Abstract
Studies on biflagellated algae Chlamydomonas reinhardtii mutants have resulted in significant contributions to our understanding of the functions of cilia/flagella components. However, visual inspection conducted under a microscope to screen and classify Chlamydomonas swimming requires considerable time, effort, and experience. In addition, it is likely that identification of mutants by this screening is biased toward individual cells with severe swimming defects, and mutants that swim slightly more slowly than wild-type cells may be missed by these screening methods. To systematically screen Chlamydomonas swimming mutants, we have here developed the cell-locating-with-nanoscale-accuracy (CLONA) method to identify the cell position to within 10-nm precision through the analysis of high-speed video images. Instead of analyzing the shape of the flagella, which is not always visible in images, we determine the position of Chlamydomonas cell bodies by determining the cross-correlation between a reference image and the image of the cell. From these positions, various parameters related to swimming, such as velocity and beat frequency, can be accurately estimated for each beat cycle. In the examination of wild-type and seven dynein arm mutants of Chlamydomonas, we found characteristic clustering on scatter plots of beat frequency versus swimming velocity. Using the CLONA method, we have screened 38 Chlamydomonas strains and detected believed-novel motility-deficient mutants that would be missed by visual screening. This CLONA method can automate the screening for mutants of Chlamydomonas and contribute to the elucidation of the functions of motility-associated proteins.