In 2013, we served as guest editors for the Glycomics special issue where the Athens guidelines for glycomic analyses were first revealed that have since been adopted by multiple journals (1). At that moment in time, we could not have easily conceived of a special issue dedicated to glycoproteomics. However, just 8 years later, we find ourselves presenting a special issue of Molecular & Cellular Proteomics on Glycoproteomics that introduces the reader to the recent explosion in front-end enrichment methods, analytical approaches, and back-end software solutions dedicated to glycoproteomics. This special issue includes nine review articles and nine research articles that introduce the reader to the exciting, challenging, and rapidly evolving field of glycoproteomics, which is so highly dependent on MS.
Glycoproteomics has been front and center this past year as scientists tackled the COVID-19 pandemic, given that the spike glycoprotein of SARS-CoV-2, which is often the basis for antibody and vaccine therapeutics, is a trimer glycoprotein consisting of 66 occupied N-linked glycosylation sites, and that the host receptor, ACE2, is also heavily glycosylated (2, 3). Thus, an issue devoted to understanding this heterogenous class of post-translational modifications seems very timely. The number of various glycan moieties that can modify proteins easily surpasses the sum of all other post-translational modifications combined, and the majority of all expressed mammalian proteins (secreted, membrane bound, and intracellular) are glycosylated (4). Thus, it is essential to be able to characterize these challenging biomolecules to better understand the fundamental roles that glycoconjugates play in nearly all aspects of physiology and pathophysiology.
A review by West et al. (5) gives us an intertaxa evolutionary perspective on glycomics, glycoproteomics, and glycogenomics. Riley et al. (6) provide a pragmatic guide to the plethora of enrichment strategies that exist for glycoproteins, while a review by Maynard and Chalkley (7) focuses specifically on enrichment and assignment of O-GlcNAc sites on proteins. Research articles by Kurz et al. (8) and Blazev et al. (9) both illustrate approaches for detailed glycomic analyses that can facilitate improved glycoproteomics. McDowell et al. (10) introduce us to imaging MS and lectin analyses for N-linked glycans applied to pancreatic cancer tissues, while Chen et al. (11) utilize enrichment combined with intact glycopeptide analyses to look at changes in glycoproteins of cerebral spinal fluid in Alzheimer’s disease. The research article by Martinez et al. (12) also exemplifies the utility of enrichment and detection methods for defining the O-GlcNAc transferase interactome.
Ye and Vakhrushev (13) tackle the analytical approach of data-independent acquisition for glycoproteomics and Pegg et al. (14) implement the data-independent acquisition approach for the analyses of glycoproteins in sparkling wine. Pepi et al. (15) introduce us to the challenging class of glycosaminoglycans–proteoglycans, and Persson et al. (16) demonstrate the power of domain mapping of glycosaminoglycans for structural characterization of proteoglycans.
Two reviews, by Cao et al. (17) and Delafield and Li (18), discuss recent advances in data analysis for glycans, intact glycopeptides, and quantification methods. The reviews by Caval et al. (19), and by Hackett and Zaia (20), tackle the challenging problem of describing the inherent heterogeneity of glycoproteins and in calculating the similarities between glycoproteins. Finally, Roushan et al. (21) illustrate the power of tools of multiple data analyses for glycoproteomics.
In this special issue, we have attempted to capture many of the recent developments in enrichment and labeling strategies, MS-based interrogation approaches, and data analysis platforms for glycoproteins that have evolved over the last decade. Although multiple challenges still exist, the existing and continuing technological advancements in glycoproteomics are making the study of glycoproteins more amenable to the scientific community at large.
Conflict of interest
The authors declare no competing interests.
Acknowledgments
Funding and additional information
G. W. H. and L. W. are both supported by the Georgia Research Alliance as an eminent scholar and distinguished investigator, respectively. L. W. is a co-director of the Thermo Fisher–appointed center of excellence in glycoproteomics. This work was supported in part by grants from the NIH (R01DK124366 (G. W. H.), R01GM116891 (G. W. H. and L. W.), U01CA233581 (L. W.), R01GM130915 (L. W.), and R01GM111939 (L. W.)). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
References
- 1.Wells L., Hart G.W., Athens Guidelines for the Publication of Glycomics Data Glycomics: Building upon proteomics to advance glycosciences. Mol. Cell. Proteomics. 2013;12:833–835. doi: 10.1074/mcp.E113.027904. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Watanabe Y., Allen J.D., Wrapp D., McLellan J.S., Crispin M. Site-specific glycan analysis of the SARS-CoV-2 spike. Science. 2020;369:330–333. doi: 10.1126/science.abb9983. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Zhao P., Praissman J.L., Grant O.C., Cai Y., Xiao T., Rosenbalm K.E., Aoki K., Kellman B.P., Bridger R., Barouch D.H., Brindley M.A., Lewis N.E., Tiemeyer M., Chen B., Woods R.J., et al. Virus-receptor interactions of glycosylated SARS-CoV-2 spike and human ACE2 receptor. Cell Host Microbe. 2020;28:586–601.e586. doi: 10.1016/j.chom.2020.08.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Varki A., Cummings R.D., Esko J.D., Stanley P., Hart G.W., Aebi M., Darvill A.G., Kinoshita T., Packer N.H., Prestegard J.H., Schnaar R.L., Seeberger P.H., editors. Essentials of Glycobiology. Cold Spring Harbor Laboratory Press; Cold Spring Harbor, NY: 2015. [PubMed] [Google Scholar]
- 5.West C.M., Malzl D., Hykollari A., Wilson I.B.H. Glycomics, glycoproteomics, and glycogenomics: An inter-taxa evolutionary perspective. Mol. Cell. Proteomics. 2021;20:100024. doi: 10.1074/mcp.R120.002263. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Riley N.M., Bertozzi C.R., Pitteri S.J. A pragmatic guide to enrichment strategies for mass spectrometry-based glycoproteomics. Mol. Cell. Proteomics. 2021;20:100029. doi: 10.1074/mcp.R120.002277. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Maynard J.C., Chalkley R.J. Methods for enrichment and assignment of N-acetylglucosamine modification sites. Mol. Cell. Proteomics. 2021;20:100031. doi: 10.1074/mcp.R120.002206. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kurz S., Sheikh M.O., Lu S., Wells L., Tiemeyer M. Separation and identification of permethylated glycan isomers by reversed phase NanoLC-NSI-MS(n) Mol. Cell. Proteomics. 2021;20:100045. doi: 10.1074/mcp.RA120.002266. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Blazev R., Ashwood C., Abrahams J.L., Chung L.H., Francis D., Yang P., Watt K.I., Qian H., Quaife-Ryan G.A., Hudson J.E., Gregorevic P., Thaysen-Andersen M., Parker B.L. Integrated glycoproteomics identifies a role of N-glycosylation and galectin-1 on myogenesis and muscle development. Mol. Cell. Proteomics. 2021;20:100030. doi: 10.1074/mcp.RA120.002166. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.McDowell C.T., Klamer Z., Hall J., West C.A., Wisniewski L., Powers T.W., Angel P.M., Mehta A.S., Lewin D.N., Haab B.B., Drake R.R. Imaging mass spectrometry and lectin analysis of N-linked glycans in carbohydrate antigen-defined pancreatic cancer tissues. Mol. Cell. Proteomics. 2021;20:100012. doi: 10.1074/mcp.RA120.002256. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Chen Z., Yu Q., Yu Q., Johnson J., Shipman R., Zhong X., Huang J., Asthana S., Carlsson C., Okonkwo O., Li L. In-depth site-specific analysis of N-glycoproteome in human cerebrospinal fluid (CSF) and glycosylation landscape changes in Alzheimer’s disease (AD) Mol. Cell. Proteomics. 2021;20:100081. doi: 10.1016/j.mcpro.2021.100081. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Martinez M., Renuse S., Kreimer S., O'Meally R., Natov P., Madugundu A.K., Nirujogi R.S., Tahir R., Cole R., Pandey A., Zachara N.E. Quantitative proteomics reveals that the OGT interactome is remodeled in response to oxidative stress. Mol. Cell. Proteomics. 2021;20:100069. doi: 10.1016/j.mcpro.2021.100069. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Ye Z., Vakhrushev S.Y. The role of data-independent acquisition for glycoproteomics. Mol. Cell. Proteomics. 2021;20:100042. doi: 10.1074/mcp.R120.002204. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Pegg C.L., Phung T.K., Caboche C.H., Niamsuphap S., Bern M., Howell K., Schulz B.L. Quantitative data-independent acquisition glycoproteomics of sparkling wine. Mol. Cell. Proteomics. 2021;20:100020. doi: 10.1074/mcp.RA120.002181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Pepi L.E., Sanderson P., Stickney M., Amster I.J. Developments in mass spectrometry for glycosaminoglycan analysis: A review. Mol. Cell. Proteomics. 2021;20:100025. doi: 10.1074/mcp.R120.002267. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Persson A., Nikpour M., Vorontsov E., Nilsson J., Larson G. Domain mapping of chondroitin/dermatan sulfate glycosaminoglycans enables structural characterization of proteoglycans. Mol. Cell. Proteomics. 2021;20:100074. doi: 10.1016/j.mcpro.2021.100074. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Cao W., Liu M., Kong S., Wu M., Zhang Y., Yang P. Recent advances in software tools for more generic and precise intact glycopeptide analysis. Mol. Cell. Proteomics. 2021:100060. doi: 10.1074/mcp.R120.002090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Delafield D.G., Li L. Recent advances in analytical approaches for glycan and glycopeptide quantitation. Mol. Cell. Proteomics. 2021;20:100054. doi: 10.1074/mcp.R120.002095. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Caval T., Heck A.J.R., Reiding K.R. Meta-heterogeneity: Evaluating and describing the diversity in glycosylation between sites on the same glycoprotein. Mol. Cell. Proteomics. 2021;20:100010. doi: 10.1074/mcp.R120.002093. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Hackett W.E., Zaia J. Calculating glycoprotein similarities from mass spectrometric data. Mol. Cell. Proteomics. 2021;20:100028. doi: 10.1074/mcp.R120.002223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Roushan A., Wilson G.M., Kletter D., Sen K.I., Tang W., Kil Y.J., Carlson E., Bern M. Peak filtering, peak annotation, and wildcard search for glycoproteomics. Mol. Cell. Proteomics. 2021;20:100011. doi: 10.1074/mcp.RA120.002260. [DOI] [PMC free article] [PubMed] [Google Scholar]
