ena-STUDY-UNIV-WUE-28-11-2013-15:51:56:516-143
DNA-based pollen identification
The identification of pollen plays an important role in ecology, palaeo-climatology, honey quality control and other areas. Currently, expert knowledge and reference collections are essential to identify pollen origin through light microscopy. Pollen identification through molecular sequencing and DNA barcoding has been proposed as an alternative approach, but so far the assessment of mixed pollen samples originating from multiple plant species is still a tedious and error-prone task that requires samples to be separated before being individually processed..   In this study we present a novel method for direct pollen assessments in mixed probes through next-generation sequencing and a bioinformatical workflow to analyse these high-throughput data with a newly created reference database. To evaluate the method, we compared results from classical identification based on light microscopy with sequencing results. We assessed in total 14 mixed pollen samples, twelve originated from honey bee colonies and two from solitary bee nests. The sequencing technique resulted in higher taxon richness (deeper assignments and more identified taxa) compared to light microscopy. Abundance estimations from sequencing data were significantly correlated with counted abundances through light microscopy. Simulation analyses of taxon specificity and sensitivity indicate that 96% of taxa present in the database are correctly identifiable at the genus level and 70% at the species level. Our approach presents a useful and efficient molecular and bioinformatic workflow to identify pollen at the genus and species level without requiring specialized palynological expert knowledge.
The identification of pollen plays an important role in ecology, palaeo-climatology, honey quality control and other areas. Currently, expert knowledge and reference collections are essential to identify pollen origin through light microscopy. Pollen identification through molecular sequencing and DNA barcoding has been proposed as an alternative approach, but so far the assessment of mixed pollen samples originating from multiple plant species is still a tedious and error-prone task that requires samples to be separated before being individually processed..   In this study we present a novel method for direct pollen assessments in mixed probes through next-generation sequencing and a bioinformatical workflow to analyse these high-throughput data with a newly created reference database. To evaluate the method, we compared results from classical identification based on light microscopy with sequencing results. We assessed in total 14 mixed pollen samples, twelve originated from honey bee colonies and two from solitary bee nests. The sequencing technique resulted in higher taxon richness (deeper assignments and more identified taxa) compared to light microscopy. Abundance estimations from sequencing data were significantly correlated with counted abundances through light microscopy. Simulation analyses of taxon specificity and sensitivity indicate that 96% of taxa present in the database are correctly identifiable at the genus level and 70% at the species level. Our approach presents a useful and efficient molecular and bioinformatic workflow to identify pollen at the genus and species level without requiring specialized palynological expert knowledge.