Image-Processing Software for High-Throughput Quantification of Colony Luminescence

Abstract

ABSTRACT Many microbiological assays include colonies that produce a luminescent or fluorescent (here generalized as “luminescent”) signal, often in the form of luminescent halos around the colonies. These signals are used as reporters for a trait of interest; therefore, exact measurements of the luminescence are often desired. However, there is currently a lack of high-throughput methods for analyzing these assays, as common automatic image analysis tools are unsuitable for identifying these halos in the presence of the inherent biological noise. In this work, we have developed CFQuant—automatic, high-throughput software for the analysis of images from colony luminescence assays. CFQuant overcomes the problems of automatic identification by relying on the luminescence halo's expected shape and provides measurements of several features of the colonies and halos. We examined the performance of CFQuant using one such colony luminescence assay, where we achieved a high correlation (R = 0.85) between the measurements of CFQuant and known protein expression levels. This demonstrates CFQuant's potential as a fast and reliable tool for analysis of colony luminescence assays. IMPORTANCE Luminescent markers are widely used as reporters for various biologically interesting traits. In colony luminescence assays, the levels of luminescence around each colony can be used to compare the levels of traits of interest for different strains, treatments, etc., using quantitative measurements of the luminescence. However, automatic methods of obtaining this data are underdeveloped, making this a laborious manual process, especially in analyzing large numbers of colonies. The significance of this work is in developing an automatic, high-throughput tool for quantitative analysis of colony luminescence assays, which will allow fast collection of qualitative data from these assays and thus increase their overall usability.

Keywords

• fluorescent-image analysis
• microbial method
• software