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.

中文翻译：

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将荧光原位杂交应用于蓝藻大量繁殖的水生系统：自发荧光抑制和高通量图像分析

由于沿海富营养化和全球气候变化，蓝藻有害藻华 (CyanoHABs) 在淡水和海洋水体中的地理范围不断扩大，并正在重构这些系统的微生物生态。蓝藻自发荧光会严重阻碍使用荧光原位杂交 (FISH) 准确识别环境样品中的原核生物分类群。这会阻碍我们准确量化并因此全面了解生态变化的能力。由于 FISH 靶细胞和自体荧光细胞的丰度通常处于相同的数量级，因此简单地减去平均自体荧光细胞浓度（通过枚举未杂交样本确定）会产生具有不可接受的大分析不确定性的表观靶细胞浓度。₄ /EtOH 化学预处理方案，可显着减少杂交环境样品中的不良自发荧光。我们对 FISH 图像应用了一种新的数据过滤程序，可以有效地从最终细胞计数中去除残留的自发荧光细胞。然后，我们将图像置于自动图像分析程序中，该程序可准确枚举探针阳性细胞。作为常规 FISH 工作流程的一部分，此方法成本低廉且易于实施。通过将这种方法应用于富含蓝藻的样品，我们可以更好地了解微生物群落的变化如何促成全球变化的生物地球化学循环。