Abstract:

Giant viruses, such as mimivirus and pandoravirus, are exceptionally large viruses that infect various microorganisms. Understanding their infection mechanisms is crucial for studying virology and microbial ecology. Conventional approaches for analyzing giant virus infections in amoeba rely on qualitative observations, which can be subjective and limited in providing detailed insights into the infection process.

In this study, we developed a novel image analysis method for time-lapse microscopy data to quantitatively analyze giant virus infections in amoeba. Our method involves image segmentation, feature extraction, and machine learning techniques. We applied this method to analyze high-throughput time-lapse imaging data of Acanthamoeba castellanii infected with the giant virus Mimivirus.

Our results provide a comprehensive analysis of the infection process, including the initial attachment of the virus to the amoeba surface, viral entry, replication, and release. Quantitative measurements, such as infection rate, viral load, and replication kinetics, were obtained and analyzed statistically. We also identified key morphological changes in infected amoeba cells throughout the infection cycle.

The developed image analysis method enables researchers to systematically study the infection mechanisms of giant viruses with high accuracy and throughput. This approach not only contributes to advancing our understanding of giant virus-host interactions but also offers a valuable tool for investigating other aspects of microbial ecology and virology.