Literature DB >> 24893805

Efficient and sensitive identification and quantification of airborne pollen using next-generation DNA sequencing.

Ken Kraaijeveld1, Letty A de Weger, Marina Ventayol García, Henk Buermans, Jeroen Frank, Pieter S Hiemstra, Johan T den Dunnen.   

Abstract

Pollen monitoring is an important and widely used tool in allergy research and creation of awareness in pollen-allergic patients. Current pollen monitoring methods are microscope-based, labour intensive and cannot identify pollen to the genus level in some relevant allergenic plant groups. Therefore, a more efficient, cost-effective and sensitive method is needed. Here, we present a method for identification and quantification of airborne pollen using DNA sequencing. Pollen is collected from ambient air using standard techniques. DNA is extracted from the collected pollen, and a fragment of the chloroplast gene trnL is amplified using PCR. The PCR product is subsequently sequenced on a next-generation sequencing platform (Ion Torrent). Amplicon molecules are sequenced individually, allowing identification of different sequences from a mixed sample. We show that this method provides an accurate qualitative and quantitative view of the species composition of samples of airborne pollen grains. We also show that it correctly identifies the individual grass genera present in a mixed sample of grass pollen, which cannot be achieved using microscopic pollen identification. We conclude that our method is more efficient and sensitive than current pollen monitoring techniques and therefore has the potential to increase the throughput of pollen monitoring.
© 2014 John Wiley & Sons Ltd.

Entities:  

Keywords:  DNA metabarcoding; molecular identification; next-generation sequencing; pollen allergy; pollen monitoring

Mesh:

Substances:

Year:  2014        PMID: 24893805     DOI: 10.1111/1755-0998.12288

Source DB:  PubMed          Journal:  Mol Ecol Resour        ISSN: 1755-098X            Impact factor:   7.090


  40 in total

1.  Validation of the Hirst-Type Spore Trap for Simultaneous Monitoring of Prokaryotic and Eukaryotic Biodiversities in Urban Air Samples by Next-Generation Sequencing.

Authors:  Andrés Núñez; Guillermo Amo de Paz; Zuzana Ferencova; Alberto Rastrojo; Raúl Guantes; Ana M García; Antonio Alcamí; A Montserrat Gutiérrez-Bustillo; Diego A Moreno
Journal:  Appl Environ Microbiol       Date:  2017-06-16       Impact factor: 4.792

Review 2.  The Future of Environmental DNA in Forensic Science.

Authors:  Julia S Allwood; Noah Fierer; Robert R Dunn
Journal:  Appl Environ Microbiol       Date:  2020-01-07       Impact factor: 4.792

3.  Mixture Analyses of Air-sampled Pollen Extracts Can Accurately Differentiate Pollen Taxa.

Authors:  Leszek J Klimczak; Cordula Ebner von Eschenbach; Peter M Thompson; Jeroen T M Buters; Geoffrey A Mueller
Journal:  Atmos Environ (1994)       Date:  2020-07-06       Impact factor: 4.798

4.  Increased efficiency in identifying mixed pollen samples by meta-barcoding with a dual-indexing approach.

Authors:  Wiebke Sickel; Markus J Ankenbrand; Gudrun Grimmer; Andrea Holzschuh; Stephan Härtel; Jonathan Lanzen; Ingolf Steffan-Dewenter; Alexander Keller
Journal:  BMC Ecol       Date:  2015-07-22       Impact factor: 2.964

Review 5.  Telling plant species apart with DNA: from barcodes to genomes.

Authors:  Peter M Hollingsworth; De-Zhu Li; Michelle van der Bank; Alex D Twyford
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2016-09-05       Impact factor: 6.237

6.  Using DNA Metabarcoding to Identify the Floral Composition of Honey: A New Tool for Investigating Honey Bee Foraging Preferences.

Authors:  Jennifer Hawkins; Natasha de Vere; Adelaide Griffith; Col R Ford; Joel Allainguillaume; Matthew J Hegarty; Les Baillie; Beverley Adams-Groom
Journal:  PLoS One       Date:  2015-08-26       Impact factor: 3.240

7.  Taxonomic Characterization of Honey Bee (Apis mellifera) Pollen Foraging Based on Non-Overlapping Paired-End Sequencing of Nuclear Ribosomal Loci.

Authors:  R Scott Cornman; Clint R V Otto; Deborah Iwanowicz; Jeffery S Pettis
Journal:  PLoS One       Date:  2015-12-23       Impact factor: 3.240

8.  Using metabarcoding to reveal and quantify plant-pollinator interactions.

Authors:  André Pornon; Nathalie Escaravage; Monique Burrus; Hélène Holota; Aurélie Khimoun; Jérome Mariette; Charlène Pellizzari; Amaia Iribar; Roselyne Etienne; Pierre Taberlet; Marie Vidal; Peter Winterton; Lucie Zinger; Christophe Andalo
Journal:  Sci Rep       Date:  2016-06-03       Impact factor: 4.379

9.  Towards Plant Species Identification in Complex Samples: A Bioinformatics Pipeline for the Identification of Novel Nuclear Barcode Candidates.

Authors:  Alexandre Angers-Loustau; Mauro Petrillo; Valentina Paracchini; Dafni M Kagkli; Patricia E Rischitor; Antonio Puertas Gallardo; Alex Patak; Maddalena Querci; Joachim Kreysa
Journal:  PLoS One       Date:  2016-01-25       Impact factor: 3.240

10.  Rank-based characterization of pollen assemblages collected by honey bees using a multi-locus metabarcoding approach.

Authors:  Rodney T Richardson; Chia-Hua Lin; Juan O Quijia; Natalia S Riusech; Karen Goodell; Reed M Johnson
Journal:  Appl Plant Sci       Date:  2015-10-30       Impact factor: 1.936

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