Deep Learning Driven Investigation of Nanoplastic Impacts on Soil Protist Behavior in Soil Chips

Abstract

Nanoplastics are emerging contaminants that have a significant impact on soil microorganisms. To fully understand the effect of plastic contamination on soil ecosystems, it is necessary to advance techniques that can monitor nanoplastic-microbe interactions under realistic conditions. In this work, we investigated the effects of nanoplastic contamination on a community of soil protists monitored through microfluidic soil chips, and analysed changes in their behavior via microscopy videos and a deep learning approach. The presented method employs a deep learning-based detection model combined with a transformer-based matching model for video frame interpolation, enabling accurate reconstruction of protist movement trajectories and velocities within soil chips. The results revealed reduced movement velocities for the groups of flagellates and ciliates under high nanoplastic conditions, a 24-30% reduction at a marginal significance level, while amoebae were unaffected. Our trajectory data provides novel insights into how protists navigate soil-like structures. By facilitating comprehensive assessments of protist-environment interactions, it opens new avenues for understanding their ecological roles and the broader implications of hazardous contaminants in both soil and aquatic ecosystems at microbial community level without need for culture extraction. This proof-of-concept system enables continuous, high-throughput monitoring of soil protist behavior and can be readily adapted to investigate protist responses to diverse chemical and physical soil hazards.