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Bai H, Hu J, Liu T, Wan L, Dong C, Luo D, Li F, Yuan Z, Tang Y, Chen T, Wang S, Gou H, Zhou Y, Ying B, Huang J, Hu WW. A sample-to-answer digital microfluidic multiplexed PCR system for syndromic pathogen detection in respiratory tract infection. LAB ON A CHIP 2025; 25:1552-1564. [PMID: 39905852 DOI: 10.1039/d4lc00704b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2025]
Abstract
Timely identification of infectious pathogens is crucial for the accurate diagnosis, management, and treatment of syndromic respiratory diseases. Nevertheless, the implementation of rapid, precise, and automated point-of-care testing (POCT) remains a significant challenge. This study introduces an advanced digital microfluidic (DMF) POCT testing system designed for the rapid molecular syndromic testing of multiple respiratory pathogens from a single untreated sample. The system seamlessly integrates magnetic beads-based nucleic acid extraction, PCR amplification, and real-time fluorescence analysis in an automatic run, facilitating sample-to-answer detection within 80 min. It accommodates various sample types, including nasopharyngeal swabs, oropharyngeal swabs, bronchoalveolar lavage fluid, and sputum. A facile sample loading method has been developed to reduce hands-on time to less than 1 min. The system exhibits high sensitivity (200-628 copies per mL) for 15 pathogens and has the capacity for up to 32 multiplexed tests per run. Validation with 255 clinical samples confirms its high sensitivity and specificity. The DMF-based system significantly reduces manual labour, enhances rapid POCT for respiratory infections, and, with optimized manufacturing processes, lowers costs for large-scale production. The system can be applied and improve clinical management near the patients as well as in resource-limited settings.
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Affiliation(s)
- Hao Bai
- Department of Laboratory Medicine, Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Precision Medicine Translational Research Center (PMTRC), West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Jie Hu
- Precision Medicine Translational Research Center (PMTRC), West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Tangyuheng Liu
- Department of Laboratory Medicine, Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Liang Wan
- Livzon Diagnostics Inc., Zhuhai, 519000, China
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410000, China
| | - Cheng Dong
- School of Intelligent Systems Science and Engineering/JNU-Industry School of Artificial Intelligence, Jinan University, Zhuhai, 519000, China
| | - Dasheng Luo
- Digifluidic Biotech Ltd., Zhuhai, 519000, China
| | - Fei Li
- Digifluidic Biotech Ltd., Zhuhai, 519000, China
| | | | - Yunmei Tang
- Livzon Diagnostics Inc., Zhuhai, 519000, China
| | | | - Shan Wang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410000, China
| | - Hongna Gou
- Livzon Diagnostics Inc., Zhuhai, 519000, China
| | - Yongzhao Zhou
- Department of Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Binwu Ying
- Department of Laboratory Medicine, Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Medical Equipment Innovation Research Center, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Jin Huang
- Medical Equipment Innovation Research Center, Med-X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Wenchuang Walter Hu
- Department of Laboratory Medicine, Clinical Laboratory Medicine Research Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Precision Medicine Translational Research Center (PMTRC), West China Hospital, Sichuan University, Chengdu, 610041, China.
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2
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Wittwer CT, Hemmert AC, Kent JO, Rejali NA. DNA melting analysis. Mol Aspects Med 2024; 97:101268. [PMID: 38489863 DOI: 10.1016/j.mam.2024.101268] [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: 10/31/2023] [Revised: 02/19/2024] [Accepted: 03/11/2024] [Indexed: 03/17/2024]
Abstract
Melting is a fundamental property of DNA that can be monitored by absorbance or fluorescence. PCR conveniently produces enough DNA to be directly monitored on real-time instruments with fluorescently labeled probes or dyes. Dyes monitor the entire PCR product, while probes focus on a specific locus within the amplicon. Advances in amplicon melting include high resolution instruments, saturating DNA dyes that better reveal multiple products, prediction programs for domain melting, barcode taxonomic identification, high speed microfluidic melting, and highly parallel digital melting. Most single base variants and small insertions or deletions can be genotyped by high resolution amplicon melting. High resolution melting also enables heterozygote scanning for any variant within a PCR product. A web application (uMelt, http://www.dna-utah.org) predicts amplicon melting curves with multiple domains, a useful tool for verifying intended products. Additional applications include methylation assessment, copy number determination and verification of sequence identity. When amplicon melting does not provide sufficient detail, unlabeled probes or snapback primers can be used instead of covalently labeled probes. DNA melting is a simple, inexpensive, and powerful tool with many research applications that is beginning to make its mark in clinical diagnostics.
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Affiliation(s)
- Carl T Wittwer
- Department of Pathology, University of Utah, Salt Lake City, UT, USA.
| | | | - Jana O Kent
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
| | - Nick A Rejali
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
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3
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Bisen M, Kharga K, Mehta S, Jabi N, Kumar L. Bacteriophages in nature: recent advances in research tools and diverse environmental and biotechnological applications. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:22199-22242. [PMID: 38411907 DOI: 10.1007/s11356-024-32535-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 02/15/2024] [Indexed: 02/28/2024]
Abstract
Bacteriophages infect and replicate within bacteria and play a key role in the environment, particularly in microbial ecosystems and bacterial population dynamics. The increasing recognition of their significance stems from their wide array of environmental and biotechnological uses, which encompass the mounting issue of antimicrobial resistance (AMR). Beyond their therapeutic potential in combating antibiotic-resistant infections, bacteriophages also find vast applications such as water quality monitoring, bioremediation, and nutrient cycling within environmental sciences. Researchers are actively involved in isolating and characterizing bacteriophages from different natural sources to explore their applications. Gaining insights into key aspects such as the life cycle of bacteriophages, their host range, immune interactions, and physical stability is vital to enhance their application potential. The establishment of diverse phage libraries has become indispensable to facilitate their wide-ranging uses. Consequently, numerous protocols, ranging from traditional to cutting-edge techniques, have been developed for the isolation, detection, purification, and characterization of bacteriophages from diverse environmental sources. This review offers an exploration of tools, delves into the methods of isolation, characterization, and the extensive environmental applications of bacteriophages, particularly in areas like water quality assessment, the food sector, therapeutic interventions, and the phage therapy in various infections and diseases.
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Affiliation(s)
- Monish Bisen
- School of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, 173229, India
| | - Kusum Kharga
- School of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, 173229, India
| | - Sakshi Mehta
- School of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, 173229, India
| | - Nashra Jabi
- School of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, 173229, India
| | - Lokender Kumar
- School of Biotechnology, Faculty of Applied Sciences and Biotechnology, Shoolini University, Solan, Himachal Pradesh, 173229, India.
- Cancer Biology Laboratory, Raj Khosla Centre for Cancer Research, Shoolini University, Himachal Pradesh, Solan, 173229, India.
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4
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Sajeer Paramabth M, Varma M. Demystifying PCR tests, challenges, alternatives, and future: A quick review focusing on COVID and fungal infections. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2023; 51:719-728. [PMID: 37485773 DOI: 10.1002/bmb.21771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 06/20/2023] [Accepted: 07/12/2023] [Indexed: 07/25/2023]
Abstract
The polymerase chain reaction (PCR) technique is one of the most potent tools in molecular biology. It is extensively used for various applications ranging from medical diagnostics to forensic science and food quality testing. This technique has facilitated to survive COVID-19 pandemic by identifying the virus-infected individuals effortlessly and effectively. This review explores the principles, recent advancements, challenges, and alternatives of PCR technique in the context of COVID-19 and fungal infections. The introduction of PCR technique for anyone new to this field is the primary aim of this review and thereby equips them to understand the science of COVID-19 and related fungal infections in a simplistic manner.
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Affiliation(s)
| | - Manoj Varma
- Center for Nano Science and Engineering (CeNSE), Indian Institute of Science, Bangalore, India
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5
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Liu DD, Muliaditan D, Viswanathan R, Cui X, Cheow LF. Melt-Encoded-Tags for Expanded Optical Readout in Digital PCR (METEOR-dPCR) Enables Highly Multiplexed Quantitative Gene Panel Profiling. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301630. [PMID: 37485651 PMCID: PMC10520687 DOI: 10.1002/advs.202301630] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/27/2023] [Indexed: 07/25/2023]
Abstract
Digital PCR (dPCR) is an important tool for precise nucleic acid quantification in clinical setting, but the limited multiplexing capability restricts its applications for quantitative gene panel profiling. Here, this work describes melt-encoded-tags for expanded optical readout in digital PCR (METEOR-dPCR), a simple two-step assay that enables simultaneous quantification of a large panel of arbitrary genes in a dPCR platform. Target genes are quantitatively converted into DNA tags with unique melting temperatures through a ligation approach. These tags are then counted and distinguished by their melt-curve profiles on a dPCR platform. A multiplexing capacity of M^N, where M is the number of resolvable melting temperature and N is the number of fluorescence channel, can be achieved. This work validates METEOR-dPCR with simultaneous DNA copy number profiling of 60 targets using dPCR in cancer cells, and demonstrates its sensitivity for estimating tumor fraction in mixed tumor and normal DNA samples. The rapid, quantitative, and highly multiplexed METEOR-dPCR assay will have wide appeal for many clinical applications.
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Affiliation(s)
- Dong Dong Liu
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
| | - Daniel Muliaditan
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
- Genome institute of SingaporeAgency for ScienceTechnology and ResearchSingapore138672Singapore
| | - Ramya Viswanathan
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
| | - Xu Cui
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
| | - Lih Feng Cheow
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
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6
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Mao Y, Xu K, Miglietta L, Kreitmann L, Moser N, Georgiou P, Holmes A, Rodriguez-Manzano J. Deep Domain Adaptation Enhances Amplification Curve Analysis for Single-Channel Multiplexing in Real-Time PCR. IEEE J Biomed Health Inform 2023; 27:3093-3103. [PMID: 37028376 DOI: 10.1109/jbhi.2023.3257727] [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: 03/17/2023]
Abstract
Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes faces several challenges, such as distribution discrepancies between different data sources (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains' information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identification. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes (blaNDM, blaIMP and blaOXA-48) illustrates a curve-level accuracy of 93.1% and a sample-level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments' capabilities in real-world clinical applications.
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7
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Cao C, You M, Tong H, Xue Z, Liu C, He W, Peng P, Yao C, Li A, Xu X, Xu F. Similar color analysis based on deep learning (SCAD) for multiplex digital PCR via a single fluorescent channel. LAB ON A CHIP 2022; 22:3837-3847. [PMID: 36073361 DOI: 10.1039/d2lc00637e] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Digital PCR (dPCR) has recently attracted great interest due to its high sensitivity and accuracy. However, the existing dPCR depends on multicolor fluorescent dyes and multiple fluorescent channels to achieve multiplex detection, resulting in increased detection cost and limited detection throughput. Here, we developed a deep learning-based similar color analysis method, namely SCAD, to achieve multiplex dPCR in a single fluorescent channel. As a demonstration, we designed a microwell chip-based diplex dPCR system for detecting two genes (blaNDM and blaVIM) with two kinds of green fluorescent probes, whose emission colors are difficult to discriminate by traditional fluorescence intensity-based methods. To verify the possibility of deep learning algorithms to distinguish the similar colors, we first applied t-distributed stochastic neighbor embedding (tSNE) to make a clustering map for the microwells with similar fluorescence. Then, we trained a Vision Transformer (ViT) model on 10 000 microwells with two similar colors and tested it with 262 202 microwells. Lastly, the trained model was proven to have highly accurate classification ability (>98% for both the training set and the test set) and precise quantification ability on both blaNDM and blaVIM (ratio difference <0.10). We envision that the developed SCAD method would significantly expand the detection throughput of dPCR without the need for other auxiliary equipment.
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Affiliation(s)
- Chaoyu Cao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Minli You
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Haoyang Tong
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Zhenrui Xue
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
- Department of Transfusion Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, P.R. China
| | - Chang Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Wanghong He
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Ping Peng
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
- Department of Transfusion Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, P.R. China
| | - Chunyan Yao
- Department of Transfusion Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, P.R. China
| | - Ang Li
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, 710049, P.R. China
| | - Xiayu Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
| | - Feng Xu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, P.R. China.
- Bioinspired Engineering and Biomechanics Center (BEBC), Xi'an Jiaotong University, Xi'an 710049, P.R. China
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8
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Hernandez MM, Banu R, Gonzalez-Reiche AS, Gray B, Shrestha P, Cao L, Chen F, Shi H, Hanna A, Ramírez JD, van de Guchte A, Sebra R, Gitman MR, Nowak MD, Cordon-Cardo C, Schutzbank TE, Simon V, van Bakel H, Sordillo EM, Paniz-Mondolfi AE. RT-PCR/MALDI-TOF Diagnostic Target Performance Reflects Circulating SARS-CoV-2 Variant Diversity in New York City. J Mol Diagn 2022; 24:738-749. [PMID: 35525388 PMCID: PMC9067105 DOI: 10.1016/j.jmoldx.2022.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 03/07/2022] [Accepted: 04/06/2022] [Indexed: 12/20/2022] Open
Abstract
As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate, multiple variants of concern have emerged. New variants pose challenges for diagnostic platforms because sequence diversity can alter primer/probe-binding sites (PBSs), causing false-negative results. The MassARRAY SARS-CoV-2 Panel (Agena Bioscience) uses RT-PCR and mass spectrometry to detect five multiplex targets across N and ORF1ab genes. Herein, we use a data set of 256 SARS-CoV-2-positive specimens collected between April 11, 2021, and August 28, 2021, to evaluate target performance with paired sequencing data. During this time frame, two targets in the N gene (N2 and N3) were subject to the greatest sequence diversity. In specimens with N3 dropout, 69% harbored the Alpha-specific A28095U polymorphism that introduces a 3'-mismatch to the N3 forward PBS and increases risk of target dropout relative to specimens with 28095A (relative risk, 20.02; 95% CI, 11.36 to 35.72; P < 0.0001). Furthermore, among specimens with N2 dropout, 90% harbored the Delta-specific G28916U polymorphism that creates a 3'-mismatch to the N2 probe PBS and increases target dropout risk (relative risk, 11.92; 95% CI, 8.17 to 14.06; P < 0.0001). These findings highlight the robust capability of MassARRAY SARS-CoV-2 Panel target results to reveal circulating virus diversity, and they underscore the power of multitarget design to capture variants of concern.
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Affiliation(s)
- Matthew M Hernandez
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Radhika Banu
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ana S Gonzalez-Reiche
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Brandon Gray
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Paras Shrestha
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Liyong Cao
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Feng Chen
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Huanzhi Shi
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ayman Hanna
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Juan David Ramírez
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Facultad de Ciencias Naturales, Universidad del Rosario, Bogotá, Colombia
| | - Adriana van de Guchte
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Sema4, a Mount Sinai venture, Stamford, Connecticut
| | - Melissa R Gitman
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Michael D Nowak
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Carlos Cordon-Cardo
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Viviana Simon
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, New York; Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; The Global Health and Emerging Pathogens Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Harm van Bakel
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Emilia Mia Sordillo
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Alberto E Paniz-Mondolfi
- Department of Pathology, Molecular, and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.
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9
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Yan Z, Zhang H, Wang X, Gaňová M, Lednický T, Zhu H, Liu X, Korabečná M, Chang H, Neužil P. An image-to-answer algorithm for fully automated digital PCR image processing. LAB ON A CHIP 2022; 22:1333-1343. [PMID: 35258048 DOI: 10.1039/d1lc01175h] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The digital polymerase chain reaction (dPCR) is an irreplaceable variant of PCR techniques due to its capacity for absolute quantification and detection of rare deoxyribonucleic acid (DNA) sequences in clinical samples. Image processing methods, including micro-chamber positioning and fluorescence analysis, determine the reliability of the dPCR results. However, typical methods demand high requirements for the chip structure, chip filling, and light intensity uniformity. This research developed an image-to-answer algorithm with single fluorescence image capture and known image-related error removal. We applied the Hough transform to identify partitions in the images of dPCR chips, the 2D Fourier transform to rotate the image, and the 3D projection transformation to locate and correct the positions of all partitions. We then calculated each partition's average fluorescence amplitudes and generated a 3D fluorescence intensity distribution map of the image. We subsequently corrected the fluorescence non-uniformity between partitions based on the map and achieved statistical results of partition fluorescence intensities. We validated the proposed algorithms using different contents of the target DNA. The proposed algorithm is independent of the dPCR chip structure damage and light intensity non-uniformity. It also provides a reliable alternative to analyze the results of chip-based dPCR systems.
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Affiliation(s)
- Zhiqiang Yan
- School of Mechanical Engineering, Department of Microsystem Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi, 710072, P.R. China.
| | - Haoqing Zhang
- School of Mechanical Engineering, Department of Microsystem Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi, 710072, P.R. China.
| | - Xinlu Wang
- School of Mechanical Engineering, Department of Microsystem Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi, 710072, P.R. China.
| | - Martina Gaňová
- Central European Institute of Technology, Brno University of Technology, Purkyňova 656/123, 612 00, Brno, Czech Republic
| | - Tomáš Lednický
- Central European Institute of Technology, Brno University of Technology, Purkyňova 656/123, 612 00, Brno, Czech Republic
| | - Hanliang Zhu
- School of Mechanical Engineering, Department of Microsystem Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi, 710072, P.R. China.
| | - Xiaocheng Liu
- School of Mechanical Engineering, Department of Microsystem Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi, 710072, P.R. China.
| | - Marie Korabečná
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital in Prague, Albertov 4, 128 00 Prague, Czech Republic
| | - Honglong Chang
- School of Mechanical Engineering, Department of Microsystem Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi, 710072, P.R. China.
| | - Pavel Neužil
- School of Mechanical Engineering, Department of Microsystem Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, Shaanxi, 710072, P.R. China.
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10
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Gaňová M, Wang X, Yan Z, Zhang H, Lednický T, Korabečná M, Neužil P. Temperature non-uniformity detection on dPCR chips and temperature sensor calibration. RSC Adv 2022; 12:2375-2382. [PMID: 35425215 PMCID: PMC8979175 DOI: 10.1039/d1ra08138a] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 01/08/2022] [Indexed: 12/12/2022] Open
Abstract
A microfluidic-based digital polymerase chain reaction (dPCR) chip requires precise temperature control as well as uniform temperature distribution to ensure PCR efficiency. However, measuring local temperature and its distribution over thousands of μL/nL-volume samples with minimum disturbance is challenging. Here, we present a method of non-contact localized temperature measurement for determination of the non-uniformity of temperature distribution over a dPCR chip. We filled the dPCR chip with a PCR solution containing amplified DNA fragments with a known melting temperature (TM). We then captured fluorescent images of the chip when it was heated from 70 to 99 °C, plotted the fluorescence intensity of each partition as a function of temperature, and calculated measured TM values from each partition. Finally, we created a 3-D map of the dPCR chip with the measured TM as the parameter. Even when the actual TM of the PCR solution was constant, the measured TM value varied between locations due to temperature non-uniformity in the dPCR chip. The method described here thereby characterized the distribution of temperature non-uniformity using a PCR solution with known TM as a temperature sensor. Among the non-contact temperature measurement methods, the proposed TM-based method can determine the temperature distribution within the chip, instead of only at the chip surface. The method also does not suffer from the undesirable photobleaching effect of fluorescein-based temperature measurement method. Temperature determination over the dPCR chip based on TM allowed us to calibrate the temperature sensor and improve the dPCR configuration and precision. This method is also suitable for determining the temperature uniformity of other microarray systems where there is no physical access to the system and thus direct temperature measurement is not possible. We present a method of non-contact localized temperature measurement for determination of the non-uniformity of temperature distribution over a dPCR chip mounted on two different thermal cycling configurations.![]()
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Affiliation(s)
- Martina Gaňová
- Central European Institute of Technology, Brno University of Technology Purkyňova 123 612 00 Brno Czech Republic
| | - Xinlu Wang
- Northwestern Polytechnical University, School of Mechanical Engineering, Department of Microsystem Engineering 127 West Youyi Road Xi'an Shaanxi 710072 PR China
| | - Zhiqiang Yan
- Northwestern Polytechnical University, School of Marine Science and Technology 127 West Youyi Road Xi'an Shaanxi 710072 PR China
| | - Haoqing Zhang
- Northwestern Polytechnical University, School of Mechanical Engineering, Department of Microsystem Engineering 127 West Youyi Road Xi'an Shaanxi 710072 PR China
| | - Tomáš Lednický
- Central European Institute of Technology, Brno University of Technology Purkyňova 123 612 00 Brno Czech Republic
| | - Marie Korabečná
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University and General University Hospital in Prague Albertov 4 128 00 Prague Czech Republic
| | - Pavel Neužil
- Northwestern Polytechnical University, School of Mechanical Engineering, Department of Microsystem Engineering 127 West Youyi Road Xi'an Shaanxi 710072 PR China
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11
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Sophian A, Purwaningsih R, Muindar M, Igirisa EPJ, Amirullah ML. Detection of Salmonella typhimurium ATCC 14028 in Powder Prepared Traditional Medicines Using Real-Time PCR. BORNEO JOURNAL OF PHARMACY 2021. [DOI: 10.33084/bjop.v4i3.1838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
The detection of Salmonella typhimurium ATCC 14028 using real-time PCR on powdered traditional medicinal products was carried out in the microbiology and molecular biology testing laboratory of the Food and Drug Administration in Gorontalo. This research aims to provide a reference for alternative testing methods in testing the products of traditional powder preparations on the market. The sample consisted of 10 traditional powder preparations spiked with positive control of S. typhimurium ATCC 14028 phase 2. The method used in the study was real-time PCR analysis using the SYBR® Green method, while DNA isolation using the direct PCR method. Data analysis was performed by analyzing the sample's melting temperature (Tm) curve and comparing it with positive control. The results showed that S. typhimurium ATCC 14028 was detected in samples at an average Tm value of 84.18°C, with ranges of 84.0-84.5°C. For positive control, the Tm value was at 85.2°C, while for the negative control, the Tm value was not detected. Based on these data, it can be concluded that S. typhimurium ATCC 14028 in traditional medicine products powder preparations can be detected using real-time PCR.
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Affiliation(s)
- Alfi Sophian
- National Agency of Drug and Food Control of Republic of Indonesia
| | | | - Muindar Muindar
- National Agency of Drug and Food Control of Republic of Indonesia in Gorontalo
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12
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Gaňová M, Zhang H, Zhu H, Korabečná M, Neužil P. Multiplexed digital polymerase chain reaction as a powerful diagnostic tool. Biosens Bioelectron 2021; 181:113155. [PMID: 33740540 DOI: 10.1016/j.bios.2021.113155] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 02/13/2021] [Accepted: 03/06/2021] [Indexed: 01/30/2023]
Abstract
The digital polymerase chain reaction (dPCR) multiplexing method can simultaneously detect and quantify closely related deoxyribonucleic acid sequences in complex mixtures. The dPCR concept is continuously improved by the development of microfluidics and micro- and nanofabrication, and different complex techniques are introduced. In this review, we introduce dPCR techniques based on sample compartmentalization, droplet- and chip-based systems, and their combinations. We then discuss dPCR multiplexing methods in both laboratory research settings and advanced or routine clinical applications. We focus on their strengths and weaknesses with regard to the character of biological samples and to the required precision of such analysis, as well as showing recently published work based on those methods. Finally, we envisage possible future achievements in this field.
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Affiliation(s)
- Martina Gaňová
- Central European Institute of Technology, Brno University of Technology, 612 00, Brno, Czech Republic
| | - Haoqing Zhang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Hanliang Zhu
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Marie Korabečná
- 1st Faculty of Medicine, Institute of Biology and Medical Genetics, Charles University and General University Hospital, 12800, Prague, Czech Republic
| | - Pavel Neužil
- Central European Institute of Technology, Brno University of Technology, 612 00, Brno, Czech Republic; School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China; The Faculty of Electrical Engineering and Communication, Brno University of Technology, 616 00, Brno, Czech Republic.
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13
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Sanchez Barea J, Kang D. Integration of Surface‐enhanced Raman Spectroscopy with
PCR
for Monitoring Single Copy of
KRAS G12D
Mutation. B KOREAN CHEM SOC 2021. [DOI: 10.1002/bkcs.12298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Joel Sanchez Barea
- Department of Chemistry Incheon National University Incheon 22012 Republic of Korea
| | - Dong‐Ku Kang
- Department of Chemistry Incheon National University Incheon 22012 Republic of Korea
- Department of Chemistry Research Institute of Basic Sciences, Incheon National University Incheon 22012 Republic of Korea
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