journal article·2020-10-27Open access

Mitigation of Carbon Nanotube Neurosensor Induced Transcriptomic and Morphological Changes in Mouse Microglia with Surface Passivation

Darwin Yang, Sarah J. Yang, Jackson Travis Del Bonis-O’Donnell, Rebecca L. Pinals, Markita P. Landry

ACS Nano· Volume 14· Issue 10

Abstract

Single-walled carbon nanotubes (SWCNT) are used in neuroscience for deep-brain imaging, neuron activity recording, measuring brain morphology, and imaging neuromodulation. However, the extent to which SWCNT-based probes impact brain tissue is not well understood. Here, we study the impact of (GT)6-SWCNT dopamine nanosensors on SIM-A9 mouse microglial cells and show SWCNT-induced morphological and transcriptomic changes in these brain immune cells. Next, we introduce a strategy to passivate (GT)6-SWCNT nanosensors with PEGylated phospholipids to improve both biocompatibility and dopamine imaging quality. We apply these passivated dopamine nanosensors to image electrically stimulated striatal dopamine release in acute mouse brain slices, and show that slices labeled with passivated nanosensors exhibit higher fluorescence response to dopamine and measure more putative dopamine release sites. Hence, this facile modification to SWCNT-based dopamine probes provides immediate improvements to both biocompatibility and dopamine imaging functionality with an approach that is readily translatable to other SWCNT-based neurotechnologies.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Neuroscience and Neural Engineering · Neuroinflammation and Neurodegeneration Mechanisms · Conducting polymers and applications

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

Affiliations

Department of Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, California 94720, United States

Innovative Genomics Institute (IGI), Berkeley, California 94720, United States

California Institute for Quantitative Biosciences, QB3, University of California, Berkeley, Berkeley, California 94720, United States

Chan-Zuckerberg Biohub, San Francisco, California 94158, United States

Funding

Foundation for Food and Agriculture Research · Defense Advanced Research Projects Agency · American Parkinson's Disease Foundation · Banatao Institute · Congressionally Directed Medical Research Programs · Arnold and Mabel Beckman Foundation · Center for Information Technology Research in the Interest of Society · National Science Foundation · U.S. Department of Agriculture

References (48)

Show source-provided references
1. 10.1021/nn502452210.1021/nn5024522 ↗
2. 10.1007/s11517-015-1430-410.1007/s11517-015-1430-4 ↗
3. 10.1038/micronano.2017.7010.1038/micronano.2017.70 ↗
4. 10.1126/scitranslmed.300359410.1126/scitranslmed.3003594 ↗
5. 10.1007/s11095-010-0141-710.1007/s11095-010-0141-7 ↗
6. 10.1523/JNEUROSCI.1664-18.201810.1523/JNEUROSCI.1664-18.2018 ↗
7. 10.1038/nnano.2008.17410.1038/nnano.2008.174 ↗
8. 10.1021/nn402857u10.1021/nn402857u ↗
9. 10.1007/s10439-010-0091-610.1007/s10439-010-0091-6 ↗
10. 10.1038/nphoton.2014.16610.1038/nphoton.2014.166 ↗
11. 10.1126/sciadv.aaw310810.1126/sciadv.aaw3108 ↗
12. 10.1126/sciadv.aay377110.1126/sciadv.aay3771 ↗
13. 10.1021/acs.biochem.8b0088310.1021/acs.biochem.8b00883 ↗
14. 10.1021/acs.nanolett.8b0293710.1021/acs.nanolett.8b02937 ↗
15. 10.1016/j.molimm.2005.02.00610.1016/j.molimm.2005.02.006 ↗
16. 10.1038/s41598-018-19521-910.1038/s41598-018-19521-9 ↗
17. 10.1021/es060589n10.1021/es060589n ↗
18. 10.1021/nn100776z10.1021/nn100776z ↗
19. 10.1016/j.toxlet.2012.08.00910.1016/j.toxlet.2012.08.009 ↗
20. 10.1007/s00401-009-0622-010.1007/s00401-009-0622-0 ↗
21. 10.1002/glia.2087410.1002/glia.20874 ↗
22. 10.3390/brainsci601000610.3390/brainsci6010006 ↗
23. 10.1016/j.tics.2019.03.00310.1016/j.tics.2019.03.003 ↗
24. 10.1002/adhm.20130017810.1002/adhm.201300178 ↗
25. 10.2131/jts.41.50110.2131/jts.41.501 ↗
26. 10.1016/j.jneumeth.2014.05.02110.1016/j.jneumeth.2014.05.021 ↗
27. 10.1021/acschemneuro.7b0046810.1021/acschemneuro.7b00468 ↗
28. 10.1007/s12035-017-0432-710.1007/s12035-017-0432-7 ↗
29. 10.1126/science.aat442210.1126/science.aat4422 ↗
30. 10.5607/en.2013.22.2.5910.5607/en.2013.22.2.59 ↗
31. 10.3389/fncel.2019.0047210.3389/fncel.2019.00472 ↗
32. 10.1016/j.celrep.2019.05.01010.1016/j.celrep.2019.05.010 ↗
33. 10.1007/s10851-009-0158-x10.1007/s10851-009-0158-x ↗
34. 10.3389/fchem.2015.0005910.3389/fchem.2015.00059 ↗
35. 10.1038/s41598-017-01746-910.1038/s41598-017-01746-9 ↗
36. 10.1186/1471-2202-13-6410.1186/1471-2202-13-64 ↗
37. 10.1093/bioinformatics/btp61610.1093/bioinformatics/btp616 ↗
38. Alexa, A.; Rahnenführer, J. TopGO: Enrichment Analysis for Gene Ontology, R package; Bioconductor, 2019.
39. 10.1093/bioinformatics/btl14010.1093/bioinformatics/btl140 ↗
40. 10.1021/jp036814u10.1021/jp036814u ↗
41. 10.1038/ncomms1024110.1038/ncomms10241 ↗
42. 10.1038/nnano.2016.24810.1038/nnano.2016.248 ↗
43. 10.1063/1.274522810.1063/1.2745228 ↗
44. 10.1021/ja211461810.1021/ja2114618 ↗
45. 10.1021/jacs.9b0961710.1021/jacs.9b09617 ↗
46. 10.2147/IJN.S19918710.2147/IJN.S199187 ↗
47. 10.1021/nn406484b10.1021/nn406484b ↗
48. 10.1093/bioinformatics/bts63510.1093/bioinformatics/bts635 ↗