1
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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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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 2024:10.1038/s44320-024-00050-9. [PMID: 38907068 DOI: 10.1038/s44320-024-00050-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 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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Beck A, Muhoberac M, Randolph CE, Beveridge CH, Wijewardhane PR, Kenttämaa HI, Chopra G. Recent Developments in Machine Learning for Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:233-246. [PMID: 38910862 PMCID: PMC11191731 DOI: 10.1021/acsmeasuresciau.3c00060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/27/2023] [Accepted: 01/22/2024] [Indexed: 06/25/2024]
Abstract
Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.
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Affiliation(s)
- Armen
G. Beck
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Matthew Muhoberac
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Caitlin E. Randolph
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Connor H. Beveridge
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Prageeth R. Wijewardhane
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Hilkka I. Kenttämaa
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
| | - Gaurav Chopra
- Department
of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States
- Department
of Computer Science (by courtesy), Purdue University, West Lafayette, Indiana 47907, United States
- Purdue
Institute for Drug Discovery, Purdue Institute for Cancer Research,
Regenstrief Center for Healthcare Engineering, Purdue Institute for
Inflammation, Immunology and Infectious Disease, Purdue Institute for Integrative Neuroscience, West Lafayette, Indiana 47907 United States
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4
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Zhang Y, Lin X, Zhai W, Shen Y, Chen S, Zhang Y, Yu Y, He X, Liu W. Machine Learning on Microstructure-Property Relationship of Lithium-Ion Conducting Oxide Solid Electrolytes. NANO LETTERS 2024; 24:5292-5300. [PMID: 38648075 DOI: 10.1021/acs.nanolett.4c00902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Understanding the structure-property relationship of lithium-ion conducting solid oxide electrolytes is essential to accelerate their development and commercialization. However, the structural complexity of nonideal materials increases the difficulty of study. Here, we develop an algorithmic framework to understand the effect of microstructure on the properties by linking the microscopic morphology images to their ionic conductivities. We adopt garnet and perovskite polycrystalline oxides as examples and quantify the microscopic morphologies via extracting determined physical parameters from the images. It directly visualizes the effect of physical parameters on their corresponding ionic conductivities. As a result, we can determine the microstructural features of a Li-ion conductor with high ionic conductivity, which can guide the synthesis of highly conductive solid electrolytes. Our work provides a novel approach to understanding the microstructure-property relationship for solid-state ionic materials, showing the potential to extend to other structural/functional ceramics with various physical properties in other fields.
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Affiliation(s)
- Yue Zhang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Xiaoyu Lin
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Wenbo Zhai
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Yanran Shen
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Shaojie Chen
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Yining Zhang
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Yi Yu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
| | - Xuming He
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
- School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai 201210, China
| | - Wei Liu
- School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Shanghai Key Laboratory of High-resolution Electron Microscopy, ShanghaiTech University, Shanghai 201210, China
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5
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Bortel P, Piga I, Koenig C, Gerner C, Martinez-Val A, Olsen JV. Systematic Optimization of Automated Phosphopeptide Enrichment for High-Sensitivity Phosphoproteomics. Mol Cell Proteomics 2024; 23:100754. [PMID: 38548019 PMCID: PMC11087715 DOI: 10.1016/j.mcpro.2024.100754] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/08/2024] [Accepted: 03/22/2024] [Indexed: 05/05/2024] Open
Abstract
Improving coverage, robustness, and sensitivity is crucial for routine phosphoproteomics analysis by single-shot liquid chromatography-tandem mass spectrometry (LC-MS/MS) from minimal peptide inputs. Here, we systematically optimized key experimental parameters for automated on-bead phosphoproteomics sample preparation with a focus on low-input samples. Assessing the number of identified phosphopeptides, enrichment efficiency, site localization scores, and relative enrichment of multiply-phosphorylated peptides pinpointed critical variables influencing the resulting phosphoproteome. Optimizing glycolic acid concentration in the loading buffer, percentage of ammonium hydroxide in the elution buffer, peptide-to-beads ratio, binding time, sample, and loading buffer volumes allowed us to confidently identify >16,000 phosphopeptides in half-an-hour LC-MS/MS on an Orbitrap Exploris 480 using 30 μg of peptides as starting material. Furthermore, we evaluated how sequential enrichment can boost phosphoproteome coverage and showed that pooling fractions into a single LC-MS/MS analysis increased the depth. We also present an alternative phosphopeptide enrichment strategy based on stepwise addition of beads thereby boosting phosphoproteome coverage by 20%. Finally, we applied our optimized strategy to evaluate phosphoproteome depth with the Orbitrap Astral MS using a cell dilution series and were able to identify >32,000 phosphopeptides from 0.5 million HeLa cells in half-an-hour LC-MS/MS using narrow-window data-independent acquisition (nDIA).
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Affiliation(s)
- Patricia Bortel
- Faculty of Chemistry, Department of Analytical Chemistry, University of Vienna, Vienna, Austria; Vienna Doctoral School in Chemistry (DoSChem), University of Vienna, Vienna, Austria
| | - Ilaria Piga
- Proteomics Program, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Claire Koenig
- Proteomics Program, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Christopher Gerner
- Faculty of Chemistry, Department of Analytical Chemistry, University of Vienna, Vienna, Austria; Joint Metabolome Facility, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Ana Martinez-Val
- Proteomics Program, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
| | - Jesper V Olsen
- Proteomics Program, Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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6
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Yang Y, Fang Q. Prediction of glycopeptide fragment mass spectra by deep learning. Nat Commun 2024; 15:2448. [PMID: 38503734 PMCID: PMC10951270 DOI: 10.1038/s41467-024-46771-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 03/11/2024] [Indexed: 03/21/2024] Open
Abstract
Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.
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Affiliation(s)
- Yi Yang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
| | - Qun Fang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
- Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
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7
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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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8
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Skinnider MA, Akinlaja MO, Foster LJ. Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry. Nat Commun 2023; 14:8365. [PMID: 38102123 PMCID: PMC10724252 DOI: 10.1038/s41467-023-44139-5] [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: 06/24/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
We present CFdb, a harmonized resource of interaction proteomics data from 411 co-fractionation mass spectrometry (CF-MS) datasets spanning 21,703 fractions. Meta-analysis of this resource charts protein abundance, phosphorylation, and interactions throughout the tree of life, including a reference map of the human interactome. We show how large-scale CF-MS data can enhance analyses of individual CF-MS datasets, and exemplify this strategy by mapping the honey bee interactome.
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Affiliation(s)
- Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ, USA
| | - Mopelola O Akinlaja
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, Canada.
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, Canada.
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9
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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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10
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Zittlau K, Nashier P, Cavarischia-Rega C, Macek B, Spät P, Nalpas N. Recent progress in quantitative phosphoproteomics. Expert Rev Proteomics 2023; 20:469-482. [PMID: 38116637 DOI: 10.1080/14789450.2023.2295872] [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: 08/11/2023] [Accepted: 12/12/2023] [Indexed: 12/21/2023]
Abstract
INTRODUCTION Protein phosphorylation is a critical post-translational modification involved in the regulation of numerous cellular processes from signal transduction to modulation of enzyme activities. Knowledge of dynamic changes of phosphorylation levels during biological processes, under various treatments or between healthy and disease models is fundamental for understanding the role of each phosphorylation event. Thereby, LC-MS/MS based technologies in combination with quantitative proteomics strategies evolved as a powerful strategy to investigate the function of individual protein phosphorylation events. AREAS COVERED State-of-the-art labeling techniques including stable isotope and isobaric labeling provide precise and accurate quantification of phosphorylation events. Here, we review the strengths and limitations of recent quantification methods and provide examples based on current studies, how quantitative phosphoproteomics can be further optimized for enhanced analytic depth, dynamic range, site localization, and data integrity. Specifically, reducing the input material demands is key to a broader implementation of quantitative phosphoproteomics, not least for clinical samples. EXPERT OPINION Despite quantitative phosphoproteomics is one of the most thriving fields in the proteomics world, many challenges still have to be overcome to facilitate even deeper and more comprehensive analyses as required in the current research, especially at single cell levels and in clinical diagnostics.
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Affiliation(s)
- Katharina Zittlau
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Payal Nashier
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Claudia Cavarischia-Rega
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Boris Macek
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Philipp Spät
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
| | - Nicolas Nalpas
- Quantitative Proteomics, Interfaculty Institute of Cell Biology, University of Tuebingen, Tuebingen , Germany
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11
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Martínez-Val A, Fort K, Koenig C, Van der Hoeven L, Franciosa G, Moehring T, Ishihama Y, Chen YJ, Makarov A, Xuan Y, Olsen JV. Hybrid-DIA: intelligent data acquisition integrates targeted and discovery proteomics to analyze phospho-signaling in single spheroids. Nat Commun 2023; 14:3599. [PMID: 37328457 PMCID: PMC10276052 DOI: 10.1038/s41467-023-39347-y] [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: 01/25/2023] [Accepted: 06/06/2023] [Indexed: 06/18/2023] Open
Abstract
Achieving sufficient coverage of regulatory phosphorylation sites by mass spectrometry (MS)-based phosphoproteomics for signaling pathway reconstitution is challenging, especially when analyzing tiny sample amounts. To address this, we present a hybrid data-independent acquisition (DIA) strategy (hybrid-DIA) that combines targeted and discovery proteomics through an Application Programming Interface (API) to dynamically intercalate DIA scans with accurate triggering of multiplexed tandem mass spectrometry (MSx) scans of predefined (phospho)peptide targets. By spiking-in heavy stable isotope labeled phosphopeptide standards covering seven major signaling pathways, we benchmark hybrid-DIA against state-of-the-art targeted MS methods (i.e., SureQuant) using EGF-stimulated HeLa cells and find the quantitative accuracy and sensitivity to be comparable while hybrid-DIA also profiles the global phosphoproteome. To demonstrate the robustness, sensitivity, and biomedical potential of hybrid-DIA, we profile chemotherapeutic agents in single colon carcinoma multicellular spheroids and evaluate the phospho-signaling difference of cancer cells in 2D vs 3D culture.
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Affiliation(s)
- Ana Martínez-Val
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Kyle Fort
- Thermo Fisher Scientific, Bremen, Germany
| | - Claire Koenig
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Leander Van der Hoeven
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Giulia Franciosa
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | | | | | | | | | - Yue Xuan
- Thermo Fisher Scientific, Bremen, Germany.
| | - Jesper V Olsen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
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12
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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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13
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Zhang MF, Wan SC, Chen WB, Yang DH, Liu WQ, Li BL, Aierken A, Du XM, Li YX, Wu WP, Yang XC, Wei YD, Li N, Peng S, Li XL, Li GP, Hua JL. Transcription factor Dmrt1 triggers the SPRY1-NF-κB pathway to maintain testicular immune homeostasis and male fertility. Zool Res 2023; 44:505-521. [PMID: 37070575 PMCID: PMC10236308 DOI: 10.24272/j.issn.2095-8137.2022.440] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/07/2023] [Indexed: 04/19/2023] Open
Abstract
Bacterial or viral infections, such as Brucella, mumps virus, herpes simplex virus, and Zika virus, destroy immune homeostasis of the testes, leading to spermatogenesis disorder and infertility. Of note, recent research shows that SARS-CoV-2 can infect male gonads and destroy Sertoli and Leydig cells, leading to male reproductive dysfunction. Due to the many side effects associated with antibiotic therapy, finding alternative treatments for inflammatory injury remains critical. Here, we found that Dmrt1 plays an important role in regulating testicular immune homeostasis. Knockdown of Dmrt1 in male mice inhibited spermatogenesis with a broad inflammatory response in seminiferous tubules and led to the loss of spermatogenic epithelial cells. Chromatin immunoprecipitation sequencing (ChIP-seq) and RNA sequencing (RNA-seq) revealed that Dmrt1 positively regulated the expression of Spry1, an inhibitory protein of the receptor tyrosine kinase (RTK) signaling pathway. Furthermore, immunoprecipitation-mass spectrometry (IP-MS) and co-immunoprecipitation (Co-IP) analysis indicated that SPRY1 binds to nuclear factor kappa B1 (NF-κB1) to prevent nuclear translocation of p65, inhibit activation of NF-κB signaling, prevent excessive inflammatory reaction in the testis, and protect the integrity of the blood-testis barrier. In view of this newly identified Dmrt1- Spry1-NF-κB axis mechanism in the regulation of testicular immune homeostasis, our study opens new avenues for the prevention and treatment of male reproductive diseases in humans and livestock.
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Affiliation(s)
- Meng-Fei Zhang
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Shi-Cheng Wan
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Wen-Bo Chen
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Dong-Hui Yang
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Wen-Qing Liu
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
- Center of Reproductive Medicine, Amsterdam Research Institute Reproduction and Development, Academic Medical Center, University of Amsterdam 1105AZ, Amsterdam, Netherlands
| | - Ba-Lun Li
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Aili Aierken
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiao-Min Du
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yun-Xiang Li
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Wen-Ping Wu
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xin-Chun Yang
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yu-Dong Wei
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Na Li
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Sha Peng
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xue-Ling Li
- Key Laboratory for Mammalian Reproductive Biology and Biotechnology, Ministry of Education, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China
| | - Guang-Peng Li
- Key Laboratory for Mammalian Reproductive Biology and Biotechnology, Ministry of Education, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China
| | - Jin-Lian Hua
- College of Veterinary Medicine, Shaanxi Centre of Stem Cells Engineering & Technology, Northwest A&F University, Yangling, Shaanxi 712100, China
- Key Laboratory of Livestock Biology, Northwest A&F University, Yangling, Shaanxi 712100, China. E-mail:
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14
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Chen M, Zhu P, Wan Q, Ruan X, Wu P, Hao Y, Zhang Z, Sun J, Nie W, Chen S. High-Coverage Four-Dimensional Data-Independent Acquisition Proteomics and Phosphoproteomics Enabled by Deep Learning-Driven Multidimensional Predictions. Anal Chem 2023; 95:7495-7502. [PMID: 37126374 DOI: 10.1021/acs.analchem.2c05414] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Four-dimensional (4D) data-independent acquisition (DIA)-based proteomics is a promising technology. However, its full performance is restricted by the time-consuming building and limited coverage of a project-specific experimental library. Herein, we developed a versatile multifunctional deep learning model Deep4D based on self-attention that could predict the collisional cross section, retention time, fragment ion intensity, and charge state with high accuracies for both the unmodified and phosphorylated peptides and thus established the complete workflows for high-coverage 4D DIA proteomics and phosphoproteomics based on multidimensional predictions. A 4D predicted library containing ∼2 million peptides was established that could realize experimental library-free DIA analysis, and 33% more proteins were identified than using an experimental library of single-shot measurement in the example of HeLa cells. These results show the great values of the convenient high-coverage 4D DIA proteomics methods.
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Affiliation(s)
- Moran Chen
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Pujia Zhu
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Qiongqiong Wan
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Xianqin Ruan
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Pengfei Wu
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Yanhong Hao
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Zhourui Zhang
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Jian Sun
- The Institute for Advanced Studies, Wuhan University, Wuhan, Hubei 430072, China
| | - Wenjing Nie
- 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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15
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Zong Y, Wang Y, Yang Y, Zhao D, Wang X, Shen C, Qiao L. DeepFLR facilitates false localization rate control in phosphoproteomics. Nat Commun 2023; 14:2269. [PMID: 37080984 PMCID: PMC10119288 DOI: 10.1038/s41467-023-38035-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 04/06/2023] [Indexed: 04/22/2023] Open
Abstract
Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.
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Affiliation(s)
- Yu Zong
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Yuxin Wang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
- Department of Computer Science, and Institute of Modern Languages and Linguistics, Fudan University, Shanghai, China
| | - Yi Yang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Dan Zhao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | | | | | - Liang Qiao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China.
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16
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Franciosa G, Locard-Paulet M, Jensen LJ, Olsen JV. Recent advances in kinase signaling network profiling by mass spectrometry. Curr Opin Chem Biol 2023; 73:102260. [PMID: 36657259 DOI: 10.1016/j.cbpa.2022.102260] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 01/19/2023]
Abstract
Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.
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Affiliation(s)
- Giulia Franciosa
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marie Locard-Paulet
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars J Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jesper V Olsen
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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17
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Searle BC, Shannon AE, Wilburn DB. Scribe: Next Generation Library Searching for DDA Experiments. J Proteome Res 2023; 22:482-490. [PMID: 36695531 DOI: 10.1021/acs.jproteome.2c00672] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.
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Affiliation(s)
- Brian C Searle
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio43210, United States.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio43210, United States.,Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio43210, United States.,Proteome Software Inc., Portland, Oregon97219, United States
| | - Ariana E Shannon
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio43210, United States.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio43210, United States.,Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio43210, United States
| | - Damien Beau Wilburn
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio43210, United States.,Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio43210, United States.,Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio43210, United States
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18
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Cox J. Prediction of peptide mass spectral libraries with machine learning. Nat Biotechnol 2023; 41:33-43. [PMID: 36008611 DOI: 10.1038/s41587-022-01424-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/11/2022] [Indexed: 01/21/2023]
Abstract
The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.
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Affiliation(s)
- Jürgen Cox
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.
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19
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Zeng WF, Zhou XX, Willems S, Ammar C, Wahle M, Bludau I, Voytik E, Strauss MT, Mann M. AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics. Nat Commun 2022; 13:7238. [PMID: 36433986 PMCID: PMC9700817 DOI: 10.1038/s41467-022-34904-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid sequence with good accuracy. However, DL is a very rapidly developing field with new neural network architectures frequently appearing, which are challenging to incorporate for proteomics researchers. Here we introduce AlphaPeptDeep, a modular Python framework built on the PyTorch DL library that learns and predicts the properties of peptides ( https://github.com/MannLabs/alphapeptdeep ). It features a model shop that enables non-specialists to create models in just a few lines of code. AlphaPeptDeep represents post-translational modifications in a generic manner, even if only the chemical composition is known. Extensive use of transfer learning obviates the need for large data sets to refine models for particular experimental conditions. The AlphaPeptDeep models for predicting retention time, collisional cross sections and fragment intensities are at least on par with existing tools. Additional sequence-based properties can also be predicted by AlphaPeptDeep, as demonstrated with a HLA peptide prediction model to improve HLA peptide identification for data-independent acquisition ( https://github.com/MannLabs/PeptDeep-HLA ).
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Affiliation(s)
- Wen-Feng Zeng
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Xie-Xuan Zhou
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Sander Willems
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Constantin Ammar
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Maria Wahle
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Isabell Bludau
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Eugenia Voytik
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Maximillian T. Strauss
- grid.5254.60000 0001 0674 042XProteomics Program, NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Matthias Mann
- grid.418615.f0000 0004 0491 845XDepartment of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany ,grid.5254.60000 0001 0674 042XProteomics Program, NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
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20
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Srinivasan A, Sing JC, Gingras AC, Röst HL. Improving Phosphoproteomics Profiling Using Data-Independent Mass Spectrometry. J Proteome Res 2022; 21:1789-1799. [PMID: 35877786 DOI: 10.1021/acs.jproteome.2c00172] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mass spectrometry-based profiling of the phosphoproteome is a powerful method of identifying phosphorylation events at a systems level. Most phosphoproteomics studies have used data-dependent acquisition (DDA) mass spectrometry as their method of choice. In this Perspective, we review some recent studies benchmarking DDA and DIA methods for phosphoproteomics and discuss data analysis options for DIA phosphoproteomics. In order to evaluate the impact of data-dependent and data-independent acquisition (DIA) on identification and quantification, we analyze a previously published phosphopeptide-enriched data set consisting of 10 replicates acquired by DDA and DIA each. We find that though more unique identifications are made in DDA data, phosphopeptides are identified more consistently across replicates in DIA. We further discuss the challenges of identifying chromatographically coeluting phosphopeptide isomers and investigate the impact on reproducibility of identifying high-confidence site-localized phosphopeptides in replicates.
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Affiliation(s)
- Aparna Srinivasan
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Justin C Sing
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Hannes L Röst
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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21
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Koenig C, Martinez-Val A, Franciosa G, Olsen JV. Optimal analytical strategies for sensitive and quantitative phosphoproteomics using TMT-based multiplexing. Proteomics 2022; 22:e2100245. [PMID: 35713889 DOI: 10.1002/pmic.202100245] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 04/28/2022] [Accepted: 06/01/2022] [Indexed: 11/10/2022]
Abstract
In large-scale quantitative mass spectrometry (MS)-based phosphoproteomics, isobaric labeling with tandem mass tags (TMTs) coupled with offline high-pH reversed-phase peptide chromatographic fractionation maximizes depth of coverage. To investigate to what extent limited sample amounts affect sensitivity and dynamic range of the analysis due to sample losses, we benchmarked TMT-based fractionation strategies against single-shot label-free quantification with spectral library-free data independent acquisition (LFQ-DIA), for different peptide input per sample. To systematically examine how peptide input amounts influence TMT-fractionation approaches in a phosphoproteomics workflow, we compared two different high-pH reversed-phase fractionation strategies, microflow (MF) and stage-tip fractionation (STF), while scaling the peptide input amount down from 12.5 to 1 μg per sample. Our results indicate that, for input amounts higher than 5 μg per sample, TMT labeling, followed by microflow fractionation (MF) and phospho-enrichment, achieves the deepest phosphoproteome coverage, even compared to single shot direct-DIA analysis. Conversely, STF of enriched phosphopeptides (STF) is optimal for lower amounts, below 5 μg/peptide per sample. As a result, we provide a decision tree to help phosphoproteomics users to choose the best workflow as a function of sample amount.
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Affiliation(s)
- Claire Koenig
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Ana Martinez-Val
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Giulia Franciosa
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Jesper V Olsen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
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22
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Ekvall M, Truong P, Gabriel W, Wilhelm M, Käll L. Prosit Transformer: A transformer for Prediction of MS2 Spectrum Intensities. J Proteome Res 2022; 21:1359-1364. [PMID: 35413196 PMCID: PMC9087333 DOI: 10.1021/acs.jproteome.1c00870] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Machine learning
has been an integral part of interpreting data
from mass spectrometry (MS)-based proteomics for a long time. Relatively
recently, a machine-learning structure appeared successful in other
areas of bioinformatics, Transformers. Furthermore, the implementation
of Transformers within bioinformatics has become relatively convenient
due to transfer learning, i.e., adapting a network trained for other
tasks to new functionality. Transfer learning makes these relatively
large networks more accessible as it generally requires less data,
and the training time improves substantially. We implemented a Transformer
based on the pretrained model TAPE to predict MS2 intensities. TAPE
is a general model trained to predict missing residues from protein
sequences. Despite being trained for a different task, we could modify
its behavior by adding a prediction head at the end of the TAPE model
and fine-tune it using the spectrum intensity from the training set
to the well-known predictor Prosit. We demonstrate that the predictor,
which we call Prosit Transformer, outperforms the recurrent neural-network-based
predictor Prosit, increasing the median angular similarity on its
hold-out set from 0.908 to 0.929. We believe that Transformers will
significantly increase prediction accuracy for other types of predictions
within MS-based proteomics.
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Affiliation(s)
- Markus Ekvall
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology─KTH, Box 1031, SE-17121 Solna, Sweden
| | - Patrick Truong
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology─KTH, Box 1031, SE-17121 Solna, Sweden
| | - Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich (TUM), D-85354 Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), D-85354 Freising, Germany
| | - Lukas Käll
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology─KTH, Box 1031, SE-17121 Solna, Sweden
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Morales-Sanfrutos J, Munoz J. UNRAVELLING THE COMPLEXITY OF THE EXTRACELLULAR VESICLE LANDSCAPE WITH ADVANCED PROTEOMICS. Expert Rev Proteomics 2022; 19:89-101. [PMID: 35290757 DOI: 10.1080/14789450.2022.2052849] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION The field of extracellular vesicles (EVs) is rapidly advancing. This progress is fuelled by the potential applications of these agents as biomarkers and also as an attractive source to encapsulate therapeutics and other agents to target specific cells. AREAS COVERED Different types of EVs, including exosomes, and other nanoparticles have been identified in the last years with key regulatory functions in cell-cell communication. However, the techniques used for their purification possess inherent limitations, resulting in heterogeneous preparations contaminated by other EVs subtypes and nano-size structures. It is therefore urgent to deconvolute the molecular constituents present in each type of EVs in order to accurately ascribe their specific functions. In this context, proteomics can profile, not only the lumen proteins and surface markers, but also their post-translational modifications, which will inform on the mechanisms of cargo selection and sorting. EXPERT OPINION Mass spectrometry-based proteomics is now a mature technique and has started to deliver new insights in the EV field. Here, we review recent developments in sample preparation, mass spectrometry (MS) and computational analysis and discuss how these technological advances, in conjunction with improved purification protocols, could impact the proteomic characterization of the complex landscape of EVs and other secreted nanoparticles.
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Affiliation(s)
| | - Javier Munoz
- Proteomics Unit, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.,Cell Signaling and Clinical Proteomics Group. Biocruces Bizkaia Health Research Institute. 48903 Barkaldo, Spain.,Ikerbasque, Basque foundation for science, Bilbao, Spain
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24
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Yang Y, Lin L, Qiao L. Deep learning approaches for data-independent acquisition proteomics. Expert Rev Proteomics 2021; 18:1031-1043. [PMID: 34918987 DOI: 10.1080/14789450.2021.2020654] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
INTRODUCTION Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field. AREAS COVERED This review discusses and provides an overview of the deep learning methods that are used for DIA data analysis, including spectral library prediction, feature scoring, and statistical control in peptide-centric analysis, as well as de novo peptide sequencing. Literature searches were performed for articles, including preprints, up to December 2021 from PubMed, Scopus, and Web of Science databases. EXPERT OPINION While spectral library prediction has broken through the limitation on proteome coverage of experimental libraries, the statistical burden due to the large query space is the remaining challenge of utilizing proteome-wide predicted libraries. Analysis of post-translational modifications is another promising direction of deep learning-based DIA methods.
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Affiliation(s)
- Yi Yang
- Department of Chemistry, Shanghai Stomatological Hospital, and Minhang Hospital, Fudan University, Shanghai China
| | - Ling Lin
- Department of Chemistry, Shanghai Stomatological Hospital, and Minhang Hospital, Fudan University, Shanghai China
| | - Liang Qiao
- Department of Chemistry, Shanghai Stomatological Hospital, and Minhang Hospital, Fudan University, Shanghai China
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Phosphoproteomics Sample Preparation Impacts Biological Interpretation of Phosphorylation Signaling Outcomes. Cells 2021; 10:cells10123407. [PMID: 34943915 PMCID: PMC8699897 DOI: 10.3390/cells10123407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/28/2021] [Accepted: 12/01/2021] [Indexed: 01/02/2023] Open
Abstract
The influence of phosphoproteomics sample preparation methods on the biological interpretation of signaling outcome is unclear. Here, we demonstrate a strong bias in phosphorylation signaling targets uncovered by comparing the phosphoproteomes generated by two commonly used methods-strong cation exchange chromatography-based phosphoproteomics (SCXPhos) and single-run high-throughput phosphoproteomics (HighPhos). Phosphoproteomes of embryonic stem cells exposed to ionizing radiation (IR) profiled by both methods achieved equivalent coverage (around 20,000 phosphosites), whereas a combined dataset significantly increased the depth (>30,000 phosphosites). While both methods reproducibly quantified a subset of shared IR-responsive phosphosites that represent DNA damage and cell-cycle-related signaling events, most IR-responsive phosphoproteins (>82%) and phosphosites (>96%) were method-specific. Both methods uncovered unique insights into phospho-signaling mediated by single (SCXPhos) versus double/multi-site (HighPhos) phosphorylation events; particularly, each method identified a distinct set of previously unreported IR-responsive kinome/phosphatome (95% disparate) directly impacting the uncovered biology.
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