Quantitative Amplicon Sequencing
This study represents a new method of meta-DNA relative abundance analysis via sequencing. Given the dramatic decline in sequencing costs as well as the sensitivity of current sequencing methods to detecting SNPs, this method may represent a lower-cost alternative to florescence-based qPCR. We also note that qAMPseq offers a higher-accuracy alternative to relative read abundance that is not subject to the issues that can arise when trying to quantify PCR products from metabarcoding (Bell et al. 2019). That said, the protocol here could be streamlined. One example is that it may be acceptable to perform fewer bead cleaning steps to reduce cost and time at the bench. Our protocol also necessitated four thermocyclers, running simultaneously. However, similar to how gradient thermocyclers vary annealing temperatures across reaction wells, we can envision a modification of thermocycler heating blocks that might allow for variation in the number of reaction cycles, and perhaps allow qAMPseq reactions to be run on the same machine, also lowering costs (e.g. Schicke and Hofmann, 2007).
One important difference between our study and studies that use markers such as ITS2 and rcbL is that we had specific target species we quantified in our samples. As such, we designed primers to amplify regions where we knew there were SNPs that differentiated our target species from each other, rather than relying on variation in ITS2 and rcbL to distinguish Clarkia from each other. It is also important to note that, because we were using closely related species within theClarkia genus, our application of the meta-barcoding approach was likely not subject to many of quantitative biases identified by Bell et al. (2019). These biases include copy number variation of the amplified gene, differences in DNA isolation efficacy among samples, and variation in primer amplification efficiency. If others are to use this method with primers that target a broader range of possibly more divergent species, these additional biases need to be carefully considered in experimental design.
Finally, the difference between estimates of relative abundance from qAMPseq versus relative read abundance depended on the tolerance with which we filtered raw RRA values. In our study, the 10% cutoff of RRA best matched the results from qAMPseq. A benefit to using qAMPseq, rather than relative read abundance, is that it did not require an arbitrary cutoff for the proportion of reads at the end of PCR that we needed to filter out. However, qAMPseq still required that we define an arbitrary number of reads as a threshold for amplification; this value is analogous to the critical Ct value in the qPCR method of quantitation. Importantly, it is likely that the similarity in relative abundances between our qAMPseq approach and RRA was due to the fact that our samples were diluted to a similar starting concentration of DNA (2 ng/uL). In studies wishing to use RRA in lieu of qAMPseq or qPCR, it should be noted that RRA may not yield accurate estimates of relative abundance if DNA concentrations are highly variable (Bell et al. 2019). We also highlight that because all of our samples were diluted to the same initial concentration, our analysis does not incorporate information about the size of the sampled pollen ball, so we cannot draw conclusions about the amount of pollen that different pollinators transport in their pollen balls. Irrespective of approach, care must be taken in determining the concentration of pre-amplification samples with any particular primer/target combination, as well the interpretation of the resulting data.