journal article·2025-04-09

Combining CRISPR-Cas12a with Microsphere Array-Enhanced Fluorescence for Portable Pathogen Nucleic Acid Detection

Menglu Gao, Chen Yang, Wu Si, Xiaodan Xi, Liangjun Chen, Zhikun Zeng, Yuan Rong, Yi Yang, Fubing Wang, Chunhui Yuan

ACS Applied Materials & Interfaces· Volume 17· Issue 14
Publisher page

Abstract

The detection of food contamination in a swift and sensitive manner is essential for safeguarding public health. Clustered regularly interspaced short palindromic repeats (CRISPR)-based assays for nucleic acid detection are renowned for their high specificity and convenient, related studies have focused on refining the Cas protein and optimizing the CRISPR (cr)RNAs design within CRISPR-based assays for enhancing the sensitivity of nucleic acid detection. Our research offers innovative insights into enhancing the fluorescence signal output intensity from a physical standpoint, thereby presenting a practical and cost-effective strategy to lower the detection thresholds in CRISPR-based assays. By a layer of microsphere arrays was spread onto the bottom of the microfluidic chip to enhance the fluorescence signal of the sample via self-assembly of the microspheres. Recombinase polymerase amplification (RPA) was used to amplify target sequences, followed by crRNA binding to activate Cas enzyme, cleaving fluorescein amidite (FAM)-labeled reporters and emitting a fluorescent signal. The method successfully identified SARS-CoV-2 positive samples (10 clinical samples and 8 environmental contamination samples) and distinguished them from negative samples. Meanwhile, it successfully detected 4 food contamination Shigella samples and 5 clinical Shigell a samples. In this study, the developed method exhibited a detection limit (LoD) of 75 fM for SARS-CoV-2 (POCT with USB camera: 50 fM) and 100 fM for Shigella (POCT with USB camera: 75 fM). It also demonstrated promising sensitivity (100%) and specificity (100%) in a small-sample validation. Combined portable and automated detection was achieved using a smartphone to receive and process the fluorescent signals obtained from the samples. The detection platform developed in this study is not only applicable for the detection of pathogens in cold-chain food products, but also extends to pathogen detection in community hospitals and resource-limited areas, providing an efficient solution for rapid pathogen screening in different settings. Moreover, different nucleic acid samples can be detected by changing the RPA primer and CRISPR crRNA. This method provides a paradigm for studying enhanced fluorescence signaling and holds significant potential to advance the commercialization and practical use of CRISPR fluorescence sensors.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: CRISPR and Genetic Engineering · Biosensors and Analytical Detection · Advanced biosensing and bioanalysis techniques

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

Affiliations

Department of Laboratory Medicine, Center for Single-Cell Omics and Tumor Liquid Biopsy

Zhongnan Hospital of Wuhan University

School of Physics & Technology, Department of Clinical Laboratory, Institute of Medicine and Physics, Zhongnan Hospital, Renmin Hospital

Wuhan University

Shenzhen Research Institute

Department of Laboratory Medicine, Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College

Huazhong University of Science and Technology

Funding

Basic and Applied Basic Research Foundation of Guangdong Province · National Natural Science Foundation of China · Zhongnan Hospital of Wuhan University · Key technology development and application of novel coronavirus detection in cold chain food and related environment

References (35)

Show source-provided references
1. 10.1038/s41598-021-01419-810.1038/s41598-021-01419-8 ↗
2. 10.1038/sj.icb710002510.1038/sj.icb7100025 ↗
3. 10.1111/1541-4337.1325810.1111/1541-4337.13258 ↗
4. 10.1080/10408398.2021.188707810.1080/10408398.2021.1887078 ↗
5. 10.1021/acsami.3c1703910.1021/acsami.3c17039 ↗
6. 10.1039/D2LC00434H10.1039/D2LC00434H ↗
7. 10.1126/science.244887510.1126/science.2448875 ↗
8. 10.1016/j.trac.2020.11598410.1016/j.trac.2020.115984 ↗
9. 10.1038/s41565-023-01372-910.1038/s41565-023-01372-9 ↗
10. 10.1021/jacs.2c0762510.1021/jacs.2c07625 ↗
11. 10.1016/j.bios.2021.11354110.1016/j.bios.2021.113541 ↗
12. 10.1038/s41467-024-50243-x10.1038/s41467-024-50243-x ↗
13. 10.1021/acsami.2c1449710.1021/acsami.2c14497 ↗
14. 10.1016/j.cell.2020.12.00110.1016/j.cell.2020.12.001 ↗
15. 10.1073/pnas.201473911710.1073/pnas.2014739117 ↗
16. 10.1038/s41551-020-00654-010.1038/s41551-020-00654-0 ↗
17. 10.1002/advs.20231006610.1002/advs.202310066 ↗
18. 10.1016/j.xcrm.2022.10076510.1016/j.xcrm.2022.100765 ↗
19. 10.1016/j.xcrm.2023.10125210.1016/j.xcrm.2023.101252 ↗
20. 10.1021/acsami.9b1776310.1021/acsami.9b17763 ↗
21. 10.1021/acsami.3c1188910.1021/acsami.3c11889 ↗
22. 10.1021/acsnano.4c0365010.1021/acsnano.4c03650 ↗
23. 10.1021/acssensors.0c0009010.1021/acssensors.0c00090 ↗
24. 10.1016/j.snb.2024.13616010.1016/j.snb.2024.136160 ↗
25. 10.1016/j.cej.2024.15473610.1016/j.cej.2024.154736 ↗
26. 10.1002/smll.20230061710.1002/smll.202300617 ↗
27. 10.1002/smtd.20240003010.1002/smtd.202400030 ↗
28. 10.1016/j.snb.2022.13221410.1016/j.snb.2022.132214 ↗
29. 10.1021/acs.analchem.6b0156610.1021/acs.analchem.6b01566 ↗
30. 10.1021/acs.analchem.8b0566810.1021/acs.analchem.8b05668 ↗
31. 10.1021/acsnano.0c0877210.1021/acsnano.0c08772 ↗
32. 10.1038/s41587-020-0513-410.1038/s41587-020-0513-4 ↗
33. 10.3389/fbioe.2022.84568810.3389/fbioe.2022.845688 ↗
34. 10.1016/j.snb.2022.13274610.1016/j.snb.2022.132746 ↗
35. 10.1007/s10544-023-00675-x10.1007/s10544-023-00675-x ↗