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 samplesyields apparent concentrations of target cells with unacceptably large analytical uncertainty. Here we present a CuSO 4 /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.

Preserved particulate organic carbon is likely derived from the subsurface sulfidic photic zone of the Proterozoic Ocean: evidence from a modern, oxygen‐deficient lake

“…At the lower oxycline boundary (20 m), where photoautotrophic purple and green sulfur bacteria first appear, the $$$ \updelta $$$ 13 C of both size fractions were most similar and were strongly 13 C‐depleted ($$$ \updelta $$$ 13 C POC values = −41.9‰ and − 39.4‰ in PA and FL fractions, respectively). At 20.5 m, where sulfur‐oxidizing photoautotrophic purple sulfur bacteria and chemoautotrophic epsilonproteobacteria were previously observed to be most abundant (Cohen et al., 2021, 2023), $$$ \updelta $$$ 13 C POC values of the two size fractions differed the most; PA fraction $$$ \updelta $$$ 13 C POC values reached their lowest value (−42.3‰) while in the FL fraction $$$ \updelta $$$ 13 C POC values returned to mid‐oxycline values (−35.1‰). Finally, local $$$ \updelta $$$ 13 C POC value maxima in the FL (−35.3‰) and PA (−39.8‰) fractions were evident at 21 m, where photoautotrophic green sulfur bacteria were most abundant.…”

“…Insitu biological methane production in the water column is an unlikely explanation because relative abundances of methanogens are multiple orders-of-magnitude lower than those of the photoautotrophs (Cohen et al, 2023), and because geochemical evidence suggests that most of the methane in the lake originates from lakebed sediment (Havig et al, 2018). δ 13 C POC at the total ICA and biomass maximum (20.5 m) was 13 Cenriched, which was expected because carbon assimilation via the rTCA pathway by green sulfur bacteria, which modestly contribute to the maximum light carbon assimilation rate (Culver & Brunskill, 1969) and chemoautotrophic epsilonproteobacteria, which are likely associated with the maximum dark carbon assimilation rate (Cohen et al, 2021) esis that a mixture of anoxygenic and oxygenic photoautotrophs contribute to the δ 13 C POC values of lakebed organic matter, superficially resembling the δ 13 C POC values of shallow-dwelling oxygenic algae (Figure 8). The apparent particle-associated δ 13 C enrichment at 40 m may reflect purple sulfur bacteria aggregates that are resistant to remineralization, so that there is less of a signal from sulfate reduction.…”

“…Other than the above‐described samples within the core of the light‐scattering layer and the deepest PA sample, the isotopic data was what we expected based on the qPCR results. The FL fraction $$$ \updelta $$$ 13 C POC at the total ICA and biomass maximum (20.5 m) was 13 C‐enriched, which was expected because carbon assimilation via the rTCA pathway by green sulfur bacteria, which modestly contribute to the maximum light carbon assimilation rate (Culver & Brunskill, 1969) and chemoautotrophic epsilonproteobacteria, which are likely associated with the maximum dark carbon assimilation rate (Cohen et al., 2021), were dominant in this fraction. The most 13 C‐enriched $$$ \updelta $$$ 13 C POC values observed in both size fractions of the turbidity layer (21 m) coincides with maxima in green sulfur bacteria and aclB gene abundances indicating that the rTCA fixation pathway dominated at that depth.…”

“…photoautotrophic purple sulfur bacteria and chemoautotrophic epsilonproteobacteria were previously observed to be most abundant(Cohen et al, 2021(Cohen et al, , 2023, δ 13 C POC values of the two size fractions differed the most; PA fraction δ 13 C POC values reached their lowest value (−42.3‰) while in the FL fraction δ 13 C POC values returned to mid-oxycline values (−35.1‰). Finally, local δ 13 C POC value maxima in the FL (−35.3‰) and PA (−39.8‰) fractions were evident at 21 m, where photoautotrophic green sulfur bacteria…”

Cutting through host autofluorescence: fluorescence lifetime imaging microscopy for visualising intracellular bacteria in Symbiodiniaceae

“…Various sample preparation approaches such as exposure of cells to sodium borohydride ( Castillo-Medina et al ., 2011 ), high light ( Webster et al ., 2001 ; Maire et al ., 2021 ), combined hydrogen peroxide and light ( Yakubovskaya et al ., 2019 ), copper sulfate and ethanol ( Cohen et al , 2021 ), fluorescence lifetime gating ( Kodama, 2016 ), TrueVIEW quenching kit ( Astafyeva et al ., 2022b ) and post image processing using deep learning tools ( Jiang et al ., 2022 ) are used for reduction of background autofluorescence. Maire et al .…”