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Goecker ZC, Burke MC, Remoroza CA, Liu Y, Mirokhin YA, Sheetlin SL, Tchekhovskoi DV, Yang X, Stein SE. Variation of Site-Specific Glycosylation Profiles of Recombinant Influenza Glycoproteins. Mol Cell Proteomics 2024:100827. [PMID: 39128790 DOI: 10.1016/j.mcpro.2024.100827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 07/08/2024] [Accepted: 08/07/2024] [Indexed: 08/13/2024] Open
Abstract
This work presents a detailed determination of site-specific N-glycan distributions of the recombinant influenza glycoproteins hemagglutinin and neuraminidase. Variation in glycosylation among recombinant glycoproteins is not predictable and can depend on details of the biomanufacturing process as well as details of protein structure. In this study, recombinant influenza proteins were analyzed from eight strains of four different suppliers. These include five hemagglutinin and three neuraminidase proteins, each produced from a HEK293 cell line. Digestion was conducted using a series of complex multi-enzymatic methods designed to isolate glycopeptides containing single N-glycosylated sites. Site-specific glycosylation profiles of intact glycopeptides were produced using a recently developed method and comparisons were made using spectral similarity scores. Variation in glycan abundances and distribution was most pronounced between different strains of virus (similarity score = 383 out of 999), whereas digestion replicates and injection replicates showed relatively little variation (similarity score = 957). Notably, glycan distributions for homologous regions of influenza glycoprotein variants showed low variability. Due to the multiple possible sources of variation and inherent analytical difficulties in site-specific glycan determinations, variations were individually examined for multiple factors, including differences in supplier, production batch, protease digestion and replicate measurement. After comparing all glycosylation distributions, four distinguishable classes could be identified for the majority of sites. Finally, attempts to identify glycosylation distributions on adjacent potential N-glycosylated sites of one HA variant were made. Only the second site (NnST) was found to be occupied using two rarely used proteases in proteomics, subtilisin and esperase, both of which did selectively cleave these adjacent sites.
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Affiliation(s)
- Zachary C Goecker
- Mass Spectrometry Data Center, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States.
| | - Meghan C Burke
- Mass Spectrometry Data Center, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Concepcion A Remoroza
- Mass Spectrometry Data Center, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Yi Liu
- Mass Spectrometry Data Center, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Yuri A Mirokhin
- Mass Spectrometry Data Center, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Sergey L Sheetlin
- Mass Spectrometry Data Center, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Dmitrii V Tchekhovskoi
- Mass Spectrometry Data Center, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Xiaoyu Yang
- Mass Spectrometry Data Center, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Stephen E Stein
- Mass Spectrometry Data Center, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
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Harvey DJ. Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2021-2022. MASS SPECTROMETRY REVIEWS 2024. [PMID: 38925550 DOI: 10.1002/mas.21873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/05/2024] [Accepted: 02/12/2024] [Indexed: 06/28/2024]
Abstract
The use of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry for the analysis of carbohydrates and glycoconjugates is a well-established technique and this review is the 12th update of the original article published in 1999 and brings coverage of the literature to the end of 2022. As with previous review, this review also includes a few papers that describe methods appropriate to analysis by MALDI, such as sample preparation, even though the ionization method is not MALDI. The review follows the same format as previous reviews. It is divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of computer software for structural identification. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other general areas such as medicine, industrial processes, natural products and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. MALDI is still an ideal technique for carbohydrate analysis, particularly in its ability to produce single ions from each analyte and advancements in the technique and range of applications show little sign of diminishing.
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Zhang NH, Deutsch EW. SpectiCal: m/ z Calibration of MS2 Peptide Spectra Using Known Low Mass Ions. J Proteome Res 2024; 23:1519-1530. [PMID: 38538550 DOI: 10.1021/acs.jproteome.3c00882] [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: 04/06/2024]
Abstract
Most tandem mass spectrometry fragmentation spectra have small calibration errors that can lead to suboptimal interpretation and annotation. We developed SpectiCal, a software tool that can read mzML files from data-dependent acquisition proteomics experiments in parallel, compute m/z calibrations for each file prior to identification analysis based on known low-mass ions, and produce information about frequently observed peaks and their explanations. Using calibration coefficients, the data can be corrected to generate new calibrated mzML files. SpectiCal was tested using five public data sets, creating a table of commonly observed low-mass ions and their identifications. Information about the calibration and individual peaks is written in PDF and TSV files. This includes information for each peak, such as the number of runs in which it appears, the percentage of spectra in which it appears, and a plot of the aggregated region surrounding each peak. SpectiCal can be used to compute MS run calibrations, examine MS runs for artifacts that might hinder downstream analysis, and generate tables of detected low-mass ions for further analysis. SpectiCal is freely available at https://github.com/PlantProteomes/SpectiCal.
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Affiliation(s)
- Nathan H Zhang
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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Remoroza CA, Burke MC, Mak TD, Sheetlin SL, Mirokhin YA, Cooper BT, Goecker ZC, Lowenthal MS, Yang X, Wang G, Tchekhovskoi DV, Stein SE. Comparison of N-Glycopeptide to Released N-Glycan Abundances and the Influence of Glycopeptide Mass and Charge States on N-Linked Glycosylation of IgG Antibodies. J Proteome Res 2024; 23:1443-1457. [PMID: 38450643 PMCID: PMC10997438 DOI: 10.1021/acs.jproteome.3c00904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
We report the comparison of mass-spectral-based abundances of tryptic glycopeptides to fluorescence abundances of released labeled glycans and the effects of mass and charge state and in-source fragmentation on glycopeptide abundances. The primary glycoforms derived from Rituximab, NISTmAb, Evolocumab, and Infliximab were high-mannose and biantennary complex galactosylated and fucosylated N-glycans. Except for Evolocumab, in-source ions derived from the loss of HexNAc or HexNAc-Hex sugars are prominent for other therapeutic IgGs. After excluding in-source fragmentation of glycopeptide ions from the results, a linear correlation was observed between fluorescently labeled N-glycan and glycopeptide abundances over a dynamic range of 500. Different charge states of human IgG-derived glycopeptides containing a wider variety of abundant attached glycans were also investigated to examine the effects of the charge state on ion abundances. These revealed a linear dependence of glycopeptide abundance on the mass of the glycan with higher charge states favoring higher-mass glycans. Findings indicate that the mass spectrometry-based bottom-up approach can provide results as accurate as those of glycan release studies while revealing the origin of each attached glycan. These site-specific relative abundances are conveniently displayed and compared using previously described glycopeptide abundance distribution spectra "GADS" representations. Mass spectrometry data are available from the MAssIVE repository (MSV000093562).
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Affiliation(s)
| | | | | | | | | | - Brian T. Cooper
- Mass Spectrometry Data Center
- Department of Chemistry, University of North Carolina at Charlotte, Charlotte, North Carolina 28223, United States
| | | | - Mark S. Lowenthal
- Bioanalytical Science Group, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive Gaithersburg, MD 20899, US
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5
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Burke MC, Liu Y, Remoroza C, Mirokhin YA, Sheetlin SL, Tchekhovskoi DV, Wang G, Yang X, Stein SE. Determining Site-Specific Glycan Profiles of Recombinant SARS-CoV-2 Spike Proteins from Multiple Sources. J Proteome Res 2023; 22:3225-3241. [PMID: 37647588 DOI: 10.1021/acs.jproteome.3c00271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Glycopeptide Abundance Distribution Spectra (GADS) were recently introduced as a means of representing, storing, and comparing glycan profiles of intact glycopeptides. Here, using that representation, an extensive analysis is made of multiple commercial sources of the recombinant SARS-CoV-2 spike protein, each containing 22 N-linked glycan sites (sequons). Multiple proteases are used along with variable energy fragmentation followed by ion trap confirmation. This enables a detailed examination of the reproducibility of the method across multiple types of variability. These results show that GADS are consistent between replicates and laboratories for sufficiently abundant glycopeptides. Derived GADS enable the examination and comparison of the glycan profiles between commercial sources of the spike protein. Multiple distinct glycopeptide distributions, generated by multiple proteases, confirm these profiles. Comparisons of GADS derived from 11 sources of recombinant spike protein reveal that sources for which protein expression methods were the same produced near-identical glycan profiles, thereby demonstrating the ability of this method to measure GADS of sufficient reliability to distinguish different glycoform distributions between commercial vendors and potentially to reliably determine and compare differences in glycosylation for any glycoprotein under different conditions of production. All mass spectrometry data files have been deposited in the MassIVE repository under the identifier MSV000091776.
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Affiliation(s)
- Meghan C Burke
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Yi Liu
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Concepcion Remoroza
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Yuri A Mirokhin
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Sergey L Sheetlin
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Dmitrii V Tchekhovskoi
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Guanghui Wang
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Xiaoyu Yang
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Stephen E Stein
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
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Bittremieux W, Levitsky L, Pilz M, Sachsenberg T, Huber F, Wang M, Dorrestein PC. Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils. J Proteome Res 2023; 22:625-631. [PMID: 36688502 DOI: 10.1021/acs.jproteome.2c00632] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.
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Affiliation(s)
- Wout Bittremieux
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium.,Biomedical Informatics Network Antwerpen (biomina), 2020 Antwerp, Belgium
| | - Lev Levitsky
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center for Chemical Physics, Russian Academy of Sciences, 119334 Moscow, Russia
| | - Matteo Pilz
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, 72076 Tübingen, Germany
| | - Timo Sachsenberg
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, 72076 Tübingen, Germany
| | - Florian Huber
- Centre for Digitalisation and Digitality, University of Applied Sciences Düsseldorf, 40476 Düsseldorf, Germany
| | - Mingxun Wang
- Department of Computer Science, University of California─Riverside, Riverside, California 92507, United States
| | - Pieter C Dorrestein
- Collaborative Mass Spectrometry Innovation Center, University of California─San Diego, La Jolla, California 92093, United States.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California─San Diego, La Jolla California 92093, United States
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7
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Remoroza CA, Burke MC, Yang X, Sheetlin S, Mirokhin Y, Markey SP, Tchekhovskoi DV, Stein SE. Mass Spectral Library Methods for Analysis of Site-Specific N-Glycosylation: Application to Human Milk Proteins. J Proteome Res 2022; 21:2421-2434. [DOI: 10.1021/acs.jproteome.2c00286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Concepcion A. Remoroza
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Meghan C. Burke
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Xiaoyu Yang
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Sergey Sheetlin
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Yuri Mirokhin
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Sanford P. Markey
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Dmitrii V. Tchekhovskoi
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
| | - Stephen E. Stein
- Mass Spectrometry Data Center, Biomolecular Measurement Division, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United States
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