The burden of formalin: metabarcoding analysis on mesozooplankton samples preserved in buffered formalin from the Gulf of Trieste
1
Department of Life Sciences, University of Trieste, Via Licio Giorgieri 5, Trieste, Trieste - 34127, Italia
2
, National Institute of Oceanography and Applied Geophysics - OGS, Via A. Piccard 54, Trieste, Trieste - 34151, Italia
3
Department of Biology, University of Florence, Via Madonna del Piano 6, Sesto Fiorentino, Firenze - 50019, Italia
4
The Vertebrate Genome Laboratory, The Rockefeller University, York Avenue, New York, - 10065, United States
Long-term ecological research and monitoring reveal how ecosystems change across decades by tracking spatial and temporal patterns. They offer snapshots on shifting communities and biodiversity loss driven by anthropogenic and climate-related stressors, thereby supporting biodiversity conservation and resource management. In the case of zooplankton, several historical collections around the world span multiple decades. Researchers often preserved samples in buffered formalin after morphological analysis. While formalin remains one of the most commonly used fixatives for maintaining structural integrity, it complicates genetic analysis due to DNA degradation and cross-links between nucleic acids and proteins. Integrating a molecular approach alongside traditional morphological analysis remains a crucial challenge, especially for understanding potential shifts in mesozooplankton communities under rapid climate change.
This study aims to establish a protocol capable of performing metabarcoding analyses on mesozooplankton samples preserved in buffered formalin for seven years.
In 2017, we collected samples monthly at the "MAMBO Station" in the Gulf of Trieste using vertical nets. The samples were then taxonomically identified and preserved in formalin. We pretreated the samples with an alkaline buffer, followed by thermal shock and extracted DNA using the E.Z.N.A.® Mollusc DNA kit (Omega Bio-Tek). Three primer sets targeting 18S and COI markers were successfully amplified. We processed raw reads in QIIME2 (2024.5) and clustered sequences with VSEARCH using abundance-based greedy clustering at a 94% similarity threshold. Taxonomic assignment was performed against reference databases (MetaZooGene for COI and SILVA for 18S). Our results demonstrate that meaningful community composition data can be recovered from archived formalin-preserved zooplankton samples, supporting their use in retrospective ecological studies, including those associated with climate change. The optimized workflow provides a valuable tool for integrating molecular and historical data to assess long-term changes in zooplankton communities.
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