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Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling
In clinical diagnostics and pathogen detection, profiling of complex samples for low-level genotypes represents a significant challenge. Advances in speed, sensitivity, and extent of multiplexing of molecular pathogen detection assays are needed to improve patient care. We report the development of...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5296755/ https://www.ncbi.nlm.nih.gov/pubmed/28176860 http://dx.doi.org/10.1038/srep42326 |
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author | Velez, Daniel Ortiz Mack, Hannah Jupe, Julietta Hawker, Sinead Kulkarni, Ninad Hedayatnia, Behnam Zhang, Yang Lawrence, Shelley Fraley, Stephanie I. |
author_facet | Velez, Daniel Ortiz Mack, Hannah Jupe, Julietta Hawker, Sinead Kulkarni, Ninad Hedayatnia, Behnam Zhang, Yang Lawrence, Shelley Fraley, Stephanie I. |
author_sort | Velez, Daniel Ortiz |
collection | PubMed |
description | In clinical diagnostics and pathogen detection, profiling of complex samples for low-level genotypes represents a significant challenge. Advances in speed, sensitivity, and extent of multiplexing of molecular pathogen detection assays are needed to improve patient care. We report the development of an integrated platform enabling the identification of bacterial pathogen DNA sequences in complex samples in less than four hours. The system incorporates a microfluidic chip and instrumentation to accomplish universal PCR amplification, High Resolution Melting (HRM), and machine learning within 20,000 picoliter scale reactions, simultaneously. Clinically relevant concentrations of bacterial DNA molecules are separated by digitization across 20,000 reactions and amplified with universal primers targeting the bacterial 16S gene. Amplification is followed by HRM sequence fingerprinting in all reactions, simultaneously. The resulting bacteria-specific melt curves are identified by Support Vector Machine learning, and individual pathogen loads are quantified. The platform reduces reaction volumes by 99.995% and achieves a greater than 200-fold increase in dynamic range of detection compared to traditional PCR HRM approaches. Type I and II error rates are reduced by 99% and 100% respectively, compared to intercalating dye-based digital PCR (dPCR) methods. This technology could impact a number of quantitative profiling applications, especially infectious disease diagnostics. |
format | Online Article Text |
id | pubmed-5296755 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-52967552017-02-10 Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling Velez, Daniel Ortiz Mack, Hannah Jupe, Julietta Hawker, Sinead Kulkarni, Ninad Hedayatnia, Behnam Zhang, Yang Lawrence, Shelley Fraley, Stephanie I. Sci Rep Article In clinical diagnostics and pathogen detection, profiling of complex samples for low-level genotypes represents a significant challenge. Advances in speed, sensitivity, and extent of multiplexing of molecular pathogen detection assays are needed to improve patient care. We report the development of an integrated platform enabling the identification of bacterial pathogen DNA sequences in complex samples in less than four hours. The system incorporates a microfluidic chip and instrumentation to accomplish universal PCR amplification, High Resolution Melting (HRM), and machine learning within 20,000 picoliter scale reactions, simultaneously. Clinically relevant concentrations of bacterial DNA molecules are separated by digitization across 20,000 reactions and amplified with universal primers targeting the bacterial 16S gene. Amplification is followed by HRM sequence fingerprinting in all reactions, simultaneously. The resulting bacteria-specific melt curves are identified by Support Vector Machine learning, and individual pathogen loads are quantified. The platform reduces reaction volumes by 99.995% and achieves a greater than 200-fold increase in dynamic range of detection compared to traditional PCR HRM approaches. Type I and II error rates are reduced by 99% and 100% respectively, compared to intercalating dye-based digital PCR (dPCR) methods. This technology could impact a number of quantitative profiling applications, especially infectious disease diagnostics. Nature Publishing Group 2017-02-08 /pmc/articles/PMC5296755/ /pubmed/28176860 http://dx.doi.org/10.1038/srep42326 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Velez, Daniel Ortiz Mack, Hannah Jupe, Julietta Hawker, Sinead Kulkarni, Ninad Hedayatnia, Behnam Zhang, Yang Lawrence, Shelley Fraley, Stephanie I. Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling |
title | Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling |
title_full | Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling |
title_fullStr | Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling |
title_full_unstemmed | Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling |
title_short | Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling |
title_sort | massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5296755/ https://www.ncbi.nlm.nih.gov/pubmed/28176860 http://dx.doi.org/10.1038/srep42326 |
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