Detection of subgroups from flow cytometry measurements of heterotrophic bacterioplankton by image analysis

Background: Flow cytometry is an invaluable tool for the analysis of large series of samples in aquatic microbial ecology. However, analysis of the re sulting data is often inefficient or does not reflect the complexity of nat ural communities. Because bacterioplankton assemblages frequently fall into several clusters with respect to their cellular properties, these subgroup s seem to be a promising level of abstraction. Image analysis was used to d etect clusters from flow cytometry data. The method was tested on a bacteri al community under heavy protozoan grazing pressure. Methods: A bivariate histogram of now cytometry data was transformed into a gray-scale image for image analysis. After low-pass filtration, regional m axima were delimited by a watershed algorithm. The resulting areas were the n used as gates on the original measurements. Results: Three clusters could be detected from the bacterial assemblage. Pr otozoan grazing had a strong impact on the bacterial community, which could be analyzed in detail at the level of individual subgroups. Conclusions: Investigation at the level of bacterial subgroups allowed a mo re detailed analysis than whole-community statistics and delivered essentia l and ecologically meaningful information. Image analysis proved to be an a dequate tool to detect the subgroups without a priori knowledge. (C) 2001 W iley-Liss, Inc.