A novel approach for pollen identification and quantification using hybrid capture-based DNA metabarcoding

Efforts to explore optimal molecular methods for identifying plant
mixtures, particularly pollen, are increasing. Pollen identification (ID)
and quantification is important in many fields, including pollination
ecology and agricultural sciences, 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
hybridisation 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.
Metabarcoding was tested using two reference libraries constructed from
publicly available sequences; the matK 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 family level,
both databases yielded comparable qualitative results, but the RefSeq
database performed better quantitatively. A restricted matK database
containing only mixture species yielded sequence proportions highly
correlated with input pollen proportions, demonstrating that hybridization
capture usefulness for metabarcoding and quantifying pollen mixtures. The
choice of reference database remains one of the most important factors
affecting qualitative and quantitative accuracy.