Quantitative amplicon sequencing—An Overview
Given the lack of sensitivity of the attempted fluorescence-based method, we developed a sequenced-based amplicon assay. We used two methods to quantify relative input DNA from the four Clarkia . First, we used a common approach to quantify relative abundance of input DNA, which simply uses the relative read abundance following the full PCR. We refer to the traditional sequencing approach—using the relative read abundance of amplicons at-or-near the PCR plateau phase—as “RRA-plateau” or “RRA”. Second, we used an approach that was specifically designed as an attempt to control for some of the biases introduced by PCR. We contrast the RRA method with our method that utilizes a PCR cycle treatment, which we refer to as “quantitative amplicon sequencing” (qAMPseq).
The premise of qAMPseq applies the theory of quantitative PCR (qPCR, A.K.A. real-time PCR) with the ability to individually index, multiplex, and sequence hundreds of metabarcoded samples (Figure 2). Quantitative PCR analysis uses a pre-determined threshold when the PCR reaction is in an exponential phase of amplification, because the PCR cycle where a reaction product moves into the exponential phase is directly related to the starting DNA concentration, unlike the plateau stage (Kubista, 2005). Realtime PCR uses fluorescence (e.g. TaqMan chemistry) quantified throughout thermocycling to determine the ‘cycle number’ where the product fluorescence is higher than a background level, as the product is in the exponential amplification phase. The estimated number of PCR cycles when the product hits this threshold is known as threshold cycle (Ct). This Ct value can then be compared across samples to compare starting DNA concentrations.
In qAMPseq, we generate the same PCR amplicon in quadruplicate, with the same starting conditions, but across different PCR cycling numbers (e.g. 20, 25, 30, and 35 cycles; Figure 2B). Subsequent cleanup and indexing steps preserve the relative DNA amounts in each of these reactions, which are then individually indexed (i.e. each original sample has four unique indexes, which correspond to the different cycle ‘treatments’) and then pooled and sequenced with all other samples (Figure 2C). Samples can then be de-multiplexed (Figure 2D) and, within each sample and treatment, reads are assigned to predicted taxonomic units (“OTUs”; in this case, the four Clarkia species). The read abundance across each sample and OTU can then be used to calculate Ct (Figure 2E), and a more robust value the relative contribution of input DNA (Figure 2F).