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Lee HJ, Seo Y, Park Y, Yi EC, Han D, Min H. Comprehensive immune cell spectral library for large-scale human primary T, B, and NK cell proteomics. Sci Data 2024; 11:871. [PMID: 39127789 PMCID: PMC11316730 DOI: 10.1038/s41597-024-03721-2] [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: 03/27/2024] [Accepted: 07/31/2024] [Indexed: 08/12/2024] Open
Abstract
Although proteomics is extensively used in immune research, there is currently no publicly accessible spectral assay library for the comprehensive proteome of immune cells. This study generated spectral assay libraries for five human immune cell lines and four primary immune cells: CD4 T, CD8 T, natural killer (NK) cells, and B cells. This was achieved by utilizing data-dependent acquisition (DDA) and employing fractionated samples from over 100 µg of proteins, which was applied to acquire the highest-quality MS/MS spectral data. In addition, Data-indedendent acquisition (DIA) was used to obtain sufficient data points for analyzing proteins from 10,000 primary CD4 T, CD8 T, NK, and B cells. The immune cell spectral assay library generated included 10,544 protein groups and 127,106 peptides. The proteomic profiles of 10,000 primary human immune cells obtained from 15 healthy volunteers analyzed using DIA revealed the highest heterogeneity of B cells among other immune cell types and the similarity between CD4 T and CD8 T cells. All data and spectral library are deposited in ProteomeXchange (PXD047742).
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Affiliation(s)
- Hyeon-Jeong Lee
- Doping Control Center, Korea Institute of Science and Technology (KIST), Seoul, 02792, Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 03080, Korea
| | - Yoondam Seo
- Doping Control Center, Korea Institute of Science and Technology (KIST), Seoul, 02792, Korea
| | - Yoon Park
- Chemical and Biological Integrative Research Center, Biomedical Research Institute, Korea Institute of Science and Technology (KIST), Seoul, 02792, Korea
| | - Eugene C Yi
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, 03080, Korea
| | - Dohyun Han
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul, 03080, Korea
| | - Hophil Min
- Doping Control Center, Korea Institute of Science and Technology (KIST), Seoul, 02792, Korea.
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2
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Humphries EM, Xavier D, Ashman K, Hains PG, Robinson PJ. High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH. J Proteome Res 2024; 23:2355-2366. [PMID: 38819404 DOI: 10.1021/acs.jproteome.4c00010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.
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Affiliation(s)
- Erin M Humphries
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
| | - Dylan Xavier
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
| | - Keith Ashman
- Sciex, 96 Ricketts Road,Mount Waverley, Victoria 3149, Australia
| | - Peter G Hains
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
| | - Phillip J Robinson
- ProCan®, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales 2145, Australia
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3
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Burns AR, Wiedrick J, Feryn A, Maes M, Midha MK, Baxter DH, Morrone SR, Prokop TJ, Kapil C, Hoopmann MR, Kusebauch U, Deutsch EW, Rappaport N, Watanabe K, Moritz RL, Miller RA, Lapidus JA, Orwoll ES. Proteomic changes induced by longevity-promoting interventions in mice. GeroScience 2024; 46:1543-1560. [PMID: 37653270 PMCID: PMC10828338 DOI: 10.1007/s11357-023-00917-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/20/2023] [Indexed: 09/02/2023] Open
Abstract
Using mouse models and high-throughput proteomics, we conducted an in-depth analysis of the proteome changes induced in response to seven interventions known to increase mouse lifespan. This included two genetic mutations, a growth hormone receptor knockout (GHRKO mice) and a mutation in the Pit-1 locus (Snell dwarf mice), four drug treatments (rapamycin, acarbose, canagliflozin, and 17α-estradiol), and caloric restriction. Each of the interventions studied induced variable changes in the concentrations of proteins across liver, kidney, and gastrocnemius muscle tissue samples, with the strongest responses in the liver and limited concordance in protein responses across tissues. To the extent that these interventions promote longevity through common biological mechanisms, we anticipated that proteins associated with longevity could be identified by characterizing shared responses across all or multiple interventions. Many of the proteome alterations induced by each intervention were distinct, potentially implicating a variety of biological pathways as being related to lifespan extension. While we found no protein that was affected similarly by every intervention, we identified a set of proteins that responded to multiple interventions. These proteins were functionally diverse but tended to be involved in peroxisomal oxidation and metabolism of fatty acids. These results provide candidate proteins and biological mechanisms related to enhancing longevity that can inform research on therapeutic approaches to promote healthy aging.
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Affiliation(s)
- Adam R Burns
- Biostatistics & Design Program, Oregon Health & Science University, Portland, OR, USA.
| | - Jack Wiedrick
- Biostatistics & Design Program, Oregon Health & Science University, Portland, OR, USA
| | - Alicia Feryn
- Biostatistics & Design Program, Oregon Health & Science University, Portland, OR, USA
| | - Michal Maes
- Institute for Systems Biology, Seattle, WA, USA
| | | | | | | | | | - Charu Kapil
- Institute for Systems Biology, Seattle, WA, USA
| | | | | | | | | | | | | | - Richard A Miller
- Department of Pathology and Geriatrics Center, University of Michigan School of Medicine, Ann Arbor, MI, USA
| | - Jodi A Lapidus
- School of Public Health, Oregon Health & Science University-Portland State University, Portland, OR, USA
| | - Eric S Orwoll
- Department of Endocrinology, Oregon Health & Science University, Portland, OR, USA
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4
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Yang Y, Fang Q. Prediction of glycopeptide fragment mass spectra by deep learning. Nat Commun 2024; 15:2448. [PMID: 38503734 PMCID: PMC10951270 DOI: 10.1038/s41467-024-46771-1] [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: 09/13/2023] [Accepted: 03/11/2024] [Indexed: 03/21/2024] Open
Abstract
Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.
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Affiliation(s)
- Yi Yang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
| | - Qun Fang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
- Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
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5
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Kotimoole CN, Ramya VK, Kaur P, Reiling N, Shandil RK, Narayanan S, Flo TH, Prasad TSK. Discovery of Species-Specific Proteotypic Peptides To Establish a Spectral Library Platform for Identification of Nontuberculosis Mycobacteria from Mass Spectrometry-Based Proteomics. J Proteome Res 2024; 23:1102-1117. [PMID: 38358903 DOI: 10.1021/acs.jproteome.3c00850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Nontuberculous mycobacteria are opportunistic bacteria pulmonary and extra-pulmonary infections in humans that closely resemble Mycobacterium tuberculosis. Although genome sequencing strategies helped determine NTMs, a common assay for the detection of coinfection by multiple NTMs with M. tuberculosis in the primary attempt of diagnosis is still elusive. Such a lack of efficiency leads to delayed therapy, an inappropriate choice of drugs, drug resistance, disease complications, morbidity, and mortality. Although a high-resolution LC-MS/MS-based multiprotein panel assay can be developed due to its specificity and sensitivity, it needs a library of species-specific peptides as a platform. Toward this, we performed an analysis of proteomes of 9 NTM species with more than 20 million peptide spectrum matches gathered from 26 proteome data sets. Our metaproteomic analyses determined 48,172 species-specific proteotypic peptides across 9 NTMs. Notably, M. smegmatis (26,008), M. abscessus (12,442), M. vaccae (6487), M. fortuitum (1623), M. avium subsp. paratuberculosis (844), M. avium subsp. hominissuis (580), and M. marinum (112) displayed >100 species-specific proteotypic peptides. Finally, these peptides and corresponding spectra have been compiled into a spectral library, FASTA, and JSON formats for future reference and validation in clinical cohorts by the biomedical community for further translation.
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Affiliation(s)
- Chinmaya Narayana Kotimoole
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India
| | - Vadageri Krishnamurthy Ramya
- Foundation for Neglected Disease Research, 20A, KIADB Industrial Area, Veerapura Village, Doddaballapur, Bengaluru 561203, India
| | - Parvinder Kaur
- Foundation for Neglected Disease Research, 20A, KIADB Industrial Area, Veerapura Village, Doddaballapur, Bengaluru 561203, India
| | - Norbert Reiling
- Microbial Interface Biology, Research Center Borstel, Leibniz Lung Center, Parkallee 22, D-23845 Borstel, Germany
- German Center for Infection Research (DZIF), Site Hamburg-Lübeck-Borstel-Riems, 23845 Borstel, Germany
| | - Radha Krishan Shandil
- Foundation for Neglected Disease Research, 20A, KIADB Industrial Area, Veerapura Village, Doddaballapur, Bengaluru 561203, India
| | - Shridhar Narayanan
- Foundation for Neglected Disease Research, 20A, KIADB Industrial Area, Veerapura Village, Doddaballapur, Bengaluru 561203, India
| | - Trude Helen Flo
- Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Kunnskapssenteret, Øya 424.04.035, Norway
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6
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Babele P, Midha MK, Rao KVS, Kumar A. Temporal Profiling of Host Proteome against Different M. tuberculosis Strains Reveals Delayed Epigenetic Orchestration. Microorganisms 2023; 11:2998. [PMID: 38138142 PMCID: PMC10745383 DOI: 10.3390/microorganisms11122998] [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: 08/31/2023] [Revised: 10/26/2023] [Accepted: 10/30/2023] [Indexed: 12/24/2023] Open
Abstract
Apart from being preventable and treatable, tuberculosis is the deadliest bacterial disease afflicting humankind owing to its ability to evade host defence responses, many of which are controlled by epigenetic mechanisms. Here, we report the temporal dynamics of the proteome of macrophage-like host cells after infecting them for 6, 18, 30, and 42 h with two laboratory strains (H37Ra and H37Rv) and two clinical strains (BND433 and JAL2287) of Mycobacterium tuberculosis (MTB). Using SWATH-MS, the proteins characterized at the onset of infection broadly represented oxidative stress and cell cytoskeleton processes. Intermediary and later stages of infection are accompanied by a reshaping of the combination of proteins implicated in histone stability, gene expression, and protein trafficking. This study provides strain-specific and time-specific variations in the proteome of the host, which might further the development of host-directed therapeutics and diagnostic tools against the pathogen. Also, our findings accentuate the importance of proteomic tools in delineating the complex recalibration of the host defence enabled as an effect of MTB infection. To the best of our knowledge, this is the first comprehensive proteomic account of the host response to avirulent and virulent strains of MTB at different time periods of the life span of macrophage-like cells. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE repository with the dataset identifier PXD022352.
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Affiliation(s)
- Prabhakar Babele
- Translational Health Science and Technology Institute, Faridabad 121001, India; (P.B.); (K.V.S.R.)
| | | | - Kanury V. S. Rao
- Translational Health Science and Technology Institute, Faridabad 121001, India; (P.B.); (K.V.S.R.)
| | - Ajay Kumar
- Translational Health Science and Technology Institute, Faridabad 121001, India; (P.B.); (K.V.S.R.)
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7
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Kitata RB, Yang JC, Chen YJ. Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape. MASS SPECTROMETRY REVIEWS 2023; 42:2324-2348. [PMID: 35645145 DOI: 10.1002/mas.21781] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/17/2021] [Accepted: 01/21/2022] [Indexed: 06/15/2023]
Abstract
The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.
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Affiliation(s)
| | - Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, Taiwan
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8
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Kusebauch U, Lorenzetti APR, Campbell DS, Pan M, Shteynberg D, Kapil C, Midha MK, López García de Lomana A, Baliga NS, Moritz RL. A comprehensive spectral assay library to quantify the Halobacterium salinarum NRC-1 proteome by DIA/SWATH-MS. Sci Data 2023; 10:697. [PMID: 37833331 PMCID: PMC10575869 DOI: 10.1038/s41597-023-02590-5] [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: 06/23/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319).
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Affiliation(s)
- Ulrike Kusebauch
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | | | - David S Campbell
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Min Pan
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - David Shteynberg
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Charu Kapil
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Mukul K Midha
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Adrián López García de Lomana
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
| | - Nitin S Baliga
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
- Departments of Biology and Microbiology, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Lawrence Berkeley National Lab, Berkeley, CA, USA
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA.
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9
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Xue Z, Zhu T, Zhang F, Zhang C, Xiang N, Qian L, Yi X, Sun Y, Liu W, Cai X, Wang L, Dai X, Yue L, Li L, Pham TV, Piersma SR, Xiao Q, Luo M, Lu C, Zhu J, Zhao Y, Wang G, Xiao J, Liu T, Liu Z, He Y, Wu Q, Gong T, Zhu J, Zheng Z, Ye J, Li Y, Jimenez CR, A J, Guo T. DPHL v.2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins. PATTERNS (NEW YORK, N.Y.) 2023; 4:100792. [PMID: 37521047 PMCID: PMC10382975 DOI: 10.1016/j.patter.2023.100792] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 04/29/2023] [Accepted: 06/12/2023] [Indexed: 08/01/2023]
Abstract
A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v.1, a previous pan-human library built from 1,096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying of 10,943 proteins. Here, we generated DPHL v.2 from 1,608 DDA-MS data. The data included 586 DDA-MS data acquired from 18 tissue types, while 1,022 files were derived from DPHL v.1. DPHL v.2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v.2 to include semi-tryptic peptides and protein isoforms. DPHL v.2 was then applied to two colorectal cancer cohorts. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v.1. Our findings show that the increased human proteome coverage of DPHL v.2 provides larger pools of potential protein biomarkers.
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Affiliation(s)
- Zhangzhi Xue
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Tiansheng Zhu
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
- College of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou, Zhejiang 311300, China
| | - Fangfei Zhang
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Cheng Zhang
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Nan Xiang
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou 310024, China
| | - Liujia Qian
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Xiao Yi
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou 310024, China
| | - Yaoting Sun
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Wei Liu
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou 310024, China
| | - Xue Cai
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Linyan Wang
- Department of Ophthalmology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China
| | - Xizhe Dai
- Department of Ophthalmology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China
| | - Liang Yue
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Lu Li
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Thang V. Pham
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, 1011 Amsterdam, the Netherlands
| | - Sander R. Piersma
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, 1011 Amsterdam, the Netherlands
| | - Qi Xiao
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Meng Luo
- Songjiang Research Institute and Songjiang Hospital, Department of Anatomy and Physiology, College of Basic Medical Science, Shanghai Jiao Tong University School of Medicine, Shanghai 201600, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yongfu Zhao
- Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, Liaoning Province 116044, China
| | - Guangzhi Wang
- Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, Liaoning Province 116044, China
| | - Junhong Xiao
- Department of General Surgery, The Second Hospital of Dalian Medical University, Dalian, Liaoning Province 116044, China
| | - Tong Liu
- Harbin Medical University Cancer Hospital, Harbin, Heilongjiang Province 150081, China
| | - Zhiyu Liu
- Department of Urology, The Second Hospital of Dalian Medical University, No.467 Zhongshan Road, Dalian, Liaoning Province 116044, China
| | - Yi He
- Department of Urology, The Second Hospital of Dalian Medical University, No.467 Zhongshan Road, Dalian, Liaoning Province 116044, China
| | - Qijun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province 110000, China
| | - Tingting Gong
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning Province 110000, China
| | - Jianqin Zhu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang 310000, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310000, China
| | - Zhiguo Zheng
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang 310000, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang 310000, China
| | - Juan Ye
- Department of Ophthalmology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310000, China
| | - Yan Li
- Songjiang Research Institute and Songjiang Hospital, Department of Anatomy and Physiology, College of Basic Medical Science, Shanghai Jiao Tong University School of Medicine, Shanghai 201600, China
| | - Connie R. Jimenez
- OncoProteomics Laboratory, Department of Medical Oncology, VU University Medical Center, VU University, 1011 Amsterdam, the Netherlands
| | - Jun A
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
| | - Tiannan Guo
- iMarker Lab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province 310024, China
- Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou, Zhejiang 310030, China
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10
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Midha MK, Kapil C, Maes M, Baxter DH, Morrone SR, Prokop TJ, Moritz RL. Vacuum Insulated Probe Heated Electrospray Ionization Source Enhances Microflow Rate Chromatography Signals in the Bruker timsTOF Mass Spectrometer. J Proteome Res 2023; 22:2525-2537. [PMID: 37294184 PMCID: PMC11060334 DOI: 10.1021/acs.jproteome.3c00305] [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] [Indexed: 06/10/2023]
Abstract
By far the largest contribution to ion detectability in liquid chromatography-driven mass spectrometry-based proteomics is the efficient generation of peptide molecular ions by the electrospray source. To maximize the transfer of peptides from the liquid to gaseous phase and allow molecular ions to enter the mass spectrometer at microspray flow rates, an efficient electrospray process is required. Here we describe the superior performance of newly design vacuum insulated probe heated electrospray ionization (VIP-HESI) source coupled to a Bruker timsTOF PRO mass spectrometer operated in microspray mode. VIP-HESI significantly improves chromatography signals in comparison to electrospray ionization (ESI) and nanospray ionization using the captivespray (CS) source and provides increased protein detection with higher quantitative precision, enhancing reproducibility of sample injection amounts. Protein quantitation of human K562 lymphoblast samples displayed excellent chromatographic retention time reproducibility (<10% coefficient of variation (CV)) with no signal degradation over extended periods of time, and a mouse plasma proteome analysis identified 12% more plasma protein groups allowing large-scale analysis to proceed with confidence (1,267 proteins at 0.4% CV). We show that the Slice-PASEF VIP-HESI mode is sensitive in identifying low amounts of peptide without losing quantitative precision. We demonstrate that VIP-HESI coupled with microflow rate chromatography achieves a higher depth of coverage and run-to-run reproducibility for a broad range of proteomic applications. Data and spectral libraries are available via ProteomeXchange (PXD040497).
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Affiliation(s)
- Mukul K Midha
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Charu Kapil
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Michal Maes
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - David H Baxter
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Seamus R Morrone
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Timothy J Prokop
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
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11
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Ahn HS, Yeom J, Yu J, Oh Y, Hong J, Kim M, Kim K. Generating Detailed Spectral Libraries for Canine Proteomes Obtained from Serum and Urine. Sci Data 2023; 10:241. [PMID: 37105983 PMCID: PMC10140049 DOI: 10.1038/s41597-023-02139-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 04/05/2023] [Indexed: 04/29/2023] Open
Abstract
Domestic dogs (Canis lupus familiaris) are popular companion animals. Increase in medical expenses associated with them and demand for extending their lifespan in a healthy manner has created the need to develop new diagnostic technology. Companion dogs also serve as important animal models for non-clinical research as they can provide various biological phenotypes. Proteomics have been increasingly used on dogs and humans to identify novel biomarkers of various diseases. Despite the growing applications of proteomics in liquid biopsy in veterinary medicine, no publicly available spectral assay libraries have been created for the proteome of canine serum and urine. In this study, we generated spectral assay libraries for the two-representative liquid-biopsy samples using mid-pH fractionation that allows in-depth understanding of proteome coverage. The resultant canine serum and urine spectral assay libraries include 1,132 and 4,749 protein groups and 5,483 and 25,228 peptides, respectively. We built these complimentary accessible resources for proteomic biomarker discovery studies through ProteomeXchange with the identifier PXD034770.
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Affiliation(s)
- Hee-Sung Ahn
- Convergence Medicine Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea
- Clinical Proteomics Core Laboratory, Convergence Medicine Research Center, Asan Medical Center, Seoul, 05505, Republic of Korea
| | - Jeonghun Yeom
- Convergence Medicine Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea
- Prometabio Research Institute, Prometabio co., ltd., Gyeonggi-do, 12939, Republic of Korea
| | - Jiyoung Yu
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea
| | - Yumi Oh
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea
- Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - JeongYeon Hong
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea
- Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Minjung Kim
- Department of Research and Development, Mjbiogen, Seoul, 04788, Republic of Korea
| | - Kyunggon Kim
- Convergence Medicine Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea.
- Clinical Proteomics Core Laboratory, Convergence Medicine Research Center, Asan Medical Center, Seoul, 05505, Republic of Korea.
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea.
- Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea.
- Bio-Medical Institute of Technology, Asan Medical Center, Seoul, 05505, Republic of Korea.
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12
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Kotimoole C, Antil N, Kasaragod S, Behera S, Arvind A, Reiling N, Flo T, Prasad T. Development of a spectral library for the discovery of altered genomic events in Mycobacterium avium associated with virulence using mass spectrometry-based proteogenomic analysis. Mol Cell Proteomics 2023; 22:100533. [PMID: 36948415 PMCID: PMC10149365 DOI: 10.1016/j.mcpro.2023.100533] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 02/02/2023] [Accepted: 03/16/2023] [Indexed: 03/24/2023] Open
Abstract
Mycobacterium avium is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. M. avium has ∼4,500 predicted protein-coding regions on average, which can help discover several variants at the proteome level. Many of them are potentially associated with virulence; thus, identifying such proteins can be a helpful feature in developing panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of M. avium with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using i) a protein database for Mycobacterium tuberculosis, ii) a M. avium genome six-frame translated database, and iii) a variant protein database of M. avium. A search of mass spectrometry data against M. avium protein database resulted in identifying 2,954 proteins. Further, proteogenomic analyses aided in identifying 1,301 novel peptide sequences and correcting translation start sites for 15 proteins. Ultimately, we created a spectral library of M. avium proteins, including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the M. avium proteome. Thus, we present an M. avium spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.
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Affiliation(s)
- ChinmayaNarayana Kotimoole
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, India
| | - Neelam Antil
- Institute of Bioinformatics, International Technology Park, Bangalore, India
| | - Sandeep Kasaragod
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, India
| | - SantoshKumar Behera
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, India
| | - Anjana Arvind
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, 575018, India
| | - Norbert Reiling
- Microbial Interface Biology, Research Center Borstel, Leibniz Lung Center, Parkallee 22, D-23845 Borstel, Germany; German Center for Infection Research (DZIF), Site Hamburg-Lübeck-Borstel-Riems, 23845 Borstel, Germany
| | - TrudeHelen Flo
- Centre of Molecular Inflammation Research, Department of Clinical and Molecular Medicine Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Kunnskapssenteret, 424.04.035, Øya, Norway
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13
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Deutsch EW, Mendoza L, Shteynberg DD, Hoopmann MR, Sun Z, Eng JK, Moritz RL. Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite. J Proteome Res 2023; 22:615-624. [PMID: 36648445 PMCID: PMC10166710 DOI: 10.1021/acs.jproteome.2c00624] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Luis Mendoza
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | | | | | - Zhi Sun
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Jimmy K Eng
- Proteomics Resource, University of Washington, Seattle, Washington 98195, United States
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
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14
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Deutsch EW, Bandeira N, Perez-Riverol Y, Sharma V, Carver J, Mendoza L, Kundu DJ, Wang S, Bandla C, Kamatchinathan S, Hewapathirana S, Pullman B, Wertz J, Sun Z, Kawano S, Okuda S, Watanabe Y, MacLean B, MacCoss M, Zhu Y, Ishihama Y, Vizcaíno J. The ProteomeXchange consortium at 10 years: 2023 update. Nucleic Acids Res 2023; 51:D1539-D1548. [PMID: 36370099 PMCID: PMC9825490 DOI: 10.1093/nar/gkac1040] [Citation(s) in RCA: 221] [Impact Index Per Article: 221.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/20/2022] [Accepted: 10/23/2022] [Indexed: 11/13/2022] Open
Abstract
Mass spectrometry (MS) is by far the most used experimental approach in high-throughput proteomics. The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) was originally set up to standardize data submission and dissemination of public MS proteomics data. It is now 10 years since the initial data workflow was implemented. In this manuscript, we describe the main developments in PX since the previous update manuscript in Nucleic Acids Research was published in 2020. The six members of the Consortium are PRIDE, PeptideAtlas (including PASSEL), MassIVE, jPOST, iProX and Panorama Public. We report the current data submission statistics, showcasing that the number of datasets submitted to PX resources has continued to increase every year. As of June 2022, more than 34 233 datasets had been submitted to PX resources, and from those, 20 062 (58.6%) just in the last three years. We also report the development of the Universal Spectrum Identifiers and the improvements in capturing the experimental metadata annotations. In parallel, we highlight that data re-use activities of public datasets continue to increase, enabling connections between PX resources and other popular bioinformatics resources, novel research and also new data resources. Finally, we summarise the current state-of-the-art in data management practices for sensitive human (clinical) proteomics data.
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Affiliation(s)
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | | | - Jeremy J Carver
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Luis Mendoza
- Institute for Systems Biology, Seattle WA 98109, USA
| | - Deepti J Kundu
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Shengbo Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Chakradhar Bandla
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Selvakumar Kamatchinathan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Suresh Hewapathirana
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Benjamin S Pullman
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Julie Wertz
- Center for Computational Mass Spectrometry, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Dept. Computer Science and Engineering, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego (UCSD), La Jolla, CA 92093, USA
| | - Zhi Sun
- Institute for Systems Biology, Seattle WA 98109, USA
| | - Shin Kawano
- Faculty of Contemporary Society, Toyama University of International Studies, Toyama 930-1292, Japan
- Database Center for Life Science (DBCLS), Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Chiba 277-0871, Japan
- School of Frontier Engineering, Kitasato University, Sagamihara 252-0373, Japan
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | - Yu Watanabe
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | | | | | - Yunping Zhu
- Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yasushi Ishihama
- Graduate School of Pharmaceutical Sciences, Kyoto University, Kyoto 606-8501, Japan
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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15
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Wang M, Weng S, Li C, Jiang Y, Qian X, Xu P, Ying W. Proteomic overview of hepatocellular carcinoma cell lines and generation of the spectral library. Sci Data 2022; 9:732. [PMID: 36446815 PMCID: PMC9708666 DOI: 10.1038/s41597-022-01845-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/14/2022] [Indexed: 12/02/2022] Open
Abstract
Cell lines are extensively used tools, therefore a comprehensive proteomic overview of hepatocellular carcinoma (HCC) cell lines and an extensive spectral library for data independent acquisition (DIA) quantification are necessary. Here, we present the proteome of nine commonly used HCC cell lines covering 9,208 protein groups, and the HCC spectral library containing 253,921 precursors, 168,811 peptides and 10,098 protein groups. The proteomic overview reveals the heterogeneity between different cell lines, and the similarity in proliferation and metastasis characteristics and drug targets-expression with tumour tissues. The HCC spectral library generating consumed 108 hours' runtime for data dependent acquisition (DDA) of 48 runs, 24 hours' runtime for database searching by MaxQuant version 2.0.3.0, and 1 hour' runtime for processing by SpectronautTM version 15.2. The HCC spectral library supports quantification of 7,637 protein groups of triples 2-hour DIA analysis of HepG2 and discovering biological alteration. This study provides valuable resources for HCC cell lines and efficient DIA quantification on LC-Orbitrap platform, further help to explore the molecular mechanism and candidate therapeutic targets.
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Affiliation(s)
- Mingchao Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing institute of Lifeomics, Beijing, China
| | - Shuang Weng
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing institute of Lifeomics, Beijing, China
| | - Chaoying Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing institute of Lifeomics, Beijing, China
| | - Ying Jiang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing institute of Lifeomics, Beijing, China
| | - Xiaohong Qian
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing institute of Lifeomics, Beijing, China
| | - Ping Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing institute of Lifeomics, Beijing, China.
| | - Wantao Ying
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing institute of Lifeomics, Beijing, China.
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16
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Baseline proteomics characterisation of the emerging host biomanufacturing organism Halomonas bluephagenesis. Sci Data 2022; 9:492. [PMID: 35963929 PMCID: PMC9376085 DOI: 10.1038/s41597-022-01610-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 08/03/2022] [Indexed: 11/12/2022] Open
Abstract
Despite its greener credentials, biomanufacturing remains financially uncompetitive compared with the higher carbon emitting, hydrocarbon-based chemical industry. Replacing traditional chassis such as E. coli with novel robust organisms, are a route to cost reduction for biomanufacturing. Extremophile bacteria such as the halophilic Halomonas bluephagenesis TD01 exemplify this potential by thriving in environments inherently inimical to other organisms, so reducing sterilisation costs. Novel chassis are inevitably less well annotated than established organisms. Rapid characterisation along with community data sharing will facilitate adoption of such organisms for biomanufacturing. The data record comprises a newly sequenced genome for the organism and evidence via LC-MS based proteomics for expression of 1160 proteins (30% of the proteome) including baseline quantification of 1063 proteins (27% of the proteome), and a spectral library enabling re-use for targeted LC-MS proteomics assays. Protein data are annotated with KEGG Orthology, enabling rapid matching of quantitative data to pathways of interest to biomanufacturing. Measurement(s) | Genome • Proteomic Profile | Technology Type(s) | PacBio Sequel System • nanoflow liquid chromatography-electrospray ionisation mass spectrometry | Sample Characteristic - Organism | Halomonas sp. TD01 |
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17
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Heil LR, Fondrie WE, McGann CD, Federation AJ, Noble WS, MacCoss MJ, Keich U. Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data. J Proteome Res 2022; 21:1382-1391. [PMID: 35549345 DOI: 10.1021/acs.jproteome.1c00895] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.
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Affiliation(s)
- Lilian R Heil
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - William E Fondrie
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Christopher D McGann
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Alexander J Federation
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States.,Paul G. Allen School for Computer Science and Engineering, University of Washington, Seattle, Washington 98105, United States
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Uri Keich
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
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18
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Qin G, Zhang P, Sun M, Fu W, Cai C. Comprehensive spectral libraries for various rabbit eye tissue proteomes. Sci Data 2022; 9:111. [PMID: 35351915 PMCID: PMC8964796 DOI: 10.1038/s41597-022-01241-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/03/2022] [Indexed: 12/14/2022] Open
Abstract
Rabbits have been widely used for studying ocular physiology and pathology due to their relatively large eye size and similar structures with human eyes. Various rabbit ocular disease models, such as dry eye, age-related macular degeneration, and glaucoma, have been established. Despite the growing application of proteomics in vision research using rabbit ocular models, there is no spectral assay library for rabbit eye proteome publicly available. Here, we generated spectral assay libraries for rabbit eye compartments, including conjunctiva, cornea, iris, retina, sclera, vitreous humor, and tears using fractionated samples and ion mobility separation enabling deep proteome coverage. The rabbit eye spectral assay library includes 9,830 protein groups and 113,593 peptides. We present the data as a freely available community resource for proteomic studies in the vision field. Instrument data and spectral libraries are available via ProteomeXchange with identifier PXD031194. Measurement(s) | database type spectral library | Technology Type(s) | ion mobility spectrometry-mass spectrometry | Sample Characteristic - Organism | Oryctolagus cuniculus | Sample Characteristic - Environment | eye | Sample Characteristic - Location | United States of America |
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19
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Zhang K, Gong X, Wang Q, Tu P, Li J, Song Y. Rapid tryptic peptide mapping of human serum albumin using DI-MS/MS ALL. RSC Adv 2022; 12:9868-9882. [PMID: 35424948 PMCID: PMC8963265 DOI: 10.1039/d1ra08717g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/13/2022] [Indexed: 11/27/2022] Open
Abstract
In recent decades, proteinic drugs, in particular monoclonal antibodies, are taking the leading role of small molecule drugs, and peptide mapping relying on liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an emerging approach to substitute the role of a ligand-binding assay for the quality control of the proteinic drugs. However, such LC-MS/MS approaches extensively suffer from time-intensive measurements, leading to a limited throughput. To achieve accelerated measurements, here, the potential of DI-MS/MSALL towards tryptic peptide mapping was evaluated through comparing with well-defined LC-MS/MS means, and human serum albumin (HSA) was employed as the representative protein for applicability illustration. Among the 55 tryptic peptides theoretically suggested by Skyline software, 47 were successfully captured by DI-MS/MSALL through acquiring the desired MS2 spectra, in comparison to 51 detected by LC-MS/MS. DI-MS/MSALL measurements merely took 5 min, which was dramatically superior to the LC-MS/MS assay. Noteworthily, different from fruitful multi-charged MS1 signals for LC-MS/MS, most quasi-molecular ions received lower charged states. DI-MS/MSALL also possessed advantages such as lower solvent consumption and facile instrumentation; however, more sample was consumed. In conclusion, DI-MS/MSALL is eligible to act as an alternative analytical tool for LC-MS/MS towards the peptide mapping of proteinic drugs, particularly when a heavy measurement workload. DI-MS/MSALL records MS2 spectrum at each 1 Da mass window through gas phase ion fractionation theory, and is eligible to act as an alternative analytical tool for LC-MS/MS towards the peptide mapping of proteinic drugs.![]()
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Affiliation(s)
- Ke Zhang
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 100029 China
| | - Xingcheng Gong
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 100029 China
| | - Qian Wang
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 100029 China
| | - Pengfei Tu
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 100029 China
| | - Jun Li
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 100029 China
| | - Yuelin Song
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 100029 China
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20
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Sun R, Lyu M, Liang S, Ge W, Wang Y, Ding X, Zhang C, Zhou Y, Chen S, Chen L, Guo T. A prostate cancer tissue specific spectral library for targeted proteomic analysis. Proteomics 2021; 22:e2100147. [PMID: 34799972 DOI: 10.1002/pmic.202100147] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 10/19/2021] [Accepted: 11/03/2021] [Indexed: 11/08/2022]
Abstract
Prostate cancer is the most common cancer in males worldwide. Mass spectrometry-based targeted proteomics has demonstrated great potential in quantifying proteins from formalin-fixed paraffin-embedded (FFPE) and (fresh) frozen biopsy tissues. Here we provide a comprehensive tissue-specific spectral library for targeted proteomic analysis of prostate tissue samples. Benign and malignant FFPE prostate tissue samples were processed into peptide samples by pressure cycling technology (PCT)-assisted sample preparation, and fractionated with high-pH reversed phase liquid chromatography (RPLC). Based on data-dependent acquisition (DDA) MS analysis using a TripleTOF 6600, we built a library containing 108,533 precursors, 84,198 peptides and 9384 unique proteins (1% FDR). The applicability of the library was demonstrated in prostate specimens.
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Affiliation(s)
- Rui Sun
- Zhejiang University, Hangzhou, Zhejiang, China.,Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Mengge Lyu
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Shuang Liang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, China
| | - Yingrui Wang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Xuan Ding
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Cheng Zhang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Yan Zhou
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
| | - Shanjun Chen
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., Hangzhou, China
| | - Lirong Chen
- Department of Pathology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Tiannan Guo
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.,Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China
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21
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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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22
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Li T, Zhou Z, Zhang K, Ma W, Chen W, Tu P, Li J, Song Q, Song Y. Direct infusion-tandem mass spectrometry combining with data mining strategies enables rapid chemome characterization of medicinal plants: A case study of Polygala tenuifolia. J Pharm Biomed Anal 2021; 204:114281. [PMID: 34333452 DOI: 10.1016/j.jpba.2021.114281] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/22/2021] [Accepted: 07/22/2021] [Indexed: 12/20/2022]
Abstract
Data-independent MS2 spectrum acquisition after fragmenting the precursor ion cohort with 1 Da bin, termed as MS/MSALL ®, offers an opportunity to achieve rapid chemome characterization when being coupled with direct infusion (DI). Some post-acquisition data processing strategies, such as mass defect filtering (MDF), diagnostic fragment ion filtering (DFIF), and neutral loss filtering (NLF), facilitate data extraction from massive dataset, and moreover, molecular weight (MW) imprinting allows rapid capturing those reported components. Here, DI-MS/MSALL ® was employed to acquire cubic spectral dataset, and the strategies such as MW imprinting, MDF, DFIF, and NLF, were subsequently applied to filter the structural information. The integrated pipeline was utilized for the chemome characterization of Polygala tenuifolia, a famous edible medicinal plant. To aid information filtering, an in-house chemical library was built by comprehensively collecting structural information from some available databases. A single analytical run was completed within 5 min. For MS1 spectrum processing, MW imprinting was firstly applied to capture the compounds in the chemical library, and "five-point" MDF frames were employed to pursue saponins, oligosaccharide esters, and xanthones. Regarding MS2 spectral plot, DFIF and NLF were deployed to search information-of-interest. Structural identification was accomplished by carefully correlating precursor ions and MS2 spectra, applying the well-defined mass cracking rules, and referring to literature information as well as available databases. A total of 109 compounds, mainly saponins (40 ones), oligosaccharide esters (29 ones), and xanthones (19 ones), were captured and structurally annotated. MS1 spectra were also implemented for chemome comparison between Polygala tenuifolia and several similar plants belonging to Polygala genus, resulting in the observation of significant inter- and intra-species differences. Above all, DI-MS/MSALL ® is a promising choice for high-throughput chemome profiling of, but not limited to, medicinal plants, in particular when being integrated with post-acquisition data processing strategies.
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Affiliation(s)
- Ting Li
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Zhizi Zhou
- Department of Genetics and Endocrinology, Guangzhou Women and Children's Medical Center, Guangzhou, 510000, China
| | - Ke Zhang
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Wen Ma
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China
| | - Wei Chen
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Pengfei Tu
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Jun Li
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Qingqing Song
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
| | - Yuelin Song
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
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23
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Su M, Zhang Z, Zhou L, Han C, Huang C, Nice EC. Proteomics, Personalized Medicine and Cancer. Cancers (Basel) 2021; 13:2512. [PMID: 34063807 PMCID: PMC8196570 DOI: 10.3390/cancers13112512] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 05/12/2021] [Accepted: 05/17/2021] [Indexed: 02/05/2023] Open
Abstract
As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.
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Affiliation(s)
- Miao Su
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, China; (M.S.); (Z.Z.); (L.Z.); (C.H.)
| | - Zhe Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, China; (M.S.); (Z.Z.); (L.Z.); (C.H.)
| | - Li Zhou
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, China; (M.S.); (Z.Z.); (L.Z.); (C.H.)
| | - Chao Han
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, China; (M.S.); (Z.Z.); (L.Z.); (C.H.)
| | - Canhua Huang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, and West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, China; (M.S.); (Z.Z.); (L.Z.); (C.H.)
| | - Edouard C. Nice
- Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia
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24
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Zhang P, Jiang J, Zhang K, Liu W, Tu P, Li J, Song Y, Zheng J, Tang L. Shotgun chemome characterization of Artemisia rupestris L. Using direct infusion-MS/MS ALL. J Chromatogr B Analyt Technol Biomed Life Sci 2021; 1176:122735. [PMID: 34020402 DOI: 10.1016/j.jchromb.2021.122735] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 04/02/2021] [Accepted: 04/21/2021] [Indexed: 11/26/2022]
Abstract
In comparison of liquid chromatography, direct infusion is a superior choice to achieve high-throughput measurements. The specificity and selectivity of tandem mass spectrometry (MS/MS) actually result in a so-called MS separation potential when chemical characterization of herbal medicines. Here, a MS/MSALL program was introduced to promote DI-MS/MS to be an eligible tool for shotgun chemome characterization of Artemisia rupestris L. that is currently drawing worldwide interests because of the promising antiviral activity. After MS1 spectral acquisition for the crude extract, the gas phase fractionation concept enabled the precursor ion cohort sequentially entered the collision cell with a stepped unit mass window (step-size as 1 Da) to generate MS2 spectra, thus generating a unique property integrating the advantages of both data-dependent and data-independent acquisition manners. Even though being free of chromatographic separation, spectrometric separations were accomplished for by MS/MSALL program unless the components shared identical nominal molecular weights. Extensive efforts such as the correlations of MS1 signals with MS2 spectra, structural annotations of fragment ion species, information retrieval in some accessible databases, and referring to the literature data, were devoted for chemical characterization, and as a result, 44 compounds, in total, were structurally identified from 50% aqueous methanol exact of A. rupestris, including 8 caffeoyl quinic acid derivatives, 13 flavonoids, 15 monomeric and dimeric sesquiterpenoids, 4 fatty acids, 2 penylpropanoids, along with 2 other compounds. However, isomers were assigned as an isomeric mixture because their precursor ions always co-existed in a single mass window. Above all, DI-MS/MSALL provides an alternative tool for chemome characterization of herbal medicines, in particular when the great measurement workload for a large sample cohort, attributing to the high-throughput advantage.
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Affiliation(s)
- Peijie Zhang
- Key Laboratory of Ethnomedicine (Minzu University of China) Ministry of Education, School of Pharmacy, Minzu University of China, Beijing 100081, China; Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Jun Jiang
- Shandong Institute for Food and Drug Control, Jinan 250101, China
| | - Ke Zhang
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Wenjing Liu
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Pengfei Tu
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Jun Li
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yuelin Song
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Jiao Zheng
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China.
| | - Li Tang
- Key Laboratory of Ethnomedicine (Minzu University of China) Ministry of Education, School of Pharmacy, Minzu University of China, Beijing 100081, China.
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25
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Kumar A, Midha MK, Rao KV. THP1 proteomics in response to mycobacterium tuberculosis infection. Data Brief 2021; 35:106803. [PMID: 33659582 PMCID: PMC7892794 DOI: 10.1016/j.dib.2021.106803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 01/22/2021] [Accepted: 01/25/2021] [Indexed: 12/01/2022] Open
Abstract
Temporal data on how the mycobacterium infection establishes itself inside the host cell is not available. We differentiated human THP1 cell line with PMA and infected them with different laboratory (H37Ra and H37Rv) and clinical strains (BND433 and JAL2287) of mycobacterium tuberculosis (Mtb). Uninfected differentiated THP1 cells were used as infection control. Host proteome was investigated at four different time points to understand the dynamics of host response to mycobacterial infection with time. The investigated time points included 6 hrs, 18 hrs, 30 hrs and 42 hrs of infection with all the Mtb strains. SWATH-MS method was used to quantitate the host proteome in response to Mtb infection and the data thus obtained are available via PRIDE repository with the dataset identifier PXD022352 (https://www.ebi.ac.uk/pride/archive/projects/PXD022352).
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Affiliation(s)
- Ajay Kumar
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India
| | - Mukul K Midha
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Kanury Vs Rao
- Translational Health Science and Technology Institute, NCR Biotech Science Cluster, Faridabad, Haryana, India
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26
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Weerakoon H, Potriquet J, Shah AK, Reed S, Jayakody B, Kapil C, Midha MK, Moritz RL, Lepletier A, Mulvenna J, Miles JJ, Hill MM. A primary human T-cell spectral library to facilitate large scale quantitative T-cell proteomics. Sci Data 2020; 7:412. [PMID: 33230158 PMCID: PMC7683684 DOI: 10.1038/s41597-020-00744-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/15/2020] [Indexed: 12/23/2022] Open
Abstract
Data independent analysis (DIA) exemplified by sequential window acquisition of all theoretical mass spectra (SWATH-MS) provides robust quantitative proteomics data, but the lack of a public primary human T-cell spectral library is a current resource gap. Here, we report the generation of a high-quality spectral library containing data for 4,833 distinct proteins from human T-cells across genetically unrelated donors, covering ~24% proteins of the UniProt/SwissProt reviewed human proteome. SWATH-MS analysis of 18 primary T-cell samples using the new human T-cell spectral library reliably identified and quantified 2,850 proteins at 1% false discovery rate (FDR). In comparison, the larger Pan-human spectral library identified and quantified 2,794 T-cell proteins in the same dataset. As the libraries identified an overlapping set of proteins, combining the two libraries resulted in quantification of 4,078 human T-cell proteins. Collectively, this large data archive will be a useful public resource for human T-cell proteomic studies. The human T-cell library is available at SWATHAtlas and the data are available via ProteomeXchange (PXD019446 and PXD019542) and PeptideAtlas (PASS01587).
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Affiliation(s)
- Harshi Weerakoon
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD, 4006, Australia
- School of Biomedical Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
- Faculty of Medicine and Allied Sciences, Rajarata University of Sri Lanka, Saliyapura, 50000, Sri Lanka
| | - Jeremy Potriquet
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD, 4006, Australia
- SCIEX Australia Pty Ltd, Mt Waverley, VIC, 3149, Australia
| | - Alok K Shah
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD, 4006, Australia
- CSL Limited, 45 Poplar Rd, Parkville, VIC, 3052, Australia
| | - Sarah Reed
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4006, Australia
| | - Buddhika Jayakody
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4006, Australia
| | - Charu Kapil
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Mukul K Midha
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | | | - Ailin Lepletier
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD, 4006, Australia
- Institute for Glycomics, Griffith University, Gold Coast, QLD, 4222, Australia
| | - Jason Mulvenna
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD, 4006, Australia
| | - John J Miles
- Australian Institute of Tropical Health and Medicine, James Cook University, Cairns, QLD, 4878, Australia.
- Centre for Molecular Therapeutics, James Cook University, Cairns, QLD, 4878, Australia.
- Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, QLD, 4878, Australia.
| | - Michelle M Hill
- QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD, 4006, Australia.
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4006, Australia.
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27
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Midha MK, Kusebauch U, Shteynberg D, Kapil C, Bader SL, Reddy PJ, Campbell DS, Baliga NS, Moritz RL. A comprehensive spectral assay library to quantify the Escherichia coli proteome by DIA/SWATH-MS. Sci Data 2020; 7:389. [PMID: 33184295 PMCID: PMC7665006 DOI: 10.1038/s41597-020-00724-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 10/05/2020] [Indexed: 02/06/2023] Open
Abstract
Data-Independent Acquisition (DIA) is a method to improve consistent identification and precise quantitation of peptides and proteins by mass spectrometry (MS). The targeted data analysis strategy in DIA relies on spectral assay libraries that are generally derived from a priori measurements of peptides for each species. Although Escherichia coli (E. coli) is among the best studied model organisms, so far there is no spectral assay library for the bacterium publicly available. Here, we generated a spectral assay library for 4,014 of the 4,389 annotated E. coli proteins using one- and two-dimensional fractionated samples, and ion mobility separation enabling deep proteome coverage. We demonstrate the utility of this high-quality library with robustness in quantitation of the E. coli proteome and with rapid-chromatography to enhance throughput by targeted DIA-MS. The spectral assay library supports the detection and quantification of 91.5% of all E. coli proteins at high-confidence with 56,182 proteotypic peptides, making it a valuable resource for the scientific community. Data and spectral libraries are available via ProteomeXchange (PXD020761, PXD020785) and SWATHAtlas (SAL00222-28).
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Affiliation(s)
- Mukul K Midha
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Ulrike Kusebauch
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - David Shteynberg
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Charu Kapil
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Samuel L Bader
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | | | - David S Campbell
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
| | - Nitin S Baliga
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA
- Departments of Biology and Microbiology, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
- Lawrence Berkeley National Lab, Berkeley, CA, USA
| | - Robert L Moritz
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, USA.
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