journal article·2022-02

Assessment of Surface Glycan Diversity on Extracellular Vesicles by Lectin Microarray and Glycoengineering Strategies for Drug Delivery Applications

Asako Shimoda, Risako Miura, Hiroaki Tateno, Naohiro Seo, Hiroshi Shiku, Shin‐ichi Sawada, Yoshihiro Sasaki, Kazunari Akiyoshi

Small Methods· Volume 6· Issue 2
Publisher page

Abstract

Abstract Extracellular vesicles (EVs) are released by all types of mammalian cells for cell–cell communication. In this study, surface glycans on EVs are compared in terms of their cell type, size, and isolation method to examine whether EV glycan profiles by lectin microarray can be used to define EV subpopulations. Moreover, EVs are glycoengineered with four distinctive surface glycan patterns and evaluated their cellular uptake efficiencies for potential drug delivery applications. Both similarities and differences in glycan patterns are identified on EVs obtained under each experimental condition. EV size‐ and isolation method‐dependent lectin‐binding patterns are observed. Moreover, cellular uptake behaviors of EVs are affected by EV glycan profiles and acceptor cells. The in vivo biodistribution of EVs is also dependent on their glycan profile. These results suggest that EV surface glycans are a potential novel indicator of EV heterogeneity, and glycoengineering is a useful approach to regulate cell–EV interactions for biomedical applications.
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Research context

OpenAlex topics: Extracellular vesicles in disease · RNA Interference and Gene Delivery · MicroRNA in disease regulation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Polymer Chemistry Graduate School of Engineering Kyoto University Katsura, Nishikyo‐ku Kyoto 615‐8510 Japan

Department of Energy and Hydrocarbon Chemistry Graduate School of Engineering Kyoto University Katsura, Nishikyo‐ku Kyoto 615‐8510 Japan

Cellular and Molecular Biotechnology Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Central 6, 1‐1‐1 Higashi Tsukuba Ibaraki 305‐8566 Japan

Department of Personalized Cancer Immunotherapy Mie University Graduate School of Medicine Edobashi Tsu Mie 514‐8507 Japan

Funding

Tokyo Medical and Dental University · Japan Society for the Promotion of Science

References (53)

Show source-provided references
1. 10.1038/s41576-020-00292-x10.1038/s41576-020-00292-x ↗
2. 10.1126/science.aau697710.1126/science.aau6977 ↗
3. 10.1038/s41556-018-0250-910.1038/s41556-018-0250-9 ↗
4. 10.1038/s41556-018-0040-410.1038/s41556-018-0040-4 ↗
5. 10.1039/D0LC00431F10.1039/D0LC00431F ↗
6. 10.3402/jev.v3.2691310.3402/jev.v3.26913 ↗
7. 10.1080/20013078.2018.153575010.1080/20013078.2018.1535750 ↗
8. 10.1038/srep2251910.1038/srep22519 ↗
9. 10.1002/mas.2163510.1002/mas.21635 ↗
10. 10.1038/s41580-020-0251-y10.1038/s41580-020-0251-y ↗
11. 10.3389/fimmu.2018.0073810.3389/fimmu.2018.00738 ↗
12. 10.3390/cells804030710.3390/cells8040307 ↗
13. 10.3390/ijms2118646610.3390/ijms21186466 ↗
14. 10.1016/j.tibtech.2020.05.01210.1016/j.tibtech.2020.05.012 ↗
15. 10.1111/aji.1336710.1111/aji.13367 ↗
16. 10.1016/j.cell.2019.02.02910.1016/j.cell.2019.02.029 ↗
17. 10.1038/srep3393510.1038/srep33935 ↗
18. 10.1080/20013078.2019.157954110.1080/20013078.2019.1579541 ↗
19. 10.1073/pnas.152123011310.1073/pnas.1521230113 ↗
20. 10.1038/s41581-019-0129-410.1038/s41581-019-0129-4 ↗
21. 10.1093/glycob/cww08610.1093/glycob/cww086 ↗
22. 10.1080/20013078.2018.144298510.1080/20013078.2018.1442985 ↗
23. 10.3390/cells1001010910.3390/cells10010109 ↗
24. 10.1080/20013078.2019.164899510.1080/20013078.2019.1648995 ↗
25. 10.1080/20013078.2020.175342010.1080/20013078.2020.1753420 ↗
26. 10.1038/s41598-019-48499-110.1038/s41598-019-48499-1 ↗
27. 10.1080/20013078.2020.171352710.1080/20013078.2020.1713527 ↗
28. 10.1021/acs.analchem.7b0545510.1021/acs.analchem.7b05455 ↗
29. 10.1016/j.lfs.2018.06.02610.1016/j.lfs.2018.06.026 ↗
30. 10.3390/biom503174110.3390/biom5031741 ↗
31. 10.1371/journal.pone.007480110.1371/journal.pone.0074801 ↗
32. 10.1016/j.bbrc.2017.07.12610.1016/j.bbrc.2017.07.126 ↗
33. 10.1038/s41598-019-47760-x10.1038/s41598-019-47760-x ↗
34. 10.1021/acs.jproteome.0c0020010.1021/acs.jproteome.0c00200 ↗
35. 10.1038/s41598-020-75561-010.1038/s41598-020-75561-0 ↗
36. 10.1038/s41577-019-0224-610.1038/s41577-019-0224-6 ↗
37. 10.1074/jbc.M114.60626910.1074/jbc.M114.606269 ↗
38. 10.1158/1541-7786.MCR-17-046710.1158/1541-7786.MCR-17-0467 ↗
39. 10.1093/glycob/cwq03910.1093/glycob/cwq039 ↗
40. 10.1016/j.addr.2012.09.03710.1016/j.addr.2012.09.037 ↗
41. 10.1248/bpb.b19-0083110.1248/bpb.b19-00831 ↗
42. 10.3402/jev.v4.2631610.3402/jev.v4.26316 ↗
43. 10.1182/blood-2014-03-56243910.1182/blood-2014-03-562439 ↗
44. 10.3892/ijmm.2014.166310.3892/ijmm.2014.1663 ↗
45. 10.1002/sctm.16-022610.1002/sctm.16-0226 ↗
46. 10.1172/jci.insight.9926310.1172/jci.insight.99263 ↗
47. 10.3390/ijms2021546810.3390/ijms20215468 ↗
48. 10.1016/j.nano.2020.10220110.1016/j.nano.2020.102201 ↗
49. 10.1126/sciadv.aba688410.1126/sciadv.aba6884 ↗
50. 10.1182/blood-2013-03-48973210.1182/blood-2013-03-489732 ↗
51. 10.1002/anie.20100089210.1002/anie.201000892 ↗
52. 10.1039/C8CC01544A10.1039/C8CC01544A ↗
53. 10.1093/nar/gkv46810.1093/nar/gkv468 ↗