journal article·2025-05-13Open access

Agarose Microgel-Based In Situ Cleavable Immuno-Rolling Circle Amplification for Multiplexed Single-Molecule Quantitation on Single Extracellular Vesicles

Juhwan Park, Michelle Feng, Jingbo Yang, Hanfei Shen, Zhiyuan Qin, Wei Guo, David A. Issadore

ACS Nano· Volume 19· Issue 18

Abstract

We have developed a platform for the multiplexed and ultrasensitive profiling of individual extracellular vesicles (EVs) directly in plasma, which we call GDEVA─Agarose micro G el-based D igital single-molecule–single EV A ssay. GDEVA achieves single-molecule sensitivity and moderate multiplexing (demonstrated 3-plex), and can achieve a throughput of ∼10 4 EVs per minute necessary to resolve EVs directly in human plasma when read out using flow cytometry. Our platform integrates a rolling circle amplification (RCA) immunoassay of EV surface proteins, which are cleaved from single EVs, and amplified within agarose microgels, followed by flow cytometry-based readout or imaging after fluorescence-activated cell sorting (FACS). It overcomes steric hindrance of RCA products, nonspecific binding of RCA templates, and the lack of quantitation of multiple proteins on EVs that have plagued earlier approaches. We evaluated the analytical capabilities of GDEVA through head-to-head comparison with conventional technology and demonstrated a ∼100× improvement in the limit of detection (LOD) of EV subpopulations. We evaluate GDEVA’s potential in cancer immunology, by analyzing single EVs in plasma samples from patients with melanoma, where EV heterogeneity plays a critical role in disease progression and response to therapy. We demonstrate profiling of individual EVs for key immune markers PD-L1, CD155, and the melanoma marker TYRP-1, and showed that GDEVA can precisely quantify EVs, offering the resolution to detect rare EV subpopulations in complex clinical specimens.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Nanoparticle-Based Drug Delivery · Micro and Nano Robotics · Advanced biosensing and bioanalysis techniques

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

Affiliations

Department of Bioengineering, School of Engineering and Applied Science

University of Pennsylvania

Department of Bio and Fermentation Convergence Technology

Kookmin University

Department of Biology, School of Arts and Science

Funding

National Cancer Institute · National Human Genome Research Institute · National Institute of Allergy and Infectious Diseases · Ministry of Environment · National Institute of Mental Health · National Institute of General Medical Sciences

References (70)

Show source-provided references
1. 10.1002/jev2.1214410.1002/jev2.12144 ↗
2. 10.1038/s41577-022-00763-810.1038/s41577-022-00763-8 ↗
3. 10.1038/s41392-024-01735-110.1038/s41392-024-01735-1 ↗
4. 10.1002/jex2.4010.1002/jex2.40 ↗
5. 10.1186/s12943-023-01898-510.1186/s12943-023-01898-5 ↗
6. 10.3389/fcimb.2021.69791910.3389/fcimb.2021.697919 ↗
7. 10.1016/j.placenta.2023.07.00610.1016/j.placenta.2023.07.006 ↗
8. 10.1007/s13577-023-00904-810.1007/s13577-023-00904-8 ↗
9. 10.1021/acsnano.2c0433710.1021/acsnano.2c04337 ↗
10. 10.1038/s41596-021-00551-z10.1038/s41596-021-00551-z ↗
11. 10.1038/s41580-023-00615-w10.1038/s41580-023-00615-w ↗
12. 10.1002/advs.20230361910.1002/advs.202303619 ↗
13. 10.1016/j.molmed.2022.05.00310.1016/j.molmed.2022.05.003 ↗
14. Gilboa, T.; Ter-Ovanesyan, D.; Wang, S.C.; Whiteman, S.; Kannarkat, G. T.; Church, G. M.; Chen-Plotkin, A. S.; Walt, D. R. Assessment of extracellular vesicle protein cargo as neurodegenerative disease biomarkers bioRxiv 2023 10.1101/2023.11.02.565137.10.1101/2023.11.02.565137 ↗
15. 10.1002/jev2.1202510.1002/jev2.12025 ↗
16. 10.1021/acsnano.1c0255610.1021/acsnano.1c02556 ↗
17. 10.1002/jev2.1204710.1002/jev2.12047 ↗
18. 10.1021/acs.nanolett.2c0027410.1021/acs.nanolett.2c00274 ↗
19. 10.1039/D4LC00331D10.1039/D4LC00331D ↗
20. 10.3389/fnint.2022.87983210.3389/fnint.2022.879832 ↗
21. 10.1002/anie.20180690110.1002/anie.201806901 ↗
22. 10.1021/acsnano.7b0778210.1021/acsnano.7b07782 ↗
23. 10.1021/acsnano.1c0078210.1021/acsnano.1c00782 ↗
24. 10.1002/jev2.1227710.1002/jev2.12277 ↗
25. 10.1038/s41467-019-11486-110.1038/s41467-019-11486-1 ↗
26. 10.1038/s41467-023-36932-z10.1038/s41467-023-36932-z ↗
27. 10.1002/jev2.1234610.1002/jev2.12346 ↗
28. 10.1126/sciadv.abc120410.1126/sciadv.abc1204 ↗
29. 10.1002/jev2.1236910.1002/jev2.12369 ↗
30. 10.1126/sciadv.adh868910.1126/sciadv.adh8689 ↗
31. 10.1021/acsnano.4c1355710.1021/acsnano.4c13557 ↗
32. 10.1002/smll.20240780910.1002/smll.202407809 ↗
33. 10.1021/ac103161b10.1021/ac103161b ↗
34. 10.1002/anie.20240154410.1002/anie.202401544 ↗
35. 10.1016/j.bios.2023.11531610.1016/j.bios.2023.115316 ↗
36. 10.1021/acsnano.1c0867510.1021/acsnano.1c08675 ↗
37. 10.1039/D2LC00229A10.1039/D2LC00229A ↗
38. 10.1021/acs.analchem.2c0347510.1021/acs.analchem.2c03475 ↗
39. 10.1038/s41586-018-0392-810.1038/s41586-018-0392-8 ↗
40. 10.1038/s12276-019-0295-210.1038/s12276-019-0295-2 ↗
41. 10.1016/j.cell.2019.02.01610.1016/j.cell.2019.02.016 ↗
42. 10.1021/acs.bioconjchem.5b0027510.1021/acs.bioconjchem.5b00275 ↗
43. Antibody and Buffer Compatibility – AlphaThera. https://alphathera.com/antibody-and-buffer-compatibility/ (accessed April 18, 2025).
44. 10.1039/C5LC01025J10.1039/C5LC01025J ↗
45. 10.1021/jo00008a01410.1021/jo00008a014 ↗
46. 10.1039/C5LC00614G10.1039/C5LC00614G ↗
47. Armbruster D. A. · Clin. Biochem. Rev. · 2008 · 29 · S49
48. 10.1038/nm73010.1038/nm730 ↗
49. 10.3390/ijms2118646610.3390/ijms21186466 ↗
50. 10.1016/j.jim.2012.02.01110.1016/j.jim.2012.02.011 ↗
51. 10.1038/s41577-019-0264-y10.1038/s41577-019-0264-y ↗
52. 10.1186/s40425-019-0768-910.1186/s40425-019-0768-9 ↗
53. 10.3389/fimmu.2019.0229810.3389/fimmu.2019.02298 ↗
54. 10.1002/adbi.20200001710.1002/adbi.202000017 ↗
55. 10.1002/jev2.1241810.1002/jev2.12418 ↗
56. 10.1136/jitc-2020-00221810.1136/jitc-2020-002218 ↗
57. 10.1186/s12943-021-01490-910.1186/s12943-021-01490-9 ↗
58. 10.1158/1078-0432.CCR-19-392510.1158/1078-0432.CCR-19-3925 ↗
59. 10.1172/jci.insight.12863310.1172/jci.insight.128633 ↗
60. 10.1016/j.oraloncology.2021.10547210.1016/j.oraloncology.2021.105472 ↗
61. 10.1038/s41598-023-29724-410.1038/s41598-023-29724-4 ↗
62. 10.1038/s41467-021-22913-710.1038/s41467-021-22913-7 ↗
63. 10.1158/0008-5472.CAN-23-399810.1158/0008-5472.CAN-23-3998 ↗
64. 10.1038/nmeth.237210.1038/nmeth.2372 ↗
65. 10.1080/20013078.2020.180976510.1080/20013078.2020.1809765 ↗
66. 10.1021/acsnano.3c1156110.1021/acsnano.3c11561 ↗
67. 10.1039/C6LC01063F10.1039/C6LC01063F ↗
68. 10.1038/s41592-020-01034-x10.1038/s41592-020-01034-x ↗
69. 10.1063/5.003776810.1063/5.0037768 ↗
70. 10.1016/j.celrep.2023.11322410.1016/j.celrep.2023.113224 ↗