Rashmi Prava Mohanty1, Mark Alan Buchheim1, Estelle Levetin2. 1. Department of Biological Sciences, University of Tulsa, Tulsa, Oklahoma. 2. Department of Biological Sciences, University of Tulsa, Tulsa, Oklahoma. Electronic address: Estelle-Levetin@utulsa.edu.
Abstract
BACKGROUND: Pollen monitoring is a common and vital tool in the field of allergy, creating awareness in pollen sensitive individuals. Traditionally, pollen monitoring has been based on conventional microscopic counting techniques that are labor intensive and limited in the identification to the genus or family level. Molecular techniques provide an alternative approach that is less labor intensive and enable identification of any species by its genetic fingerprint. OBJECTIVE: To use quantitative polymerase chain reaction (qPCR) to evaluate pollen concentrations in air samples. METHODS: Juniperus pollen was selected as our model because of the importance of this pollen in the southcentral United States. We analyzed 105 air samples collected with a Burkard spore trap from 2013 to 2015 using species-specific primers and probes. To evaluate the feasibility of a molecular approach, we used duplicate air samples that allowed us to compare results from classical identification based on light microscopy with our qPCR results. RESULTS: Pollen concentrations from the qPCR data were significantly correlated with concentrations determined through light microscopy (R = 0.902, P < .001). We also confirmed an overlap in the pollination seasons between Juniperus ashei and Juniperus pinchotii and between J ashei and Juniperus virginiana. CONCLUSION: We found that this method correctly identified different Juniperus species present in mixed air samples in the southcentral United States, an accomplishment that cannot be achieved using microscopic identification. We conclude that the qPCR method is more accurate and sensitive than current pollen monitoring techniques and, therefore, has the potential to be used in various pollen monitoring stations.
BACKGROUND: Pollen monitoring is a common and vital tool in the field of allergy, creating awareness in pollen sensitive individuals. Traditionally, pollen monitoring has been based on conventional microscopic counting techniques that are labor intensive and limited in the identification to the genus or family level. Molecular techniques provide an alternative approach that is less labor intensive and enable identification of any species by its genetic fingerprint. OBJECTIVE: To use quantitative polymerase chain reaction (qPCR) to evaluate pollen concentrations in air samples. METHODS:Juniperus pollen was selected as our model because of the importance of this pollen in the southcentral United States. We analyzed 105 air samples collected with a Burkard spore trap from 2013 to 2015 using species-specific primers and probes. To evaluate the feasibility of a molecular approach, we used duplicate air samples that allowed us to compare results from classical identification based on light microscopy with our qPCR results. RESULTS: Pollen concentrations from the qPCR data were significantly correlated with concentrations determined through light microscopy (R = 0.902, P < .001). We also confirmed an overlap in the pollination seasons between Juniperus ashei and Juniperus pinchotii and between J ashei and Juniperus virginiana. CONCLUSION: We found that this method correctly identified different Juniperus species present in mixed air samples in the southcentral United States, an accomplishment that cannot be achieved using microscopic identification. We conclude that the qPCR method is more accurate and sensitive than current pollen monitoring techniques and, therefore, has the potential to be used in various pollen monitoring stations.