1
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Jiang Y, Rex DA, Schuster D, Neely BA, Rosano GL, Volkmar N, Momenzadeh A, Peters-Clarke TM, Egbert SB, Kreimer S, Doud EH, Crook OM, Yadav AK, Vanuopadath M, Hegeman AD, Mayta M, Duboff AG, Riley NM, Moritz RL, Meyer JG. Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:338-417. [PMID: 39193565 PMCID: PMC11348894 DOI: 10.1021/acsmeasuresciau.3c00068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 05/03/2024] [Accepted: 05/03/2024] [Indexed: 08/29/2024]
Abstract
Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.
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Affiliation(s)
- Yuming Jiang
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Devasahayam Arokia
Balaya Rex
- Center for
Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India
| | - Dina Schuster
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
- Department
of Biology, Institute of Molecular Biology
and Biophysics, ETH Zurich, Zurich 8093, Switzerland
- Laboratory
of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, Villigen 5232, Switzerland
| | - Benjamin A. Neely
- Chemical
Sciences Division, National Institute of
Standards and Technology, NIST, Charleston, South Carolina 29412, United States
| | - Germán L. Rosano
- Mass
Spectrometry
Unit, Institute of Molecular and Cellular
Biology of Rosario, Rosario, 2000 Argentina
| | - Norbert Volkmar
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
| | - Amanda Momenzadeh
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Trenton M. Peters-Clarke
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California, 94158, United States
| | - Susan B. Egbert
- Department
of Chemistry, University of Manitoba, Winnipeg, Manitoba, R3T 2N2 Canada
| | - Simion Kreimer
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Emma H. Doud
- Center
for Proteome Analysis, Indiana University
School of Medicine, Indianapolis, Indiana, 46202-3082, United States
| | - Oliver M. Crook
- Oxford
Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United
Kingdom
| | - Amit Kumar Yadav
- Translational
Health Science and Technology Institute, NCR Biotech Science Cluster 3rd Milestone Faridabad-Gurgaon
Expressway, Faridabad, Haryana 121001, India
| | | | - Adrian D. Hegeman
- Departments
of Horticultural Science and Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota 55108, United States
| | - Martín
L. Mayta
- School
of Medicine and Health Sciences, Center for Health Sciences Research, Universidad Adventista del Plata, Libertador San Martin 3103, Argentina
- Molecular
Biology Department, School of Pharmacy and Biochemistry, Universidad Nacional de Rosario, Rosario 2000, Argentina
| | - Anna G. Duboff
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Nicholas M. Riley
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Robert L. Moritz
- Institute
for Systems biology, Seattle, Washington 98109, United States
| | - Jesse G. Meyer
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
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2
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Harrison A, Eder JG, Lalli PM, Munoz N, Gao Y, Clendinen CS, Orton DJ, Zheng X, Williams SM, Couvillion SP, Chu RK, Balasubramanian VK, Bhattacharjee A, Anderton CR, Pomraning KR, Burnum-Johnson KE, Liu T, Kyle JE, Bilbao A. PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 39013167 DOI: 10.1021/jasms.4c00146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.
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Affiliation(s)
- Andrea Harrison
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Josie G Eder
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Priscila M Lalli
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Nathalie Munoz
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- US Department of Energy Agile BioFoundry, Emeryville, California 94608, United States
| | - Yuqian Gao
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- US Department of Energy Agile BioFoundry, Emeryville, California 94608, United States
| | - Chaevien S Clendinen
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Daniel J Orton
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Xueyun Zheng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sneha P Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Rosalie K Chu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Vimal K Balasubramanian
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Arunima Bhattacharjee
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Christopher R Anderton
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Kyle R Pomraning
- Energy Processes & Materials Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- US Department of Energy Agile BioFoundry, Emeryville, California 94608, United States
| | - Kristin E Burnum-Johnson
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- US Department of Energy Agile BioFoundry, Emeryville, California 94608, United States
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Jennifer E Kyle
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Aivett Bilbao
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
- US Department of Energy Agile BioFoundry, Emeryville, California 94608, United States
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3
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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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4
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Assress H, Ferruzzi MG, Lan RS. Optimization of Mass Spectrometric Parameters in Data Dependent Acquisition for Untargeted Metabolomics on the Basis of Putative Assignments. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:1621-1631. [PMID: 37419493 PMCID: PMC10402710 DOI: 10.1021/jasms.3c00084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/09/2023]
Abstract
Optimization of mass spectrometric parameters for a data dependent acquisition (DDA) experiment is essential to increase the MS/MS coverage and hence increase metabolite identifications in untargeted metabolomics. We explored the influence of mass spectrometric parameters including mass resolution, radio frequency (RF) level, signal intensity threshold, number of MS/MS events, cycle time, collision energy, maximum ion injection time (MIT), dynamic exclusion, and automatic gain control (AGC) target value on metabolite annotations on an Exploris 480-Orbitrap mass spectrometer. Optimal annotation results were obtained by performing ten data dependent MS/MS scans with a mass isolation window of 2.0 m/z and a minimum signal intensity threshold of 1 × 104 at a mass resolution of 180,000 for MS and 30,000 for MS/MS, while maintaining the RF level at 70%. Furthermore, combining an AGC target value of 5 × 106 and MIT of 100 ms for MS and an AGC target value of 1 × 105 and an MIT of 50 ms for MS/MS scans provided an improved number of annotated metabolites. A 10 s exclusion duration and a two stepped collision energy were optimal for higher spectral quality. These findings confirm that MS parameters do influence metabolomics results, and propose strategies for increasing metabolite coverage in untargeted metabolomics. A limitation of this work is that our parameters were only optimized for one RPLC method on single matrix and may be different for other protocols. Additionally, no metabolites were identified at level 1 confidence. The results presented here are based on metabolite annotations and need to be validated with authentic standards.
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Affiliation(s)
- Hailemariam
Abrha Assress
- Arkansas
Children’s Nutrition Center, Little Rock, Arkansas 72202, United States
- Department
of Pediatrics, University of Arkansas for
Medical Sciences, Little
Rock, Arkansas 72205, United States
| | - Mario G. Ferruzzi
- Arkansas
Children’s Nutrition Center, Little Rock, Arkansas 72202, United States
- Department
of Pediatrics, University of Arkansas for
Medical Sciences, Little
Rock, Arkansas 72205, United States
| | - Renny S. Lan
- Arkansas
Children’s Nutrition Center, Little Rock, Arkansas 72202, United States
- Department
of Pediatrics, University of Arkansas for
Medical Sciences, Little
Rock, Arkansas 72205, United States
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5
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Bowser BL, Robinson RAS. Enhanced Multiplexing Technology for Proteomics. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2023; 16:379-400. [PMID: 36854207 DOI: 10.1146/annurev-anchem-091622-092353] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The identification of thousands of proteins and their relative levels of expression has furthered understanding of biological processes and disease and stimulated new systems biology hypotheses. Quantitative proteomics workflows that rely on analytical assays such as mass spectrometry have facilitated high-throughput measurements of proteins partially due to multiplexing. Multiplexing allows proteome differences across multiple samples to be measured simultaneously, resulting in more accurate quantitation, increased statistical robustness, reduced analysis times, and lower experimental costs. The number of samples that can be multiplexed has evolved from as few as two to more than 50, with studies involving more than 10 samples being denoted as enhanced multiplexing or hyperplexing. In this review, we give an update on emerging multiplexing proteomics techniques and highlight advantages and limitations for enhanced multiplexing strategies.
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Affiliation(s)
- Bailey L Bowser
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA;
| | - Renã A S Robinson
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA;
- Department of Neurology, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Memory and Alzheimer's Center, Nashville, Tennessee, USA
- Vanderbilt Institute of Chemical Biology, Vanderbilt School of Medicine, Nashville, Tennessee, USA
- Vanderbilt Brain Institute, Vanderbilt School of Medicine, Nashville, Tennessee, USA
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6
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Lombard-Banek C, Pohl KI, Kwee EJ, Elliott JT, Schiel JE. A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies. J Proteome Res 2022; 21:1229-1239. [PMID: 35404046 PMCID: PMC9087334 DOI: 10.1021/acs.jproteome.1c00887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Indexed: 11/29/2022]
Abstract
Mass spectrometry (MS)-based proteomic measurements are uniquely poised to impact the development of cell and gene therapies. With the adoption of rigorous instrumental performance qualifications (PQs), large-scale proteomics can move from a research to a manufacturing control tool. Especially suited, data-independent acquisition (DIA) approaches have distinctive qualities to extend multiattribute method (MAM) principles to characterize the proteome of cell therapies. Here, we describe the development of a DIA method for the sensitive identification and quantification of proteins on a Q-TOF instrument. Using the improved acquisition parameters, we defined a control strategy and highlighted some metrics to improve the reproducibility of SWATH acquisition-based proteomic measurements. Finally, we applied the method to analyze the proteome of Jurkat cells that here serves as a model for human T-cells. Raw and processed data were deposited in PRIDE (PXD029780).
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Affiliation(s)
- Camille Lombard-Banek
- National
Institute of Standards and Technology, Material and Measurements Laboratory, Gaithersburg, Maryland 20899, United States
- Institute
for Bioscience and Bioengineering Research, Rockville, Maryland 20850, United States
| | | | - Edward J. Kwee
- National
Institute of Standards and Technology, Material and Measurements Laboratory, Gaithersburg, Maryland 20899, United States
| | - John T. Elliott
- National
Institute of Standards and Technology, Material and Measurements Laboratory, Gaithersburg, Maryland 20899, United States
| | - John E. Schiel
- National
Institute of Standards and Technology, Material and Measurements Laboratory, Gaithersburg, Maryland 20899, United States
- Institute
for Bioscience and Bioengineering Research, Rockville, Maryland 20850, United States
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7
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Dong Y, Kazachkova Y, Gou M, Morgan L, Wachsman T, Gazit E, Birkler RID. RawHummus: an R Shiny app for automated raw data quality control in metabolomics. Bioinformatics 2022; 38:2072-2074. [PMID: 35080628 DOI: 10.1093/bioinformatics/btac040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 01/16/2022] [Accepted: 01/24/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Robust and reproducible data is essential to ensure high-quality analytical results and is particularly important for large-scale metabolomics studies where detector sensitivity drifts, retention time and mass accuracy shifts frequently occur. Therefore, raw data need to be inspected before data processing to detect measurement bias and verify system consistency. RESULTS Here, we present RawHummus, an R Shiny app for an automated raw data quality control (QC) in metabolomics studies. It produces a comprehensive QC report, which contains interactive plots and tables, summary statistics and detailed explanations. The versatility and limitations of RawHummus are tested with 13 metabolomics/lipidomics datasets and 1 proteomics dataset obtained from 5 different liquid chromatography mass spectrometry platforms. AVAILABILITY AND IMPLEMENTATION RawHummus is released on CRAN repository (https://cran.r-project.org/web/packages/RawHummus), with source code being available on GitHub (https://github.com/YonghuiDong/RawHummus). The web application can be executed locally from the R console using the command 'runGui()'. Alternatively, it can be freely accessed at https://bcdd.shinyapps.io/RawHummus/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yonghui Dong
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
| | - Yana Kazachkova
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Meng Gou
- College of Life Science, Liaoning Normal University, Dalian 116081, China
| | - Liat Morgan
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tal Wachsman
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ehud Gazit
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
| | - Rune Isak Dupont Birkler
- Metabolite Medicine Division, BLAVATNIK CENTER for Drug Discovery, Tel Aviv University, Tel Aviv 69978, Israel
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8
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Kovalchik KA, Ma Q, Wessling L, Saab F, Despault J, Kubiniok P, Hamelin DJ, Faridi P, Li C, Purcell AW, Jang A, Paramithiotis E, Tognetti M, Reiter L, Bruderer R, Lanoix J, Bonneil É, Courcelles M, Thibault P, Caron E, Sirois I. MhcVizPipe: A Quality Control Software for Rapid Assessment of Small- to Large-Scale Immunopeptidome Data Sets. Mol Cell Proteomics 2021; 21:100178. [PMID: 34798331 PMCID: PMC8717601 DOI: 10.1016/j.mcpro.2021.100178] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 12/12/2022] Open
Abstract
Mass spectrometry (MS)-based immunopeptidomics is maturing into an automatized, high-throughput technology, producing small- to large-scale datasets of clinically relevant MHC class I- and II-associated peptides. Consequently, the development of quality control (QC) and quality assurance (QA) systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semi-automated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition and MHC-specificity to greatly accelerate the 'pass-fail' QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community.
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Affiliation(s)
| | - Qing Ma
- School of Electrical Engineering and Computer Science, Faculty of Engineering, University of Ottawa, ON K1N 6N5, Canada
| | - Laura Wessling
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Frederic Saab
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Jérôme Despault
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Peter Kubiniok
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - David J Hamelin
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada
| | - Pouya Faridi
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Chen Li
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anthony W Purcell
- Infection and Immunity Program and Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria 3800, Australia
| | - Anne Jang
- CellCarta, Montreal, QC H2X 3Y7, Canada
| | | | | | - Lukas Reiter
- Biognosys, Wagistrasse 21, 8952 Schlieren, Switzerland
| | | | - Joël Lanoix
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Éric Bonneil
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Mathieu Courcelles
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada
| | - Pierre Thibault
- Institute of Research in Immunology and Cancer, Montreal, QC H3T 1J4, Canada; Department of Chemistry, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Etienne Caron
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada; Department of Pathology and Cellular Biology, Faculty of Medicine, Université de Montréal, QC H3T 1J4, Canada.
| | - Isabelle Sirois
- CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada.
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9
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McIlvin MR, Saito MA. Online Nanoflow Two-Dimension Comprehensive Active Modulation Reversed Phase-Reversed Phase Liquid Chromatography High-Resolution Mass Spectrometry for Metaproteomics of Environmental and Microbiome Samples. J Proteome Res 2021; 20:4589-4597. [PMID: 34384028 DOI: 10.1021/acs.jproteome.1c00588] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Metaproteomics is a powerful analytical approach that can assess the functional capabilities deployed by microbial communities in both environmental and biomedical microbiome settings. Yet, the mass spectra resulting from these mixed biological communities are challenging to obtain due to the high number of low intensity peak features. The use of multiple dimensions of chromatographic separation prior to mass spectrometry analyses has been applied to proteomics previously but can require increased sampling handling and instrument time. Here, we demonstrate an automated online comprehensive active modulation two-dimensional liquid chromatography method for metaproteome sample analysis. A high pH PLRP-S column was used in the first dimension followed by low pH separation in the second dimension using dual modulating C18 traps and a C18 column. This method increased the number of unique peptides found in ocean metaproteome samples by more than 50% when compared to a one-dimension separation while using the same amount of sample and instrument time.
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Affiliation(s)
- Matthew R McIlvin
- Marine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02563, United States
| | - Mak A Saito
- Marine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02563, United States
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