Environmental DNA (Nov 2022)
An efficient early‐pooling protocol for environmental DNA metabarcoding
Abstract
Abstract Environmental DNA (eDNA) metabarcoding, a method that applies high‐throughput sequencing and universal primer sets to eDNA analysis, has been a promising approach for efficient, comprehensive biodiversity monitoring. However, significant money‐, labor‐, and time‐costs are still required for performing eDNA metabarcoding. In this study, we assessed the performance of an “early‐pooling” protocol (a protocol based on 1st PCR tagging) to reduce the experimental costs of library preparation for eDNA metabarcoding. Specifically, we performed three experiments to investigate the effects of 1st PCR‐tagging and 2nd PCR‐indexing protocols on the community composition revealed by eDNA metabarcoding, the effects of post‐1st PCR exonuclease purification on tag jumping (corresponds to index hopping in 2nd PCR indexing), and the effects of the number of PCR replicates and the eDNA template volume on the number of detected OTUs. Analyses of 204 eDNA libraries from three natural aquatic ecosystems and one mock eDNA sample showed that (i) 1st PCR tagging does not cause clear biases in the outcomes of eDNA metabarcoding, (ii) post‐1st PCR exonuclease purification reduces the risk of tag jumping, and (iii) increasing the eDNA template volume may increase the number of detected OTUs and reduce variations in the detected community compositions, similar to increasing the number of 1st PCR replicates. Our results show that an early‐pooling protocol with post‐1st PCR exonuclease purification and an increased amount of the DNA template reduces the risk of tag jumping, the costs for consumables and reagents (except for many tagged 1st PCR primers), and the handling time in library preparation, and produces similar results to a 2nd PCR‐indexing protocol. Therefore, once a target metabarcoding region is selected and a set of tagged‐1st PCR primers is prepared, the early‐pooling protocol provides a cost, labor, and time‐efficient approach for processing a large number of samples.
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