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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...

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Autores principales: Velez, Daniel Ortiz, Mack, Hannah, Jupe, Julietta, Hawker, Sinead, Kulkarni, Ninad, Hedayatnia, Behnam, Zhang, Yang, Lawrence, Shelley, Fraley, Stephanie I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
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.
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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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