1
|
Goshia T, Aralar A, Wiederhold N, Jenks JD, Mehta SR, Karmakar A, E S M, Sharma A, Sun H, Kebadireng R, White PL, Sinha M, Hoenigl M, Fraley SI. Universal digital high-resolution melting for the detection of pulmonary mold infections. J Clin Microbiol 2024; 62:e0147623. [PMID: 38695528 PMCID: PMC11237519 DOI: 10.1128/jcm.01476-23] [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: 11/08/2023] [Accepted: 02/21/2024] [Indexed: 05/14/2024] Open
Abstract
Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (Aspergillus, Mucorales, Lomentospora, and Fusarium) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be >8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRM's speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes. IMPORTANCE Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.
Collapse
Affiliation(s)
- Tyler Goshia
- Department of Bioengineering, University of California San Diego, San Diego, California, USA
| | - April Aralar
- Department of Bioengineering, University of California San Diego, San Diego, California, USA
| | - Nathan Wiederhold
- Department of Pathology, University of Texas Health Science Center, San Antonio, Texas, USA
| | - Jeffrey D Jenks
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
- Durham County Department of Public Health, Durham, North Carolina, USA
| | - Sanjay R Mehta
- Department of Medicine, University of California San Diego, San Diego, California, USA
- San Diego Veterans Administration Medical Center, San Diego, California, USA
| | | | - Monish E S
- MelioLabs Inc., Santa Clara, California, USA
| | | | - Haoxiang Sun
- Department of Bioengineering, University of California San Diego, San Diego, California, USA
| | - Refilwe Kebadireng
- Department of Bioengineering, University of California San Diego, San Diego, California, USA
| | - P Lewis White
- Public Health Wales Microbiology Cardiff, Cardiff University, UHW, Cardiff, United Kingdom
- Centre for Trials Research, Division of Infection and Immunity, Cardiff University, UHW, Cardiff, United Kingdom
| | - Mridu Sinha
- MelioLabs Inc., Santa Clara, California, USA
| | - Martin Hoenigl
- Department of Internal Medicine, Medical University of Graz, Graz, Austria
- ECMM Excellence Center for Medical Mycology, Medical University of Graz, Graz, Austria
| | - Stephanie I Fraley
- Department of Bioengineering, University of California San Diego, San Diego, California, USA
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Li C, Kang N, Ye S, Huang W, Wang X, Wang C, Li Y, Liu YF, Lan Y, Ma L, Zhao Y, Han Y, Fu J, Shen D, Dong L, Du W. All-In-One OsciDrop Digital PCR System for Automated and Highly Multiplexed Molecular Diagnostics. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2309557. [PMID: 38516754 DOI: 10.1002/advs.202309557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/29/2024] [Indexed: 03/23/2024]
Abstract
Digital PCR (dPCR) holds immense potential for precisely detecting nucleic acid markers essential for personalized medicine. However, its broader application is hindered by high consumable costs, complex procedures, and restricted multiplexing capabilities. To address these challenges, an all-in-one dPCR system is introduced that eliminates the need for microfabricated chips, offering fully automated operations and enhanced multiplexing capabilities. Using this innovative oscillation-induced droplet generation technique, OsciDrop, this system supports a comprehensive dPCR workflow, including precise liquid handling, pipette-based droplet printing, in situ thermocycling, multicolor fluorescence imaging, and machine learning-driven analysis. The system's reliability is demonstrated by quantifying reference materials and evaluating HER2 copy number variation in breast cancer. Its multiplexing capability is showcased with a quadruplex dPCR assay that detects key EGFR mutations, including 19Del, L858R, and T790M in lung cancer. Moreover, the digital stepwise melting analysis (dSMA) technique is introduced, enabling high-multiplex profiling of seven major EGFR variants spanning 35 subtypes. This innovative dPCR system presents a cost-effective and versatile alternative, overcoming existing limitations and paving the way for transformative advances in precision diagnostics.
Collapse
Affiliation(s)
- Caiming Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, 101408, China
| | - Nan Kang
- Department of Pathology, Peking University People's Hospital, Beijing, 100044, China
| | - Shun Ye
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Weihang Huang
- Center for Corpus Research, Department of English Language and Linguistics, University of Birmingham, Edgbaston, Birmingham, B152TT, UK
| | - Xia Wang
- Center for Advanced Measurement Science, National Institute of Metrology, Beijing, 100013, China
| | - Cheng Wang
- Department of Breast Surgery Huangpu Branch, Shanghai Ninth People's Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Yuchen Li
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- Biomedical Sciences College & Shandong Medical Biotechnology Centre, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250000, China
| | - Yan-Fei Liu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- Research Center for Analytical Sciences, Northeastern University, Shenyang, 110819, China
| | - Ying Lan
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Liang Ma
- Maccura Biotechnology Co., Ltd, Chengdu, 611730, China
| | - Yuhang Zhao
- Maccura Biotechnology Co., Ltd, Chengdu, 611730, China
| | - Yong Han
- Maccura Biotechnology Co., Ltd, Chengdu, 611730, China
| | - Jun Fu
- Maccura Biotechnology Co., Ltd, Chengdu, 611730, China
| | - Danhua Shen
- Department of Pathology, Peking University People's Hospital, Beijing, 100044, China
| | - Lianhua Dong
- Center for Advanced Measurement Science, National Institute of Metrology, Beijing, 100013, China
| | - Wenbin Du
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
- College of Life Sciences, University of the Chinese Academy of Sciences, Beijing, 101408, China
- Savaid Medical School, University of the Chinese Academy of Sciences, Beijing, 101408, China
| |
Collapse
|
4
|
Boussina A, Langouche L, Obirieze AC, Sinha M, Mack H, Leineweber W, Aralar A, Pride DT, Coleman TP, Fraley SI. Machine learning based DNA melt curve profiling enables automated novel genotype detection. BMC Bioinformatics 2024; 25:185. [PMID: 38730317 PMCID: PMC11088152 DOI: 10.1186/s12859-024-05747-0] [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: 05/15/2023] [Accepted: 03/14/2024] [Indexed: 05/12/2024] Open
Abstract
Surveillance for genetic variation of microbial pathogens, both within and among species, plays an important role in informing research, diagnostic, prevention, and treatment activities for disease control. However, large-scale systematic screening for novel genotypes remains challenging in part due to technological limitations. Towards addressing this challenge, we present an advancement in universal microbial high resolution melting (HRM) analysis that is capable of accomplishing both known genotype identification and novel genotype detection. Specifically, this novel surveillance functionality is achieved through time-series modeling of sequence-defined HRM curves, which is uniquely enabled by the large-scale melt curve datasets generated using our high-throughput digital HRM platform. Taking the detection of bacterial genotypes as a model application, we demonstrate that our algorithms accomplish an overall classification accuracy over 99.7% and perform novelty detection with a sensitivity of 0.96, specificity of 0.96 and Youden index of 0.92. Since HRM-based DNA profiling is an inexpensive and rapid technique, our results add support for the feasibility of its use in surveillance applications.
Collapse
Affiliation(s)
- Aaron Boussina
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Lennart Langouche
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Augustine C Obirieze
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Mridu Sinha
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Hannah Mack
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - William Leineweber
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - April Aralar
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - David T Pride
- Department of Pathology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Todd P Coleman
- Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
| | - Stephanie I Fraley
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA.
| |
Collapse
|
5
|
Aralar A, Goshia T, Ramchandar N, Lawrence SM, Karmakar A, Sharma A, Sinha M, Pride DT, Kuo P, Lecrone K, Chiu M, Mestan KK, Sajti E, Vanderpool M, Lazar S, Crabtree M, Tesfai Y, Fraley SI. Universal Digital High-Resolution Melt Analysis for the Diagnosis of Bacteremia. J Mol Diagn 2024; 26:349-363. [PMID: 38395408 PMCID: PMC11090205 DOI: 10.1016/j.jmoldx.2024.01.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 01/04/2024] [Accepted: 01/30/2024] [Indexed: 02/25/2024] Open
Abstract
Fast and accurate diagnosis of bloodstream infection is necessary to inform treatment decisions for septic patients, who face hourly increases in mortality risk. Blood culture remains the gold standard test but typically requires approximately 15 hours to detect the presence of a pathogen. We, therefore, assessed the potential for universal digital high-resolution melt (U-dHRM) analysis to accomplish faster broad-based bacterial detection, load quantification, and species-level identification directly from whole blood. Analytical validation studies demonstrated strong agreement between U-dHRM load measurement and quantitative blood culture, indicating that U-dHRM detection is highly specific to intact organisms. In a pilot clinical study of 17 whole blood samples from pediatric patients undergoing simultaneous blood culture testing, U-dHRM achieved 100% concordance when compared with blood culture and 88% concordance when compared with clinical adjudication. Moreover, U-dHRM identified the causative pathogen to the species level in all cases where the organism was represented in the melt curve database. These results were achieved with a 1-mL sample input and sample-to-answer time of 6 hours. Overall, this pilot study suggests that U-dHRM may be a promising method to address the challenges of quickly and accurately diagnosing a bloodstream infection.
Collapse
Affiliation(s)
- April Aralar
- Department of Bioengineering, University of California, San Diego, La Jolla, California
| | - Tyler Goshia
- Department of Bioengineering, University of California, San Diego, La Jolla, California
| | - Nanda Ramchandar
- Department of Pediatrics, Naval Medical Center San Diego, San Diego, California; Division of Infectious Diseases, Department of Pediatrics, University of California, San Diego, La Jolla, California
| | - Shelley M Lawrence
- Division of Neonatology, Department of Pediatrics, The University of Utah, Salt Lake City, Utah
| | | | | | | | - David T Pride
- Department of Pathology, University of California, San Diego, La Jolla, California
| | - Peiting Kuo
- Department of Pathology, University of California, San Diego, La Jolla, California
| | - Khrissa Lecrone
- Department of Pathology, University of California, San Diego, La Jolla, California
| | - Megan Chiu
- Department of Pathology, University of California, San Diego, La Jolla, California
| | - Karen K Mestan
- Division of Neonatology, Department of Pediatrics, University of California, San Diego, La Jolla, California
| | - Eniko Sajti
- Division of Neonatology, Department of Pediatrics, University of California, San Diego, La Jolla, California
| | - Michelle Vanderpool
- Department of Pathology and Laboratory Medicine, Rady Children's Hospital-San Diego, San Diego, California
| | - Sarah Lazar
- Division of Neonatology, Department of Pediatrics, University of California, San Diego, La Jolla, California
| | - Melanie Crabtree
- Division of Neonatology, Department of Pediatrics, University of California, San Diego, La Jolla, California
| | - Yordanos Tesfai
- Division of Neonatology, Department of Pediatrics, University of California, San Diego, La Jolla, California
| | - Stephanie I Fraley
- Department of Bioengineering, University of California, San Diego, La Jolla, California.
| |
Collapse
|
6
|
Traylor A, Lee PW, Hsieh K, Wang TH. Improving bacteria identification from digital melt assay via oligonucleotide-based temperature calibration. Anal Chim Acta 2024; 1297:342371. [PMID: 38438240 PMCID: PMC11082877 DOI: 10.1016/j.aca.2024.342371] [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: 11/10/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 03/06/2024]
Abstract
BACKGROUND Bacterial infections, especially polymicrobial infections, remain a threat to global health and require advances in diagnostic technologies for timely and accurate identification of all causative species. Digital melt - microfluidic chip-based digital PCR combined with high resolution melt (HRM) - is an emerging method for identification and quantification of polymicrobial bacterial infections. Despite advances in recent years, existing digital melt instrumentation often delivers nonuniform temperatures across digital chips, resulting in nonuniform digital melt curves for individual bacterial species. This nonuniformity can lead to inaccurate species identification and reduce the capacity for differentiating bacterial species with similar digital melt curves. RESULTS We introduce herein a new temperature calibration method for digital melt by incorporating an unamplified, synthetic DNA fragment with a known melting temperature as a calibrator. When added at a tuned concentration to an established digital melt assay amplifying the commonly targeted 16S V1 - V6 region, this calibrator produced visible low temperature calibrator melt curves across-chip along with the target bacterial melt curves. This enables alignment of the bacterial melt curves and correction of heating-induced nonuniformities. Using this calibration method, we were able to improve the uniformity of digital melt curves from three causative species of bacteria. Additionally, we assessed calibration's effects on identification accuracy by performing machine learning identification of three polymicrobial mixtures comprised of two bacteria with similar digital melt curves in different ratios. Calibration greatly improved mixture composition prediction. SIGNIFICANCE To the best of our knowledge, this work represents the first DNA calibrator-supplemented assay and calibration method for nanoarray digital melt. Our results suggest that this calibration method can be flexibly used to improve identification accuracy and reduce melt curve variabilities across a variety of pathogens and assays. Therefore, this calibration method has the potential to elevate the diagnostic capabilities of digital melt toward polymicrobial bacterial infections and other infectious diseases.
Collapse
Affiliation(s)
- Amelia Traylor
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, United States
| | - Pei-Wei Lee
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, United States
| | - Kuangwen Hsieh
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, United States
| | - Tza-Huei Wang
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, 21218, United States; Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21205, United States; Institute of NanoBioTechnology, Johns Hopkins University, Baltimore, MD, 21218, United States.
| |
Collapse
|
7
|
Lawrence SM, Goshia T, Sinha M, Fraley SI, Williams M. Decoding human cytomegalovirus for the development of innovative diagnostics to detect congenital infection. Pediatr Res 2024; 95:532-542. [PMID: 38146009 PMCID: PMC10837078 DOI: 10.1038/s41390-023-02957-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 11/14/2023] [Accepted: 11/27/2023] [Indexed: 12/27/2023]
Abstract
Cytomegalovirus is the most common cause of congenital infectious disease and the leading nongenetic etiology of sensorineural hearing loss. Although most infected neonates are asymptomatic at birth, congenital cytomegalovirus infection is responsible for nearly 400 infant deaths annually in the United States and may lead to significant long-term neurodevelopmental impairments in survivors. The resulting financial and social burdens of congenital cytomegalovirus infection have led many medical centers to initiate targeted testing after birth, with a growing advocacy to advance universal newborn screening. While no cures or vaccines are currently available to eliminate or prevent cytomegalovirus infection, much has been learned over the last five years regarding disease pathophysiology and viral replication cycles that may enable the development of innovative diagnostics and therapeutics. This Review will detail our current understanding of congenital cytomegalovirus infection, while focusing our discussion on routine and emerging diagnostics for viral detection, quantification, and long-term prognostication. IMPACT: This review highlights our current understanding of the fetal transmission of human cytomegalovirus. It details clinical signs and physical findings of congenital cytomegalovirus infection. This submission discusses currently available cytomegalovirus diagnostics and introduces emerging platforms that promise improved sensitivity, specificity, limit of detection, viral quantification, detection of genomic antiviral resistance, and infection staging (primary, latency, reactivation, reinfection).
Collapse
Affiliation(s)
- Shelley M Lawrence
- University of Utah, College of Medicine, Department of Pediatrics, Division of Neonatology, Salt Lake City, UT, USA.
| | - Tyler Goshia
- Department of Bioengineering, University of California, San Diego, San Diego, CA, USA
| | | | - Stephanie I Fraley
- Department of Bioengineering, University of California, San Diego, San Diego, CA, USA
| | - Marvin Williams
- University of Oklahoma, College of Medicine, Department of Obstetrics and Gynecology, Division of Fetal-Maternal Medicine, Oklahoma City, OK, USA
| |
Collapse
|
8
|
Kota PK, Vu HA, LeJeune D, Han M, Syed S, Baraniuk RG, Drezek RA. Expanded Multiplexing on Sensor-Constrained Microfluidic Partitioning Systems. Anal Chem 2023; 95:17458-17466. [PMID: 37971927 PMCID: PMC10759160 DOI: 10.1021/acs.analchem.3c01176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Microfluidics can split samples into thousands or millions of partitions, such as droplets or nanowells. Partitions capture analytes according to a Poisson distribution, and in diagnostics, the analyte concentration is commonly inferred with a closed-form solution via maximum likelihood estimation (MLE). Here, we present a new scalable approach to multiplexing analytes. We generalize MLE with microfluidic partitioning and extend our previously developed Sparse Poisson Recovery (SPoRe) inference algorithm. We also present the first in vitro demonstration of SPoRe with droplet digital PCR (ddPCR) toward infection diagnostics. Digital PCR is intrinsically highly sensitive, and SPoRe helps expand its multiplexing capacity by circumventing its channel limitations. We broadly amplify bacteria with 16S ddPCR and assign barcodes to nine pathogen genera by using five nonspecific probes. Given our two-channel ddPCR system, we measured two probes at a time in multiple groups of droplets. Although individual droplets are ambiguous in their bacterial contents, we recover the concentrations of bacteria in the sample from the pooled data. We achieve stable quantification down to approximately 200 total copies of the 16S gene per sample, enabling a suite of clinical applications given a robust upstream microbial DNA extraction procedure. We develop a new theory that generalizes the application of this framework to many realistic sensing modalities, and we prove scaling rules for system design to achieve further expanded multiplexing. The core principles demonstrated here could impact many biosensing applications with microfluidic partitioning.
Collapse
Affiliation(s)
- Pavan K. Kota
- Department of Bioengineering, Rice University, Houston, Texas 77005
| | - Hoang-Anh Vu
- Department of Bioengineering, Rice University, Houston, Texas 77005
| | - Daniel LeJeune
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005
| | - Margaret Han
- Department of Biosciences, Rice University, Houston, Texas 77005
| | - Saamiya Syed
- Department of Engineering Technology, University of Houston, Houston, Texas 77204
| | - Richard G. Baraniuk
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005
| | | |
Collapse
|
9
|
Edwards RL, Takach JE, McAndrew MJ, Menteer J, Lestz RM, Whitman D, Baxter-Lowe LA. Next generation multiplexing for digital PCR using a novel melt-based hairpin probe design. Front Genet 2023; 14:1272964. [PMID: 38028620 PMCID: PMC10667681 DOI: 10.3389/fgene.2023.1272964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
Digital PCR (dPCR) is a powerful tool for research and diagnostic applications that require absolute quantification of target molecules or detection of rare events, but the number of nucleic acid targets that can be distinguished within an assay has limited its usefulness. For most dPCR systems, one target is detected per optical channel and the total number of targets is limited by the number of optical channels on the platform. Higher-order multiplexing has the potential to dramatically increase the usefulness of dPCR, especially in scenarios with limited sample. Other potential benefits of multiplexing include lower cost, additional information generated by more probes, and higher throughput. To address this unmet need, we developed a novel melt-based hairpin probe design to provide a robust option for multiplexing digital PCR. A prototype multiplex digital PCR (mdPCR) assay using three melt-based hairpin probes per optical channel in a 16-well microfluidic digital PCR platform accurately distinguished and quantified 12 nucleic acid targets per well. For samples with 10,000 human genome equivalents, the probe-specific ranges for limit of blank were 0.00%-0.13%, and those for analytical limit of detection were 0.00%-0.20%. Inter-laboratory reproducibility was excellent (r 2 = 0.997). Importantly, this novel melt-based hairpin probe design has potential to achieve multiplexing beyond the 12 targets/well of this prototype assay. This easy-to-use mdPCR technology with excellent performance characteristics has the potential to revolutionize the use of digital PCR in research and diagnostic settings.
Collapse
Affiliation(s)
- Rebecca L. Edwards
- Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA, United States
| | | | | | - Jondavid Menteer
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Division of Cardiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States
| | - Rachel M. Lestz
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Division of Nephrology, Children’s Hospital Los Angeles, Los Angeles, CA, United States
| | - Douglas Whitman
- Luminex Corporation, A Diasorin Company, Austin, TX, United States
| | - Lee Ann Baxter-Lowe
- Department of Pathology and Laboratory Medicine, Children’s Hospital Los Angeles, Los Angeles, CA, United States
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| |
Collapse
|
10
|
Goshia T, Aralar A, Wiederhold N, Jenks JD, Mehta SR, Sinha M, Karmakar A, Sharma A, Shrivastava R, Sun H, White PL, Hoenigl M, Fraley SI. Universal Digital High Resolution Melt for the detection of pulmonary mold infections. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.09.566457. [PMID: 37986859 PMCID: PMC10659414 DOI: 10.1101/2023.11.09.566457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Background Invasive mold infections (IMIs) such as aspergillosis, mucormycosis, fusariosis, and lomentosporiosis are associated with high morbidity and mortality, particularly in immunocompromised patients, with mortality rates as high as 40% to 80%. Outcomes could be substantially improved with early initiation of appropriate antifungal therapy, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high resolution melting analysis (U-dHRM) may enable rapid and robust diagnosis of IMI. This technology aims to accomplish timely pathogen detection at the single genome level by conducting broad-based amplification of microbial barcoding genes in a digital polymerase chain reaction (dPCR) format, followed by high-resolution melting of the DNA amplicons in each digital reaction to generate organism-specific melt curve signatures that are identified by machine learning. Methods A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these 19 fungal melt curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage (BAL) samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. Results U-dHRM achieved an average of 97% fungal organism identification accuracy and a turn-around-time of 4hrs. Pathogenic molds (Aspergillus, Mucorales, Lomentospora and Fusarium) were detected by U-dHRM in 73% of BALF samples suspected of IMI. Mixtures of pathogenic molds were detected in 19%. U-dHRM demonstrated good sensitivity for IMI, as defined by current diagnostic criteria, when clinical findings were also considered. Conclusions U-dHRM showed promising performance as a separate or combination diagnostic approach to standard mycological tests. The speed of U-dHRM and its ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples as well as detect emerging opportunistic pathogens may provide information that could aid in treatment decisions and improve patient outcomes.
Collapse
Affiliation(s)
- Tyler Goshia
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
| | - April Aralar
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
| | - Nathan Wiederhold
- Department of Pathology, University of Texas Health Science Center, San Antonio, TX, USA
| | - Jeffrey D. Jenks
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham County Department of Public Health, Durham, NC, USA
| | - Sanjay R. Mehta
- Department of Medicine, University of California San Diego, San Diego, CA, USA
- San Diego Veterans Administration Medical Center, San Diego, CA, USA
| | | | | | | | | | - Haoxiang Sun
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
| | - P. Lewis White
- Public Health Wales Microbiology Cardiff, and Cardiff University Centre for Trials Research/Division of Infection/Immunity, University Hospital of Wales, Cardiff, United Kingdom
| | - Martin Hoenigl
- Department of Medicine, Medical University of Graz, Graz, Austria
| | - Stephanie I. Fraley
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
| |
Collapse
|
11
|
Lee PW, Chen L, Hsieh K, Traylor A, Wang TH. Harnessing Variabilities in Digital Melt Curves for Accurate Identification of Bacteria. Anal Chem 2023; 95:15522-15530. [PMID: 37812586 DOI: 10.1021/acs.analchem.3c01654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023]
Abstract
Digital PCR combined with high resolution melt (HRM) is an emerging method for identifying pathogenic bacteria with single cell resolution via species-specific digital melt curves. Currently, the development of such digital PCR-HRM assays entails first identifying PCR primers to target hypervariable gene regions within the target bacteria panel, next performing bulk-based PCR-HRM to examine whether the resulting species-specific melt curves possess sufficient interspecies variability (i.e., variability between bacterial species), and then digitizing the bulk-based PCR-HRM assays with melt curves that have high interspecies variability via microfluidics. In this work, we first report our discovery that the current development workflow can be inadequate because a bulk-based PCR-HRM assay that produces melt curves with high interspecies variability can, in fact, lead to a digital PCR-HRM assay that produces digital melt curves with unwanted intraspecies variability (i.e., variability within the same bacterial species), consequently hampering bacteria identification accuracy. Our subsequent investigation reveals that such intraspecies variability in digital melt curves can arise from PCR primers that target nonidentical gene copies or amplify nonspecifically. We then show that computational in silico HRM opens a window to inspect both interspecies and intraspecies variabilities and thus provides the missing link between bulk-based PCR-HRM and digital PCR-HRM. Through this new development workflow, we report a new digital PCR-HRM assay with improved bacteria identification accuracy. More broadly, this work can serve as the foundation for enhancing the development of future digital PCR-HRM assays toward identifying causative pathogens and combating infectious diseases.
Collapse
Affiliation(s)
- Pei-Wei Lee
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Liben Chen
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Kuangwen Hsieh
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Amelia Traylor
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Tza-Huei Wang
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, United States
- Institute of NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland 21218, United States
| |
Collapse
|
12
|
Aralar A, Goshia T, Ramchandar N, Lawrence SM, Karmakar A, Sharma A, Sinha M, Pride DT, Kuo P, Lecrone K, Chiu M, Mestan K, Sajti E, Vanderpool M, Lazar S, Crabtree M, Tesfai Y, Fraley SI. Universal digital high resolution melt analysis for the diagnosis of bacteremia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.07.23295215. [PMID: 37732245 PMCID: PMC10508820 DOI: 10.1101/2023.09.07.23295215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Fast and accurate diagnosis of bloodstream infection is necessary to inform treatment decisions for septic patients, who face hourly increases in mortality risk. Blood culture remains the gold standard test but typically requires ∼15 hours to detect the presence of a pathogen. Here, we assess the potential for universal digital high-resolution melt (U-dHRM) analysis to accomplish faster broad-based bacterial detection, load quantification, and species-level identification directly from whole blood. Analytical validation studies demonstrated strong agreement between U-dHRM load measurement and quantitative blood culture, indicating that U-dHRM detection is highly specific to intact organisms. In a pilot clinical study of 21 whole blood samples from pediatric patients undergoing simultaneous blood culture testing, U-dHRM achieved 100% concordance when compared with blood culture and 90.5% concordance when compared with clinical adjudication. Moreover, U-dHRM identified the causative pathogen to the species level in all cases where the organism was represented in the melt curve database. These results were achieved with a 1 mL sample input and sample-to-answer time of 6 hrs. Overall, this pilot study suggests that U-dHRM may be a promising method to address the challenges of quickly and accurately diagnosing a bloodstream infection. Universal digital high resolution melt analysis for the diagnosis of bacteremia April Aralar, Tyler Goshia, Nanda Ramchandar, Shelley M. Lawrence, Aparajita Karmakar, Ankit Sharma, Mridu Sinha, David Pride, Peiting Kuo, Khrissa Lecrone, Megan Chiu, Karen Mestan, Eniko Sajti, Michelle Vanderpool, Sarah Lazar, Melanie Crabtree, Yordanos Tesfai, Stephanie I. Fraley.
Collapse
|
13
|
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: 1] [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.
Collapse
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
| |
Collapse
|
14
|
Jenks JD, White PL, Kidd SE, Goshia T, Fraley SI, Hoenigl M, Thompson GR. An update on current and novel molecular diagnostics for the diagnosis of invasive fungal infections. Expert Rev Mol Diagn 2023; 23:1135-1152. [PMID: 37801397 PMCID: PMC10842420 DOI: 10.1080/14737159.2023.2267977] [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: 07/07/2023] [Accepted: 10/04/2023] [Indexed: 10/08/2023]
Abstract
BACKGROUND Invasive fungal infections cause millions of infections annually, but diagnosis remains challenging. There is an increased need for low-cost, easy to use, highly sensitive and specific molecular assays that can differentiate between colonized and pathogenic organisms from different clinical specimens. AREAS COVERED We reviewed the literature evaluating the current state of molecular diagnostics for invasive fungal infections, focusing on current and novel molecular tests such as polymerase chain reaction (PCR), digital PCR, high-resolution melt (HRM), and metagenomics/next generation sequencing (mNGS). EXPERT OPINION PCR is highly sensitive and specific, although performance can be impacted by prior/concurrent antifungal use. PCR assays can identify mutations associated with antifungal resistance, non-Aspergillus mold infections, and infections from endemic fungi. HRM is a rapid and highly sensitive diagnostic modality that can identify a wide range of fungal pathogens, including down to the species level, but multiplex assays are limited and HRM is currently unavailable in most healthcare settings, although universal HRM is working to overcome this limitation. mNGS offers a promising approach for rapid and hypothesis-free diagnosis of a wide range of fungal pathogens, although some drawbacks include limited access, variable performance across platforms, the expertise and costs associated with this method, and long turnaround times in real-world settings.
Collapse
Affiliation(s)
- Jeffrey D Jenks
- Durham County Department of Public Health, Durham, North Carolina, USA
- Division of Infectious Diseases, Department of Medicine, Duke University, Durham, North Carolina, USA
| | - P Lewis White
- Public Health Wales Microbiology Cardiff, UHW, United Kingdom and Centre for trials research/Division of Infection/Immunity, Cardiff University, Cardiff, UK
| | - Sarah E Kidd
- National Mycology Reference Centre, SA Pathology, Adelaide, Australia
- School of Biological Sciences, Faculty of Sciences, University of Adelaide, Adelaide, Australia
| | - Tyler Goshia
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Stephanie I Fraley
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Martin Hoenigl
- Division of Infectious Diseases, Medical University of Graz, Graz, Austria
- BioTechMed, Graz, Austria
| | - George R Thompson
- University of California Davis Center for Valley Fever, Sacramento, CA, USA
- Department of Internal Medicine, Division of Infectious Diseases, University of California Davis Medical Center, Sacramento, CA, USA
- Department of Medical Microbiology and Immunology, University of California Davis, Davis, CA, USA
| |
Collapse
|
15
|
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: 2] [Impact Index Per Article: 2.0] [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.
Collapse
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
| |
Collapse
|
16
|
Cai D, Wang Y, Zou J, Li Z, Huang E, Ouyang X, Que Z, Luo Y, Chen Z, Jiang Y, Zhang G, Wu H, Liu D. Droplet Encoding-Pairing Enabled Multiplexed Digital Loop-Mediated Isothermal Amplification for Simultaneous Quantitative Detection of Multiple Pathogens. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2205863. [PMID: 36646503 PMCID: PMC9982564 DOI: 10.1002/advs.202205863] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/06/2022] [Indexed: 06/01/2023]
Abstract
Despite the advantages of digital nucleic acid analysis (DNAA) in terms of sensitivity, precision, and resolution, current DNAA methods commonly suffer a limitation in multiplexing capacity. To address this issue, a droplet encoding-pairing enabled DNAA multiplexing strategy is developed, wherein unique tricolor combinations are deployed to index individual primer droplets. The template droplets and primer droplets are sequentially introduced into a microfluidic chip with a calabash-shaped microwell array and are pairwise trapped and merged in the microwells. Pre-merging and post-amplification image analysis with a machine learning algorithm is used to identify, enumerate, and address the droplets. By incorporating the amplification signals with droplet encoding information, simultaneous quantitative detection of multiple targets is achieved. This strategy allows for the establishment of flexible multiplexed DNAA by simply adjusting the primer droplet library. Its flexibility is demonstrated by establishing two multiplexed (8-plex) droplet digital loop-mediated isothermal amplification (mddLAMP) assays for individually detecting lower respiratory tract infection and urinary tract infection causative pathogens. Clinical sample analysis shows that the microbial detection outcomes of the mddLAMP assays are consistent with those of the conventional assay. This DNAA multiplexing strategy can achieve flexible high-order multiplexing on demand, making it a desirable tool for high-content pathogen detection.
Collapse
Affiliation(s)
- Dongyang Cai
- Department of Laboratory Medicinethe Second Affiliated HospitalSchool of MedicineSouth China University of TechnologyGuangzhou510180China
| | - Yu Wang
- Department of Laboratory Medicinethe Second Affiliated HospitalSchool of MedicineSouth China University of TechnologyGuangzhou510180China
| | - Jingjing Zou
- College of Food Science and EngineeringSouth China University of TechnologyGuangzhou510640China
| | - Zhujun Li
- Department of Laboratory Medicinethe Second Affiliated HospitalSchool of MedicineSouth China University of TechnologyGuangzhou510180China
| | - Enqi Huang
- Department of Laboratory Medicinethe Second Affiliated HospitalSchool of MedicineSouth China University of TechnologyGuangzhou510180China
| | - Xiuyun Ouyang
- College of Food Science and EngineeringSouth China University of TechnologyGuangzhou510640China
| | - Zhiquan Que
- Department of Laboratory Medicinethe Second Affiliated HospitalSchool of MedicineSouth China University of TechnologyGuangzhou510180China
| | - Yanzhang Luo
- Department of Laboratory Medicinethe Second Affiliated HospitalSchool of MedicineSouth China University of TechnologyGuangzhou510180China
| | - Zhenhua Chen
- Department of Laboratory Medicinethe Second Affiliated HospitalSchool of MedicineSouth China University of TechnologyGuangzhou510180China
| | - Yanqing Jiang
- Beijing Baicare Biotechnology Co., LtdBeijing102206China
| | - Guohao Zhang
- Beijing Baicare Biotechnology Co., LtdBeijing102206China
| | - Hongkai Wu
- Department of ChemistryHong Kong University of Science and TechnologyHong KongChina
| | - Dayu Liu
- Department of Laboratory Medicinethe Second Affiliated HospitalSchool of MedicineSouth China University of TechnologyGuangzhou510180China
- Guangdong Engineering Technology Research Center of Microfluidic Chip Medical DiagnosisGuangzhou510180China
- Clinical Molecular Medicine and Molecular Diagnosis Key Laboratory of Guangdong ProvinceGuangzhou510180China
| |
Collapse
|
17
|
Luo Y, Cui X, Cheruba E, Chua YK, Ng C, Tan RZ, Tan KK, Cheow LF. SAMBA: A Multicolor Digital Melting PCR Platform for Rapid Microbiome Profiling. SMALL METHODS 2022; 6:e2200185. [PMID: 35652511 DOI: 10.1002/smtd.202200185] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 04/27/2022] [Indexed: 06/15/2023]
Abstract
During the past decade, breakthroughs in sequencing technology have provided the basis for studies of the myriad ways in which microbial communities in and on the human body influence human health and disease. In almost every medical specialty, there is now a growing interest in accurate and quantitative profiling of the microbiota for use in diagnostic and therapeutic applications. However, the current next-generation sequencing approach for microbiome profiling is costly, requires laborious library preparation, and is challenging to scale up for routine diagnostics. Split, Amplify, and Melt analysis of BActeria-community (SAMBA), a novel multicolor digital melting polymerase chain reaction platform with unprecedented multiplexing capability is presented, and the capability to distinguish and quantify 16 bacteria species in mixtures is demonstrated. Subsequently, SAMBA is applied to measure the compositions of bacteria in the gut microbiome to identify microbial dysbiosis related to colorectal cancer. This rapid, low cost, and high-throughput approach will enable the implementation of microbiome diagnostics in clinical laboratories and routine medical practice.
Collapse
Affiliation(s)
- Yongqiang Luo
- Department of Biomedical Engineering & Institute for Health Innovation and Technology, National University of Singapore, Singapore, 119077, Singapore
| | - Xu Cui
- Department of Biomedical Engineering & Institute for Health Innovation and Technology, National University of Singapore, Singapore, 119077, Singapore
| | - Elsie Cheruba
- Department of Biomedical Engineering & Institute for Health Innovation and Technology, National University of Singapore, Singapore, 119077, Singapore
| | - Yong Kang Chua
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
| | - Charmaine Ng
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
| | - Rui Zhen Tan
- Engineering Cluster, Singapore Institute of Technology, Singapore, 138683, Singapore
| | - Ker-Kan Tan
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore
- Division of Colorectal Surgery, National University Hospital, Singapore, 119074, Singapore
| | - Lih Feng Cheow
- Department of Biomedical Engineering & Institute for Health Innovation and Technology, National University of Singapore, Singapore, 119077, Singapore
| |
Collapse
|
18
|
Zhang H, Laššáková S, Yan Z, Wang X, Šenkyřík P, Gaňová M, Chang H, Korabecna M, Neuzil P. Digital polymerase chain reaction duplexing method in a single fluorescence channel. Anal Chim Acta 2022; 1238:340243. [DOI: 10.1016/j.aca.2022.340243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 11/24/2022]
|
19
|
Kota PK, LeJeune D, Drezek RA, Baraniuk RG. Extreme Compressed Sensing of Poisson Rates from Multiple Measurements. IEEE TRANSACTIONS ON SIGNAL PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2022; 70:2388-2401. [PMID: 36082267 PMCID: PMC9447484 DOI: 10.1109/tsp.2022.3172028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Compressed sensing (CS) is a signal processing technique that enables the efficient recovery of a sparse high-dimensional signal from low-dimensional measurements. In the multiple measurement vector (MMV) framework, a set of signals with the same support must be recovered from their corresponding measurements. Here, we present the first exploration of the MMV problem where signals are independently drawn from a sparse, multivariate Poisson distribution. We are primarily motivated by a suite of biosensing applications of microfluidics where analytes (such as whole cells or biomarkers) are captured in small volume partitions according to a Poisson distribution. We recover the sparse parameter vector of Poisson rates through maximum likelihood estimation with our novel Sparse Poisson Recovery (SPoRe) algorithm. SPoRe uses batch stochastic gradient ascent enabled by Monte Carlo approximations of otherwise intractable gradients. By uniquely leveraging the Poisson structure, SPoRe substantially outperforms a comprehensive set of existing and custom baseline CS algorithms. Notably, SPoRe can exhibit high performance even with one-dimensional measurements and high noise levels. This resource efficiency is not only unprecedented in the field of CS but is also particularly potent for applications in microfluidics in which the number of resolvable measurements per partition is often severely limited. We prove the identifiability property of the Poisson model under such lax conditions, analytically develop insights into system performance, and confirm these insights in simulated experiments. Our findings encourage a new approach to biosensing and are generalizable to other applications featuring spatial and temporal Poisson signals.
Collapse
Affiliation(s)
- Pavan K Kota
- Department of Bioengineering, Rice University, Houston, TX 77005 USA
| | - Daniel LeJeune
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005 USA
| | - Rebekah A Drezek
- Department of Bioengineering, Rice University, Houston, TX 77005 USA
| | - Richard G Baraniuk
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005 USA
| |
Collapse
|
20
|
Carvalho J, Yadav S, Garrido-Maestu A, Azinheiro S, Trujillo I, Barros-Velázquez J, Prado M. Evaluation of simple sequence repeats (SSR) and single nucleotide polymorphism (SNP)-based methods in olive varieties from the Northwest of Spain and potential for miniaturization. FOOD CHEMISTRY: MOLECULAR SCIENCES 2021; 3:100038. [PMID: 35415648 PMCID: PMC8991621 DOI: 10.1016/j.fochms.2021.100038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 07/10/2021] [Accepted: 07/31/2021] [Indexed: 11/17/2022]
Abstract
SSR- and SNP-based methods were evaluated for the identification of olive varieties. SNP identification was performed for the first time for two autochthonous varieties. The potential for future miniaturization of the genotyping methods was evaluated. Allele-specific PCR provided the best results for the tested olive varieties.
Miniaturization of DNA-based techniques can bring interesting advantages for food analysis, such as portability of complex analytical procedures. In the olive oil industry, miniaturization can be particularly interesting for authenticity and traceability applications, through in situ control of raw materials before production and/or the final products. However, variety identification is challenging, and implementation on miniaturized settings must be carefully evaluated, starting from the selected analytical approach. In this work, SSR- and SNP-based genotyping strategies were investigated for the identification and differentiation of two olive varieties from the Northwest of Spain. For the selected SNPs two genotyping methods were tested: real-time allele-specific PCR and high resolution melting analysis. These methods were compared and evaluated regarding their potential for integration in a microfluidic device. Both SNP-based methods proved to be successful for identification of the selected varieties, however real-time allele-specific PCR was the one that achieved the best results when analyzing mixtures, allowing the identification of both monovarietal samples and mixtures of the varieties tested with up to 25%.
Collapse
|
21
|
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: 22] [Impact Index Per Article: 7.3] [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.
Collapse
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.
| |
Collapse
|
22
|
Langouche L, Aralar A, Sinha M, Lawrence SM, Fraley SI, Coleman TP. Data-driven noise modeling of digital DNA melting analysis enables prediction of sequence discriminating power. Bioinformatics 2020; 36:5337-5343. [PMID: 33355665 PMCID: PMC8016452 DOI: 10.1093/bioinformatics/btaa1053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 12/04/2020] [Accepted: 12/09/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION The need to rapidly screen complex samples for a wide range of nucleic acid targets, like infectious diseases, remains unmet. Digital High-Resolution Melt (dHRM) is an emerging technology with potential to meet this need by accomplishing broad-based, rapid nucleic acid sequence identification. Here, we set out to develop a computational framework for estimating the resolving power of dHRM technology for defined sequence profiling tasks. By deriving noise models from experimentally generated dHRM datasets and applying these to in silico predicted melt curves, we enable the production of synthetic dHRM datasets that faithfully recapitulate real-world variations arising from sample and machine variables. We then use these datasets to identify the most challenging melt curve classification tasks likely to arise for a given application and test the performance of benchmark classifiers. RESULTS This toolbox enables the in silico design and testing of broad-based dHRM screening assays and the selection of optimal classifiers. For an example application of screening common human bacterial pathogens, we show that human pathogens having the most similar sequences and melt curves are still reliably identifiable in the presence of experimental noise. Further, we find that ensemble methods outperform whole series classifiers for this task and are in some cases able to resolve melt curves with single-nucleotide resolution. AVAILABILITY Data and code available on https://github.com/lenlan/dHRM-noise-modeling. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Lennart Langouche
- Department of Nanoengineering, University of California, San Diego, La Jolla, CA, USA
| | - April Aralar
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Mridu Sinha
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Shelley M Lawrence
- Department of Pediatrics, Division of Neonatal-Perinatal Medicine, University of California, San Diego, La Jolla, CA, USA.,Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA.,Rady Children's Hospital of San Diego, San Diego, CA, USA
| | - Stephanie I Fraley
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.,Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Todd P Coleman
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| |
Collapse
|
23
|
Zhang H, Gaňová M, Yan Z, Chang H, Neužil P. PCR Multiplexing Based on a Single Fluorescent Channel Using Dynamic Melting Curve Analysis. ACS OMEGA 2020; 5:30267-30273. [PMID: 33251461 PMCID: PMC7689941 DOI: 10.1021/acsomega.0c04766] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 10/29/2020] [Indexed: 06/12/2023]
Abstract
Since its invention in 1986, the polymerase chain reaction (PCR), has become a well-established method for the detection and amplification of deoxyribonucleic acid (DNA) with a specific sequence. Incorporating fluorescent probes, known as TaqMan probes, or DNA intercalating dyes, such as SYBR Green, into the PCR mixture allows real-time monitoring of the reaction progress and extraction of quantitative information. Previously reported real-time PCR product detection using intercalating dyes required melting curve analysis (MCA) to be performed following thermal cycling. Here, we propose a technique to perform dynamic MCA during each thermal cycle, based on a continuous fluorescence monitoring method, providing qualitative and quantitative sample information. We applied the proposed method in multiplexing detection of hepatitis B virus DNA and complementary DNA of human immunodeficiency virus as well as glyceraldehyde 3-phosphate dehydrogenase in different concentration ratios. We extracted the DNA melting curve and its derivative from each PCR cycle during the transition from the elongation to the denaturation temperature with a set heating rate of 0.8 K·s-1and then used the data to construct individual PCR amplification curves for each gene to determine the initial concentration of DNA in the sample. Our proposed method allows researchers to look inside the PCR in each thermal cycle, determining the PCR product specificity in real time instead of waiting until the end of the PCR. Additionally, the slow transition rate from elongation to denaturation provides a dynamic multiplexing assay, allowing the detection of at least three genes in real time.
Collapse
Affiliation(s)
- Haoqing Zhang
- Ministry
of Education Key Laboratory of Micro/Nano Systems for Aerospace, Department
of Microsystem Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an, Shaanxi 710072, P. R. China
| | - Martina Gaňová
- Ministry
of Education Key Laboratory of Micro/Nano Systems for Aerospace, Department
of Microsystem Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an, Shaanxi 710072, P. R. China
- Central
European Institute of Technology, Brno University
of Technology, Purkyňova 123, 612 00 Brno, Czech Republic
| | - ZhiQiang Yan
- Ministry
of Education Key Laboratory of Micro/Nano Systems for Aerospace, Department
of Microsystem Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an, Shaanxi 710072, P. R. China
| | - Honglong Chang
- Ministry
of Education Key Laboratory of Micro/Nano Systems for Aerospace, Department
of Microsystem Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an, Shaanxi 710072, P. R. China
| | - Pavel Neužil
- Ministry
of Education Key Laboratory of Micro/Nano Systems for Aerospace, Department
of Microsystem Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, 127 West Youyi Road, Xi’an, Shaanxi 710072, P. R. China
- Central
European Institute of Technology, Brno University
of Technology, Purkyňova 123, 612 00 Brno, Czech Republic
- Department
of Microelectronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 3058/10, 616 00 Brno, Czech Republic
| |
Collapse
|
24
|
Moniri A, Miglietta L, Holmes A, Georgiou P, Rodriguez-Manzano J. High-Level Multiplexing in Digital PCR with Intercalating Dyes by Coupling Real-Time Kinetics and Melting Curve Analysis. Anal Chem 2020; 92:14181-14188. [PMID: 32954724 DOI: 10.1021/acs.analchem.0c03298] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Digital polymerase chain reaction (dPCR) is a mature technique that has enabled scientific breakthroughs in several fields. However, this technology is primarily used in research environments with high-level multiplexing, representing a major challenge. Here, we propose a novel method for multiplexing, referred to as amplification and melting curve analysis (AMCA), which leverages the kinetic information in real-time amplification data and the thermodynamic melting profile using an affordable intercalating dye (EvaGreen). The method trains a system composed of supervised machine learning models for accurate classification, by virtue of the large volume of data from dPCR platforms. As a case study, we develop a new 9-plex assay to detect mobilized colistin resistant genes as clinically relevant targets for antimicrobial resistance. Over 100,000 amplification events have been analyzed, and for the positive reactions, the AMCA approach reports a classification accuracy of 99.33 ± 0.13%, an increase of 10.0% over using melting curve analysis. This work provides an affordable method of high-level multiplexing without fluorescent probes, extending the benefits of dPCR in research and clinical settings.
Collapse
Affiliation(s)
- Ahmad Moniri
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K
| | - Luca Miglietta
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K
| | - Alison Holmes
- NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London W12 0NN, U.K
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K
| | - Jesus Rodriguez-Manzano
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K.,NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Infectious Disease, Imperial College London, London W12 0NN, U.K
| |
Collapse
|
25
|
Gaddes DE, Lee PW, Trick AY, Athamanolap P, O'Keefe CM, Puleo C, Hsieh K, Wang TH. Facile Coupling of Droplet Magnetofluidic-Enabled Automated Sample Preparation for Digital Nucleic Acid Amplification Testing and Analysis. Anal Chem 2020; 92:13254-13261. [PMID: 32869628 PMCID: PMC8549765 DOI: 10.1021/acs.analchem.0c02454] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Digital nucleic acid amplification testing (dNAAT) and analysis techniques, such as digital polymerase chain reaction (PCR), have become useful clinical diagnostic tools. However, nucleic acid (NA) sample preparation preceding dNAAT is generally laborious and performed manually, thus creating the need for a simple sample preparation technique and a facile coupling strategy for dNAAT. Therefore, we demonstrate a simple workflow which automates magnetic bead-based extraction of NAs with a one-step transfer to dNAAT. Specifically, we leverage droplet magnetofluidics (DM) to automate the movement of magnetic beads between small volumes of reagents commonly employed for NA extraction and purification. Importantly, the buffer typically used to elute the NAs off the magnetic beads is replaced by a carefully selected PCR solution, enabling direct transfer from sample preparation to dNAAT. Moreover, we demonstrate the potential for multiplexing using a digital high-resolution melt (dHRM) after the digital PCR (dPCR). The utility of this workflow is demonstrated with duplexed detection of bacteria in a sample imitating a coinfection. We first purify the bacterial DNA into a PCR solution using our DM-based sample preparation. We then transfer the purified bacterial DNA to our microfluidic nanoarray to amplify 16S rRNA using dPCR and then perform dHRM to identify the two bacterial species.
Collapse
Affiliation(s)
- David E Gaddes
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Pei-Wei Lee
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Alexander Y Trick
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Pornpat Athamanolap
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Nakorn Pathom 73170, Thailand
| | - Christine M O'Keefe
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Chris Puleo
- Electronics Organization, GE Global Research Center, Niskayuna, New York 12309, United States
| | - Kuangwen Hsieh
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Tza-Huei Wang
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21205, United States
| |
Collapse
|
26
|
Nakagawa T, Tanaka J, Harada K, Shiratori A, Shimazaki Y, Yokoi T, Uematsu C, Kohara Y. 10-Plex Digital Polymerase Chain Reaction with Four-Color Melting Curve Analysis for Simultaneous KRAS and BRAF Genotyping. Anal Chem 2020; 92:11705-11713. [PMID: 32786457 DOI: 10.1021/acs.analchem.0c01704] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Digital PCR (dPCR) is a promising method for performing liquid biopsies that quantifies nucleic acids more sensitively than real-time PCR. However, dPCR shows large fluctuations in the fluorescence intensity of droplets or wells due to insufficient PCR amplification in the small partitions, limiting the multiplexing capability of using the fluorescence intensity. In this study, we propose a measurement method that combines dPCR with melting curve analysis for highly multiplexed genotyping. A sample was digitized into a silicon chip with up to 2 × 104 wells in which asymmetric PCR was performed to obtain more single-stranded amplicons that were complementary to molecular beacon probes. Fluorescence images were captured while controlling the temperature of the chip, and the melting curve was measured for each well. Then, genotyping was performed by using the fluorescence intensity, the dye color of the probe, and the melting temperature (Tm). Because the Tm of the PCR products is not highly dependent on the amplification efficiency of PCR, genotyping accuracy is improved by using Tm values, enabling highly multiplexed genotyping. The concept was confirmed by simultaneously identifying wild-type KRAS, BRAF, and eight mutants of these genes (G12D, G12R, G12V, G13D, G12A, G12C, G12S, and V600E) through four-color melting curve analysis. To the best of our knowledge, this is the first demonstration of the genotyping of 10 DNA groups including single mutations of cancer-related genes by combining dPCR with four-color melting curve analysis.
Collapse
Affiliation(s)
- Tatsuo Nakagawa
- Biosystems Research Department, Research & Development Group, Hitachi, Ltd.,1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Junko Tanaka
- Biosystems Research Department, Research & Development Group, Hitachi, Ltd.,1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Kunio Harada
- Biosystems Research Department, Research & Development Group, Hitachi, Ltd.,1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Akiko Shiratori
- Biosystems Research Department, Research & Development Group, Hitachi, Ltd.,1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Yuzuru Shimazaki
- Biosystems Research Department, Research & Development Group, Hitachi, Ltd.,1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Takahide Yokoi
- Biosystems Research Department, Research & Development Group, Hitachi, Ltd.,1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Chihiro Uematsu
- Biosystems Research Department, Research & Development Group, Hitachi, Ltd.,1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| | - Yoshinobu Kohara
- Biosystems Research Department, Research & Development Group, Hitachi, Ltd.,1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo 185-8601, Japan
| |
Collapse
|
27
|
Improving Quantitative Power in Digital PCR through Digital High-Resolution Melting. J Clin Microbiol 2020; 58:JCM.00325-20. [PMID: 32295887 DOI: 10.1128/jcm.00325-20] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 04/05/2020] [Indexed: 12/23/2022] Open
Abstract
Applying digital PCR (dPCR) technology to challenging clinical and industrial detection tasks has become more prevalent because of its capability for absolute quantification and rare target detection. However, practices learned from quantitative PCR (qPCR) that promote assay robustness and wide-ranging utility are not readily applied in dPCR. These include internal amplification controls to account for false-negative reactions and amplicon high-resolution melt (HRM) analysis to distinguish true positives from false positives. Incorporation of internal amplification controls in dPCR is challenging because of the limited fluorescence channels available on most machines, and the application of HRM analysis is hindered by the separation of heating and imaging functions on most dPCR systems. We use a custom digital HRM platform to assess the utility of HRM-based approaches for mitigation of false positives and false negatives in dPCR. We show that detection of an exogenous internal control using dHRM analysis reduces the inclusion of false-negative partitions, changing the calculated DNA concentration up to 52%. The integration of dHRM analysis enables classification of partitions that would otherwise be considered ambiguous "rain," which accounts for up to ∼3% and ∼10% of partitions in intercalating dye and hydrolysis probe dPCR, respectively. We focused on developing an internal control method that would be compatible with broad-based microbial detection in dPCR-dHRM. Our approach can be applied to a number of DNA detection methods including microbial profiling and may advance the utility of dPCR in clinical applications where accurate quantification is imperative.
Collapse
|
28
|
Rolando JC, Jue E, Barlow JT, Ismagilov RF. Real-time kinetics and high-resolution melt curves in single-molecule digital LAMP to differentiate and study specific and non-specific amplification. Nucleic Acids Res 2020; 48:e42. [PMID: 32103255 PMCID: PMC7144905 DOI: 10.1093/nar/gkaa099] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 01/08/2020] [Accepted: 02/06/2020] [Indexed: 12/15/2022] Open
Abstract
Isothermal amplification assays, such as loop-mediated isothermal amplification (LAMP), show great utility for the development of rapid diagnostics for infectious diseases because they have high sensitivity, pathogen-specificity and potential for implementation at the point of care. However, elimination of non-specific amplification remains a key challenge for the optimization of LAMP assays. Here, using chlamydia DNA as a clinically relevant target and high-throughput sequencing as an analytical tool, we investigate a potential mechanism of non-specific amplification. We then develop a real-time digital LAMP (dLAMP) with high-resolution melting temperature (HRM) analysis and use this single-molecule approach to analyze approximately 1.2 million amplification events. We show that single-molecule HRM provides insight into specific and non-specific amplification in LAMP that are difficult to deduce from bulk measurements. We use real-time dLAMP with HRM to evaluate differences between polymerase enzymes, the impact of assay parameters (e.g. time, rate or florescence intensity), and the effect background human DNA. By differentiating true and false positives, HRM enables determination of the optimal assay and analysis parameters that leads to the lowest limit of detection (LOD) in a digital isothermal amplification assay.
Collapse
Affiliation(s)
- Justin C Rolando
- Division of Chemistry and Chemical Engineering, California Institute of Technology 1200 E. California Boulevard, Pasadena, CA 91125, USA
| | - Erik Jue
- Division of Biology and Biological Engineering, California Institute of Technology 1200 E. California Boulevard, Pasadena, CA 91125, USA
| | - Jacob T Barlow
- Division of Biology and Biological Engineering, California Institute of Technology 1200 E. California Boulevard, Pasadena, CA 91125, USA
| | - Rustem F Ismagilov
- Division of Chemistry and Chemical Engineering, California Institute of Technology 1200 E. California Boulevard, Pasadena, CA 91125, USA
- Division of Biology and Biological Engineering, California Institute of Technology 1200 E. California Boulevard, Pasadena, CA 91125, USA
| |
Collapse
|
29
|
Athamanolap P, Hsieh K, O'Keefe CM, Zhang Y, Yang S, Wang TH. Nanoarray Digital Polymerase Chain Reaction with High-Resolution Melt for Enabling Broad Bacteria Identification and Pheno-Molecular Antimicrobial Susceptibility Test. Anal Chem 2019; 91:12784-12792. [PMID: 31525952 DOI: 10.1021/acs.analchem.9b02344] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Toward combating infectious diseases caused by pathogenic bacteria, there remains an unmet need for diagnostic tools that can broadly identify the causative bacteria and determine their antimicrobial susceptibilities from complex and even polymicrobial samples in a timely manner. To address this need, a microfluidic and machine-learning-based platform that performs broad bacteria identification (ID) and rapid yet reliable antimicrobial susceptibility testing (AST) is developed. Specifically, this platform builds on "pheno-molecular AST", a strategy that transforms nucleic acid amplification tests (NAATs) into phenotypic AST through quantitative detection of bacterial genomic replication, and utilizes digital polymerase chain reaction (PCR) and digital high-resolution melt (HRM) to quantify and identify bacterial DNA molecules. Bacterial species are identified using integrated experiment-machine learning algorithm via HRM profiles. Digital DNA quantification allows for rapid growth measurement that reflects susceptibility profiles of each bacterial species within only 30 min of antibiotic exposure. As a demonstration, multiple bacterial species and their susceptibility profiles in a spiked-in polymicrobial urine specimen were correctly identified with a total turnaround time of ∼4 h. With further development and clinical validation, this platform holds the potential for improving clinical diagnostics and enabling targeted antibiotic treatments.
Collapse
Affiliation(s)
- Pornpat Athamanolap
- Department of Biomedical Engineering , Johns Hopkins School of Medicine , Baltimore , Maryland 21205 , United States
| | | | - Christine M O'Keefe
- Department of Biomedical Engineering , Johns Hopkins School of Medicine , Baltimore , Maryland 21205 , United States
| | - Ye Zhang
- Department of Biomedical Engineering , Johns Hopkins School of Medicine , Baltimore , Maryland 21205 , United States
| | - Samuel Yang
- Department of Emergency Medicine , Stanford University , Stanford , California 94304 , United States
| | - Tza-Huei Wang
- Department of Biomedical Engineering , Johns Hopkins School of Medicine , Baltimore , Maryland 21205 , United States.,The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins , Baltimore , Maryland 21287 , United States
| |
Collapse
|
30
|
Zhang Y, Hu A, Andini N, Yang S. A 'culture' shift: Application of molecular techniques for diagnosing polymicrobial infections. Biotechnol Adv 2019; 37:476-490. [PMID: 30797092 PMCID: PMC6447436 DOI: 10.1016/j.biotechadv.2019.02.013] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 02/04/2019] [Accepted: 02/19/2019] [Indexed: 12/11/2022]
Abstract
With the advancement of microbiological discovery, it is evident that many infections, particularly bloodstream infections, are polymicrobial in nature. Consequently, new challenges have emerged in identifying the numerous etiologic organisms in an accurate and timely manner using the current diagnostic standard. Various molecular diagnostic methods have been utilized as an effort to provide a fast and reliable identification in lieu or parallel to the conventional culture-based methods. These technologies are mostly based on nucleic acid, proteins, or physical properties of the pathogens with differing advantages and limitations. This review evaluates the different molecular methods and technologies currently available to diagnose polymicrobial infections, which will help determine the most appropriate option for future diagnosis.
Collapse
Affiliation(s)
- Yi Zhang
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore.
| | - Anne Hu
- Emergency Medicine, Stanford University, Stanford, California 94305, USA
| | - Nadya Andini
- Emergency Medicine, Stanford University, Stanford, California 94305, USA
| | - Samuel Yang
- Emergency Medicine, Stanford University, Stanford, California 94305, USA.
| |
Collapse
|
31
|
KRAS genotyping by digital PCR combined with melting curve analysis. Sci Rep 2019; 9:2626. [PMID: 30796246 PMCID: PMC6384904 DOI: 10.1038/s41598-019-38822-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Accepted: 01/10/2019] [Indexed: 01/05/2023] Open
Abstract
Digital PCR (dPCR) has been developed as a method that can quantify nucleic acids more sensitively than real-time PCR. However, dPCR exhibits large fluctuations in the fluorescence intensity of the compartment, resulting in low accuracy. The main cause is most likely due to insufficient PCR. In this study, we proposed a new method that combines dPCR with melting curve analysis and applied that method to KRAS genotyping. Since the melting temperature (Tm) of the PCR product hardly depends on the amplification efficiency, genotyping accuracy is improved by using the Tm value. The results showed that the peaks of the distribution of the Tm values of DNA in the wells were 68.7, 66.3, and 62.6 °C for wild-type KRAS, the G12R mutant, and the G12D mutant, respectively, and the standard deviation of the Tm values was 0.2 °C for each genotype. This result indicates that the proposed method is capable of discriminating between the wild-type sequence and the two mutants. To the best of our knowledge, this is the first demonstration of the genotyping of single mutations by combining melting curve analysis and dPCR. The application of this approach could be useful for the quantification and genotyping of cancer-related genes in low-abundance samples.
Collapse
|
32
|
O’Keefe CM, Pisanic TR, Zec H, Overman MJ, Herman JG, Wang TH. Facile profiling of molecular heterogeneity by microfluidic digital melt. SCIENCE ADVANCES 2018; 4:eaat6459. [PMID: 30263958 PMCID: PMC6157960 DOI: 10.1126/sciadv.aat6459] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 08/14/2018] [Indexed: 05/05/2023]
Abstract
This work presents a digital microfluidic platform called HYPER-Melt (high-density profiling and enumeration by melt) for highly parallelized copy-by-copy DNA molecular profiling. HYPER-Melt provides a facile means of detecting and assessing sequence variations of thousands of individual DNA molecules through digitization in a nanowell microchip array, allowing amplification and interrogation of individual template molecules by detecting HRM fluorescence changes due to sequence-dependent denaturation. As a model application, HYPER-Melt is used here for the detection and assessment of intermolecular heterogeneity of DNA methylation within the promoters of classical tumor suppressor genes. The capabilities of this platform are validated through serial dilutions of mixed epialleles, with demonstrated detection limits as low as 1 methylated variant in 2 million unmethylated templates (0.00005%) of a classic tumor suppressor gene, CDKN2A (p14ARF). The clinical potential of the platform is demonstrated using a digital assay for NDRG4, a tumor suppressor gene that is commonly methylated in colorectal cancer, in liquid biopsies of healthy and colorectal cancer patients. Overall, the platform provides the depth of information, simplicity of use, and single-molecule sensitivity necessary for rapid assessment of intermolecular variation contributing to genetic and epigenetic heterogeneity for challenging applications in embryogenesis, carcinogenesis, and rare biomarker detection.
Collapse
Affiliation(s)
- Christine M. O’Keefe
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
| | - Thomas R. Pisanic
- Johns Hopkins Institute for NanoBioTechnology, Baltimore, MD 21218, USA
| | - Helena Zec
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
| | - Michael J. Overman
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - James G. Herman
- Division of Hematology/Oncology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15232, USA
| | - Tza-Huei Wang
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
- Johns Hopkins Institute for NanoBioTechnology, Baltimore, MD 21218, USA
- Division of Hematology/Oncology, Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15232, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Corresponding author.
| |
Collapse
|
33
|
Sinha M, Mack H, Coleman TP, Fraley SI. A High-Resolution Digital DNA Melting Platform for Robust Sequence Profiling and Enhanced Genotype Discrimination. SLAS Technol 2018; 23:580-591. [DOI: 10.1177/2472630318769846] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
DNA melting analysis provides a rapid method for genotyping a target amplicon directly after PCR amplification. To transform melt genotyping into a broad-based profiling approach for heterogeneous samples, we previously proposed the integration of universal PCR and melt analysis with digital PCR. Here, we advanced this concept by developing a high-resolution digital melt platform with precise thermal control to accomplish reliable, high-throughput heat ramping of microfluidic chip digital PCR reactions. Using synthetic DNA oligos with defined melting temperatures, we characterized sources of melting variability and minimized run-to-run variations. Within-run comparisons throughout a 20,000-reaction chip revealed that high-melting-temperature sequences were significantly less prone to melt variation. Further optimization using bacterial 16S amplicons revealed a strong dependence of the number of melting transitions on the heating rate during curve generation. These studies show that reliable high-resolution melt curve genotyping can be achieved in digital, picoliter-scale reactions and demonstrate that rate-dependent melt signatures may be useful for enhancing automated melt genotyping.
Collapse
Affiliation(s)
- Mridu Sinha
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Clinical Translational Research Institute, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Hannah Mack
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Clinical Translational Research Institute, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Todd P. Coleman
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| | - Stephanie I. Fraley
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Clinical Translational Research Institute, University of California, San Diego, La Jolla, CA, USA
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA
| |
Collapse
|
34
|
Sinha M, Jupe J, Mack H, Coleman TP, Lawrence SM, Fraley SI. Emerging Technologies for Molecular Diagnosis of Sepsis. Clin Microbiol Rev 2018; 31:e00089-17. [PMID: 29490932 PMCID: PMC5967692 DOI: 10.1128/cmr.00089-17] [Citation(s) in RCA: 186] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Rapid and accurate profiling of infection-causing pathogens remains a significant challenge in modern health care. Despite advances in molecular diagnostic techniques, blood culture analysis remains the gold standard for diagnosing sepsis. However, this method is too slow and cumbersome to significantly influence the initial management of patients. The swift initiation of precise and targeted antibiotic therapies depends on the ability of a sepsis diagnostic test to capture clinically relevant organisms along with antimicrobial resistance within 1 to 3 h. The administration of appropriate, narrow-spectrum antibiotics demands that such a test be extremely sensitive with a high negative predictive value. In addition, it should utilize small sample volumes and detect polymicrobial infections and contaminants. All of this must be accomplished with a platform that is easily integrated into the clinical workflow. In this review, we outline the limitations of routine blood culture testing and discuss how emerging sepsis technologies are converging on the characteristics of the ideal sepsis diagnostic test. We include seven molecular technologies that have been validated on clinical blood specimens or mock samples using human blood. In addition, we discuss advances in machine learning technologies that use electronic medical record data to provide contextual evaluation support for clinical decision-making.
Collapse
Affiliation(s)
- Mridu Sinha
- Bioengineering Department, University of California, San Diego, San Diego, California, USA
| | - Julietta Jupe
- Donald Danforth Plant Science Center, Saint Louis, Missouri, USA
| | - Hannah Mack
- Bioengineering Department, University of California, San Diego, San Diego, California, USA
| | - Todd P Coleman
- Bioengineering Department, University of California, San Diego, San Diego, California, USA
- Center for Microbiome Innovation, University of California, San Diego, San Diego, California, USA
| | - Shelley M Lawrence
- Department of Pediatrics, Division of Neonatal-Perinatal Medicine, University of California, San Diego, San Diego, California, USA
- Rady Children's Hospital of San Diego, San Diego, California, USA
- Clinical Translational Research Institute, University of California, San Diego, San Diego, California, USA
- Center for Microbiome Innovation, University of California, San Diego, San Diego, California, USA
| | - Stephanie I Fraley
- Bioengineering Department, University of California, San Diego, San Diego, California, USA
- Clinical Translational Research Institute, University of California, San Diego, San Diego, California, USA
- Center for Microbiome Innovation, University of California, San Diego, San Diego, California, USA
| |
Collapse
|
35
|
Shah K, Bentley E, Tyler A, Richards KSR, Wright E, Easterbrook L, Lee D, Cleaver C, Usher L, Burton JE, Pitman JK, Bruce CB, Edge D, Lee M, Nazareth N, Norwood DA, Moschos SA. Field-deployable, quantitative, rapid identification of active Ebola virus infection in unprocessed blood. Chem Sci 2017; 8:7780-7797. [PMID: 29163915 PMCID: PMC5694917 DOI: 10.1039/c7sc03281a] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 09/20/2017] [Indexed: 01/01/2023] Open
Abstract
The West African Ebola virus outbreak underlined the importance of delivering mass diagnostic capability outside the clinical or primary care setting in effectively containing public health emergencies caused by infectious disease. Yet, to date, there is no solution for reliably deploying at the point of need the gold standard diagnostic method, real time quantitative reverse transcription polymerase chain reaction (RT-qPCR), in a laboratory infrastructure-free manner. In this proof of principle work, we demonstrate direct performance of RT-qPCR on fresh blood using far-red fluorophores to resolve fluorogenic signal inhibition and controlled, rapid freeze/thawing to achieve viral genome extraction in a single reaction chamber assay. The resulting process is entirely free of manual or automated sample pre-processing, requires no microfluidics or magnetic/mechanical sample handling and thus utilizes low cost consumables. This enables a fast, laboratory infrastructure-free, minimal risk and simple standard operating procedure suited to frontline, field use. Developing this novel approach on recombinant bacteriophage and recombinant human immunodeficiency virus (HIV; Lentivirus), we demonstrate clinical utility in symptomatic EBOV patient screening using live, infectious Filoviruses and surrogate patient samples. Moreover, we evidence assay co-linearity independent of viral particle structure that may enable viral load quantification through pre-calibration, with no loss of specificity across an 8 log-linear maximum dynamic range. The resulting quantitative rapid identification (QuRapID) molecular diagnostic platform, openly accessible for assay development, meets the requirements of resource-limited countries and provides a fast response solution for mass public health screening against emerging biosecurity threats.
Collapse
Affiliation(s)
- Kavit Shah
- Westminster Genomic Services , Department of Biomedical Sciences , Faculty of Science and Technology , University of Westminster , 115 New Cavendish Str , London W1W 6UW , UK
- BGResearch Ltd. , 6 The Business Centre, Harvard Way, Harvard Industrial Estate , Kimbolton , Huntingdon PE28 0NJ , UK
| | - Emma Bentley
- Department of Biomedical Sciences , Faculty of Science and Technology , University of Westminster , 115 New Cavendish Str , London W1W 6UW , UK
| | - Adam Tyler
- BioGene Ltd. , 8 The Business Centre, Harvard Way, Harvard Industrial Estate , Kimbolton , Huntingdon PE28 0NJ , UK
| | - Kevin S R Richards
- Public Health England , National Infection Service , High Containment Microbiology Department , Porton Down , Salisbury , Wiltshire SP4 0JG , UK
| | - Edward Wright
- Department of Biomedical Sciences , Faculty of Science and Technology , University of Westminster , 115 New Cavendish Str , London W1W 6UW , UK
| | - Linda Easterbrook
- Public Health England , National Infection Service , High Containment Microbiology Department , Porton Down , Salisbury , Wiltshire SP4 0JG , UK
| | - Diane Lee
- Fluorogenics LIMITED , Building 227, Tetricus Science Park, Dstl Porton Down , Salisbury , Wiltshire SP4 0JQ , UK
| | - Claire Cleaver
- Fluorogenics LIMITED , Building 227, Tetricus Science Park, Dstl Porton Down , Salisbury , Wiltshire SP4 0JQ , UK
| | - Louise Usher
- Westminster Genomic Services , Department of Biomedical Sciences , Faculty of Science and Technology , University of Westminster , 115 New Cavendish Str , London W1W 6UW , UK
| | - Jane E Burton
- Public Health England , National Infection Service , High Containment Microbiology Department , Porton Down , Salisbury , Wiltshire SP4 0JG , UK
| | - James K Pitman
- Public Health England , National Infection Service , High Containment Microbiology Department , Porton Down , Salisbury , Wiltshire SP4 0JG , UK
| | - Christine B Bruce
- Public Health England , National Infection Service , High Containment Microbiology Department , Porton Down , Salisbury , Wiltshire SP4 0JG , UK
| | - David Edge
- BioGene Ltd. , 8 The Business Centre, Harvard Way, Harvard Industrial Estate , Kimbolton , Huntingdon PE28 0NJ , UK
| | - Martin Lee
- Fluorogenics LIMITED , Building 227, Tetricus Science Park, Dstl Porton Down , Salisbury , Wiltshire SP4 0JQ , UK
| | - Nelson Nazareth
- BioGene Ltd. , 8 The Business Centre, Harvard Way, Harvard Industrial Estate , Kimbolton , Huntingdon PE28 0NJ , UK
| | - David A Norwood
- Diagnostic Systems Division and Virology Division , United States Army Medical Research Institute of Infectious Diseases , Fort Detrick , MD 21701-5011 , USA
| | - Sterghios A Moschos
- Westminster Genomic Services , Department of Biomedical Sciences , Faculty of Science and Technology , University of Westminster , 115 New Cavendish Str , London W1W 6UW , UK
- Department of Biomedical Sciences , Faculty of Science and Technology , University of Westminster , 115 New Cavendish Str , London W1W 6UW , UK
- Department of Applied Sciences , Faculty of Health and Life Sciences , Northumbria University , C4.03 Ellison Building, Ellison Place , Newcastle Upon Tyne , Tyne and Wear NE1 8ST , UK . ; Tel: +44(0) 191 215 6623
| |
Collapse
|