Inter-tissue glycan heterogeneity: site-specific glycoform analysis of mouse tissue N-glycoproteomes using MS1-based glycopeptide detection method assisted by lectin microarray.
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| Titel: | Inter-tissue glycan heterogeneity: site-specific glycoform analysis of mouse tissue N-glycoproteomes using MS1-based glycopeptide detection method assisted by lectin microarray. |
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| Autoren: | Nagai-Okatani C; Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan. chiaki-okatani@aist.go.jp., Tomioka A; Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan., Tominaga D; Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan., Sakaue H; Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan., Kuno A; Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan., Kaji H; Molecular and Cellular Glycoproteomics Research Group, Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8565, Japan. kaji.hiroyuki.j8@f.mail.nagoya-u.ac.jp.; Institute for Glyco-core Research (iGCORE), Nagoya University, Furo-Cho, Chikusa, Nagoya, Aichi, 464-8601, Japan. kaji.hiroyuki.j8@f.mail.nagoya-u.ac.jp. |
| Quelle: | Analytical and bioanalytical chemistry [Anal Bioanal Chem] 2025 Feb; Vol. 417 (5), pp. 973-988. Date of Electronic Publication: 2024 Dec 16. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Springer-Verlag Country of Publication: Germany NLM ID: 101134327 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1618-2650 (Electronic) Linking ISSN: 16182642 NLM ISO Abbreviation: Anal Bioanal Chem Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Heidelberg : Springer-Verlag, 2002- |
| MeSH-Schlagworte: | Glycopeptides*/analysis , Glycopeptides*/chemistry , Polysaccharides*/analysis , Polysaccharides*/chemistry , Lectins*/chemistry , Glycoproteins*/chemistry , Glycoproteins*/analysis , Protein Array Analysis*/methods , Proteome*, Animals ; Mice ; Glycosylation ; Male ; Proteomics/methods ; Tandem Mass Spectrometry/methods |
| Abstract: | To understand the biological and pathological functions of protein glycosylation, the glycan heterogeneities for each glycosite in a single glycoprotein need to be elucidated depending on the type and status of cells. For this aim, a reliable strategy is needed to compare site-specific glycoforms of multiple glycoprotein samples in a comprehensive manner. To analyze this "inter-heterogeneity" of samples, we previously introduced an MS1-based glycopeptide detection method, "Glyco-RIDGE." Here, to elucidate inter-tissue glycan heterogeneities, this estimation method was applied to site-specific N-glycoforms of glycoproteins from six normal mouse tissues (liver, kidney, spleen, pancreas, stomach, and testis). The analyses of desialylated glycopeptides estimated 11,325 site-specific N-glycoforms with 239 glycan compositions at 1260 sites (1122 non-redundant core peptides) in 800 glycoproteins, revealing inter-tissue differences in macro-, micro-, and meta-glycan heterogeneities. To obtain detailed information on their glycan features and tissue distribution, the Glyco-RIDGE results were compared with laser microdissection-assisted lectin microarray (LMD-LMA)-based mouse tissue glycome mapping data deposited on LM-GlycomeAtlas, as well as MS-based mouse tissue glycome data deposited on GlycomeAtlas. This integrated approach supported the certainty of Glyco-RIDGE results and suggested that inter-tissue differences exist in glycan motifs, such as alpha-galactose and bisecting N-acetylglucosamine, in both whole tissue glycomes and specific glycoproteins, Anpep and Lamc1. In addition, the comparison with LMD-LMA-based tissue glycome mapping data suggested the possibility of estimating the tissue distribution of the assigned glycans and glycopeptides. Taken together, these findings demonstrate the utility of an integrated approach using MS assisted by LMA for large-scale analyses. (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.) |
| Competing Interests: | Declarations. Ethics approval, source of biological material, and statement on animal welfare: C57BL/6 J mice were purchased from Japan SLC (Shizuoka, Japan). All animal experiments were performed in accordance with the relevant guidelines and regulations. The animal study protocol was approved by the Institutional Animal Care and Use Committee of AIST (no. 2022–082; date June approval, 22/06/2022). Conflict of interest: The authors declare no competing interests. |
| References: | Varki A, Cummings RD, Esko JD, Stanley P, Hart GW, Aebi M, et al. Essentials of glycobiology. 4th ed. Cold Spring Harbor: Cold Spring Harbor Laboratory Press; 2022. Narimatsu H, Sawaki H, Kuno A, Kaji H, Ito H, Ikehara Y. A strategy for discovery of cancer glyco-biomarkers in serum using newly developed technologies for glycoproteomics. FEBS J. 2010;277:95–105. https://doi.org/10.1111/j.1742-4658.2009.07430.x . (PMID: 10.1111/j.1742-4658.2009.07430.x19919546) Cai L, Gu Z, Zhong J, Wen D, Chen G, He L, Wu J, Gu Z. Advances in glycosylation-mediated cancer-targeted drug delivery. Drug Discov Today. 2018;23:1126–38. https://doi.org/10.1016/j.drudis.2018.02.009 . (PMID: 10.1016/j.drudis.2018.02.00929501708) Narimatsu H, Kaji H, Vakhrushev SY, Clausen H, Zhang H, Noro E, Togayachi A, Nagai-Okatani C, Kuno A, Zou X, Cheng L, Tao SC, Sun Y. Current technologies for complex glycoproteomics and their applications to biology/disease-driven glycoproteomics. J Proteome Res. 2018;17:4097–112. https://doi.org/10.1021/acs.jproteome.8b00515 . (PMID: 10.1021/acs.jproteome.8b0051530359034) He K, Baniasad M, Kwon H, Caval T, Xu G, Lebrilla C, Hommes DW, Bertozzi C. Decoding the glycoproteome: a new frontier for biomarker discovery in cancer. J Hermatol Oncol. 2024;17:12. https://doi.org/10.1186/S13045-024-01532-X . (PMID: 10.1186/S13045-024-01532-X) Čaval T, Heck AJR, Reiding KR. Meta-heterogeneity: evaluating and eescribing the diversity in glycosylation between sites on the same glycoprotein. Mol Cell Proteomics. 2021;20:100010. https://doi.org/10.1074/MCP.R120.002093 . (PMID: 10.1074/MCP.R120.00209333561609) Kawahara R, Chernykh A, Alagesan K, Bern M, Cao W, Chalkley RJ, Cheng K, Choo MS, Edwards N, Goldman R, Hoffmann M, Hu Y, Huang Y, Kim JY, Kletter D, Liquet B, Liu M, Mechref Y, Meng B, Neelamegham S, Nguyen-Khuong T, Nilsson J, Pap A, Park GW, Parker BL, Pegg CL, Penninger JM, Phung TK, Pioch M, Rapp E, Sakalli E, Sanda M, Schulz BL, Scott NE, Sofronov G, Stadlmann J, Vakhrushev SY, Woo CM, Wu HY, Yang P, Ying W, Zhang H, Zhang Y, Zhao J, Zaia J, Haslam SM, Palmisano G, Yoo JS, Larson G, Khoo KH, Medzihradszky KF, Kolarich D, Packer NH, Thaysen-Andersen M. Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis. Nat Methods. 2021;18:1304–16. https://doi.org/10.1038/S41592-021-01309-X . (PMID: 10.1038/S41592-021-01309-X347254848566223) Noro E, Togayachi A, Sato T, Tomioka A, Fujita M, Sukegawa M, Suzuki N, Kaji H, Narimatsu H. Large-scale identification of N-glycan glycoproteins carrying Lewis x and site-specific N-glycan alterations in Fut9 knockout mice. J Proteome Res. 2015;14:3823–34. https://doi.org/10.1021/acs.jproteome.5b00178 . (PMID: 10.1021/acs.jproteome.5b0017826244810) Togayachi A, Tomioka A, Fujita M, Sukegawa M, Noro E, Takakura D, Miyazaki M, Shikanai T, Narimatsu H, Kaji H. Identification of poly-N-acetyllactosamine-carrying glycoproteins from HL-60 human promyelocytic leukemia cells using a site-specific glycome analysis method. Glyco-RIDGE J Am Soc Mass Spectrom. 2018;29:1138–52. https://doi.org/10.1007/s13361-018-1938-6 . (PMID: 10.1007/s13361-018-1938-629675740) Kaji H, Saito H, Yamauchi Y, Shinkawa T, Taoka M, Hirabayashi J, Kasai K, Takahashi N, Isobe T. Lectin affinity capture, isotope-coded tagging and mass spectrometry to identify N-linked glycoproteins. Nat Biotechnol. 2003;21:667–72. https://doi.org/10.1038/nbt829 . (PMID: 10.1038/nbt82912754521) Kaji H, Yamauchi Y, Takahashi N, Isobe T. Mass spectrometric identification of N-linked glycopeptides using lectin-mediated affinity capture and glycosylation site-specific stable isotope tagging. Nat Protoc. 2006;1:3019–27. https://doi.org/10.1038/nprot.2006.444 . (PMID: 10.1038/nprot.2006.44417406563) Nagai-okatani C, Tominaga D, Tomioka A, Sakaue H, Goda N, Ko S, Kuno A, Kaji H. (2024) GRable version 1.0: a software tool for site- specific glycoform analysis with improved MS1-based glycopeptide detection with parallel clustering and confidence evaluation with MS2 information. Moll Cell Proteomics. 2024;23:100833. https://doi.org/10.1016/j.mcpro.2024.100833 . (PMID: 10.1016/j.mcpro.2024.100833) Hiono T, Sakaue H, Tomioka A, Kaji H, Sasaki M, Orba Y, Sawa H, Kuno A. Combinatorial approach with mass spectrometry and lectin microarray dissected site-specific glycostem and glycoleaf features of the virion-derived spike protein of ancestral and γ variant SARS-CoV-2 strains. J Proteome Res. 2024;23:1408–19. https://doi.org/10.1021/ACS.JPROTEOME.3C00874 . (PMID: 10.1021/ACS.JPROTEOME.3C0087438536229) Noro E, Matsuda A, Kyoutou T, Sato T, Tomioka A, Nagai M, Sogabe M, Tsuruno C, Takahama Y, Kuno A, Tanaka Y, Kaji H, Narimatsu H. N-Glycan structures of Wisteria floribunda agglutinin-positive Mac2 binding protein in the serum of patients with liver fibrosis. Glycobiology. 2021;31:1268–78. https://doi.org/10.1093/GLYCOB/CWAB060 . (PMID: 10.1093/GLYCOB/CWAB06034192302) Yamaguchi Y, Ishii K, Koizumi S, Sakaue H, Maruno T, Fukuhara M, Shibuya R, Tsunaka Y, Matsushita A, Bandoh K, Torisu T, Murata-Kishimoto C, Tomioka A, Mizukado S, Kaji H, Kashiwakura Y, Ohmori T, Kuno A, Uchiyama S. Glycosylation of recombinant adeno-associated virus serotype 6. Mol Ther Methods Clin Dev. 2024;32:101256. https://doi.org/10.1016/J.OMTM.2024.101256 . (PMID: 10.1016/J.OMTM.2024.1012563877458211107246) Denti V, Capitoli G, Piga I, Clerici F, Pagani L, Criscuolo L, Bindi G, Principi L, Chinello C, Paglia G, Magni F, Smith A. Spatial multiomics of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section. J Proteome Res. 2022;21:2798–809. https://doi.org/10.1021/acs.jproteome.2c00601 . (PMID: 10.1021/acs.jproteome.2c00601362597559639202) Hinneburg H, Korać P, Schirmeister F, Gasparov S, Seeberger PH, Zoldoš V, Kolarich D. Unlocking cancer glycomes from histopathological formalin-fixed and paraffin-embedded (FFPE) tissue microdissections. Mol Cell Proteomics. 2017;16:524–36. https://doi.org/10.1074/mcp.M116.062414 . (PMID: 10.1074/mcp.M116.062414281229435383776) Zou X, Yoshida M, Nagai-Okatani C, Iwaki J, Matsuda A, Tan B, Hagiwara K, Sato T, Itakura Y, Noro E, Kaji H, Toyoda M, Zhang Y, Narimatsu H, Kuno A. A standardized method for lectin microarray-based tissue glycome mapping. Sci Rep. 2017;7:43560. https://doi.org/10.1038/srep43560 . (PMID: 10.1038/srep43560282627095337905) Hiono T, Nagai-Okatani C, Kuno A. Application of glycan-related microarrays. Compr Glycosci. 2021;4:134–48. https://doi.org/10.1016/B978-0-12-819475-1.00059-6 . (PMID: 10.1016/B978-0-12-819475-1.00059-6) Nagai-Okatani C, Zou X, Fujita N, Sogabe I, Arakawa K, Nagai M, Angata K, Zhang Y, Aoki-Kinoshita KF, Kuno A. LM-GlycomeAtlas Ver. 2.0: an integrated visualization for lectin microarray-based mouse tissue glycome mapping data with lectin histochemistry. J Proteome Res. 2021;20:2069–75. https://doi.org/10.1021/acs.jproteome.0c00907 . (PMID: 10.1021/acs.jproteome.0c0090733657805) Konishi Y, Aoki-Kinoshita KF. The GlycomeAtlas tool for visualizing and querying glycome data. Bioinformatics. 2012;28:2849–50. https://doi.org/10.1093/BIOINFORMATICS/BTS516 . (PMID: 10.1093/BIOINFORMATICS/BTS51622923294) Kaji H, Shikanai T, Sasaki-Sawa A, Wen H, Fujita M, Suzuki Y, Sugahara D, Sawaki H, Yamauchi Y, Shinkawa T, Taoka M, Takahashi N, Isobe T, Narimatsu H. Large-scale identification of N-glycosylated proteins of mouse tissues and construction of a glycoprotein database. GlycoProtDB J Proteome Res. 2012;11:4553–66. https://doi.org/10.1021/pr300346c . (PMID: 10.1021/pr300346c22823882) Cooper CA, Gasteiger E, Packer NH. GlycoMod-a software tool for determining glycosylation compositions from mass spectro-metric data. Proteomics. 2001;1:340–9. https://doi.org/10.1002/1615-9861(200102)1:2 . (PMID: 10.1002/1615-9861(200102)1:211680880) Nielsen H. Predicting secretory proteins with SignalP. Methods Mol Biol. 2017;1611:59–73. https://doi.org/10.1007/978-1-4939-7015-5_6 . (PMID: 10.1007/978-1-4939-7015-5_628451972) Krogh A, Larsson B, Von Heijne G, Sonnhammer ELL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305:567–80. https://doi.org/10.1006/JMBI.2000.4315 . (PMID: 10.1006/JMBI.2000.431511152613) Hirokawa T, Boon-Chieng S, Mitaku S. SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics. 1998;14:378–9. https://doi.org/10.1093/BIOINFORMATICS/14.4.378 . (PMID: 10.1093/BIOINFORMATICS/14.4.3789632836) Nagai-Okatani C, Aoki-Kinoshita KF, Kakuda S, Nagai M, Hagiwara K, Kiyohara K, Fujita N, Suzuki Y, Sato T, Angata K, Kuno A. LM-GlycomeAtlas Ver. 1.0: a novel visualization tool for lectin microarray-based glycomic profiles of mouse tissue sections. Molecules. 2019;24:2962. https://doi.org/10.3390/molecules24162962 . (PMID: 10.3390/molecules24162962314432786719194) Otaki M, Hirane N, Natsume-Kitatani Y, Nogami Itoh M, Shindo M, Kurebayashi Y, Nishimura SI. Mouse tissue glycome atlas 2022 highlights inter-organ variation in major N-glycan profiles. Sci Rep. 2022;12:17804. https://doi.org/10.1038/s41598-022-21758-4 . (PMID: 10.1038/s41598-022-21758-4362807479592591) Chau TH, Chernykh A, Kawahara R, Thaysen-Andersen M. Critical considerations in N-glycoproteomics. Curr Opin Chem Biol. 2023;73:102272. https://doi.org/10.1016/J.CBPA.2023.102272 . (PMID: 10.1016/J.CBPA.2023.10227236758418) Riley NM, Hebert AS, Westphall MS, Coon JJ. Capturing site-specific heterogeneity with large-scale N-glycoproteome analysis. Nat Commun. 2019;10:1311. https://doi.org/10.1038/s41467-019-09222-w . (PMID: 10.1038/s41467-019-09222-w308990046428843) Zielinska DF, Gnad F, Wiśniewski JR, Mann M. Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints. Cell. 2010;141:897–907. https://doi.org/10.1016/J.CELL.2010.04.012 . (PMID: 10.1016/J.CELL.2010.04.01220510933) Liu MQ, Zeng WF, Fang P, Cao WQ, Liu C, Yan GQ, Zhang Y, Peng C, Wu JQ, Zhang XJ, Tu HJ, Chi H, Sun RX, Cao Y, Dong MQ, Jiang BY, Huang JM, Shen HL, Wong CCL, He SM, Yang PY. pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification. Nat Commun. 2017;8:438. https://doi.org/10.1038/S41467-017-00535-2 . (PMID: 10.1038/S41467-017-00535-2288747125585273) Mebius RE, Kraal G. Structure and function of the spleen. Nat Rev Immunol. 2005;5:606–16. https://doi.org/10.1038/NRI1669 . (PMID: 10.1038/NRI166916056254) Wollscheid B, Bausch-Fluck D, Henderson C, O’Brien R, Bibel M, Schiess R, Aebersold R, Watts JD. Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins. Nat Biotechnol. 2009;27:378–86. https://doi.org/10.1038/NBT.1532 . (PMID: 10.1038/NBT.1532193499732829300) Li J, Jia L, Hao Z, Xu Y, Shen J, Ma C, Wu J, Zhao T, Zhi Y, Li P, Li J, Zhu B, Sun S. Site-specific N-glycoproteomic analysis reveals upregulated sialylation and core fucosylation during transient regeneration loss in neonatal mouse hearts. J Proteome Res. 2020;19:3191–200. https://doi.org/10.1021/ACS.JPROTEOME.0C00172 . (PMID: 10.1021/ACS.JPROTEOME.0C0017232425043) Mina-Osorio P. The moonlighting enzyme CD13: old and new functions to target. Trends Mol Med. 2008;14:361–71. https://doi.org/10.1016/J.MOLMED.2008.06.003 . (PMID: 10.1016/J.MOLMED.2008.06.003186034727106361) Stadlmann J, Taubenschmid J, Wenzel D, Gattinger A, Dürnberger G, Dusberger F, Elling U, Mach L, Mechtler K, Penninger JM. Comparative glycoproteomics of stem cells identifies new players in ricin toxicity. Nature. 2017;549:538–42. https://doi.org/10.1038/NATURE24015 . (PMID: 10.1038/NATURE24015289599626003595) Boottanun P, Nagai-Okatani C, Nagai M, Ungkulpasvich U, Yamane S, Yamada M, Kuno A. An improved evanescent fluorescence scanner suitable for high-resolution glycome mapping of formalin-fixed paraffin-embedded tissue sections. Anal Bioanal Chem. 2023;415:6975–84. https://doi.org/10.1007/S00216-023-04824-2 . (PMID: 10.1007/S00216-023-04824-237395746) Wallace EN, West CA, McDowell CT, Lu X, Bruner E, Mehta AS, Aoki-Kinoshita KF, Angel PM, Drake RR. An N-glycome tissue atlas of 15 human normal and cancer tissue types determined by MALDI-imaging mass spectrometry. Sci Rep. 2024;14:489. https://doi.org/10.1038/S41598-023-50957-W . (PMID: 10.1038/S41598-023-50957-W3817719210766640) Sun S, Hu Y, Ao M, Shah P, Chen J, Yang W, Jia X, Tian Y, Thomas S, Zhang H. N-GlycositeAtlas: a database resource for mass spectrometry-based human N-linked glycoprotein and glycosylation site mapping. Clin Proteomics. 2019;16:35. https://doi.org/10.1186/S12014-019-9254-0 . (PMID: 10.1186/S12014-019-9254-0315164006731604) |
| Grant Information: | JP20ae0101021h0005 Japan Agency for Medical Research and Development; JP15K14426 Japan Society for the Promotion of Science |
| Contributed Indexing: | Keywords: Glycan heterogeneity; Glycoproteomics; Lectin microarray; Mass spectrometry; Site-specific glycoform; Tissue glycome mapping |
| Substance Nomenclature: | 0 (Glycopeptides) 0 (Polysaccharides) 0 (Lectins) 0 (Glycoproteins) 0 (Proteome) |
| Entry Date(s): | Date Created: 20241215 Date Completed: 20250427 Latest Revision: 20250427 |
| Update Code: | 20250428 |
| DOI: | 10.1007/s00216-024-05686-y |
| PMID: | 39676134 |
| Datenbank: | MEDLINE |
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