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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.
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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
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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.
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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.
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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.
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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.
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Huang Q, Xun Z, Lin J, Xie R, Zhu C, Wang L, Shang H, Wu S, Ou Q, Liu C. A novel microfluidic chip-based digital PCR method for enhanced sensitivity in the early diagnosis of colorectal cancer via mSEPT9. Clin Chim Acta 2024; 554:117781. [PMID: 38224929 DOI: 10.1016/j.cca.2024.117781] [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: 11/18/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 01/17/2024]
Abstract
BACKGROUND To enhance the sensitivity of plasma methylated Septin9 gene (mSEPT9) detection in colorectal cancer (CRC) screening, we developed a microfluidic chip-based digital PCR (dPCR) method suitable for low-concentration samples, aiming to apply it for mSEPT9 detection in CRC diagnosis. METHODS Our microfluidic chip-based dPCR method utilized specific primers and probes with locked nucleic acids (LNAs) modifications for mSEPT9 detection. We evaluated its performance, including detection limit, specificity, and linear range, comparing it with a commercial qPCR reagent kit using the same samples (95 CRC, 23 non-CRC). RESULTS The LNAs-modified dPCR method showed a linear range of 100-104 copies/μL and a detection limit of 100 copies/μL. Clinical testing revealed that our dPCR method exhibited a sensitivity of 82.11 % and specificity of 95.65 % for CRC diagnosis, outperforming the commercial qPCR kit (sensitivity: 58.95 %, specificity: 91.30 %), particularly in Stage I with a diagnostic sensitivity of 90.91 %. Combining mSEPT9 and carcinoembryonic antigen (CEA) improved diagnostic sensitivity to 91.49 %. CONCLUSIONS Our accurate microfluidic chip-based dPCR method, especially in combination with CEA, holds promise for effective CRC screening and timely interventions, offering enhanced mSEPT9 quantification over conventional qPCR.
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Affiliation(s)
- Qunfang Huang
- Department of Laboratory Medicine, Fujian Key Laboratory of Laboratory Medicine, Gene Diagnosis Research Center, Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian, China; Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, Fujian, China
| | - Zhen Xun
- Department of Laboratory Medicine, Fujian Key Laboratory of Laboratory Medicine, Gene Diagnosis Research Center, Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian, China; Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, Fujian, China; The First Clinical College, Fujian Medical University, Fuzhou 350005, Fujian, China
| | - Junyu Lin
- The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, Fujian, China
| | - Rubing Xie
- Department of Laboratory Medicine, Fujian Key Laboratory of Laboratory Medicine, Gene Diagnosis Research Center, Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian, China; The First Clinical College, Fujian Medical University, Fuzhou 350005, Fujian, China
| | - Chenggong Zhu
- Department of Laboratory Medicine, Fujian Key Laboratory of Laboratory Medicine, Gene Diagnosis Research Center, Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian, China; The First Clinical College, Fujian Medical University, Fuzhou 350005, Fujian, China
| | - Long Wang
- Department of Laboratory Medicine, Fujian Key Laboratory of Laboratory Medicine, Gene Diagnosis Research Center, Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian, China; The First Clinical College, Fujian Medical University, Fuzhou 350005, Fujian, China
| | - Hongyan Shang
- Department of Laboratory Medicine, Fujian Key Laboratory of Laboratory Medicine, Gene Diagnosis Research Center, Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian, China; Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, Fujian, China
| | - Songhang Wu
- Department of Laboratory Medicine, Fujian Key Laboratory of Laboratory Medicine, Gene Diagnosis Research Center, Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian, China; Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, Fujian, China
| | - Qishui Ou
- Department of Laboratory Medicine, Fujian Key Laboratory of Laboratory Medicine, Gene Diagnosis Research Center, Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian, China; Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, Fujian, China; The First Clinical College, Fujian Medical University, Fuzhou 350005, Fujian, China.
| | - Can Liu
- Department of Laboratory Medicine, Fujian Key Laboratory of Laboratory Medicine, Gene Diagnosis Research Center, Fujian Clinical Research Center for Laboratory Medicine of Immunology, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian, China; Department of Laboratory Medicine, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, Fujian, China; The First Clinical College, Fujian Medical University, Fuzhou 350005, Fujian, China.
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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).
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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
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6
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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.
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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
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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.
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Liu DD, Muliaditan D, Viswanathan R, Cui X, Cheow LF. Melt-Encoded-Tags for Expanded Optical Readout in Digital PCR (METEOR-dPCR) Enables Highly Multiplexed Quantitative Gene Panel Profiling. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301630. [PMID: 37485651 PMCID: PMC10520687 DOI: 10.1002/advs.202301630] [Citation(s) in RCA: 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.
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Affiliation(s)
- Dong Dong Liu
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
| | - Daniel Muliaditan
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
- Genome institute of SingaporeAgency for ScienceTechnology and ResearchSingapore138672Singapore
| | - Ramya Viswanathan
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
| | - Xu Cui
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
| | - Lih Feng Cheow
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
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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.
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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
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Suchy FP, Nishimura T, Seki S, Wilkinson AC, Higuchi M, Hsu I, Zhang J, Bhadury J, Nakauchi H. Streamlined and quantitative detection of chimerism using digital PCR. Sci Rep 2022; 12:10223. [PMID: 35715477 PMCID: PMC9206010 DOI: 10.1038/s41598-022-14467-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 06/07/2022] [Indexed: 12/28/2022] Open
Abstract
Animal chimeras are widely used for biomedical discoveries, from developmental biology to cancer research. However, the accurate quantitation of mixed cell types in chimeric and mosaic tissues is complicated by sample preparation bias, transgenic silencing, phenotypic similarity, and low-throughput analytical pipelines. Here, we have developed and characterized a droplet digital PCR single-nucleotide discrimination assay to detect chimerism among common albino and non-albino mouse strains. In addition, we validated that this assay is compatible with crude lysate from all solid organs, drastically streamlining sample preparation. This chimerism detection assay has many additional advantages over existing methods including its robust nature, minimal technical bias, and ability to report the total number of cells in a prepared sample. Moreover, the concepts discussed here are readily adapted to other genomic loci to accurately measure mixed cell populations in any tissue.
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Affiliation(s)
- Fabian P Suchy
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
| | - Toshiya Nishimura
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Shinsuke Seki
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Experimental Animal Division, Bioscience Education and Research Support Center, Akita University, Akita, 010-8543, Japan
| | - Adam C Wilkinson
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Maimi Higuchi
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Ian Hsu
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Jinyu Zhang
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Joydeep Bhadury
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Institute of Biomedicine, Sahlgrenska University Hospital, University of Gothenburg, 41345, Gothenburg, SE, Sweden
| | - Hiromitsu Nakauchi
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA.
- Division of Stem Cell Therapy, Institute of Medical Science, University of Tokyo, Tokyo, 108-8639, Japan.
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA.
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11
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Teal CN, Coykendall DK, Campbell MR, Eardley DL, Delomas TA, Shira JT, Schill DJ, Bonar SA, Culver M. Sex-specific markers undetected in green sunfish Lepomis cyanellus using restriction-site associated DNA sequencing. JOURNAL OF FISH BIOLOGY 2022; 100:1528-1540. [PMID: 35439326 DOI: 10.1111/jfb.15063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
We used restriction-site associated DNA sequencing for SNP discovery and genotyping of known-sex green sunfish Lepomis cyanellus DNA samples to search for sex-diagnostic single nucleotide polymorphisms (SNPs) and restriction-site associated sequences present in one sex and absent in the other. The bioinformatic analyses discovered candidate SNPs and sex-specific restriction-site associated sequences that fit patterns of male or female heterogametic sex determination systems. However, when primers were developed and tested, no candidates reliably identified phenotypic sex. The top performing SNP candidate (ZW_218) correlated with phenotypic sex 63.0% of the time and the presence-absence loci universally amplified in both sexes. We recommend further investigations that interrogate a larger fraction of the L. cyanellus genome. Additionally, studies on the effect of temperature and rearing density on sex determination, as well as breeding of sex-reversed individuals, could provide more insights into the sex determination system of L. cyanellus.
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Affiliation(s)
- Chad N Teal
- Arizona Cooperative Fish and Wildlife Research Unit, School of Natural Resources and the Environment, Tucson, Arizona, USA
| | - D Katharine Coykendall
- Pacific States Marine Fisheries Commission, Eagle Fish Genetics Lab, Eagle, Idaho, USA
- Idaho Department of Fish and Game, Eagle Fish Genetics Lab, Eagle, Idaho, USA
| | - Matthew R Campbell
- Idaho Department of Fish and Game, Eagle Fish Genetics Lab, Eagle, Idaho, USA
| | - Daniel L Eardley
- Pacific States Marine Fisheries Commission, Eagle Fish Genetics Lab, Eagle, Idaho, USA
- Idaho Department of Fish and Game, Eagle Fish Genetics Lab, Eagle, Idaho, USA
| | - Thomas A Delomas
- Pacific States Marine Fisheries Commission, Eagle Fish Genetics Lab, Eagle, Idaho, USA
- Idaho Department of Fish and Game, Eagle Fish Genetics Lab, Eagle, Idaho, USA
| | - James T Shira
- University of Arizona Genetics Core, Tucson, Arizona, USA
| | | | - Scott A Bonar
- US Geological Survey, Arizona Cooperative Fish and Wildlife Research Unit, School of Natural Resources and the Environment, University of Arizona, ENR2, Tucson, Arizona, USA
| | - Melanie Culver
- US Geological Survey, Arizona Cooperative Fish and Wildlife Research Unit, School of Natural Resources and the Environment, University of Arizona, ENR2, Tucson, Arizona, USA
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12
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Yao J, Luo Y, Zhang Z, Li J, Li C, Li C, Guo Z, Wang L, Zhang W, Zhao H, Zhou L. The development of real-time digital PCR technology using an improved data classification method. Biosens Bioelectron 2021; 199:113873. [PMID: 34953301 DOI: 10.1016/j.bios.2021.113873] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/26/2021] [Accepted: 12/06/2021] [Indexed: 02/09/2023]
Abstract
For digital polymerase chain reaction (PCR), data classification is always a crucial task. The dynamic real-time amplification process information of each partition is always ignored in typical digital PCR analysis, which can easily lead to inaccurate outcomes. In this work, an integrated device that offers real-time chip-based digital PCR analysis was established. In addition, an enhanced process-based classification model (PAM) was built and trained. And then the device and the analytical model were employed in classification tasks for different concentrations of Epstein-Barr Virus (EBV) plasmid quantification assays. The results indicated that the real-time analysis device achieved a linearity of 0.97, the classification method was able to distinguish the false-positive curves, and the recognition error of positive wells was decreased by 64.4% compared with typical static analysis techniques when low concentrations of samples were tested. With these advantages, it is supposed that the real-time digital PCR analysis apparatus and the improved classification method can be employed to enhance the performance of digital PCR technology.
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Affiliation(s)
- Jia Yao
- School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Yuanyuan Luo
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Zhiqi Zhang
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Jinze Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Chuanyu Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Chao Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Zhen Guo
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Lirong Wang
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Wei Zhang
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
| | - Heming Zhao
- School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China.
| | - Lianqun Zhou
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
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13
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Gaňová M, Zhang H, Zhu H, Korabečná M, Neužil P. Multiplexed digital polymerase chain reaction as a powerful diagnostic tool. Biosens Bioelectron 2021; 181:113155. [PMID: 33740540 DOI: 10.1016/j.bios.2021.113155] [Citation(s) in RCA: 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.
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Affiliation(s)
- Martina Gaňová
- Central European Institute of Technology, Brno University of Technology, 612 00, Brno, Czech Republic
| | - Haoqing Zhang
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Hanliang Zhu
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China
| | - Marie Korabečná
- 1st Faculty of Medicine, Institute of Biology and Medical Genetics, Charles University and General University Hospital, 12800, Prague, Czech Republic
| | - Pavel Neužil
- Central European Institute of Technology, Brno University of Technology, 612 00, Brno, Czech Republic; School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, PR China; The Faculty of Electrical Engineering and Communication, Brno University of Technology, 616 00, Brno, Czech Republic.
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