Automated Image Analysis for Quantitative Fluorescence 1 In Situ Hybridization with Environmental Samples 2 Running title : AUTOMATED IMAGE ANALYSIS FOR QUANTITATIVE FISH 3 4

1 When performing fluorescence in situ hybridization (FISH) analysis on complex 2 environmental samples, difficulties related to the presence of microbial cell aggregates 3 and non-uniform background fluorescence often are encountered. The objective of this 4 study was to develop a robust and automated quantitative FISH method for complex 5 environmental samples, such as manure and soil. The method and duration of sample 6 dispersion were optimized to reduce the interference of cell aggregates. An automated 7 image analysis program that detects cells from 4', 6-diamidino-2-phenylindole (DAPI) 8 micrographs and extracts the maximum and mean fluorescence intensities for each cell 9 from corresponding FISH images was developed with the software Visilog. Intensity 10 thresholds were not consistent even for duplicate analyses, so alternative ways of 11 classifying signals were investigated. In the resulting method, the intensity data were 12 divided into clusters using fuzzy c-means (FCM) clustering and the resulting clusters 13 were classified as target (positive) or non-target (negative). A manual quality control 14 confirmed this classification. With this method, 50.4%, 72.1%, and 64.9% of the cells in 15 two swine manure samples and a soil sample were positive with a 16S rRNA-targeted 16 bacterial probe (S-D-Bact-0338-a-A-18), respectively. Manual counting resulted in 17 2.5% of the cells in two swine manure samples and a soil sample were positive with an 19 archaeal probe (S-D-Arch-0915-a-A-20), respectively. Manual counting resulted in 20 corresponding values of 22.4%, 14.0%, and 2.9%, respectively. This automated method 21 will facilitate quantitative analysis of FISH images in a variety of complex environmental 22 samples.