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Chakrabarty S, Wang S, Roychowdhury T, Ginsberg SD, Chiosis G. Introducing dysfunctional Protein-Protein Interactome (dfPPI) - A platform for systems-level protein-protein interaction (PPI) dysfunction investigation in disease. Curr Opin Struct Biol 2024; 88:102886. [PMID: 39003916 PMCID: PMC11392609 DOI: 10.1016/j.sbi.2024.102886] [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: 05/05/2024] [Revised: 06/19/2024] [Accepted: 06/21/2024] [Indexed: 07/16/2024]
Abstract
Protein-protein interactions (PPIs) play a crucial role in cellular function and disease manifestation, with dysfunctions in PPI networks providing a direct link between stressors and phenotype. The dysfunctional Protein-Protein Interactome (dfPPI) platform, formerly known as epichaperomics, is a newly developed chemoproteomic method aimed at detecting dynamic changes at the systems level in PPI networks under stressor-induced cellular perturbations within disease states. This review provides an overview of dfPPIs, emphasizing the novel methodology, data analytics, and applications in disease research. dfPPI has applications in cancer research, where it identifies dysfunctions integral to maintaining malignant phenotypes and discovers strategies to enhance the efficacy of current therapies. In neurodegenerative disorders, dfPPI uncovers critical dysfunctions in cellular processes and stressor-specific vulnerabilities. Challenges, including data complexity and the potential for integration with other omics datasets are discussed. The dfPPI platform is a potent tool for dissecting disease systems biology by directly informing on dysfunctions in PPI networks and holds promise for advancing disease identification and therapeutics.
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Affiliation(s)
- Souparna Chakrabarty
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Shujuan Wang
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Tanaya Roychowdhury
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
| | - Stephen D Ginsberg
- Departments of Psychiatry, Neuroscience & Physiology & the NYU Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, 10016, USA; Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, 10962, USA
| | - Gabriela Chiosis
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA; Department of Medicine, Division of Solid Tumors, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
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2
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Huang CF, Hollas MA, Sanchez A, Bhattacharya M, Ho G, Sundaresan A, Caldwell MA, Zhao X, Benz R, Siddiqui A, Kelleher NL. Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-Down Proteomics. J Proteome Res 2024. [PMID: 39312774 DOI: 10.1021/acs.jproteome.4c00621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
The dynamic range challenge for the detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundance proteins selectively and reproducibly from human plasma and use top-down proteomics to quantify differential enrichment for the 2841 detected proteoforms from 114 proteins. Furthermore, nanoparticle enrichment allowed top-down detection of proteoforms between ∼1 μg/mL and ∼10 pg/mL in absolute abundance, providing up to a 105-fold increase in proteome depth over neat plasma in which only proteoforms from abundant proteins (>1 μg/mL) were detected. The ability to monitor medium and some low-abundant proteoforms through reproducible enrichment significantly extends the applicability of proteoform research by adding depth beyond albumin, immunoglobins, and apolipoproteins to uncover many involved in immunity and cell signaling. As proteoforms carry unique information content relative to peptides, this report opens the door to deeper proteoform sequencing in clinical proteomics of disease or aging cohorts.
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Affiliation(s)
- Che-Fan Huang
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - Michael A Hollas
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - Aniel Sanchez
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | | | - Giang Ho
- Seer Inc., Redwood City, California 94065, United States
| | | | - Michael A Caldwell
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - Xiaoyan Zhao
- Seer Inc., Redwood City, California 94065, United States
| | - Ryan Benz
- Seer Inc., Redwood City, California 94065, United States
| | - Asim Siddiqui
- Seer Inc., Redwood City, California 94065, United States
| | - Neil L Kelleher
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
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3
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Sadeghi SA, Ashkarran AA, Wang Q, Zhu G, Mahmoudi M, Sun L. Mass Spectrometry-Based Top-Down Proteomics in Nanomedicine: Proteoform-Specific Measurement of Protein Corona. ACS NANO 2024. [PMID: 39276099 DOI: 10.1021/acsnano.4c04675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/16/2024]
Abstract
Conventional mass spectrometry (MS)-based bottom-up proteomics (BUP) analysis of the protein corona [i.e., an evolving layer of biomolecules, mostly proteins, formed on the surface of nanoparticles (NPs) during their interactions with biomolecular fluids] enabled the nanomedicine community to partly identify the biological identity of NPs. Such an approach, however, fails to pinpoint the specific proteoforms─distinct molecular variants of proteins in the protein corona. The proteoform-level information could potentially advance the prediction of the biological fate and pharmacokinetics of nanomedicines. Recognizing this limitation, this study pioneers a robust and reproducible MS-based top-down proteomics (TDP) technique for characterizing proteoforms in the protein corona. Our TDP approach has successfully identified about 900 proteoforms in the protein corona of polystyrene NPs, ranging from 2 to 70 kDa, revealing proteoforms of 48 protein biomarkers with combinations of post-translational modifications, signal peptide cleavages, and/or truncations─details that BUP could not fully discern. This advancement in MS-based TDP offers a more advanced approach to characterize NP protein coronas, deepening our understanding of NPs' biological identities. We, therefore, propose using both TDP and BUP strategies to obtain more comprehensive information about the protein corona, which, in turn, can further enhance the diagnostic and therapeutic efficacy of nanomedicine technologies.
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Affiliation(s)
- Seyed Amirhossein Sadeghi
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
| | - Ali Akbar Ashkarran
- Department of Radiology and Precision Health Program, Michigan State University, East Lansing, Michigan 48824, United States
| | - Qianyi Wang
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
| | - Guijie Zhu
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
| | - Morteza Mahmoudi
- Department of Radiology and Precision Health Program, Michigan State University, East Lansing, Michigan 48824, United States
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
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4
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Cobley JN. Exploring the unmapped cysteine redox proteoform landscape. Am J Physiol Cell Physiol 2024; 327:C844-C866. [PMID: 39099422 DOI: 10.1152/ajpcell.00152.2024] [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: 03/07/2024] [Revised: 07/16/2024] [Accepted: 07/16/2024] [Indexed: 08/06/2024]
Abstract
Cysteine redox proteoforms define the diverse molecular states that proteins with cysteine residues can adopt. A protein with one cysteine residue must adopt one of two binary proteoforms: reduced or oxidized. Their numbers scale: a protein with 10 cysteine residues must assume one of 1,024 proteoforms. Although they play pivotal biological roles, the vast cysteine redox proteoform landscape comprising vast numbers of theoretical proteoforms remains largely uncharted. Progress is hampered by a general underappreciation of cysteine redox proteoforms, their intricate complexity, and the formidable challenges that they pose to existing methods. The present review advances cysteine redox proteoform theory, scrutinizes methodological barriers, and elaborates innovative technologies for detecting unique residue-defined cysteine redox proteoforms. For example, chemistry-enabled hybrid approaches combining the strengths of top-down mass spectrometry (TD-MS) and bottom-up mass spectrometry (BU-MS) for systematically cataloguing cysteine redox proteoforms are delineated. These methods provide the technological means to map uncharted redox terrain. To unravel hidden redox regulatory mechanisms, discover new biomarkers, and pinpoint therapeutic targets by mining the theoretical cysteine redox proteoform space, a community-wide initiative termed the "Human Cysteine Redox Proteoform Project" is proposed. Exploring the cysteine redox proteoform landscape could transform current understanding of redox biology.
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Affiliation(s)
- James N Cobley
- School of Life Sciences, University of Dundee, Dundee, United Kingdom
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5
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Des Soye BJ, McGee JP, Hollas MAR, Forte E, Fellers RT, Melani RD, Wilkins JT, Compton PD, Kafader JO, Kelleher NL. Automated Immunoprecipitation, Sample Preparation, and Individual Ion Mass Spectrometry Platform for Proteoforms. Anal Chem 2024. [PMID: 39143757 DOI: 10.1021/acs.analchem.4c01962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Charge detection mass spectrometry (CDMS) is a well-established technique that provides direct mass spectral outputs regardless of analyte heterogeneity or molecular weight. Over the past few years, it has been demonstrated that CDMS can be multiplexed on Orbitrap analyzers utilizing an integrated approach termed individual ion mass spectrometry (I2MS). To further increase adaptability, robustness, and throughput of this technique, here, we present a method that utilizes numerous integrated equipment components including a Kingfisher system, SampleStream platform, and Q Exactive mass spectrometer to provide a fully automated workflow for immunoprecipitation, sample preparation, injection, and subsequent I2MS acquisition. This automated workflow has been applied to a cohort of 58 test subjects to determine individualized patient antibody responses to SARS-CoV-2 antigens. Results from a range of serum donors include 37 subject I2MS spectra that contained a positive COVID-19 antibody response and 21 I2MS spectra that contained a negative COVID-19 antibody response. This high-throughput automated I2MS workflow can currently process over 100 samples per week and is general for making immunoprecipitation-MS workflows achieve proteoform resolution.
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Affiliation(s)
- Benjamin J Des Soye
- Departments of Chemistry and Molecular Biosciences, the Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - John P McGee
- Departments of Chemistry and Molecular Biosciences, the Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
- ImmPro, Evanston, Illinois 60201, United States
| | - Michael A R Hollas
- Departments of Chemistry and Molecular Biosciences, the Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - Eleonora Forte
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
- Comprehensive Transplant Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, United States
| | - Ryan T Fellers
- Departments of Chemistry and Molecular Biosciences, the Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - Rafael D Melani
- Departments of Chemistry and Molecular Biosciences, the Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - John T Wilkins
- Departments of Chemistry and Molecular Biosciences, the Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
- Departments of Medicine (Cardiology) and Preventive Medicine (Epidemiology), Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, United States
| | - Philip D Compton
- Integrated Protein Technologies, San Jose, California 95134, United States
| | - Jared O Kafader
- Departments of Chemistry and Molecular Biosciences, the Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
| | - Neil L Kelleher
- Departments of Chemistry and Molecular Biosciences, the Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois 60208, United States
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6
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Zhong J, Song X, Wang S. FREE: Enhanced Feature Representation for Isotopic Envelope Evaluation in Top-Down Mass Spectra Deconvolution. Anal Chem 2024; 96:12602-12615. [PMID: 39037184 DOI: 10.1021/acs.analchem.4c00152] [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: 07/23/2024]
Abstract
The aim of deconvolution of top-down mass spectra is to recognize monoisotopic peaks from the experimental envelopes in raw mass spectra. So accurate assessment of similarity between theoretical and experimental envelopes is a critical step in mass spectra data deconvolution. Existing evaluation methods primarily rely on intensity differences and m/z similarity, potentially lacking a comprehensive assessment. To overcome this constraint and facilitate a comprehensive and refined assessment of the similarity between theoretical and experimental envelopes, there exists an imperative to systematically explore and identify increasingly efficacious features for assessing this correspondence. We present enhanced feature representation for isotopic envelope evaluation (FREE) that derives diverse feature representations, encapsulating fundamental physical attributes of envelopes, including peak intensity and envelope shape. We trained FREE and evaluated its performance on both the ovarian tumor (OT) (human OT cells) data set and zebrafish (ZF) (brain in mature female ZF) data set. Specifically, comparing the state-of-art method, FREE demonstrates higher performance in multiple evaluation metrics across both the OT and ZF data sets, with a particular emphasis on precision, and it demonstrates accurate predictions of a greater number of positive envelopes among the top-ranked envelopes based on their scores. Moreover, within a cross-species data set of ZF, FREE identified a higher number of proteoform-spectrum matches (PrSMs), increasing the count from 50,795 to 52,927 compared to EnvCNN, the amalgamation of FREE with TopFD also exhibits a commendable capacity to discern 117,883 fragment ions, thus surpassing the 97,554 fragment ions identified through the application of EnvCNN in conjunction with TopFD. To further validate the performance of FREE, we have tested 10 a cross-species top-down proteomes containing 36 subdata set from ProteomeXchange. The results reveal that, after deconvolution with TopFD + FREE, TopPIC identifies more PrSMs across these 10 data sets in both the first and second rounds of experiments. These findings underscore the robustness and generalization capabilities of the FREE approach in diverse proteomes.
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Affiliation(s)
- Jiancheng Zhong
- College of Information Science and Engineering, Hunan Normal University, ChangSha 410081, China
| | - Xingran Song
- College of Information Science and Engineering, Hunan Normal University, ChangSha 410081, China
| | - Shaokai Wang
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo N2L 3G1, Canada
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7
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Cesnik A, Schaffer LV, Gaur I, Jain M, Ideker T, Lundberg E. Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes. Annu Rev Biomed Data Sci 2024; 7:369-389. [PMID: 38748859 PMCID: PMC11343683 DOI: 10.1146/annurev-biodatasci-102423-113534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2024]
Abstract
While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.
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Affiliation(s)
- Anthony Cesnik
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Leah V Schaffer
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Ishan Gaur
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Mayank Jain
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Trey Ideker
- Departments of Computer Science and Engineering and Bioengineering, University of California San Diego, La Jolla, California, USA
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Emma Lundberg
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Pathology, Stanford University, Palo Alto, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA;
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8
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Huang CF, Hollas MA, Sanchez A, Bhattacharya M, Ho G, Sundaresan A, Caldwell MA, Zhao X, Benz R, Siddiqui A, Kelleher NL. Deep Profiling of Plasma Proteoforms with Engineered Nanoparticles for Top-down Proteomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.20.604425. [PMID: 39071411 PMCID: PMC11275834 DOI: 10.1101/2024.07.20.604425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
The dynamic range challenge for detection of proteins and their proteoforms in human plasma has been well documented. Here, we use the nanoparticle protein corona approach to enrich low-abundant proteins selectively and reproducibly from human plasma and use top-down proteomics to quantify differential enrichment for the 2841 detected proteoforms from 114 proteins. Furthermore, nanoparticle enrichment allowed top-down detection of proteoforms between ∼1 µg/mL and ∼10 pg/mL in absolute abundance, providing up to 10 5 -fold increase in proteome depth over neat plasma in which only proteoforms from abundant proteins (>1 µg/mL) were detected. The ability to monitor medium and some low abundant proteoforms through reproducible enrichment significantly extends the applicability of proteoform research by adding depth beyond albumin, immunoglobins and apolipoproteins to uncover many involved in immunity and cell signaling. As proteoforms carry unique information content relative to peptides, this report opens the door to deeper proteoform sequencing in clinical proteomics of disease or aging cohorts.
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9
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Xu T, Wang Q, Wang Q, Sun L. Mass spectrometry-intensive top-down proteomics: an update on technology advancements and biomedical applications. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:4664-4682. [PMID: 38973469 PMCID: PMC11257149 DOI: 10.1039/d4ay00651h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 06/25/2024] [Indexed: 07/09/2024]
Abstract
Proteoforms are all forms of protein molecules from the same gene because of variations at the DNA, RNA, and protein levels, e.g., alternative splicing and post-translational modifications (PTMs). Delineation of proteins in a proteoform-specific manner is crucial for understanding their biological functions. Mass spectrometry (MS)-intensive top-down proteomics (TDP) is promising for comprehensively characterizing intact proteoforms in complex biological systems. It has achieved substantial progress in technological development, including sample preparation, proteoform separations, MS instrumentation, and bioinformatics tools. In a single TDP study, thousands of proteoforms can be identified and quantified from a cell lysate. It has also been applied to various biomedical research to better our understanding of protein function in regulating cellular processes and to discover novel proteoform biomarkers of diseases for early diagnosis and therapeutic development. This review covers the most recent technological development and biomedical applications of MS-intensive TDP.
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Affiliation(s)
- Tian Xu
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI 48824, USA.
| | - Qianjie Wang
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI 48824, USA.
| | - Qianyi Wang
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI 48824, USA.
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI 48824, USA.
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10
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Zemaitis KJ, Fulcher JM, Kumar R, Degnan DJ, Lewis LA, Liao YC, Veličković M, Williams SM, Moore RJ, Bramer LM, Veličković D, Zhu Y, Zhou M, Paša-Tolić L. Spatial top-down proteomics for the functional characterization of human kidney. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580062. [PMID: 38405958 PMCID: PMC10888776 DOI: 10.1101/2024.02.13.580062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background The Human Proteome Project has credibly detected nearly 93% of the roughly 20,000 proteins which are predicted by the human genome. However, the proteome is enigmatic, where alterations in amino acid sequences from polymorphisms and alternative splicing, errors in translation, and post-translational modifications result in a proteome depth estimated at several million unique proteoforms. Recently mass spectrometry has been demonstrated in several landmark efforts mapping the human proteoform landscape in bulk analyses. Herein, we developed an integrated workflow for characterizing proteoforms from human tissue in a spatially resolved manner by coupling laser capture microdissection, nanoliter-scale sample preparation, and mass spectrometry imaging. Results Using healthy human kidney sections as the case study, we focused our analyses on the major functional tissue units including glomeruli, tubules, and medullary rays. After laser capture microdissection, these isolated functional tissue units were processed with microPOTS (microdroplet processing in one-pot for trace samples) for sensitive top-down proteomics measurement. This provided a quantitative database of 616 proteoforms that was further leveraged as a library for mass spectrometry imaging with near-cellular spatial resolution over the entire section. Notably, several mitochondrial proteoforms were found to be differentially abundant between glomeruli and convoluted tubules, and further spatial contextualization was provided by mass spectrometry imaging confirming unique differences identified by microPOTS, and further expanding the field-of-view for unique distributions such as enhanced abundance of a truncated form (1-74) of ubiquitin within cortical regions. Conclusions We developed an integrated workflow to directly identify proteoforms and reveal their spatial distributions. Where of the 20 differentially abundant proteoforms identified as discriminate between tubules and glomeruli by microPOTS, the vast majority of tubular proteoforms were of mitochondrial origin (8 of 10) where discriminate proteoforms in glomeruli were primarily hemoglobin subunits (9 of 10). These trends were also identified within ion images demonstrating spatially resolved characterization of proteoforms that has the potential to reshape discovery-based proteomics because the proteoforms are the ultimate effector of cellular functions. Applications of this technology have the potential to unravel etiology and pathophysiology of disease states, informing on biologically active proteoforms, which remodel the proteomic landscape in chronic and acute disorders.
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Affiliation(s)
- Kevin J. Zemaitis
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - James M. Fulcher
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Rashmi Kumar
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - David J. Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Logan A. Lewis
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Yen-Chen Liao
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Marija Veličković
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Sarah M. Williams
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J. Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lisa M. Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Dušan Veličković
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
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11
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Hansen J, Jain AR, Nenov P, Robinson PN, Iyengar R. From transcriptomics to digital twins of organ function. Front Cell Dev Biol 2024; 12:1240384. [PMID: 38989060 PMCID: PMC11234175 DOI: 10.3389/fcell.2024.1240384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 05/30/2024] [Indexed: 07/12/2024] Open
Abstract
Cell level functions underlie tissue and organ physiology. Gene expression patterns offer extensive views of the pathways and processes within and between cells. Single cell transcriptomics provides detailed information on gene expression within cells, cell types, subtypes and their relative proportions in organs. Functional pathways can be scalably connected to physiological functions at the cell and organ levels. Integrating experimentally obtained gene expression patterns with prior knowledge of pathway interactions enables identification of networks underlying whole cell functions such as growth, contractility, and secretion. These pathways can be computationally modeled using differential equations to simulate cell and organ physiological dynamics regulated by gene expression changes. Such computational systems can be thought of as parts of digital twins of organs. Digital twins, at the core, need computational models that represent in detail and simulate how dynamics of pathways and networks give rise to whole cell level physiological functions. Integration of transcriptomic responses and numerical simulations could simulate and predict whole cell functional outputs from transcriptomic data. We developed a computational pipeline that integrates gene expression timelines and systems of coupled differential equations to generate cell-type selective dynamical models. We tested our integrative algorithm on the eicosanoid biosynthesis network in macrophages. Converting transcriptomic changes to a dynamical model allowed us to predict dynamics of prostaglandin and thromboxane synthesis and secretion by macrophages that matched published lipidomics data obtained in the same experiments. Integration of cell-level system biology simulations with genomic and clinical data using a knowledge graph framework will allow us to create explicit predictive models that mechanistically link genomic determinants to organ function. Such integration requires a multi-domain ontological framework to connect genomic determinants to gene expression and cell pathways and functions to organ level phenotypes in healthy and diseased states. These integrated scalable models of tissues and organs as accurate digital twins predict health and disease states for precision medicine.
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Affiliation(s)
- Jens Hansen
- Department of Pharmacological Science and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Abhinav R Jain
- Department of Pharmacological Science and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Philip Nenov
- Department of Pharmacological Science and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - Peter N Robinson
- Berlin Institute of Health at Charité Rahel Hirsch Center for Translational Medicine, Berlin, Germany
| | - Ravi Iyengar
- Department of Pharmacological Science and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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12
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Forte E, Sanders JM, Pla I, Kanchustambham VL, Hollas MAR, Huang CF, Sanchez A, Peterson KN, Melani RD, Huang A, Polineni P, Doll JM, Dietch Z, Kelleher NL, Ladner DP. Top-Down Proteomics Identifies Plasma Proteoform Signatures of Liver Cirrhosis Progression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599662. [PMID: 38948836 PMCID: PMC11212939 DOI: 10.1101/2024.06.19.599662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Cirrhosis, advanced liver disease, affects 2-5 million Americans. While most patients have compensated cirrhosis and may be fairly asymptomatic, many decompensate and experience life-threatening complications such as gastrointestinal bleeding, confusion (hepatic encephalopathy), and ascites, reducing life expectancy from 12 to less than 2 years. Among patients with compensated cirrhosis, identifying patients at high risk of decompensation is critical to optimize care and reduce morbidity and mortality. Therefore, it is important to preferentially direct them towards specialty care which cannot be provided to all patients with cirrhosis. We used discovery Top-down Proteomics (TDP) to identify differentially expressed proteoforms (DEPs) in the plasma of patients with progressive stages of liver cirrhosis with the ultimate goal to identify candidate biomarkers of disease progression. In this pilot study, we identified 209 DEPs across three stages of cirrhosis (compensated, compensated with portal hypertension, and decompensated), of which 115 derived from proteins enriched in the liver at a transcriptional level and discriminated the three stages of cirrhosis. Enrichment analyses demonstrated DEPs are involved in several metabolic and immunological processes known to be impacted by cirrhosis progression. We have preliminarily defined the plasma proteoform signatures of cirrhosis patients, setting the stage for ongoing discovery and validation of biomarkers for early diagnosis, risk stratification, and disease monitoring.
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Affiliation(s)
- Eleonora Forte
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, 60208, USA
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Jes M. Sanders
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Indira Pla
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, 60208, USA
| | | | - Michael A. R. Hollas
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, 60208, USA
| | - Che-Fan Huang
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, 60208, USA
| | - Aniel Sanchez
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, 60208, USA
| | - Katrina N. Peterson
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, 60208, USA
| | - Rafael D. Melani
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, 60208, USA
| | - Alexander Huang
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Praneet Polineni
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Julianna M. Doll
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Zachary Dietch
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Neil L. Kelleher
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, 60208, USA
- Department of Chemistry, Northwestern University, Evanston, IL, 60208, USA
- Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Daniela P. Ladner
- Northwestern University Transplant Outcomes Research Collaborative (NUTORC), Comprehensive Transplant Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
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13
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Roberts DS, Loo JA, Tsybin YO, Liu X, Wu S, Chamot-Rooke J, Agar JN, Paša-Tolić L, Smith LM, Ge Y. Top-down proteomics. NATURE REVIEWS. METHODS PRIMERS 2024; 4:38. [PMID: 39006170 PMCID: PMC11242913 DOI: 10.1038/s43586-024-00318-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/24/2024] [Indexed: 07/16/2024]
Abstract
Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.
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Affiliation(s)
- David S Roberts
- Department of Chemistry, Stanford University, Stanford, CA, USA
- Sarafan ChEM-H, Stanford University, Stanford, CA, USA
| | - Joseph A Loo
- Department of Chemistry and Biochemistry, Department of Biological Chemistry, University of California - Los Angeles, Los Angeles, CA, USA
| | | | - Xiaowen Liu
- Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA, USA
| | - Si Wu
- Department of Chemistry and Biochemistry, The University of Alabama, Tuscaloosa, AL, USA
| | | | - Jeffrey N Agar
- Departments of Chemistry and Chemical Biology and Pharmaceutical Sciences, Northeastern University, Boston, MA, USA
| | - Ljiljana Paša-Tolić
- Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
| | - Ying Ge
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
- Department of Cell and Regenerative Biology, Human Proteomics Program, University of Wisconsin - Madison, Madison, WI, USA
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14
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Su P, Hollas MAR, Butun FA, Kanchustambham VL, Rubakhin S, Ramani N, Greer JB, Early BP, Fellers RT, Caldwell MA, Sweedler JV, Kafader JO, Kelleher NL. Single Cell Analysis of Proteoforms. J Proteome Res 2024; 23:1883-1893. [PMID: 38497708 PMCID: PMC11406863 DOI: 10.1021/acs.jproteome.4c00075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
We introduce single cell Proteoform imaging Mass Spectrometry (scPiMS), which realizes the benefit of direct solvent extraction and MS detection of intact proteins from single cells dropcast onto glass slides. Sampling and detection of whole proteoforms by individual ion mass spectrometry enable a scalable approach to single cell proteomics. This new scPiMS platform addresses the throughput bottleneck in single cell proteomics and boosts the cell processing rate by several fold while accessing protein composition with higher coverage.
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Affiliation(s)
- Pei Su
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Michael A R Hollas
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Fatma Ayaloglu Butun
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
| | - Vijaya Lakshmi Kanchustambham
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
| | - Stanislav Rubakhin
- Beckman Institute and Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Namrata Ramani
- Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, United States
| | - Joseph B Greer
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Bryan P Early
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Ryan T Fellers
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Michael A Caldwell
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
| | - Jonathan V Sweedler
- Beckman Institute and Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Jared O Kafader
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
| | - Neil L Kelleher
- Departments of Molecular Biosciences, Chemistry, Chemical and Biological Engineering, and Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
- Proteomics Center of Excellence, Chemistry of Life Processes Institute, Northwestern University, Evanston, Illinois 60208, United States
- Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, United States
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15
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Mi Y, Burnham KL, Charles PD, Heilig R, Vendrell I, Whalley J, Torrance HD, Antcliffe DB, May SM, Neville MJ, Berridge G, Hutton P, Geoghegan CG, Radhakrishnan J, Nesvizhskii AI, Yu F, Davenport EE, McKechnie S, Davies R, O'Callaghan DJP, Patel P, Del Arroyo AG, Karpe F, Gordon AC, Ackland GL, Hinds CJ, Fischer R, Knight JC. High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response. Sci Transl Med 2024; 16:eadh0185. [PMID: 38838133 DOI: 10.1126/scitranslmed.adh0185] [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: 02/05/2023] [Accepted: 05/08/2024] [Indexed: 06/07/2024]
Abstract
Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.
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Affiliation(s)
- Yuxin Mi
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Katie L Burnham
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Philip D Charles
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Raphael Heilig
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Iolanda Vendrell
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford OX3 7BN, UK
| | - Justin Whalley
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Hew D Torrance
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
| | - David B Antcliffe
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
- Department of Critical Care, Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - Shaun M May
- Translational Medicine and Therapeutics, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Matt J Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LE, UK
- NIHR Oxford Biomedical Research Centre, Oxford OX3 9DU, UK
| | - Georgina Berridge
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
| | - Paula Hutton
- Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7JX, UK
| | - Cyndi G Geoghegan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Jayachandran Radhakrishnan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | | | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emma E Davenport
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
| | - Stuart McKechnie
- Oxford University Hospitals NHS Foundation Trust, Oxford OX3 7JX, UK
| | - Roger Davies
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
| | - David J P O'Callaghan
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
- Department of Critical Care, Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - Parind Patel
- Department of Critical Care, Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - Ana G Del Arroyo
- Translational Medicine and Therapeutics, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LE, UK
- NIHR Oxford Biomedical Research Centre, Oxford OX3 9DU, UK
| | - Anthony C Gordon
- Division of Anaesthetics, Pain Medicine and Intensive Care, Imperial College, London SW7 2AZ, UK
- Department of Critical Care, Imperial College Healthcare NHS Trust, London W2 1NY, UK
| | - Gareth L Ackland
- Translational Medicine and Therapeutics, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Charles J Hinds
- Translational Medicine and Therapeutics, William Harvey Research Institute, Queen Mary University of London, London EC1M 6BQ, UK
| | - Roman Fischer
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7FZ, UK
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford OX3 7BN, UK
| | - Julian C Knight
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
- Chinese Academy of Medical Science Oxford Institute, University of Oxford, Oxford OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, Oxford OX3 9DU, UK
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16
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Zhai Z, Mavridou D, Damian M, Mutti FG, Schoenmakers PJ, Gargano AFG. Characterization of Complex Proteoform Mixtures by Online Nanoflow Ion-Exchange Chromatography-Native Mass Spectrometry. Anal Chem 2024; 96:8880-8885. [PMID: 38771719 PMCID: PMC11154664 DOI: 10.1021/acs.analchem.4c01760] [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: 04/03/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 05/23/2024]
Abstract
The characterization of proteins and complexes in biological systems is essential to establish their critical properties and to understand their unique functions in a plethora of bioprocesses. However, it is highly difficult to analyze low levels of intact proteins in their native states (especially those exceeding 30 kDa) with liquid chromatography (LC)-mass spectrometry (MS). Herein, we describe for the first time the use of nanoflow ion-exchange chromatography directly coupled with native MS to resolve mixtures of intact proteins. Reference proteins and protein complexes with molecular weights between 10 and 150 kDa and a model cell lysate were separated using a salt-mediated pH gradient method with volatile additives. The method allowed for low detection limits (0.22 pmol of monoclonal antibodies), while proteins presented nondenatured MS (low number of charges and limited charge state distributions), and the oligomeric state of the complexes analyzed was mostly kept. Excellent chromatographic separations including the resolution of different proteoforms of large proteins (>140 kDa) and a peak capacity of 82 in a 30 min gradient were obtained. The proposed setup and workflows show great potential for analyzing diverse proteoforms in native top-down proteomics, opening unprecedented opportunities for clinical studies and other sample-limited applications.
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Affiliation(s)
- Ziran Zhai
- Analytical
Chemistry Group and Biocatalysis Group, Van’t Hoff Institute
for Molecular Sciences (HIMS), University
of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- Centre
for Analytical Sciences Amsterdam, Van’t Hoff Institute for
Molecular Sciences (HIMS), University of
Amsterdam, Science Park
904, 1098 XH Amsterdam, The Netherlands
| | - Despoina Mavridou
- Analytical
Chemistry Group and Biocatalysis Group, Van’t Hoff Institute
for Molecular Sciences (HIMS), University
of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- Centre
for Analytical Sciences Amsterdam, Van’t Hoff Institute for
Molecular Sciences (HIMS), University of
Amsterdam, Science Park
904, 1098 XH Amsterdam, The Netherlands
| | - Matteo Damian
- Analytical
Chemistry Group and Biocatalysis Group, Van’t Hoff Institute
for Molecular Sciences (HIMS), University
of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Francesco G. Mutti
- Analytical
Chemistry Group and Biocatalysis Group, Van’t Hoff Institute
for Molecular Sciences (HIMS), University
of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
| | - Peter J. Schoenmakers
- Analytical
Chemistry Group and Biocatalysis Group, Van’t Hoff Institute
for Molecular Sciences (HIMS), University
of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- Centre
for Analytical Sciences Amsterdam, Van’t Hoff Institute for
Molecular Sciences (HIMS), University of
Amsterdam, Science Park
904, 1098 XH Amsterdam, The Netherlands
| | - Andrea F. G. Gargano
- Analytical
Chemistry Group and Biocatalysis Group, Van’t Hoff Institute
for Molecular Sciences (HIMS), University
of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- Centre
for Analytical Sciences Amsterdam, Van’t Hoff Institute for
Molecular Sciences (HIMS), University of
Amsterdam, Science Park
904, 1098 XH Amsterdam, The Netherlands
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17
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Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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18
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Naryzhny S. Puzzle of Proteoform Variety-Where Is a Key? Proteomes 2024; 12:15. [PMID: 38804277 PMCID: PMC11130821 DOI: 10.3390/proteomes12020015] [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: 01/31/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
One of the human proteome puzzles is an imbalance between the theoretically calculated and experimentally measured amounts of proteoforms. Considering the possibility of combinations of different post-translational modifications (PTMs), the quantity of possible proteoforms is huge. An estimation gives more than a million different proteoforms in each cell type. But, it seems that there is strict control over the production and maintenance of PTMs. Although the potential complexity of proteoforms due to PTMs is tremendous, available information indicates that only a small part of it is being implemented. As a result, a protein could have many proteoforms according to the number of modification sites, but because of different systems of personal regulation, the profile of PTMs for a given protein in each organism is slightly different.
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Affiliation(s)
- Stanislav Naryzhny
- B. P. Konstantinov Petersburg Nuclear Physics Institute, National Research Center "Kurchatov Institute", Leningrad Region, Gatchina 188300, Russia
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19
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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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20
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Liu Z, Liu H, Vera AM, Yang B, Tinnefeld P, Nash MA. Engineering an artificial catch bond using mechanical anisotropy. Nat Commun 2024; 15:3019. [PMID: 38589360 PMCID: PMC11001878 DOI: 10.1038/s41467-024-46858-9] [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: 11/13/2023] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Catch bonds are a rare class of protein-protein interactions where the bond lifetime increases under an external pulling force. Here, we report how modification of anchor geometry generates catch bonding behavior for the mechanostable Dockerin G:Cohesin E (DocG:CohE) adhesion complex found on human gut bacteria. Using AFM single-molecule force spectroscopy in combination with bioorthogonal click chemistry, we mechanically dissociate the complex using five precisely controlled anchor geometries. When tension is applied between residue #13 on CohE and the N-terminus of DocG, the complex behaves as a two-state catch bond, while in all other tested pulling geometries, including the native configuration, it behaves as a slip bond. We use a kinetic Monte Carlo model with experimentally derived parameters to simulate rupture force and lifetime distributions, achieving strong agreement with experiments. Single-molecule FRET measurements further demonstrate that the complex does not exhibit dual binding mode behavior at equilibrium but unbinds along multiple pathways under force. Together, these results show how mechanical anisotropy and anchor point selection can be used to engineer artificial catch bonds.
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Affiliation(s)
- Zhaowei Liu
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland
- Department of Bionanoscience, Delft University of Technology, 2629HZ, Delft, the Netherlands
| | - Haipei Liu
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland
| | - Andrés M Vera
- Faculty of Chemistry and Center for NanoScience, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Byeongseon Yang
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland
- Botnar Research Centre for Child Health, 4051, Basel, Switzerland
- National Center for Competence in Research (NCCR) Molecular Systems Engineering, 4058, Basel, Switzerland
| | - Philip Tinnefeld
- Faculty of Chemistry and Center for NanoScience, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Michael A Nash
- Institute of Physical Chemistry, Department of Chemistry, University of Basel, 4058, Basel, Switzerland.
- Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.
- Botnar Research Centre for Child Health, 4051, Basel, Switzerland.
- National Center for Competence in Research (NCCR) Molecular Systems Engineering, 4058, Basel, Switzerland.
- Swiss Nanoscience Institute, 4056, Basel, Switzerland.
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21
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Su J, Yang L, Sun Z, Zhan X. Personalized Drug Therapy: Innovative Concept Guided With Proteoformics. Mol Cell Proteomics 2024; 23:100737. [PMID: 38354979 PMCID: PMC10950891 DOI: 10.1016/j.mcpro.2024.100737] [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: 12/05/2023] [Revised: 01/29/2024] [Accepted: 02/09/2024] [Indexed: 02/16/2024] Open
Abstract
Personalized medicine can reduce adverse effects, enhance drug efficacy, and optimize treatment outcomes, which represents the essence of personalized medicine in the pharmacy field. Protein drugs are crucial in the field of personalized drug therapy and are currently the mainstay, which possess higher target specificity and biological activity than small-molecule chemical drugs, making them efficient in regulating disease-related biological processes, and have significant potential in the development of personalized drugs. Currently, protein drugs are designed and developed for specific protein targets based on patient-specific protein data. However, due to the rapid development of two-dimensional gel electrophoresis and mass spectrometry, it is now widely recognized that a canonical protein actually includes multiple proteoforms, and the differences between these proteoforms will result in varying responses to drugs. The variation in the effects of different proteoforms can be significant and the impact can even alter the intended benefit of a drug, potentially making it harmful instead of lifesaving. As a result, we propose that protein drugs should shift from being targeted through the lens of protein (proteomics) to being targeted through the lens of proteoform (proteoformics). This will enable the development of personalized protein drugs that are better equipped to meet patients' specific needs and disease characteristics. With further development in the field of proteoformics, individualized drug therapy, especially personalized protein drugs aimed at proteoforms as a drug target, will improve the understanding of disease mechanisms, discovery of new drug targets and signaling pathways, provide a theoretical basis for the development of new drugs, aid doctors in conducting health risk assessments and making more cost-effective targeted prevention strategies conducted by artificial intelligence/machine learning, promote technological innovation, and provide more convenient treatment tailored to individualized patient profile, which will benefit the affected individuals and society at large.
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Affiliation(s)
- Junwen Su
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Lamei Yang
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Ziran Sun
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Xianquan Zhan
- Medical Science and Technology Innovation Center, Shandong Provincial Key Medical and Health Laboratory of Ovarian Cancer Multiomics, & Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, China.
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22
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Huang CF, Kline JT, Negrão F, Robey MT, Toby TK, Durbin KR, Fellers RT, Friedewald JJ, Levitsky J, Abecassis MMI, Melani RD, Kelleher NL, Fornelli L. Targeted Quantification of Proteoforms in Complex Samples by Proteoform Reaction Monitoring. Anal Chem 2024; 96:3578-3586. [PMID: 38354049 PMCID: PMC11008684 DOI: 10.1021/acs.analchem.3c05578] [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] [Indexed: 02/28/2024]
Abstract
Existing mass spectrometric assays used for sensitive and specific measurements of target proteins across multiple samples, such as selected/multiple reaction monitoring (SRM/MRM) or parallel reaction monitoring (PRM), are peptide-based methods for bottom-up proteomics. Here, we describe an approach based on the principle of PRM for the measurement of intact proteoforms by targeted top-down proteomics, termed proteoform reaction monitoring (PfRM). We explore the ability of our method to circumvent traditional limitations of top-down proteomics, such as sensitivity and reproducibility. We also introduce a new software program, Proteoform Finder (part of ProSight Native), specifically designed for the easy analysis of PfRM data. PfRM was initially benchmarked by quantifying three standard proteins. The linearity of the assay was shown over almost 3 orders of magnitude in the femtomole range, with limits of detection and quantification in the low femtomolar range. We later applied our multiplexed PfRM assay to complex samples to quantify biomarker candidates in peripheral blood mononuclear cells (PBMCs) from liver-transplanted patients, suggesting their possible translational applications. These results demonstrate that PfRM has the potential to contribute to the accurate quantification of protein biomarkers for diagnostic purposes and to improve our understanding of disease etiology at the proteoform level.
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Affiliation(s)
- Che-Fan Huang
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Jake T Kline
- School of Biological Sciences, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Fernanda Negrão
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Matthew T Robey
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
- Proteinaceous, Inc., Evanston, Illinois 60201, United States
| | - Timothy K Toby
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Kenneth R Durbin
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
- Proteinaceous, Inc., Evanston, Illinois 60201, United States
| | - Ryan T Fellers
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
- Proteinaceous, Inc., Evanston, Illinois 60201, United States
| | - John J Friedewald
- Comprehensive Transplant Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, United States
| | - Josh Levitsky
- Comprehensive Transplant Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, United States
| | - Michael M I Abecassis
- Comprehensive Transplant Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, United States
| | - Rafael D Melani
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Neil L Kelleher
- Departments of Molecular Biosciences, Chemistry, and the Feinberg School of Medicine, Northwestern University, Evanston, Illinois 60208, United States
| | - Luca Fornelli
- School of Biological Sciences, University of Oklahoma, Norman, Oklahoma 73019, United States
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
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23
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Lermyte F. The need for open and FAIR data in top-down proteomics. Proteomics 2024; 24:e2300354. [PMID: 38088481 DOI: 10.1002/pmic.202300354] [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: 10/18/2023] [Accepted: 10/24/2023] [Indexed: 02/15/2024]
Abstract
In recent years, there has been a tremendous evolution in the high-throughput, tandem mass spectrometry-based analysis of intact proteins, also known as top-down proteomics (TDP). Both hardware and software have developed to the point that the technique has largely entered the mainstream, and large-scale, ambitious, multi-laboratory initiatives have started to make their appearance in the literature. For this, however, more convenient and robust data sharing and reuse will be required. Walzer et al. have created TopDownApp, a customisable, open platform for visualisation and analysis of TDP data, which they hope will be a step in this direction. As they point out, other benefits of such data sharing and interoperability would include reanalysis of published datasets, as well as the prospect of using large amounts of data to train machine learning algorithms. In time, this work could prove to be a valuable resource in the move towards a future of greater TDP data findability, accessibility, interoperability and reusability.
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Affiliation(s)
- Frederik Lermyte
- Department of Chemistry, Clemens-Schöpf Institute of Organic Chemistry and Biochemistry, Technical University of Darmstadt, Darmstadt, Germany
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24
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Takemori A, Kaulich PT, Konno R, Kawashima Y, Hamazaki Y, Hoshino A, Tholey A, Takemori N. GeLC-FAIMS-MS workflow for in-depth middle-down proteomics. Proteomics 2024; 24:e2200431. [PMID: 37548120 DOI: 10.1002/pmic.202200431] [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: 04/14/2023] [Revised: 06/20/2023] [Accepted: 07/20/2023] [Indexed: 08/08/2023]
Abstract
Middle-down proteomics (MDP) is an analytical approach in which protein samples are digested with proteases such as Glu-C to generate large peptides (>3 kDa) that are analyzed by mass spectrometry (MS). This method is useful for characterizing high-molecular-weight proteins that are difficult to detect by top-down proteomics (TDP), in which intact proteins are analyzed by MS. In this study, we applied GeLC-FAIMS-MS, a multidimensional separation workflow that combines gel-based prefractionation with LC-FAIMS MS, for deep MDP. Middle-down peptides generated by optimized limited Glu-C digestion conditions were first size-fractionated by polyacrylamide gel electrophoresis, followed by C4 reversed-phase liquid chromatography separation and additional ion mobility fractionation, resulting in a significant increase in peptide length detectable by MS. In addition to global analysis, the GeLC-FAIMS-MS concept can also be applied to targeted MDP, where only proteins in the desired molecular weight range are gel-fractionated and their Glu-C digestion products are analyzed, as demonstrated by targeted analysis of integrins in exosomes. In-depth MDP achieved by global and targeted GeLC-FAIMS-MS supports the exploration of proteoform information not covered by conventional TDP by increasing the number of detectable protein groups or post-translational modifications (PTMs) and improving the sequence coverage.
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Affiliation(s)
- Ayako Takemori
- Advanced Research Support Center, Institute for Promotion of Science and Technology, Ehime University, Ehime, Japan
| | - Philipp T Kaulich
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Ryo Konno
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba, Japan
| | - Yusuke Kawashima
- Department of Applied Genomics, Kazusa DNA Research Institute, Chiba, Japan
| | - Yuto Hamazaki
- School of Life Science and Technology, Tokyo Institute of Technology, Kanagawa, Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Ayuko Hoshino
- School of Life Science and Technology, Tokyo Institute of Technology, Kanagawa, Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Andreas Tholey
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Nobuaki Takemori
- Advanced Research Support Center, Institute for Promotion of Science and Technology, Ehime University, Ehime, Japan
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25
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Walzer M, Jeong K, Tabb DL, Vizcaíno JA. TopDownApp: An open and modular platform for analysis and visualisation of top-down proteomics data. Proteomics 2024; 24:e2200403. [PMID: 37787899 DOI: 10.1002/pmic.202200403] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 10/04/2023]
Abstract
Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise their adoption. In this context, there are numerous improvements that are possible in the area of open science practices, including a greater application of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. These include, for example, increased data sharing practices and readily available open data standards. Additionally, the field would benefit from the development of open data analysis workflows that can enable data reuse of public datasets, something that is increasingly common in other proteomics fields.
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Affiliation(s)
- Mathias Walzer
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Kyowon Jeong
- Applied Bioinformatics, Computer Science Department, University of Tübingen, Tübingen, Germany
| | - David L Tabb
- Institut Pasteur, Université Paris Cité, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris, France
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
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26
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Adair LR, Jones I, Cramer R. Utilizing Precursor Ion Connectivity of Different Charge States to Improve Peptide and Protein Identification in MS/MS Analysis. Anal Chem 2024; 96:985-990. [PMID: 38193749 PMCID: PMC10809226 DOI: 10.1021/acs.analchem.3c03061] [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: 07/12/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 01/10/2024]
Abstract
Tandem mass spectrometry (MS/MS) has become a key method for the structural analysis of biomolecules such as peptides and proteins. A pervasive problem in MS/MS analyses, especially for top-down proteomics, is the occurrence of chimeric spectra, when two or more precursor ions are co-isolated and fragmented, thus leading to complex MS/MS spectra that are populated with fragment ions originating from different precursor ions. This type of convoluted data typically results in low sequence database search scores due to the vast number of mixed-source fragment ions, of which only a fraction originates from a specific precursor ion. Herein, we present a novel workflow that deconvolutes the data of chimeric MS/MS spectra, improving the protein search scores and sequence coverages in database searching and thus providing a more confident peptide and protein identification. Previously misidentified proteins or proteins with insignificant search scores can be correctly and significantly identified following the presented data acquisition and analysis workflow with search scores increasing by a factor of 3-4 for smaller precursor ions (peptides) and >6 for larger precursor ions such as intact ubiquitin and cytochrome C.
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Affiliation(s)
- Lily R. Adair
- Department
of Chemistry, University of Reading, Whiteknights, Reading RG6 6DX, United Kingdom
| | - Ian Jones
- School
of Biological Sciences, University of Reading, Whiteknights, Reading RG6 6AJ, United Kingdom
| | - Rainer Cramer
- Department
of Chemistry, University of Reading, Whiteknights, Reading RG6 6DX, United Kingdom
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27
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Po A, Eyers CE. Top-Down Proteomics and the Challenges of True Proteoform Characterization. J Proteome Res 2023; 22:3663-3675. [PMID: 37937372 PMCID: PMC10696603 DOI: 10.1021/acs.jproteome.3c00416] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/09/2023] [Accepted: 10/20/2023] [Indexed: 11/09/2023]
Abstract
Top-down proteomics (TDP) aims to identify and profile intact protein forms (proteoforms) extracted from biological samples. True proteoform characterization requires that both the base protein sequence be defined and any mass shifts identified, ideally localizing their positions within the protein sequence. Being able to fully elucidate proteoform profiles lends insight into characterizing proteoform-unique roles, and is a crucial aspect of defining protein structure-function relationships and the specific roles of different (combinations of) protein modifications. However, defining and pinpointing protein post-translational modifications (PTMs) on intact proteins remains a challenge. Characterization of (heavily) modified proteins (>∼30 kDa) remains problematic, especially when they exist in a population of similarly modified, or kindred, proteoforms. This issue is compounded as the number of modifications increases, and thus the number of theoretical combinations. Here, we present our perspective on the challenges of analyzing kindred proteoform populations, focusing on annotation of protein modifications on an "average" protein. Furthermore, we discuss the technical requirements to obtain high quality fragmentation spectral data to robustly define site-specific PTMs, and the fact that this is tempered by the time requirements necessary to separate proteoforms in advance of mass spectrometry analysis.
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Affiliation(s)
- Allen Po
- Centre
for Proteome Research, Faculty of Health & Life Sciences, University of Liverpool, Liverpool L69 7ZB, U.K.
- Department
of Biochemistry, Cell & Systems Biology, Institute of Systems,
Molecular & Integrative Biology, Faculty of Health & Life
Sciences, University of Liverpool, Liverpool L69 7ZB, U.K.
| | - Claire E. Eyers
- Centre
for Proteome Research, Faculty of Health & Life Sciences, University of Liverpool, Liverpool L69 7ZB, U.K.
- Department
of Biochemistry, Cell & Systems Biology, Institute of Systems,
Molecular & Integrative Biology, Faculty of Health & Life
Sciences, University of Liverpool, Liverpool L69 7ZB, U.K.
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28
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Huang CF, Su P, Fisher TD, Levitsky J, Kelleher NL, Forte E. Mass spectrometry-based proteomics for advancing solid organ transplantation research. FRONTIERS IN TRANSPLANTATION 2023; 2:1286881. [PMID: 38993855 PMCID: PMC11235370 DOI: 10.3389/frtra.2023.1286881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 11/13/2023] [Indexed: 07/13/2024]
Abstract
Scarcity of high-quality organs, suboptimal organ quality assessment, unsatisfactory pre-implantation procedures, and poor long-term organ and patient survival are the main challenges currently faced by the solid organ transplant (SOT) field. New biomarkers for assessing graft quality pre-implantation, detecting, and predicting graft injury, rejection, dysfunction, and survival are critical to provide clinicians with invaluable prediction tools and guidance for personalized patients' treatment. Additionally, new therapeutic targets are also needed to reduce injury and rejection and improve transplant outcomes. Proteins, which underlie phenotypes, are ideal candidate biomarkers of health and disease statuses and therapeutic targets. A protein can exist in different molecular forms, called proteoforms. As the function of a protein depends on its exact composition, proteoforms can offer a more accurate basis for connection to complex phenotypes than protein from which they derive. Mass spectrometry-based proteomics has been largely used in SOT research for identification of candidate biomarkers and therapeutic intervention targets by so-called "bottom-up" proteomics (BUP). However, such BUP approaches analyze small peptides in lieu of intact proteins and provide incomplete information on the exact molecular composition of the proteins of interest. In contrast, "Top-down" proteomics (TDP), which analyze intact proteins retaining proteoform-level information, have been only recently adopted in transplantation studies and already led to the identification of promising proteoforms as biomarkers for organ rejection and dysfunction. We anticipate that the use of top-down strategies in combination with new technological advancements in single-cell and spatial proteomics could drive future breakthroughs in biomarker and therapeutic target discovery in SOT.
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Affiliation(s)
- Che-Fan Huang
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, United States
| | - Pei Su
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, United States
- Department of Chemistry, Northwestern University, Evanston, IL, United States
| | - Troy D. Fisher
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, United States
| | - Josh Levitsky
- Division of Gastroenterology and Hepatology, Comprehensive Transplant Center Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Neil L. Kelleher
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, United States
- Department of Chemistry, Northwestern University, Evanston, IL, United States
- Department of Biochemistry and Molecular Genetics, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
- Department of Surgery, Feinberg School of Medicine, Comprehensive Transplant Center, Northwestern University, Chicago, IL, United States
| | - Eleonora Forte
- Proteomics Center of Excellence, Northwestern University, Evanston, IL, United States
- Department of Surgery, Feinberg School of Medicine, Comprehensive Transplant Center, Northwestern University, Chicago, IL, United States
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29
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Zhang W, Cao L, Yang J, Zhang S, Zhao J, Shi Z, Liao K, Wang H, Chen B, Qian Z, Xu H, Wu L, Liu H, Wang H, Ma C, Qiu Y, Ge J, Chen J, Lin Y. AEP-cleaved DDX3X induces alternative RNA splicing events to mediate cancer cell adaptation in harsh microenvironments. J Clin Invest 2023; 134:e173299. [PMID: 37988165 PMCID: PMC10849765 DOI: 10.1172/jci173299] [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: 06/27/2023] [Accepted: 11/14/2023] [Indexed: 11/23/2023] Open
Abstract
Oxygen and nutrient deprivation are common features of solid tumors. Although abnormal alternative splicing (AS) has been found to be an important driving force in tumor pathogenesis and progression, the regulatory mechanisms of AS that underly the adaptation of cancer cells to harsh microenvironments remain unclear. Here, we found that hypoxia- and nutrient deprivation-induced asparagine endopeptidase (AEP) specifically cleaved DDX3X in a HIF1A-dependent manner. This cleavage yields truncated carboxyl-terminal DDX3X (tDDX3X-C), which translocates and aggregates in the nucleus. Unlike intact DDX3X, nuclear tDDX3X-C complexes with an array of splicing factors and induces AS events of many pre-mRNAs; for example, enhanced exon skipping (ES) in exon 2 of the classic tumor suppressor PRDM2 leads to a frameshift mutation of PRDM2. Intriguingly, the isoform ARRB1-Δexon 13 binds to glycolytic enzymes and regulates glycolysis. By utilizing in vitro assays, glioblastoma organoids, and animal models, we revealed that AEP/tDDX3X-C promoted tumor malignancy via these isoforms. More importantly, high AEP/tDDX3X-C/ARRB1-Δexon 13 in cancerous tissues was tightly associated with poor patient prognosis. Overall, our discovery of the effect of AEP-cleaved DDX3X switching on alternative RNA splicing events identifies a mechanism in which cancer cells adapt to oxygen and nutrient shortages and provides potential diagnostic and/or therapeutic targets.
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Affiliation(s)
- Wenrui Zhang
- Brain Injury Center, Shanghai Institute of Head Trauma and
- Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lu Cao
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Yang
- Department of Neurosurgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuai Zhang
- Department of Neurosurgery, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jianyi Zhao
- Brain Injury Center, Shanghai Institute of Head Trauma and
- Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhonggang Shi
- Brain Injury Center, Shanghai Institute of Head Trauma and
- Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Keman Liao
- Brain Injury Center, Shanghai Institute of Head Trauma and
| | - Haiwei Wang
- Fujian Key Laboratory for Prenatal Diagnosis and Birth Defects, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Binghong Chen
- Department of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Zhongrun Qian
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, China
| | - Haoping Xu
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linshi Wu
- Department of Biliary-Pancreatic Surgery and
| | - Hua Liu
- Department of General Surgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hongxiang Wang
- Department of Neurosurgery, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chunhui Ma
- Department of Orthopedics, Shanghai General Hospital of Shanghai Jiao Tong University, Shanghai, China
| | - Yongming Qiu
- Brain Injury Center, Shanghai Institute of Head Trauma and
| | - Jianwei Ge
- Department of Neurosurgery, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiayi Chen
- Department of Radiation Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingying Lin
- Brain Injury Center, Shanghai Institute of Head Trauma and
- Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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30
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Kline JT, Belford MW, Boeser CL, Huguet R, Fellers RT, Greer JB, Greer SM, Horn DM, Durbin KR, Dunyach JJ, Ahsan N, Fornelli L. Orbitrap Mass Spectrometry and High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Enable the in-Depth Analysis of Human Serum Proteoforms. J Proteome Res 2023; 22:3418-3426. [PMID: 37774690 PMCID: PMC10629265 DOI: 10.1021/acs.jproteome.3c00488] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Indexed: 10/01/2023]
Abstract
Blood serum and plasma are arguably the most commonly analyzed clinical samples, with dozens of proteins serving as validated biomarkers for various human diseases. Top-down proteomics may provide additional insights into disease etiopathogenesis since this approach focuses on protein forms, or proteoforms, originally circulating in blood, potentially providing access to information about relevant post-translational modifications, truncations, single amino acid substitutions, and many other sources of protein variation. However, the vast majority of proteomic studies on serum and plasma are carried out using peptide-centric, bottom-up approaches that cannot recapitulate the original proteoform content of samples. Clinical laboratories have been slow to adopt top-down analysis, also due to higher sample handling requirements. In this study, we describe a straightforward protocol for intact proteoform sample preparation based on the depletion of albumin and immunoglobulins, followed by simplified protein fractionation via polyacrylamide gel electrophoresis. After molecular weight-based fractionation, we supplemented the traditional liquid chromatography-tandem mass spectrometry (LC-MS2) data acquisition with high-field asymmetric waveform ion mobility spectrometry (FAIMS) to further simplify serum proteoform mixtures. This LC-FAIMS-MS2 method led to the identification of over 1000 serum proteoforms < 30 kDa, outperforming traditional LC-MS2 data acquisition and more than doubling the number of proteoforms identified in previous studies.
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Affiliation(s)
- Jake T. Kline
- Department
of Biology, University of Oklahoma, Norman, Oklahoma 73019, United States
| | | | | | - Romain Huguet
- Thermo
Scientific, San Jose, California 95134, United States
| | - Ryan T. Fellers
- Proteinaceous,
Inc., Evanston, Illinois 60204, United
States
| | - Joseph B. Greer
- Proteinaceous,
Inc., Evanston, Illinois 60204, United
States
| | | | - David M. Horn
- Thermo
Scientific, San Jose, California 95134, United States
| | | | | | - Nagib Ahsan
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019, United States
- Mass
Spectrometry, Proteomics and Metabolomics Core Facility, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Luca Fornelli
- Department
of Biology, University of Oklahoma, Norman, Oklahoma 73019, United States
- Department
of Chemistry and Biochemistry, University
of Oklahoma, Norman, Oklahoma 73019, United States
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31
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Dam SH, Olsen LR, Vitting-Seerup K. Expression and splicing mediate distinct biological signals. BMC Biol 2023; 21:220. [PMID: 37858135 PMCID: PMC10588054 DOI: 10.1186/s12915-023-01724-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: 12/01/2022] [Accepted: 10/04/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND Through alternative splicing, most human genes produce multiple isoforms in a cell-, tissue-, and disease-specific manner. Numerous studies show that alternative splicing is essential for development, diseases, and their treatments. Despite these important examples, the extent and biological relevance of splicing are currently unknown. RESULTS To solve this problem, we developed pairedGSEA and used it to profile transcriptional changes in 100 representative RNA-seq datasets. Our systematic analysis demonstrates that changes in splicing, on average, contribute to 48.1% of the biological signal in expression analyses. Gene-set enrichment analysis furthermore indicates that expression and splicing both convey shared and distinct biological signals. CONCLUSIONS These findings establish alternative splicing as a major regulator of the human condition and suggest that most contemporary RNA-seq studies likely miss out on critical biological insights. We anticipate our results will contribute to the transition from a gene-centric to an isoform-centric research paradigm.
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Affiliation(s)
- Søren Helweg Dam
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Lars Rønn Olsen
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Kristoffer Vitting-Seerup
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
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32
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Robey MT, Utley D, Greer JB, Fellers RT, Kelleher NL, Durbin KR. Advancing Intact Protein Quantitation with Updated Deconvolution Routines. Anal Chem 2023; 95:14954-14962. [PMID: 37750863 PMCID: PMC10840078 DOI: 10.1021/acs.analchem.3c02345] [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] [Indexed: 09/27/2023]
Abstract
Analysis of intact proteins by mass spectrometry enables direct quantitation of the specific proteoforms present in a sample and is an increasingly important tool for biopharmaceutical and academic research. Interpreting and quantifying intact protein species from mass spectra typically involves many challenges including mass deconvolution and peak processing as well as determining optimal spectral averaging parameters and matching masses to theoretical proteoforms. Each of these steps can present informatic hurdles, as parameters often need to be tailored specifically to the data sets. To reduce intact mass deconvolution data analysis burdens, we built upon the widely used "sliding window" mass deconvolution technique with several additional concepts. First, we found that how spectra are averaged and the overlap in spectral windows can be tuned to favor either sensitivity or speed. A multiple window averaging approach was found to be the most effective way to increase mass detection and yielded a >2-fold increase in the number of masses detected. We also developed a targeted feature-finding routine that boosted sensitivity by >2-fold, decreased coefficient of variation across replicates by 50%, and increased the quality of mass elution profiles through 3-fold more detected time points. Lastly, we furthered existing approaches for annotating detected masses with potential proteoforms through spectral fitting for possible proteoform family modifications and network viewing. These proteoform annotation approaches ultimately produced a more accurate way of finding related, but previously unknown proteoforms from intact mass-only data. Together, these quantitation workflow improvements advance the information obtainable from intact protein mass spectrometry analyses.
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Affiliation(s)
- Matthew T Robey
- Proteinaceous, Inc., Evanston, Illinois 60201, United States
- Northwestern University, Evanston, Illinois 60208, United States
| | - Daisha Utley
- Proteinaceous, Inc., Evanston, Illinois 60201, United States
| | - Joseph B Greer
- Proteinaceous, Inc., Evanston, Illinois 60201, United States
- Northwestern University, Evanston, Illinois 60208, United States
| | - Ryan T Fellers
- Proteinaceous, Inc., Evanston, Illinois 60201, United States
- Northwestern University, Evanston, Illinois 60208, United States
| | - Neil L Kelleher
- Proteinaceous, Inc., Evanston, Illinois 60201, United States
- Northwestern University, Evanston, Illinois 60208, United States
| | - Kenneth R Durbin
- Proteinaceous, Inc., Evanston, Illinois 60201, United States
- Northwestern University, Evanston, Illinois 60208, United States
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33
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Dey AK, Banarjee R, Boroumand M, Rutherford DV, Strassheim Q, Nyunt T, Olinger B, Basisty N. Translating Senotherapeutic Interventions into the Clinic with Emerging Proteomic Technologies. BIOLOGY 2023; 12:1301. [PMID: 37887011 PMCID: PMC10604147 DOI: 10.3390/biology12101301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
Cellular senescence is a state of irreversible growth arrest with profound phenotypic changes, including the senescence-associated secretory phenotype (SASP). Senescent cell accumulation contributes to aging and many pathologies including chronic inflammation, type 2 diabetes, cancer, and neurodegeneration. Targeted removal of senescent cells in preclinical models promotes health and longevity, suggesting that the selective elimination of senescent cells is a promising therapeutic approach for mitigating a myriad of age-related pathologies in humans. However, moving senescence-targeting drugs (senotherapeutics) into the clinic will require therapeutic targets and biomarkers, fueled by an improved understanding of the complex and dynamic biology of senescent cell populations and their molecular profiles, as well as the mechanisms underlying the emergence and maintenance of senescence cells and the SASP. Advances in mass spectrometry-based proteomic technologies and workflows have the potential to address these needs. Here, we review the state of translational senescence research and how proteomic approaches have added to our knowledge of senescence biology to date. Further, we lay out a roadmap from fundamental biological discovery to the clinical translation of senotherapeutic approaches through the development and application of emerging proteomic technologies, including targeted and untargeted proteomic approaches, bottom-up and top-down methods, stability proteomics, and surfaceomics. These technologies are integral for probing the cellular composition and dynamics of senescent cells and, ultimately, the development of senotype-specific biomarkers and senotherapeutics (senolytics and senomorphics). This review aims to highlight emerging areas and applications of proteomics that will aid in exploring new senescent cell biology and the future translation of senotherapeutics.
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Affiliation(s)
| | | | | | | | | | | | | | - Nathan Basisty
- Translational Geroproteomics Unit, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA; (A.K.D.); (R.B.); (M.B.); (D.V.R.); (Q.S.); (T.N.); (B.O.)
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Merola J, Emond JC, Levitsky J. Novel Noninvasive Biomarkers in Liver Transplantation: A Tool on the Doorstep of Clinical Utilization. Transplantation 2023; 107:2120-2125. [PMID: 37019173 DOI: 10.1097/tp.0000000000004580] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Biomarkers have the potential to transform the detection, treatment, and outcomes of liver transplant complications, though their application is limited because of the lack of prospective validation. Although many genetic, proteomic, and immune markers correlating with allograft rejection and graft dysfunction have been described, evaluation of these markers in combination and validation among a broad liver transplant recipient population remain understudied. In this review, we present evidence supporting biomarker applications in 5 clinical liver transplant scenarios: (i) diagnosis of allograft rejection, (ii) prediction of allograft rejection, (iii) minimization of immunosuppression, (iv) detection of fibrosis and recurrent disease, and (v) prediction of renal recovery following liver transplantation. Current limitations for biomarker utilization and opportunities for further investigation are discussed. Accurate risk assessment, diagnosis, and evaluation of treatment responses using such noninvasive tools will pave the way for a more personalized and precise approach to management of the liver transplant patients that has profound potential to reduce morbidity and improve graft and patient longevity.
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Affiliation(s)
- Jonathan Merola
- Center for Liver Disease and Transplantation, Columbia University Medical Center, New York, NY
| | - Jean C Emond
- Center for Liver Disease and Transplantation, Columbia University Medical Center, New York, NY
| | - Josh Levitsky
- Division of Gastroenterology and Hepatology, Northwestern University Feinberg School of Medicine, Chicago, IL
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35
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Daly LA, Clarke CJ, Po A, Oswald SO, Eyers CE. Considerations for defining +80 Da mass shifts in mass spectrometry-based proteomics: phosphorylation and beyond. Chem Commun (Camb) 2023; 59:11484-11499. [PMID: 37681662 PMCID: PMC10521633 DOI: 10.1039/d3cc02909c] [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: 06/16/2023] [Accepted: 08/21/2023] [Indexed: 09/09/2023]
Abstract
Post-translational modifications (PTMs) are ubiquitous and key to regulating protein function. Understanding the dynamics of individual PTMs and their biological roles requires robust characterisation. Mass spectrometry (MS) is the method of choice for the identification and quantification of protein modifications. This article focusses on the MS-based analysis of those covalent modifications that induce a mass shift of +80 Da, notably phosphorylation and sulfation, given the challenges associated with their discrimination and pinpointing the sites of modification on a polypeptide chain. Phosphorylation in particular is highly abundant, dynamic and can occur on numerous residues to invoke specific functions, hence robust characterisation is crucial to understanding biological relevance. Showcasing our work in the context of other developments in the field, we highlight approaches for enrichment and site localisation of phosphorylated (canonical and non-canonical) and sulfated peptides, as well as modification analysis in the context of intact proteins (top down proteomics) to explore combinatorial roles. Finally, we discuss the application of native ion-mobility MS to explore the effect of these PTMs on protein structure and ligand binding.
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Affiliation(s)
- Leonard A Daly
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.
| | - Christopher J Clarke
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.
| | - Allen Po
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.
| | - Sally O Oswald
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.
| | - Claire E Eyers
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, UK.
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36
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Yang L, Li C, Song T, Zhan X. Growth hormone proteoformics atlas created to promote predictive, preventive, and personalized approach in overall management of pituitary neuroendocrine tumors. EPMA J 2023; 14:443-456. [PMID: 37605654 PMCID: PMC10439873 DOI: 10.1007/s13167-023-00329-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 06/14/2023] [Indexed: 08/23/2023]
Abstract
Human growth hormone (GH) is the indispensable hormone for the maintenance of normal physiological functions of the human body, including the growth, development, metabolism, and even immunoregulation. The GH is synthesized, secreted, and stored by somatotroph cells in adenohypophysis. Abnormal GH is associated with various GH-related diseases, such as acromegaly, dwarfism, diabetes, and cancer. Currently, some studies found there are dozens or even hundreds of GH proteoforms in tissue and serum as well as a series of GH-binding protein (GHBP) proteoforms and GH receptor (GHR) proteoforms were also identified. The structure-function relationship of protein hormone proteoforms is significantly important to reveal their overall physiological and pathophysiological mechanisms. We propose the use of proteoformics to study the relationship between every GH proteoform and different physiological/pathophysiological states to clarify the pathogenic mechanism of GH-related disease such as pituitary neuroendocrine tumor and conduct precise molecular classification to promote predictive preventive personalized medicine (PPPM / 3P medicine). This article reviews GH proteoformics in GH-related disease such as pituitary neuroendocrine tumor, which has the potential role to provide novel insight into pathogenic mechanism, discover novel therapeutic targets, identify effective GH proteoform biomarker for patient stratification, predictive diagnosis, and prognostic assessment, improve therapy method, and further accelerate the development of 3P medicine.
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Affiliation(s)
- Lamei Yang
- Medical Science and Technology Innovation Center, and Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
| | - Chunling Li
- Department of Anesthesiology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 People’s Republic of China
| | - Tao Song
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Weiqi Road, Jinan, Shandong 250021 People’s Republic of China
| | - Xianquan Zhan
- Medical Science and Technology Innovation Center, and Shandong Key Laboratory of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University & Shandong Academy of Medical Sciences, 440 Jiyan Road, Jinan, Shandong 250117 People’s Republic of China
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37
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Kourti M, Aivaliotis M, Hatzipantelis E. Proteomics in Childhood Acute Lymphoblastic Leukemia: Challenges and Opportunities. Diagnostics (Basel) 2023; 13:2748. [PMID: 37685286 PMCID: PMC10487225 DOI: 10.3390/diagnostics13172748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/20/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
Acute lymphoblastic leukemia (ALL) is the most common cancer in children and one of the success stories in cancer therapeutics. Risk-directed therapy based on clinical, biologic and genetic features has played a significant role in this accomplishment. Despite the observed improvement in survival rates, leukemia remains one of the leading causes of cancer-related deaths. Implementation of next-generation genomic and transcriptomic sequencing tools has illustrated the genomic landscape of ALL. However, the underlying dynamic changes at protein level still remain a challenge. Proteomics is a cutting-edge technology aimed at deciphering the mechanisms, pathways, and the degree to which the proteome impacts leukemia subtypes. Advances in mass spectrometry enable high-throughput collection of global proteomic profiles, representing an opportunity to unveil new biological markers and druggable targets. The purpose of this narrative review article is to provide a comprehensive overview of studies that have utilized applications of proteomics in an attempt to gain insight into the pathogenesis and identification of biomarkers in childhood ALL.
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Affiliation(s)
- Maria Kourti
- Third Department of Pediatrics, School of Medicine, Aristotle University and Hippokration General Hospital, 54642 Thessaloniki, Greece
| | - Michalis Aivaliotis
- Laboratory of Biological Chemistry, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Emmanouel Hatzipantelis
- Children & Adolescent Hematology-Oncology Unit, Second Department of Pediatrics, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
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38
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Su T, Hollas MAR, Fellers RT, Kelleher NL. Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics. Annu Rev Biomed Data Sci 2023; 6:357-376. [PMID: 37561601 PMCID: PMC10840079 DOI: 10.1146/annurev-biodatasci-020722-044021] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.
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Affiliation(s)
- Taojunfeng Su
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA;
| | - Michael A R Hollas
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois, USA
| | - Ryan T Fellers
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois, USA
| | - Neil L Kelleher
- Department of Molecular Biosciences, Northwestern University, Evanston, Illinois, USA;
- Proteomics Center of Excellence, Northwestern University, Evanston, Illinois, USA
- Department of Chemistry, Northwestern University, Evanston, Illinois, USA
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39
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Jain S, Pei L, Spraggins JM, Angelo M, Carson JP, Gehlenborg N, Ginty F, Gonçalves JP, Hagood JS, Hickey JW, Kelleher NL, Laurent LC, Lin S, Lin Y, Liu H, Naba A, Nakayasu ES, Qian WJ, Radtke A, Robson P, Stockwell BR, Van de Plas R, Vlachos IS, Zhou M, Börner K, Snyder MP. Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nat Cell Biol 2023; 25:1089-1100. [PMID: 37468756 PMCID: PMC10681365 DOI: 10.1038/s41556-023-01194-w] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/22/2023] [Indexed: 07/21/2023]
Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.
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Affiliation(s)
- Sanjay Jain
- Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA.
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
| | - Liming Pei
- Center for Mitochondrial and Epigenomic Medicine, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, and Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jeffrey M Spraggins
- Department of Cell and Developmental Biology and the Mass Spectrometry Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
| | - Michael Angelo
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - James P Carson
- Texas Advanced Computing Center, University of Texas at Austin, Austin, TX, USA
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Joana P Gonçalves
- Department of Intelligent Systems, Delft University of Technology, Delft, Netherlands
| | - James S Hagood
- Department of Pediatrics (Pulmonology) and Program for Rare and Interstitial Lung Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - John W Hickey
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Neil L Kelleher
- Departments of Medicine, Chemistry and Molecular Biosciences, Northwestern University, Evanston, IL, USA
| | - Louise C Laurent
- Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Shin Lin
- Division of Cardiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
| | - Huiping Liu
- Departments of Pharmacology, Medicine (Hematology and Oncology), Lurie Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Alexandra Naba
- Department of Physiology and Biophysics, University of Illinois at Chicago, Chicago, IL, USA
| | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Andrea Radtke
- Lymphocyte Biology Section and Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Paul Robson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Brent R Stockwell
- Department of Biological Sciences and Department of Chemistry, Columbia University, New York, NY, USA
| | - Raf Van de Plas
- Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands
| | - Ioannis S Vlachos
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Spatial Technologies Unit, Harvard Medical School Initiative for RNA Medicine, Department of Pathology, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Katy Börner
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA.
| | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA.
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40
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Guo Y, Cupp‐Sutton KA, Zhao Z, Anjum S, Wu S. Multidimensional Separations in Top-Down Proteomics. ANALYTICAL SCIENCE ADVANCES 2023; 4:181-203. [PMID: 38188188 PMCID: PMC10769458 DOI: 10.1002/ansa.202300016] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/21/2023] [Accepted: 05/01/2023] [Indexed: 01/09/2024]
Abstract
Top-down proteomics (TDP) identifies, quantifies, and characterizes proteins at the intact proteoform level in complex biological samples to understand proteoform function and cellular mechanisms. However, analyzing complex biological samples using TDP is still challenging due to high sample complexity and wide dynamic range. High-resolution separation methods are often applied prior to mass spectrometry (MS) analysis to decrease sample complexity and increase proteomics throughput. These separation methods, however, may not be efficient enough to characterize low abundance intact proteins in complex samples. As such, multidimensional separation techniques (combination of two or more separation methods with high orthogonality) have been developed and applied that demonstrate improved separation resolution and more comprehensive identification in TDP. A suite of multidimensional separation methods that couple various types of liquid chromatography (LC), capillary electrophoresis (CE), and/or gel electrophoresis-based separation approaches have been developed and applied in TDP to analyze complex biological samples. Here, we reviewed multidimensional separation strategies employed for TDP, summarized current applications, and discussed the gaps that may be addressed in the future.
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Affiliation(s)
- Yanting Guo
- Department of Chemistry and BiochemistryUniversity of OklahomaOklahomaNormanUSA
| | | | - Zhitao Zhao
- Department of Chemistry and BiochemistryUniversity of OklahomaOklahomaNormanUSA
| | - Samin Anjum
- Department of Chemistry and BiochemistryUniversity of OklahomaOklahomaNormanUSA
| | - Si Wu
- Department of Chemistry and BiochemistryUniversity of OklahomaOklahomaNormanUSA
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41
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Wang Q, Fang F, Sun L. Pilot investigation of magnetic nanoparticle-based immobilized metal affinity chromatography for efficient enrichment of phosphoproteoforms for mass spectrometry-based top-down proteomics. Anal Bioanal Chem 2023; 415:4521-4531. [PMID: 37017721 PMCID: PMC10540245 DOI: 10.1007/s00216-023-04677-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/23/2023] [Accepted: 03/28/2023] [Indexed: 04/06/2023]
Abstract
Protein phosphorylation is a vital and common post-translational modification (PTM) in cells, modulating various biological processes and diseases. Comprehensive top-down proteomics of phosphorylated proteoforms (phosphoproteoforms) in cells and tissues is essential for a better understanding of the roles of protein phosphorylation in fundamental biological processes and diseases. Mass spectrometry (MS)-based top-down proteomics of phosphoproteoforms remains challenging due to their relatively low abundance. Herein, we investigated magnetic nanoparticle-based immobilized metal affinity chromatography (IMAC, Ti4+, and Fe3+) for selective enrichment of phosphoproteoforms for MS-based top-down proteomics. The IMAC method achieved reproducible and highly efficient enrichment of phosphoproteoforms from simple and complex protein mixtures. It outperformed one commercial phosphoprotein enrichment kit regarding the capture efficiency and recovery of phosphoproteins. Reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) analyses of yeast cell lysates after IMAC (Ti4+ or Fe3+) enrichment produced roughly 100% more phosphoproteoform identifications compared to without IMAC enrichment. Importantly, the phosphoproteoforms identified after Ti4+-IMAC or Fe3+-IMAC enrichment correspond to proteins with much lower overall abundance compared to that identified without the IMAC treatment. We also revealed that Ti4+-IMAC and Fe3+-IMAC could enrich different pools of phosphoproteoforms from complex proteomes and the combination of those two methods will be useful for further improving the phosphoproteoform coverage from complex samples. The results clearly demonstrate the value of our magnetic nanoparticle-based Ti4+-IMAC and Fe3+-IMAC for advancing top-down MS characterization of phosphoproteoforms in complex biological systems.
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Affiliation(s)
- Qianyi Wang
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI, 48824, USA
| | - Fei Fang
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI, 48824, USA
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI, 48824, USA.
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42
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Xu T, Wang Q, Wang Q, Sun L. Coupling High-Field Asymmetric Waveform Ion Mobility Spectrometry with Capillary Zone Electrophoresis-Tandem Mass Spectrometry for Top-Down Proteomics. Anal Chem 2023; 95:9497-9504. [PMID: 37254456 PMCID: PMC10540249 DOI: 10.1021/acs.analchem.3c00551] [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] [Indexed: 06/01/2023]
Abstract
Capillary zone electrophoresis-tandem mass spectrometry (CZE-MS/MS) has emerged as an essential technique for top-down proteomics (TDP), providing superior separation efficiency and high detection sensitivity for proteoform analysis. Here, we aimed to further enhance the performance of CZE-MS/MS for TDP via coupling online gas-phase proteoform fractionation using high-field asymmetric waveform ion mobility spectrometry (FAIMS). When the compensation voltage (CV) of FAIMS was changed from -50 to 30 V, the median mass of identified proteoforms increased from less than 10 kDa to about 30 kDa, suggesting that FAIMS can efficiently fractionate proteoforms by their size. CZE-FAIMS-MS/MS boosted the number of proteoform identifications from a yeast sample by nearly 3-fold relative to CZE-MS/MS alone. It particularly benefited the identification of relatively large proteoforms, improving the number of proteoforms in a mass range of 20-45 kDa by 6-fold compared to CZE-MS/MS alone. FAIMS fractionation gained nearly 20-fold better signal-to-noise ratios of randomly selected proteoforms than no FAIMS. We expect that CZE-FAIMS-MS/MS will be a useful tool for further advancing the sensitivity and coverage of TDP. This work shows the first example of coupling CE with ion mobility spectrometry (IMS) for TDP.
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Affiliation(s)
- Tian Xu
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
| | - Qianjie Wang
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
| | - Qianyi Wang
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
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43
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Kline JT, Belford MW, Huang J, Greer JB, Bergen D, Fellers RT, Greer SM, Horn DM, Zabrouskov V, Huguet R, Boeser CL, Durbin KR, Fornelli L. Improved Label-Free Quantification of Intact Proteoforms Using Field Asymmetric Ion Mobility Spectrometry. Anal Chem 2023; 95:9090-9096. [PMID: 37252723 PMCID: PMC11149911 DOI: 10.1021/acs.analchem.3c01534] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The high-throughput quantification of intact proteoforms using a label-free approach is typically performed on proteins in the 0-30 kDa mass range extracted from whole cell or tissue lysates. Unfortunately, even when high-resolution separation of proteoforms is achieved by either high-performance liquid chromatography or capillary electrophoresis, the number of proteoforms that can be identified and quantified is inevitably limited by the inherent sample complexity. Here, we benchmark label-free quantification of proteoforms of Escherichia coli by applying gas-phase fractionation (GPF) via field asymmetric ion mobility spectrometry (FAIMS). Recent advances in Orbitrap instrumentation have enabled the acquisition of high-quality intact and fragmentation mass spectra without the need for averaging time-domain transients prior to Fourier transform. The resulting speed improvements allowed for the application of multiple FAIMS compensation voltages in the same liquid chromatography-tandem mass spectrometry experiment without increasing the overall data acquisition cycle. As a result, the application of FAIMS to label-free quantification based on intact mass spectra substantially increases the number of both identified and quantified proteoforms without penalizing quantification accuracy in comparison to traditional label-free experiments that do not adopt GPF.
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Affiliation(s)
- Jake T. Kline
- Department of Biology, University of Oklahoma, Norman, Oklahoma 73019, United States
| | | | - Jingjing Huang
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Joseph B. Greer
- Proteinaceous, Inc., Evanston, Illinois 60208, United States
| | - David Bergen
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Ryan T. Fellers
- Proteinaceous, Inc., Evanston, Illinois 60208, United States
| | | | - David M. Horn
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Vlad Zabrouskov
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | - Romain Huguet
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | | | | | - Luca Fornelli
- Department of Biology, University of Oklahoma, Norman, Oklahoma 73019, United States
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
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44
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Leonard B, Danna V, Gorham L, Davison M, Chrisler W, Kim DN, Gerbasi VR. Shaping Nanobodies and Intrabodies against Proteoforms. Anal Chem 2023; 95:8747-8751. [PMID: 37235478 PMCID: PMC10269583 DOI: 10.1021/acs.analchem.3c00958] [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: 03/03/2023] [Accepted: 05/23/2023] [Indexed: 05/28/2023]
Abstract
Proteoforms expand genomic diversity and direct developmental processes. While high-resolution mass spectrometry has accelerated characterization of proteoforms, molecular techniques working to bind and disrupt the function of specific proteoforms have lagged behind. In this study, we worked to develop intrabodies capable of binding specific proteoforms. We employed a synthetic camelid nanobody library expressed in yeast to identify nanobody binders of different SARS-CoV-2 receptor binding domain (RBD) proteoforms. Importantly, employment of the positive and negative selection mechanisms inherent to the synthetic system allowed for amplification of nanobody-expressing yeast that bind to the original (Wuhan strain RBD) but not the E484 K (Beta variant) mutation. Nanobodies raised against specific RBD proteoforms were validated by yeast-2-hybrid analysis and sequence comparisons. These results provide a framework for development of nanobodies and intrabodies that target proteoforms.
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Affiliation(s)
- Bojana Leonard
- Pacific
Northwest National Laboratory, Richland, Washington 99354, United States
| | - Vincent Danna
- Pacific
Northwest National Laboratory, Richland, Washington 99354, United States
| | - Leo Gorham
- Pacific
Northwest National Laboratory, Richland, Washington 99354, United States
| | - Michelle Davison
- Pacific
Northwest National Laboratory, Richland, Washington 99354, United States
| | - William Chrisler
- Pacific
Northwest National Laboratory, Richland, Washington 99354, United States
| | - Doo Nam Kim
- Pacific
Northwest National Laboratory, Richland, Washington 99354, United States
| | - Vincent R. Gerbasi
- Pacific
Northwest National Laboratory, Richland, Washington 99354, United States
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45
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Basharat AR, Zang Y, Sun L, Liu X. TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics. Anal Chem 2023; 95:8189-8196. [PMID: 37196155 DOI: 10.1021/acs.analchem.2c05244] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms and generates mass spectra containing peaks of proteoforms with various isotopic compositions, charge states, and retention times. An essential step in top-down MS data analysis is proteoform feature detection, which aims to group these peaks into peak sets (features), each containing all peaks of a proteoform. Accurate protein feature detection enhances the accuracy in MS-based proteoform identification and quantification. Here, we present TopFD, a software tool for top-down MS feature detection that integrates algorithms for proteoform feature detection, feature boundary refinement, and machine learning models for proteoform feature evaluation. We performed extensive benchmarking of TopFD, ProMex, FlashDeconv, and Xtract using seven top-down MS data sets and demonstrated that TopFD outperforms other tools in feature accuracy, reproducibility, and feature abundance reproducibility.
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Affiliation(s)
- Abdul Rehman Basharat
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Yong Zang
- Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xiaowen Liu
- Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana 70112, United States
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46
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Kurzawa N, Leo IR, Stahl M, Kunold E, Becher I, Audrey A, Mermelekas G, Huber W, Mateus A, Savitski MM, Jafari R. Deep thermal profiling for detection of functional proteoform groups. Nat Chem Biol 2023:10.1038/s41589-023-01284-8. [PMID: 36941476 PMCID: PMC10374440 DOI: 10.1038/s41589-023-01284-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 02/09/2023] [Indexed: 03/23/2023]
Abstract
The complexity of the functional proteome extends considerably beyond the coding genome, resulting in millions of proteoforms. Investigation of proteoforms and their functional roles is important to understand cellular physiology and its deregulation in diseases but challenging to perform systematically. Here we applied thermal proteome profiling with deep peptide coverage to detect functional proteoform groups in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detected 15,846 proteoforms, capturing differently spliced, cleaved and post-translationally modified proteins expressed from 9,290 genes. We identified differential co-aggregation of proteoform pairs and established links to disease biology. Moreover, we systematically made use of measured biophysical proteoform states to find specific biomarkers of drug sensitivity. Our approach, thus, provides a powerful and unique tool for systematic detection and functional annotation of proteoform groups.
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Affiliation(s)
- Nils Kurzawa
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Isabelle Rose Leo
- Clinical Proteomics Mass Spectrometry, Department of Oncology-Pathology Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Matthias Stahl
- Clinical Proteomics Mass Spectrometry, Department of Oncology-Pathology Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Elena Kunold
- Clinical Proteomics Mass Spectrometry, Department of Oncology-Pathology Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Isabelle Becher
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Anastasia Audrey
- Clinical Proteomics Mass Spectrometry, Department of Oncology-Pathology Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Georgios Mermelekas
- Clinical Proteomics Mass Spectrometry, Department of Oncology-Pathology Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Wolfgang Huber
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - André Mateus
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Mikhail M Savitski
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
| | - Rozbeh Jafari
- Clinical Proteomics Mass Spectrometry, Department of Oncology-Pathology Karolinska Institutet, Science for Life Laboratory, Solna, Sweden.
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47
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Wu D, Guo M, Robinson CV. Connecting single-nucleotide polymorphisms, glycosylation status, and interactions of plasma serine protease inhibitors. Chem 2023; 9:665-681. [PMID: 38455847 PMCID: PMC10914678 DOI: 10.1016/j.chempr.2022.11.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/06/2022] [Accepted: 11/22/2022] [Indexed: 12/23/2022]
Abstract
Understanding the combined impacts of genetic variances and post-translational modifications requires new approaches. Here, we delineate proteoforms of plasma serine protease inhibitors and relate specific proteoforms to their interactions in complexes through the use of native mass spectrometry (MS). First, we dissect the proteoform repertoire of an acute-phase plasma protein, serine protease inhibitor A1 (SERPINA1), resolving four SERPINA1 variants (M1V, M1A, M2, and M3) with common single-nucleotide polymorphisms (SNPs). Investigating the glycosylation status of these variants and their ability to form complexes with a serine protease, elastase, we find that fucosylation stabilizes the interaction of the SERPINA1 M1V variant through its core fucosylation on Asn271. In contrast, antennary fucosylation on Asn271 destabilizes SERPINA1-elastase interactions. We unveil the same opposing effects of core and antennary fucosylation on SERPINA3 interactions with chymotrypsin. Together, our native MS results highlight the modulating effects of fucosylation with different linkages on glycoprotein interactions.
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Affiliation(s)
- Di Wu
- Department of Chemistry, University of Oxford, Oxford OX1 3QZ, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford OX1 3QU, UK
| | - Manman Guo
- Botnar Research Centre, NIHR Biomedical Research Unit Oxford, Nuffield Department of Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Carol V. Robinson
- Department of Chemistry, University of Oxford, Oxford OX1 3QZ, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford OX1 3QU, UK
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48
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Ercan H, Resch U, Hsu F, Mitulovic G, Bileck A, Gerner C, Yang JW, Geiger M, Miller I, Zellner M. A Practical and Analytical Comparative Study of Gel-Based Top-Down and Gel-Free Bottom-Up Proteomics Including Unbiased Proteoform Detection. Cells 2023; 12:747. [PMID: 36899884 PMCID: PMC10000902 DOI: 10.3390/cells12050747] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 02/22/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
Proteomics is an indispensable analytical technique to study the dynamic functioning of biological systems via different proteins and their proteoforms. In recent years, bottom-up shotgun has become more popular than gel-based top-down proteomics. The current study examined the qualitative and quantitative performance of these two fundamentally different methodologies by the parallel measurement of six technical and three biological replicates of the human prostate carcinoma cell line DU145 using its two most common standard techniques, label-free shotgun and two-dimensional differential gel electrophoresis (2D-DIGE). The analytical strengths and limitations were explored, finally focusing on the unbiased detection of proteoforms, exemplified by discovering a prostate cancer-related cleavage product of pyruvate kinase M2. Label-free shotgun proteomics quickly yields an annotated proteome but with reduced robustness, as determined by three times higher technical variation compared to 2D-DIGE. At a glance, only 2D-DIGE top-down analysis provided valuable, direct stoichiometric qualitative and quantitative information from proteins to their proteoforms, even with unexpected post-translational modifications, such as proteolytic cleavage and phosphorylation. However, the 2D-DIGE technology required almost 20 times as much time per protein/proteoform characterization with more manual work. Ultimately, this work should expose both techniques' orthogonality with their different contents of data output to elucidate biological questions.
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Affiliation(s)
- Huriye Ercan
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
- Immunology Outpatient Clinic, 1090 Vienna, Austria
| | - Ulrike Resch
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Felicia Hsu
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Goran Mitulovic
- Proteomics Core Facility, Clinical Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria
| | - Andrea Bileck
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
- Joint Metabolome Facility, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria
| | - Christopher Gerner
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria
- Joint Metabolome Facility, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria
| | - Jae-Won Yang
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Margarethe Geiger
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
| | - Ingrid Miller
- Institute of Medical Biochemistry, University of Veterinary Medicine Vienna, 1210 Vienna, Austria
| | - Maria Zellner
- Centre for Physiology and Pharmacology, Medical University of Vienna, 1090 Vienna, Austria
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49
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Song Q, Ruffalo M, Bar-Joseph Z. Using single cell atlas data to reconstruct regulatory networks. Nucleic Acids Res 2023; 51:e38. [PMID: 36762475 PMCID: PMC10123116 DOI: 10.1093/nar/gkad053] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 12/16/2022] [Accepted: 01/19/2023] [Indexed: 02/11/2023] Open
Abstract
Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.
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Affiliation(s)
- Qi Song
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Matthew Ruffalo
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.,Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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50
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Martin EA, Fulcher JM, Zhou M, Monroe ME, Petyuk VA. TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis. J Proteome Res 2023; 22:399-409. [PMID: 36631391 DOI: 10.1021/acs.jproteome.2c00570] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with the interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. We extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across data sets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS data sets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.
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Affiliation(s)
- Evan A Martin
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington99352, United States
| | - James M Fulcher
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington99352, United States
| | - Mowei Zhou
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington99352, United States
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington99352, United States
| | - Vladislav A Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington99352, United States
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