Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling - PubMed (original) (raw)
Massively parallel digital high resolution melt for rapid and absolutely quantitative sequence profiling
Daniel Ortiz Velez et al. Sci Rep. 2017.
Abstract
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.
Conflict of interest statement
The authors are inventors on a patent filed for the platform presented herein.
Figures
Figure 1. Massively parallel U-dHRM device.
(A) Poisson distribution of DNA in a 96-well plate versus a 20,000 well digital PCR chip, showing the distribution of molecules per well. (B) Schematic of the U-dHRM platform. (C) Image of the actual U-dHRM heating setup. (D) Fluorescent image of a small portion of chip where background dye (red) and intercalating dye (green) are overlaid. 3D intensity plot of the green channel is shown in inset.
Figure 2. On-chip U-dHRM process characterization and optimization.
(A) Image of a portion of a chip, which has been saturated with synthetic DNA such that nearly all wells exhibit green fluorescence of intercalating dye. Upon controlled heating, fluorescence is lost as DNA denatures. (B) Melting of three synthetic temperature calibrator sequences (pre-made and applied in high concentration to the chip, not PCR amplified) containing different GC content. Optimized ramp rate on-chip compared to bulk qPCR HRM. The mean and standard deviation of the calibration sequence melt curves are shown. (C) A plot of the relationship between voltage and temperature for 5 runs, showing it remains linear throughout the HRM temperature range of interest. Standard deviation reaches a maximum of 1.22 °C at 91.6 °C.
Figure 3. U-dHRM sampling and ramp rate optimization on-chip.
(A,B) L. monocytogenes melt curves generated with a low imaging rate on qPCR HRM and U-dHRM platforms respectively. (C,D) L. monocytogenes melt curves generated using a high imaging rate on qPCR HRM and U-dHRM platforms respectively. The synthetic temperature calibrator sequence mean melting temperature and standard deviation are shown in all. Black circle highlights a melt curve shape feature unique to L. monocytogenes 16S sequence, which is dependent on sampling rate.
Figure 4. OVO SVM classification of L. monocytogenes and S. pneumoniae.
(A) Two-thousand normalized S. pneumoniae (top) and L. monocytogenes (bottom) U-dHRM melt curves aligned to 0.1 –dF/dT, respectively. These curves were used to train the OVO SVM to classify each bacteria. (B) Histogram of fluorescence intensity values of digital reaction wells with PDF overlay and the intensity value chosen to classify positive from negative marked by dotted line (top). Histogram showing the Tm of each digital reaction with PDF overlay and the Tm value chosen to classify positive from negative marked by dotted line (bottom). Both graphs correspond to a concentration of 458 genomes of L. monocytogenes per chip. (C) U-dHRM dilution series of L. monocytogenes with U-dHRM measured values plotted against spectrometer measured values for DNA content. The sample mean and sample standard deviation are shown. (D) In blue: qPCR melt curve generated from a 1:1 mix of 20 ng total DNA input of S. pneumoniae and L. monocytogenes. In red: qPCR melt curve generated from a 1:1 mix of 0.02 ng total DNA input of S. pneumoniae and L. monocytogenes. This concentration and reaction mixture is similar to that used for digital chip experiments. In grey: qPCR melt curve generated from a negative template control (NTC) with no bacterial DNA added. (E) U-dHRM and OVO SVM classification of L. monocytogenes and S. pneumoniae in two distinct mixture compositions, demonstrating polymicrobial detection capability. Table 2 shows enumeration of detected curves in panel E.
Figure 5. Identification of L. monocytogenes in mock blood sample.
(A) Histogram showing the Tm of each digital reaction with PDF overlay and the calculated Tm threshold (dotted line) used to classify true positive from off-target amplification. (B) Bacterial DNA melt curves from reactions identified as positive using the Tm and peak height thresholds adjusted for human DNA background. (C) Melt curves from reactions identified as negative using thresholds specific for human DNA background. This plot highlights the high background noise associated with the addition of human DNA to our sample (D) L. monocytogenes melt curves from panel B normalized and aligned to 0.1 − dF/dT.
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