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Universal digital high-resolution melt: a novel approach to broad-based profiling of heterogeneous biological samples.

ABSTRACT: Comprehensive profiling of nucleic acids in genetically heterogeneous samples is important for clinical and basic research applications. Universal digital high-resolution melt (U-dHRM) is a new approach to broad-based PCR diagnostics and profiling technologies that can overcome issues of poor sensitivity due to contaminating nucleic acids and poor specificity due to primer or probe hybridization inaccuracies for single nucleotide variations. The U-dHRM approach uses broad-based primers or ligated adapter sequences to universally amplify all nucleic acid molecules in a heterogeneous sample, which have been partitioned, as in digital PCR. Extensive assay optimization enables direct sequence identification by algorithm-based matching of melt curve shape and Tm to a database of known sequence-specific melt curves. We show that single-molecule detection and single nucleotide sensitivity is possible. The feasibility and utility of U-dHRM is demonstrated through detection of bacteria associated with polymicrobial blood infection and microRNAs (miRNAs) associated with host response to infection. U-dHRM using broad-based 16S rRNA gene primers demonstrates universal single cell detection of bacterial pathogens, even in the presence of larger amounts of contaminating bacteria; U-dHRM using universally adapted Lethal-7 miRNAs in a heterogeneous mixture showcases the single copy sensitivity and single nucleotide specificity of this approach.

SUBMITTER: Fraley SI

PROVIDER: S-EPMC3794612 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Universal digital high-resolution melt: a novel approach to broad-based profiling of heterogeneous biological samples.

Fraley Stephanie I SI Hardick Justin J Masek Billie J BJ Athamanolap Pornpat P Rothman Richard E RE Gaydos Charlotte A CA Carroll Karen C KC Wakefield Teresa T Wang Tza-Huei TH Yang Samuel S

Nucleic acids research 20130809 18

Comprehensive profiling of nucleic acids in genetically heterogeneous samples is important for clinical and basic research applications. Universal digital high-resolution melt (U-dHRM) is a new approach to broad-based PCR diagnostics and profiling technologies that can overcome issues of poor sensitivity due to contaminating nucleic acids and poor specificity due to primer or probe hybridization inaccuracies for single nucleotide variations. The U-dHRM approach uses broad-based primers or ligate ...[more]

PMID: 23935121

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Project description:Positive identification of the nature of biological material present on evidentiary items can be crucial for understanding the circumstances surrounding a crime. However, traditional protein-based methods do not permit the identification of all body fluids and tissues, and thus molecular based strategies for the conclusive identification of all forensically relevant biological fluids and tissues need to be developed. Messenger RNA (mRNA) profiling is an example of such a molecular-based approach. Current mRNA body fluid identification assays involve capillary electrophoresis (CE) or quantitative RT-PCR (qRT-PCR) platforms, each with its own limitations. Both platforms require the use of expensive fluorescently labeled primers or probes. CE-based assays require separate amplification and detection steps thus increasing the analysis time. For qRT-PCR assays, only 3-4 markers can be included in a single reaction since each requires a different fluorescent dye. To simplify mRNA profiling assays, and reduce the time and cost of analysis, we have developed single- and multiplex body fluid High Resolution Melt (HRM) assays for the identification of common forensically relevant biological fluids and tissues. The incorporated biomarkers include IL19 (vaginal secretions), IL1F7 (skin), ALAS2 (blood), MMP10 (menstrual blood), HTN3 (saliva) and TGM4 (semen). The HRM assays require only unlabeled PCR primers and a single saturating intercalating fluorescent dye (Eva Green). Each body-fluid-specific marker can easily be identified by the presence of a distinct melt peak. Usually, HRM assays are used to detect variants or isoforms for a single gene target. However, we have uniquely developed duplex and triplex HRM assays to permit the simultaneous detection of multiple targets per reaction. Here we describe the development and initial performance evaluation of the developed HRM assays. The results demonstrate the potential use of HRM assays for rapid, and relatively inexpensive, screening of biological evidence.

Project description:BackgroundFor heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.ResultsExperimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.ConclusionsThe deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.

Dataset Information

Universal digital high-resolution melt: a novel approach to broad-based profiling of heterogeneous biological samples.

Publications

Universal digital high-resolution melt: a novel approach to broad-based profiling of heterogeneous biological samples.

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