Comparative Analysis of Light Microscopy and High-Throughput Sequencing for Phytoplankton Detection in Rivers Flowing into the Sea
Year: 2025
Type: article
Abstract: Phytoplankton are essential indicators of aquatic ecosystem health. Traditional phytoplankton detection methods using microscopy struggle to identify species with small particle sizes or unclear morphological characteristics. In contrast, molecular methods have high accuracy but struggle to simultaneously detect prokaryotic and eukaryotic organisms due to primer specificity. As algal blooms can be caused by both prokaryotes and eukaryotes, methods that can detect both are required. This study used both microscopic detection and high-throughput sequencing methods to analyze phytoplankton in seagoing waters in eastern coastal China. Two high-throughput sequencing primers targeting 16S rDNA for prokaryotes and 18S rDNA for eukaryotes were used, and the results were compared with those of microscopic analysis. Microscopy identified 230 phytoplankton species across 73 genera, mainly belonging to Bacillariophyta, Chlorophyta, Euglenophyta, Cyanophyta, Dinophyta, and Chrysophyta. Twenty-four species across 16 sampling stations exceeded 1 million cells/L. High-throughput sequencing yielded 8642 prokaryotic and 7375 eukaryotic operational taxonomic units, with 432 identified as phytoplankton. Chlorophyta and Bacillariophyta had the highest species richness, accounting for 34% and 17%, respectively. High-throughput sequencing detected more species than microscopic detection but relied on gene reference databases and provided only the relative abundance of species based on operational taxonomic unit counts.
Source: Water
DOI: 10.3390/w17111559
Authors: Xiaokun Hu, Yunlong Liu, Rui Wang, Zhaojun Dong, Kuixuan Lin, Shucong Lv, Yuchen Liu, Wenze Lu, Lusan Liu
Institution: Chinese Research Academy of Environmental Sciences
Language: English
Subfield: Ecology
Field: Environmental Science
Domain: Physical Sciences
SDG: Life below water
Open Access status: gold
Estimated APC paid: $3,137
Award: G1873595379
OpenAlex ID: W4410589103
