Performance Comparison of Five Methods for Tetrahymena Number Counting on the ImageJ Platform: Assessing the Built-in Tool and Machine-Learning-Based Extension

Deep Learning-Based Automatic Duckweed Counting Using StarDist and Its Application on Measuring Growth Inhibition Potential of Rare Earth Elements as Contaminants of Emerging Concerns

In recent years, there have been efforts to utilize surface water as a power source, material, and food. However, these efforts are impeded due to the vast amounts of contaminants and emerging contaminants introduced by anthropogenic activities. Herbicides such as Glyphosate and Glufosinate are commonly known to contaminate surface water through agricultural industries. In contrast, some emerging contaminants, such as rare earth elements, have started to enter the surface water from the production and waste of electronic products. Duckweeds are angiosperms from the Lemnaceae family and have been used for toxicity tests in aquatic environments, mainly those from the genus Lemna, and have been approved by OECD. In this study, we used duckweed from the genus Wolffia, which is smaller and considered a good indicator of metal pollutants in the aquatic environment. The growth rate of duckweed is the most common endpoint in observing pollutant toxicity. In order to observe and mark the fronds automatically, we used StarDist, a machine learning-based tool. StarDist is available as a plugin in ImageJ, simplifying and assisting the counting process. Python also helps arrange, manage, and calculate the inhibition percentage after duckweeds are exposed to contaminants. The toxicity test results showed Dysprosium to be the most toxic, with an IC50 value of 14.6 ppm, and Samarium as the least toxic, with an IC50 value of 279.4 ppm. In summary, we can provide a workflow for automatic frond counting using StarDist integrated with ImageJ and Python to simplify the detection, counting, data management, and calculation process.

Establishing a High-Throughput Locomotion Tracking Method for Multiple Biological Assessments in Tetrahymena

Protozoa are eukaryotic, unicellular microorganisms that have an important ecological role, are easy to handle, and grow rapidly, which makes them suitable for ecotoxicity assessment. Previous methods for locomotion tracking in protozoa are largely based on software with the drawback of high cost and/or low operation throughput. This study aimed to develop an automated pipeline to measure the locomotion activity of the ciliated protozoan Tetrahymena thermophila using a machine learning-based software, TRex, to conduct tracking. Behavioral endpoints, including the total distance, velocity, burst movement, angular velocity, meandering, and rotation movement, were derived from the coordinates of individual cells. To validate the utility, we measured the locomotor activity in either the knockout mutant of the dynein subunit DYH7 or under starvation. Significant reduction of locomotion and alteration of behavior was detected in either the dynein mutant or in the starvation condition. We also analyzed how Tetrahymena locomotion was affected by the exposure to copper sulfate and showed that our method indeed can be used to conduct a toxicity assessment in a high-throughput manner. Finally, we performed a principal component analysis and hierarchy clustering to demonstrate that our analysis could potentially differentiate altered behaviors affected by different factors. Taken together, this study offers a robust methodology for Tetrahymena locomotion tracking in a high-throughput manner for the first time.