journal article·2023-02-09Open access

Magnetic transferrin nanoparticles (MTNs) assay as a novel isolation approach for exosomal biomarkers in neurological diseases

Yoon Ok Jang, Hee-Sung Ahn, Thuy Nguyen Thi Dao, JeongYeon Hong, Wangyong Shin, Young-Min Lim, Sun Ju Chung, Jae-Hong Lee, Huifang Liu, Bonhan Koo, Myoung Gyu Kim, Kyunggon Kim, Eun-Jae Lee, Yong Shin

Biomaterials Research· Volume 27· Issue 1

Abstract

Abstract Background Brain-derived exosomes released into the blood are considered a liquid biopsy to investigate the pathophysiological state, reflecting the aberrant heterogeneous pathways of pathological progression of the brain in neurological diseases. Brain-derived blood exosomes provide promising prospects for the diagnosis of neurological diseases, with exciting possibilities for the early and sensitive diagnosis of such diseases. However, the capability of traditional exosome isolation assays to specifically isolate blood exosomes and to characterize the brain-derived blood exosomal proteins by high-throughput proteomics for clinical specimens from patients with neurological diseases cannot be assured. We report a magnetic transferrin nanoparticles (MTNs) assay, which combined transferrin and magnetic nanoparticles to isolate brain-derived blood exosomes from clinical samples. Methods The principle of the MTNs assay is a ligand-receptor interaction through transferrin on MTNs and transferrin receptor on exosomes, and electrostatic interaction via positively charged MTNs and negatively charged exosomes to isolate brain-derived blood exosomes. In addition, the MTNs assay is simple and rapid (< 35 min) and does not require any large instrument. We confirmed that the MTNs assay accurately and efficiently isolated exosomes from serum samples of humans with neurodegenerative diseases, such as dementia, Parkinson's disease (PD), and multiple sclerosis (MS). Moreover, we isolated exosomes from serum samples of 30 patients with three distinct neurodegenerative diseases and performed unbiased proteomic analysis to explore the pilot value of brain-derived blood protein profiles as biomarkers. Results Using comparative statistical analysis, we found 21 candidate protein biomarkers that were significantly different among three groups of neurodegenerative diseases. Conclusion The MTNs assay is a convenient approach for the specific and affordable isolation of extracellular vesicles from body fluids for minimally-invasive diagnosis of neurological diseases.
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Research context

OpenAlex topics: Extracellular vesicles in disease · Barrier Structure and Function Studies · Long-Term Effects of COVID-19

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

Affiliations

Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea.

Department of Convergence Medicine, Asan Medical Center, Seoul 05505, Republic of Korea.

Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Republic of Korea.

Department of Biomedical Sciences, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.

Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.

Funding

National Research Foundation of Korea

References (100+)

Show source-provided references
1. Malatinsky J, Vigas M, Jurcovicova J, Jezova D, Garayova S, Minarikova M. The patterns of endocrine response to surgical stress during different types of anesthesia and surgery in man. Acta Anaesthesiol Belg. 1986;37(1):23–32. 1:STN:280:DyaL283htFSmtQ%3D%3D
2. 10.1016/j.ceb.2013.08.00110.1016/j.ceb.2013.08.001 ↗
3. 10.1038/s41582-018-0058-z10.1038/s41582-018-0058-z ↗
4. 10.3389/fneur.2022.86573010.3389/fneur.2022.865730 ↗
5. 10.1038/nbt.164110.1038/nbt.1641 ↗
6. 10.1001/jamaneurol.2019.153410.1001/jamaneurol.2019.1534 ↗
7. 10.1038/s41582-021-00616-310.1038/s41582-021-00616-3 ↗
8. 10.3389/fnins.2021.68993810.3389/fnins.2021.689938 ↗
9. 10.3389/fnmol.2020.0007910.3389/fnmol.2020.00079 ↗
10. 10.3402/jev.v4.2706610.3402/jev.v4.27066 ↗
11. 10.1093/biosci/biv08410.1093/biosci/biv084 ↗
12. 10.1073/pnas.101419410710.1073/pnas.1014194107 ↗
13. 10.3389/fimmu.2014.0065810.3389/fimmu.2014.00658 ↗
14. 10.1016/j.ccr.2022.21500610.1016/j.ccr.2022.215006 ↗
15. 10.1146/annurev-biochem-013118-11190210.1146/annurev-biochem-013118-111902 ↗
16. 10.3402/jev.v3.2359210.3402/jev.v3.23592 ↗
17. 10.1096/fj.14-26204810.1096/fj.14-262048 ↗
18. 10.1158/1940-6207.CAPR-17-040910.1158/1940-6207.CAPR-17-0409 ↗
19. 10.1080/20013078.2018.149014510.1080/20013078.2018.1490145 ↗
20. 10.1038/ki.2013.50210.1038/ki.2013.502 ↗
21. 10.4049/jimmunol.179.3.196910.4049/jimmunol.179.3.1969 ↗
22. 10.1038/s41467-019-11534-w10.1038/s41467-019-11534-w ↗
23. 10.1002/stem.177110.1002/stem.1771 ↗
24. 10.1182/blood-2008-08-17409410.1182/blood-2008-08-174094 ↗
25. 10.1038/sj.leu.240413210.1038/sj.leu.2404132 ↗
26. 10.1074/jbc.R116.75795510.1074/jbc.R116.757955 ↗
27. 10.1186/s12967-016-0927-410.1186/s12967-016-0927-4 ↗
28. 10.1016/j.tcm.2018.10.01010.1016/j.tcm.2018.10.010 ↗
29. 10.33160/yam.2019.06.00210.33160/yam.2019.06.002 ↗
30. 10.1016/j.biochi.2013.06.01610.1016/j.biochi.2013.06.016 ↗
31. 10.1146/annurev-cellbio-101512-12232610.1146/annurev-cellbio-101512-122326 ↗
32. 10.1093/annonc/mdx54210.1093/annonc/mdx542 ↗
33. 10.1212/WNL.000000000000170210.1212/WNL.0000000000001702 ↗
34. 10.1177/135245851769659710.1177/1352458517696597 ↗
35. 10.1002/mds.2863910.1002/mds.28639 ↗
36. 10.3389/fnmol.2020.0003810.3389/fnmol.2020.00038 ↗
37. 10.1186/s40035-022-00301-510.1186/s40035-022-00301-5 ↗
38. 10.3390/biom1107098010.3390/biom11070980 ↗
39. 10.4103/1673-5374.33583210.4103/1673-5374.335832 ↗
40. 10.1186/s13287-018-0923-010.1186/s13287-018-0923-0 ↗
41. 10.1007/978-1-4939-7253-1_810.1007/978-1-4939-7253-1_8 ↗
42. 10.1016/j.chroma.2014.10.02610.1016/j.chroma.2014.10.026 ↗
43. 10.3390/mi1006039210.3390/mi10060392 ↗
44. 10.1186/s12967-017-1374-610.1186/s12967-017-1374-6 ↗
45. 10.1038/s41598-020-57497-710.1038/s41598-020-57497-7 ↗
46. 10.1039/C5LC00240K10.1039/C5LC00240K ↗
47. 10.1039/B916199F10.1039/B916199F ↗
48. 10.1038/nature1458110.1038/nature14581 ↗
49. 10.1073/pnas.161808811410.1073/pnas.1618088114 ↗
50. 10.1038/ki.2010.10610.1038/ki.2010.106 ↗
51. 10.1002/jev2.1219510.1002/jev2.12195 ↗
52. 10.1038/s12276-019-0223-510.1038/s12276-019-0223-5 ↗
53. 10.1093/ije/dym27610.1093/ije/dym276 ↗
54. 10.1016/S1474-4422(17)30470-210.1016/S1474-4422(17)30470-2 ↗
55. 10.1002/mds.2642410.1002/mds.26424 ↗
56. American Psychiatric Association DSM-5 Task Force. Diagnostic and statistical manual of mental disorders: DSM-5™ (5th ed.). American Psychiatric Publishing Inc. (2013). https://doi.org/10.1176/appi.books.9780890425596.10.1176/appi.books.9780890425596 ↗
57. 10.3390/bios1110037310.3390/bios11100373 ↗
58. 10.1039/C9LC00367C10.1039/C9LC00367C ↗
59. Deutsch EW, Bandeira N, Sharma V, Perez-Riverol Y, Carver JJ, Kundu DJ, et al. The proteomexchange consortium in 2020: enabling 'big data' approaches in proteomics. Nucleic Acids Res. 2020;48(D1):D1145–D1152. 1:CAS:528:DC%2BB3cXhs1Glt7zF
60. 10.1093/nar/28.1.4510.1093/nar/28.1.45 ↗
61. 10.1038/nmeth111310.1038/nmeth1113 ↗
62. 10.1007/978-1-4939-7493-1_710.1007/978-1-4939-7493-1_7 ↗
63. 10.1186/1755-8794-1-4210.1186/1755-8794-1-42 ↗
64. Guo Y, Zhao S, Su PF, Li CI, Ye F, Flynn CR, et al. Statistical strategies for microRNAseq batch effect reduction. Transl Cancer Res. 2014;3(3):260–265. 1:CAS:528:DC%2BC2MXitVGnur7L
65. 10.1016/j.tibtech.2017.02.01210.1016/j.tibtech.2017.02.012 ↗
66. 10.1080/20013078.2017.132145510.1080/20013078.2017.1321455 ↗
67. 10.1186/1471-2105-14-12810.1186/1471-2105-14-128 ↗
68. 10.1093/bioinformatics/btp10110.1093/bioinformatics/btp101 ↗
69. 10.1038/nmeth.221210.1038/nmeth.2212 ↗
70. 10.1016/j.neuron.2019.05.00210.1016/j.neuron.2019.05.002 ↗
71. 10.1002/pro.330710.1002/pro.3307 ↗
72. 10.1007/s40097-019-0303-z10.1007/s40097-019-0303-z ↗
73. 10.1186/2228-5326-3-2310.1186/2228-5326-3-23 ↗
74. 10.1371/journal.pbio.100145010.1371/journal.pbio.1001450 ↗
75. 10.1093/brain/awac02110.1093/brain/awac021 ↗
76. 10.1371/journal.pone.009568410.1371/journal.pone.0095684 ↗
77. Maierhaba Yang X, Mahmut M, Yao Y, Jia Y. iTRAQ-based proteomics analysis of hippocampus and striatum in rats with hyperglycemia and parkinsons disease. Int J Clin Exp Med. 2018;11(3):1959–1972.
78. 10.1159/00026007010.1159/000260070 ↗
79. 10.3389/fimmu.2019.0203310.3389/fimmu.2019.02033 ↗
80. 10.1016/S0165-6147(02)01899-010.1016/S0165-6147(02)01899-0 ↗
81. Sun MK, Alkon DL. Pharmacological enhancement of synaptic efficacy, spatial learning, and memory through carbonic anhydrase activation in rats. J Pharmacol Exp Ther. 2001;297(3):961–967. 1:CAS:528:DC%2BD3MXjvVSiu7s%3D
82. 10.1038/nrd246710.1038/nrd2467 ↗
83. 10.3233/JPD-21273010.3233/JPD-212730 ↗
84. 10.1073/pnas.080243710510.1073/pnas.0802437105 ↗
85. 10.3389/fnagi.2017.0007310.3389/fnagi.2017.00073 ↗
86. Teismann P. Myeloperoxidase in the neurodegenerative process of parkinson's disease. Dtsch Med Wochenschr. 2014;139(3):99–102. 1:CAS:528:DC%2BC2MXivVahs7k%3D
87. 10.1093/brain/awv18410.1093/brain/awv184 ↗
88. 10.1523/JNEUROSCI.0970-05.200510.1523/JNEUROSCI.0970-05.2005 ↗
89. 10.3390/biomedicines1009223910.3390/biomedicines10092239 ↗
90. 10.1016/j.gene.2011.03.00610.1016/j.gene.2011.03.006 ↗
91. 10.3389/fimmu.2022.94861510.3389/fimmu.2022.948615 ↗
92. 10.1093/glycob/cwx02210.1093/glycob/cwx022 ↗
93. 10.1182/blood.V100.7.250610.1182/blood.V100.7.2506 ↗
94. Durrenberger PF, Grünblatt E, Fernando FS, Monoranu CM, et al. Inflammatory pathways in parkinson's disease. A BNE Microarray Study Parkinsons Dis. 2012;2012
95. 10.1186/s12974-018-1284-410.1186/s12974-018-1284-4 ↗
96. 10.2147/NDT.S17843510.2147/NDT.S178435 ↗
97. 10.1016/j.jalz.2016.10.00510.1016/j.jalz.2016.10.005 ↗
98. 10.1093/nar/gkz38910.1093/nar/gkz389 ↗
99. 10.1038/s41582-020-0333-710.1038/s41582-020-0333-7 ↗
100. 10.1016/j.pneurobio.2019.01.00510.1016/j.pneurobio.2019.01.005 ↗