Chlamydia trachomatis is a major health concern with over 200 million people with active urogenital or ocular infection each year worldwide. Chlamydia are obligate intracellular bacteria with a unique biphasic developmental cycle. A better understanding of that biphasic cycle can lead to inhibitors that are specific for chlamydial infection in order to avoid overuse of antibiotics. Individual Chlamydia are too small and tightly packed to be spatially separated with conventional light microscopes, and 3D SEM is too labor-intensive for inhibitor studies. We will use a new sample preparation method that physically expands the sample with polymers termed Expansion Microscopy or ExM. Expanded samples can then be imaged with a traditional confocal microscope, and high-content analysis performed automatically using machine learning methods such as pixel classification and novelty detection. Prepared samples can be imaged and analyzed in under an hour instead of the multiple days required for 3D SEM. This R03 grant will develop an innovative high-content screening platform, called Expansion Microscopy Aided Phenotyping (ExMAP), for the quantification of changes in Chlamydia development after treatment. ExMAP can be paired with Chlamydia transformed with promoters for EUO and IhtA (RB cell types) and the promoters for HctB and Tarp (EB cell types). The combination of expansion microscopy, machine learning, and chlamydial transformation will make ExMAP a powerful tool for research on both the developmental cycle and new therapy development.
This project will develop a new high-content platform, termed ExMAP, that will physically expand the sample of interest and then utilize machine learning for image analysis. At the completion of the project, we expect to have developed a new method for the study of the Chlamydia developmental cycle and inhibitors that disrupt that cycle.
