1
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Chen X, Peng R, Wang RQ, Du K. Sheath-enhanced concentration and on-chip detection of bacteria from an extremely low-concentration level. LAB ON A CHIP 2024. [PMID: 39688032 DOI: 10.1039/d4lc00698d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2024]
Abstract
Microfluidic-based sheath flow focusing methods have been widely used for efficiently isolating, concentrating, and detecting pathogenic bacteria for various biomedical applications due to their enhanced sensitivity and exceptional integration. However, such a microfluidic device usually needs complicated device fabrication and sample dilution, hampering the efficient and sensitive identification of target bacteria. In this study, we develop and fabricate a sheath-assisted and pneumatic-induced nano-sieve device for achieving the improved on-chip concentration and sensitive detection of Staphylococcus aureus (MRSA). The optimized nanochannel design with diverging configuration is beneficial to the regulation of the hydrodynamic flow while the sheath flow is focusing the sample to the confined region as expected. Per the experimental finding, a high flow ratio (sheath flow/sample flow) presents enhanced target concentration by comparing with a low flow ratio. With this setup, MRSA bacteria with an extremely low concentration of ∼100 CFU mL-1 are successfully and sensitively detected under a fluorescence microscope, less than 30 min, demonstrating a reliable sheath-enhanced concentration and on-chip detection for target bacteria. Additionally, the theoretical model introduced here further rationalizes the working principle of our nano-sieve device, potentially guiding the optimization of next generation devices for highly sensitive and accurate on-chip bacteria detection at a much lower concentration level below 100 CFU mL-1.
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Affiliation(s)
- Xinye Chen
- Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA 92507, USA.
| | - Ruonan Peng
- Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA 92507, USA.
| | - Ruo-Qian Wang
- Department of Civil and Environmental Engineering, Rutgers, The State University of New Jersey, New Brunswick, NJ 08854, USA
| | - Ke Du
- Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA 92507, USA.
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2
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Fang M, Wang Y, Yang T, Zhang J, Yu H, Luo Z, Su B, Lin X. Nucleic Acid Plate Culture: Label-Free and Naked-Eye-Based Digital Loop-Mediated Isothermal Amplification in Hydrogel with Machine Learning. ACS Sens 2024; 9:2010-2019. [PMID: 38602267 DOI: 10.1021/acssensors.3c02807] [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: 04/12/2024]
Abstract
Digital nucleic acid amplification enables the absolute quantification of single molecules. However, due to the ultrasmall reaction volume in the digital system (i.e., short light path), most digital systems are limited to fluorescence signals, while label-free and naked-eye readout remain challenging. In this work, we report a digital nucleic acid plate culture method for label-free, ultrasimple, and naked-eye nucleic acid analysis. As simple as the bacteria culture, the nanoconfined digital loop-mediated isothermal amplification was performed by using polyacrylamide (PAM) hydrogel as the amplification matrix. The nanoconfinement of PAM hydrogel with an ionic polymer chain can remarkably accelerate the amplification of target nucleic acids and the growth of inorganic byproducts, namely, magnesium pyrophosphate particles (MPPs). Compared to that in aqueous solutions, MPPs trapped in the hydrogel with enhanced light scattering characteristics are clearly visible to the naked eye, forming white "colony" spots that can be simply counted in a label-free and instrument-free manner. The MPPs can also be photographed by a smartphone and automatically counted by a machine-learning algorithm to realize the absolute quantification of antibiotic-resistant pathogens in diverse real samples.
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Affiliation(s)
- Mei Fang
- College of Biosystems Engineering and Food Science, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China
| | - Yiru Wang
- College of Biosystems Engineering and Food Science, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China
| | - Tao Yang
- College of Biosystems Engineering and Food Science, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China
| | - Jing Zhang
- Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
| | - Hanry Yu
- Critical Analytics for Manufacturing Personalized Medicine Interdisciplinary Research Group, Singapore-MIT Alliance for Research and Technology, Singapore 138602, Singapore
| | - Zisheng Luo
- College of Biosystems Engineering and Food Science, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China
- Ningbo Innovation Center, Zhejiang University, Ningbo 315100, China
| | - Bin Su
- Institute of Analytical Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, China
| | - Xingyu Lin
- College of Biosystems Engineering and Food Science, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China
- Ningbo Innovation Center, Zhejiang University, Ningbo 315100, China
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3
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Xue P, Peng Y, Wang R, Wu Q, Chen Q, Yan C, Chen W, Xu J. Advances, challenges, and opportunities for food safety analysis in the isothermal nucleic acid amplification/CRISPR-Cas12a era. Crit Rev Food Sci Nutr 2024:1-16. [PMID: 38659323 DOI: 10.1080/10408398.2024.2343413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Global food safety stands out as a prominent public concern, affecting populations worldwide. The recurrent challenge of food safety incidents reveals the need for a robust inspection framework. In recent years, the integration of isothermal nucleic acid amplification with CRISPR-Cas12a techniques has emerged as a promising tool for molecular detection of food hazards, presenting next generation of biosensing for food safety detection. This paper provides a comprehensive review of the current state of research on the synergistic application of isothermal nucleic acid amplification and CRISPR-Cas12a technology in the field of food safety. This innovative combination not only enriches the analytical tools, but also improving assay performance such as sensitivity and specificity, addressing the limitations of traditional methods. The review summarized various detection methodologies by the integration of isothermal nucleic acid amplification and CRISPR-Cas12a technology for diverse food safety concerns, including pathogenic bacterium, viruses, mycotoxins, food adulteration, and genetically modified foods. Each section elucidates the specific strategies employed and highlights the advantages conferred. Furthermore, the paper discussed the challenges faced by this technology in the context of food safety, offering insightful discussions on potential solutions and future prospects.
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Affiliation(s)
- Pengpeng Xue
- Engineering Research Center of Bio-process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, P. R. China
| | - Yubo Peng
- Engineering Research Center of Bio-process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, P. R. China
| | - Renjing Wang
- Engineering Research Center of Bio-process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, P. R. China
| | - Qian Wu
- Engineering Research Center of Bio-process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, P. R. China
| | - Qi Chen
- Engineering Research Center of Bio-process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, P. R. China
| | - Chao Yan
- School of Life Science, Anhui University, Hefei, P. R. China
| | - Wei Chen
- Engineering Research Center of Bio-process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, P. R. China
| | - Jianguo Xu
- Engineering Research Center of Bio-process, Ministry of Education, School of Food and Biological Engineering, Hefei University of Technology, Hefei, P. R. China
- Jiaxing Key Laboratory of Molecular Recognition and Sensing, College of Biological, Chemical Sciences and Engineering, Jiaxing University, Zhejiang, P. R. China
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4
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Jin M, Ding J, Zhou Y, Chen J, Wang Y, Li Z. StratoLAMP: Label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of precipitate. Proc Natl Acad Sci U S A 2024; 121:e2314030121. [PMID: 38165933 PMCID: PMC10786297 DOI: 10.1073/pnas.2314030121] [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: 08/14/2023] [Accepted: 12/05/2023] [Indexed: 01/04/2024] Open
Abstract
Multiplex, digital nucleic acid detections have important biomedical applications, but the multiplexity of existing methods is predominantly achieved using fluorescent dyes or probes, making the detection complicated and costly. Here, we present the StratoLAMP for label-free, multiplex digital loop-mediated isothermal amplification based on visual stratification of the precipitate byproduct. The StratoLAMP designates two sets of primers with different concentrations to achieve different precipitate yields when amplifying different nucleic acid targets. In the detection, deep learning image analysis is used to stratify the precipitate within each droplet and determine the encapsulated targets for nucleic acid quantification. We investigated the effect of the amplification reagents and process on the precipitate generation and optimized the assay conditions. We then implemented a deep-learning image analysis pipeline for droplet detection, achieving an overall accuracy of 94.3%. In the application, the StratoLAMP successfully achieved the simultaneous quantification of two nucleic acid targets with high accuracy. By eliminating the need for fluorescence, StratoLAMP represents a unique concept toward label-free, multiplex nucleic acid assays and an analytical tool with great cost-effectiveness.
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Affiliation(s)
- Meichi Jin
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
| | - Jingyi Ding
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
| | - Yu Zhou
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
- Smart Medical Imaging, Learning and Engineering Lab, Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
| | - Jiazhao Chen
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
- Smart Medical Imaging, Learning and Engineering Lab, Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
| | - Yi Wang
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
- Smart Medical Imaging, Learning and Engineering Lab, Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
| | - Zida Li
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen518060, China
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5
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Yang C, Gan X, Zeng Y, Xu Z, Xu L, Hu C, Ma H, Chai B, Hu S, Chai Y. Advanced design and applications of digital microfluidics in biomedical fields: An update of recent progress. Biosens Bioelectron 2023; 242:115723. [PMID: 37832347 DOI: 10.1016/j.bios.2023.115723] [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: 07/05/2023] [Revised: 09/11/2023] [Accepted: 09/29/2023] [Indexed: 10/15/2023]
Abstract
Significant breakthroughs have been made in digital microfluidic (DMF)-based technologies over the past decades. DMF technology has attracted great interest in bioassays depending on automatic microscale liquid manipulations and complicated multi-step processing. In this review, the recent advances of DMF platforms in the biomedical field were summarized, focusing on the integrated design and applications of the DMF system. Firstly, the electrowetting-on-dielectric principle, fabrication of DMF chips, and commercialization of the DMF system were elaborated. Then, the updated droplets and magnetic beads manipulation strategies with DMF were explored. DMF-based biomedical applications were comprehensively discussed, including automated sample preparation strategies, immunoassays, molecular diagnosis, blood processing/testing, and microbe analysis. Emerging applications such as enzyme activity assessment and DNA storage were also explored. The performance of each bioassay was compared and discussed, providing insight into the novel design and applications of the DMF technology. Finally, the advantages, challenges, and future trends of DMF systems were systematically summarized, demonstrating new perspectives on the extensive applications of DMF in basic research and commercialization.
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Affiliation(s)
- Chengbin Yang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
| | - Xiangyu Gan
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
| | - Yuping Zeng
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
| | - Zhourui Xu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
| | - Longqian Xu
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
| | - Chenxuan Hu
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
| | - Hanbin Ma
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China; Guangdong ACXEL Micro & Nano Tech Co., Ltd, Foshan, China.
| | - Bao Chai
- Department of Dermatology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China; Department of Dermatology, The 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China.
| | - Siyi Hu
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
| | - Yujuan Chai
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
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6
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Jiang L, Lan X, Ren L, Jin Z, Shan X, Yang M, Chang L. Single-molecule RNA capture-assisted droplet digital loop-mediated isothermal amplification for ultrasensitive and rapid detection of infectious pathogens. MICROSYSTEMS & NANOENGINEERING 2023; 9:118. [PMID: 37767528 PMCID: PMC10519972 DOI: 10.1038/s41378-023-00576-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/27/2023] [Accepted: 06/16/2023] [Indexed: 09/29/2023]
Abstract
To minimize and control the transmission of infectious diseases, a sensitive, accurate, rapid, and robust assay strategy for application on-site screening is critical. Here, we report single-molecule RNA capture-assisted digital RT-LAMP (SCADL) for point-of-care testing of infectious diseases. Target RNA was captured and enriched by specific capture probes and oligonucleotide probes conjugated to magnetic beads, replacing laborious RNA extraction. Droplet generation, amplification, and the recording of results are all integrated on a microfluidic chip. In assaying commercial standard samples, quantitative results precisely corresponded to the actual concentration of samples. This method provides a limit of detection of 10 copies mL-1 for the N gene within 1 h, greatly reducing the need for skilled personnel and precision instruments. The ultrasensitivity, specificity, portability, rapidity and user-friendliness make SCADL a competitive candidate for the on-site screening of infectious diseases.
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Affiliation(s)
- Liying Jiang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, 450002 Zhengzhou, China
- Academy for Quantum Science and Technology, Zhengzhou University of Light Industry, 450002 Zhengzhou, China
| | - Xianghao Lan
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, 450002 Zhengzhou, China
| | - Linjiao Ren
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, 450002 Zhengzhou, China
| | - Zhiyuan Jin
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China
| | - Xuchen Shan
- School of Physics, Beihang University, 100191 Beijing, China
| | - Mingzhu Yang
- Beijing Research Institute of Mechanical Equipment, 100143 Beijing, China
| | - Lingqian Chang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, 100191 Beijing, China
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7
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Mao S, Zhao J, Ding X, Vuong VA, Song J, Que L. Integrated Sensing Chip for Ultrasensitive Label-Free Detection of the Products of Loop-Mediated Isothermal Amplification. ACS Sens 2023; 8:2255-2262. [PMID: 37276452 DOI: 10.1021/acssensors.3c00227] [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: 06/07/2023]
Abstract
Loop-mediated isothermal amplification (LAMP) is a nucleic acid amplification technique that has been widely used for the detection of pathogens in many organisms. Current LAMP-based sensors usually require the LAMP products to be labeled in order for them to be detected. Here, we present a novel label-free LAMP chip, which consists of a nanopore thin-film sensor embedded inside a LAMP reaction chamber. A fraction of LAMP primers is immobilized on the sensor surface, allowing the LAMP products to be synthesized and bound to the sensor surface via immobilized primers. After the LAMP reaction components are removed from the reaction chamber, the amplified LAMP products bound to the sensor surface give rise to significantly increased transducing signals, which can be measured by a portable optical spectrometer through an optical fiber probe. As a demonstration, we used the LAMP chip to detect the causal agent of late blight, Phytophthora infestans, which is one of the most devastating plant pathogens and poses a major threat to sustainable crop production worldwide. We show that this chip can detect as low as 1 fg/μL of P. infestans DNA in 30 min, which corresponds to an attomolar level of 1.6 × 10-6 attomole/μL and is at least 10 times more sensitive than the currently available methods. This label-free sensing technology holds great promise to open up a new avenue for ultrasensitive, highly specific, rapid, and cost-effective point-of-care diagnostics of plant, animal, human, and foodborne pathogens.
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Affiliation(s)
- Subin Mao
- Electrical and Computer Engineering Department, Iowa State University, Ames, Iowa 50011, United States
| | - Jinping Zhao
- Texas A&M AgriLife Research Center at Dallas, Texas A&M University System, Dallas, Texas 75252, United States
| | - Xiaoke Ding
- Electrical and Computer Engineering Department, Iowa State University, Ames, Iowa 50011, United States
| | - Van Anh Vuong
- Texas A&M AgriLife Research Center at Dallas, Texas A&M University System, Dallas, Texas 75252, United States
| | - Junqi Song
- Texas A&M AgriLife Research Center at Dallas, Texas A&M University System, Dallas, Texas 75252, United States
- Department of Plant Pathology & Microbiology, Texas A&M University, College Station, Texas 77843, United States
| | - Long Que
- Electrical and Computer Engineering Department, Iowa State University, Ames, Iowa 50011, United States
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8
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 PMCID: PMC10141994 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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9
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Wu K, Fang Q, Zhao Z, Li Z. CoID-LAMP: Color-Encoded, Intelligent Digital LAMP for Multiplex Nucleic Acid Quantification. Anal Chem 2023; 95:5069-5078. [PMID: 36892003 DOI: 10.1021/acs.analchem.2c05665] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Multiplex, digital nucleic acid tests have important biomedical applications, but existing methods mostly use fluorescent probes that are target-specific and difficult to optimize, limiting their widespread applications. Here, we report color-encoded, intelligent digital loop-mediated isothermal amplification (CoID-LAMP) for the coidentification of multiple nucleic acid targets. CoID-LAMP supplements different primer solutions with different dyes, generates primer droplets and sample droplets, and collectively pairs these two types of droplets in a microwell array device to perform LAMP. After imaging, the droplet colors were analyzed to decode the primer information, and the precipitate byproducts within droplets were detected to determine the target occupancy and calculate the concentrations. We first established an image analysis pipeline based on a deep learning algorithm for reliable droplet detection and validated the analytical performance in nucleic acid quantification. We then implemented CoID-LAMP using fluorescent dyes as the coding materials and established an 8-plex digital nucleic acid assay, confirming the reliable coding performance and the capability of multiplex nucleic acid quantification. We further implemented CoID-LAMP using brightfield dyes for a 4-plex assay, suggesting that the assay could be realized solely by brightfield imaging with minimal demand on the optics. Leveraging the advantages of droplet microfluidics in multiplexing and deep learning in intelligent image analysis, CoID-LAMP offers a useful tool for multiplex nucleic acid quantification.
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Affiliation(s)
- Kai Wu
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
| | - Qi Fang
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
| | - Zhantao Zhao
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
| | - Zida Li
- Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Department of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen 518060, China
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10
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Wu C, Liu L, Ye Z, Gong J, Hao P, Ping J, Ying Y. TriD-LAMP: A pump-free microfluidic chip for duplex droplet digital loop-mediated isothermal amplification analysis. Anal Chim Acta 2022; 1233:340513. [DOI: 10.1016/j.aca.2022.340513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/21/2022] [Accepted: 10/10/2022] [Indexed: 11/01/2022]
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11
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Chen L, Ding J, Yuan H, Chen C, Li Z. Deep-dLAMP: Deep Learning-Enabled Polydisperse Emulsion-Based Digital Loop-Mediated Isothermal Amplification. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2105450. [PMID: 35072353 PMCID: PMC8948574 DOI: 10.1002/advs.202105450] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/06/2022] [Indexed: 06/14/2023]
Abstract
Digital nucleic acid amplification tests enable absolute quantification of nucleic acids, but the generation of uniform compartments and reading of the fluorescence requires specialized instruments that are costly, limiting their widespread applications. Here, the authors report deep learning-enabled polydisperse emulsion-based digital loop-mediated isothermal amplification (deep-dLAMP) for label-free, low-cost nucleic acid quantification. deep-dLAMP performs LAMP reaction in polydisperse emulsions and uses a deep learning algorithm to segment and determine the occupancy status of each emulsion in images based on precipitated byproducts. The volume and occupancy data of the emulsions are then used to infer the nucleic acid concentration based on the Poisson distribution. deep-dLAMP can accurately predict the sizes and occupancy status of each emulsion and provide accurate measurements of nucleic acid concentrations with a limit of detection of 5.6 copies µl-1 and a dynamic range of 37.2 to 11000 copies µl-1 . In addition, deep-dLAMP shows robust performance under various parameters, such as the vortexing time and image qualities. Leveraging the state-of-the-art deep learning models, deep-dLAMP represents a significant advancement in digital nucleic acid tests by significantly reducing the instrument cost. We envision deep-dLAMP would be readily adopted by biomedical laboratories and be developed into a point-of-care digital nucleic acid test system.
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Affiliation(s)
- Linzhe Chen
- Department of Biomedical EngineeringSchool of MedicineShenzhen UniversityShenzhen518060China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound ImagingDepartment of Biomedical EngineeringSchool of MedicineShenzhen UniversityShenzhen518060China
| | - Jingyi Ding
- Department of Biomedical EngineeringSchool of MedicineShenzhen UniversityShenzhen518060China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound ImagingDepartment of Biomedical EngineeringSchool of MedicineShenzhen UniversityShenzhen518060China
| | - Hao Yuan
- School of Life Sciences and EngineeringSouthwest Jiaotong UniversityChengduSichuan610031China
| | - Chi Chen
- Department of NanoengineeringUniversity of California San DiegoLa JollaCA92093USA
| | - Zida Li
- Department of Biomedical EngineeringSchool of MedicineShenzhen UniversityShenzhen518060China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound ImagingDepartment of Biomedical EngineeringSchool of MedicineShenzhen UniversityShenzhen518060China
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12
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High-throughput selection of cells based on accumulated growth and division using PicoShell particles. Proc Natl Acad Sci U S A 2022; 119:2109430119. [PMID: 35046027 PMCID: PMC8794849 DOI: 10.1073/pnas.2109430119] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/16/2021] [Indexed: 01/19/2023] Open
Abstract
Production of high-energy lipids by microalgae may provide a sustainable energy source that can help tackle climate change. However, microalgae engineered to produce more lipids usually grow slowly, leading to reduced overall yields. Unfortunately, culture vessels used to select cells based on growth while maintaining high biomass production, such as well plates, water-in-oil droplet emulsions, and nanowell arrays, do not provide production-relevant environments that cells experience in scaled-up cultures (e.g., bioreactors or outdoor cultivation farms). As a result, strains that are developed in the laboratory may not exhibit the same beneficial phenotypic behavior when transferred to industrial production. Here, we introduce PicoShells, picoliter-scale porous hydrogel compartments, that enable >100,000 individual cells to be compartmentalized, cultured in production-relevant environments, and selected based on growth and bioproduct accumulation traits using standard flow cytometers. PicoShells consist of a hollow inner cavity where cells are encapsulated and a porous outer shell that allows for continuous solution exchange with the external environment. PicoShells allow for cell growth directly in culture environments, such as shaking flasks and bioreactors. We experimentally demonstrate that Chlorella sp., Saccharomyces cerevisiae, and Chinese hamster ovary cells, used for bioproduction, grow to significantly larger colony sizes in PicoShells than in water-in-oil droplet emulsions (P < 0.05). We also demonstrate that PicoShells containing faster dividing and growing Chlorella clonal colonies can be selected using a fluorescence-activated cell sorter and regrown. Using the PicoShell process, we select a Chlorella population that accumulates chlorophyll 8% faster than does an unselected population after a single selection cycle.
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13
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Zheng J, Cole T, Zhang Y, Kim J, Tang SY. Exploiting machine learning for bestowing intelligence to microfluidics. Biosens Bioelectron 2021; 194:113666. [PMID: 34600338 DOI: 10.1016/j.bios.2021.113666] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 02/06/2023]
Abstract
Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.
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Affiliation(s)
- Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Jeeson Kim
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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14
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Fu R, Du W, Jin X, Wang R, Lin X, Su Y, Yang H, Shan X, Lv W, Zheng Z, Huang G. Microfluidic Biosensor for Rapid Nucleic Acid Quantitation Based on Hyperspectral Interferometric Amplicon-Complex Analysis. ACS Sens 2021; 6:4057-4066. [PMID: 34694791 DOI: 10.1021/acssensors.1c01491] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Nucleic acid detection plays a vital role in both biomedical research and clinical medicine. The temperature circulation changes of the widely used polymerase chain reaction technique are time-consuming and technically challenging for system development. Recombinase polymerase amplification (RPA) is an isothermal method for rapid nucleic acid detection. However, current RPA amplicon detection methods are complicated and expensive and easily generate false positives, restricting the promotion of RPA techniques. In this work, a hyperspectral interferometric amplicon-complex quantitation method is presented, combined with asymmetric dipole complex strategy optical scattering analysis. GelRed dye was utilized to form amplicon-complex particles, and the Fourier domain spectrum computation contributed to complex scattering quantitation. With this method, a supporting microfluidic chip and automatic system were developed to achieve integrated, rapid, quantitative, and miniscule nucleic acid detection. The Plasmodium falciparum dhfr gene was utilized as an example for targeted nucleic acid quantitation and single nucleotide polymorphism detection. The total reaction time was decreased to merely 20 min, and the limit of detection was only 3.17 ng/μL. The minimum measurable concentration of target was 1.68 copies/μL, 31.67 times more sensitive than turbidity detection, and the single reaction chamber was only 9.33 μL. No scattering increase occurred for template-free control, and thus, false positives caused by primer dimers and nonspecific products could be avoided. The experimental results prove that the provided method and system can detect single-base mutations in the dhfr gene and is a reasonable technique for rapid, automatic, and low-cost nucleic acid detection.
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Affiliation(s)
- Rongxin Fu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Wenli Du
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xiangyu Jin
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Ruliang Wang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xue Lin
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Ya Su
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Han Yang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Xiaohui Shan
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Wenqi Lv
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Zhi Zheng
- Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing 100730, China
| | - Guoliang Huang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China
- National Engineering Research Center for Beijing Biochip Technology, Beijing 102206, China
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15
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Han T, Cong H, Shen Y, Yu B. Recent advances in detection technologies for COVID-19. Talanta 2021; 233:122609. [PMID: 34215093 PMCID: PMC8196236 DOI: 10.1016/j.talanta.2021.122609] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 06/10/2021] [Indexed: 12/16/2022]
Abstract
Corona Virus Disease 2019 (COVID-19) is a highly infectious respiratory illness that was caused by the SARS-CoV-2. It spread around the world in just a few months and became a worldwide pandemic. Quick and accurate diagnosis of infected patients is very important for controlling transmission. In addition to the commonly used Real-time reverse-transcription polymerase chain reaction (RT-PCR) detection techniques, other diagnostic techniques are also emerging endlessly. This article reviews the current diagnostic methods for COVID-19 and discusses their advantages and disadvantages. It provides an important reference for the diagnosis of COVID-19.
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Affiliation(s)
- Tingting Han
- Institute of Biomedical Materials and Engineering, College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Qingdao University, Qingdao, 266071, China
| | - Hailin Cong
- Institute of Biomedical Materials and Engineering, College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Qingdao University, Qingdao, 266071, China; State Key Laboratory of Bio-Fibers and Eco-Textiles, Qingdao University, Qingdao, 266071, China
| | - Youqing Shen
- Institute of Biomedical Materials and Engineering, College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Qingdao University, Qingdao, 266071, China
| | - Bing Yu
- Institute of Biomedical Materials and Engineering, College of Chemistry and Chemical Engineering, College of Materials Science and Engineering, Qingdao University, Qingdao, 266071, China; State Key Laboratory of Bio-Fibers and Eco-Textiles, Qingdao University, Qingdao, 266071, China.
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16
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Wang Y, Shah V, Lu A, Pachler E, Cheng B, Di Carlo D. Counting of enzymatically amplified affinity reactions in hydrogel particle-templated drops. LAB ON A CHIP 2021; 21:3438-3448. [PMID: 34378611 PMCID: PMC11288628 DOI: 10.1039/d1lc00344e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Counting of numerous compartmentalized enzymatic reactions underlies quantitative and high sensitivity immunodiagnostic assays. However, digital enzyme-linked immunosorbent assays (ELISA) require specialized instruments which have slowed adoption in research and clinical labs. We present a lab-on-a-particle solution to digital counting of thousands of single enzymatic reactions. Hydrogel particles are used to bind enzymes and template the formation of droplets that compartmentalize reactions with simple pipetting steps. These hydrogel particles can be made at a high throughput, stored, and used during the assay to create ∼500 000 compartments within 2 minutes. These particles can also be dried and rehydrated with sample, amplifying the sensitivity of the assay by driving affinity interactions on the hydrogel surface. We demonstrate digital counting of β-galactosidase enzyme at a femtomolar detection limit with a dynamic range of 3 orders of magnitude using standard benchtop equipment and experiment techniques. This approach can faciliate the development of digital ELISAs with reduced need for specialized microfluidic devices, instruments, or imaging systems.
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Affiliation(s)
- Yilian Wang
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
| | - Vishwesh Shah
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
| | - Angela Lu
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
| | - Ella Pachler
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
| | - Brian Cheng
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
| | - Dino Di Carlo
- Department of Bioengineering, University of California, Los Angeles, CA, USA.
- Department of Mechanical and Aerospace Engineering, California NanoSystems Institute, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
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17
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Du RS, Liu L, Ng S, Sambandam S, Hernandez Adame B, Perez H, Ha K, Falcon C, de Rutte J, Di Carlo D, Bertozzi AL. Statistical energy minimization theory for systems of drop-carrier particles. Phys Rev E 2021; 104:015109. [PMID: 34412304 DOI: 10.1103/physreve.104.015109] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 06/04/2021] [Indexed: 11/07/2022]
Abstract
Drop-carrier particles (DCPs) are solid microparticles designed to capture uniform microscale drops of a target solution without using costly microfluidic equipment and techniques. DCPs are useful for automated and high-throughput biological assays and reactions, as well as single-cell analyses. Surface energy minimization provides a theoretical prediction for the volume distribution in pairwise droplet splitting, showing good agreement with macroscale experiments. We develop a probabilistic pairwise interaction model for a system of such DCPs exchanging fluid volume to minimize surface energy. This leads to a theory for the number of pairwise interactions of DCPs needed to reach a uniform volume distribution. Heterogeneous mixtures of DCPs with different sized particles require fewer interactions to reach a minimum energy distribution for the system. We optimize the DCP geometry for minimal required target solution and uniformity in droplet volume.
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Affiliation(s)
- Ryan Shijie Du
- Department of Mathematics, University of California, Los Angeles, California, USA
| | - Lily Liu
- Department of Mathematics, University of Chicago, Chicago, Illinois, USA
| | - Simon Ng
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Sneha Sambandam
- Department of Mathematics, University of California, Los Angeles, California, USA
| | | | - Hansell Perez
- Department of Mathematics, University of California, Merced, California, USA
| | - Kyung Ha
- Department of Mathematics, University of California, Los Angeles, California, USA
| | - Claudia Falcon
- Department of Mathematics, University of California, Los Angeles, California, USA
| | - Joseph de Rutte
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Dino Di Carlo
- Department of Bioengineering, University of California, Los Angeles, California, USA.,Department of Mechanical Engineering, University of California, Los Angeles, California, USA
| | - Andrea L Bertozzi
- Department of Mathematics, University of California, Los Angeles, California, USA.,Department of Mechanical Engineering, University of California, Los Angeles, California, USA
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18
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Hairpin DNA-Mediated isothermal amplification (HDMIA) techniques for nucleic acid testing. Talanta 2021; 226:122146. [PMID: 33676697 DOI: 10.1016/j.talanta.2021.122146] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 01/21/2021] [Accepted: 01/24/2021] [Indexed: 01/19/2023]
Abstract
Nucleic acid detection is of great importance in a variety of areas, from life science and clinical diagnosis to environmental monitoring and food safety. Unfortunately, nucleic acid targets are always found in trace amounts and their response signals are difficult to be detected. Amplification mechanisms are then practically needed to either duplicate nucleic acid targets or enhance the detection signals. Polymerase chain reaction (PCR) is one of the most popular and powerful techniques for nucleic acid analysis. But the requirement of costly devices for precise thermo-cycling procedures in PCR has severely hampered the wide applications of PCR. Fortunately, isothermal molecular reactions have emerged as promising alternatives. The past decade has witnessed significant progress in the research of isothermal molecular reactions utilizing hairpin DNA probes (HDPs). Based on the nucleic acid strand interaction mechanisms, the hairpin DNA-mediated isothermal amplification (HDMIA) techniques can be mainly divided into three categories: strand assembly reactions, strand decomposition reactions, and strand creation reactions. In this review, we introduce the basics of HDMIA methods, including the sensing principles, the basic and advanced designs, and their wide applications, especially those benefiting from the utilization of G-quadruplexes and nanomaterials during the past decade. We also discuss the current challenges encountered, highlight the potential solutions, and point out the possible future directions in this prosperous research area.
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19
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Choopara I, Suea-Ngam A, Teethaisong Y, Howes PD, Schmelcher M, Leelahavanichkul A, Thunyaharn S, Wongsawaeng D, deMello AJ, Dean D, Somboonna N. Fluorometric Paper-Based, Loop-Mediated Isothermal Amplification Devices for Quantitative Point-of-Care Detection of Methicillin-Resistant Staphylococcus aureus (MRSA). ACS Sens 2021; 6:742-751. [PMID: 33439634 DOI: 10.1021/acssensors.0c01405] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Loop-mediated isothermal amplification (LAMP) has been widely used to detect many infectious diseases. However, minor inconveniences during the steps of adding reaction ingredients and lack of simple color results hinder point-of-care detection. We therefore invented a fluorometric paper-based LAMP by incorporating LAMP reagents, including a biotinylated primer, onto a cellulose membrane paper, with a simple DNA fluorescent dye incubation that demonstrated rapid and accurate results parallel to quantitative polymerase chain reaction (qPCR) methods. This technology allows for instant paper strip detection of methicillin-resistant Staphylococcus aureus (MRSA) in the laboratory and clinical samples. MRSA represents a major public health problem as it can cause infections in different parts of the human body and yet is resistant to commonly used antibiotics. In this study, we optimized LAMP reaction ingredients and incubation conditions following a central composite design (CCD) that yielded the shortest reaction time with high sensitivity. These CCD components and conditions were used to construct the paper-based LAMP reaction by immobilizing the biotinylated primer and the rest of the LAMP reagents to produce the ready-to-use MRSA diagnostic device. Our paper-based LAMP device could detect as low as 10 ag (equivalent to 1 copy) of the MRSA gene mecA within 36-43 min, was evaluated using both laboratory (individual cultures of MRSA and non-MRSA bacteria) and clinical blood samples to be 100% specific and sensitive compared to qPCR results, and had 35 day stability under 25 °C storage. Furthermore, the color readout allows for quantitation of MRSA copies. Hence, this device is applicable for point-of-care MRSA detection.
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Affiliation(s)
- Ilada Choopara
- Program in Biotechnology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
| | - Akkapol Suea-Ngam
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, 8093 Zürich, Switzerland
| | - Yothin Teethaisong
- Department of Microbiology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
| | - Philip D. Howes
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, 8093 Zürich, Switzerland
| | - Mathias Schmelcher
- Department of Health Sciences and Technology, ETH Zürich, 8092 Zürich, Switzerland
| | - Asada Leelahavanichkul
- Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
- Center of Excellence in Immunology and Immune-mediated Diseases, Department of Microbiology, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, Thailand
- STAR on Craniofacial and Skeleton Disorders, Faculty of Dentistry, Chulalongkorn University, Bangkok 10330, Thailand
| | - Sudaluck Thunyaharn
- Faculty of Medical Technology, Nakhonratchasima College, Nakhon Ratchasima 30000, Thailand
| | - Doonyapong Wongsawaeng
- Department of Nuclear Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
| | - Andrew J. deMello
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, 8093 Zürich, Switzerland
| | - Deborah Dean
- Center for Immunobiology and Vaccine Development, UCSF Benioff Children’s Hospital Oakland Research Institute, Oakland, California 94609, United States
- Department of Medicine and Pediatrics, University of California, San Francisco, California 94143, United States
- UC Berkeley/UCSF Graduate Program in Bioengineering, University of California, Berkeley, California 94720, United States
| | - Naraporn Somboonna
- Department of Microbiology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Microbiome Research Unit for Probiotics in Food and Cosmetics, Chulalongkorn University, Bangkok 10330, Thailand
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20
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Wang M, Nai MH, Huang RYJ, Leo HL, Lim CT, Chen CH. High-throughput functional profiling of single adherent cells via hydrogel drop-screen. LAB ON A CHIP 2021; 21:764-774. [PMID: 33506832 DOI: 10.1039/d0lc01294g] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Single-adherent-cell phenotyping on an extracellular matrix (ECM) is essential to determine cellular biological functions, such as morphological adaptations and biomolecule secretions, correlated to medical treatments and metastasis, yet there is no available platform for such high-throughput screening. Here, a novel hydrogel drop-screen device was developed to rapidly measure large-scale single-cell morphologies and multiple secretions on substrates for phenotype profiling. Single cells were first anchored to microfluidically fabricated gelatin particles providing mechanical stimulations similar to those from ECM in vivo. The cellular morphologies were then examined by quantifying the amount of cytoskeleton expressed on the particles. With droplet encapsulation, adherent single-cell multiplexed secretion analysis of a disintegrin and metalloproteinases (ADAMs) and matrix metalloproteinases (MMPs) was conducted at a throughput of ∼102 cells per second, revealing distinct functional heterogeneities associated with extracellular mechanical stimulations. The level of cell heterogeneity increased with increasing substrate stuffiness. Moreover, because of the promising screening capability, a database related to both nontumorigenic and tumorigenic breast cells (MCF10A, MCF-7, and MDA-MB-231) was constructed. The respective cell distributions and heterogeneities based on the morphologies and secreted bioindicators, such as MMP-2, MMP-3, MMP-9, and ADAM-8, were measured and found to correspond to the progress of tumor metastasis.
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Affiliation(s)
- Ming Wang
- NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, 21 Lower Kent Ridge Road, 119077 Singapore and Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore and Institute for Health Innovation and Technology (iHealthtech), MD6, 14 Medical Drive 14-01, 117599 Singapore
| | - Mui Hoon Nai
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore
| | - Ruby Yun-Ju Huang
- College of Medicine, National Taiwan University, No.1 Jen-Ai Road, Taipei, 10051, Taiwan and Graduate Institute of Oncology, College of Medicine, National Taiwan University, No. 1, Sec. 4, Roosevelt road, Taipei, 10617, Taiwan and Department of Biomedical Engineering, National Taiwan University, No.1, Sec.1, Jen-Ai Road, Taipei, 10051, Taiwan
| | - Hwa Liang Leo
- NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, 21 Lower Kent Ridge Road, 119077 Singapore and Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore
| | - Chwee Teck Lim
- NUS Graduate School for Integrative Sciences & Engineering, National University of Singapore, 21 Lower Kent Ridge Road, 119077 Singapore and Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, 117583 Singapore and Institute for Health Innovation and Technology (iHealthtech), MD6, 14 Medical Drive 14-01, 117599 Singapore and Mechanobiology Institute, National University of Singapore, 117411 Singapore
| | - Chia-Hung Chen
- Department of Biomedical Engineering, City University of Hong Kong, Y6700, 83 Tat Chee Avenue, Hong Kong SAR, China.
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21
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Destgeer G, Ouyang M, Wu CY, Di Carlo D. Fabrication of 3D concentric amphiphilic microparticles to form uniform nanoliter reaction volumes for amplified affinity assays. LAB ON A CHIP 2020; 20:3503-3514. [PMID: 32895694 DOI: 10.1039/d0lc00698j] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Reactions performed in uniform microscale volumes have enabled numerous applications in the analysis of rare entities (e.g. cells and molecules). Here, highly monodisperse aqueous droplets are formed by simply mixing microscale multi-material particles, consisting of concentric hydrophobic outer and hydrophilic inner layers, with oil and water. The particles are manufactured in batch using a 3D printed device to co-flow four concentric streams of polymer precursors which are polymerized with UV light. The cross-sectional shapes of the particles are altered by microfluidic nozzle design in the 3D printed device. Once a particle encapsulates an aqueous volume, each "dropicle" provides uniform compartmentalization and customizable shape-coding for each sample volume to enable multiplexing of uniform reactions in a scalable manner. We implement an enzymatically-amplified immunoassay using the dropicle system, yielding a detection limit of <1 pM with a dynamic range of at least 3 orders of magnitude. Multiplexing using two types of shape-coded particles was demonstrated without cross talk, laying a foundation for democratized single-entity assays.
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Affiliation(s)
- Ghulam Destgeer
- Department of Bioengineering, University of California, Los Angeles, CA 90095, USA.
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22
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Abstract
Ubiquitous post-transcriptional regulators in eukaryotes, microRNAs are currently emerging as promising biomarkers of physiological and pathological processes. Multiplex and digital detection of microRNAs represents a major challenge toward the use of microRNA signatures in clinical settings. The classical reverse transcription polymerase chain reaction quantification approach has important limitations because of the need for thermocycling and a reverse transcription step. Simpler, isothermal alternatives have been proposed, yet none could be adapted in both a digital and multiplex format. This is either because of a lack of sensitivity that forbids single molecule detection or molecular cross-talk reactions that are responsible for nonspecific amplification. Building on an ultrasensitive isothermal amplification mechanism, we present a strategy to suppress cross-talk reactions, allowing for robust isothermal and multiplex detection of microRNA targets. Our approach relies on target-specific DNA circuits interconnected with DNA-encoded inhibitors that repress nonspecific signal amplification. We demonstrate the one-step, isothermal, digital, and simultaneous quantification of various pairs of important microRNA targets.
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Affiliation(s)
- Yannick Rondelez
- Gulliver Laboratory, ESPCI Paris—Université PSL, 10 rue Vauquelin, 75005 Paris, France
| | - Guillaume Gines
- Gulliver Laboratory, ESPCI Paris—Université PSL, 10 rue Vauquelin, 75005 Paris, France
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23
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Yuan X, Yang C, He Q, Chen J, Yu D, Li J, Zhai S, Qin Z, Du K, Chu Z, Qin P. Current and Perspective Diagnostic Techniques for COVID-19. ACS Infect Dis 2020; 6:1998-2016. [PMID: 32677821 PMCID: PMC7409380 DOI: 10.1021/acsinfecdis.0c00365] [Citation(s) in RCA: 106] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Indexed: 02/08/2023]
Abstract
Since late December 2019, the coronavirus pandemic (COVID-19; previously known as 2019-nCoV) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been surging rapidly around the world. With more than 1,700,000 confirmed cases, the world faces an unprecedented economic, social, and health impact. The early, rapid, sensitive, and accurate diagnosis of viral infection provides rapid responses for public health surveillance, prevention, and control of contagious diffusion. More than 30% of the confirmed cases are asymptomatic, and the high false-negative rate (FNR) of a single assay requires the development of novel diagnostic techniques, combinative approaches, sampling from different locations, and consecutive detection. The recurrence of discharged patients indicates the need for long-term monitoring and tracking. Diagnostic and therapeutic methods are evolving with a deeper understanding of virus pathology and the potential for relapse. In this Review, a comprehensive summary and comparison of different SARS-CoV-2 diagnostic methods are provided for researchers and clinicians to develop appropriate strategies for the timely and effective detection of SARS-CoV-2. The survey of current biosensors and diagnostic devices for viral nucleic acids, proteins, and particles and chest tomography will provide insight into the development of novel perspective techniques for the diagnosis of COVID-19.
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Affiliation(s)
- Xi Yuan
- Center
of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong 518055, China
| | - Chengming Yang
- Southern
University of Science and Technology Hospital, Shenzhen, Guangdong 518055, China
| | - Qian He
- Center
of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong 518055, China
| | - Junhu Chen
- National
Institute of Parasitic Diseases, Chinese
Center for Disease Control and Prevention, Shanghai 200025, China
| | - Dongmei Yu
- Center
of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong 518055, China
- Department
of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Jie Li
- Center
of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong 518055, China
- Kunming
Dog Base of Police Security, Ministry of Public Security, Kunming, Yunnan 650204, China
| | - Shiyao Zhai
- Center
of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong 518055, China
| | - Zhifeng Qin
- Animal &
Plant Inspection and Quarantine Technology Center, Shenzhen Customs District People’s Republic of China, Shenzhen, Guangdong 518045, China
| | - Ke Du
- Department
of Mechanical Engineering, Rochester Institute
of Technology, Rochester, New York 14623, United States
| | - Zhenhai Chu
- Southern
University of Science and Technology Hospital, Shenzhen, Guangdong 518055, China
| | - Peiwu Qin
- Center
of Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Shenzhen, Guangdong 518055, China
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