Chapter 4 : Digital PCR and Its Potential Application to Microbiology

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A digital PCR (dPCR) takes a PCR and subdivides it across a large number of smaller reactions, termed partitions, so that a number of the partitions contain no template molecules. Many of the ideas that underpin dPCR were described in the late 1980s and early 1990s (1) and applied at that time using conventional or nested PCR. However, the procedure originally required partitioning of a single sample using individual tubes or a 96-well plate, followed by amplicon detection by agarose gel electrophoresis. This initial format represented a very unwieldy technique and an inefficient use of time and resources.

Citation: Huggett J, Garson J, Whale A. 2016. Digital PCR and Its Potential Application to Microbiology, p 49-57. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch4
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Image of FIGURE 1

Influence of the number of positive partitions on the estimated copy calculations. Graph demonstrating the linear relationship between the number of positive partitions and estimated copies (red line) when a total of 10,000 partitions are measured. (A) When λ is low (<0.1), the number of positive partitions is almost equal to the number of molecules (blue dashed lines). (B) As λ increases, the total number of estimated copies exceeds the number of positive partitions (blue dashed lines), so the dynamic range is greater than the total number of partitions.

Citation: Huggett J, Garson J, Whale A. 2016. Digital PCR and Its Potential Application to Microbiology, p 49-57. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch4
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Image of FIGURE 2

Influence of the number of positive partitions on precision. Graph demonstrating the correlation between the precision and the proportion of positive partitions observed. At very low or high λ, the precision is reduced. The highest precision is observed when 1.5 < λ < 1.6, where the number of positive partitions is between 7,700 and 8,000. Graph calculated using Ucount (https://dna.utah.edu/ucount/uc.html).

Citation: Huggett J, Garson J, Whale A. 2016. Digital PCR and Its Potential Application to Microbiology, p 49-57. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch4
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Image of FIGURE 3

Detection of single nucleotide polymorphisms using droplet dPCR. Example of graphs produced with the QuantaSoft Software from the QX200 Droplet Digital PCR System (Bio-Rad). The horizontal and vertical pink lines represent the thresholds between negative and positive droplets. (A) One-dimensional plot illustrating positive and negative droplets using a uniplex reaction containing a mutant-specific probe only. The mutant-only sample (MT) gives two distinct droplet populations: positive (blue droplets) and negative (gray droplets). The presence of the wild type sequence in the wild type-only sample (WT) and mixed sample (MT & WT) generates a second population of droplets that falls between the negative and positive droplets due to the probe binding with lower affinity to the wild type sequence. (B) Two-dimensional plot of the mixed sample shown in (A) with a duplex reaction containing both the mutant and wild type probes. Each droplet has both a mutant and wild type signal, so four possible outcomes are observed: MT only (blue), WT only (green), MT & WT (orange), and negative (gray). (C) Two-dimensional plot of a mixed sample showing partition-specific competitive PCR. Droplets containing both MT and WT sequences (orange) merge with the MT only (blue) and WT only (green) droplets to form an “arc” across the plot with reduced amplitude of one or both signals, thus making confident positioning of the thresholds difficult.

Citation: Huggett J, Garson J, Whale A. 2016. Digital PCR and Its Potential Application to Microbiology, p 49-57. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch4
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Generic image for table

Details of the currently available dPCR instruments

Citation: Huggett J, Garson J, Whale A. 2016. Digital PCR and Its Potential Application to Microbiology, p 49-57. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch4

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