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Deng A, Mao Z, Jin X, Lv W, Huang L, Zhong H, Wang S, Shi Y, Zhou T, Zhao J, Huang Q, Luo X, Ma L, Zou H, Fu R, Huang G. ID-CRISPR: A CRISPR/Cas12a platform for label-free and sensitive detection of rare mutant alleles using self-interference DNA hydrogel reporter. Biosens Bioelectron 2025; 278:117309. [PMID: 40020637 DOI: 10.1016/j.bios.2025.117309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2024] [Revised: 02/16/2025] [Accepted: 02/23/2025] [Indexed: 03/03/2025]
Abstract
Accurate and sensitive detection of single nucleotide variants (SNVs) is paramount for cancer diagnosis and treatment. The CRISPR/Cas12a system shows promise for SNV detection due to its high sensitivity and single-base specificity. However, most CRISPR/Cas12a-based methods rely on F/Q-labeled single-stranded DNA (ssDNA) reporters, which are susceptible to fluorescence fluctuations, thereby reducing accuracy. To address these limitations, researchers have proposed using DNA hydrogels as signal transducers in CRISPR/Cas12a systems. Yet, the encapsulation of indicators into DNA hydrogels introduces additional instability, which could compromise both detection sensitivity and linearity. In this study, we integrated hyperspectral interferometry into a DNA hydrogel-based CRISPR/Cas12a detection platform (ID-CRISPR) to achieve sensitive label-free SNV detection. Using EGFR L858R SNV as a model target, we demonstrated that ID-CRISPR can detect mutant allele frequencies (MAFs) as low as 0.1% with a limit of detection (LOD) of 5 aM, while also showing its potential for quantifying SNV abundance. Its clinical utility was confirmed through analysis of lung tumor samples, with results consistent with sequencing data. Therefore, ID-CRISPR provides a sensitive, label-free, and user-friendly platform for SNV detection, offering new insights into combining optical sensing with DNA hydrogel technology in CRISPR/Cas assays.
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Affiliation(s)
- Anni Deng
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Zeyin Mao
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Xiangyu Jin
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Wenqi Lv
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Leyang Huang
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Hao Zhong
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Shihong Wang
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Yixuan Shi
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Tianqi Zhou
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Jianxin Zhao
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Qin Huang
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Xianbo Luo
- National Engineering Research Center for Beijing Biochip Technology, Beijing, 102206, China
| | - Li Ma
- National Engineering Research Center for Beijing Biochip Technology, Beijing, 102206, China
| | - Heng Zou
- Department of Respiratory Medicine, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
| | - Rongxin Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China; Engineering Research Center of Integrated Acousto-opto-electronic Microsystems (Ministry of Education of China), Beijing Institute of Technology, Beijing, 100081, China.
| | - Guoliang Huang
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China; National Engineering Research Center for Beijing Biochip Technology, Beijing, 102206, China
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2
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Yang F, Fu R, Liu Y, Dong W, Liu X, Song Y, Li G, Zhou T, Hu H, Li S, Jin X, Zhang J, Li H, Lu Y, Guan Y, Xu T, Ding H, Huang G, Xie H, Zhang S. Automated Electroosmotic Digital Optofluidics for Rapid and Label-Free Protein Detection. NANO LETTERS 2025; 25:5325-5333. [PMID: 40091223 DOI: 10.1021/acs.nanolett.5c00270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2025]
Abstract
Rapid protein detection is crucial for medical diagnosis, clinical trials, and drug development but often faces challenges in balancing sensitivity with multiplex detection, low reagent consumption, and a short detection time. In this work, we developed an automated and sensitive electroosmotic digital optofluidics (e-DOF) platform for rapid and label-free protein biomarker quantification in microliter blood samples. The hyperspectral computation reveals nanoscale morphology changes caused by target protein capture, eliminating multifarious enzyme-linked labeling. Electroosmosis-driven molecular circulation accelerates the immuno-hybridization, enhancing sensitivity (with a detection limit of 0.21 nM) and reducing the detection time to 15 min, compared to 2-3 h for a traditional enzyme-linked immunosorbent assay. In multiplex detection of hepatitis A and E IgM in 17 clinical samples, the results were completely consistent with clinical trial outcomes. This e-DOF system presents an automated, rapid, and label-free platform for multiplex detection in microliter samples, highlighting potential applications in clinical diagnosis and immunoassay research.
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Affiliation(s)
- Fan Yang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10081, China
| | - Rongxin Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Engineering Research Center of Integrated Acousto-optoelectronic Microsystems (Ministry of Education of China), Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Beijing 100081, China
- Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou 450000, China
| | - Yitong Liu
- Department of Chemistry, University of Toronto, Toronto, ON M5S 3H6, Canada
| | - Wenbo Dong
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10081, China
| | - Xuekai Liu
- Clinical laboratory, Aerospace Center Hospital, Beijing 100049, China
| | - Yan Song
- Clinical laboratory, Aerospace Center Hospital, Beijing 100049, China
| | - Gong Li
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Tianqi Zhou
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Hanqi Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Shanglin Li
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Xiangyu Jin
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Jiangjiang Zhang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Hang Li
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Engineering Research Center of Integrated Acousto-optoelectronic Microsystems (Ministry of Education of China), Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Beijing 100081, China
- Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou 450000, China
| | - Yao Lu
- Engineering Research Center of Integrated Acousto-optoelectronic Microsystems (Ministry of Education of China), Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Beijing 100081, China
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Yanfang Guan
- School of Mechanical and Electrical Engineering, Henan University of Technology, Henan 450052, China
| | - Tianming Xu
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - He Ding
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Guoliang Huang
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Huikai Xie
- Engineering Research Center of Integrated Acousto-optoelectronic Microsystems (Ministry of Education of China), Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Beijing 100081, China
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
| | - Shuailong Zhang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 10081, China
- Engineering Research Center of Integrated Acousto-optoelectronic Microsystems (Ministry of Education of China), Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Beijing 100081, China
- Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou 450000, China
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, China
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3
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Zhou T, Fu R, Hou J, Yang F, Chai F, Mao Z, Deng A, Li F, Guan Y, Hu H, Li H, Lu Y, Huang G, Zhang S, Xie H. Self-Interference Digital Optofluidic Genotyping for Integrated and Automated Label-Free Pathogen Detection. ACS Sens 2024; 9:6411-6420. [PMID: 39561298 DOI: 10.1021/acssensors.4c01520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2024]
Abstract
Pathogen, prevalent in both natural and human environments, cause approximately 4.95 million deaths annually, ranking them among the top contributors to global mortality. Traditional pathogen detection methods, reliant on microscopy and cultivation, are slow and labor-intensive and often produce subjective results. While nucleic acid amplification techniques such as polymerase chain reaction offer genetic accuracy, they necessitate costly laboratory equipment and skilled personnel. Consequently, isothermal amplification methods like recombinase polymerase amplification (RPA) have attracted interest for their rapid and straightforward operations. However, these methods face challenges in specificity and automated sample processing. In this study, we introduce a self-interferometric digital optofluidic platform incorporating asymmetric direct solid-phase RPA for real-time, label-free, and automated pathogen genotyping. By integration of digital microfluidics with a DNA monolayer detection method using hyperspectral interferometry, this platform enables rapid, specific, and sensitive pathogen detection without the need for exogenous labeling or complex procedures. The system demonstrated high sensitivity (10 CFU·mL-1), specificity (differentiating four Candida species), detection efficiency (fully automated within 50 min for Gram-negative bacteria), and throughput (simultaneous detection of four indices). This integrated approach to pathogen quantitation on a single microfluidic chip represents a significant advancement in rapid pathogen diagnostics, providing a practical solution for timely pathogen detection and analysis.
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Affiliation(s)
- Tianqi Zhou
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Rongxin Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou, Henan 450000, China
- School of Integrated Circuits and Electronics, Engineering Research Center of Integrated Acousto-Optoelectronic Microsystem (Ministry of Education of China), Beijing Institute of Technology, Beijing 100081, China
- Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Chongqing 400000, China
| | - Jialu Hou
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou, Henan 450000, China
| | - Fan Yang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Fengli Chai
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zeyin Mao
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Anni Deng
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Fenggang Li
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Yanfang Guan
- School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou, Henan 450052, China
| | - Hanqi Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Hang Li
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou, Henan 450000, China
- School of Integrated Circuits and Electronics, Engineering Research Center of Integrated Acousto-Optoelectronic Microsystem (Ministry of Education of China), Beijing Institute of Technology, Beijing 100081, China
- Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Chongqing 400000, China
| | - Yao Lu
- School of Integrated Circuits and Electronics, Engineering Research Center of Integrated Acousto-Optoelectronic Microsystem (Ministry of Education of China), Beijing Institute of Technology, Beijing 100081, China
- Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Chongqing 400000, China
| | - Guoliang Huang
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Shuailong Zhang
- Zhengzhou Research Institute, Beijing Institute of Technology, Zhengzhou, Henan 450000, China
- School of Integrated Circuits and Electronics, Engineering Research Center of Integrated Acousto-Optoelectronic Microsystem (Ministry of Education of China), Beijing Institute of Technology, Beijing 100081, China
- Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Chongqing 400000, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Huikai Xie
- School of Integrated Circuits and Electronics, Engineering Research Center of Integrated Acousto-Optoelectronic Microsystem (Ministry of Education of China), Beijing Institute of Technology, Beijing 100081, China
- Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Chongqing 400000, China
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Su Y, Jin X, Yang F, Liu X, Li F, Zhao Q, Hou J, Zhang S, Li H, Huang G, Fu R. A compact microfluidic platform for rapid multiplex detection of respiratory viruses via centrifugal polar-absorbance spectroscopy. Talanta 2024; 280:126733. [PMID: 39173249 DOI: 10.1016/j.talanta.2024.126733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2024] [Revised: 08/16/2024] [Accepted: 08/17/2024] [Indexed: 08/24/2024]
Abstract
Nucleic acid detection technology has become a crucial tool in cutting-edge research within the life sciences and clinical diagnosis domains. Its significance is particularly highlighted during the respiratory virus pandemic, where nucleic acid testing plays a pivotal role in accurately detecting the virus. Isothermal amplification technologies have been developed and offer advantages such as rapidity, mild reaction conditions and excellent stability. Among these methods, recombinase polymerase amplification (RPA) has gained significant attention due to its simple primer design and resistance to multiple reaction inhibitors. However, the detection of RPA amplicons hinders the widespread adoption of this technology, leading to a research focus on cost-effective and convenient detection methods for RPA nucleic acid testing. In this study, we propose a novel computational absorption spectrum approach that utilizes the polar GelRed dye to efficiently detect RPA amplicons. By exploiting the asymmetry of GelRed molecules upon binding with DNA, polar electric dipoles are formed, leading to precipitate formation through centrifugal vibration and electrostatic interaction. The quantification of amplicon content is achieved by measuring the residual GelRed concentration in the supernatant. Our proposed portable and integrated microfluidic device successfully detected five respiratory virus genes simultaneously. The optimized linear detection was achieved and the sensitivity for all the targets reached 100 copies/μL. The total experiment could be finished in 27 min. The clinical experiments demonstrated the practicality and accuracy. This cost-effective and convenient detection scheme presents a promising biosensor for rapid virus detection, contributing to the advancement of RPA technology.
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Affiliation(s)
- Ya Su
- School of Medical Technology, Zhengzhou Academy of Intelligent Technology, Beijing Institute of Technology, Beijing, 100081, China; School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Xiangyu Jin
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Fan Yang
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 10081, China
| | - Xuekai Liu
- Clinical laboratory, Aerospace Center Hospital, Beijing, 100049, China
| | - Fenggang Li
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 10081, China
| | - Qingchen Zhao
- School of Medical Technology, Zhengzhou Academy of Intelligent Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Jialu Hou
- School of Medical Technology, Zhengzhou Academy of Intelligent Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Shuailong Zhang
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, 100081, China; Engineering Research Center of Integrated Acousto-opto-electronic Microsystems (Ministry of Education of China), Beijing Institute of Technology, Beijing, 100081, China; Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Chongqing, 400000, China
| | - Hang Li
- School of Medical Technology, Zhengzhou Academy of Intelligent Technology, Beijing Institute of Technology, Beijing, 100081, China; Engineering Research Center of Integrated Acousto-opto-electronic Microsystems (Ministry of Education of China), Beijing Institute of Technology, Beijing, 100081, China
| | - Guoliang Huang
- School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
| | - Rongxin Fu
- School of Medical Technology, Zhengzhou Academy of Intelligent Technology, Beijing Institute of Technology, Beijing, 100081, China; Engineering Research Center of Integrated Acousto-opto-electronic Microsystems (Ministry of Education of China), Beijing Institute of Technology, Beijing, 100081, China; Chongqing Institute of Microelectronics and Microsystems, Beijing Institute of Technology, Chongqing, 400000, China.
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5
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Mao Z, Deng A, Jin X, Zhou T, Zhang S, Li M, Lv W, Huang L, Zhong H, Wang S, Shi Y, Zhang L, Liao Q, Fu R, Huang G. Highly Specific and Rapid Multiplex Identification of Candida Species Using Digital Microfluidics Integrated with a Semi-Nested Genoarray. Anal Chem 2024; 96:18797-18805. [PMID: 39548967 DOI: 10.1021/acs.analchem.4c04265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2024]
Abstract
Candida species are the most common cause of fungal infections around the world, associated with superficial and even deep-seated infections. In clinical practice, there is great significance in identifying different Candida species because of their respective characteristics. However, current technologies have difficulty in onsite species identification due to long turnover time, high cost of reagents and instruments, or limited detection performance. We developed a semi-nested recombinase polymerase amplification (RPA) genoarray as well as an integrated system for highly specific identification of four Candida species with a simple design of primers, high detection sensitivity, fast turnover time, and good cost-effectiveness. The system constructed to perform the assay consists of a rapid sample processing module for nucleic acid release from fungal samples in 15 min and a digital microfluidic platform for precise and efficient detection reactions in 35 min. Therefore, our system could automatically identify specific Candida species, with a reagent consumption of only 2.5 μL of the RPA reaction mixture per target and no cross-reaction. Its detection sensitivity for four Candida species achieved 101-102 CFU/mL, which was 10-fold better than conventional RPA and even comparable to a common polymerase chain reaction. Evaluated by using cultured samples and 24 clinical samples, our system shows great applicability to onsite multiplex nucleic acid analysis.
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Affiliation(s)
- Zeyin Mao
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Anni Deng
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Xiangyu Jin
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Tianqi Zhou
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Shuailong Zhang
- School of Integrated Circuits and Electronics, Zhengzhou Research Institute, Beijing Institute of Technology, Beijing 100081, China
- Engineering Research Center of Integrated Acousto-opto-electronic Microsystems (Ministry of Education of China), Beijing Institute of Technology, Beijing 100081, China
| | - Meng Li
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - Wenqi Lv
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Leyang Huang
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Hao Zhong
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Shihong Wang
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Yixuan Shi
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Lei Zhang
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - Qinping Liao
- Department of Obstetrics and Gynecology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
| | - Rongxin Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Engineering Research Center of Integrated Acousto-opto-electronic Microsystems (Ministry of Education of China), Beijing Institute of Technology, Beijing 100081, China
| | - Guoliang Huang
- School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
- National Engineering Research Center for Beijing Biochip Technology, Beijing 102206, China
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Liu J, Fu R, Zhang S, Hou J, Ma H, Hu S, Li H, Zhang Y, Wang W, Qiao B, Zang B, Min X, Zhang F, Du J, Yan S. Rapid and multi-target genotyping of Helicobacter pylori with digital microfluidics. Biosens Bioelectron 2024; 256:116282. [PMID: 38626615 DOI: 10.1016/j.bios.2024.116282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/18/2024] [Accepted: 04/08/2024] [Indexed: 04/18/2024]
Abstract
Helicobacter pylori (H. pylori) infection correlates closely with gastric diseases such as gastritis, ulcers, and cancer, influencing more than half of the world's population. Establishing a rapid, precise, and automated platform for H. pylori diagnosis is an urgent clinical need and would significantly benefit therapeutic intervention. Recombinase polymerase amplification (RPA)-CRISPR recently emerged as a promising molecular diagnostic assay due to its rapid detection capability, high specificity, and mild reaction conditions. In this work, we adapted the RPA-CRISPR assay on a digital microfluidics (DMF) system for automated H. pylori detection and genotyping. The system can achieve multi-target parallel detection of H. pylori nucleotide conservative genes (ureB) and virulence genes (cagA and vacA) across different samples within 30 min, exhibiting a detection limit of 10 copies/rxn and no false positives. We further conducted tests on 80 clinical saliva samples and compared the results with those derived from real-time quantitative polymerase chain reaction, demonstrating 100% diagnostic sensitivity and specificity for the RPA-CRISPR/DMF method. By automating the assay process on a single chip, the DMF system can significantly reduce the usage of reagents and samples, minimize the cross-contamination effect, and shorten the reaction time, with the additional benefit of losing the chance of experiment failure/inconsistency due to manual operations. The DMF system together with the RPA-CRISPR assay can be used for early detection and genotyping of H. pylori with high sensitivity and specificity, and has the potential to become a universal molecular diagnostic platform.
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Affiliation(s)
- Jinsong Liu
- Department of Laboratory Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, 563006, China; College of Laboratory Medicine, Zunyi Medical University, Zunyi, 563000, China
| | - Rongxin Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China; Engineering Research Center of Integrated Acousto-opto-electronic Microsystems, Ministry of Education of China, Beijing, 100081, China
| | - Shuailong Zhang
- School of Integrated Circuits and Electronic, Beijing Institute of Technology, Beijing, 100081, China; Engineering Research Center of Integrated Acousto-opto-electronic Microsystems, Ministry of Education of China, Beijing, 100081, China.
| | - Jialu Hou
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Hanbin Ma
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Siyi Hu
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Hang Li
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China; Engineering Research Center of Integrated Acousto-opto-electronic Microsystems, Ministry of Education of China, Beijing, 100081, China
| | - Yanli Zhang
- Department of Gastroenterology, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Weian Wang
- Department of Gastroenterology, The Third Medical Center of People's Liberation Army (PLA) General Hospital, Beijing, 100039, China
| | - Bokang Qiao
- Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China; Beijing Institute of Heart, Lung and Vascular Diseases, Key Laboratory of Remodeling-Related Cardiovascular Diseases, Beijing, 100029, China
| | - Baisheng Zang
- Zhejiang Anji GeneDetective Medical Technology Co. Ltd., Anji, 313300, China
| | - Xun Min
- Department of Laboratory Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, 563006, China; College of Laboratory Medicine, Zunyi Medical University, Zunyi, 563000, China
| | - Feng Zhang
- Department of Laboratory Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, 563006, China; College of Laboratory Medicine, Zunyi Medical University, Zunyi, 563000, China
| | - Jie Du
- Beijing Anzhen Hospital, Capital Medical University, Beijing, 100029, China; Beijing Institute of Heart, Lung and Vascular Diseases, Key Laboratory of Remodeling-Related Cardiovascular Diseases, Beijing, 100029, China.
| | - Shengkai Yan
- Department of Laboratory Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, 563006, China; College of Laboratory Medicine, Zunyi Medical University, Zunyi, 563000, China.
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7
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Zhang J, Li Z, Guo C, Guan X, Avery L, Banach D, Liu C. Intrinsic RNA Targeting Triggers Indiscriminate DNase Activity of CRISPR-Cas12a. Angew Chem Int Ed Engl 2024; 63:e202403123. [PMID: 38516796 PMCID: PMC11073899 DOI: 10.1002/anie.202403123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 03/23/2024]
Abstract
The CRISPR-Cas12a system has emerged as a powerful tool for next-generation nucleic acid-based molecular diagnostics. However, it has long been believed to be effective only on DNA targets. Here, we investigate the intrinsic RNA-enabled trans-cleavage activity of AsCas12a and LbCas12a and discover that they can be directly activated by full-size RNA targets, although LbCas12a exhibits weaker trans-cleavage activity than AsCas12a on both single-stranded DNA and RNA substrates. Remarkably, we find that the RNA-activated Cas12a possesses higher specificity in recognizing mutated target sequences compared to DNA activation. Based on these findings, we develop the "Universal Nuclease for Identification of Virus Empowered by RNA-Sensing" (UNIVERSE) assay for nucleic acid testing. We incorporate a T7 transcription step into this assay, thereby eliminating the requirement for a protospacer adjacent motif (PAM) sequence in the target. Additionally, we successfully detect multiple PAM-less targets in HIV clinical samples that are undetectable by the conventional Cas12a assay based on double-stranded DNA activation, demonstrating unrestricted target selection with the UNIVERSE assay. We further validate the clinical utility of the UNIVERSE assay by testing both HIV RNA and HPV 16 DNA in clinical samples. We envision that the intrinsic RNA targeting capability may bring a paradigm shift in Cas12a-based nucleic acid detection and further enhance the understanding of CRISPR-Cas biochemistry.
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Affiliation(s)
- Jiongyu Zhang
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, Connecticut 06030, United States
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Ziyue Li
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, Connecticut 06030, United States
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Chong Guo
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, Connecticut 06030, United States
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Xin Guan
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, Connecticut 06030, United States
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Lori Avery
- Department of Pathology and Laboratory Medicine, University of Connecticut Health Center, Farmington, Connecticut 06030, United States
| | - David Banach
- Department of Medicine, Division of Infectious Diseases, University of Connecticut Health Center, Farmington, Connecticut 06030, United States
| | - Changchun Liu
- Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, Connecticut 06030, United States
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Feng X, Liu Y, Zhao Y, Sun Z, Xu N, Zhao C, Xia W. Recombinase Polymerase Amplification-Based Biosensors for Rapid Zoonoses Screening. Int J Nanomedicine 2023; 18:6311-6331. [PMID: 37954459 PMCID: PMC10637217 DOI: 10.2147/ijn.s434197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 10/21/2023] [Indexed: 11/14/2023] Open
Abstract
Recent, outbreaks of new emergency zoonotic diseases have prompted an urgent need to develop fast, accurate, and portable screening assays for pathogen infections. Recombinase polymerase amplification (RPA) is sensitive and specific and can be conducted at a constant low temperature with a short response time, making it especially suitable for on-site screening and making it a powerful tool for preventing or controlling the spread of zoonoses. This review summarizes the design principles of RPA-based biosensors as well as various signal output or readout technologies involved in fluorescence detection, lateral flow assays, enzymatic catalytic reactions, spectroscopic techniques, electrochemical techniques, chemiluminescence, nanopore sequencing technologies, microfluidic digital RPA, and clustered regularly interspaced short palindromic repeats/CRISPR-associated systems. The current status and prospects of the application of RPA-based biosensors in zoonoses screening are highlighted. RPA-based biosensors demonstrate the advantages of rapid response, easy-to-read result output, and easy implementation for on-site detection, enabling development toward greater portability, automation, and miniaturization. Although there are still problems such as high cost with unstable signal output, RPA-based biosensors are increasingly becoming one of the most important means of on-site pathogen screening in complex samples involving environmental, water, food, animal, and human samples for controlling the spread of zoonotic diseases.
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Affiliation(s)
- Xinrui Feng
- College of Public Health, Jilin Medical University, Jilin, 132013, People’s Republic of China
- Medical College, Yanbian University, Yanji, 136200, People’s Republic of China
| | - Yan Liu
- College of Public Health, Jilin Medical University, Jilin, 132013, People’s Republic of China
| | - Yang Zhao
- Department of Emergency and Intensive Medicine, No. 965 Hospital of PLA Joint Logistic Support Force, Jilin, 132013, People’s Republic of China
| | - Zhe Sun
- College of Public Health, Jilin Medical University, Jilin, 132013, People’s Republic of China
- College of Medical Technology, Beihua University, Jilin, 132013, People’s Republic of China
| | - Ning Xu
- State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun, 130062, People’s Republic of China
| | - Chen Zhao
- College of Public Health, Jilin Medical University, Jilin, 132013, People’s Republic of China
| | - Wei Xia
- College of Medical Technology, Beihua University, Jilin, 132013, People’s Republic of China
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Zheng T, Li X, Si Y, Wang M, Zhou Y, Yang Y, Liang N, Ying B, Wu P. Specific lateral flow detection of isothermal nucleic acid amplicons for accurate point-of-care testing. Biosens Bioelectron 2023; 222:114989. [PMID: 36538868 DOI: 10.1016/j.bios.2022.114989] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/01/2022] [Accepted: 12/04/2022] [Indexed: 12/23/2022]
Abstract
For point-of-care testing (POCT), coupling isothermal nucleic acid amplification schemes (e.g., recombinase polymerase amplification, RPA) with lateral flow assay (LFA) readout is an ideal platform, since such integration offers both high sensitivity and deployability. However, isothermal schemes typically suffers from non-specific amplification, which is difficult to be differentiated by LFA and thus results in false-positives. Here, we proposed an accurate POCT platform by specific recognition of target amplicons with peptide nucleic acid (PNA, assisted by T7 Exonuclease), which could be directly plugged into the existing RPA kits and commercial LFA test strips. With SARS-CoV-2 as the model, the proposed method (RPA-TeaPNA-LFA) efficiently eliminated the false-positives, exhibiting a lowest detection concentration of 6.7 copies/μL of RNA and 90 copies/μL of virus. Using dual-gene (orf1ab and N genes of SARS-CoV-2) as the targets, RPA-TeaPNA-LFA offered a high specificity (100%) and sensitivity (RT-PCR Ct < 31, 100%; Ct < 40, 71.4%), and is valuable for on-site screening or self-testing during isolation. In addition, the dual test lines in the test strips were successfully explored for simultaneous detection of SARS-CoV-2 and H1N1, showing great potential in response to future pathogen-based pandemics.
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Affiliation(s)
- Ting Zheng
- Analytical & Testing Center, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610064, China
| | - Xianming Li
- Analytical & Testing Center, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610064, China
| | - Yanjun Si
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Minjin Wang
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Yuzhen Zhou
- Chengdu Center for Disease Control and Prevention, Chengdu, 610041, China
| | - Yusheng Yang
- Chengdu Center for Disease Control and Prevention, Chengdu, 610041, China
| | - Na Liang
- Chengdu Center for Disease Control and Prevention, Chengdu, 610041, China
| | - Binwu Ying
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Peng Wu
- Analytical & Testing Center, State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610064, China; School of Chemistry and Chemical Engineering, Henan Normal University, Xinxiang, 453007, China.
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Zou X, Dong C, Ni Y, Gao Q. Rapid Detection of Strawberry Mild Yellow Edge Virus with a Lateral Flow Strip Reverse Transcription Recombinase Polymerase Amplification Assay. Curr Microbiol 2022; 79:365. [PMID: 36253613 DOI: 10.1007/s00284-022-03045-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 09/16/2022] [Indexed: 11/03/2022]
Abstract
Strawberry mild yellow edge virus (SMYEV) is a latent virus that severely affects the yield and quality of strawberry fruit. The technology suitable for rapid and accurate detection of SMYEV on site is important to effectively control its spread. In this study, a reverse transcription recombinase polymerase amplification combined with lateral flow strip (SMYEV-RT-RPA-LF), targeting the conserved genome of Beijing SMYEV isolate, was established to diagnose SMYEV in strawberries. The SMYEV-RT-RPA-LF assay showed no cross-reaction with other strawberry viruses. The sensitivity of SMYEV-RT-RPA-L assay was 100 times higher than that of RT-PCR (10 pg/μL). In addition, through the detection of suspected samples in the field, it was found that the accuracy of SMYEV-RT-RPA-L assay was consistent with the RT-PCR results. However, compared with RT-PCR, SMYEV-RT-RPA-LF assay has the advantages of simple operation, time savings, and high specificity and sensitivity, indicating the potential application of SMYEV-RT-RPA-LF in the rapid field diagnosis of SMYEV.
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Affiliation(s)
- Xiaohua Zou
- Shanghai Key Laboratory of Protected Horticultural Technology, Forestry and Fruit Tree Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 201403, China.
| | - Chao Dong
- Shanghai Key Laboratory of Protected Horticultural Technology, Forestry and Fruit Tree Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 201403, China
| | - Yiduo Ni
- Shanghai Key Laboratory of Protected Horticultural Technology, Forestry and Fruit Tree Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 201403, China
| | - Qinghua Gao
- Shanghai Key Laboratory of Protected Horticultural Technology, Forestry and Fruit Tree Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 201403, China.
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