journal article·2025-12-24

Quantum Dot-Enriched Dendritic Mesoporous Silica Nanospheres for Chamber-Free Digital Detection: High Sensitivity, Easy Operation, and Clinical Accessibility

Feiyang Ou, Yida Huang, Yutong Zhang, Fang Gao, Hongchen Gu, Xinyu Zhao, Chang Lei, Chengzhong Yu, Hong Xu

ACS Applied Materials & Interfaces· Volume 17· Issue 51
Publisher page

Abstract

Digital highly sensitive biodetection is increasingly crucial for early disease diagnosis and treatment, particularly for accurate quantification of low-abundance biomarkers. Current digital biodetection methods represented as single-molecule assay (Simoa), enzyme- or DNA-assisted signal amplification strategy, the fluorescence nanosphere labeling-based digital biodetection, etc. often rely on microcompartments or time-consuming signal amplification steps, facing challenges in terms of cumbersome workflow, insufficient signal strength, and limited clinical accessibility. To address these limitations, we propose a high-sensitivity chamber-free digital immunoassay strategy based on a standard flow cytometer, which utilizes dendritic mesoporous silica nanoparticles (DMSNs) doped with quantum dots (QDs) forming ultrabright fluorescence nanospheres (DMSN@QDs@Silica, DQS) as signal label. Structure-function relationship (SFR) of DQS was systematically investigated to obtain DQS with optimal labeling kinetics and fluorescence brightness. The synthesized DQS are significantly brighter than commercial label fluorescent nanospheres, enabling direct digital signal detection via flow cytometry, which eliminates the need for isolation chambers and multistep signal amplification processes, simplifying the assay and reducing associated costs. We demonstrate that the high-sensitive DQS-based digital assay provides approximately 24-fold improvement in sensitivity over traditional flow cytometry fluorescence immunoassay, achieving a detection limit of 0.37 pg/mL for interleukin-6 (IL-6). This chamber-free digital detection approach holds significant promise for low-abundance biomarker detection, offering enhanced sensitivity, simplicity, and clinical applicability.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Biosensors and Analytical Detection · Advanced biosensing and bioanalysis techniques · Advanced Biosensing Techniques and Applications

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

Affiliations

School of Biomedical Engineering, Med-X Research Institute

Shanghai Jiao Tong University

Department of Clinical Laboratory

Xinqiao Hospital, Army Medical University

Institute of Energy Materials Science

University of Shanghai for Science and Technology

The First Affiliated Hospital of Zhengzhou University

Australian Institute for Bioengineering and Nanotechnology

The University of Queensland

School of Medical Sciences, Faculty of Medicine and Health

The University of Sydney

Charles Perkins Centre

School of Chemistry and Molecular Engineering

East China Normal University

Funding

National Key Research and Development Program of China · Fundamental Research Funds for the Central Universities · National Natural Science Foundation of China

References (34)

Show source-provided references
1. 10.1039/D3SD00144J10.1039/D3SD00144J ↗
2. 10.1038/srep1103410.1038/srep11034 ↗
3. 10.1016/j.bios.2016.12.01410.1016/j.bios.2016.12.014 ↗
4. 10.1016/j.snb.2024.13678010.1016/j.snb.2024.136780 ↗
5. 10.1016/j.snb.2023.13496110.1016/j.snb.2023.134961 ↗
6. 10.1016/j.talanta.2020.12172210.1016/j.talanta.2020.121722 ↗
7. 10.1038/nbt.164110.1038/nbt.1641 ↗
8. 10.1038/s41551-020-00611-x10.1038/s41551-020-00611-x ↗
9. 10.1002/anie.20211070210.1002/anie.202110702 ↗
10. 10.1021/acsomega.1c0018010.1021/acsomega.1c00180 ↗
11. 10.1021/acsnano.0c0237810.1021/acsnano.0c02378 ↗
12. 10.1021/jacs.0c0659910.1021/jacs.0c06599 ↗
13. 10.1021/acsnano.3c0964210.1021/acsnano.3c09642 ↗
14. 10.1016/j.talanta.2023.12552910.1016/j.talanta.2023.125529 ↗
15. 10.1021/acs.analchem.6b0114810.1021/acs.analchem.6b01148 ↗
16. 10.1021/jacs.0c0433110.1021/jacs.0c04331 ↗
17. Taylor, M. S.; Wu, C.; Fridy, P. C.; Senussi, Y.; Cheng, W. C.; Heaps, J.; Mori, K.; Cohen, L.; Molloy, K. R.; Chait, B. T. Ultrasensitive detection of circulating LINE-1 ORF1p as a specific multi-cancer biomarker bioRxiv 2023 10.1101/2023.01.25.525462.10.1101/2023.01.25.525462 ↗
18. 10.1021/acssensors.0c0166110.1021/acssensors.0c01661 ↗
19. 10.1039/D5LC00103J10.1039/D5LC00103J ↗
20. 10.1016/j.snb.2023.13454710.1016/j.snb.2023.134547 ↗
21. 10.1021/acs.analchem.3c0533610.1021/acs.analchem.3c05336 ↗
22. 10.1016/j.snb.2021.12979410.1016/j.snb.2021.129794 ↗
23. 10.1002/adfm.20160196310.1002/adfm.201601963 ↗
24. 10.1021/acs.chemmater.7b0381110.1021/acs.chemmater.7b03811 ↗
25. 10.1002/smll.20210031510.1002/smll.202100315 ↗
26. 10.1021/acsami.0c1152310.1021/acsami.0c11523 ↗
27. 10.1021/acsami.4c1282910.1021/acsami.4c12829 ↗
28. 10.1021/acssensors.5c0002110.1021/acssensors.5c00021 ↗
29. 10.1002/smtd.20200092410.1002/smtd.202000924 ↗
30. 10.1039/c3cc48405j10.1039/c3cc48405j ↗
31. 10.1021/acssensors.4c0319010.1021/acssensors.4c03190 ↗
32. 10.1016/B978-0-08-097037-0.00016-610.1016/B978-0-08-097037-0.00016-6 ↗
33. 10.1021/acsami.0c0509910.1021/acsami.0c05099 ↗
34. 10.1002/adfm.20170538010.1002/adfm.201705380 ↗