Deep learning-driven investigation of nanoplastic impacts on soil protist behavior in soil chips

Nanoplastics are emerging environmental contaminants that increasingly threaten soil ecosystems, yet their effects on microbial behavior remain poorly understood. This is mainly due to the lack of experimental tools capable of directly observing microbial dynamics in situ under realistic soil-like conditions. Here, we present a proof-of-concept system that enables real-time, high-throughput monitoring of soil protists within microfluidic soil chips under nanoplastic exposure. Using microscopy video analysis integrated with a deep learning-based detection model and a transformer-based trajectory reconstruction algorithm, we quantitatively measured the movement of three morpho-/locomotion type groups-flagellates, ciliates, and amoebae-across a gradient of nanoplastic concentrations (0, 2, and 10 mg/L). Our results showed reduced movement velocities for flagellates and ciliates under high nanoplastic conditions with a 24%-30% reduction in speed, while no effect on amoebae was detected. The trajectory data also provides novel insights into how protists navigate soil-like structures. Beyond these specific findings, our approach establishes a transformative framework for observing microbial life directly within its microenvironment, comparable to how animal behavior is monitored in ecological studies. By bridging real-time imaging and artificial intelligence, this method offers a new angle to study protist-environment interactions without the need for culture extraction. It opens the door to rethinking how microbial ecology, soil contamination, and biotic responses to environmental stressors are investigated, advancing opportunities from static, population-level measurements to dynamic, behavioral-level understanding within realistic habitats.