In‐Depth Cell‐Type‐Specific Proteome Landscape of the Brain from Human Amyloid‐β Overexpression Mouse Model
Source abstract
Abstract
Abstract Amyloid‐β (Aβ) plays a crucial role in Alzheimer's disease pathogenesis. Understanding how Aβ overexpression alters the proteome of individual brain cell types is essential but challenging due to the nature of brain tissue, which contains intermingled various cell types. The current methods for cell‐type‐specific proteomics either require genetic modifications or complex cell isolation, limiting their use. This study introduces a novel method, in situ cell‐type‐specific proteome analysis using antibody‐mediated biotinylation (iCAB), which applies immunohistochemistry with biotin‐tyramide to target cell‐specific proteins directly in tissue. Applied to 5xFAD mice, iCAB enables us to identify ≈8000 cell‐type‐specific proteomes with significantly more differentially expressed proteins than traditional bulk proteome methods, pinpointing unique pathways such as mRNA processing, calcium regulation, and phagocytosis for neurons, astrocytes, and microglia, respectively. This study reports in‐depth the cell‐type‐specific brain proteome landscape of the human Aβ overexpression mouse model for the first time using an innovative tool that is powerful, straightforward, and applicable to both animal models and human tissues, without the need for prior genetic alterations.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Alzheimer's disease research and treatments · Neuroinflammation and Neurodegeneration Mechanisms · Biotin and Related Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Neurology Johns Hopkins University School of Medicine Baltimore MD 21205 USA
Neuroregeneration and Stem Cell Programs Institute for Cell Engineering Johns Hopkins University School of Medicine Baltimore MD 21205 USA
Department of Brain and Cognitive Sciences Korea Advanced Institute of Science and Technology Daejeon 34141 South Korea
Funding
National Institutes of Health
References (71)
Show source-provided references
1. 10.3233/JAD-17993510.3233/JAD-179935 ↗
2. 10.1159/00036910110.1159/000369101 ↗
3. 10.15252/emmm.20160621010.15252/emmm.201606210 ↗
4. 10.1038/nature1994910.1038/nature19949 ↗
5. 10.1038/s41467-021-27778-410.1038/s41467-021-27778-4 ↗
6. 10.1002/pmic.20190022610.1002/pmic.201900226 ↗
7. 10.1038/nbt.401610.1038/nbt.4016 ↗
8. 10.7554/eLife.7092110.7554/eLife.70921 ↗
9. 10.1016/j.neuron.2022.09.02510.1016/j.neuron.2022.09.025 ↗
10. 10.1038/s41467-022-30623-x10.1038/s41467-022-30623-x ↗
11. 10.1038/s41386-018-0260-010.1038/s41386-018-0260-0 ↗
12. 10.1021/acs.jproteome.8b0098110.1021/acs.jproteome.8b00981 ↗
13. 10.1021/pr400246t10.1021/pr400246t ↗
14. 10.3389/fcell.2016.0011610.3389/fcell.2016.00116 ↗
15. 10.1038/nmeth.453310.1038/nmeth.4533 ↗
16. 10.1073/pnas.211440511910.1073/pnas.2114405119 ↗
17. 10.1111/jnc.1579610.1111/jnc.15796 ↗
18. 10.1016/j.neuron.2017.09.05610.1016/j.neuron.2017.09.056 ↗
19. 10.1038/s41380-021-01249-010.1038/s41380-021-01249-0 ↗
20. 10.3390/ijms23211292410.3390/ijms232112924 ↗
21. 10.1038/s41591-023-02476-410.1038/s41591-023-02476-4 ↗
22. 10.1126/scitranslmed.adg412210.1126/scitranslmed.adg4122 ↗
23. 10.1038/s41597-023-02158-310.1038/s41597-023-02158-3 ↗
24. 10.1038/s41467-021-25144-y10.1038/s41467-021-25144-y ↗
25. 10.1016/j.aca.2018.01.03710.1016/j.aca.2018.01.037 ↗
26. 10.1186/s13073-023-01206-210.1186/s13073-023-01206-2 ↗
27. 10.1186/s13024-018-0282-410.1186/s13024-018-0282-4 ↗
28. 10.1016/j.celrep.2019.08.10410.1016/j.celrep.2019.08.104 ↗
29. 10.3389/fnmol.2023.123787410.3389/fnmol.2023.1237874 ↗
30. 10.3389/fnagi.2023.119168010.3389/fnagi.2023.1191680 ↗
31. 10.1016/j.neuroscience.2020.03.02510.1016/j.neuroscience.2020.03.025 ↗
32. 10.3389/fnsyn.2023.113857710.3389/fnsyn.2023.1138577 ↗
33. 10.1186/1750-1326-9-3310.1186/1750-1326-9-33 ↗
34. 10.1007/s11481-020-09908-910.1007/s11481-020-09908-9 ↗
35. 10.2147/IDR.S31640210.2147/IDR.S316402 ↗
36. 10.1093/jnen/nlad00610.1093/jnen/nlad006 ↗
37. 10.1074/jbc.M70828120010.1074/jbc.M708281200 ↗
38. 10.1016/j.molimm.2009.01.01410.1016/j.molimm.2009.01.014 ↗
39. 10.1016/S0306-4522(98)00361-310.1016/S0306-4522(98)00361-3 ↗
40. 10.1021/jacs.1c0860710.1021/jacs.1c08607 ↗
41. 10.1093/glycob/cwp15210.1093/glycob/cwp152 ↗
42. 10.1186/s12974-023-02722-z10.1186/s12974-023-02722-z ↗
43. 10.1007/s10571-023-01376-y10.1007/s10571-023-01376-y ↗
44. 10.5483/BMBRep.2020.53.10.15810.5483/BMBRep.2020.53.10.158 ↗
45. 10.1186/s12861-019-0200-110.1186/s12861-019-0200-1 ↗
46. 10.1002/1873-3468.1406310.1002/1873-3468.14063 ↗
47. 10.1155/2016/984769610.1155/2016/9847696 ↗
48. 10.7554/eLife.2379810.7554/eLife.23798 ↗
49. 10.1126/scitranslmed.adf014110.1126/scitranslmed.adf0141 ↗
50. 10.1126/sciadv.abf693510.1126/sciadv.abf6935 ↗
51. 10.1523/JNEUROSCI.19-17-07516.199910.1523/JNEUROSCI.19-17-07516.1999 ↗
52. 10.1093/nar/gks104210.1093/nar/gks1042 ↗
53. 10.1002/ctm2.107610.1002/ctm2.1076 ↗
54. 10.1016/j.mcpro.2022.10045210.1016/j.mcpro.2022.100452 ↗
55. 10.1038/nmeth.425610.1038/nmeth.4256 ↗
56. 10.1016/j.molcel.2018.06.01310.1016/j.molcel.2018.06.013 ↗
57. 10.1038/nmeth.390110.1038/nmeth.3901 ↗
58. 10.1073/pnas.09106249810.1073/pnas.091062498 ↗
59. 10.1093/nar/gkw37710.1093/nar/gkw377 ↗
60. 10.1016/j.cell.2021.04.04810.1016/j.cell.2021.04.048 ↗
61. 10.1038/s41586-019-1629-x10.1038/s41586-019-1629-x ↗
62. 10.1038/s41596-022-00710-w10.1038/s41596-022-00710-w ↗
63. 10.1038/nprot.2008.21110.1038/nprot.2008.211 ↗
64. 10.1186/1471-2105-15-29310.1186/1471-2105-15-293 ↗
65. 10.1093/brain/awae22210.1093/brain/awae222 ↗
66. 10.1186/1559-0275-11-410.1186/1559-0275-11-4 ↗
67. 10.3390/molecules2703113710.3390/molecules27031137 ↗
68. 10.1002/path.542010.1002/path.5420 ↗
69. 10.1093/bioinformatics/btv30010.1093/bioinformatics/btv300 ↗
70. 10.1093/nar/gkg03410.1093/nar/gkg034 ↗
71. 10.1186/1471-2105-9-55910.1186/1471-2105-9-559 ↗