A Novel Approach for Pollen Identification and Quantification Using Hybrid Capture-Based DNA Metabarcoding.

Pollen identification (ID) and quantification is important in many fields, including pollination ecology and agricultural sciences, and efforts to explore optimal molecular methods for identifying low concentrations of DNA from plant mixtures are increasing, but quantifying mixture proportions remains challenging. Traditional pollen ID using microscopy is time-consuming, requires expertise and has limited accuracy and throughput. Molecular barcoding approaches being explored offer improved accuracy and throughput. The common approach, amplicon sequencing, employs PCR amplification to isolate DNA barcodes, but introduces significant bias, impairing downstream quantification. We apply a novel molecular hybrid capture approach to artificial pollen mixtures to improve upon current taxon ID and quantification methods. The method randomly fragments DNA and uses RNA baits to capture DNA barcodes, which allows for PCR duplicate removal, reducing downstream quantification bias. Four reference databases were used to explore identification and quantification. A restricted database containing only mixture species yielded sequence proportions highly correlated with input pollen proportions, demonstrating the potential usefulness of hybrid capture for metabarcoding and quantifying pollen mixtures. Identification power was further tested using two reference libraries constructed from publicly available sequences: the plastid barcode and RefSeq complete chloroplast references. Single barcode-based taxon ID did not consistently resolve to species or genus level. The RefSeq chloroplast database performed better qualitatively but had limited taxon coverage (relative to species used here) and introduced ID issues. At the family level, both databases yielded comparable qualitative results, but the RefSeq database performed better quantitatively. Whilst the method developed here has tremendous potential, the choice and expansion of reference databases remains one of the most important factors allowing qualitative and quantitative accuracy using the full set of genomic regions screened by this hybrid capture method.