Applying fluorescence in situ hybridization to aquatic systems with cyanobacteria blooms: Autofluorescence suppression and high‐throughput image analysis

Abstract

Cyanobacterial Harmful Algal Blooms (CyanoHABs) are expanding geographically in both fresh and marine water bodies due to coastal eutrophication and global climate change and are restructuring the microbial ecology of these systems. Cyanobacterial autofluorescence can pose a significant impediment to accurately identifying prokaryotic taxonomic groups in environmental samples using fluorescence in situ hybridization (FISH). This can hinder our ability to accurately quantify, and therefore fully understand ecological changes. As abundances of FISH target cells and autofluorescent cells can often be of the same the order of magnitude, simply subtracting average autofluorescent cell concentrations—determined from enumerating unhybridized samples— yields apparent concentrations of target cells with unacceptably large analytical uncertainty. Here we present a CuSO4/EtOH chemical pretreatment protocol that significantly reduces undesirable autofluorescence in hybridized environmental samples. We apply a novel data filtration routine to FISH images that efficiently removes residual autofluorescent cells from final cell counts. We then subject images to an automated image analysis routine that accurately enumerates probe-positive cells. This method is inexpensive and easy to implement as part of a routine FISH workflow. By applying this method to cyanobacteria rich samples, we can better understand how microbial community changes are contributing to globally changing biogeochemical cycles. Cyanobacteria harmful algal blooms (CyanoHABs) are becoming more geographically extensive in fresh and marine water bodies as a consequence of coastal eutrophication and global climate change and appear to be restructuring the microbial ecology of these systems (Hallegraeff 2010; O’Neil et al. 2012; Wells et al. 2015). Lower trophic level organisms, such as prokaryotes and protists, tolerate much lower concentrations of cyano-toxins than those at higher trophic levels. CyanoHABS therefore weaken the efficiency of the microbial loop in the biological carbon pump, which results in greater organic matter deposition in sediment. In turn, the higher sedimentary organic matter content causes hypoxic or anoxic conditions lethal to benthic macrofauna living near the sediment–water interface. Additionally, if the affected microbes are key to the food web, their decimation causes trophic cascades (Christoffersen 1996). One major focus of current research on bloom-forming harmful cyanobacteria is quantifying how cyanobacteria interact with other microbes and affect the overall microbial ecology (e.g., Parulekar et al. 2017; Tromas et al. 2017). Tromas et al. (2017) even used such information to develop a predictive cyanobacteria bloom model. As such, accurately quantifying population dynamics within microbial communities over time is critical. Relative and absolute abundances of taxa derived from rRNA libraries often used in such studies are poorly constrained proxies for cell abundances because of potential PCR biases and because prokaryotes can have anywhere between 1 and 15 copies of the 16S rRNA gene per cell (Kembel et al. 2012). This uncertainty seems to be even more pronounced for 18S rRNA libraries representing microbial eukaryotes. For example, Gong and Marchetti (2019) demonstrated that among a handful of eukaryotic microalgae, 18S gene copies can vary from 3 to 160 per cell as evident from draft/closed genomes. In the common situation that environmental or experimental samples have to be split for many analyses, sample volumes can also often be too small to extract sufficient DNA for metagenomic sequencing or quantitative PCR. *Correspondence: [email protected] Author Contribution Statement: A.N. and C.W. contributed equally. Additional Supporting Information may be found in the online version of this article.