1
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Habeck T, Brown KA, Des Soye B, Lantz C, Zhou M, Alam N, Hossain MA, Jung W, Keener JE, Volny M, Wilson JW, Ying Y, Agar JN, Danis PO, Ge Y, Kelleher NL, Li H, Loo JA, Marty MT, Paša-Tolić L, Sandoval W, Lermyte F. Top-down mass spectrometry of native proteoforms and their complexes: a community study. Nat Methods 2024:10.1038/s41592-024-02279-6. [PMID: 38744918 DOI: 10.1038/s41592-024-02279-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 04/10/2024] [Indexed: 05/16/2024]
Abstract
The combination of native electrospray ionization with top-down fragmentation in mass spectrometry (MS) allows simultaneous determination of the stoichiometry of noncovalent complexes and identification of their component proteoforms and cofactors. Although this approach is powerful, both native MS and top-down MS are not yet well standardized, and only a limited number of laboratories regularly carry out this type of research. To address this challenge, the Consortium for Top-Down Proteomics initiated a study to develop and test protocols for native MS combined with top-down fragmentation of proteins and protein complexes across 11 instruments in nine laboratories. Here we report the summary of the outcomes to provide robust benchmarks and a valuable entry point for the scientific community.
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Affiliation(s)
- Tanja Habeck
- Technische Universität Darmstadt, Darmstadt, Germany
| | - Kyle A Brown
- University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Mowei Zhou
- Pacific Northwest National Laboratory, Richland, WA, USA
- Zhejiang University, Zhejiang, China
| | | | | | | | | | | | - Jesse W Wilson
- Pacific Northwest National Laboratory, Richland, WA, USA
| | - Yujia Ying
- Sun Yat-sen University, Guangzhou, China
| | - Jeffrey N Agar
- Northeastern University, Boston, MA, USA
- Consortium for Top-Down Proteomics, Cambridge, MA, USA
| | - Paul O Danis
- Consortium for Top-Down Proteomics, Cambridge, MA, USA
| | - Ying Ge
- University of Wisconsin-Madison, Madison, WI, USA
- Consortium for Top-Down Proteomics, Cambridge, MA, USA
| | - Neil L Kelleher
- Northwestern University, Evanston, IL, USA
- Consortium for Top-Down Proteomics, Cambridge, MA, USA
| | - Huilin Li
- Sun Yat-sen University, Guangzhou, China
| | - Joseph A Loo
- University of California, Los Angeles, CA, USA
- Consortium for Top-Down Proteomics, Cambridge, MA, USA
| | | | - Ljiljana Paša-Tolić
- Pacific Northwest National Laboratory, Richland, WA, USA
- Consortium for Top-Down Proteomics, Cambridge, MA, USA
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2
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Sachdeva S, Bhatia S, Al Harrasi A, Shah YA, Anwer K, Philip AK, Shah SFA, Khan A, Ahsan Halim S. Unraveling the role of cloud computing in health care system and biomedical sciences. Heliyon 2024; 10:e29044. [PMID: 38601602 PMCID: PMC11004887 DOI: 10.1016/j.heliyon.2024.e29044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 03/24/2024] [Accepted: 03/28/2024] [Indexed: 04/12/2024] Open
Abstract
Cloud computing has emerged as a transformative force in healthcare and biomedical sciences, offering scalable, on-demand resources for managing vast amounts of data. This review explores the integration of cloud computing within these fields, highlighting its pivotal role in enhancing data management, security, and accessibility. We examine the application of cloud computing in various healthcare domains, including electronic medical records, telemedicine, and personalized patient care, as well as its impact on bioinformatics research, particularly in genomics, proteomics, and metabolomics. The review also addresses the challenges and ethical considerations associated with cloud-based healthcare solutions, such as data privacy and cybersecurity. By providing a comprehensive overview, we aim to assist readers in understanding the significance of cloud computing in modern medical applications and its potential to revolutionize both patient care and biomedical research.
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Affiliation(s)
| | - Saurabh Bhatia
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
- School of Health Science, University of Petroleum and Energy Studies, Prem Nagar, Dehradun, Uttarakhand, 248007, India
| | - Ahmed Al Harrasi
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
| | - Yasir Abbas Shah
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
| | - Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Anil K. Philip
- School of Pharmacy, University of Nizwa, Birkat Al Mouz, Nizwa, 616, Oman
| | - Syed Faisal Abbas Shah
- Faculty of Computer Science & Information Technology, Virtual University of Pakistan, Lahore, 54000, Pakistan
| | - Ajmal Khan
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
| | - Sobia Ahsan Halim
- Natural & Medical Sciences Research Center, University of Nizwa, P.O. Box 33, 616 Birkat Al Mauz, Nizwa, Oman
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3
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Popova L, Carr RA, Carabetta VJ. Recent Contributions of Proteomics to Our Understanding of Reversible N ε-Lysine Acylation in Bacteria. J Proteome Res 2024. [PMID: 38442041 DOI: 10.1021/acs.jproteome.3c00912] [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] [Indexed: 03/07/2024]
Abstract
Post-translational modifications (PTMs) have been extensively studied in both eukaryotes and prokaryotes. Lysine acetylation, originally thought to be a rare occurrence in bacteria, is now recognized as a prevalent and important PTM in more than 50 species. This expansion in interest in bacterial PTMs became possible with the advancement of mass spectrometry technology and improved reagents such as acyl-modification specific antibodies. In this Review, we discuss how mass spectrometry-based proteomic studies of lysine acetylation and other acyl modifications have contributed to our understanding of bacterial physiology, focusing on recently published studies from 2018 to 2023. We begin with a discussion of approaches used to study bacterial PTMs. Next, we discuss newly characterized acylomes, including acetylomes, succinylomes, and malonylomes, in different bacterial species. In addition, we examine proteomic contributions to our understanding of bacterial virulence and biofilm formation. Finally, we discuss the contributions of mass spectrometry to our understanding of the mechanisms of acetylation, both enzymatic and nonenzymatic. We end with a discussion of the current state of the field and possible future research avenues to explore.
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Affiliation(s)
- Liya Popova
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, New Jersey 08103, United States
| | - Rachel A Carr
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, New Jersey 08103, United States
| | - Valerie J Carabetta
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, New Jersey 08103, United States
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4
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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 and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - James M Fulcher
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Rashmi Kumar
- Environmental and Biological 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 and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Marija Veličković
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Sarah M Williams
- Environmental and Biological 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 and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Department of Microchemistry, Proteomics, Lipidomics and Next Generation Sequencing, Genentech, 1 DNA Way, San Francisco, CA 94080, United States
| | - Mowei Zhou
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ljiljana Paša-Tolić
- Environmental and Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
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5
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Jeong K, Kaulich PT, Jung W, Kim J, Tholey A, Kohlbacher O. Precursor deconvolution error estimation: The missing puzzle piece in false discovery rate in top-down proteomics. Proteomics 2024; 24:e2300068. [PMID: 37997224 DOI: 10.1002/pmic.202300068] [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: 04/26/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023]
Abstract
Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics (BUP) that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification. The most widely used FDR estimation scheme is based on the target-decoy approach (TDA), which has primarily been established for BUP. We present evidence that the TDA-based FDR estimation may not work at the proteoform-level due to an overlooked factor, namely the erroneous deconvolution of precursor masses, which leads to incorrect FDR estimation. We argue that the conventional TDA-based FDR in proteoform identification is in fact protein-level FDR rather than proteoform-level FDR unless precursor deconvolution error rate is taken into account. To address this issue, we propose a formula to correct for proteoform-level FDR bias by combining TDA-based FDR and precursor deconvolution error rate.
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Affiliation(s)
- Kyowon Jeong
- Applied Bioinformatics, Computer Science Department, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Philipp T Kaulich
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Wonhyeuk Jung
- Department of Cell Biology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Jihyung Kim
- Applied Bioinformatics, Computer Science Department, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Andreas Tholey
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Computer Science Department, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
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6
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [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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7
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Fulcher JM, Swensen AC, Chen YC, Verchere CB, Petyuk VA, Qian WJ. Top-Down Proteomics of Mouse Islets With Beta Cell CPE Deletion Reveals Molecular Details in Prohormone Processing. Endocrinology 2023; 164:bqad160. [PMID: 37967211 PMCID: PMC10650973 DOI: 10.1210/endocr/bqad160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Indexed: 11/17/2023]
Abstract
Altered prohormone processing, such as with proinsulin and pro-islet amyloid polypeptide (proIAPP), has been reported as an important feature of prediabetes and diabetes. Proinsulin processing includes removal of several C-terminal basic amino acids and is performed principally by the exopeptidase carboxypeptidase E (CPE), and mutations in CPE or other prohormone convertase enzymes (PC1/3 and PC2) result in hyperproinsulinemia. A comprehensive characterization of the forms and quantities of improperly processed insulin and other hormone products following Cpe deletion in pancreatic islets has yet to be attempted. In the present study we applied top-down proteomics to globally evaluate the numerous proteoforms of hormone processing intermediates in a β-cell-specific Cpe knockout mouse model. Increases in dibasic residue-containing proinsulin and other novel proteoforms of improperly processed proinsulin were found, and we could classify several processed proteoforms as novel substrates of CPE. Interestingly, some other known substrates of CPE remained unaffected despite its deletion, implying that paralogous processing enzymes such as carboxypeptidase D (CPD) can compensate for CPE loss and maintain near normal levels of hormone processing. In summary, our quantitative results from top-down proteomics of islets provide unique insights into the complexity of hormone processing products and the regulatory mechanisms.
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Affiliation(s)
- James M Fulcher
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Adam C Swensen
- Integrative Omics, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Yi-Chun Chen
- Department of Surgery, BC Children’s Hospital Research Institute and University of British Columbia, Vancouver, British Columbia, V5Z 4H4, Canada
| | - C Bruce Verchere
- Department of Surgery, BC Children’s Hospital Research Institute and University of British Columbia, Vancouver, British Columbia, V5Z 4H4, Canada
- Department of Pathology and Laboratory Medicine, BC Children’s Hospital Research Institute and University of British Columbia, Vancouver, British Columbia, V5Z 4H4, Canada
| | - Vladislav A Petyuk
- Integrative Omics, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Wei-Jun Qian
- Integrative Omics, Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
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8
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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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9
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Chen W, Ding Z, Zang Y, Liu X. Characterization of Proteoform Post-Translational Modifications by Top-Down and Bottom-Up Mass Spectrometry in Conjunction with Annotations. J Proteome Res 2023; 22:3178-3189. [PMID: 37728997 PMCID: PMC10563160 DOI: 10.1021/acs.jproteome.3c00207] [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: 04/07/2023] [Indexed: 09/22/2023]
Abstract
Many proteoforms can be produced from a gene due to genetic mutations, alternative splicing, post-translational modifications (PTMs), and other variations. PTMs in proteoforms play critical roles in cell signaling, protein degradation, and other biological processes. Mass spectrometry (MS) is the primary technique for investigating PTMs in proteoforms, and two alternative MS approaches, top-down and bottom-up, have complementary strengths. The combination of the two approaches has the potential to increase the sensitivity and accuracy in PTM identification and characterization. In addition, protein and PTM knowledge bases, such as UniProt, provide valuable information for PTM characterization and verification. Here, we present a software pipeline PTM-TBA (PTM characterization by Top-down and Bottom-up MS and Annotations) for identifying and localizing PTMs in proteoforms by integrating top-down and bottom-up MS as well as PTM annotations. We assessed PTM-TBA using a technical triplicate of bottom-up and top-down MS data of SW480 cells. On average, database search of the top-down MS data identified 2000 mass shifts, 814.5 (40.7%) of which were matched to 11 common PTMs and 423 of which were localized. Of the mass shifts identified by top-down MS, PTM-TBA verified 435 mass shifts using the bottom-up MS data and UniProt annotations.
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Affiliation(s)
- Wenrong Chen
- Department
of BioHealth Informatics, Indiana University-Purdue
University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Zhengming Ding
- Department
of Computer Science, Tulane School of Science and Engineering, Tulane University, New Orleans, Louisiana 70118, United States
| | - Yong Zang
- Department
of Biostatics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
- Center
for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
| | - Xiaowen Liu
- Tulane
Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana 70112, United States
- Deming Department
of Medicine, Tulane University, New Orleans, Louisiana 70112, United States
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10
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Degnan DJ, Zemaitis KJ, Lewis LA, McCue LA, Bramer LM, Fulcher JM, Veličković D, Paša-Tolić L, Zhou M. IsoMatchMS: Open-Source Software for Automated Annotation and Visualization of High Resolution MALDI-MS Spectra. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2061-2064. [PMID: 37523489 DOI: 10.1021/jasms.3c00180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Due to its speed, accuracy, and adaptability to various sample types, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has become a popular method to identify molecular isotope profiles from biological samples. Often MALDI-MS data do not include tandem MS fragmentation data, and thus the identification of compounds in samples requires external databases so that the accurate mass of detected signals can be matched to known molecular compounds. Most relevant MALDI-MS software tools developed to confirm compound identifications are focused on small molecules (e.g., metabolites, lipids) and cannot be easily adapted to protein data due to their more complex isotopic distributions. Here, we present an R package called IsoMatchMS for the automated annotation of MALDI-MS data for multiple datatypes: intact proteins, peptides, and glycans. This tool accepts already derived molecular formulas or, for proteomics applications, can derive molecular formulas from a list of input peptides or proteins including proteins with post-translational modifications. Visualization of all matched isotopic profiles is provided in a highly accessible HTML format called a trelliscope display, which allows users to filter and sort by several parameters such as match scores and the number of peaks matched. IsoMatchMS simplifies the annotation and visualization of MALDI-MS data for downstream analyses.
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Affiliation(s)
- David J Degnan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Kevin J Zemaitis
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Logan A Lewis
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Lee Ann McCue
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Lisa M Bramer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - James M Fulcher
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Dušan Veličković
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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11
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Peng W, den Boer MA, Tamara S, Mokiem NJ, van der Lans SPA, Bondt A, Schulte D, Haas PJ, Minnema MC, Rooijakkers SHM, van Zuilen AD, Heck AJR, Snijder J. Direct Mass Spectrometry-Based Detection and Antibody Sequencing of Monoclonal Gammopathy of Undetermined Significance from Patient Serum: A Case Study. J Proteome Res 2023; 22:3022-3028. [PMID: 37499263 PMCID: PMC10476240 DOI: 10.1021/acs.jproteome.3c00330] [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: 06/04/2023] [Indexed: 07/29/2023]
Abstract
Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell disorder characterized by the presence of a predominant monoclonal antibody (i.e., M-protein) in serum, without clinical symptoms. Here we present a case study in which we detect MGUS by liquid-chromatography coupled with mass spectrometry (LC-MS) profiling of IgG1 in human serum. We detected a Fab-glycosylated M-protein and determined the full heavy and light chain sequences by bottom-up proteomics techniques using multiple proteases, further validated by top-down LC-MS. Moreover, the composition and location of the Fab-glycan could be determined in CDR1 of the heavy chain. The outlined approach adds to an expanding mass spectrometry-based toolkit to characterize monoclonal gammopathies such as MGUS and multiple myeloma, with fine molecular detail. The ability to detect monoclonal gammopathies and determine M-protein sequences straight from blood samples by mass spectrometry provides new opportunities to understand the molecular mechanisms of such diseases.
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Affiliation(s)
- Weiwei Peng
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584CH Utrecht, The Netherlands
| | - Maurits A. den Boer
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584CH Utrecht, The Netherlands
| | - Sem Tamara
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584CH Utrecht, The Netherlands
| | - Nadia J. Mokiem
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584CH Utrecht, The Netherlands
| | - Sjors P. A. van der Lans
- Medical
Microbiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Albert Bondt
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584CH Utrecht, The Netherlands
| | - Douwe Schulte
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584CH Utrecht, The Netherlands
| | - Pieter-Jan Haas
- Medical
Microbiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Monique C. Minnema
- Department
of Hematology, University Medical Center
Utrecht, Utrecht University, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Suzan H. M. Rooijakkers
- Medical
Microbiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Arjan D. van Zuilen
- Department
of Nephrology and Hypertension, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584CX Utrecht, The Netherlands
| | - Albert J. R. Heck
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584CH Utrecht, The Netherlands
| | - Joost Snijder
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584CH Utrecht, The Netherlands
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12
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Lermyte F, Habeck T, Brown K, Des Soye B, Lantz C, Zhou M, Alam N, Hossain MA, Jung W, Keener J, Volny M, Wilson J, Ying Y, Agar J, Danis P, Ge Y, Kelleher N, Li H, Loo J, Marty M, Pasa-Tolic L, Sandoval W. Top-down mass spectrometry of native proteoforms and their complexes: A community study. RESEARCH SQUARE 2023:rs.3.rs-3228472. [PMID: 37674709 PMCID: PMC10479449 DOI: 10.21203/rs.3.rs-3228472/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
The combination of native electrospray ionisation with top-down fragmentation in mass spectrometry allows simultaneous determination of the stoichiometry of noncovalent complexes and identification of their component proteoforms and co-factors. While this approach is powerful, both native mass spectrometry and top-down mass spectrometry are not yet well standardised, and only a limited number of laboratories regularly carry out this type of research. To address this challenge, the Consortium for Top-Down Proteomics (CTDP) initiated a study to develop and test protocols for native mass spectrometry combined with top-down fragmentation of proteins and protein complexes across eleven instruments in nine laboratories. The outcomes are summarised in this report to provide robust benchmarks and a valuable entry point for the scientific community.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Jeffrey Agar
- Department of Chemistry and Chemical Biology, Northeastern University
| | | | - Ying Ge
- University of Wisconsin-Madison
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13
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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: 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: 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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14
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Larson EJ, Pergande MR, Moss ME, Rossler KJ, Wenger RK, Krichel B, Josyer H, Melby JA, Roberts DS, Pike K, Shi Z, Chan HJ, Knight B, Rogers HT, Brown KA, Ong IM, Jeong K, Marty MT, McIlwain SJ, Ge Y. MASH Native: a unified solution for native top-down proteomics data processing. Bioinformatics 2023; 39:btad359. [PMID: 37294807 PMCID: PMC10283151 DOI: 10.1093/bioinformatics/btad359] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/13/2023] [Accepted: 06/07/2023] [Indexed: 06/11/2023] Open
Abstract
MOTIVATION Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. RESULTS We have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a "one-stop shop" for characterizing both native protein complexes and proteoforms. AVAILABILITY AND IMPLEMENTATION The MASH Native app, video tutorials, written tutorials, and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file.
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Affiliation(s)
- Eli J Larson
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Melissa R Pergande
- Department of Cell and Regenerative Biology, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Michelle E Moss
- Department of Cell and Regenerative Biology, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Kalina J Rossler
- Department of Cell and Regenerative Biology, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - R Kent Wenger
- Department of Cell and Regenerative Biology, University of Wisconsin–Madison, Madison, WI 53705, United States
- Human Proteomics Program, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Boris Krichel
- Department of Cell and Regenerative Biology, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Harini Josyer
- Department of Cell and Regenerative Biology, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Jake A Melby
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - David S Roberts
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Kyndalanne Pike
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Zhuoxin Shi
- Department of Cell and Regenerative Biology, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Hsin-Ju Chan
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Bridget Knight
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Holden T Rogers
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Kyle A Brown
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Irene M Ong
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53705, United States
- University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI 53705, United States
- Department of Obstetrics and Gynecology, University of Wisconsin–Madison, Madison, WI 53705, United States
| | - Kyowon Jeong
- Department of Applied Bioinformatics, University of Tübingen, Tübingen 72704, Germany
| | - Michael T Marty
- Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ 85719, United States
| | - Sean J McIlwain
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Madison, WI 53705, United States
- University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI 53705, United States
| | - Ying Ge
- Department of Chemistry, University of Wisconsin–Madison, Madison, WI 53705, United States
- Department of Cell and Regenerative Biology, University of Wisconsin–Madison, Madison, WI 53705, United States
- Human Proteomics Program, School of Medicine and Public Health, University of Wisconsin–Madison, Madison, WI 53705, United States
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15
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Tabb DL, Jeong K, Druart K, Gant MS, Brown KA, Nicora C, Zhou M, Couvillion S, Nakayasu E, Williams JE, Peterson HK, McGuire MK, McGuire MA, Metz TO, Chamot-Rooke J. Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection. J Proteome Res 2023. [PMID: 37235544 DOI: 10.1021/acs.jproteome.2c00673] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.
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Affiliation(s)
- David L Tabb
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
| | - Kyowon Jeong
- Applied Bioinformatics, Computer Science Department, University of Tübingen, Tübingen 72076, Germany
| | - Karen Druart
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
| | - Megan S Gant
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
| | - Kyle A Brown
- School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Carrie Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Sneha Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ernesto Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Janet E Williams
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Haley K Peterson
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Michelle K McGuire
- Margaret Ritchie School of Family and Consumer Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Mark A McGuire
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Julia Chamot-Rooke
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
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16
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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: 0] [Impact Index Per Article: 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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17
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Chen W, Ding Z, Zang Y, Liu X. Characterization of proteoform post-translational modifications by top-down and bottom-up mass spectrometry in conjunction with UniProt annotations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.04.535618. [PMID: 37066296 PMCID: PMC10104052 DOI: 10.1101/2023.04.04.535618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Many proteoforms can be produced from a gene due to genetic mutations, alternative splicing, post-translational modifications (PTMs), and other variations. PTMs in proteoforms play critical roles in cell signaling, protein degradation, and other biological processes. Mass spectrometry (MS) is the primary technique for investigating PTMs in proteoforms, and two alternative MS approaches, top-down and bottom-up, have complementary strengths. The combination of the two approaches has the potential to increase the sensitivity and accuracy in PTM identification and characterization. In addition, protein and PTM knowledgebases, such as UniProt, provide valuable information for PTM characterization and validation. Here, we present a software pipeline called PTM-TBA (PTM characterization by Top-down, Bottom-up MS and Annotations) for identifying and localizing PTMs in proteoforms by integrating top-down and bottom-up MS as well as UniProt annotations. We identified 1,662 mass shifts from a top-down MS data set of SW480 cells, 545 (33%) of which were matched to 12 common PTMs, and 351 of which were localized. PTM-TBA validated 346 of the 1,662 mass shifts using UniProt annotations or a bottom-up MS data set of SW480 cells.
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18
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [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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19
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Zemaitis KJ, Zhou M, Kew W, Paša-Tolić L. 193 nm Ultraviolet Photodissociation for the Characterization of Singly Charged Proteoforms Generated by MALDI. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:328-332. [PMID: 36622763 PMCID: PMC10084724 DOI: 10.1021/jasms.2c00302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
MALDI imaging allows for the near-cellular profiling of proteoforms directly from microbial, plant, and mammalian samples. Despite detecting hundreds of proteoforms, identification of unknowns with only intact mass information remains a distinct challenge, even with high mass resolving power and mass accuracy. To this end, many supplementary methods have been used to create experimental databases for accurate mass matching, including bulk or spatially resolved bottom-up and/or top-down proteomics. Herein, we describe the application of 193 nm ultraviolet photodissociation (UVPD) for fragmentation of quadrupole isolated singly charged ubiquitin (m/z 8565) by MALDI-UVPD on a UHMR HF Orbitrap. This platform permitted the high-resolution accurate mass measurement of not just terminal fragments but also large internal fragments. The outlined workflow demonstrates the feasibility of top-down analyses of isolated MALDI protein ions and the potential toward more comprehensive characterization of proteoforms in MALDI imaging applications.
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Affiliation(s)
- Kevin J Zemaitis
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - William Kew
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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20
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Cain RL, Webb IK. Online protein unfolding characterized by ion mobility electron capture dissociation mass spectrometry: cytochrome C from neutral and acidic solutions. Anal Bioanal Chem 2023; 415:749-758. [PMID: 36622393 DOI: 10.1007/s00216-022-04501-w] [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: 10/08/2022] [Revised: 12/02/2022] [Accepted: 12/20/2022] [Indexed: 01/10/2023]
Abstract
Electrospray ionization mass spectrometry (ESI-MS) experiments, including ion mobility spectrometry mass spectrometry (ESI-IMS-MS) and electron capture dissociation (ECD) of proteins ionized from aqueous solutions, have been used for the study of solution-like structures of intact proteins. By mixing aqueous proteins with denaturants online before ESI, the amount of protein unfolding can be precisely controlled and rapidly analyzed, permitting the characterization of protein folding intermediates in protein folding pathways. Herein, we mixed various pH solutions online with aqueous cytochrome C for unfolding and characterizing its unfolding intermediates with ESI-MS charge state distribution measurements, IMS, and ECD. The presence of folding intermediates and unfolded cytochrome c structures were detected from changes in charge states, arrival time distributions (ATDs), and ECD. We also compared structures from nondenaturing and denaturing solution mixtures measured under "gentle" (i.e., low energy) ion transmission conditions with structures measured under "harsh" (i.e., higher energy) transmission. This work confirms that when using "gentle" instrument conditions, the gas-phase cytochrome c ions reflect attributes of the various solution-phase structures. However, "harsh" conditions that maximize ion transmission produce extended structures that no longer correlate with changes in solution structure.
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Affiliation(s)
- Rebecca L Cain
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA
| | - Ian K Webb
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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21
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Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform. Mol Cell Proteomics 2023; 22:100491. [PMID: 36603806 PMCID: PMC9944986 DOI: 10.1016/j.mcpro.2022.100491] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 12/10/2022] [Accepted: 12/20/2022] [Indexed: 01/04/2023] Open
Abstract
Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples-based sample preparation system and an laser capture microdissection-based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection-isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry-based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification-specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.
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22
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Juan H, Huang H. Quantitative analysis of high‐throughput biological data. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Hsueh‐Fen Juan
- Department of Life Science, Institute of Biomedical Electronics and Bioinformatics, and Center for Systems Biology National Taiwan University Taipei Taiwan
- Taiwan AI Labs Taipei Taiwan
| | - Hsuan‐Cheng Huang
- Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan
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23
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Larson EJ, Pergande MR, Moss ME, Rossler KJ, Wenger RK, Krichel B, Josyer H, Melby JA, Roberts DS, Pike K, Shi Z, Chan HJ, Knight B, Rogers HT, Brown KA, Ong IM, Jeong K, Marty M, McIlwain SJ, Ge Y. MASH Native: A Unified Solution for Native Top-Down Proteomics Data Processing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.02.522513. [PMID: 36711733 PMCID: PMC9881860 DOI: 10.1101/2023.01.02.522513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. Herein, we have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a one-stop shop for characterizing both native protein complexes and proteoforms. The MASH Native app, video tutorials, written tutorials and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHNativeSoftware.php . All data files shown in user tutorials are included with the MASH Native software in the download .zip file.
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24
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Seeing the complete picture: proteins in top-down mass spectrometry. Essays Biochem 2022; 67:283-300. [PMID: 36468679 DOI: 10.1042/ebc20220098] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022]
Abstract
Abstract
Top-down protein mass spectrometry can provide unique insights into protein sequence and structure, including precise proteoform identification and study of protein–ligand and protein–protein interactions. In contrast with the commonly applied bottom-up approach, top-down approaches do not include digestion of the protein of interest into small peptides, but instead rely on the ionization and subsequent fragmentation of intact proteins. As such, it is fundamentally the only way to fully characterize the composition of a proteoform. Here, we provide an overview of how a top-down protein mass spectrometry experiment is performed and point out recent applications from the literature to the reader. While some parts of the top-down workflow are broadly applicable, different research questions are best addressed with specific experimental designs. The most important divide is between studies that prioritize sequence information (i.e., proteoform identification) versus structural information (e.g., conformational studies, or mapping protein–protein or protein–ligand interactions). Another important consideration is whether to work under native or denaturing solution conditions, and the overall complexity of the sample also needs to be taken into account, as it determines whether (chromatographic) separation is required prior to MS analysis. In this review, we aim to provide enough information to support both newcomers and more experienced readers in the decision process of how to answer a potential research question most efficiently and to provide an overview of the methods that exist to answer these questions.
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25
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Cheung See Kit M, Webb IK. Application of Multiple Length Cross-linkers to the Characterization of Gaseous Protein Structure. Anal Chem 2022; 94:13301-13310. [DOI: 10.1021/acs.analchem.2c03044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Melanie Cheung See Kit
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Ian K. Webb
- Department of Chemistry and Chemical Biology, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
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26
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Winkels K, Koudelka T, Kaulich PT, Leippe M, Tholey A. Validation of Top-Down Proteomics Data by Bottom-Up-Based N-Terminomics Reveals Pitfalls in Top-Down-Based Terminomics Workflows. J Proteome Res 2022; 21:2185-2196. [PMID: 35972260 DOI: 10.1021/acs.jproteome.2c00277] [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: 11/29/2022]
Abstract
Bottom-up proteomics (BUP)-based N-terminomics techniques have become standard to identify protein N-termini. While these methods rely on the identification of N-terminal peptides only, top-down proteomics (TDP) comes with the promise to provide additional information about post-translational modifications and the respective C-termini. To evaluate the potential of TDP for terminomics, two established TDP workflows were employed for the proteome analysis of the nematode Caenorhabditis elegans. The N-termini of the identified proteoforms were validated using a BUP-based N-terminomics approach. The TDP workflows used here identified 1658 proteoforms, the N-termini of which were verified by BUP in 25% of entities only. Caveats in both the BUP- and TDP-based workflows were shown to contribute to this low overlap. In BUP, the use of trypsin prohibits the detection of arginine-rich or arginine-deficient N-termini, while in TDP, the formation of artificially generated termini was observed in particular in a workflow encompassing sample treatment with high acid concentrations. Furthermore, we demonstrate the applicability of reductive dimethylation in TDP to confirm biological N-termini. Overall, our study shows not only the potential but also current limitations of TDP for terminomics studies and also presents suggestions for future developments, for example, for data quality control, allowing improvement of the detection of protein termini by TDP.
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Affiliation(s)
- Konrad Winkels
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Tomas Koudelka
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Philipp T Kaulich
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Matthias Leippe
- Comparative Immunobiology, Zoological Institute, Christian-Albrechts-Universität zu Kiel, 24098 Kiel, Germany
| | - Andreas Tholey
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
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27
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Yang M, Hu H, Su P, Thomas PM, Camarillo JM, Greer JB, Early BP, Fellers RT, Kelleher NL, Laskin J. Proteoform-Selective Imaging of Tissues Using Mass Spectrometry. Angew Chem Int Ed Engl 2022; 61:e202200721. [PMID: 35446460 PMCID: PMC9276647 DOI: 10.1002/anie.202200721] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Indexed: 01/28/2023]
Abstract
Unraveling the complexity of biological systems relies on the development of new approaches for spatially resolved proteoform‐specific analysis of the proteome. Herein, we employ nanospray desorption electrospray ionization mass spectrometry imaging (nano‐DESI MSI) for the proteoform‐selective imaging of biological tissues. Nano‐DESI generates multiply charged protein ions, which is advantageous for their structural characterization using tandem mass spectrometry (MS/MS) directly on the tissue. Proof‐of‐concept experiments demonstrate that nano‐DESI MSI combined with on‐tissue top‐down proteomics is ideally suited for the proteoform‐selective imaging of tissue sections. Using rat brain tissue as a model system, we provide the first evidence of differential proteoform expression in different regions of the brain.
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Affiliation(s)
- Manxi Yang
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN 47907USA
| | - Hang Hu
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN 47907USA
| | - Pei Su
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN 47907USA
- Departments of Chemistry and Molecular BiosciencesNorthwestern University2145 Sheridan RoadEvanstonIL 60208USA
| | - Paul M. Thomas
- Departments of Chemistry and Molecular BiosciencesNorthwestern University2145 Sheridan RoadEvanstonIL 60208USA
| | - Jeannie M. Camarillo
- Departments of Chemistry and Molecular BiosciencesNorthwestern University2145 Sheridan RoadEvanstonIL 60208USA
| | - Joseph B. Greer
- Departments of Chemistry and Molecular BiosciencesNorthwestern University2145 Sheridan RoadEvanstonIL 60208USA
| | - Bryan P. Early
- Departments of Chemistry and Molecular BiosciencesNorthwestern University2145 Sheridan RoadEvanstonIL 60208USA
| | - Ryan T. Fellers
- Departments of Chemistry and Molecular BiosciencesNorthwestern University2145 Sheridan RoadEvanstonIL 60208USA
| | - Neil L. Kelleher
- Departments of Chemistry and Molecular BiosciencesNorthwestern University2145 Sheridan RoadEvanstonIL 60208USA
| | - Julia Laskin
- Department of ChemistryPurdue University560 Oval DriveWest LafayetteIN 47907USA
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Méndez V, Holland S, Bhardwaj S, McDonald J, Khan S, O'Carroll D, Pickford R, Richards S, O'Farrell C, Coleman N, Lee M, Manefield MJ. Aerobic biotransformation of 6:2 fluorotelomer sulfonate by Dietzia aurantiaca J3 under sulfur-limiting conditions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 829:154587. [PMID: 35306084 DOI: 10.1016/j.scitotenv.2022.154587] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 03/11/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
The polyfluorinated alkyl substance 6:2 fluorotelomer sulfonate (6:2 FTS) has been detected in diverse environments impacted by aqueous film-forming foams used for firefighting. In this study, a bacterial strain (J3) using 6:2 FTS as a sulfur source was isolated from landfill leachate previously exposed to polyfluoroalkyl substances in New South Wales, Australia. Strain J3 shares 99.9% similarity with the 16S rRNA gene of Dietzia aurantiaca CCUG 35676T. Genome sequencing yielded a draft genome sequence of 37 contigs with a G + C content of 69.7%. A gene cluster related to organic sulfur utilisation and assimilation was identified, that included an alkanesulfonate monooxygenase component B (ssuD), an alkanesulfonate permease protein (ssuC), an ABC transporter (ssuB), and an alkanesulfonate-binding protein (ssuA). Proteomic analyses comparing strain J3 cultures using sulfate and 6:2 FTS as sulfur source indicated that the ssu gene cluster was involved in 6:2 FTS biodegradation. Upregulated proteins included the SsuD monooxygenase, the SsuB transporter, the ABC transporter permease (SsuC), an alkanesulfonate-binding protein (SsuA), and a nitrilotriacetate monooxygenase component B. 6:2 Fluorotelomer carboxylic acid (6:2 FTCA) and 6:2 fluorotelomer unsaturated acid (6:2 FTUA) were detected as early degradation products in cultures (after 72 h) while 5:3 fluorotelomer acid (5:3 FTCA), perfluorohexanoic acid (PFHxA) and perfluoropentanoic acid (PFPeA) were detected as later degradation products (after 168 h). This work provides biochemical and metabolic insights into 6:2 FTS biodegradation by the Actinobacterium D. aurantiaca J3, informing the fate of PFAS in the environment.
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Affiliation(s)
- Valentina Méndez
- UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia
| | - Sophie Holland
- UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia
| | - Shefali Bhardwaj
- UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia
| | - James McDonald
- UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia
| | - Stuart Khan
- UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia
| | - Denis O'Carroll
- UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia
| | - Russell Pickford
- UNSW Mark Wainwright Analytical Centre, UNSW, Sydney, NSW 2052, Australia
| | | | | | - Nicholas Coleman
- School of Life and Environmental Sciences, University of Sydney, NSW 2006, Australia
| | - Matthew Lee
- UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia
| | - Michael J Manefield
- UNSW Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, NSW 2052, Australia.
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Liu R, Xia S, Li H. Native top-down mass spectrometry for higher-order structural characterization of proteins and complexes. MASS SPECTROMETRY REVIEWS 2022:e21793. [PMID: 35757976 DOI: 10.1002/mas.21793] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
Progress in structural biology research has led to a high demand for powerful and yet complementary analytical tools for structural characterization of proteins and protein complexes. This demand has significantly increased interest in native mass spectrometry (nMS), particularly native top-down mass spectrometry (nTDMS) in the past decade. This review highlights recent advances in nTDMS for structural research of biological assemblies, with a particular focus on the extra multi-layers of information enabled by TDMS. We include a short introduction of sample preparation and ionization to nMS, tandem fragmentation techniques as well as mass analyzers and software/analysis pipelines used for nTDMS. We highlight unique structural information offered by nTDMS and examples of its broad range of applications in proteins, protein-ligand interactions (metal, cofactor/drug, DNA/RNA, and protein), therapeutic antibodies and antigen-antibody complexes, membrane proteins, macromolecular machineries (ribosome, nucleosome, proteosome, and viruses), to endogenous protein complexes. The challenges, potential, along with perspectives of nTDMS methods for the analysis of proteins and protein assemblies in recombinant and biological samples are discussed.
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Affiliation(s)
- Ruijie Liu
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Shujun Xia
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Huilin Li
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
- Guangdong Key Laboratory of Chiral Molecule and Drug Discovery, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
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30
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Chen W, McCool EN, Sun L, Zang Y, Ning X, Liu X. Evaluation of Machine Learning Models for Proteoform Retention and Migration Time Prediction in Top-Down Mass Spectrometry. J Proteome Res 2022; 21:1736-1747. [PMID: 35616364 PMCID: PMC9250612 DOI: 10.1021/acs.jproteome.2c00124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Reversed-phase liquid
chromatography (RPLC) and capillary zone
electrophoresis (CZE) are two primary proteoform separation methods
in mass spectrometry (MS)-based top-down proteomics. Proteoform retention
time (RT) prediction in RPLC and migration time (MT) prediction in
CZE provide additional information for accurate proteoform identification
and quantification. While existing methods are mainly focused on peptide
RT and MT prediction in bottom-up MS, there is still a lack of methods
for proteoform RT and MT prediction in top-down MS. We systematically
evaluated eight machine learning models and a transfer learning method
for proteoform RT prediction and five models and the transfer learning
method for proteoform MT prediction. Experimental results showed that
a gated recurrent unit (GRU)-based model with transfer learning achieved
a high accuracy (R = 0.978) for proteoform RT prediction
and that the GRU-based model and a fully connected neural network
model obtained a high accuracy of R = 0.982 and 0.981
for proteoform MT prediction, respectively.
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Affiliation(s)
- Wenrong Chen
- Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United Staes
| | - Elijah N McCool
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United Staes
| | - Liangliang Sun
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, United Staes
| | - Yong Zang
- Department of Biostatics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana 46202, United Staes
| | - Xia Ning
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, United Staes.,Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio 43210, United Staes.,Translational Data Analytics Institute, The Ohio State University, Columbus, Ohio 43210, United Staes
| | - Xiaowen Liu
- Tulane Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, Louisiana 70112, United Staes.,Deming Department of Medicine, Tulane University, New Orleans, Louisiana 70112, United Staes
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31
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Yang M, Hu H, Su P, Thomas PM, Camarillo JM, Greer JB, Early BP, Fellers RT, Kelleher NL, Laskin J. Proteoform‐Selective Imaging of Tissues Using Mass Spectrometry. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202200721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Manxi Yang
- Purdue University Department of Chemistry chemistry 560 Oval Dr. 47906 West Lafayette UNITED STATES
| | - Hang Hu
- Purdue University Chemistry UNITED STATES
| | - Pei Su
- Northwestern University Chemistry and Molecular Biosciences UNITED STATES
| | - Paul M. Thomas
- Northwestern University Chemistry and Molecular Biosciences UNITED STATES
| | | | - Joseph B. Greer
- Northwestern University Chemistry and Molecular Biosciences UNITED STATES
| | - Bryan P. Early
- Northwestern University Chemistry and Molecular Biosciences UNITED STATES
| | - Ryan T. Fellers
- Northwestern University Chemistry and Molecular Biosciences UNITED STATES
| | - Neil L. Kelleher
- Northwestern University Chemistry and Molecular Biosciences UNITED STATES
| | - Julia Laskin
- Purdue University Department of Chemistry 560 Oval Dr. 47907 West Lafayette UNITED STATES
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32
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Wilson J, Bilbao A, Wang J, Liao YC, Velickovic D, Wojcik R, Passamonti M, Zhao R, Gargano AFG, Gerbasi VR, Pas̆a-Tolić L, Baker SE, Zhou M. Online Hydrophilic Interaction Chromatography (HILIC) Enhanced Top-Down Mass Spectrometry Characterization of the SARS-CoV-2 Spike Receptor-Binding Domain. Anal Chem 2022; 94:5909-5917. [PMID: 35380435 PMCID: PMC9003935 DOI: 10.1021/acs.analchem.2c00139] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 03/25/2022] [Indexed: 12/13/2022]
Abstract
SARS-CoV-2 cellular infection is mediated by the heavily glycosylated spike protein. Recombinant versions of the spike protein and the receptor-binding domain (RBD) are necessary for seropositivity assays and can potentially serve as vaccines against viral infection. RBD plays key roles in the spike protein's structure and function, and thus, comprehensive characterization of recombinant RBD is critically important for biopharmaceutical applications. Liquid chromatography coupled to mass spectrometry has been widely used to characterize post-translational modifications in proteins, including glycosylation. Most studies of RBDs were performed at the proteolytic peptide (bottom-up proteomics) or released glycan level because of the technical challenges in resolving highly heterogeneous glycans at the intact protein level. Herein, we evaluated several online separation techniques: (1) C2 reverse-phase liquid chromatography (RPLC), (2) capillary zone electrophoresis (CZE), and (3) acrylamide-based monolithic hydrophilic interaction chromatography (HILIC) to separate intact recombinant RBDs with varying combinations of glycosylations (glycoforms) for top-down mass spectrometry (MS). Within the conditions we explored, the HILIC method was superior to RPLC and CZE at separating RBD glycoforms, which differ significantly in neutral glycan groups. In addition, our top-down analysis readily captured unexpected modifications (e.g., cysteinylation and N-terminal sequence variation) and low abundance, heavily glycosylated proteoforms that may be missed by using glycopeptide data alone. The HILIC top-down MS platform holds great potential in resolving heterogeneous glycoproteins for facile comparison of biosimilars in quality control applications.
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Affiliation(s)
- Jesse
W. Wilson
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Aivett Bilbao
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Juan Wang
- Biological
Sciences Division, Pacific Northwest National
Laboratories, 902 Battelle
Boulevard, Richland, Washington 99354, United States
| | - Yen-Chen Liao
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Dusan Velickovic
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Roza Wojcik
- National
Security Directorate, Pacific Northwest
National Laboratories, 902 Battelle Boulevard, Richland, Washington 99354, United States
| | - Marta Passamonti
- Centre
for Analytical Sciences Amsterdam, Amsterdam 1098 XH, The
Netherlands
- Van’t
Hoff Institute for Molecular Sciences, University
of Amsterdam, Amsterdam 1098 XH, The Netherlands
| | - Rui Zhao
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Andrea F. G. Gargano
- Centre
for Analytical Sciences Amsterdam, Amsterdam 1098 XH, The
Netherlands
- Van’t
Hoff Institute for Molecular Sciences, University
of Amsterdam, Amsterdam 1098 XH, The Netherlands
| | - Vincent R. Gerbasi
- Biological
Sciences Division, Pacific Northwest National
Laboratories, 902 Battelle
Boulevard, Richland, Washington 99354, United States
| | - Ljiljana Pas̆a-Tolić
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Scott E. Baker
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
| | - Mowei Zhou
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, 3335 Innovation Boulevard, Richland, Washington 99354, United States
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33
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Yang R, Ma J, Zhang S, Zheng Y, Wang L, Zhu D. mzMD: visualization-oriented MS data storage and retrieval. Bioinformatics 2022; 38:2333-2340. [PMID: 35171986 DOI: 10.1093/bioinformatics/btac098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/23/2022] [Accepted: 02/14/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Drawing peaks in a data window of an MS dataset happens at all time in MS data visualization applications. This asks to retrieve from an MS dataset some selected peaks in a data window whose image in a display window reflects the visual feature of all peaks in the data window. If an algorithm for this purpose is asked to output high-quality solutions in real time, then the most fundamental dependence of it is on the storage format of the MS dataset. RESULTS We present mzMD, a new storage format of MS datasets and an algorithm to query this format of a storage system for a summary (a set of selected representative peaks) of a given data window. We propose a criterion Q-score to examine the quality of data window summaries. Experimental statistics on real MS datasets verified the high speed of mzMD in retrieving high-quality data window summaries. mzMD reported summaries of data windows whose Q-score outperforms those mzTree reported. The query speed of mzMD is the same as that of mzTree whereas its query speed stability is better than that of mzTree. AVAILABILITY AND IMPLEMENTATION The source code is freely available at https://github.com/yrm9837/mzMD-java. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Runmin Yang
- School of Computer Science and Technology, Shandong University, Qingdao 266237, China
| | - Jingjing Ma
- School of Computer Science and Technology, Shandong University, Qingdao 266237, China
| | - Shu Zhang
- School of Computer Science and Technology, Shandong University, Qingdao 266237, China
| | - Yu Zheng
- School of Computer Science and Technology, Shandong University, Qingdao 266237, China
| | - Lusheng Wang
- Department of Computer Science, City University of Hong Kong, Hong Kong, China.,City University of Hong Kong Shenzhen Research Institute, Shenzhen 518057, China
| | - Daming Zhu
- School of Computer Science and Technology, Shandong University, Qingdao 266237, China
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34
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Sun RX, Wang RM, Luo L, Liu C, Chi H, Zeng WF, He SM. Accurate Proteoform Identification and Quantitation Using pTop 2.0. Methods Mol Biol 2022; 2500:105-129. [PMID: 35657590 DOI: 10.1007/978-1-0716-2325-1_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The remarkable advancement of top-down proteomics in the past decade is driven by the technological development in separation, mass spectrometry (MS) instrumentation, novel fragmentation, and bioinformatics. However, the accurate identification and quantification of proteoforms, all clearly-defined molecular forms of protein products from a single gene, remain a challenging computational task. This is in part due to the complicated mass spectra from intact proteoforms when compared to those from the digested peptides. Herein, pTop 2.0 is developed to fill in the gap between the large-scale complex top-down MS data and the shortage of high-accuracy bioinformatic tools. Compared with pTop 1.0, the first version, pTop 2.0 concentrates mainly on the identification of the proteoforms with unexpected modifications or a terminal truncation. The quantitation based on isotopic labeling is also a new function, which can be carried out by the convenient and user-friendly "one-key operation," integrated together with the qualitative identifications. The accuracy and running speed of pTop 2.0 is significantly improved on the test data sets. This chapter will introduce the main features, step-by-step running operations, and algorithmic developments of pTop 2.0 in order to push the identification and quantitation of intact proteoforms to a higher-accuracy level in top-down proteomics.
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Affiliation(s)
- Rui-Xiang Sun
- National Institute of Biological Sciences, Beijing, China.
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
| | - Rui-Min Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Lan Luo
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Chao Liu
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Hao Chi
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Wen-Feng Zeng
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Si-Min He
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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35
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Tiambeng TN, Wu Z, Melby JA, Ge Y. Size Exclusion Chromatography Strategies and MASH Explorer for Large Proteoform Characterization. Methods Mol Biol 2022; 2500:15-30. [PMID: 35657584 PMCID: PMC9703982 DOI: 10.1007/978-1-0716-2325-1_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Top-down mass spectrometry (MS)-based analysis of larger proteoforms (>50 kDa) is typically challenging due to an exponential decay in the signal-to-noise ratio with increasing protein molecular weight (MW) and coelution with low-MW proteoforms. Size exclusion chromatography (SEC) fractionates proteins based on their size, separating larger proteoforms from those of smaller size in the proteome. In this protocol, we initially describe the use of SEC to fractionate high-MW proteoforms from low-MW proteoforms. Subsequently, the SEC fractions containing the proteoforms of interest are subjected to reverse-phase liquid chromatography (RPLC) coupled online with high-resolution MS. Finally, proteoforms are characterized using MASH Explorer, a user-friendly software environment for in-depth proteoform characterization.
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Affiliation(s)
- Timothy N. Tiambeng
- Department of Chemistry, University of Wisconsin – Madison, Madison, WI 53706
| | - Zhijie Wu
- Department of Chemistry, University of Wisconsin – Madison, Madison, WI 53706
| | - Jake A. Melby
- Department of Chemistry, University of Wisconsin – Madison, Madison, WI 53706
| | - Ying Ge
- Department of Chemistry, University of Wisconsin – Madison, Madison, WI 53706,Department of Cell and Regenerative Biology, University of Wisconsin – Madison, Madison, WI 53705,Human Proteomic Program, University of Wisconsin – Madison, Madison WI 53705,To whom correspondence may be addressed: Dr. Ying Ge, 8551 WIMR-II, 1111 Highland Ave., Madison, Wisconsin 53705, USA. ; Tel: 608-265-4744
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36
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Wilson JW, Zhou M. Discovery of Unknown Posttranslational Modifications by Top-Down Mass Spectrometry. Methods Mol Biol 2022; 2500:181-199. [PMID: 35657594 DOI: 10.1007/978-1-0716-2325-1_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Protein encoding genes can undergo modifications posttranscriptionally and posttranslationally, yielding many different "proteoforms." The chemical diversity of such modifications is known to be important biomarkers of function within biological systems but is not completely understood. Top-down mass spectrometry is a valuable tool for the characterization of proteoforms, especially for histones that have complex combinations of posttranslational modifications (PTMs). In this chapter, we present a top-down liquid chromatography-mass spectrometry experimental and data analysis workflow for the identification of novel, unexpected modifications on histones. Proteoforms of interest are first discovered using the "open" modification search in TopPIC. Then target proteoforms are manually confirmed using the data visualization tool-LcMsSpectator, part of the Informed-Proteomics package. The workflow can be very helpful in targeted PTM analysis and can be expanded to other types of proteins for discovery of unknown PTMs.
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Affiliation(s)
- Jesse W Wilson
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA.
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37
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Jeong K, Kim J, Kohlbacher O. Mass Deconvolution of Top-Down Mass Spectrometry Datasets by FLASHDeconv. Methods Mol Biol 2022; 2500:145-157. [PMID: 35657592 DOI: 10.1007/978-1-0716-2325-1_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Mass deconvolution, the determination of proteoform precursor and fragment masses, is crucial for top-down proteomics data analysis. Here we describe the detailed procedure to run FLASHDeconv, an ultrafast, high-quality mass deconvolution tool. Both spectrum- and feature-level deconvolution results are obtainable in various output formats by FLASHDeconv. FLASHDeconv is runnable in different environments such as the command line and OpenMS workflows.
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Affiliation(s)
- Kyowon Jeong
- Department of Computer Science, University of Tübingen, Tübingen, Germany.
| | - Jihyung Kim
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Oliver Kohlbacher
- Department of Computer Science, University of Tübingen, Tübingen, Germany
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38
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Greisch JF, den Boer MA, Lai SH, Gallagher K, Bondt A, Commandeur J, Heck AJR. Extending Native Top-Down Electron Capture Dissociation to MDa Immunoglobulin Complexes Provides Useful Sequence Tags Covering Their Critical Variable Complementarity-Determining Regions. Anal Chem 2021; 93:16068-16075. [PMID: 34813704 PMCID: PMC8655740 DOI: 10.1021/acs.analchem.1c03740] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
![]()
Native top-down mass
spectrometry (MS) is gaining traction for
the analysis and sequencing of intact proteins and protein assemblies,
giving access to their mass and composition, as well as sequence information
useful for identification. Herein, we extend and apply native top-down
MS, using electron capture dissociation, to two submillion Da IgM-
and IgG-based oligomeric immunoglobulins. Despite structural similarities,
these two systems are quite different. The ∼895 kDa noncovalent
IgG hexamer consists of six IgG subunits hexamerizing in solution
due to three specifically engineered mutations in the Fc region, whereas
the ∼935 kDa IgM oligomer results from the covalent assembly
of one joining (J) chain and 5 IgM subunits into an asymmetric “pentamer”
stabilized by interchain disulfide bridges. Notwithstanding their
size, structural differences, and complexity, we observe that their
top-down electron capture dissociation spectra are quite similar and
straightforward to interpret, specifically providing informative sequence
tags covering the highly variable CDR3s and FR4s of the Ig subunits
they contain. Moreover, we show that the electron capture dissociation
fragmentation spectra of immunoglobulin oligomers are essentially
identical to those obtained for their respective monomers. Demonstrated
for recombinantly produced systems, the approach described here opens
up new prospects for the characterization and identification of IgMs
circulating in plasma, which is important since IgMs play a critical
role in the early immune response to pathogens such as viruses and
bacteria.
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Affiliation(s)
- Jean-Francois Greisch
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.,Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Maurits A den Boer
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.,Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Szu-Hsueh Lai
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.,Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Kelly Gallagher
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.,Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Albert Bondt
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.,Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Jan Commandeur
- MSVision, Televisieweg 40, 1322 AM Almere, The Netherlands
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands.,Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
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39
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Zhou M, Laureanti JA, Bell CJ, Kwon M, Meng Q, Novikova IV, Thomas DG, Nicora CD, Sontag RL, Bedgar DL, O'Bryon I, Merkley ED, Ginovska B, Cort JR, Davin LB, Lewis NG. De novo sequencing and native mass spectrometry revealed hetero-association of dirigent protein homologs and potential interacting proteins in Forsythia × intermedia. Analyst 2021; 146:7670-7681. [PMID: 34806721 DOI: 10.1039/d1an01476e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The discovery of dirigent proteins (DPs) and their functions in plant phenol biochemistry was made over two decades ago with Forsythia × intermedia. Stereo-selective, DP-guided, monolignol-derived radical coupling in vitro was then reported to afford the optically active lignan, (+)-pinoresinol from coniferyl alcohol, provided one-electron oxidase/oxidant capacity was present. It later became evident that DPs have several distinct sub-families, presumably with different functions. Some known DPs require other essential enzymes/proteins (e.g. oxidases) for their functions. However, the lack of a fully sequenced genome for Forsythia × intermedia made it difficult to profile other components co-purified with the (+)-pinoresinol forming DP. Herein, we used an integrated bottom-up, top-down, and native mass spectrometry (MS) approach to de novo sequence the extracted proteins via adaptation of our initial report of DP solubilization and purification. Using publicly available transcriptome and genomic data from closely related species, we identified 14 proteins that were putatively associated with either DP function or the cell wall. Although their co-occurrence after extraction and chromatographic separation is suggestive for potential protein-protein interactions, none were found to form stable protein complexes with DPs in native MS under the specific experimental conditions we have explored. Interestingly, two new DP homologs were found and they formed hetero-trimers. Molecular dynamics simulations suggested that similar hetero-trimers were possible between Arabidopsis DP homologs with comparable sequence similarities. Nevertheless, our integrated mass spectrometry method development helped prepare for future investigations directed to the discovery of novel proteins and protein-protein interactions. These advantages can be highly beneficial for plant and microbial research where fully sequenced genomes may not be readily available.
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Affiliation(s)
- Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA.
| | - Joseph A Laureanti
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Callum J Bell
- National Center for Genome Resources, Santa Fe, NM, USA
| | - Mi Kwon
- Institute of Biological Chemistry, Washington State University, Pullman, WA, USA
| | - Qingyan Meng
- Institute of Biological Chemistry, Washington State University, Pullman, WA, USA
| | - Irina V Novikova
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, USA.
| | - Dennis G Thomas
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Ryan L Sontag
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Diana L Bedgar
- Institute of Biological Chemistry, Washington State University, Pullman, WA, USA
| | - Isabelle O'Bryon
- Chemical and Biological Signatures Group, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Eric D Merkley
- Chemical and Biological Signatures Group, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Bojana Ginovska
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - John R Cort
- Institute of Biological Chemistry, Washington State University, Pullman, WA, USA.,Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Laurence B Davin
- Institute of Biological Chemistry, Washington State University, Pullman, WA, USA
| | - Norman G Lewis
- Institute of Biological Chemistry, Washington State University, Pullman, WA, USA
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40
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Kaulich PT, Winkels K, Kaulich TB, Treitz C, Cassidy L, Tholey A. MSTopDiff: A Tool for the Visualization of Mass Shifts in Deconvoluted Top-Down Proteomics Data for the Database-Independent Detection of Protein Modifications. J Proteome Res 2021; 21:20-29. [PMID: 34818005 DOI: 10.1021/acs.jproteome.1c00766] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Top-down proteomics analyzes intact proteoforms with all of their post-translational modifications and genetic and RNA splice variants. In addition, modifications introduced either deliberately or inadvertently during sample preparation, that is, via oxidation, alkylation, or labeling reagents, or through the formation of noncovalent adducts (e.g., detergents) further increase the sample complexity. To facilitate the recognition of protein modifications introduced during top-down analysis, we developed MSTopDiff, a software tool with a graphical user interface written in Python, which allows one to detect protein modifications by calculating and visualizing mass differences in top-down data without the prerequisite of a database search. We demonstrate the successful application of MSTopDiff for the detection of artifacts originating from oxidation, formylation, overlabeling during isobaric labeling, and adduct formation with cations or sodium dodecyl sulfate. MSTopDiff offers several modes of data representation using deconvoluted MS1 or MS2 spectra. In addition to artificial modifications, the tool enables the visualization of biological modifications such as phosphorylation and acetylation. MSTopDiff provides an overview of the artificial and biological modifications in top-down proteomics samples, which makes it a valuable tool in quality control of standard workflows and for parameter evaluation during method development.
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Affiliation(s)
- Philipp T Kaulich
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Konrad Winkels
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Tobias B Kaulich
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Christian Treitz
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Liam Cassidy
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Andreas Tholey
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
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41
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Novikova IV, Zhou M, Evans JE, Du C, Parra M, Kim DN, VanAernum ZL, Shaw JB, Hellmann H, Wysocki VH. Tunable Heteroassembly of a Plant Pseudoenzyme-Enzyme Complex. ACS Chem Biol 2021; 16:2315-2325. [PMID: 34520180 PMCID: PMC9979268 DOI: 10.1021/acschembio.1c00475] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Pseudoenzymes have emerged as key regulatory elements in all kingdoms of life despite being catalytically nonactive. Yet many factors defining why one protein is active while its homologue is inactive remain uncertain. For pseudoenzyme-enzyme pairs, the similarity of both subunits can often hinder conventional characterization approaches. In plants, a pseudoenzyme, PDX1.2, positively regulates vitamin B6 production by association with its active catalytic homologues such as PDX1.3 through an unknown assembly mechanism. Here we used an integrative experimental approach to learn that such pseudoenzyme-enzyme pair associations result in heterocomplexes of variable stoichiometry, which are unexpectedly tunable. We also present the atomic structure of the PDX1.2 pseudoenzyme as well as the population averaged PDX1.2-PDX1.3 pseudoenzyme-enzyme pair. Finally, we dissected hetero-dodecamers of each stoichiometry to understand the arrangement of monomers in the heterocomplexes and identified symmetry-imposed preferences in PDX1.2-PDX1.3 interactions. Our results provide a new model of pseudoenzyme-enzyme interactions and their native heterogeneity.
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Affiliation(s)
- Irina V. Novikova
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - James E. Evans
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States; School of Biological Sciences, Washington State University, Pullman, Washington 99164, United States
| | - Chen Du
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States; Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, Ohio 43210, United States
| | - Marcelina Parra
- School of Biological Sciences, Washington State University, Pullman, Washington 99164, United States
| | - Doo Nam Kim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Zachary L. VanAernum
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States; Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, Ohio 43210, United States
| | - Jared B. Shaw
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Hanjo Hellmann
- School of Biological Sciences, Washington State University, Pullman, Washington 99164, United States
| | - Vicki H. Wysocki
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, United States; Resource for Native Mass Spectrometry Guided Structural Biology, The Ohio State University, Columbus, Ohio 43210, United States
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42
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Evaluating the Performance of 193 nm Ultraviolet Photodissociation for Tandem Mass Tag Labeled Peptides. ANALYTICA 2021. [DOI: 10.3390/analytica2040014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Despite the successful application of tandem mass tags (TMT) for peptide quantitation, missing reporter ions in higher energy collisional dissociation (HCD) spectra remains a challenge for consistent quantitation, especially for peptides with labile post-translational modifications. Ultraviolet photodissociation (UVPD) is an alternative ion activation method shown to provide superior coverage for sequencing of peptides and intact proteins. Here, we optimized and evaluated 193 nm UVPD for the characterization of TMT-labeled model peptides, HeLa proteome, and N-glycopeptides from model proteins. UVPD yielded the same TMT reporter ions as HCD, at m/z 126–131. Additionally, UVPD produced a wide range of fragments that yielded more complete characterization of glycopeptides and less frequent missing TMT reporter ion channels, whereas HCD yielded a strong tradeoff between characterization and quantitation of TMT-labeled glycopeptides. However, the lower fragmentation efficiency of UVPD yielded fewer peptide identifications than HCD. Overall, 193 nm UVPD is a valuable tool that provides an alternative to HCD for the quantitation of large and highly modified peptides with labile PTMs. Continued development of instrumentation specific to UVPD will yield greater fragmentation efficiency and fulfil the potential of UVPD to be an all-in-one spectrum ion activation method for broad use in the field of proteomics.
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43
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Abstract
![]()
Native mass spectrometry
(MS) involves the analysis and characterization
of macromolecules, predominantly intact proteins and protein complexes,
whereby as much as possible the native structural features of the
analytes are retained. As such, native MS enables the study of secondary,
tertiary, and even quaternary structure of proteins and other biomolecules.
Native MS represents a relatively recent addition to the analytical
toolbox of mass spectrometry and has over the past decade experienced
immense growth, especially in enhancing sensitivity and resolving
power but also in ease of use. With the advent of dedicated mass analyzers,
sample preparation and separation approaches, targeted fragmentation
techniques, and software solutions, the number of practitioners and
novel applications has risen in both academia and industry. This review
focuses on recent developments, particularly in high-resolution native
MS, describing applications in the structural analysis of protein
assemblies, proteoform profiling of—among others—biopharmaceuticals
and plasma proteins, and quantitative and qualitative analysis of
protein–ligand interactions, with the latter covering lipid,
drug, and carbohydrate molecules, to name a few.
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Affiliation(s)
- Sem Tamara
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, The Netherlands.,Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Maurits A den Boer
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, The Netherlands.,Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Padualaan 8, 3584 CH Utrecht, The Netherlands.,Netherlands Proteomics Center, Padualaan 8, 3584 CH Utrecht, The Netherlands
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44
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Lui KW, Ngai SM. PrSM-Level Side-by-Side Comparison of Online LC-MS Methods with Intact Histone H3 and H4 Proteoforms. J Proteome Res 2021; 20:4331-4345. [PMID: 34327993 DOI: 10.1021/acs.jproteome.1c00308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The heterogeneity of histone H3 proteoforms makes histone H3 top-down analysis challenging. To enhance the detection coverage of the proteoforms, performing liquid chromatography (LC) front-end to mass spectrometry (MS) detection is recommended. Here, using optimized electron-transfer/high-energy collision dissociation (EThcD) parameters, we have conducted a proteoform-spectrum match (PrSM)-level side-by-side comparison of reversed-phase LC-MS (RPLC-MS), "dual-gradient" weak cation-exchange/hydrophilic interaction LC-MS (dual-gradient WCX/HILIC-MS), and "organic-rich" WCX/HILIC-MS on the top-down analyses of H3.1, H3.2, and H4 proteins extracted from a HeLa cell culture. While both dual-gradient WCX/HILIC and organic-rich WCX/HILIC could resolve intact H3 and H4 proteoforms by the number of acetylations, the organic-rich method could enhance the separations of different trimethyl/acetyl near-isobaric H3 proteoforms. In comparison with RPLC-MS, both of the WCX/HILIC-MS methods enhanced the qualities of the H3 PrSMs and remarkably improved the range, reproducibility, and confidence in the identifications of H3 proteoforms.
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Affiliation(s)
- Kin-Wing Lui
- School of Life Sciences, The Chinese University of Hong Kong, Sha Tin, Hong Kong 999077, P. R. China
| | - Sai-Ming Ngai
- School of Life Sciences, The Chinese University of Hong Kong, Sha Tin, Hong Kong 999077, P. R. China.,State Key Laboratory of Agrobiotechnology and School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong 999077, P. R. China
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45
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Rommelfanger S, Zhou M, Shaghasi H, Tzeng SC, Evans BS, Paša-Tolić L, Umen JG, Pesavento JJ. An Improved Top-Down Mass Spectrometry Characterization of Chlamydomonas reinhardtii Histones and Their Post-translational Modifications. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1671-1688. [PMID: 34165968 PMCID: PMC9236284 DOI: 10.1021/jasms.1c00029] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 06/01/2023]
Abstract
We present an updated analysis of the linker and core histone proteins and their proteoforms in the green microalga Chlamydomonas reinhardtii by top-down mass spectrometry (TDMS). The combination of high-resolution liquid chromatographic separation, robust fragmentation, high mass spectral resolution, the application of a custom search algorithm, and extensive manual analysis enabled the characterization of 86 proteoforms across all four core histones H2A, H2B, H3, and H4 and the linker histone H1. All canonical H2A paralogs, which vary in their C-termini, were identified, along with the previously unreported noncanonical variant H2A.Z that had high levels of acetylation and C-terminal truncations. Similarly, a majority of the canonical H2B paralogs were identified, along with a smaller noncanonical variant, H2B.v1, that was highly acetylated. Histone H4 exhibited a novel acetylation profile that differs significantly from that found in other organisms. A majority of H3 was monomethylated at K4 with low levels of co-occuring acetylation, while a small fraction of H3 was trimethylated at K4 with high levels of co-occuring acetylation.
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Affiliation(s)
- Sarah
R. Rommelfanger
- Donald
Danforth Plant Science Center, St. Louis, Missouri 63132, United States
- Washington
University in St. Louis, St. Louis, Missouri 63130, United States
| | - Mowei Zhou
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - Henna Shaghasi
- Saint
Mary’s College of California, Moraga, California 94575, United States
| | - Shin-Cheng Tzeng
- Donald
Danforth Plant Science Center, St. Louis, Missouri 63132, United States
| | - Bradley S. Evans
- Donald
Danforth Plant Science Center, St. Louis, Missouri 63132, United States
| | - Ljiljana Paša-Tolić
- Environmental
Molecular Sciences Laboratory, Pacific Northwest
National Laboratory, Richland, Washington 99354, United States
| | - James G. Umen
- Donald
Danforth Plant Science Center, St. Louis, Missouri 63132, United States
- Washington
University in St. Louis, St. Louis, Missouri 63130, United States
| | - James J. Pesavento
- Saint
Mary’s College of California, Moraga, California 94575, United States
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46
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Khalid MF, Iman K, Ghafoor A, Saboor M, Ali A, Muaz U, Basharat AR, Tahir T, Abubakar M, Akhter MA, Nabi W, Vanderbauwhede W, Ahmad F, Wajid B, Chaudhary SU. PERCEPTRON: an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics. Nucleic Acids Res 2021; 49:W510-W515. [PMID: 33999207 PMCID: PMC8262694 DOI: 10.1093/nar/gkab368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/10/2021] [Accepted: 04/25/2021] [Indexed: 11/12/2022] Open
Abstract
PERCEPTRON is a next-generation freely available web-based proteoform identification and characterization platform for top-down proteomics (TDP). PERCEPTRON search pipeline brings together algorithms for (i) intact protein mass tuning, (ii) de novo sequence tags-based filtering, (iii) characterization of terminal as well as post-translational modifications, (iv) identification of truncated proteoforms, (v) in silico spectral comparison, and (vi) weight-based candidate protein scoring. High-throughput performance is achieved through the execution of optimized code via multiple threads in parallel, on graphics processing units (GPUs) using NVidia Compute Unified Device Architecture (CUDA) framework. An intuitive graphical web interface allows for setting up of search parameters as well as for visualization of results. The accuracy and performance of the tool have been validated on several TDP datasets and against available TDP software. Specifically, results obtained from searching two published TDP datasets demonstrate that PERCEPTRON outperforms all other tools by up to 135% in terms of reported proteins and 10-fold in terms of runtime. In conclusion, the proposed tool significantly enhances the state-of-the-art in TDP search software and is publicly available at https://perceptron.lums.edu.pk. Users can also create in-house deployments of the tool by building code available on the GitHub repository (http://github.com/BIRL/Perceptron).
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Affiliation(s)
- Muhammad Farhan Khalid
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Kanzal Iman
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Amna Ghafoor
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Mujtaba Saboor
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Ahsan Ali
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Urwa Muaz
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Abdul Rehman Basharat
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Taha Tahir
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Muhammad Abubakar
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Momina Amer Akhter
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
| | - Waqar Nabi
- School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Wim Vanderbauwhede
- School of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Fayyaz Ahmad
- Department of Statistics, University of Gujrat, Gujrat, Pakistan
| | - Bilal Wajid
- Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan
- Department of Computer Science, University of Management and Technology, Lahore, Pakistan
- Division of Research and Development, Sabz-Qalam, Lahore, Pakistan
| | - Safee Ullah Chaudhary
- Biomedical Informatics Research Laboratory, Department of Biology, Lahore University of Management Sciences, Lahore, Pakistan
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47
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Abstract
Proteoform identification is required to fully understand the biological diversity present in a sample. However, these identifications are often ambiguous because of the challenges in analyzing full length proteins by mass spectrometry. A five-level proteoform classification system was recently developed to delineate the ambiguity of proteoform identifications and to allow for comparisons across software platforms and acquisition methods. Widespread adoption of this system requires software tools to provide classification of the proteoform identifications. We describe here an implementation of the five-level classification system in the software program MetaMorpheus, which provides both bottom-up and top-down identifications. Additionally, we developed a stand-alone program called ProteoformClassifier that allows users to classify proteoform results from any search program, provided that the program writes output that includes the information necessary to evaluate proteoform ambiguity. This stand-alone program includes a small test file and database to evaluate if a given program provides sufficient information to evaluate ambiguity. If the program does not, then ProteoformClassifier provides meaningful feedback to assist developers with implementing the classification system. We tested currently available top-down software programs and found that none of them (other than MetaMorpheus) provided sufficient information regarding identification ambiguity to permit classification.
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Affiliation(s)
- Zach Rolfs
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
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48
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Lu L, Scalf M, Shortreed MR, Smith LM. Mesh Fragmentation Improves Dissociation Efficiency in Top-down Proteomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1319-1325. [PMID: 33754701 PMCID: PMC8783543 DOI: 10.1021/jasms.0c00462] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Top-down proteomics is a key mass spectrometry-based technology for comprehensive analysis of proteoforms. Proteoforms exhibit multiple high charge states and isotopic forms in full MS scans. The dissociation behavior of proteoforms in different charge states and subjected to different collision energies is highly variable. The current widely employed data-dependent acquisition (DDA) method selects a narrow m/z range (corresponding to a single proteoform charge state) for dissociation from the most abundant precursors. We describe here Mesh, a novel dissociation strategy, to dissociate multiple charge states of one proteoform with multiple collision energies. We show that the Mesh strategy has the potential to generate fragment ions with improved sequence coverage and improve identification ratios in top-down proteomic analyses of complex samples. The strategy is implemented within an open-source instrument control software program named MetaDrive to perform real time deconvolution and precursor selection.
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Affiliation(s)
- Lei Lu
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, United States
| | - Mark Scalf
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, United States
| | - Michael R. Shortreed
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, United States
| | - Lloyd M. Smith
- Department of Chemistry, University of Wisconsin, Madison, Wisconsin 53706, United States
- Corresponding Author Phone: (608) 263-2594. Fax: (608) 265-6780.
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Choi IK, Jiang T, Kankara SR, Wu S, Liu X. TopMSV: A Web-Based Tool for Top-Down Mass Spectrometry Data Visualization. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1312-1318. [PMID: 33780241 PMCID: PMC8172439 DOI: 10.1021/jasms.0c00460] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Top-down mass spectrometry (MS) investigates intact proteoforms for proteoform identification, characterization, and quantification. Data visualization plays an essential role in top-down MS data analysis because proteoform identification and characterization often involve manual data inspection to determine the molecular masses of highly charged ions and validate unexpected alterations in identified proteoforms. While many software tools have been developed for MS data visualization, there is still a lack of web-based visualization software designed for top-down MS. Here, we present TopMSV, a web-based tool for top-down MS data processing and visualization. TopMSV provides interactive views of top-down MS data using a web browser. It integrates software tools for spectral deconvolution and proteoform identification and uses analysis results of the tools to annotate top-down MS data.
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Affiliation(s)
- In Kwon Choi
- Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Tianze Jiang
- Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Sreekanth Reddy Kankara
- Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States
| | - Si Wu
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States
| | - Xiaowen Liu
- Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana 46202, United States
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States
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Greisch JF, den Boer MA, Beurskens F, Schuurman J, Tamara S, Bondt A, Heck AJR. Generating Informative Sequence Tags from Antigen-Binding Regions of Heavily Glycosylated IgA1 Antibodies by Native Top-Down Electron Capture Dissociation. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1326-1335. [PMID: 33570406 PMCID: PMC8176452 DOI: 10.1021/jasms.0c00461] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Immunoglobulins A (IgA) include some of the most abundant human antibodies and play an important role in defending mucosal surfaces against pathogens. The unique structural features of the heavy chain of IgA subclasses (called IgA1 and IgA2) enable them to polymerize via the joining J-chain, resulting in IgA dimers but also higher oligomers. While secretory sIgA oligomers are dominant in milk and saliva, IgAs exist primarily as monomers in serum. No method currently allows disentangling the millions of unique IgAs potentially present in the human antibody repertoire. Obtaining unambiguous sequence reads of their hypervariable antigen-binding regions is a prerequisite for IgA identification. We here report a mass spectrometric method that uses electron capture dissociation (ECD) to produce straightforward-to-read sequence ladders of the variable parts of both the light and heavy chains of IgA1s, in particular, of the functionally critical CDR3 regions. We directly compare the native top-down ECD spectra of a heavily and heterogeneously N- and O-glycosylated anti-CD20 IgA1, the corresponding N-glycosylated anti-CD20 IgG1, and their Fab parts. We show that while featuring very different MS1 spectra, the native top-down ECD MS2 spectra of all four species are nearly identical, with cleavages occurring specifically within the CDR3 and FR4 regions of both the heavy and light chain. From the sequence-informative ECD data of an intact glycosylated IgA1, we foresee that native top-down ECD will become a valuable complementary tool for the de novo sequencing of IgA1s from milk, saliva, or serum.
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Affiliation(s)
- Jean-Francois Greisch
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Maurits A. den Boer
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Frank Beurskens
- Genmab,
Utrecht, Uppsalalaan
15, 3584 CT Utrecht, The Netherlands
| | - Janine Schuurman
- Genmab,
Utrecht, Uppsalalaan
15, 3584 CT Utrecht, The Netherlands
| | - Sem Tamara
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Albert Bondt
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Albert J. R. Heck
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
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