New Tools for Comparing Microscopy Images: Quantitative Analysis of Cell Types in Bacillus subtilis

Abstract

Fluorescence microscopy is a method commonly used to examine individual differences between bacterial cells, yet many studies still lack a quantitative analysis of fluorescence microscopy data. Here we introduce some simple tools that microbiologists can use to analyze and compare their microscopy images. We show how image data can be converted to distribution data. These data can be subjected to a cluster analysis that makes it possible to objectively compare microscopy images. The distribution data can further be analyzed using distribution fitting. We illustrate our methods by scrutinizing two independently acquired data sets, each containing microscopy images of a doubly labeled Bacillus subtilis strain. For the first data set, we examined the expression of srfA and tapA, two genes which are expressed in surfactin-producing and matrix-producing cells, respectively. For the second data set, we examined the expression of eps and tapA; these genes are expressed in matrix-producing cells. We show that srfA is expressed by all cells in the population, a finding which contrasts with a previously reported bimodal distribution of srfA expression. In addition, we show that eps and tapA do not always have the same expression profiles, despite being expressed in the same cell type: both operons are expressed in cell chains, while single cells mainly express eps. These findings exemplify that the quantification and comparison of microscopy data can yield insights that otherwise would go unnoticed.