journal article·2020-03-25

Magnetic Particle Spectroscopy for Detection of Influenza A Virus Subtype H1N1

Kai Wu, Jinming Liu, Renata Saha, Diqing Su, Venkatramana D. Krishna, Maxim C.-J. Cheeran, Jian-Ping Wang

ACS Applied Materials & Interfaces· Volume 12· Issue 12
Publisher page

Abstract

Magnetic nanoparticles (MNPs) with proper surface functionalization have been extensively applied as labels for magnetic immunoassays, carriers for controlled drug/gene delivery, tracers and contrasts for magnetic imaging, etc. Here, we introduce a new biosensing scheme based on magnetic particle spectroscopy (MPS) and the self-assembly of MNPs to quantitatively detect H1N1 nucleoprotein molecules. MPS monitors the harmonics of oscillating MNPs as a metric for the freedom of rotational process, thus indicating the bound states of MNPs. These harmonics can be readily collected from nanogram quantities of iron oxide nanoparticles within 10 s. The H1N1 nucleoprotein molecule hosts multiple different epitopes that forms binding sites for many IgG polyclonal antibodies. Anchoring IgG polyclonal antibodies onto MNPs triggers the cross-linking between MNPs and H1N1 nucleoprotein molecules, thereby forming MNP self-assemblies. Using MPS and the self-assembly of MNPs, we were able to detect as low as 44 nM (4.4 pmole) H1N1 nucleoprotein. In addition, the morphologies and the hydrodynamic sizes of the MNP self-assemblies are characterized to verify the MPS results. Different MNP self-assembly models such as classical cluster, open ring tetramer, and chain model as well as multimers (from dimer to pentamer) are proposed in this paper. Herein, we claim the feasibility of using MPS and the self-assembly of MNPs as a new biosensing scheme for detecting ultralow concentrations of target biomolecules, which can be employed as rapid, sensitive, and wash-free magnetic immunoassays.
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Research context

OpenAlex topics: Characterization and Applications of Magnetic Nanoparticles · Microfluidic and Bio-sensing Technologies · Acoustic Wave Resonator Technologies

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

Affiliations

Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota 55455, United States

Department of Chemical Engineering and Material Science, University of Minnesota, Minneapolis, Minnesota 55455, United States

Department of Veterinary Population Medicine, University of Minnesota, St. Paul, Minnesota 55108, United States

Funding

National Institute of Food and Agriculture · Institute of Engineering in Medicine, University of Minnesota

References (58)

Show source-provided references
1. 10.32776/revbiomed.v17i1.44010.32776/revbiomed.v17i1.440 ↗
2. 10.1007/s00705-018-3708-y10.1007/s00705-018-3708-y ↗
3. Influenza (Seasonal). World Health Organization. https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal) (accessed 2020-03-12).
4. 10.1128/JVI.69.6.3799-3806.199510.1128/JVI.69.6.3799-3806.1995 ↗
5. 10.1126/science.122817210.1126/science.1228172 ↗
6. 10.1126/science.122727010.1126/science.1227270 ↗
7. 10.1099/0022-1317-76-4-100910.1099/0022-1317-76-4-1009 ↗
8. 10.1128/JVI.67.5.2723-2729.199310.1128/JVI.67.5.2723-2729.1993 ↗
9. 10.1099/0022-1317-83-4-72310.1099/0022-1317-83-4-723 ↗
10. 10.1021/acs.analchem.6b0050210.1021/acs.analchem.6b00502 ↗
11. 10.1097/00008480-200302000-0001310.1097/00008480-200302000-00013 ↗
12. 10.1016/j.bios.2014.10.05210.1016/j.bios.2014.10.052 ↗
13. 10.1099/jmm.0.017244-010.1099/jmm.0.017244-0 ↗
14. 10.1016/j.jviromet.2017.01.00810.1016/j.jviromet.2017.01.008 ↗
15. 10.1016/j.jviromet.2017.01.01910.1016/j.jviromet.2017.01.019 ↗
16. 10.7326/0003-4819-156-7-201204030-0040310.7326/0003-4819-156-7-201204030-00403 ↗
17. 10.7326/M17-084810.7326/M17-0848 ↗
18. 10.1007/s10096-015-2328-710.1007/s10096-015-2328-7 ↗
19. 10.1542/peds.2009-306010.1542/peds.2009-3060 ↗
20. 10.1021/acssensors.7b0043210.1021/acssensors.7b00432 ↗
21. 10.3389/fmicb.2016.0040010.3389/fmicb.2016.00400 ↗
22. 10.3389/fmicb.2019.0107710.3389/fmicb.2019.01077 ↗
23. 10.1088/1361-6463/ab03c010.1088/1361-6463/ab03c0 ↗
24. 10.1016/j.jmmm.2006.10.116410.1016/j.jmmm.2006.10.1164 ↗
25. 10.1016/j.jmmm.2006.10.118010.1016/j.jmmm.2006.10.1180 ↗
26. 10.1002/smll.20160413510.1002/smll.201604135 ↗
27. 10.1016/j.bios.2013.06.04910.1016/j.bios.2013.06.049 ↗
28. 10.1063/1.492805710.1063/1.4928057 ↗
29. 10.1088/0031-9155/54/19/L0110.1088/0031-9155/54/19/L01 ↗
30. 10.1088/0031-9155/59/5/110910.1088/0031-9155/59/5/1109 ↗
31. 10.1021/ac303075b10.1021/ac303075b ↗
32. 10.1021/acsami.9b0523310.1021/acsami.9b05233 ↗
33. 10.1021/ja017773n10.1021/ja017773n ↗
34. 10.1021/acsami.6b1424710.1021/acsami.6b14247 ↗
35. 10.1021/acsnano.5b0237910.1021/acsnano.5b02379 ↗
36. 10.1021/acs.chemrev.5b0032110.1021/acs.chemrev.5b00321 ↗
37. 10.1039/C4TB01828A10.1039/C4TB01828A ↗
38. 10.1016/j.bios.2018.10.04610.1016/j.bios.2018.10.046 ↗
39. 10.1063/1.494504210.1063/1.4945042 ↗
40. 10.1103/PhysRevMaterials.2.05441510.1103/PhysRevMaterials.2.054415 ↗
41. Thanh N. T. · 201810.1201/9781315168258 ↗
42. 10.1016/j.bios.2015.12.04910.1016/j.bios.2015.12.049 ↗
43. 10.1016/j.bios.2016.05.09010.1016/j.bios.2016.05.090 ↗
44. 10.1021/acssensors.8b0004810.1021/acssensors.8b00048 ↗
45. 10.3390/s1606082810.3390/s16060828 ↗
46. 10.1186/s13036-017-0053-810.1186/s13036-017-0053-8 ↗
47. 10.1039/C3LC50621E10.1039/C3LC50621E ↗
48. 10.1016/j.foodcont.2016.06.02810.1016/j.foodcont.2016.06.028 ↗
49. Jackman L. M. · 2013
50. 10.1088/0957-4484/22/8/08550110.1088/0957-4484/22/8/085501 ↗
51. 10.1186/s12951-015-0069-510.1186/s12951-015-0069-5 ↗
52. 10.7567/JJAP.55.02BD0210.7567/JJAP.55.02BD02 ↗
53. 10.3390/s1911259910.3390/s19112599 ↗
54. 10.1016/j.bios.2016.02.03710.1016/j.bios.2016.02.037 ↗
55. 10.1016/j.jmmm.2017.10.07810.1016/j.jmmm.2017.10.078 ↗
56. 10.1039/C8NR01511B10.1039/C8NR01511B ↗
57. 10.1002/mp.1298310.1002/mp.12983 ↗
58. 10.1118/1.342629410.1118/1.3426294 ↗