journal article·2025-01-14

Concurrent Detection of Protein and miRNA at the Single Extracellular Vesicle Level Using a Digital Dual CRISPR-Cas Assay

Xun Xu, Yuanyue Zhang, Jiajia Liu, Shujin Wei, Nan Li, Xintong Yao, Muxue Wang, Xiaohan Su, Gaoshan Jing, Junquan Xu, Yan Liu, Ying Lu, Jing Cheng, Youchun Xu

ACS Nano· Volume 19· Issue 1
Publisher page

Abstract

The simultaneous detection of proteins and microRNA (miRNA) at the single extracellular vesicle (EV) level shows great promise for precise disease profiling, owing to the heterogeneity and scarcity of tumor-derived EVs. However, a highly reliable method for multiple-target analysis of single EVs remains to be developed. In this study, a d igital d ual CRISPR-Cas-powered S ingle E V E valuation ( ddSEE ) system was proposed to enable the concurrent detection of surface protein and inner miRNA of EVs at the single-molecule level. By optimizing simultaneous reaction conditions of CRISPR-Cas12a and CRISPR-Cas13a, the surface protein of EVs was detected by Cas12a using antibody-DNA conjugates to transfer the signal of the protein to DNA, while the inner miRNA was analyzed by Cas13a through EV-liposome fusion. A microfluidic chip containing 188,000 microwells was used to convert the CRISPR-Cas system into a digital assay format to enable the absolute quantification of miRNA/protein-positive EVs without bias through fluorescence imaging, which can detect as few as 214 EVs/μL. Finally, a total of 31 blood samples, 21 from breast cancer patients and 10 from healthy donors, were collected and tested, achieving a diagnostic accuracy of 92% in distinguishing patients with breast cancer from healthy donors. With its absolute quantification, ease of use, and multiplexed detection capability, the ddSEE system demonstrates its great potential for both EV research and clinical applications.
Source: openalex_inverted_index · Quality: baseline

Research context

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

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

Affiliations

School of Biomedical Engineering, Tsinghua University, Beijing 100084, China

CapitalBio Technology, Beijing 101111, China

State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China

Iomics Biosciences, Beijing 101318, China

National Engineering Research Center for Beijing Biochip Technology, Beijing 102200, China

Funding

National Natural Science Foundation of China · Beijing Nova Program · Beijing Lab Foundation

References (74)

Show source-provided references
1. 10.1126/science.aau697710.1126/science.aau6977 ↗
2. 10.1073/pnas.140830111110.1073/pnas.1408301111 ↗
3. 10.3389/fimmu.2018.0073810.3389/fimmu.2018.00738 ↗
4. 10.1016/j.mam.2018.02.00210.1016/j.mam.2018.02.002 ↗
5. 10.1021/acs.analchem.8b0590910.1021/acs.analchem.8b05909 ↗
6. 10.1021/acs.chemrev.7b0053410.1021/acs.chemrev.7b00534 ↗
7. 10.1016/j.molmed.2022.05.00310.1016/j.molmed.2022.05.003 ↗
8. 10.1021/acsnano.3c0317210.1021/acsnano.3c03172 ↗
9. 10.1002/adbi.20190030710.1002/adbi.201900307 ↗
10. 10.1021/acsnano.1c0078210.1021/acsnano.1c00782 ↗
11. 10.1021/acsnano.7b0706010.1021/acsnano.7b07060 ↗
12. 10.1002/advs.20230361910.1002/advs.202303619 ↗
13. 10.1038/s41467-017-01196-x10.1038/s41467-017-01196-x ↗
14. 10.1126/sciadv.abc120410.1126/sciadv.abc1204 ↗
15. 10.7150/thno.3368310.7150/thno.33683 ↗
16. 10.1093/nar/gkg21210.1093/nar/gkg212 ↗
17. 10.1016/j.biomaterials.2018.08.03810.1016/j.biomaterials.2018.08.038 ↗
18. 10.1126/science.aam932110.1126/science.aam9321 ↗
19. 10.1038/s41551-021-00760-710.1038/s41551-021-00760-7 ↗
20. 10.1126/science.aar624510.1126/science.aar6245 ↗
21. 10.1016/j.bios.2021.11345010.1016/j.bios.2021.113450 ↗
22. 10.1021/acssensors.4c0043210.1021/acssensors.4c00432 ↗
23. 10.1021/acs.analchem.3c0449210.1021/acs.analchem.3c04492 ↗
24. 10.1021/acs.est.1c0673310.1021/acs.est.1c06733 ↗
25. 10.1021/acs.nanolett.1c0071510.1021/acs.nanolett.1c00715 ↗
26. 10.1021/acsnano.0c0816510.1021/acsnano.0c08165 ↗
27. 10.1021/acsnano.2c1014310.1021/acsnano.2c10143 ↗
28. 10.1002/smll.20200449210.1002/smll.202004492 ↗
29. 10.1021/ac050597710.1021/ac0505977 ↗
30. 10.1038/nbt.164110.1038/nbt.1641 ↗
31. 10.1177/221106821558958010.1177/2211068215589580 ↗
32. 10.1038/s41551-023-01017-110.1038/s41551-023-01017-1 ↗
33. 10.1002/advs.20230176610.1002/advs.202301766 ↗
34. 10.1002/anie.20190199710.1002/anie.201901997 ↗
35. 10.1038/s41565-021-00939-810.1038/s41565-021-00939-8 ↗
36. 10.1016/j.nantod.2022.10159910.1016/j.nantod.2022.101599 ↗
37. 10.1021/acsnano.4c0531210.1021/acsnano.4c05312 ↗
38. 10.1038/s41467-020-18615-110.1038/s41467-020-18615-1 ↗
39. 10.1016/j.isci.2021.10299610.1016/j.isci.2021.102996 ↗
40. 10.1038/nature1980210.1038/nature19802 ↗
41. 10.1016/j.bios.2024.11623910.1016/j.bios.2024.116239 ↗
42. 10.1038/s42003-022-03433-610.1038/s42003-022-03433-6 ↗
43. 10.1021/acs.analchem.2c0563210.1021/acs.analchem.2c05632 ↗
44. 10.1016/j.bios.2021.11370110.1016/j.bios.2021.113701 ↗
45. 10.1021/acs.analchem.2c0455510.1021/acs.analchem.2c04555 ↗
46. 10.1016/j.molcel.2017.03.01610.1016/j.molcel.2017.03.016 ↗
47. 10.1016/j.cell.2018.09.00110.1016/j.cell.2018.09.001 ↗
48. 10.3389/fmicb.2021.76646410.3389/fmicb.2021.766464 ↗
49. 10.1016/j.snb.2022.13276710.1016/j.snb.2022.132767 ↗
50. 10.1039/D0AN00664E10.1039/D0AN00664E ↗
51. 10.1039/C5OB01451D10.1039/C5OB01451D ↗
52. 10.1016/j.bios.2022.11493910.1016/j.bios.2022.114939 ↗
53. 10.1002/anie.20210173010.1002/anie.202101730 ↗
54. 10.1002/jev2.1240410.1002/jev2.12404 ↗
55. 10.1038/s41577-019-0264-y10.1038/s41577-019-0264-y ↗
56. 10.1038/s41586-018-0392-810.1038/s41586-018-0392-8 ↗
57. 10.1186/s13058-016-0753-x10.1186/s13058-016-0753-x ↗
58. 10.1016/j.omtn.2020.03.00310.1016/j.omtn.2020.03.003 ↗
59. 10.1039/C4CS00370E10.1039/C4CS00370E ↗
60. 10.1186/1479-5876-7-10510.1186/1479-5876-7-105 ↗
61. 10.1126/sciadv.aba571410.1126/sciadv.aba5714 ↗
62. 10.1021/acsnano.2c0433710.1021/acsnano.2c04337 ↗
63. 10.1038/nmeth.263310.1038/nmeth.2633 ↗
64. 10.1021/acsami.8b1397110.1021/acsami.8b13971 ↗
65. 10.1021/ac401983w10.1021/ac401983w ↗
66. 10.1021/acs.bioconjchem.5b0061310.1021/acs.bioconjchem.5b00613 ↗
67. 10.3390/jcm1002031910.3390/jcm10020319 ↗
68. 10.1126/sciadv.adh868910.1126/sciadv.adh8689 ↗
69. 10.1021/acsnano.3c0579910.1021/acsnano.3c05799 ↗
70. 10.1021/acsbiomaterials.3c0145910.1021/acsbiomaterials.3c01459 ↗
71. 10.1155/2022/593868810.1155/2022/5938688 ↗
72. 10.1245/s10434-018-07117-610.1245/s10434-018-07117-6 ↗
73. 10.1016/S0092-8674(04)00045-510.1016/S0092-8674(04)00045-5 ↗
74. 10.3389/fmicb.2023.111939510.3389/fmicb.2023.1119395 ↗