1
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Lancaster NM, Sinitcyn P, Forny P, Peters-Clarke TM, Fecher C, Smith AJ, Shishkova E, Arrey TN, Pashkova A, Robinson ML, Arp N, Fan J, Hansen J, Galmozzi A, Serrano LR, Rojas J, Gasch AP, Westphall MS, Stewart H, Hock C, Damoc E, Pagliarini DJ, Zabrouskov V, Coon JJ. Fast and deep phosphoproteome analysis with the Orbitrap Astral mass spectrometer. Nat Commun 2024; 15:7016. [PMID: 39147754 PMCID: PMC11327265 DOI: 10.1038/s41467-024-51274-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 08/02/2024] [Indexed: 08/17/2024] Open
Abstract
Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method, we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology is benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We apply this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detect 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of phosphorylation events relevant to mitochondrial and brain biology.
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Affiliation(s)
- Noah M Lancaster
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Patrick Forny
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Caroline Fecher
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew J Smith
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Evgenia Shishkova
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
- National Center for Quantitative Biology of Complex Systems, Madison, WI, USA
| | | | - Anna Pashkova
- Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany
| | - Margaret Lea Robinson
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Nicholas Arp
- Morgridge Institute for Research, Madison, WI, USA
- Cellular and Molecular Biology Graduate Program, University of Wisconsin-Madison, Madison, WI, USA
| | - Jing Fan
- Morgridge Institute for Research, Madison, WI, USA
- Cellular and Molecular Biology Graduate Program, University of Wisconsin-Madison, Madison, WI, USA
- Department of Nutritional Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Juli Hansen
- Department of Medicine, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA
| | - Andrea Galmozzi
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medicine, University of Wisconsin-Madison, School of Medicine and Public Health, Madison, WI, USA
- University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Lia R Serrano
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
| | - Julie Rojas
- Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI, USA
| | - Audrey P Gasch
- Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI, USA
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, USA
| | - Michael S Westphall
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA
- National Center for Quantitative Biology of Complex Systems, Madison, WI, USA
| | | | | | - Eugen Damoc
- Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany
| | - David J Pagliarini
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, MO, USA
- Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Joshua J Coon
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biomolecular Chemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Morgridge Institute for Research, Madison, WI, USA.
- National Center for Quantitative Biology of Complex Systems, Madison, WI, USA.
- Center for Genomic Science Innovation, University of Wisconsin-Madison, Madison, WI, USA.
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2
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Oliinyk D, Will A, Schneidmadel FR, Böhme M, Rinke J, Hochhaus A, Ernst T, Hahn N, Geis C, Lubeck M, Raether O, Humphrey SJ, Meier F. µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics. Mol Syst Biol 2024; 20:972-995. [PMID: 38907068 PMCID: PMC11297287 DOI: 10.1038/s44320-024-00050-9] [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: 08/25/2023] [Revised: 06/06/2024] [Accepted: 06/11/2024] [Indexed: 06/23/2024] Open
Abstract
Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos ('microPhos'), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in <8 h total processing time. By greatly minimizing transfer steps and liquid volumes, we demonstrate increased sensitivity, >90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation.
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Affiliation(s)
- Denys Oliinyk
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
| | - Andreas Will
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
| | - Felix R Schneidmadel
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
| | - Maximilian Böhme
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Jenny Rinke
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Andreas Hochhaus
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Thomas Ernst
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany
- Klinik für Innere Medizin II, Jena University Hospital, 07747, Jena, Germany
| | - Nina Hahn
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, 07747, Jena, Germany
| | - Christian Geis
- Section of Translational Neuroimmunology, Department of Neurology, Jena University Hospital, 07747, Jena, Germany
- Center for Sepsis Control and Care, Jena University Hospital, 07747, Jena, Germany
| | - Markus Lubeck
- Bruker Daltonics GmbH & Co. KG, 28359, Bremen, Germany
| | | | - Sean J Humphrey
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, 3052, Victoria, Australia.
| | - Florian Meier
- Functional Proteomics, Jena University Hospital, 07747, Jena, Germany.
- Comprehensive Cancer Center Central Germany, 07747, Jena, Germany.
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3
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Lapcik P, Synkova K, Janacova L, Bouchalova P, Potesil D, Nenutil R, Bouchal P. A hybrid DDA/DIA-PASEF based assay library for a deep proteotyping of triple-negative breast cancer. Sci Data 2024; 11:794. [PMID: 39025866 PMCID: PMC11258311 DOI: 10.1038/s41597-024-03632-2] [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: 02/14/2024] [Accepted: 07/10/2024] [Indexed: 07/20/2024] Open
Abstract
Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and deeper proteome coverage is needed for its molecular characterization. We present comprehensive library of targeted mass spectrometry assays specific for TNBC and demonstrate its applicability. Proteins were extracted from 105 TNBC tissues and digested. Aliquots were pooled, fractionated using hydrophilic chromatography and analyzed by LC-MS/MS in data-dependent acquisition (DDA) parallel accumulation-serial fragmentation (PASEF) mode on timsTOF Pro LC-MS system. 16 individual lysates were analyzed in data-independent acquisition (DIA)-PASEF mode. Hybrid library was generated in Spectronaut software and covers 244,464 precursors, 168,006 peptides and 11,564 protein groups (FDR = 1%). Application of our library for pilot quantitative analysis of 16 tissues increased identification numbers in Spectronaut 18.5 and DIA-NN 1.8.1 software compared to library-free setting, with Spectronaut achieving the best results represented by 190,310 precursors, 140,566 peptides, and 10,463 protein groups. In conclusion, we introduce assay library that offers the deepest coverage of TNBC proteome to date. The TNBC library is available via PRIDE repository (PXD047793).
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Grants
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- NU22-08-00230 Ministerstvo Zdravotnictví Ceské Republiky (Ministry of Health of the Czech Republic)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LX22NPO5102 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- CZ.02.1.01/0.0/0.0/18_046/0015974 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
- LM2023033 Ministerstvo Školství, Mládeže a Tělovýchovy (Ministry of Education, Youth and Sports)
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Affiliation(s)
- Petr Lapcik
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Klara Synkova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Lucia Janacova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Pavla Bouchalova
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - David Potesil
- Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Rudolf Nenutil
- Department of Oncological Pathology, Masaryk Memorial Cancer Institute, Brno, Czech Republic
| | - Pavel Bouchal
- Department of Biochemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.
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4
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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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5
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Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [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] [Received: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
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Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
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6
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Pham TV, Henneman AA, Truong NX, Jimenez CR. msproteomics sitereport: reporting DIA-MS phosphoproteomics experiments at site level with ease. Bioinformatics 2024; 40:btae432. [PMID: 38944032 PMCID: PMC11239223 DOI: 10.1093/bioinformatics/btae432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 06/07/2024] [Accepted: 06/28/2024] [Indexed: 07/01/2024] Open
Abstract
SUMMARY Identification and quantification of phosphorylation sites are essential for biological interpretation of a phosphoproteomics experiment. For data independent acquisition mass spectrometry-based (DIA-MS) phosphoproteomics, extracting a site-level report from the output of current processing software is not straightforward as multiple peptides might contribute to a single site, multiple phosphorylation sites can occur on the same peptides, and protein isoforms complicate site specification. Currently only limited support is available from a commercial software package via a platform-specific solution with a rather simple site quantification method. Here, we present sitereport, a software tool implemented in an extendable Python package called msproteomics to report phosphosites and phosphopeptides from a DIA-MS phosphoproteomics experiment with a proven quantification method called MaxLFQ. We demonstrate the use of sitereport for downstream data analysis at site level, allowing benchmarking different DIA-MS processing software tools. AVAILABILITY AND IMPLEMENTATION sitereport is available as a command line tool in the Python package msproteomics, released under the Apache License 2.0 and available from the Python Package Index (PyPI) at https://pypi.org/project/msproteomics and GitHub at https://github.com/tvpham/msproteomics.
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Affiliation(s)
- Thang V Pham
- Amsterdam UMC location Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Medical Oncology, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Alex A Henneman
- Amsterdam UMC location Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Medical Oncology, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
| | - Nam X Truong
- Thuyloi University, Faculty of Computer Science and Engineering, 175 Tay Son, Hanoi, Vietnam
| | - Connie R Jimenez
- Amsterdam UMC location Vrije Universiteit Amsterdam, OncoProteomics Laboratory, Medical Oncology, De Boelelaan 1117, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, the Netherlands
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7
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He G, He Q, Cheng J, Yu R, Shuai J, Cao Y. ProPept-MT: A Multi-Task Learning Model for Peptide Feature Prediction. Int J Mol Sci 2024; 25:7237. [PMID: 39000344 PMCID: PMC11241495 DOI: 10.3390/ijms25137237] [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: 05/28/2024] [Revised: 06/26/2024] [Accepted: 06/28/2024] [Indexed: 07/16/2024] Open
Abstract
In the realm of quantitative proteomics, data-independent acquisition (DIA) has emerged as a promising approach, offering enhanced reproducibility and quantitative accuracy compared to traditional data-dependent acquisition (DDA) methods. However, the analysis of DIA data is currently hindered by its reliance on project-specific spectral libraries derived from DDA analyses, which not only limits proteome coverage but also proves to be a time-intensive process. To overcome these challenges, we propose ProPept-MT, a novel deep learning-based multi-task prediction model designed to accurately forecast key features such as retention time (RT), ion intensity, and ion mobility (IM). Leveraging advanced techniques such as multi-head attention and BiLSTM for feature extraction, coupled with Nash-MTL for gradient coordination, ProPept-MT demonstrates superior prediction performance. Integrating ion mobility alongside RT, mass-to-charge ratio (m/z), and ion intensity forms 4D proteomics. Then, we outline a comprehensive workflow tailored for 4D DIA proteomics research, integrating the use of 4D in silico libraries predicted by ProPept-MT. Evaluation on a benchmark dataset showcases ProPept-MT's exceptional predictive capabilities, with impressive results including a 99.9% Pearson correlation coefficient (PCC) for RT prediction, a median dot product (DP) of 96.0% for fragment ion intensity prediction, and a 99.3% PCC for IM prediction on the test set. Notably, ProPept-MT manifests efficacy in predicting both unmodified and phosphorylated peptides, underscoring its potential as a valuable tool for constructing high-quality 4D DIA in silico libraries.
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Affiliation(s)
- Guoqiang He
- Postgraduate Training Base Alliance, Wenzhou Medical University, Wenzhou 325000, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, China
| | - Qingzu He
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China
| | - Jinyan Cheng
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, China
| | - Rongwen Yu
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, China
| | - Jianwei Shuai
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, China
| | - Yi Cao
- Postgraduate Training Base Alliance, Wenzhou Medical University, Wenzhou 325000, China
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325000, China
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8
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Fröhlich K, Fahrner M, Brombacher E, Seredynska A, Maldacker M, Kreutz C, Schmidt A, Schilling O. Data-independent acquisition: A milestone and prospect in clinical mass spectrometry-based proteomics. Mol Cell Proteomics 2024:100800. [PMID: 38880244 DOI: 10.1016/j.mcpro.2024.100800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 06/08/2024] [Accepted: 06/13/2024] [Indexed: 06/18/2024] Open
Abstract
Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given mass-to-charge range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.
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Affiliation(s)
- Klemens Fröhlich
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
| | - Eva Brombacher
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Germany; Spemann Graduate School of Biology and Medicine (SGBM), University of Freiburg, Germany; Faculty of Biology, University of Freiburg, Germany
| | - Adrianna Seredynska
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany; Faculty of Biology, University of Freiburg, Germany
| | - Maximilian Maldacker
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; Faculty of Biology, University of Freiburg, Germany
| | - Clemens Kreutz
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center-University of Freiburg, Germany; Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Germany
| | - Alexander Schmidt
- Proteomics Core Facility, Biozentrum Basel, University of Basel, Basel, Switzerland
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK) and Cancer Research Center (DKFZ), Freiburg, Germany
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9
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Wen B, Freestone J, Riffle M, MacCoss MJ, Noble WS, Keich U. Assessment of false discovery rate control in tandem mass spectrometry analysis using entrapment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.01.596967. [PMID: 38895431 PMCID: PMC11185562 DOI: 10.1101/2024.06.01.596967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
A pressing statistical challenge in the field of mass spectrometry proteomics is how to assess whether a given software tool provides accurate error control. Each software tool for searching such data uses its own internally implemented methodology for reporting and controlling the error. Many of these software tools are closed source, with incompletely documented methodology, and the strategies for validating the error are inconsistent across tools. In this work, we identify three different methods for validating false discovery rate (FDR) control in use in the field, one of which is invalid, one of which can only provide a lower bound rather than an upper bound, and one of which is valid but under-powered. The result is that the field has a very poor understanding of how well we are doing with respect to FDR control, particularly for the analysis of data-independent acquisition (DIA) data. We therefore propose a new, more powerful method for evaluating FDR control in this setting, and we then employ that method, along with an existing lower bounding technique, to characterize a variety of popular search tools. We find that the search tools for analysis of data-dependent acquisition (DDA) data generally seem to control the FDR at the peptide level, whereas none of the DIA search tools consistently controls the FDR at the peptide level across all the datasets we investigated. Furthermore, this problem becomes much worse when the latter tools are evaluated at the protein level. These results may have significant implications for various downstream analyses, since proper FDR control has the potential to reduce noise in discovery lists and thereby boost statistical power.
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Affiliation(s)
- Bo Wen
- Department of Genome Sciences, University of Washington
| | - Jack Freestone
- School of Mathematics and Statistics, University of Sydney
| | | | | | - William S Noble
- Department of Genome Sciences, University of Washington
- Paul G. Allen School of Computer Science and Engineering, University of Washington
| | - Uri Keich
- School of Mathematics and Statistics, University of Sydney
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10
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Li S, Luo H, Tang P, Tian C, Hu J, Lu H, Shui W. Generation of a Deep Mouse Brain Spectral Library for Transmembrane Proteome Profiling in Mental Disease Models. Mol Cell Proteomics 2024; 23:100777. [PMID: 38670310 PMCID: PMC11137342 DOI: 10.1016/j.mcpro.2024.100777] [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] [Received: 11/28/2023] [Revised: 04/21/2024] [Accepted: 04/23/2024] [Indexed: 04/28/2024] Open
Abstract
Transmembrane (TM) proteins constitute over 30% of the mammalian proteome and play essential roles in mediating cell-cell communication, synaptic transmission, and plasticity in the central nervous system. Many of these proteins, especially the G protein-coupled receptors (GPCRs), are validated or candidate drug targets for therapeutic development for mental diseases, yet their expression profiles are underrepresented in most global proteomic studies. Herein, we establish a brain TM protein-enriched spectral library based on 136 data-dependent acquisition runs acquired from various brain regions of both naïve mice and mental disease models. This spectral library comprises 3043 TM proteins including 171 GPCRs, 231 ion channels, and 598 transporters. Leveraging this library, we analyzed the data-independent acquisition data from different brain regions of two mouse models exhibiting depression- or anxiety-like behaviors. By integrating multiple informatics workflows and library sources, our study significantly expanded the mental stress-perturbed TM proteome landscape, from which a new GPCR regulator of depression was verified by in vivo pharmacological testing. In summary, we provide a high-quality mouse brain TM protein spectral library to largely increase the TM proteome coverage in specific brain regions, which would catalyze the discovery of new potential drug targets for the treatment of mental disorders.
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Affiliation(s)
- Shanshan Li
- Institutes of Biomedical Sciences and Department of Chemistry, Fudan University, Shanghai, China; iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Huoqing Luo
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China; Department of Anesthesiology & Perioperative Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Pan Tang
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China; University of Chinese Academy of Sciences, Beijing, China
| | - Cuiping Tian
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Ji Hu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
| | - Haojie Lu
- Institutes of Biomedical Sciences and Department of Chemistry, Fudan University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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11
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Padhye BD, Nawaz U, Hains PG, Reddel RR, Robinson PJ, Zhong Q, Poulos RC. Proteomic insights into paediatric cancer: Unravelling molecular signatures and therapeutic opportunities. Pediatr Blood Cancer 2024; 71:e30980. [PMID: 38556739 DOI: 10.1002/pbc.30980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 04/02/2024]
Abstract
Survival rates in some paediatric cancers have improved greatly over recent decades, in part due to the identification of diagnostic, prognostic and predictive molecular signatures, and the development of risk-directed therapies. However, other paediatric cancers have proved difficult to treat, and there is an urgent need to identify novel biomarkers that reveal therapeutic opportunities. The proteome is the total set of expressed proteins present in a cell or tissue at a point in time, and is vastly more dynamic than the genome. Proteomics holds significant promise for cancer research, as proteins are ultimately responsible for cellular phenotype and are the target of most anticancer drugs. Here, we review the discoveries, opportunities and challenges of proteomic analyses in paediatric cancer, with a focus on mass spectrometry (MS)-based approaches. Accelerating incorporation of proteomics into paediatric precision medicine has the potential to improve survival and quality of life for children with cancer.
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Affiliation(s)
- Bhavna D Padhye
- Cancer Centre for Children, The Children's Hospital at Westmead, Westmead, New South Wales, Australia
- Kids Research, Children's Cancer Research Unit, The Children's Hospital at Westmead, Westmead, New South Wales, Australia
| | - Urwah Nawaz
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Peter G Hains
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Roger R Reddel
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Phillip J Robinson
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Qing Zhong
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
| | - Rebecca C Poulos
- ProCan, Children's Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, New South Wales, Australia
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12
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Lancaster NM, Sinitcyn P, Forny P, Peters-Clarke TM, Fecher C, Smith AJ, Shishkova E, Arrey TN, Pashkova A, Robinson ML, Arp N, Fan J, Hansen J, Galmozzi A, Serrano LR, Rojas J, Gasch AP, Westphall MS, Stewart H, Hock C, Damoc E, Pagliarini DJ, Zabrouskov V, Coon JJ. Fast and Deep Phosphoproteome Analysis with the Orbitrap Astral Mass Spectrometer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.21.568149. [PMID: 38045259 PMCID: PMC10690147 DOI: 10.1101/2023.11.21.568149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Owing to its roles in cellular signal transduction, protein phosphorylation plays critical roles in myriad cell processes. That said, detecting and quantifying protein phosphorylation has remained a challenge. We describe the use of a novel mass spectrometer (Orbitrap Astral) coupled with data-independent acquisition (DIA) to achieve rapid and deep analysis of human and mouse phosphoproteomes. With this method we map approximately 30,000 unique human phosphorylation sites within a half-hour of data collection. The technology was benchmarked to other state-of-the-art MS platforms using both synthetic peptide standards and with EGF-stimulated HeLa cells. We applied this approach to generate a phosphoproteome multi-tissue atlas of the mouse. Altogether, we detected 81,120 unique phosphorylation sites within 12 hours of measurement. With this unique dataset, we examine the sequence, structural, and kinase specificity context of protein phosphorylation. Finally, we highlight the discovery potential of this resource with multiple examples of novel phosphorylation events relevant to mitochondrial and brain biology.
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13
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Murfuni MS, Prestagiacomo LE, Giuliano A, Gabriele C, Signoretti S, Cuda G, Gaspari M. Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis. Int J Mol Sci 2024; 25:5141. [PMID: 38791181 PMCID: PMC11121386 DOI: 10.3390/ijms25105141] [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: 03/20/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/26/2024] Open
Abstract
The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (Human, Soybean and Pisum sativum) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95-0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349.
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14
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Peng H, Wang H, Kong W, Li J, Goh WWB. Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference. Nat Commun 2024; 15:3922. [PMID: 38724498 PMCID: PMC11082229 DOI: 10.1038/s41467-024-47899-w] [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: 06/19/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.
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Affiliation(s)
- Hui Peng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - He Wang
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Weijia Kong
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
| | - Jinyan Li
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Wilson Wen Bin Goh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
- Center for Biomedical Informatics, Nanyang Technological University, Singapore, Singapore.
- Center of AI in Medicine, Nanyang Technological University, Singapore, Singapore.
- Division of Neurology, Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
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15
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Kurgan N, Kjærgaard Larsen J, Deshmukh AS. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia 2024; 67:783-797. [PMID: 38345659 DOI: 10.1007/s00125-024-06097-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/20/2023] [Indexed: 03/21/2024]
Abstract
Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.
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Affiliation(s)
- Nigel Kurgan
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jeppe Kjærgaard Larsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Atul S Deshmukh
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
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16
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Basharat AR, Xiong X, Xu T, Zang Y, Sun L, Liu X. TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.05.588302. [PMID: 38645171 PMCID: PMC11030422 DOI: 10.1101/2024.04.05.588302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Top-down mass spectrometry is widely used for proteoform identification, characterization, and quantification owing to its ability to analyze intact proteoforms. In the last decade, top-down proteomics has been dominated by top-down data-dependent acquisition mass spectrometry (TD-DDA-MS), and top-down data-independent acquisition mass spectrometry (TD-DIA-MS) has not been well studied. While TD-DIA-MS produces complex multiplexed tandem mass spectrometry (MS/MS) spectra, which are challenging to confidently identify, it selects more precursor ions for MS/MS analysis and has the potential to increase proteoform identifications compared with TD-DDA-MS. Here we present TopDIA, the first software tool for proteoform identification by TD-DIA-MS. It generates demultiplexed pseudo MS/MS spectra from TD-DIA-MS data and then searches the pseudo MS/MS spectra against a protein sequence database for proteoform identification. We compared the performance of TD-DDA-MS and TD-DIA-MS using Escherichia coli K-12 MG1655 cells and demonstrated that TD-DIA-MS with TopDIA increased proteoform and protein identifications compared with TD-DDA-MS.
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Affiliation(s)
- Abdul Rehman Basharat
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA
| | - Xingzhao Xiong
- Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, 70112, USA
| | - Tian Xu
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824, USA
| | - Yong Zang
- Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, East Lansing, MI, 48824, USA
| | - Xiaowen Liu
- Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, 70112, USA
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17
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Koutsilieri S, Mickols E, Végvári Á, Lauschke VM. Proteomic workflows for deep phenotypic profiling of 3D organotypic liver models. Biotechnol J 2024; 19:e2300684. [PMID: 38509783 DOI: 10.1002/biot.202300684] [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: 12/03/2023] [Revised: 02/07/2024] [Accepted: 02/15/2024] [Indexed: 03/22/2024]
Abstract
Organotypic human tissue models constitute promising systems to facilitate drug discovery and development. They allow to maintain native cellular phenotypes and functions, which enables long-term pharmacokinetic and toxicity studies, as well as phenotypic screening. To trace relevant phenotypic changes back to specific targets or signaling pathways, comprehensive proteomic profiling is the gold-standard. A multitude of proteomic workflows have been applied on 3D tissue models to quantify their molecular phenotypes; however, their impact on analytical results and biological conclusions in this context has not been evaluated. The performance of twelve mass spectrometry-based global proteomic workflows that differed in the amount of cellular input, lysis protocols and quantification methods was compared for the analysis of primary human liver spheroids. Results differed majorly between protocols in the total number and subcellular compartment bias of identified proteins, which is particularly relevant for the reliable quantification of transporters and drug metabolizing enzymes. Using a model of metabolic dysfunction-associated steatotic liver disease, we furthermore show that critical disease pathways are robustly identified using a standardized high throughput-compatible workflow based on thermal lysis, even using only individual spheroids (1500 cells) as input. The results increase the applicability of proteomic profiling to phenotypic screens in organotypic microtissues and provide a scalable platform for deep phenotyping from limited biological material.
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Affiliation(s)
- Stefania Koutsilieri
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Evgeniya Mickols
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | - Ákos Végvári
- Division of Chemistry I, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Volker M Lauschke
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
- Dr Margarete Fischer-Bosch Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
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18
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [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] [Received: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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19
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Kardell O, von Toerne C, Merl-Pham J, König AC, Blindert M, Barth TK, Mergner J, Ludwig C, Tüshaus J, Eckert S, Müller SA, Breimann S, Giesbertz P, Bernhardt AM, Schweizer L, Albrecht V, Teupser D, Imhof A, Kuster B, Lichtenthaler SF, Mann M, Cox J, Hauck SM. Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens. J Proteome Res 2024; 23:117-129. [PMID: 38015820 PMCID: PMC10775142 DOI: 10.1021/acs.jproteome.3c00473] [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: 07/31/2023] [Revised: 11/02/2023] [Accepted: 11/14/2023] [Indexed: 11/30/2023]
Abstract
The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.
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Affiliation(s)
- Oliver Kardell
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Christine von Toerne
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Juliane Merl-Pham
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Ann-Christine König
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Marcel Blindert
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
| | - Teresa K. Barth
- Clinical
Protein Analysis Unit (ClinZfP), Biomedical Center (BMC), Faculty
of Medicine, Ludwig-Maximilians-University
(LMU) Munich, Großhaderner Straße 9, Martinsried 82152, Germany
| | - Julia Mergner
- Bavarian
Center for Biomolecular Mass Spectrometry at Klinikum Rechts der Isar
(BayBioMS@MRI), Technical University of
Munich, Munich 80333, Germany
| | - Christina Ludwig
- Bavarian
Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of
Life Sciences, Technical University of Munich, Freising 85354, Germany
| | - Johanna Tüshaus
- Chair
of Proteomics and Bioanalytics, Technical
University of Munich, Freising 85354, Germany
| | - Stephan Eckert
- Chair
of Proteomics and Bioanalytics, Technical
University of Munich, Freising 85354, Germany
| | - Stephan A. Müller
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
| | - Stephan Breimann
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
| | - Pieter Giesbertz
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
| | - Alexander M. Bernhardt
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Department
of Neurology, Ludwig-Maximilians-Universität
München, Munich 80539, Germany
| | - Lisa Schweizer
- Department
of Proteomics and Signal Transduction, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Vincent Albrecht
- Department
of Proteomics and Signal Transduction, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Daniel Teupser
- Institute
of Laboratory Medicine, University Hospital,
LMU Munich, Munich 81377, Germany
| | - Axel Imhof
- Clinical
Protein Analysis Unit (ClinZfP), Biomedical Center (BMC), Faculty
of Medicine, Ludwig-Maximilians-University
(LMU) Munich, Großhaderner Straße 9, Martinsried 82152, Germany
| | - Bernhard Kuster
- Bavarian
Center for Biomolecular Mass Spectrometry (BayBioMS), TUM School of
Life Sciences, Technical University of Munich, Freising 85354, Germany
- Chair
of Proteomics and Bioanalytics, Technical
University of Munich, Freising 85354, Germany
| | - Stefan F. Lichtenthaler
- German
Center
for Neurodegenerative Diseases (DZNE) Munich, DZNE, Munich 81377, Germany
- Neuroproteomics,
School of Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich 81675, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich 81377, Germany
| | - Matthias Mann
- Department
of Proteomics and Signal Transduction, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Jürgen Cox
- Computational Systems
Biochemistry Research Group, Max-Planck
Institute of Biochemistry, Martinsried 82152, Germany
| | - Stefanie M. Hauck
- Metabolomics
and Proteomics Core (MPC), Helmholtz Zentrum
München,German Research Center for Environmental Health (GmbH), Munich 80939, Germany
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20
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Low RRJ, Fung KY, Dagley LF, Yousef J, Emery-Corbin SJ, Putoczki TL. Unbiased Quantitative Proteomics of Organoid Models of Pancreatic Cancer. Methods Mol Biol 2024; 2823:77-93. [PMID: 39052215 DOI: 10.1007/978-1-0716-3922-1_6] [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: 07/27/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a lethal solid malignancy with many patients succumbing to the disease within 6 months of diagnosis. The mechanisms that underlie PDAC initiation and progression are poorly understood. Current treatment options are primarily limited to chemotherapy, which is often provided with palliative intent. Unfortunately, there are no robust biomarkers to guide treatment selection or monitor treatment response. This is concerning given the increasing incidence of this cancer. We and others have generated organoid models to explore the biology underlying PDAC with the goal of identifying new therapeutic targets. Here we provide protocols to generate a preclinical PDAC organoid model and methods to use these to define the proteomic landscape of this cancer.
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Affiliation(s)
- Ronnie Ren Jie Low
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
- Currently at the DSB Repair Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Ka Yee Fung
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Laura F Dagley
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Jumana Yousef
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Samantha J Emery-Corbin
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia
| | - Tracy L Putoczki
- The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
- Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia.
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21
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Bianco M, Ventura G, Calvano CD, Losito I, Cataldi TRI. Food allergen detection by mass spectrometry: From common to novel protein ingredients. Proteomics 2023; 23:e2200427. [PMID: 37691088 DOI: 10.1002/pmic.202200427] [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] [Received: 05/30/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 09/12/2023]
Abstract
Food allergens are molecules, mainly proteins, that trigger immune responses in susceptible individuals upon consumption even when they would otherwise be harmless. Symptoms of a food allergy can range from mild to acute; this last effect is a severe and potentially life-threatening reaction. The European Union (EU) has identified 14 common food allergens, but new allergens are likely to emerge with constantly changing food habits. Mass spectrometry (MS) is a promising alternative to traditional antibody-based assays for quantifying multiple allergenic proteins in complex matrices with high sensitivity and selectivity. Here, the main allergenic proteins and the advantages and drawbacks of some MS acquisition protocols, such as multiple reaction monitoring (MRM) and data-dependent analysis (DDA) for identifying and quantifying common allergenic proteins in processed foodstuffs are summarized. Sections dedicated to novel foods like microalgae and insects as new sources of allergenic proteins are included, emphasizing the significance of establishing stable marker peptides and validated methods using database searches. The discussion involves the in-silico digestion of allergenic proteins, providing insights into their potential impact on immunogenicity. Finally, case studies focussing on microalgae highlight the value of MS as an effective analytical tool for ensuring regulatory compliance throughout the food control chain.
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Affiliation(s)
- Mariachiara Bianco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Giovanni Ventura
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Centro interdipartimentale SMART, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Cosima D Calvano
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Centro interdipartimentale SMART, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Ilario Losito
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Centro interdipartimentale SMART, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Tommaso R I Cataldi
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Centro interdipartimentale SMART, Università degli Studi di Bari Aldo Moro, Bari, Italy
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22
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Zhang B, Bassani-Sternberg M. Current perspectives on mass spectrometry-based immunopeptidomics: the computational angle to tumor antigen discovery. J Immunother Cancer 2023; 11:e007073. [PMID: 37899131 PMCID: PMC10619091 DOI: 10.1136/jitc-2023-007073] [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] [Accepted: 07/21/2023] [Indexed: 10/31/2023] Open
Abstract
Identification of tumor antigens presented by the human leucocyte antigen (HLA) molecules is essential for the design of effective and safe cancer immunotherapies that rely on T cell recognition and killing of tumor cells. Mass spectrometry (MS)-based immunopeptidomics enables high-throughput, direct identification of HLA-bound peptides from a variety of cell lines, tumor tissues, and healthy tissues. It involves immunoaffinity purification of HLA complexes followed by MS profiling of the extracted peptides using data-dependent acquisition, data-independent acquisition, or targeted approaches. By incorporating DNA, RNA, and ribosome sequencing data into immunopeptidomics data analysis, the proteogenomic approach provides a powerful means for identifying tumor antigens encoded within the canonical open reading frames of annotated coding genes and non-canonical tumor antigens derived from presumably non-coding regions of our genome. We discuss emerging computational challenges in immunopeptidomics data analysis and tumor antigen identification, highlighting key considerations in the proteogenomics-based approach, including accurate DNA, RNA and ribosomal sequencing data analysis, careful incorporation of predicted novel protein sequences into reference protein database, special quality control in MS data analysis due to the expanded and heterogeneous search space, cancer-specificity determination, and immunogenicity prediction. The advancements in technology and computation is continually enabling us to identify tumor antigens with higher sensitivity and accuracy, paving the way toward the development of more effective cancer immunotherapies.
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Affiliation(s)
- Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, Texas, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Department of Oncology, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
- Agora Cancer Research Centre, Lausanne, Switzerland
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23
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Hao Y, Chen M, Huang X, Xu H, Wu P, Chen S. 4D-diaXLMS: Proteome-wide Four-Dimensional Data-Independent Acquisition Workflow for Cross-Linking Mass Spectrometry. Anal Chem 2023; 95:14077-14085. [PMID: 37691250 DOI: 10.1021/acs.analchem.3c02824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Cross-linking mass spectrometry (XL-MS) is a powerful tool for examining protein structures and interactions. Nevertheless, analysis of low-abundance cross-linked peptides is often limited in the data-dependent acquisition (DDA) mode due to its semistochastic nature. To address this issue, we introduced a workflow called 4D-diaXLMS, representing the first-ever application of four-dimensional data-independent acquisition for proteome-wide cross-linking analysis. Cross-linking studies of the HeLa cell proteome were evaluated using the classical cross-linker disuccinimidyl suberate as an example. Compared with the DDA analysis, 4D-diaXLMS exhibited marked improvement in the identification coverage of cross-linked peptides, with a total increase of 36% in single-shot analysis across all 16 SCX fractions. This advantage was further amplified when reducing the fraction number to 8 and 4, resulting in 125 and 149% improvements, respectively. Using 4D-diaXLMS, up to 83% of the cross-linked peptides were repeatedly identified in three replicates, more than twice the 38% in the DDA mode. Furthermore, 4D-diaXLMS showed good performance in the quantitative analysis of yeast cross-linked peptides even in a 15-fold excess amount of HeLa cell matrix, with a low coefficient of variation and high quantitative accuracies in all concentrations. Overall, 4D-diaXLMS was proven to have high coverage, good reproducibility, and accurate quantification for in-depth XL-MS analysis in complex samples, demonstrating its immense potential for advances in the field.
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Affiliation(s)
- Yanhong Hao
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Moran Chen
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Xiao Huang
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Hui Xu
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Pengfei Wu
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Suming Chen
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
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24
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Zhang F, Ge W, Huang L, Li D, Liu L, Dong Z, Xu L, Ding X, Zhang C, Sun Y, A J, Gao J, Guo T. A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry. Mol Cell Proteomics 2023; 22:100623. [PMID: 37481071 PMCID: PMC10458344 DOI: 10.1016/j.mcpro.2023.100623] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/12/2023] [Accepted: 07/18/2023] [Indexed: 07/24/2023] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.
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Affiliation(s)
- Fangfei Zhang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China.
| | - Weigang Ge
- Westlake Omics, Ltd, Hangzhou, Zhejiang Province, China
| | | | - Dan Li
- Westlake Omics, Ltd, Hangzhou, Zhejiang Province, China
| | - Lijuan Liu
- Westlake Omics, Ltd, Hangzhou, Zhejiang Province, China
| | - Zhen Dong
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Luang Xu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Xuan Ding
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Cheng Zhang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Yingying Sun
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Jun A
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Jinlong Gao
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
| | - Tiannan Guo
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang Province, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China.
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25
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George AL, Sidgwick FR, Watt JE, Martin MP, Trost M, Marín-Rubio JL, Dueñas ME. Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling. J Proteome Res 2023; 22:2629-2640. [PMID: 37439223 PMCID: PMC10407934 DOI: 10.1021/acs.jproteome.3c00111] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Indexed: 07/14/2023]
Abstract
Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMTs) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukemia cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable of TMT-DDA in their ability to detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labeled quantitation in the TPP pipeline.
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Affiliation(s)
- Amy L. George
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Frances R. Sidgwick
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Jessica E. Watt
- Newcastle
Cancer Centre, Northern Institute for Cancer Research, Medical School, Newcastle University, Paul O’Gorman Building, Framlington Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Mathew P. Martin
- Newcastle
Cancer Centre, Northern Institute for Cancer Research, Medical School, Newcastle University, Paul O’Gorman Building, Framlington Place, Newcastle upon Tyne NE2 4HH, U.K.
| | - Matthias Trost
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - José Luis Marín-Rubio
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
| | - Maria Emilia Dueñas
- Laboratory
for Biological Mass Spectrometry, Biosciences Institute, Newcastle University, Newcastle-upon-Tyne NE2 4HH, U.K.
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26
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Yu F, Teo GC, Kong AT, Fröhlich K, Li GX, Demichev V, Nesvizhskii AI. Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nat Commun 2023; 14:4154. [PMID: 37438352 PMCID: PMC10338508 DOI: 10.1038/s41467-023-39869-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/28/2023] [Indexed: 07/14/2023] Open
Abstract
Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.
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Affiliation(s)
- Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Andy T Kong
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Klemens Fröhlich
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Ginny Xiaohe Li
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Vadim Demichev
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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27
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Fan Z, Jia W. Lactobacillus casei-Derived Postbiotics Elevate the Bioaccessibility of Proteins via Allosteric Regulation of Pepsin and Trypsin and Introduction of Endopeptidases. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023. [PMID: 37410960 DOI: 10.1021/acs.jafc.3c02125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
The potential of probiotics to benefit digestion has been widely reported, while its utilization in high-risk patients and potential adverse reactions have focused interest on postbiotics. A variable data-independent acquisition (vDIA)-based spatial-omics strategy integrated with unsupervised variational autoencoders was applied to profile the functional mechanism underlying the action of Lactobacillus casei-derived postbiotic supplementation in goat milk digestion in an infant digestive system, from a metabolomics-peptidomics-proteomics perspective. Amide and olefin derivatives were proved to elevate the activities of pepsin and trypsin through hydrogen bonding and hydrophobic forces based on allosteric effects, and recognition of nine endopeptidases and their cleavage to serine, proline, and aspartate were introduced by postbiotics, thereby promoting the generation of hydrophilic peptides and elevating the bioaccessibility of goat milk protein. The peptides originating from αs1-casein, β-casein, β-lactoglobulin, Ig-like domain-containing protein, κ-casein, and serum amyloid A protein, with multiple bioactivities including angiotensin I-converting enzyme (ACE)-inhibitory, osteoanabolic, dipeptidyl peptidase IV (DPP-IV) inhibitory, antimicrobial, bradykinin-potentiating, antioxidant, and anti-inflammatory activities, were significantly increased in the postbiotic supplementation group, which was also considered to potentially prevent necrotizing enterocolitis through inhibiting the multiplication of pathogenic bacteria and blocking signal transducer and activator of transcription 1 and nuclear factor kappa-light-chain-enhancer of activated B cells inflammatory pathways. This research deepened the understanding of the mechanism underlying the postbiotics affecting goat milk digestion, which established a critical groundwork for the clinical application of postbiotics in infant complementary foods.
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Affiliation(s)
- Zibian Fan
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
| | - Wei Jia
- School of Food and Biological Engineering, Shaanxi University of Science & Technology, Xi'an 710021, China
- Shaanxi Research Institute of Agricultural Products Processing Technology, Xi'an 710021, China
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28
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Souza Junior DR, Silva ARM, Ronsein GE. Strategies for consistent and automated quantification of HDL proteome using data-independent acquisition (DIA). J Lipid Res 2023:100397. [PMID: 37286042 PMCID: PMC10339053 DOI: 10.1016/j.jlr.2023.100397] [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: 01/31/2023] [Revised: 05/11/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
The introduction of mass spectrometry-based proteomics has revolutionized HDL field, with the description, characterization and implication of HDL-associated proteins in an array of pathologies. However, acquiring robust, reproducible data is still a challenge in the quantitative assessment of HDL proteome. Data-independent acquisition (DIA) is a mass spectrometry methodology that allows the acquisition of reproducible data, but data analysis remains a challenge in the field. Up to date, there is no consensus in how to process DIA-derived data for HDL proteomics. Here, we developed a pipeline aiming to standardize HDL proteome quantification. We optimized instrument parameters, and compared the performance of four freely available, user-friendly software tools (DIA-NN, EncyclopeDIA, MaxDIA and Skyline) in processing DIA data. Importantly, pooled samples were used as quality controls throughout our experimental setup. A carefully evaluation of precision, linearity, and detection limits, first using E. coli background for HDL proteomics, and second using HDL proteome and synthetic peptides, was undertaken. Finally, as a proof of concept, we employed our optimized and automated pipeline to quantify the proteome of HDL and apolipoprotein B (APOB)-containing lipoproteins. Our results show that determination of precision is key to confidently and consistently quantify HDL proteins. Taking this precaution, any of the available software tested here would be appropriate for quantification of HDL proteome, although their performance varied considerably.
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Affiliation(s)
| | | | - Graziella Eliza Ronsein
- Department of Biochemistry, Institute of Chemistry, University of São Paulo, São Paulo, Brazil.
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29
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Wen C, Wu X, Lin G, Yan W, Gan G, Xu X, Chen XY, Chen X, Liu X, Fu G, Zhong CQ. Evaluation of DDA Library-Free Strategies for Phosphoproteomics and Ubiquitinomics Data-Independent Acquisition Data. J Proteome Res 2023. [PMID: 37256709 DOI: 10.1021/acs.jproteome.2c00735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Phosphoproteomics and ubiquitinomics data-independent acquisition (DIA) mass spectrometry (MS) data is typically analyzed by using a data-dependent acquisition (DDA) spectral library. The performance of various library-free strategies for analyzing phosphoproteomics and ubiquitinomics DIA MS data has not been evaluated. In this study, we systematically compare four commonly used DDA library-free approaches including Spectronaut's directDIA, DIA-Umpire, DIA-MSFragger, and in silico-predicted library for analysis of phosphoproteomics SWATH, DIA, and diaPASEF data as well as ubiquitinomics diaPASEF data. Spectronaut's directDIA shows the highest sensitivity for phosphopeptide detection not only in synthetic phosphopeptide samples but also in phosphoproteomics SWATH-MS and DIA data from real biological samples, when compared to the other three library-free strategies. For phosphoproteomics diaPASEF data, Spectronaut's directDIA and the in silico-predicted library based on DIA-NN identify almost the same number of phosphopeptides as a project-specific DDA spectral library. However, only about 30% of the total phosphopeptides are commonly identified, suggesting that the library-free strategies for phospho-diaPASEF data need further improvement in terms of sensitivity. For ubiquitinomics diaPASEF data, the in silico-predicted library performs the best among the four workflows and detects ∼50% more K-GG peptides than a project-specific DDA spectral library. Our results demonstrate that Spectronaut's directDIA is suitable for the analysis of phosphoproteomics SWATH-MS and DIA MS data, while the in silico-predicted library based on DIA-NN shows substantial advantages for ubiquitinomics diaPASEF MS data.
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Affiliation(s)
- Chengwen Wen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen 361005, Fujian, China
| | - Xiurong Wu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen 361005, Fujian, China
| | - Guanzhong Lin
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen 361005, Fujian, China
| | - Wei Yan
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen 361005, Fujian, China
| | - Guohong Gan
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen 361005, Fujian, China
| | - Xiao Xu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen 361005, Fujian, China
| | - Xiang-Yu Chen
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen 361005, Fujian, China
| | - Xi Chen
- SpecAlly Life Technology Co., Ltd., Wuhan 430074, Hubei, China
| | - Xianming Liu
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200030, China
| | - Guo Fu
- School of Medicine, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen 361005, Fujian, China
| | - Chuan-Qi Zhong
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen 361005, Fujian, China
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30
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Tabaei S, Haghshenas MR, Webster TJ, Ghaderi A. Proteomics strategies for urothelial bladder cancer diagnosis, prognosis and treatment: Trends for tumor biomarker sources. Anal Biochem 2023; 666:115074. [PMID: 36738874 DOI: 10.1016/j.ab.2023.115074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023]
Abstract
Urothelial bladder cancer (UBC) is a heterogeneous multifactorial malignancy with a high recurrence rate. Current procedures for UBC diagnosis suffering from the lack of clinical sensitivity and specificity screening tests. Therefore, biomarkers have promising values to predict pathological conditions and can be considered as effective targets for early diagnosis, prognosis and antitumor immunotherapy. Recently, researchers have been interested for tumor proteins as biomarkers for different diseases. At present, proteomics methods have rapidly progressive that has potential identified biomarkers of UBC. Specifically, there has been several studies on the potential application of proteomics for the identification, quantification, and profiling of proteins for UBC in different sources. Based on these studies, using the panel of biomarkers as proteomic patterns may achieve higher sensitivity and specificity than single proteins in the diagnosis of UBC. In the present review, we evaluate recent literature related to the UBC proteome focusing especially on new proteomics techniques. Moreover, we classify UBC tumor biomarkers as diagnostic, prognostic, and therapeutic targets based on their sources (urine, serum/plasm, cell line, and tumor tissue) and we also discuss the advantages and limitations of each source. In this manner, this review article provides a critical assessment presentation of the advances in proteomics for all aspects of UBC diagnosis, prognosis, and treatment based on sources.
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Affiliation(s)
- Samira Tabaei
- Shiraz Institute for Cancer Research, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Immunology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Reza Haghshenas
- Shiraz Institute for Cancer Research, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Thomas J Webster
- School of Biomedical Engineering and Health Sciences, Hebei University of Technology, Tianjin, China
| | - Abbas Ghaderi
- Shiraz Institute for Cancer Research, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; Department of Immunology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
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