Over the past few decades, advancements in cultivation methods and the rapid development of new and advancing technologies has been the driving force behind the “-omics era” and has greatly enhanced the rate of discovery for a myriad of microorganisms. However, applications of microarray and Next Generation Sequencing (NGS) technologies typically are very limited in determining the relative abundances of individual targets and visualization of cells, which are features of which fluorescence in situ hybridization (FISH) excels. For that reason, scientists have frequently turned to FISH as an effective methodology for driving microbiome research around the world.

Several FISH-,qPCR- or RNA sequencing-based methods can measureRNA expression at the single-cell level. One technique that is suitablefor high-throughput analysis is flow cytometry-based fluorescence in situ hybridization, or Flow-FISH.

Analyzing microscopic image datasets is difficult. It is even more so with the rapid increase of PixelBiotech’s customer base. We are con-stantly receiving requests for HuluREAD to analyze datasets that are acquired using different microscopes, under various imaging conditions and on different species. In this article,we discuss important lessons we learned in building HuluREAD into an efficient and scalable algorith-mic platform for customized HuluFISH image data analysis.