Glycopeptide variable window SWATH for improved data independent acquisition glycoprotein analysis

Zhou, Chun; Schulz, Benjamin L.

doi:10.1016/j.ab.2020.113667

Cited by 22 publications

(19 citation statements)

References 62 publications

(100 reference statements)

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“…To the best of our knowledge, the methods for statistical control of glycopeptide error rates in DIA analysis have not been well established. In previous studies, spectral libraries were generated from deglycosylated peptides 28 , 32 or peptides with truncated glycans 33 by shifting the precursor mass, and DIA data analysis of glycopeptides was performed using tools designed for peptide analysis. Therefore, as a baseline for comparison, we built another two glycan entrapment libraries using the peptide sequences from yeast and glycans from human, one without Y ions and the other including Y ions, to analyze the fission yeast sample using the peptide-only FDR control approach with peptide decoys only (Supplementary Note 2 , Supplementary Data 9 and Supplementary Fig.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, glycoform inference was enabled when multiple glycopeptide precursors with the same peptide sequence and different glycans are arranged to be fragmented in one isolation window. Despite the herein proposed strategies to control error rates when using wide isolation windows, we still recommend to design the isolation windows properly according to the mass distribution of glycopeptides 32 , if possible, for improving the detection sensitivity. Recent advances in ion mobility spectrometry (IMS) including high field asymmetric waveform ion mobility spectrometry (FAIMS) 51 and parallel accumulation-serial fragmentation (diaPASEF) 52 have achieved rapid improvements in the sensitivity of DIA analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Pan et al built a spectral library containing both peptide fragments and glycan Y ions, achieving site-specific N-glycosylation analysis of six glycosites in IgM 31 . Zhou et al developed a SWATH-MS method with optimized variable windows for a set of target glycopeptides to allow accurate glycoform measurement 32 . These methods have achieved better sensitivity than DDA for targeted analysis of glycoforms of several or a dozen of glycoproteins.…”

Section: Introductionmentioning

confidence: 99%

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GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control

et al. 2021

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Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control

et al. 2021

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“…However, due to the large mass addition of glycans, glycopeptides are not evenly distributed along this range and are concentrated between 950 and 1200 m/z . As such, Zhou and Schulz ( 182 ) validated a more effective strategy, GP-SWATH that narrows selection window width across the glycopeptide region to provide more accurate and robust glycopeptide detection and quantification. A notable limitation in DIA analysis is the deconvolution of tandem MS spectra as DIA experiments commonly lose precursor information, making identification of posttranslationally modified peptides a challenge—especially for O-glycopeptides.…”

Section: Glycopeptide Quantitationmentioning

confidence: 99%

Recent Advances in Analytical Approaches for Glycan and Glycopeptide Quantitation

Delafield

2021

Molecular & Cellular Proteomics

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Growing implications of glycosylation in physiological occurrences and human disease have prompted intensive focus on revealing glycomic perturbations through absolute and relative quantification. Empowered by seminal methodologies and increasing capacity for detection, identification, and characterization, the past decade has provided a significant increase in the number of suitable strategies for glycan and glycopeptide quantification. Mass spectrometry-based strategies for glycomic quantitation have grown to include metabolic incorporation of stable isotopes, deposition of mass difference and mass defect isotopic labels, and isobaric chemical labeling, providing researchers with ample tools for accurate and robust quantitation. Beyond this, workflows have been designed to harness instrument capability for label-free quantification and numerous software packages have been developed to facilitate reliable spectrum scoring. In this review, we present and highlight the most recent advances in chemical labeling and associated techniques for glycan and glycopeptide quantification.

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“…While SWATH‐MS is not as quantitatively accurate as DDA it identifies more peptides and associated proteins and is more reproducible than traditional DDA (Bruderer et al, 2015; Kelstrup et al, 2018). While this technique has not been used in viral glycomics or glycoproteomics yet, we have included it here because of its increasing use in proteomics and recent introduction into glycoproteomic studies (Xu, Bailey, & Schulz, 2015; Zacchi & Schulz, 2016, 2019; Zhou & Schulz, 2020).…”

Section: Instrumentation and Fragmentation Modes: Targeting The Desirmentioning

confidence: 99%

Glycomics and glycoproteomics of viruses: Mass spectrometry applications and insights toward structure–function relationships

Cipollo

Parsons

2020

Mass Spectrometry Reviews

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The advancement of viral glycomics has paralleled that of the mass spectrometry glycomics toolbox. In some regard the glycoproteins studied have provided the impetus for this advancement. Viral proteins are often highly glycosylated, especially those targeted by the host immune system. Glycosylation tends to be dynamic over time as viruses propagate in host populations leading to increased number of and/or “movement” of glycosylation sites in response to the immune system and other pressures. This relationship can lead to highly glycosylated, difficult to analyze glycoproteins that challenge the capabilities of modern mass spectrometry. In this review, we briefly discuss five general areas where glycosylation is important in the viral niche and how mass spectrometry has been used to reveal key information regarding structure–function relationships between viral glycoproteins and host cells. We describe the recent past and current glycomics toolbox used in these analyses and give examples of how the requirement to analyze these complex glycoproteins has provided the incentive for some advances seen in glycomics mass spectrometry. A general overview of viral glycomics, special cases, mass spectrometry methods and work‐flows, informatics and complementary chemical techniques currently used are discussed. © 2020 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd. Mass Spec Rev

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Glycopeptide variable window SWATH for improved data independent acquisition glycoprotein analysis

Cited by 22 publications

References 62 publications

GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control

GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control

Recent Advances in Analytical Approaches for Glycan and Glycopeptide Quantitation

Glycomics and glycoproteomics of viruses: Mass spectrometry applications and insights toward structure–function relationships

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