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Deep learning-enhanced hyperspectral imaging for rapid screening of Co-metabolic microplastic-degrading bacteria in environmental samples
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Researchers developed a novel method combining hyperspectral imaging with deep learning to rapidly screen for microplastic-degrading bacteria directly from culture media. The approach analyzes chemical changes in the growth medium caused by bacterial degradation activity, allowing indirect identification of effective degraders without lengthy traditional screening. The method successfully identified a PBAT-degrading bacterial strain, demonstrating potential for accelerating the discovery of microorganisms useful for bioremediation of plastic pollution.
Microbial biodegradation of microplastic (MP) emerges as an environmentally benign and highly promising strategy for alleviating MP pollution in the ecosystem. Conventional approaches for screening MP-degrading bacteria use pollutants as the sole carbon source. Co-metabolism plays an essential role in microbial screening, as it enables the discovery of additional degrading microorganisms. However, identifying co-metabolic degrading bacteria is challenging and time-intensive, as not all microorganisms on a co-metabolic medium exhibit degradation capability, increasing the need for refined screening methods. In this study, we propose a novel hyperspectral imaging (HSI) approach to rapidly screen polybutylene adipate terephthalate (PBAT) degrading bacteria directly from co-metabolic media. Hyperspectral images of solid media cultures were acquired, capturing both spatial (image) and spectral (chemical) information. Chemical components in the solid medium exhibit distinct changes under the influence of degrading and non-degrading bacteria. By analyzing the spectral information using machine and deep learning algorithms, it was possible to monitor the PBAT concentration changes in the solid medium, indirectly identifying degrading and non-degrading bacteria. This HSI-based model successfully screened out one kind of PBAT-degrading bacteria validated by traditional method, demonstrating potential for rapid screening of MP-degrading bacteria. With artificial intelligence (AI) technology attracting extensive attention across diverse fields, this study pioneers a new approach for the efficient screening of degrading microorganisms by combining AI algorithms with HSI. This innovative methodology is expected to display significant application potential, thus facilitating the research and development in related fields. SYNOPSIS: This study introduces a highly efficient method to screen co-metabolic MP-degrading bacteria. By combining HSI with deep learning, MP-degrading bacteria can be directly identified on co-metabolism solid media, greatly enhancing the efficiency of screening for MP-degrading microorganisms.
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