapeutics, Boston, MA, 02139, USA
| | - H Rodger Harvey
- Old Dominion University, Department of Ocean, Earth, and Atmospheric Sciences, Norfolk, VA, 23529, USA
| | - Brook L Nunn
- University of Washington, Department of Genome Sciences, Seattle, WA, 98195, USA.
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122
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IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts. Proc Natl Acad Sci U S A 2018; 115:E4767-E4776. [PMID: 29743190 DOI: 10.1073/pnas.1800541115] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set (n = 20). We demonstrated the usage of IonStar by a large-scale investigation of traumatic injuries and pharmacological treatments in rat brains (n = 100), quantifying >7,000 unique protein groups (>99.8% without missing data across the 100 samples) with a low false discovery rate (FDR), two or more unique peptides per protein, and high quantitative precision. IonStar represents a reliable and robust solution for precise and reproducible protein measurement in large cohorts.
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123
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Jin P, Lan J, Wang K, Baker MS, Huang C, Nice EC. Pathology, proteomics and the pathway to personalised medicine. Expert Rev Proteomics 2018; 15:231-243. [PMID: 29310484 DOI: 10.1080/14789450.2018.1425618] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Ping Jin
- Key Laboratory of Tropical Diseases and Translational Medicine of Ministry of Education & Department of Neurology, The Affiliated Hospital of Hainan Medical College, Haikou, P.R. China
| | - Jiang Lan
- Key Laboratory of Tropical Diseases and Translational Medicine of Ministry of Education & Department of Neurology, The Affiliated Hospital of Hainan Medical College, Haikou, P.R. China
- West China School of Basic Medical Sciences & Forensic Medicine, and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, 610041, P.R. China
| | - Kui Wang
- West China School of Basic Medical Sciences & Forensic Medicine, and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, 610041, P.R. China
| | - Mark S. Baker
- Department of Biomedical Sciences, Faculty of Medicine & Health Sciences, Macquarie University, Sydney, Australia
| | - Canhua Huang
- Key Laboratory of Tropical Diseases and Translational Medicine of Ministry of Education & Department of Neurology, The Affiliated Hospital of Hainan Medical College, Haikou, P.R. China
- West China School of Basic Medical Sciences & Forensic Medicine, and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, 610041, P.R. China
| | - Edouard C. Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, Australia and Visiting Professor, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, 610041, P.R. China
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124
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Timmins-Schiffman EB, Crandall GA, Vadopalas B, Riffle ME, Nunn BL, Roberts SB. Integrating Discovery-driven Proteomics and Selected Reaction Monitoring To Develop a Noninvasive Assay for Geoduck Reproductive Maturation. J Proteome Res 2017; 16:3298-3309. [PMID: 28730805 DOI: 10.1021/acs.jproteome.7b00288] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Geoduck clams (Panopea generosa) are an increasingly important fishery and aquaculture product along the eastern Pacific coast from Baja California, Mexico, to Alaska. These long-lived clams are highly fecund, although sustainable hatchery production of genetically diverse larvae is hindered by the lack of sexual dimorphism, resulting in asynchronous spawning of broodstock, unequal sex ratios, and low numbers of breeders. The development of assays of gonad physiology could indicate sex and maturation stage as well as be used to assess the status of natural populations. Proteomic profiles were determined for three reproductive maturation stages in both male and female clams using data-dependent acquisition (DDA) of gonad proteins. Gonad proteomes became increasingly divergent between males and females as maturation progressed. The DDA data were used to develop targets analyzed with selected reaction monitoring (SRM) in gonad tissue as well as hemolymph. The SRM assay yielded a suite of indicator peptides that can be used as an efficient assay to determine geoduck gonad maturation status. Application of SRM in hemolymph samples demonstrates that this procedure could effectively be used to assess reproductive status in marine mollusks in a nonlethal manner.
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Affiliation(s)
- Emma B Timmins-Schiffman
- Department of Genome Sciences, University of Washington , Seattle, Washington 98105, United States
| | - Grace A Crandall
- School of Aquatic and Fishery Sciences, University of Washington , Seattle, Washington 98105, United States
| | - Brent Vadopalas
- School of Aquatic and Fishery Sciences, University of Washington , Seattle, Washington 98105, United States
| | - Michael E Riffle
- Department of Biochemistry, University of Washington , Seattle, Washington 98105, United States
| | - Brook L Nunn
- Department of Genome Sciences, University of Washington , Seattle, Washington 98105, United States
| | - Steven B Roberts
- School of Aquatic and Fishery Sciences, University of Washington , Seattle, Washington 98105, United States
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