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Son A, Kim W, Lee W, Park J, Kim H. Applicability of selected reaction monitoring for precise screening tests. Expert Rev Proteomics 2024:1-10. [PMID: 38697802 DOI: 10.1080/14789450.2024.2350975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/27/2024] [Indexed: 05/05/2024]
Abstract
INTRODUCTION The proactive identification of diseases through screening tests has long been endorsed as a means to preempt symptomatic onset. However, such screening endeavors are fraught with complications, such as diagnostic inaccuracies, procedural risks, and patient unease during examinations. These challenges are amplified when screenings for multiple diseases are administered concurrently. Selected Reaction Monitoring (SRM) offers a unique advantage, allowing for the high-throughput quantification of hundreds of analytes with minimal interferences. AREAS COVERED Our research posits that SRM-based assays, traditionally tailored for single-disease biomarker profiling, can be repurposed for multi-disease screening. This innovative approach has the potential to substantially alleviate time, labor, and cost demands on healthcare systems and patients alike. Nonetheless, there are formidable methodological hurdles to overcome. These include difficulties in detecting low-abundance proteins and the risk of model overfitting due to the multiple functionalities of single proteins across different disease spectrums - issues especially pertinent in blood-based assays where detection sensitivity is constrained. As we move forward, technological strides in sample preparation, online extraction, throughput, and automation are expected to ameliorate these limitations. EXPERT OPINION The maturation of mass spectrometry's integration into clinical laboratories appears imminent, positioning it as an invaluable asset for delivering highly sensitive, reproducible, and precise diagnostic results.
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Affiliation(s)
- Ahrum Son
- Department of Molecular Medicine, The Scripps Research Institute, La Jolla, CA, USA
| | - Woojin Kim
- Department of Bio-AI convergence Chungnam National University,Daejeon, South Korea
| | - Wonseok Lee
- Department of Bio-AI convergence Chungnam National University,Daejeon, South Korea
| | - Jongham Park
- Department of Bio-AI convergence Chungnam National University,Daejeon, South Korea
| | - Hyunsoo Kim
- Department of Bio-AI convergence Chungnam National University,Daejeon, South Korea
- Department of Convergent Bioscience and Informatics, Chungnam National University, Daejeon, Republic of Korea
- SCICS, Daejeon, Republic of Korea
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Tsantilas KA, Merrihew GE, Robbins JE, Johnson RS, Park J, Plubell DL, Huang E, Riffle M, Sharma V, MacLean BX, Eckels J, Wu CC, Bereman MS, Spencer SE, Hoofnagle AN, MacCoss MJ. A framework for quality control in quantitative proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589318. [PMID: 38645098 PMCID: PMC11030400 DOI: 10.1101/2024.04.12.589318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.
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Affiliation(s)
- Kristine A. Tsantilas
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Gennifer E. Merrihew
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Julia E. Robbins
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Richard S. Johnson
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Jea Park
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Deanna L. Plubell
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Eric Huang
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Michael Riffle
- Department of Biochemistry, University of Washington, Washington 98195, United States
| | - Vagisha Sharma
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Brendan X. MacLean
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Josh Eckels
- LabKey, 500 Union St #1000, Seattle, Washington 98101, United States
| | - Christine C. Wu
- Department of Genome Sciences, University of Washington, Washington 98195, United States
| | - Michael S. Bereman
- Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27607
| | - Sandra E. Spencer
- Canada’s Michael Smith Genome Sciences Centre (BC Cancer Research Institute), University of British Columbia, Vancouver, British Columbia V5Z 4S6, Canada
| | - Andrew N. Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, United States
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, Washington 98195, United States
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Wenk D, Zuo C, Kislinger T, Sepiashvili L. Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers. Clin Proteomics 2024; 21:6. [PMID: 38287260 PMCID: PMC10826105 DOI: 10.1186/s12014-024-09452-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/14/2024] [Indexed: 01/31/2024] Open
Abstract
Routine measurement of cancer biomarkers is performed for early detection, risk classification, and treatment monitoring, among other applications, and has substantially contributed to better clinical outcomes for patients. However, there remains an unmet need for clinically validated assays of cancer protein biomarkers. Protein tumor markers are of particular interest since proteins carry out the majority of biological processes and thus dynamically reflect changes in cancer pathophysiology. Mass spectrometry-based targeted proteomics is a powerful tool for absolute peptide and protein quantification in biological matrices with numerous advantages that make it attractive for clinical applications in oncology. The use of liquid chromatography-tandem mass spectrometry (LC-MS/MS) based methodologies has allowed laboratories to overcome challenges associated with immunoassays that are more widely used for tumor marker measurements. Yet, clinical implementation of targeted proteomics methodologies has so far been limited to a few cancer markers. This is due to numerous challenges associated with paucity of robust validation studies of new biomarkers and the labor-intensive and operationally complex nature of LC-MS/MS workflows. The purpose of this review is to provide an overview of targeted proteomics applications in cancer, workflows used in targeted proteomics, and requirements for clinical validation and implementation of targeted proteomics assays. We will also discuss advantages and challenges of targeted MS-based proteomics assays for clinical cancer biomarker analysis and highlight some recent developments that will positively contribute to the implementation of this technique into clinical laboratories.
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Affiliation(s)
- Deborah Wenk
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Charlotte Zuo
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Thomas Kislinger
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
- Princess Margaret Cancer Research Tower, Room 9-807, 101 College Street, Toronto, ON, M5G 1L7, Canada.
| | - Lusia Sepiashvili
- Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, 555 University Ave, Rm 3606, Toronto, ON, M5G 1X8, Canada.
- Department of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, ON, Canada.
- Sickkids Research Institute, Toronto, ON, Canada.
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Saul MC, Litkowski EM, Hadad N, Dunn AR, Boas SM, Wilcox JAL, Robbins JE, Wu Y, Philip VM, Merrihew GE, Park J, De Jager PL, Bridges DE, Menon V, Bennett DA, Hohman TJ, MacCoss MJ, Kaczorowski CC. Hippocampus Glutathione S Reductase Potentially Confers Genetic Resilience to Cognitive Decline in the AD-BXD Mouse Population. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574219. [PMID: 38260300 PMCID: PMC10802440 DOI: 10.1101/2024.01.09.574219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Alzheimer's disease (AD) is a prevalent and costly age-related dementia. Heritable factors account for 58-79% of variation in late-onset AD, but substantial variation remains in age-of- onset, disease severity, and whether those with high-risk genotypes acquire AD. To emulate the diversity of human populations, we utilized the AD-BXD mouse panel. This genetically diverse resource combines AD genotypes with multiple BXD strains to discover new genetic drivers of AD resilience. Comparing AD-BXD carriers to noncarrier littermates, we computed a novel quantitative metric for resilience to cognitive decline in the AD-BXDs. Our quantitative AD resilience trait was heritable and genetic mapping identified a locus on chr8 associated with resilience to AD mutations that resulted in amyloid brain pathology. Using a hippocampus proteomics dataset, we nominated the mitochondrial glutathione S reductase protein (GR or GSHR) as a resilience factor, finding that the DBA/2J genotype was associated with substantially higher GR abundance. By mapping protein QTLs (pQTLs), we identified synaptic organization and mitochondrial proteins coregulated in trans with a cis-pQTL for GR. We found four coexpression modules correlated with the quantitative resilience score in aged 5XFAD mice using paracliques, which were related to cell structure, protein folding, and postsynaptic densities. Finally, we found significant positive associations between human GSR transcript abundance in the brain and better outcomes on AD-related cognitive and pathology traits in the Religious Orders Study/Memory and Aging project (ROSMAP). Taken together, these data support a framework for resilience in which neuronal antioxidant pathway activity provides for stability of synapses within the hippocampus.
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Lee HJ, Jeon M, Seo Y, Kang I, Jeong W, Son J, Yi EC, Min H. Application of Skyline software for detecting prohibited substances in doping control analysis. PLoS One 2023; 18:e0295065. [PMID: 38051722 DOI: 10.1371/journal.pone.0295065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 11/14/2023] [Indexed: 12/07/2023] Open
Abstract
As the number of prohibited drugs has been progressively increasing and analytical methods for detecting such substances are renewed continuously for doping control, the need for more sensitive and accurate doping analysis has increased. To address the urgent need for high throughput and accurate analysis, liquid chromatography with tandem mass spectrometry is actively utilized in case of most of the newly designated prohibited substances. However, because all mass spectrometer vendors provide data processing software that is incapable of handling other instrumental data, it is difficult to cover all doping analysis procedures, from method development to result reporting, on one platform. Skyline is an open-source and vendor-neutral software program invented for the method development and data processing of targeted proteomics. Recently, the utilization of Skyline has been expanding for the quantitative analysis of small molecules and lipids. Herein, we demonstrated Skyline as a simple platform for unifying overall doping control, including the optimization of analytical methods, monitoring of data quality, discovery of suspected doping samples, and validation of analytical methods for detecting newly prohibited substances. For method optimization, we selected the optimal collision energies for 339 prohibited substances. Notably, 195 substances exhibited a signal intensity increase of >110% compared with the signal intensity of the original collision energy. All data related to method validation and quantitative analysis were efficiently visualized, extracted, or calculated using Skyline. Moreover, a comparison of the time consumed and the number of suspicious samples screened in the initial test procedure highlighted the advantages of using Skyline over the commercially available software TraceFinder in doping control.
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Affiliation(s)
- Hyeon-Jeong Lee
- Doping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Yeongeon-dong, Jongno-gu, Seoul, Republic of Korea
| | - Mijin Jeon
- Doping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea
| | - Yoondam Seo
- Doping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea
| | - Inseon Kang
- Doping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea
| | - Wooyeon Jeong
- Doping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea
| | - Junghyun Son
- Doping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea
| | - Eugene C Yi
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Yeongeon-dong, Jongno-gu, Seoul, Republic of Korea
| | - Hophil Min
- Doping Control Center, Korea Institute of Science and Technology, Seongbuk-gu, Seoul, Republic of Korea
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Hay BN, Akinlaja MO, Baker TC, Houfani AA, Stacey RG, Foster LJ. Integration of data-independent acquisition (DIA) with co-fractionation mass spectrometry (CF-MS) to enhance interactome mapping capabilities. Proteomics 2023; 23:e2200278. [PMID: 37144656 DOI: 10.1002/pmic.202200278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 05/06/2023]
Abstract
Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.
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Affiliation(s)
- Brenna N Hay
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Mopelola O Akinlaja
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Teesha C Baker
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Aicha Asma Houfani
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - R Greg Stacey
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories and Department of Biochemistry & Molecular Biology, University of British Columbia, Vancouver, BC, Canada
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Bowser BL, Patterson KL, Robinson RA. Evaluating cPILOT Data toward Quality Control Implementation. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:1741-1752. [PMID: 37459602 DOI: 10.1021/jasms.3c00179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
Multiplexing enables the monitoring of hundreds to thousands of proteins in quantitative proteomics analyses and increases sample throughput. In most mass-spectrometry-based proteomics workflows, multiplexing is achieved by labeling biological samples with heavy isotopes via precursor isotopic labeling or isobaric tagging. Enhanced multiplexing strategies, such as combined precursor isotopic labeling and isobaric tagging (cPILOT), combine multiple technologies to afford an even higher sample throughput. Critical to enhanced multiplexing analyses is ensuring that analytical performance is optimal and that missingness of sample channels is minimized. Automation of sample preparation steps and use of quality control (QC) metrics can be incorporated into multiplexing analyses and reduce the likelihood of missing information, thus maximizing the amount of usable quantitative data. Here, we implemented QC metrics previously developed in our laboratory to evaluate a 36-plex cPILOT experiment that encompassed 144 mouse samples of various tissue types, time points, genotypes, and biological replicates. The evaluation focuses on the use of a sample pool generated from all samples in the experiment to monitor the daily instrument performance and to provide a means for data normalization across sample batches. Our results show that tracking QC metrics enabled the quantification of ∼7000 proteins in each sample batch, of which ∼70% had minimal missing values across up to 36 sample channels. Implementation of QC metrics for future cPILOT studies as well as other enhanced multiplexing strategies will help yield high-quality data sets.
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Affiliation(s)
- Bailey L Bowser
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Khiry L Patterson
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Renã As Robinson
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
- Vanderbilt Memory & Alzheimer's Center, Nashville, Tennessee 37212, United States
- Vanderbilt Institute of Chemical Biology, Nashville, Tennessee 37232, United States
- Vanderbilt Brain Institute, Nashville, Tennessee 37232, United States
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8
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Fecke A, Saw NMMT, Kale D, Kasarla SS, Sickmann A, Phapale P. Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics. Metabolites 2023; 13:844. [PMID: 37512551 PMCID: PMC10383057 DOI: 10.3390/metabo13070844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.
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Affiliation(s)
- Antonia Fecke
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
- Department Hamm 2, Hochschule Hamm-Lippstadt, Marker-Allee 76-78, 59063 Hamm, Germany
| | - Nay Min Min Thaw Saw
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Dipali Kale
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Siva Swapna Kasarla
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
| | - Prasad Phapale
- Leibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Str. 6b, 44227 Dortmund, Germany
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Patterson KL, Arul AB, Choi MJ, Oliver NC, Whitaker MD, Bodrick AC, Libby JB, Hansen S, Dumitrescu L, Gifford KA, Jefferson AL, Hohman TJ, Robinson RAS. Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023. [PMID: 37163770 DOI: 10.1021/jasms.3c00050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Proteomics research has been transformed due to high-throughput liquid chromatography (LC-MS/MS) tandem mass spectrometry instruments combined with highly sophisticated automated sample preparation and multiplexing workflows. However, scaling proteomics experiments to large sample cohorts (hundreds to thousands) requires thoughtful quality control (QC) protocols. Robust QC protocols can help with reproducibility, quantitative accuracy, and provide opportunities for more decisive troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of N = 335 patient samples using tandem mass tag (TMTpro) 16-plex batches. Over the course of a 10-month data acquisition period for this cohort we collected 271 pooled QC LC-MS/MS result files obtained from MS/MS analysis of a patient-derived pooled plasma sample, representative of the entire cohort population. This sample was tagged with TMTzero or TMTpro reagents and used to inform the daily performance of the LC-MS/MS instruments and to allow within and across sample batch normalization. Analytical variability of a number of instrumental and data analysis metrics including protein and peptide identifications, peptide spectral matches (PSMs), number of obtained MS/MS spectra, average peptide abundance, percent of peptides with a Δ m/z between ±0.003 Da, percent of MS/MS spectra obtained at the maximum injection time, and the retention time of selected tracking peptides were evaluated to help inform the design of a robust LC-MS/MS QC workflow for use in future cohort studies. This study also led to general tips for using selected metrics to inform real-time troubleshooting of LC-MS/MS performance issues with daily QC checks.
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Affiliation(s)
- Khiry L Patterson
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Albert B Arul
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Min Ji Choi
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Nekesa C Oliver
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Marsalas D Whitaker
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Angel C Bodrick
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Cancer Biology, Neuroscience, and Pharmacology, Meharry Medical College, Nashville, Tennessee 37208, United States
| | - Julia B Libby
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Shania Hansen
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
| | - Logan Dumitrescu
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Katherine A Gifford
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
| | - Angela L Jefferson
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Renã A S Robinson
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, Tennessee 37232, United States
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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11
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Mellinger AL, McCoy K, Minior DAT, Williams TI. Discovery proteomics of human placental tissue. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2023; 38 Suppl 1:e9189. [PMID: 34486781 PMCID: PMC9218992 DOI: 10.1002/rcm.9189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/02/2021] [Accepted: 09/01/2021] [Indexed: 05/11/2023]
Abstract
We describe a label-free proteomics protocol for the interrogation of the placental proteome. Step-by-step directions, including tissue cleanup and preparation, proteolytic digestion, nanoLC-MS/MS data collection and data analysis, are provided. The workflow has been applied toward exploring differential protein expression patterns in placentas from women who have been exposed to drugs during pregnancy relative to those who have not. We collected 20 tissue specimens, each representing a combination of spatially diverse sections across the placenta. These specimens were analyzed in the work described here, to survey information across the entire organ. This protocol can be scaled up or down as needed.
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Affiliation(s)
- Allyson L. Mellinger
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Krista McCoy
- Department of Biology, East Carolina University, Greenville, North Carolina 27834*
| | - Duy An T. Minior
- Division of Neonatal-Perinatal Medicine, East Carolina University/The Brody School of Medicine, Greenville, North Carolina 27834
| | - Taufika Islam Williams
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
- Molecular Education, Technology and Research Innovation Center (METRIC), North Carolina State University, Raleigh, North Carolina 27695
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12
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Van Puyvelde B, Van Uytfanghe K, Van Oudenhove L, Gabriels R, Van Royen T, Matthys A, Razavi M, Yip R, Pearson T, Drouin N, Claereboudt J, Foley D, Wardle R, Wyndham K, Hankemeier T, Jones D, Saelens X, Martens G, Stove CP, Deforce D, Martens L, Vissers JPC, Anderson NL, Dhaenens M. Cov 2MS: An Automated and Quantitative Matrix-Independent Assay for Mass Spectrometric Measurement of SARS-CoV-2 Nucleocapsid Protein. Anal Chem 2022; 94:17379-17387. [PMID: 36490367 PMCID: PMC9773173 DOI: 10.1021/acs.analchem.2c01610] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The pandemic readiness toolbox needs to be extended, targeting different biomolecules, using orthogonal experimental set-ups. Here, we build on our Cov-MS effort using LC-MS, adding SISCAPA technology to enrich proteotypic peptides of the SARS-CoV-2 nucleocapsid (N) protein from trypsin-digested patient samples. The Cov2MS assay is compatible with most matrices including nasopharyngeal swabs, saliva, and plasma and has increased sensitivity into the attomole range, a 1000-fold improvement compared to direct detection in a matrix. A strong positive correlation was observed with qPCR detection beyond a quantification cycle of 30-31, the level where no live virus can be cultured. The automatable sample preparation and reduced LC dependency allow analysis of up to 500 samples per day per instrument. Importantly, peptide enrichment allows detection of the N protein in pooled samples without sensitivity loss. Easily multiplexed, we detect variants and propose targets for Influenza A and B detection. Thus, the Cov2MS assay can be adapted to test for many different pathogens in pooled samples, providing longitudinal epidemiological monitoring of large numbers of pathogens within a population as an early warning system.
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Affiliation(s)
- Bart Van Puyvelde
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, 9000 Ghent, Belgium
| | - Katleen Van Uytfanghe
- Laboratory of Toxicology, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, 9000 Ghent, Belgium
| | | | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Tessa Van Royen
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biochemistry and Microbiology, Ghent University, Ghent 9000 Belgium
| | - Arne Matthys
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biochemistry and Microbiology, Ghent University, Ghent 9000 Belgium
| | - Morteza Razavi
- SISCAPA Assay Technologies, Inc., Box 53309, Washington, DC 20009, United States.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8P 5C2, Canada
| | - Richard Yip
- SISCAPA Assay Technologies, Inc., Box 53309, Washington, DC 20009, United States.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8P 5C2, Canada
| | - Terry Pearson
- SISCAPA Assay Technologies, Inc., Box 53309, Washington, DC 20009, United States.,Department of Biochemistry and Microbiology, University of Victoria, Victoria, BC V8P 5C2, Canada
| | - Nicolas Drouin
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | | | - Dominic Foley
- Waters Corporation, Wilmslow SK9 4AX, United Kingdom.,Waters Corporation, Milford, Massachusetts 01757, United States
| | - Robert Wardle
- Waters Corporation, Wilmslow SK9 4AX, United Kingdom.,Waters Corporation, Milford, Massachusetts 01757, United States
| | - Kevin Wyndham
- Waters Corporation, Wilmslow SK9 4AX, United Kingdom.,Waters Corporation, Milford, Massachusetts 01757, United States
| | - Thomas Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, 2333 AL Leiden, The Netherlands
| | - Donald Jones
- Leicester Cancer Research Centre, RKCSB, Cardiovascular Research Centre, Glenfield Hospital, University of Leicester, Leicester LE1 7RH, United Kingdom.,John and Lucille van Geest Biomarker Facility, Leicester LE3 9QP, United Kingdom.,The Department of Chemical Pathology and Metabolic Diseases, Leicester Royal Infirmary, Level 4, Sandringham Building, Leicester LE1 7RH, United Kingdom
| | - Xavier Saelens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biochemistry and Microbiology, Ghent University, Ghent 9000 Belgium
| | - Geert Martens
- AZ Delta Medical Laboratories, AZ Delta General Hospital, 8800 Roeselare, Belgium
| | - Christophe P Stove
- Laboratory of Toxicology, Department of Bioanalysis, Faculty of Pharmaceutical Sciences, Ghent University, 9000 Ghent, Belgium
| | - Dieter Deforce
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, 9000 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, 9000 Ghent, Belgium.,Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Johannes P C Vissers
- Waters Corporation, Wilmslow SK9 4AX, United Kingdom.,Waters Corporation, Milford, Massachusetts 01757, United States
| | - N Leigh Anderson
- SISCAPA Assay Technologies, Inc., Box 53309, Washington, DC 20009, United States
| | - Maarten Dhaenens
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, 9000 Ghent, Belgium
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13
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Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts. Proteomes 2022; 10:proteomes10030026. [PMID: 35997438 PMCID: PMC9397030 DOI: 10.3390/proteomes10030026] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/13/2022] [Accepted: 07/23/2022] [Indexed: 01/25/2023] Open
Abstract
Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.
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14
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Wang X, Chen C, Yang Y, Wang L, Li M, Zhang P, Deng S, Liang S. Proteome-Based Serotyping of the Food-Borne Pathogens Salmonella Enterica by Label-Free Mass Spectrometry. Molecules 2022; 27:molecules27144334. [PMID: 35889206 PMCID: PMC9321705 DOI: 10.3390/molecules27144334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/27/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022] Open
Abstract
Food-borne diseases caused by Salmonella enterica of 2500 serovars represent a serious public health problem worldwide. A quick identification for the pathogen serovars is critical for controlling food pollution and disease spreading. Here, we applied a mass spectrum-based proteomic profiling for identifying five epidemiologically important Salmonella enterica subsp. enterica serovars (Enteritidis, Typhimurium, London, Rissen and Derby) in China. By label-free analysis, the 53 most variable serovar-related peptides, which were almost all enzymes related to nucleoside phosphate and energy metabolism, were screened as potential peptide biomarkers, and based on which a C5.0 predicted model for Salmonella enterica serotyping with four predictor peptides was generated with the accuracy of 94.12%. In comparison to the classic gene patterns by PFGE analysis, the high-throughput proteomic fingerprints were also effective to determine the genotypic similarity among Salmonella enteric isolates according to each strain of proteome profiling, which is indicative of the potential breakout of food contamination. Generally, the proteomic dissection on Salmonella enteric serovars provides a novel insight and real-time monitoring of food-borne pathogens.
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Affiliation(s)
- Xixi Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and National Collaborative Innovation Center for Biotherapy, Chengdu 610041, China;
- Chengdu Center for Disease Control and Prevention, Chengdu 610041, China; (Y.Y.); (L.W.); (M.L.)
| | - Chen Chen
- Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China;
| | - Yang Yang
- Chengdu Center for Disease Control and Prevention, Chengdu 610041, China; (Y.Y.); (L.W.); (M.L.)
| | - Lian Wang
- Chengdu Center for Disease Control and Prevention, Chengdu 610041, China; (Y.Y.); (L.W.); (M.L.)
| | - Ming Li
- Chengdu Center for Disease Control and Prevention, Chengdu 610041, China; (Y.Y.); (L.W.); (M.L.)
| | - Peng Zhang
- Department of Urinary Surgery, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China; (P.Z.); (S.D.)
| | - Shi Deng
- Department of Urinary Surgery, West China Hospital, West China Medical School, Sichuan University, Chengdu 610041, China; (P.Z.); (S.D.)
| | - Shufang Liang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and National Collaborative Innovation Center for Biotherapy, Chengdu 610041, China;
- Correspondence:
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15
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A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics. Sci Data 2022; 9:126. [PMID: 35354825 PMCID: PMC8967878 DOI: 10.1038/s41597-022-01216-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/23/2022] [Indexed: 12/23/2022] Open
Abstract
In the last decade, a revolution in liquid chromatography-mass spectrometry (LC-MS) based proteomics was unfolded with the introduction of dozens of novel instruments that incorporate additional data dimensions through innovative acquisition methodologies, in turn inspiring specialized data analysis pipelines. Simultaneously, a growing number of proteomics datasets have been made publicly available through data repositories such as ProteomeXchange, Zenodo and Skyline Panorama. However, developing algorithms to mine this data and assessing the performance on different platforms is currently hampered by the lack of a single benchmark experimental design. Therefore, we acquired a hybrid proteome mixture on different instrument platforms and in all currently available families of data acquisition. Here, we present a comprehensive Data-Dependent and Data-Independent Acquisition (DDA/DIA) dataset acquired using several of the most commonly used current day instrumental platforms. The dataset consists of over 700 LC-MS runs, including adequate replicates allowing robust statistics and covering over nearly 10 different data formats, including scanning quadrupole and ion mobility enabled acquisitions. Datasets are available via ProteomeXchange (PXD028735). Measurement(s) | Digital Data Repository | Technology Type(s) | Digital Data Repository |
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16
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Marsh AN, Sharma V, Mani SK, Vitek O, MacCoss MJ, MacLean BX. Skyline Batch: An Intuitive User Interface for Batch Processing with Skyline. J Proteome Res 2021; 21:289-294. [PMID: 34919405 PMCID: PMC8749956 DOI: 10.1021/acs.jproteome.1c00749] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
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Skyline Batch is
a newly developed Windows forms application that
enables the easy and consistent reprocessing of data with Skyline.
Skyline has made previous advances in this direction; however, none
enable seamless automated reprocessing of local and remote files.
Skyline keeps a log of all of the steps that were taken in the document;
however, reproducing these steps takes time and allows room for human
error. Skyline also has a command-line interface, enabling it to be
run from a batch script, but using the program in this way requires
expertise in editing these scripts. By formalizing the workflow of
a highly used set of batch scripts into an intuitive and powerful
user interface, Skyline Batch can reprocess data stored in remote
repositories just by opening and running a Skyline Batch configuration
file. When run, a Skyline Batch configuration downloads all necessary
remote files and then runs a four-step Skyline workflow. By condensing
the steps needed to reprocess the data into one file, Skyline Batch
gives researchers the opportunity to publish their processing along
with their data and other analysis files. These easily run configuration
files will greatly increase the transparency and reproducibility of
published work. Skyline Batch is freely available at https://skyline.ms/batch.url.
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Affiliation(s)
- Alexandra N Marsh
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Vagisha Sharma
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Surya K Mani
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Olga Vitek
- Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts 02115, United States
| | - Michael J MacCoss
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Brendan X MacLean
- Genome Sciences, University of Washington, Seattle, Washington 98195, United States
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17
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Improving data quality in liquid chromatography-mass spectrometry metabolomics of human urine. J Chromatogr A 2021; 1654:462457. [PMID: 34404016 DOI: 10.1016/j.chroma.2021.462457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/29/2021] [Accepted: 07/31/2021] [Indexed: 11/23/2022]
Abstract
Signal variation is a common drawback in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS), mainly due to the complexity of biological matrices and reduced sample preparation, which results in the accumulation of sample components in the column and the ion source. Here we propose a simple, easy to implement approach to improve data quality in untargeted metabolomics by LC-MS. This approach involves the use of a divert valve to direct the column effluent to waste at the beginning of the chromatographic run and during column cleanup and equilibration, in combination with longer column cleanups in between injections. Our approach was tested using urine samples collected from patients after renal transplantation. Analytical responses were contrasted before and after introducing these modifications by analyzing a batch of untargeted metabolomics data. A significant improvement in peak area repeatability was observed for the quality controls, with relative standard deviations (RSDs) for several metabolites decreasing from ∼60% to ∼10% when our approach was introduced. Similarly, RSDs of peak areas for internal standards improved from ∼40% to ∼10%. Furthermore, calibrant solutions were more consistent after introducing these modifications when comparing peak areas of solutions injected at the beginning and the end of each analytical sequence. Therefore, we recommend the use of a divert valve and extended column cleanup as a powerful strategy to improve data quality in untargeted metabolomics, especially for very complex types of samples where minimum sample preparation is required, such as in this untargeted metabolomics study with urine from renal transplanted patients.
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18
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Methods for Proteomic Analyses of Mycobacteria. Methods Mol Biol 2021. [PMID: 34235669 DOI: 10.1007/978-1-0716-1460-0_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
The use of proteomic technologies to characterize and study the proteome of mycobacteria has provided important information in terms of function, diversity, protein-protein interactions, and host-pathogen interactions in Mycobacterium spp. There are many different mass spectrometry methodologies that can be applied to proteomics studies of mycobacteria and microorganisms in general. Sample processing and appropriate study design are critical to generating high-quality data regardless of the mass spectrometry method applied. Appropriate study design relies on statistical rigor and data curation using bioinformatics approaches that are widely applicable regardless of the organism or system studied. Sample processing, on the other hand, is often a niched process specific to the physiology of the organism or system under investigation. Therefore, in this chapter, we will provide protocols for processing mycobacterial protein samples for the specific application of Top-down and Bottom-up proteomic analyses.
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19
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Tisdale RH, Zobel RW, Burkey KO. Tropospheric ozone rapidly decreases root growth by altering carbon metabolism and detoxification capability in growing soybean roots. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 766:144292. [PMID: 33418251 DOI: 10.1016/j.scitotenv.2020.144292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/27/2020] [Accepted: 11/28/2020] [Indexed: 06/12/2023]
Abstract
High tropospheric ozone (O3) concentrations lead to significant global soybean (Glycine max) yield reductions. Research concerning O3 impacts on soybean has focused on the contributions of above-ground tissues. In this study, Mandarin (Ottawa) (O3-sensitive) and Fiskeby III (O3-tolerant) soybean genotypes provide contrasting materials to investigate O3 effects on root growth. We compared root morphological and proteomic changes when 16-day-old plants were treated with charcoal-filtered (CF) air or elevated O3 (80 ppb O3 for 7 h/day) in continuously stirred-tank reactors (CSTR) for 7 days. Our results showed that in Mandarin (Ottawa), decreased expression of enzymes involved in the tricarboxylic acid (TCA) cycle contributes to reduction of root biomass and diameter under elevated O3. In contrast, O3 tolerance in Fiskeby III roots was associated with O3-dependent induction of enzymes involved in glycolysis and O3-independent expression of enzymes involved in the ascorbate-glutathione cycle. We conclude that a decreased abundance of key redox enzymes in roots due to limited carbon availability rapidly alters root growth under O3 stress. However, maintaining a high abundance of enzymes associated with redox status and detoxification capability contributes to overall O3 tolerance in roots.
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Affiliation(s)
- Ripley H Tisdale
- U.S. Department of Agriculture, Agricultural Research Service, Plant Science Research Unit, Raleigh, 27607, NC, USA; Department of Crop and Soil Sciences, North Carolina State University, Raleigh, 27695, NC, USA.
| | - Richard W Zobel
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, 27695, NC, USA
| | - Kent O Burkey
- U.S. Department of Agriculture, Agricultural Research Service, Plant Science Research Unit, Raleigh, 27607, NC, USA; Department of Crop and Soil Sciences, North Carolina State University, Raleigh, 27695, NC, USA
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20
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Morgenstern D, Barzilay R, Levin Y. RawBeans: A Simple, Vendor-Independent, Raw-Data Quality-Control Tool. J Proteome Res 2021; 20:2098-2104. [PMID: 33657803 PMCID: PMC8041395 DOI: 10.1021/acs.jproteome.0c00956] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
![]()
Every laboratory performing mass-spectrometry-based
proteomics
strives to generate high-quality data. Among the many factors that
impact the outcome of any experiment in proteomics is the LC–MS
system performance, which should be monitored within each specific
experiment and also long term. This process is termed quality control
(QC). We present an easy-to-use tool that rapidly produces a visual,
HTML-based report that includes the key parameters needed to monitor
the LC–MS system performance, with a focus on monitoring the
performance within an experiment. The tool, named RawBeans, generates
a report for individual files or for a set of samples from a whole
experiment. We anticipate that it will help proteomics users and experts
evaluate raw data quality independent of data processing. The tool
is available at https://bitbucket.org/incpm/prot-qc/downloads. The mass-spectrometry proteomics data have been deposited to the
ProteomeXchange Consortium via the PRIDE partner repository with the
data set identifier PXD022816.
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Affiliation(s)
- David Morgenstern
- de Botton Institute for Protein Profiling, The Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Rotem Barzilay
- Ilana and Pascal Mantoux Institute for Bioinformatics, The Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yishai Levin
- de Botton Institute for Protein Profiling, The Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Rehovot 76100, Israel
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21
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Cardozo KHM, Lebkuchen A, Okai GG, Schuch RA, Viana LG, Olive AN, Lazari CDS, Fraga AM, Granato CFH, Pintão MCT, Carvalho VM. Establishing a mass spectrometry-based system for rapid detection of SARS-CoV-2 in large clinical sample cohorts. Nat Commun 2020; 11:6201. [PMID: 33273458 PMCID: PMC7713649 DOI: 10.1038/s41467-020-19925-0] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 11/05/2020] [Indexed: 12/13/2022] Open
Abstract
The outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is pressing public health systems around the world, and large population testing is a key step to control this pandemic disease. Here, we develop a high-throughput targeted proteomics assay to detect SARS-CoV-2 nucleoprotein peptides directly from nasopharyngeal and oropharyngeal swabs. A modified magnetic particle-based proteomics approach implemented on a robotic liquid handler enables fully automated preparation of 96 samples within 4 hours. A TFC-MS system allows multiplexed analysis of 4 samples within 10 min, enabling the processing of more than 500 samples per day. We validate this method qualitatively (Tier 3) and quantitatively (Tier 1) using 985 specimens previously analyzed by real-time RT-PCR, and detect up to 84% of the positive cases with up to 97% specificity. The presented strategy has high sample stability and should be considered as an option for SARS-CoV-2 testing in large populations.
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Affiliation(s)
| | - Adriana Lebkuchen
- Division of Research and Development, Fleury Group, 04344-070, São Paulo, SP, Brazil
| | | | | | - Luciana Godoy Viana
- Division of Research and Development, Fleury Group, 04344-070, São Paulo, SP, Brazil
| | - Aline Nogueira Olive
- Division of Research and Development, Fleury Group, 04344-070, São Paulo, SP, Brazil
| | | | - Ana Maria Fraga
- Division of Research and Development, Fleury Group, 04344-070, São Paulo, SP, Brazil
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22
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Pino LK, Searle BC, Bollinger JG, Nunn B, MacLean B, MacCoss MJ. The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics. MASS SPECTROMETRY REVIEWS 2020; 39:229-244. [PMID: 28691345 PMCID: PMC5799042 DOI: 10.1002/mas.21540] [Citation(s) in RCA: 390] [Impact Index Per Article: 97.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 06/01/2017] [Indexed: 05/03/2023]
Abstract
Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.
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Affiliation(s)
- Lindsay K Pino
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brian C Searle
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - James G Bollinger
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brook Nunn
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Brendan MacLean
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington
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23
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Adams KJ, Pratt B, Bose N, Dubois LG, St John-Williams L, Perrott KM, Ky K, Kapahi P, Sharma V, MacCoss MJ, Moseley MA, Colton CA, MacLean BX, Schilling B, Thompson JW. Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics. J Proteome Res 2020; 19:1447-1458. [PMID: 31984744 DOI: 10.1021/acs.jproteome.9b00640] [Citation(s) in RCA: 216] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Vendor-independent software tools for quantification of small molecules and metabolites are lacking, especially for targeted analysis workflows. Skyline is a freely available, open-source software tool for targeted quantitative mass spectrometry method development and data processing with a 10 year history supporting six major instrument vendors. Designed initially for proteomics analysis, we describe the expansion of Skyline to data for small molecule analysis, including selected reaction monitoring, high-resolution mass spectrometry, and calibrated quantification. This fundamental expansion of Skyline from a peptide-sequence-centric tool to a molecule-centric tool makes it agnostic to the source of the molecule while retaining Skyline features critical for workflows in both peptide and more general biomolecular research. The data visualization and interrogation features already available in Skyline, such as peak picking, chromatographic alignment, and transition selection, have been adapted to support small molecule data, including metabolomics. Herein, we explain the conceptual workflow for small molecule analysis using Skyline, demonstrate Skyline performance benchmarked against a comparable instrument vendor software tool, and present additional real-world applications. Further, we include step-by-step instructions on using Skyline for small molecule quantitative method development and data analysis on data acquired with a variety of mass spectrometers from multiple instrument vendors.
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Affiliation(s)
- Kendra J Adams
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States.,Department of Neurology, Duke University, Durham, North Carolina 27710, United States
| | - Brian Pratt
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Neelanjan Bose
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - Laura G Dubois
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
| | - Lisa St John-Williams
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
| | - Kevin M Perrott
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - Karina Ky
- University of California San Francisco, San Francisco, California 94143, United States
| | - Pankaj Kapahi
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - Vagisha Sharma
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - M Arthur Moseley
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States
| | - Carol A Colton
- Department of Neurology, Duke University, Durham, North Carolina 27710, United States
| | - Brendan X MacLean
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Birgit Schilling
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - J Will Thompson
- Proteomics and Metabolomics Shared Resource, Duke University, Durham, North Carolina 27701, United States.,Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina 27710, United States
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24
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Ding H, Fazelinia H, Spruce LA, Weiss DA, Zderic SA, Seeholzer SH. Urine Proteomics: Evaluation of Different Sample Preparation Workflows for Quantitative, Reproducible, and Improved Depth of Analysis. J Proteome Res 2020; 19:1857-1862. [DOI: 10.1021/acs.jproteome.9b00772] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Hua Ding
- Proteomics Core Facility, Children’s Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, Pennsylvania 19104, United States
| | - Hossein Fazelinia
- Proteomics Core Facility, Children’s Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, Pennsylvania 19104, United States
| | - Lynn A. Spruce
- Proteomics Core Facility, Children’s Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, Pennsylvania 19104, United States
| | - Dana A. Weiss
- Division of Urology, Children’s Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, Pennsylvania 19104, United States
| | - Stephen A. Zderic
- Division of Urology, Children’s Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, Pennsylvania 19104, United States
| | - Steven H. Seeholzer
- Proteomics Core Facility, Children’s Hospital of Philadelphia, 3615 Civic Center Boulevard, Philadelphia, Pennsylvania 19104, United States
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25
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Lavergne FD, Broeckling CD, Brown KJ, Cockrell DM, Haley SD, Peairs FB, Pearce S, Wolfe LM, Jahn CE, Heuberger AL. Differential Stem Proteomics and Metabolomics Profiles for Four Wheat Cultivars in Response to the Insect Pest Wheat Stem Sawfly. J Proteome Res 2020; 19:1037-1051. [PMID: 31995381 DOI: 10.1021/acs.jproteome.9b00561] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Common wheat (Triticum aestivum L.) is a global staple crop, and insect pests can impact grain yield. The wheat stem sawfly (Cephus cinctus, WSS) is a major wheat pest, and while partial resistance has been deployed by breeding for a solid-stem trait, this trait is affected by environment. Here, a proteomics and metabolomics study was performed on four wheat cultivars to characterize a molecular response to WSS infestation. The cultivars Hatcher (hollow-stem partially tolerant), Conan (semisolid-stem-resistant), and Denali and Reeder (hollow-stem-susceptible) were infested with WSS, and changes in stem proteins and metabolites were characterized using liquid chromatography-mass spectrometry. The proteome was characterized as 1830 proteins that included five major biological processes, including metabolic processes and response to stimuli, and the metabolome (1823 metabolites) spanned eight chemical superclasses, including alkaloids, benzenoids, and lipids. All four varieties had a molecular response to WSS following infestation. Hatcher had the most distinct changes, whereby 62 proteins and 29 metabolites varied in metabolic pathways involving enzymatic detoxification, proteinase inhibition, and antiherbivory compound production via benzoxazinoids, neolignans, and phenolics. Taken together, these data demonstrate variation in the wheat stem molecular response to WSS infestation and support breeding for molecular resistance in hollow-stem cultivars.
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Affiliation(s)
- Florent D Lavergne
- Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Corey D Broeckling
- Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, Colorado 80523, United States.,Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Kitty J Brown
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Darren M Cockrell
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Scott D Haley
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Frank B Peairs
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Stephen Pearce
- Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Lisa M Wolfe
- Proteomics and Metabolomics Facility, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Courtney E Jahn
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Adam L Heuberger
- Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins, Colorado 80523, United States.,Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
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26
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Global targeting of functional tyrosines using sulfur-triazole exchange chemistry. Nat Chem Biol 2019; 16:150-159. [PMID: 31768034 PMCID: PMC6982592 DOI: 10.1038/s41589-019-0404-5] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 10/08/2019] [Indexed: 12/22/2022]
Abstract
Covalent probes serve as valuable tools for global investigation of protein function and ligand binding capacity. Despite efforts to expand coverage of residues available for chemical proteomics (e.g. cysteine and lysine), a large fraction of the proteome remains inaccessible with current activity-based probes. Here, we introduce sulfur-triazole exchange (SuTEx) chemistry as a tunable platform for developing covalent probes with broad applications for chemical proteomics. We show modifications to the triazole leaving group can furnish sulfonyl probes with ~5-fold enhanced chemoselectivity for tyrosines over other nucleophilic amino acids to investigate, for the first time, more than 10,000 tyrosine sites in lysates and live cells. We discover that tyrosines with enhanced nucleophilicity are enriched in enzymatic, protein-protein interaction, and nucleotide recognition domains. We apply SuTEx as a chemical phosphoproteomics strategy to monitor activation of phosphotyrosine sites. Collectively, we describe SuTEx as a biocompatible chemistry for chemical biology investigations of the human proteome.
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27
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Yeyeodu ST, Martin ME, Reaves DK, Enders JR, Costantini LM, Fleming JM. Experimental data demonstrating the effects of silver nanoparticles on basement membrane gene and protein expression in cultured colon, mammary and bronchial epithelia. Data Brief 2019; 26:104464. [PMID: 31667234 PMCID: PMC6811978 DOI: 10.1016/j.dib.2019.104464] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/18/2019] [Accepted: 08/23/2019] [Indexed: 12/01/2022] Open
Abstract
This data article is related to the research article entitled “Silver nanoparticles alter epithelial basement membrane integrity, cell adhesion molecule expression and TGF-beta secretion”, available in the journal Nanomedicine: Nanotechnology, Biology, and Medicine [1]. This Data in Brief consists of data that describe changes in the expression of basement membrane (BM)-associated genes and proteins in three non-transformed epithelial cell lines following acute (6 h) and chronic (24 h plus 7-day chase) exposure to silver nanoparticles (AgNPs). Human BEAS2B (lung), MCF10AI (breast), and CCD-18Co (colon) cultured epithelia were analyzed for protein expression by LC-MS/MS and for gene expression by pathway-focused QRT-PCR arrays of 168 focal adhesion, integrin, and extracellular matrix (ECM) genes known to be localized to the plasma membrane, the BM/ECM, or secreted into the extracellular space. Ingenuity pathway analysis (IPA) of combined gene and protein expression datasets was then used to predict canonical pathways affected by AgNP exposure.
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Affiliation(s)
| | - Megan E Martin
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, NC, USA
| | - Denise K Reaves
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, NC, USA
| | - Jeffrey R Enders
- Molecular Education, Technology and Research Innovation Center, North Carolina State University, Raleigh, NC, USA.,Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA
| | - Lindsey M Costantini
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, NC, USA
| | - Jodie M Fleming
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, NC, USA.,Molecular Education, Technology and Research Innovation Center, North Carolina State University, Raleigh, NC, USA.,Lineberger Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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28
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Affiliation(s)
- Jennifer E Van Eyk
- The Advanced Clinical Biosystems Research Institute, The Barbra Streisand Women's Heart Center at the Smidt Heart Institute and Department of Medicine (J.E.V.E.), Cedars-Sinai Medical Center, Los Angeles, CA.,Cedars-Sinai Medical Center Precision Biomarkers Laboratories (J.E.V.E., K.S.), Cedars-Sinai Medical Center, Los Angeles, CA
| | - Kimia Sobhani
- Department of Pathology (K.S.), Cedars-Sinai Medical Center, Los Angeles, CA.,Cedars-Sinai Medical Center Precision Biomarkers Laboratories (J.E.V.E., K.S.), Cedars-Sinai Medical Center, Los Angeles, CA
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29
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Laumont CM, Vincent K, Hesnard L, Audemard É, Bonneil É, Laverdure JP, Gendron P, Courcelles M, Hardy MP, Côté C, Durette C, St-Pierre C, Benhammadi M, Lanoix J, Vobecky S, Haddad E, Lemieux S, Thibault P, Perreault C. Noncoding regions are the main source of targetable tumor-specific antigens. Sci Transl Med 2018; 10:10/470/eaau5516. [DOI: 10.1126/scitranslmed.aau5516] [Citation(s) in RCA: 256] [Impact Index Per Article: 42.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 11/15/2018] [Indexed: 12/15/2022]
Abstract
Tumor-specific antigens (TSAs) represent ideal targets for cancer immunotherapy, but few have been identified thus far. We therefore developed a proteogenomic approach to enable the high-throughput discovery of TSAs coded by potentially all genomic regions. In two murine cancer cell lines and seven human primary tumors, we identified a total of 40 TSAs, about 90% of which derived from allegedly noncoding regions and would have been missed by standard exome-based approaches. Moreover, most of these TSAs derived from nonmutated yet aberrantly expressed transcripts (such as endogenous retroelements) that could be shared by multiple tumor types. Last, we demonstrated that, in mice, the strength of antitumor responses after TSA vaccination was influenced by two parameters that can be estimated in humans and could serve for TSA prioritization in clinical studies: TSA expression and the frequency of TSA-responsive T cells in the preimmune repertoire. In conclusion, the strategy reported herein could considerably facilitate the identification and prioritization of actionable human TSAs.
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30
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Dogu E, Taheri SM, Olivella R, Marty F, Lienert I, Reiter L, Sabido E, Vitek O. MSstatsQC 2.0: R/Bioconductor Package for Statistical Quality Control of Mass Spectrometry-Based Proteomics Experiments. J Proteome Res 2018; 18:678-686. [DOI: 10.1021/acs.jproteome.8b00732] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Eralp Dogu
- Department of Statistics, Muğla Sitki Koçman University, Muğla 48000, Turkey
| | - Sara Mohammad Taheri
- College of Computer Science, Northeastern University, Boston, Massachusetts 02115,United States
| | - Roger Olivella
- Proteomics Unit, Centre de Regulaci Genmica, Barcelona Institute of Science and Technology, Universitat Pompeu Fabra, 08002 Barcelona, Spain
| | | | | | | | - Eduard Sabido
- Proteomics Unit, Centre de Regulaci Genmica, Barcelona Institute of Science and Technology, Universitat Pompeu Fabra, 08002 Barcelona, Spain
| | - Olga Vitek
- College of Computer Science, Northeastern University, Boston, Massachusetts 02115,United States
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31
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Bereman MS, Beri J, Enders JR, Nash T. Machine Learning Reveals Protein Signatures in CSF and Plasma Fluids of Clinical Value for ALS. Sci Rep 2018; 8:16334. [PMID: 30397248 PMCID: PMC6218542 DOI: 10.1038/s41598-018-34642-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 10/23/2018] [Indexed: 11/14/2022] Open
Abstract
We use shotgun proteomics to identify biomarkers of diagnostic and prognostic value in individuals diagnosed with amyotrophic lateral sclerosis. Matched cerebrospinal and plasma fluids were subjected to abundant protein depletion and analyzed by nano-flow liquid chromatography high resolution tandem mass spectrometry. Label free quantitation was used to identify differential proteins between individuals with ALS (n = 33) and healthy controls (n = 30) in both fluids. In CSF, 118 (p-value < 0.05) and 27 proteins (q-value < 0.05) were identified as significantly altered between ALS and controls. In plasma, 20 (p-value < 0.05) and 0 (q-value < 0.05) proteins were identified as significantly altered between ALS and controls. Proteins involved in complement activation, acute phase response and retinoid signaling pathways were significantly enriched in the CSF from ALS patients. Subsequently various machine learning methods were evaluated for disease classification using a repeated Monte Carlo cross-validation approach. A linear discriminant analysis model achieved a median area under the receiver operating characteristic curve of 0.94 with an interquartile range of 0.88–1.0. Three proteins composed a prognostic model (p = 5e-4) that explained 49% of the variation in the ALS-FRS scores. Finally we investigated the specificity of two promising proteins from our discovery data set, chitinase-3 like 1 protein and alpha-1-antichymotrypsin, using targeted proteomics in a separate set of CSF samples derived from individuals diagnosed with ALS (n = 11) and other neurological diseases (n = 15). These results demonstrate the potential of a panel of targeted proteins for objective measurements of clinical value in ALS.
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Affiliation(s)
- Michael S Bereman
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, 27695, USA. .,Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA. .,Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, 27695, USA.
| | - Joshua Beri
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Jeffrey R Enders
- Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, 27695, USA
| | - Tara Nash
- Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, 27695, USA
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32
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Toghi Eshghi S, Auger P, Mathews WR. Quality assessment and interference detection in targeted mass spectrometry data using machine learning. Clin Proteomics 2018; 15:33. [PMID: 30323719 PMCID: PMC6173846 DOI: 10.1186/s12014-018-9209-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 09/24/2018] [Indexed: 12/24/2022] Open
Abstract
Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions’ ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.
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Affiliation(s)
- Shadi Toghi Eshghi
- OMNI-Biomarker Development, Genentech Inc., South San Francisco, CA 94080 USA
| | - Paul Auger
- OMNI-Biomarker Development, Genentech Inc., South San Francisco, CA 94080 USA
| | - W Rodney Mathews
- OMNI-Biomarker Development, Genentech Inc., South San Francisco, CA 94080 USA
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33
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Titz B, Gadaleta RM, Lo Sasso G, Elamin A, Ekroos K, Ivanov NV, Peitsch MC, Hoeng J. Proteomics and Lipidomics in Inflammatory Bowel Disease Research: From Mechanistic Insights to Biomarker Identification. Int J Mol Sci 2018; 19:ijms19092775. [PMID: 30223557 PMCID: PMC6163330 DOI: 10.3390/ijms19092775] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 09/11/2018] [Accepted: 09/12/2018] [Indexed: 02/06/2023] Open
Abstract
Inflammatory bowel disease (IBD) represents a group of progressive disorders characterized by recurrent chronic inflammation of the gut. Ulcerative colitis and Crohn's disease are the major manifestations of IBD. While our understanding of IBD has progressed in recent years, its etiology is far from being fully understood, resulting in suboptimal treatment options. Complementing other biological endpoints, bioanalytical "omics" methods that quantify many biomolecules simultaneously have great potential in the dissection of the complex pathogenesis of IBD. In this review, we focus on the rapidly evolving proteomics and lipidomics technologies and their broad applicability to IBD studies; these range from investigations of immune-regulatory mechanisms and biomarker discovery to studies dissecting host⁻microbiome interactions and the role of intestinal epithelial cells. Future studies can leverage recent advances, including improved analytical methodologies, additional relevant sample types, and integrative multi-omics analyses. Proteomics and lipidomics could effectively accelerate the development of novel targeted treatments and the discovery of complementary biomarkers, enabling continuous monitoring of the treatment response of individual patients; this may allow further refinement of treatment and, ultimately, facilitate a personalized medicine approach to IBD.
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Affiliation(s)
- Bjoern Titz
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Raffaella M Gadaleta
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Giuseppe Lo Sasso
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Ashraf Elamin
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Kim Ekroos
- Lipidomics Consulting Ltd., Irisviksvägen 31D, 02230 Esbo, Finland.
| | - Nikolai V Ivanov
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Manuel C Peitsch
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
| | - Julia Hoeng
- PMI R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, CH-2000 Neuchatel, Switzerland.
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34
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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: 578] [Impact Index Per Article: 96.3] [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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35
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Trachsel C, Panse C, Kockmann T, Wolski WE, Grossmann J, Schlapbach R. rawDiag: An R Package Supporting Rational LC–MS Method Optimization for Bottom-up Proteomics. J Proteome Res 2018; 17:2908-2914. [DOI: 10.1021/acs.jproteome.8b00173] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Christian Trachsel
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology Zurich, University of Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland
| | - Christian Panse
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology Zurich, University of Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland
| | - Tobias Kockmann
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology Zurich, University of Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland
| | - Witold E. Wolski
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology Zurich, University of Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland
| | - Jonas Grossmann
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology Zurich, University of Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland
| | - Ralph Schlapbach
- Functional Genomics Center Zurich, Swiss Federal Institute of Technology Zurich, University of Zurich, Winterthurerstr. 190, CH-8057 Zurich, Switzerland
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36
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Andjelković U, Josić D. Mass spectrometry based proteomics as foodomics tool in research and assurance of food quality and safety. Trends Food Sci Technol 2018. [DOI: 10.1016/j.tifs.2018.04.008] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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37
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Martinović T, Šrajer Gajdošik M, Josić D. Sample preparation in foodomic analyses. Electrophoresis 2018; 39:1527-1542. [DOI: 10.1002/elps.201800029] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 03/12/2018] [Accepted: 03/27/2018] [Indexed: 12/30/2022]
Affiliation(s)
| | | | - Djuro Josić
- Department of Biotechnology; University of Rijeka; Rijeka Croatia
- Department of Medicine; Brown Medical School; Brown University; Providence RI USA
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38
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Zhang X, Liu Q, Zhou W, Li P, Alolga RN, Qi LW, Yin X. A comparative proteomic characterization and nutritional assessment of naturally- and artificially-cultivated Cordyceps sinensis. J Proteomics 2018; 181:24-35. [PMID: 29609095 DOI: 10.1016/j.jprot.2018.03.029] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 03/27/2018] [Accepted: 03/27/2018] [Indexed: 01/24/2023]
Abstract
Cordyceps sinensis has gained increasing attention due to its nutritional and medicinal properties. Herein, we employed label-free quantitative mass spectrometry to explore the proteome differences between naturally- and artificially-cultivated C. sinensis. A total of 22,829 peptides with confidence ≥95%, corresponding to 2541 protein groups were identified from the caterpillar bodies/stromata of 12 naturally- and artificially-cultivated samples of C. sinensis. Among them, 165 proteins showed significant differences between the samples of natural and artificial cultivation. These proteins were mainly involved in energy production/conversion, amino acid transport/metabolism, and transcription regulation. The proteomic results were confirmed by the identification of 4 significantly changed metabolites, thus, lysine, threonine, serine, and arginine via untargeted metabolomics. The change tendencies of these metabolites were partly in accordance with changes in abundance of the proteins, which was upstream of their synthetic pathways. In addition, the nutritional value in terms of the levels of nucleosides, nucleotides, and adenosine between the artificially- and naturally-cultivated samples was virtually same. These proteomic data will be useful for understanding the medicinal value of C. sinensis and serve as reference for its artificial cultivation. SIGNIFICANCE C. sinensis is a precious and valued medicinal product, the current basic proteome dataset would provide useful information to understand its development/infection processes as well as help to artificially cultivate it. This work would also provide basic proteome profile for further study of C. sinensis.
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Affiliation(s)
- Xu Zhang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Qun Liu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Wei Zhou
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Ping Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Raphael N Alolga
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China
| | - Lian-Wen Qi
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China; Clinical Metabolomics Center, China Pharmaceutical University, Nanjing 211198, China.
| | - Xiaojian Yin
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 210009, China.
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39
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Misra BB. Updates on resources, software tools, and databases for plant proteomics in 2016-2017. Electrophoresis 2018; 39:1543-1557. [PMID: 29420853 DOI: 10.1002/elps.201700401] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 01/23/2018] [Accepted: 02/02/2018] [Indexed: 11/05/2022]
Abstract
Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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Chiva C, Olivella R, Borràs E, Espadas G, Pastor O, Solé A, Sabidó E. QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories. PLoS One 2018; 13:e0189209. [PMID: 29324744 PMCID: PMC5764250 DOI: 10.1371/journal.pone.0189209] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 11/21/2017] [Indexed: 01/03/2023] Open
Abstract
The increasing number of biomedical and translational applications in mass spectrometry-based proteomics poses new analytical challenges and raises the need for automated quality control systems. Despite previous efforts to set standard file formats, data processing workflows and key evaluation parameters for quality control, automated quality control systems are not yet widespread among proteomics laboratories, which limits the acquisition of high-quality results, inter-laboratory comparisons and the assessment of variability of instrumental platforms. Here we present QCloud, a cloud-based system to support proteomics laboratories in daily quality assessment using a user-friendly interface, easy setup, automated data processing and archiving, and unbiased instrument evaluation. QCloud supports the most common targeted and untargeted proteomics workflows, it accepts data formats from different vendors and it enables the annotation of acquired data and reporting incidences. A complete version of the QCloud system has successfully been developed and it is now open to the proteomics community (http://qcloud.crg.eu). QCloud system is an open source project, publicly available under a Creative Commons License Attribution-ShareAlike 4.0.
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Affiliation(s)
- Cristina Chiva
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Roger Olivella
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Eva Borràs
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Guadalupe Espadas
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Olga Pastor
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Amanda Solé
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
| | - Eduard Sabidó
- Proteomics Unit, Centre de Regulació Genòmica (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Barcelona
- Universitat Pompeu Fabra (UPF), Barcelona, Barcelona
- * E-mail:
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Diversity of Amyloid-beta Proteoforms in the Alzheimer's Disease Brain. Sci Rep 2017; 7:9520. [PMID: 28842697 PMCID: PMC5572664 DOI: 10.1038/s41598-017-10422-x] [Citation(s) in RCA: 104] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Accepted: 08/08/2017] [Indexed: 12/31/2022] Open
Abstract
Amyloid-beta (Aβ) plays a key role in the pathogenesis of Alzheimer’s disease (AD), but little is known about the proteoforms present in AD brain. We used high-resolution mass spectrometry to analyze intact Aβ from soluble aggregates and insoluble material in brains of six cases with severe dementia and pathologically confirmed AD. The soluble aggregates are especially relevant because they are believed to be the most toxic form of Aβ. We found a diversity of Aβ peptides, with 26 unique proteoforms including various N- and C-terminal truncations. N- and C-terminal truncations comprised 73% and 30%, respectively, of the total Aβ proteoforms detected. The Aβ proteoforms segregated between the soluble and more insoluble aggregates with N-terminal truncations predominating in the insoluble material and C- terminal truncations segregating into the soluble aggregates. In contrast, canonical Aβ comprised the minority of the identified proteoforms (15.3%) and did not distinguish between the soluble and more insoluble aggregates. The relative abundance of many truncated Aβ proteoforms did not correlate with post-mortem interval, suggesting they are not artefacts. This heterogeneity of Aβ proteoforms deepens our understanding of AD and offers many new avenues for investigation into pathological mechanisms of the disease, with implications for therapeutic development.
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Beri J, Nash T, Martin RM, Bereman MS. Exposure to BMAA mirrors molecular processes linked to neurodegenerative disease. Proteomics 2017; 17. [PMID: 28837265 DOI: 10.1002/pmic.201700161] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Revised: 07/17/2017] [Accepted: 07/19/2017] [Indexed: 12/14/2022]
Abstract
The goal of this study is to investigate the molecular pathways perturbed by in vitro exposure of beta-methylamino-L-alanine (BMAA) to NSC-34 cells via contemporary proteomics. Our analysis of differentially regulated proteins reveals significant enrichment (p < 0.01) of pathways related to ER stress, protein ubiquitination, the unfolded protein response, and mitochondrial dysfunction. Upstream regulator analysis indicates that exposure to BMAA induces activation of transcription factors (X-box binding protein 1; nuclear factor 2 erythroid like 2; promyelocytic leukemia) involved in regulation of the UPR, oxidative stress, and cellular senescence. Furthermore, the authors examine the hypothesis that BMAA causes protein damage via misincorporation in place of L-Serine. The authors are unable to detect misincorporation of BMAA into protein via analysis of cellular protein, secreted protein, targeted detection of BMAA after protein hydrolysis, or through the use of in vitro protein translation kits.
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Affiliation(s)
- Joshua Beri
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA
| | - Tara Nash
- Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA
| | - Rubia M Martin
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Michael S Bereman
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.,Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA.,Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
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Dogu E, Mohammad-Taheri S, Abbatiello SE, Bereman MS, MacLean B, Schilling B, Vitek O. MSstatsQC: Longitudinal System Suitability Monitoring and Quality Control for Targeted Proteomic Experiments. Mol Cell Proteomics 2017; 16:1335-1347. [PMID: 28483925 PMCID: PMC5500765 DOI: 10.1074/mcp.m116.064774] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Revised: 04/12/2017] [Indexed: 01/14/2023] Open
Abstract
Selected Reaction Monitoring (SRM) is a powerful tool for targeted detection and quantification of peptides in complex matrices. An important objective of SRM is to obtain peptide quantifications that are (1) suitable for the investigation, and (2) reproducible across laboratories and runs. The first objective is achieved by system suitability tests (SST), which verify that mass spectrometric instrumentation performs as specified. The second objective is achieved by quality control (QC), which provides in-process quality assurance of the sample profile. A common aspect of SST and QC is the longitudinal nature of the data. Although SST and QC have received a lot of attention in the proteomic community, the currently used statistical methods are limited. This manuscript improves upon the statistical methodology for SST and QC that is currently used in proteomics. It adapts the modern methods of longitudinal statistical process control, such as simultaneous and time weighted control charts and change point analysis, to SST and QC of SRM experiments, discusses their advantages, and provides practical guidelines. Evaluations on simulated data sets, and on data sets from the Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium, demonstrated that these methods substantially improve our ability of real time monitoring, early detection and prevention of chromatographic and instrumental problems. We implemented the methods in an open-source R-based software package MSstatsQC and its web-based graphical user interface. They are available for use stand-alone, or for integration with automated pipelines. Although the examples focus on targeted proteomics, the statistical methods in this manuscript apply more generally to quantitative proteomics.
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Affiliation(s)
- Eralp Dogu
- From the ‡College of Computer and Information Science, Northeastern University, Massachusetts 02115
- §College of Science, Mugla Sitki Kocman University 48000, Turkey
| | - Sara Mohammad-Taheri
- From the ‡College of Computer and Information Science, Northeastern University, Massachusetts 02115
| | | | - Michael S Bereman
- ‖Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, North Carolina 27695
| | - Brendan MacLean
- **Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195
| | - Birgit Schilling
- ‡‡Buck Institute for Research on Aging, Novato, California 94945;
| | - Olga Vitek
- From the ‡College of Computer and Information Science, Northeastern University, Massachusetts 02115;
- §§College of Science, Northeastern University, Massachusetts 02115
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Meyer JG, Schilling B. Clinical applications of quantitative proteomics using targeted and untargeted data-independent acquisition techniques. Expert Rev Proteomics 2017; 14:419-429. [PMID: 28436239 PMCID: PMC5671767 DOI: 10.1080/14789450.2017.1322904] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
INTRODUCTION While selected/multiple-reaction monitoring (SRM or MRM) is considered the gold standard for quantitative protein measurement, emerging data-independent acquisition (DIA) using high-resolution scans have opened a new dimension of high-throughput, comprehensive quantitative proteomics. These newer methodologies are particularly well suited for discovery of biomarker candidates from human disease samples, and for investigating and understanding human disease pathways. Areas covered: This article reviews the current state of targeted and untargeted DIA mass spectrometry-based proteomic workflows, including SRM, parallel-reaction monitoring (PRM) and untargeted DIA (e.g., SWATH). Corresponding bioinformatics strategies, as well as application in biological and clinical studies are presented. Expert commentary: Nascent application of highly-multiplexed untargeted DIA, such as SWATH, for accurate protein quantification from clinically relevant and disease-related samples shows great potential to comprehensively investigate biomarker candidates and understand disease.
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Affiliation(s)
- Jesse G Meyer
- a Mass Spectrometry Core , Buck Institute for Research on Aging , Novato , CA , USA
| | - Birgit Schilling
- a Mass Spectrometry Core , Buck Institute for Research on Aging , Novato , CA , USA
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