Abstract
The green algae Chlamydomonas reinhardtii is a model system for motility in unicellular organisms. Photo-, gravi-, and chemotaxis have previously been associated with C. reinhardtii, and observing the extent of these responses within a population of cells is crucial for refining our understanding of how this organism responds to changing environmental conditions. However, manually tracking and modeling a statistically viable number of samples of these microorganisms is an unreasonable task. We hypothesized that automated particle tracking systems are now sufficiently advanced to effectively characterize such populations. Here, we present an automated method to observe C. reinhardtii motility that allows us to identify individual cells as well as global information on direction, speed, and size. Nutrient availability effects on wild-type C. reinhardtii swimming speeds, as well as changes in speed and directionality in response to light, were characterized using this method. We also provide for the first time the swimming speeds of several motility-deficient mutant lines. While our present effort is focused around the unicellular green algae, C. reinhardtii, we confirm the general utility of this approach using Chlamydomonas moewusii, another member of this genus which contains over 300 species. Our work provides new tools for evaluating and modeling motility in this model organism and establishes the methodology for conducting similar experiments on other unicellular microorganisms.