Unveiling distribution patterns of freshwater phytoplankton by a next generation sequencing based approach

PLoS One. 2013;8(1):e53516. doi: 10.1371/journal.pone.0053516. Epub 2013 Jan 22.

Abstract

The recognition and discrimination of phytoplankton species is one of the foundations of freshwater biodiversity research and environmental monitoring. This step is frequently a bottleneck in the analytical chain from sampling to data analysis and subsequent environmental status evaluation. Here we present phytoplankton diversity data from 49 lakes including three seasonal surveys assessed by next generation sequencing (NGS) of 16S ribosomal RNA chloroplast and cyanobacterial gene amplicons and also compare part of these datasets with identification based on morphology. Direct comparison of NGS to microscopic data from three time-series showed that NGS was able to capture the seasonality in phytoplankton succession as observed by microscopy. Still, the PCR-based approach was only semi-quantitative, and detailed NGS and microscopy taxa lists had only low taxonomic correspondence. This is probably due to, both, methodological constraints and current discrepancies in taxonomic frameworks. Discrepancies included Euglenophyta and Heterokonta that were scarce in the NGS but frequently detected by microscopy and Cyanobacteria that were in general more abundant and classified with high resolution by NGS. A deep-branching taxonomically unclassified cluster was frequently detected by NGS but could not be linked to any group identified by microscopy. NGS derived phytoplankton composition differed significantly among lakes with different trophic status, showing that our approach can resolve phytoplankton communities at a level relevant for ecosystem management. The high reproducibility and potential for standardization and parallelization makes our NGS approach an excellent candidate for simultaneous monitoring of prokaryotic and eukaryotic phytoplankton in inland waters.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Food Chain
  • Fresh Water*
  • High-Throughput Nucleotide Sequencing*
  • Phytoplankton / classification
  • Phytoplankton / genetics*
  • RNA, Ribosomal, 16S / genetics
  • Sequence Analysis, RNA*
  • Time Factors

Substances

  • RNA, Ribosomal, 16S

Grant support

This work was supported by the Swedish foundation for strategic research (grant to AE), the Swedish Research Council (separate grants to ESL and SB) and the Czech Grant Agency (grant 206/08/0015 to KS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.