The ability to identify a plant species through pollen samples has played a role in various technological, industrial, and scientific fields throughout the years. For example, airborne pollen monitoring to manage seasonal respiratory diseases, food quality control of honey, plant-pollinator interactions, and forensic botany. The two most commonly used methods for pollen identification are microscopy and DNA metabarcoding. Microscopy is considered to be the gold standard for quantitative and qualitative pollen analysis. Pollen assemblages, consisting of hundreds of grains, are typically stained, and morphology is determined under a microscope. However, regardless of the field, pollen identification relies on expert knowledge examining the samples and high manual effort leading to a considerable cost, time-consuming, and low sample throughput. Another disadvantage to this method is that identification can only occur at the family level or higher for many species and may fail to detect rarer species. DNA metabarcoding identifies large quantities of pollen samples at the species level using standardized barcode marker gene regions. To date, five genetic markers have been used for DNA barcoding and metabarcoding: matK, rbcL, ITS2, trnH-psbA, and trnL. One standard method to determine one species from another is through sequence variation using polymerase chain reaction (PCR). qPCR analysis of the chloroplast genetic markers rbcL and matK, the second nuclear ribosomal ITS2 region, was used for each plant species. The species evaluated were Ambrosia artemisiifolia (common ragweed), Paspalum notatum (bahiagrass), Artemisia vulgaris (common mugwort), Quercus virginiana (southern live oak), Pinus echinata (shortleaf pine), Taxodium distichum (bald cypress), Phalaris arundinacea (reed canary grass), and Plantago lanceolata (ribwort plantain). In addition, the combined data obtained from single grains opens the door for other downstream applications, including plant species identification at the individual grain level in soil, air collection, and clothing swabs samples.

Multi-parametric analysis of single pollen grains for forensic applications

ACS Spring 2022

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