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

Zou, Hanbang LU ; Ying, Wei ; Mafla-Endara, Paola M. LU ; Klinghammer, Fredrik LU ; Bai, Jingmo ; Kang, Hanwen and Hammer, Edith C. LU orcid (2026) In Environmental Pollution 389.
Abstract

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... (More)

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.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Microbial AI tracking, Microfluidics, Nano plastic, Soil Chip, Soil protist
in
Environmental Pollution
volume
389
article number
127414
publisher
Elsevier
external identifiers
  • scopus:105023098522
  • pmid:41274594
ISSN
0269-7491
DOI
10.1016/j.envpol.2025.127414
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2025
id
8d513709-7148-40ed-9824-fd05a008386d
date added to LUP
2026-01-15 15:28:08
date last changed
2026-01-20 13:39:58
@article{8d513709-7148-40ed-9824-fd05a008386d,
  abstract     = {{<p>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.</p>}},
  author       = {{Zou, Hanbang and Ying, Wei and Mafla-Endara, Paola M. and Klinghammer, Fredrik and Bai, Jingmo and Kang, Hanwen and Hammer, Edith C.}},
  issn         = {{0269-7491}},
  keywords     = {{Microbial AI tracking; Microfluidics; Nano plastic; Soil Chip; Soil protist}},
  language     = {{eng}},
  month        = {{01}},
  publisher    = {{Elsevier}},
  series       = {{Environmental Pollution}},
  title        = {{Deep learning-driven investigation of nanoplastic impacts on soil protist behavior in soil chips}},
  url          = {{http://dx.doi.org/10.1016/j.envpol.2025.127414}},
  doi          = {{10.1016/j.envpol.2025.127414}},
  volume       = {{389}},
  year         = {{2026}},
}