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       
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both the glycan and peptide portions can be obtained through distinct yet comple­mentary tandem fragmentation strategies. Indeed, hybrid fragmentation methods
3
combining parallel (alternating) or sequential (MS
) fragmentation approaches pro­vide more comprehensive structural information on both the peptidic and glycan moieties of glycopeptides[198, 199]. Collision‐induced dissociation (CID) repre­sents one of the most prevalent approaches for the tandem fragmentation of glycans and glycoconjugates and for the generation of diagnostic ions. Lower energy CID‐ based fragmentation preferentially cleaves the glycosidic bonds between monosac­charides, which are more labile than peptidic amide bonds, generating B‐ and Y‐ions that inform glycan composition and sequence[200]. Higher collision energies, on the other hand, generate cross‐ring A‐ and X‐ions and internal double cleavage frag­ments for the identification of glycan branching sites and the resolution of isobaric structures[199, 201]. This kind of fragmentation does not produce information on the glycopeptide peptidic sequence and, thus, is rarely used individually for glyco­peptide assignment[201]. Indeed, CID‐derived parent B‐ and Y‐ions with sufficient
3
signal intensity can be selected to undergo sequential fragmentation cycles (MS
), where complementary b‐ and y‐ions from the peptidic backbone can be generated by higher collision energies. Since distinct collision energies give rise to differential fragmentation, current glycan‐ and glycopeptide‐directed MS workflows make use of collision energy‐stepping, i.e. the use of incremental CID fragmentation energies on the same set of precursor ions within a single mass spectrum scan, which yields richer and more informative fragmentation spectra[198, 202]. Higher energy colli­sional dissociation (HCD), a higher energy variant of CID, is unique to more mod­ern Orbitrap‐based instruments. HCD offers improved accuracy in the detection of small diagnostic oxonium ions, particularly in the low m/z range[201]. HCD pro­duces both glycan B‐ and Y‐oxonium ions and b‐ and y‐type ions derived from pep­tide backbone fragmentation, which tend to lose all or part of their glycan modifications during fragmentation. HCD‐based stepping workflows not only pro­duce both glycan and peptide backbone fragments from a single fragmentation event but also generate more detectable cross‐ring fragment ions, which are funda­mental to pinpoint glycosidic linkages and identify isomeric species [198, 199]. Electron transfer dissociation (ETD) constitutes a widely used fragmentation method for intact glycopeptide analysis. In ETD‐based fragmentation, protonated glycopeptides receive electrons generated from radical anions, which cause frag­mentation along the peptidic backbone in the form of c‐ and z‐type ions [203]. Distinctively from CID‐based fragmentation, ETD retains labile post-translational modifications such as glycosylation on the protein backbone and, therefore, does not generate valuable information on glycan composition or sequence[199, 204]. However, generated c‐ and z‐type ions are extremely useful to pinpoint glycosylation sites and retrieve peptidic sequences.
Aiming to gather more comprehensive structural information on both peptide and glycan moieties of glycopeptides, many modern‐day Orbitrap‐based mass spectrom­eters are able to combine complementary dissociation workflows. For instance, in a product‐triggered targeted method, the detection of HCD‐derived glycan oxonium ions generated by glycopeptide fragmentation triggers subsequent ETD
181
 
182
fragmentation events of the respective parent ions, while nonglycosylated peptides are not selected for tandem MS analysis[205]. This strategy has been employed to comprehensively characterize the site‐specific glycosylation profiles of the ErbB2 and EGFR RTKs in gastric and colorectal carcinoma cells, respectively, using the HexNAc oxonium ion (m/z 204.087) as a product ion trigger to select HexNAc‐ containing parent ions for additional fragmentation rounds [48, 206]. Product‐ triggered fragmentation approaches have been successfully used to unravel the target‐specific O‐glycoproteome of colorectal and leukemia cancer cell lines[207, 208].
The manual interpretation of data generated from the MS‐based analysis of free glycans or glycoproteins is challenging, laborious, time‐consuming, and requires sig­nificant expertise. The inclusion of fragmentation spectral data further increases the complexity of this analytical exercise. Contrarily to genomic databases, the establish­ment of both glycomic and glycoproteomic software‐based tools represents a more challenging task since glycans constitute secondary gene products whose structure cannot be fully predicted by a given genetic sequence. The establishment of compre­hensive glycan structural databases rather relies on the global deposition of glycan analytical data and the development of bioinformatic tools capable of querying this data in a high‐throughput manner for the quick and targeted prediction of a given glycan 3D structure, binding partner, biosynthetic pathway, associated pathologies, and host organism[209]. Several open‐source and commercial software tools have been developed to aid in the interpretation of glycopeptide mass spectra. These include: SimGlycan[210], Glyco‐DIA[211], GlypID 2.0/GlycoFragWork[212, 213], ByOnic [214], MassyTools [215], GlycoPepGrader [216], SweetSEQer [217], ArMone[218], GlycoPepDetector[219], GRIP[220], MAGIC[221], GP Finder[222], Sweet‐Heart [223], GPQuest [224], GlycoFinder [201], GlycopeptideSearch (GPS) [225], pGlyco 2.0 [226], O‐Pair Search [227], GPSeeker [228], pMatch­Glyco[229], glyXtoolMS[230], SugarQB[231], GlycoPAT[232], I‐GPA [233], and GlycoMasterDB[234].
6.8 Concluding Remarks
Glycans undoubtedly play an essential role in maintaining mammalian cell homeo­stasis. Strategically localized at the interface of a cell and its surrounding microen­vironment, glycans act as crucial molecular gatekeepers, actively governing virtually every cellular process. Moreover, since the posttranslational glycosylation cellular process is highly sensitive to a cell’s physiological state, glycans have been widely established as robust molecular reporters of pathological conditions and, in particu­lar, of the neoplastic transformation of human tissues. The aberrant expression of cancer‐specific glycan antigens by neoplastic cells, which stems from a variety of dysregulated mechanisms affecting the cellular glycosylation machinery, has been linked to the majority of malignant features supporting tumor cell survival, prolif­eration, differentiation, immune evasion, and local or distant organ dissemination. At the cellular level, TACAs drive malignant transformation by either disrupting the
homeostatic functions of tumor‐suppressive elements or by enhancing the onco-
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genic properties of proteins controlling cell adhesion, programmed cell death, mito­genic signaling, and extracellular communication. However, the mechanistic basis underlying such alterations, as well as the functional impact that these may have on tumor onset, development, and metastatic spread, warrant further investigation. Indeed, several biosynthetic and structural features of glycans pose unique chal­lenges to their compositional characterization, structural elucidation, carrier assign­ment, and functional study. The vast array of monosaccharide substrate donors and transporters and the partial redundant and overlapping specificities depicted by GTs and glycosidases exponentially increase the structural complexity of the human gly­cosylation landscape. Moreover, the nontemplate‐driven nature of glycosylation greatly hinders the unequivocal analytical‐based structural elucidation of complex glycan chains. In recent years, we have witnessed the exponential technological development of cellular‐, imaging‐, and analytical‐based tools and platforms that have been instrumental in the unprecedented association between a glycan struc­ture and its function within complex biological systems. The rapidly expanding tool­box of gene editing technologies has allowed the generation of cellular and animal models depicting cleaner and well‐defined glycosylation backgrounds in which the study of glycan biosynthesis and function can be performed in the absence of con­founding factors such as GT redundant and compensatory activity. Increasingly complex imaging systems have prompted the comprehensive characterization of glycan expression patterns and distribution across histologically defined regions, as well as the identification and mapping of tumor‐relevant glycan–protein interac­tions at the single complex level, in tissue specimens from oncological patients. The emerging glycan microarray technology has fueled the high‐throughput interroga­tion and dissection of the glycan‐binding specificities of disease‐relevant carbohy­drate‐based molecular interactions actively underpinning numerous features of malignant cells, including evasion to immune recognition and surveillance. The MS‐based elucidation of the composition, 3D structure, and site of attachment of cancer‐associated glycan antigens has the potential to revolutionize the structure‐ guided design and optimization of cancer therapeutics. In conclusion, glycans undeniably represent a novel set of tools that will become indispensable in the field of modern‐era precision oncology, either as robust molecular biomarkers bearing diagnostic, prognostic, predictive, and stratifying value, or as promising targets for the delivery of personalized anticancer therapeutic agents.
183
List ofAbbreviations
capillary electrophoresis CE carcinoembryonic antigen CEA Consortium for Functional Glycomics CFG collision‐induced dissociation CID concanavalin A ConA
 
184
CRISPR‐associated protein 9 CRISPR/Cas9 extracellular matrix ECM epidermal growth factor receptor EGFR electrospray ionization ESI electron transfer dissociation ETD formalin‐fixed paraffin‐embedded FFPE N‐acetylgalactosamine GalNAc glycan‐binding protein GBP N‐acetylglucosamine GlcNAc guide RNA gRNA glycosyltransferase GT higher energy collisional dissociation HCD high‐performance liquid chromatography HPLC hematoxylin/eosin H&E Helicobacter pylori H. pylori imaging mass spectrometry IMS jacalin JAC knock‐in KI knock‐out KO Lewis b Le monoclonal antibody MAb Maackia amurensis lectin MAL matrix‐assisted laser desorption/ionization MALDI
2
MALDI‐(time‐of‐flight)
‐MS MALDI‐TOF/TOF‐MS mass spectrometry MS N‐acetylgalactosaminyltransferases ppGalNAcT porous graphitized carbon PGC proximity ligation assay PLA peptide‐N‐glycosidase F PNGase F nonhomologous end joining NHRJ sialic acid Neu5Ac open reading frame ORF patient‐derived xenograft PDX receptor tyrosine kinase RTK sialyl Lewis a sLe sialyl Lewis x sLe Sambucus nigra agglutinin SNA so lid‐phase hydrophilic interaction
liquid chromatography SPE‐HILIC surface plasmon resonance SPR sialyl Tn STn tumor‐associated carbohydrate antigen TACA tr anscription activator‐like effector
nucleases TALEN
b
a
x
 
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185
titanium dioxide TiO
2
tissue microarray TMA Vicia villosa agglutinin wheat germ agglutinin Wisteria floribunda agglutinin zinc‐finger nuclease
VVA WGA
WFA
ZFN
References
1 Varki, A., Cummings, R.D., Esko, J.D. etal. (2015). Essentials of Glycobiology. Cold
Spring Harbor, NY: Cold Spring Harbor Laboratory Press.
2 Schjoldager, K.T., Narimatsu, Y., Joshi, H.J., and Clausen, H. (2020). Global view
of human protein glycosylation pathways and functions. Nature Reviews Molecular Cell Biology 21 (12): 729–749.
3 Varki, A. (2017). Biological roles of glycans. Glycobiology 27 (1): 3–49. 4 Taniguchi, N. and Kizuka, Y. (2015). Glycans and cancer: role of N‐glycans in
cancer biomarker, progression and metastasis, and therapeutics. Advances in Cancer Research 126: 11–51.
5 Julien, S., Ivetic, A., Grigoriadis, A. etal. (2011). Selectin ligand sialyl‐Lewis x
antigen drives metastasis of hormone‐dependent breast cancers. Cancer Research 71 (24): 7683–7693.
6 Blanas, A., Sahasrabudhe, N.M., Rodríguez, E. etal. (2018). Fucosylated antigens
in cancer: an alliance toward tumor progression, metastasis, and resistance to chemotherapy. Frontiers in Oncology 8: 39.
7 Stowell, S.R., Ju, T., and Cummings, R.D. (2015). Protein glycosylation in cancer.
Annual Review of Pathology: Mechanisms of Disease 10: 473–510.
8 Radhakrishnan, P., Dabelsteen, S., Madsen, F.B. etal. (2014). Immature truncated
O‐glycophenotype of cancer directly induces oncogenic features. Proceedings of the National Academy of Sciences of the United States of America 111 (39): E4066–E4075.
9 Pinho, S.S. and Reis, C.A. (2015). Glycosylation in cancer: mechanisms and
clinical implications. Nature Reviews Cancer 15 (9): 540–555.
10 Mereiter, S., Balmaña, M., Campos, D. etal. (2019). Glycosylation in the era of
cancer‐targeted therapy: where are we heading? Cancer Cell 36 (1): 6–16.
11 Magalhães, A., Duarte, H.O., and Reis, C.A. (2021). The role of O‐glycosylation in
human disease. Molecular Aspects of Medicine 79: 100964.
12 RodrÍguez, E., Schetters, S.T., and van Kooyk, Y. (2018). The tumour glyco‐code as
a novel immune checkpoint for immunotherapy. Nature Reviews Immunology 18 (3): 204–211.
13 Rodrigues, J.G., Duarte, H.O., Reis, C.A., and Gomes, J. (2021). Aberrant protein
glycosylation in cancer: implications in targeted therapy. Biochemical Society Transactions 49 (2): 843–854.
 
186
14 Flynn, R.A., Pedram, K., Malaker, S.A. etal. (2021). Small RNAs are modified with
N‐glycans and displayed on the surface of living cells. Cell 184 (12): 3109–3124.
15 Reis, C.A., Osorio, H., Silva, L. etal. (2010). Alterations in glycosylation as
biomarkers for cancer detection. Journal of Clinical Pathology 63 (4): 322–329.
16 Marcos, N.T., Pinho, S., Grandela, C. etal. (2004). Role of the human
ST6GalNAc‐I and ST6GalNAc‐II in the synthesis of the cancer‐associated sialyl‐Tn antigen. Cancer Research 64 (19): 7050–7057.
17 Warnock, M., Stoloff, A., and Thor, A. (1988). Differentiation of adenocarcinoma
of the lung from mesothelioma. Periodic acid‐Schiff, monoclonal antibodies B72.3, and Leu M1. The American Journal of Pathology 133 (1): 30.
18 O’Boyle, K.P., Markowitz, A.L., Khorshidi, M. etal. (1996). Specificity analysis of
murine monoclonal antibodies reactive with Tn, sialylated Tn, T, and monosialylated (2 → 6) T antigens. Hybridoma 15 (6): 401–408.
19 Köhler, G. and Milstein, C. (1975). Continuous cultures of fused cells secreting
antibody of predefined specificity. Nature 256 (5517): 495–497.
20 Cummings, R.D. (2009). The repertoire of glycan determinants in the human
glycome. Molecular BioSystems 5 (10): 1087–1104.
21 Briard, J.G., Jiang, H., Moremen, K.W. etal. (2018). Cell‐based glycan arrays for
probing glycan–glycan binding protein interactions. Nature Communications 9 (1): 1–11.
22 Blaschke, C.R.K., McDowell, C.T., Black, A.P. etal. (2021). Glycan imaging mass
spectrometry: progress in developing clinical diagnostic assays for tissues, biofluids, and cells. Clinics in Laboratory Medicine 41 (2): 247–266.
23 Powers, T.W., Neely, B.A., Shao, Y. etal. (2014). MALDI imaging mass
spectrometry profiling of N‐glycans in formalin‐fixed paraffin embedded clinical tissue blocks and tissue microarrays. PLoS One 9 (9): e106255.
24 Everest‐Dass, A.V., Briggs, M.T., Kaur, G. etal. (2016). N‐glycan MALDI imaging
mass spectrometry on formalin‐fixed paraffin‐embedded tissue enables the delineation of ovarian cancer tissues. Molecular and Cellular Proteomics 15 (9): 3003–3016.
25 Kunzke, T., Balluff, B., Feuchtinger, A. etal. (2017). Native glycan fragments
detected by MALDI‐FT‐ICR mass spectrometry imaging impact gastric cancer biology and patient outcome. Oncotarget 8 (40): 68012.
26 Briggs, M.T., Condina, M.R., Ho, Y.Y. etal. (2019). MALDI mass spectrometry
imaging of early‐and late‐stage serous ovarian cancer tissue reveals stage‐specific N‐glycans. Proteomics 19 (21, 22): 1800482.
27 Heijs, B., Holst‐Bernal, S., de Graaff, M.A. etal. (2020). Molecular signatures of
tumor progression in myxoid liposarcoma identified by N‐glycan mass spectrometry imaging. Laboratory Investigation 100 (9): 1252–1261.
28 Boyaval, F., Van Zeijl, R., Dalebout, H. etal. (2021). N‐glycomic signature of stage
II colorectal cancer and its association with the tumor microenvironment. Molecular and Cellular Proteomics 20: 100057.
29 Holst, S., Heijs, B., De Haan, N. etal. (2016). Linkage‐specific in situ sialic acid
derivatization for N‐glycan mass spectrometry imaging of formalin‐fixed paraffin‐ embedded tissues. Analytical Chemistry 88 (11): 5904–5913.
 
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
30 Norris, J.L., Porter, N.A., and Caprioli, R.M. (2005). Combination detergent/
MALDI matrix: functional cleavable detergents for mass spectrometry. Analytical Chemistry 77 (15): 5036–5040.
31 Lemaire, R., Tabet, J., Ducoroy, P. etal. (2006). Solid ionic matrixes for direct
tissue analysis and MALDI imaging. Analytical Chemistry 78 (3): 809–819.
32 Zhang, H., Shi, X., Vu, N.Q. etal. (2020). On‐tissue derivatization with Girard’s
reagent P enhances N‐glycan signals for formalin‐fixed paraffin‐embedded tissue sections in MALDI mass spectrometry imaging. Analytical Chemistry 92 (19): 13361–13368.
33 Arentz, G., Mittal, P., Zhang, C. etal. (2017). Applications of mass spectrometry
imaging to cancer. Advances in Cancer Research 134: 27–66.
34 Powers, T.W., Jones, E.E., Betesh, L.R. etal. (2013). Matrix assisted laser
desorption ionization imaging mass spectrometry workflow for spatial profiling analysis of N‐linked glycan expression in tissues. Analytical Chemistry 85 (20): 9799–9806.
35 Powers, T.W., Holst, S., Wuhrer, M. etal. (2015). Two‐dimensional N‐glycan
distribution mapping of hepatocellular carcinoma tissues by MALDI‐imaging mass spectrometry. Biomolecules 5 (4): 2554–2572.
36 Drake, R.R., Powers, T.W., Norris‐Caneda, K. etal. (2018). In situ imaging of
N‐glycans by MALDI imaging mass spectrometry of fresh or formalin‐fixed paraffin‐embedded tissue. Current Protocols in Protein Science 94 (1): e68.
37 Drake, R.R., Powers, T.W., Jones, E.E. etal. (2017). MALDI mass spectrometry
imaging of N‐linked glycans in cancer tissues. Advances in Cancer Research 134: 85–116.
38 Heijs, B., Holst, S., Briaire‐de Bruijn, I.H. etal. (2016). Multimodal mass
spectrometry imaging of N‐glycans and proteins from the same tissue section. Analytical Chemistry 88 (15): 7745–7753.
39 Fredriksson, S., Gullberg, M., Jarvius, J. etal. (2002). Protein detection using
proximity‐dependent DNA ligation assays. Nature Biotechnology 20 (5): 473–477.
40 Oliveira, F.M.S., Mereiter, S., Lönn, P. etal. (2018). Detection of post‐translational
modifications using solid‐phase proximity ligation assay. New Biotechnology 45: 51–59.
41 Söderberg, O., Leuchowius, K.‐J., Gullberg, M. etal. (2008). Characterizing
proteins and their interactions in cells and tissues using the in situ proximity ligation assay. Methods 45 (3): 227–232.
42 Pinto, R., Carvalho, A.S., Conze, T. etal. (2012). Identification of new cancer
biomarkers based on aberrant mucin glycoforms by in situ proximity ligation. Journal of Cellular and Molecular Medicine 16 (7): 1474–1484.
43 Campos, D., Freitas, D., Gomes, J. etal. (2015). Probing the O‐glycoproteome of
gastric cancer cell lines for biomarker discovery. Molecular and Cellular Proteomics 14 (6): 1616–1629.
44 Mereiter, S., Martins, Á.M., Gomes, C. etal. (2019). O‐glycan truncation enhances
cancer‐related functions of CD44in gastric cancer. FEBS Letters 593 (13): 1675–1689.
187
 
188
45 Mereiter, S., Magalhães, A., Adamczyk, B. etal. (2016). Glycomic analysis of
gastric carcinoma cells discloses glycans as modulators of RON receptor tyrosine kinase activation in cancer. Biochimica et Biophysica Acta (BBA), General Subjects 1860 (8): 1795–1808.
46 Gomes, C., Almeida, A., Barreira, A. etal. (2019). Carcinoembryonic antigen
carrying SLe(X) as a new biomarker of more aggressive gastric carcinomas. Theranostics 9 (24): 7431–7446.
47 Carvalho, S., Catarino, T.A., Dias, A.M. etal. (2016). Preventing E‐cadherin
aberrant N‐glycosylation at Asn‐554 improves its critical function in gastric cancer. Oncogene 35 (13): 1619–1631.
48 Duarte, H.O., Rodrigues, J.G., Gomes, C. etal. (2021). ST6Gal1 targets the
sensitivity to trastuzumab. Oncogene 40 (21): 3719–3733.
49 Magalhães, A., Rossez, Y., Robbe‐Masselot, C. etal. (2016). Muc5ac gastric mucin
glycosylation is shaped by FUT2 activity and functionally impacts Helicobacter pylori binding. Scientific Reports 6 (1): 25575.
50 Li, X., Jiang, X., Xu, X. etal. (2017). Imaging of protein‐specific glycosylation by
glycan metabolic tagging and in situ proximity ligation. Carbohydrate Research 448: 148–154.
51 Freeze, H.H. (2006). Genetic defects in the human glycome. Nature Reviews
Genetics 7 (7): 537–551.
52 Wang, D., Liu, S., Trummer, B.J. etal. (2002). Carbohydrate microarrays for the
recognition of cross‐reactive molecular markers of microbes and host cells. Nature Biotechnology 20 (3): 275–281.
53 Fukui, S., Feizi, T., Galustian, C. etal. (2002). Oligosaccharide microarrays for
high‐throughput detection and specificity assignments of carbohydrate‐protein interactions. Nature Biotechnology 20 (10): 1011–1017.
54 Gao, C., Wei, M., McKitrick, T.R. etal. (2019). Glycan microarrays as chemical
tools for identifying glycan recognition by immune proteins. Frontiers in Chemistry 7 (833): 833.
55 Song, X., Heimburg‐Molinaro, J., Cummings, R.D., and Smith, D.F. (2014).
Chemistry of natural glycan microarrays. Current Opinion in Chemical Biology 18: 70–77.
56 Hirabayashi, J., Yamada, M., Kuno, A., and Tateno, H. (2013). Lectin microarrays:
concept, principle and applications. Chemical Society Reviews 42 (10): 4443–4458.
57 Li, Z. and Feizi, T. (2018). The neoglycolipid (NGL) technology‐based microarrays
and future prospects. FEBS Letters 592 (23): 3976–3991.
58 Smith, D.F., Song, X., and Cummings, R.D. (2010). Chapter Nineteen– Use of
glycan microarrays to explore specificity of glycan‐binding proteins. In: Methods in Enzymology, vol. 480 (ed. M. Fukuda), 417–444. Academic Press.
59 Drickamer, K. and Taylor, M.E. (2002). Glycan arrays for functional glycomics.
Genome Biology 3 (12): reviews1034.1.
60 Patwa, T., Li, C., Simeone, D.M., and Lubman, D.M. (2010). Glycoprotein analysis
using protein microarrays and mass spectrometry. Mass Spectrometry Reviews 29 (5): 830–844.
 
Данная книга находится в списке для перевода на русский язык сайта https://meduniver.com/
61 Tse‐Wen, C. (1983). Binding of cells to matrixes of distinct antibodies coated on
solid surface. Journal of Immunological Methods 65 (1): 217–223.
62 Song, X., Heimburg‐Molinaro, J., Smith, D.F., and Cummings, R.D. (2015). Glycan
microarrays of fluorescently‐tagged natural glycans. Glycoconjugate Journal 32 (7): 465–473.
63 Palma, A.S., Feizi, T., Zhang, Y. etal. (2006). Ligands for the β‐glucan receptor,
dectin‐1, assigned using “designer” microarrays of oligosaccharide probes (neoglycolipids) generated from glucan polysaccharides. Journal of Biological Chemistry 281 (9): 5771–5779.
64 Liu, L., Prudden, A.R., Capicciotti, C.J. etal. (2019). Streamlining the
chemoenzymatic synthesis of complex N‐glycans by a stop and go strategy. Nature Chemistry 11 (2): 161–169.
65 Fair, R.J., Hahm, H.S., and Seeberger, P.H. (2015). Combination of automated
solid‐phase and enzymatic oligosaccharide synthesis provides access to α(2,3)‐ sialylated glycans. Chemical Communications 51 (28): 6183–6185.
66 Esposito, D., Hurevich, M., Castagner, B. etal. (2012). Automated synthesis of
sialylated oligosaccharides. Beilstein Journal of Organic Chemistry 8: 1601–1609.
67 Li, T., Liu, L., Wei, N. etal. (2019). An automated platform for the enzyme‐
mediated assembly of complex oligosaccharides. Nature Chemistry 11 (3): 229–236.
68 Liang, C.F., Hahm, H.S., and Seeberger, P.H. (2015). Automated synthesis of
chondroitin sulfate oligosaccharides. Methods in Molecular Biology 1229: 3–10.
69 Song, X., Xia, B., Stowell, S.R. etal. (2009). Novel fluorescent glycan microarray
strategy reveals ligands for galectins. Chemistry and Biology 16 (1): 36–47.
70 Xia, B., Kawar, Z.S., Ju, T. etal. (2005). Versatile fluorescent derivatization of
glycans for glycomic analysis. Nature Methods 2 (11): 845–850.
71 de Boer, A.R., Hokke, C.H., Deelder, A.M., and Wuhrer, M. (2007). General
microarray technique for immobilization and screening of natural glycans. Analytical Chemistry 79 (21): 8107–8113.
72 McQuillan, A.M., Byrd‐Leotis, L., Heimburg‐Molinaro, J., and Cummings,
R.D. (2019). Natural and synthetic sialylated glycan microarrays and their applications. Frontiers in Molecular Biosciences 6: 88.
73 Temme, J.S., Campbell, C.T., and Gildersleeve, J.C. (2019). Factors contributing to
variability of glycan microarray binding profiles. Faraday Discussions 219: 90–111.
74 Wang, R., Liu, S., Shah, D., and Wang, D. (2005). A practical protocol for
carbohydrate microarrays. In: Chemical Genomics: Reviews and Protocols (ed. E.D. Zanders), 241–252. Totowa, NJ: Humana Press.
75 Wang, C.‐C., Huang, Y.‐L., Ren, C.‐T. etal. (2008). Glycan microarray of Globo H
and related structures for quantitative analysis of breast cancer. Proceedings of the National Academy of Sciences of the United States of America 105 (33): 11661–11666.
76 Gao, C., Liu, Y., Zhang, H. etal. (2014). Carbohydrate sequence of the prostate
cancer‐associated antigen F77 assigned by a Mucin O‐glycome designer array. Journal of Biological Chemistry 289 (23): 16462–16477.
189
 
190
77 Blixt, O., Head, S., Mondala, T. etal. (2004). Printed covalent glycan array for
ligand profiling of diverse glycan binding proteins. Proceedings of the National Academy of Sciences of the United States of America 101 (49): 17033–17038.
78 Song, X., Xia, B., Lasanajak, Y. etal. (2008). Quantifiable fluorescent glycan
microarrays. Glycoconjugate Journal 25 (1): 15–25.
79 Chen, S., LaRoche, T., Hamelinck, D. etal. (2007). Multiplexed analysis of glycan
variation on native proteins captured by antibody microarrays. Nature Methods 4 (5): 437–444.
80 Park, H., Jung, J., Rodrigues, E. etal. (2020). Mass spectrometry‐based shotgun
glycomics for discovery of natural ligands of glycan‐binding proteins. Analytical Chemistry 92 (20): 14012–14020.
81 Padler‐Karavani, V., Song, X., Yu, H. etal. (2012). Cross‐comparison of protein
recognition of sialic acid diversity on two novel sialoglycan microarrays. The Journal of Biological Chemistry 287 (27): 22593–22608.
82 Büll, C., Nason, R., Sun, L. etal. (2021). Probing the binding specificities of
human Siglecs by cell‐based glycan arrays. Proceedings of the National Academy of Sciences of the United States of America 118 (17): e2026102118.
83 Wang, S., Chen, C., Guan, M. etal. (2021). Terminal epitope‐dependent branch
preference of siglecs toward N‐glycans. Frontiers in Molecular Biosciences 8 (142): 645999.
84 Arthur, C.M., Rodrigues, L.C., Baruffi, M.D. etal. (2015). Examining galectin
binding specificity using glycan microarrays. Methods in Molecular Biology (Clifton, N.J.) 1207: 115–131.
85 Padler‐Karavani, V., Hurtado‐Ziola, N., Pu, M. etal. (2011). Human xeno‐
autoantibodies against a non‐human sialic acid serve as novel serum biomarkers and immunotherapeutics in cancer. Cancer Research 71 (9): 3352–3363.
86 Lawrie, C.H., Marafioti, T., Hatton, C.S.R. etal. (2006). Cancer‐associated
carbohydrate identification in Hodgkin’s lymphoma by carbohydrate array profiling. International Journal of Cancer 118 (12): 3161–3166.
87 Huang, W.‐L., Li, Y.‐G., Lv, Y.‐C. etal. (2014). Use of lectin microarray to
differentiate gastric cancer from gastric ulcer. World Journal of Gastroenterology 20 (18): 5474–5482.
88 Purohit, S., Li, T., Guan, W. etal. (2018). Multiplex glycan bead array for high
throughput and high content analyses of glycan binding proteins. Nature Communications 9 (1): 258.
89 Pedersen, J.W., Blixt, O., Bennett, E.P. etal. (2011). Seromic profiling of colorectal
cancer patients with novel glycopeptide microarray. International Journal of Cancer 128 (8): 1860–1871.
90 Zhao, J., Patwa, T.H., Qiu, W. etal. (2007). Glycoprotein microarrays with
multi‐lectin detection: unique lectin binding patterns as a tool for classifying normal, chronic pancreatitis and pancreatic cancer sera. Journal of Proteome Research 6 (5): 1864–1874.
91 Burford, B., Gentry‐Maharaj, A., Graham, R. etal. (2013). Autoantibodies to
MUC1 glycopeptides cannot be used as a screening assay for early detection of