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Data · dataset · 2026

Dataset for "Single-Cell Raman-Guided Identification and Recovery of Active Microplastic-Transforming Bacteria from Complex Microbiota"

Listed in ScienceDB

Microbial biotransformation presents a sustainable strategy for mitigating global microplastics (MPs) pollution but is fundamentally constrained by the difficulty to directly identify and recover functionally active MPs-transforming microorganisms from complex microbial communities.

Description

Here, we established a function-driven, single-cell Raman spectroscopic framework that integrated deuterium-based metabolic profiling, Raman-activated cell sorting (RACS), targeted sequencing, and cultivation.

To circumvent the high cost and technical hurdles of using isotope-labeled plastics to simulate multiple types of MPs in environment, a universal deuterium-tracing strategy that reliably identifies MP-responsive cells based on their metabolic activity under plastic-dependent conditions was developed, directly linking phenotype to genotype and enabling isolate recovery. Applying this platform to the gut microbiota of plastic-fed mealworms, we discovered previously unrecognized polystyrene (PS)- biotransforming bacteria and associated candidate enzymes through RACS and targeted metagenomic analysis, with several of these enzymes being functionally validated via heterologous prokaryotic expression.

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By integrating enzymatic functions with detected conversion products, and physiochemical changes, a detailed three-stage PS biotransformation pathway was delineated, enhancing mechanistic understanding of gut microbiota-mediated PS conversion. Importantly, Raman-activated live-cell sorting successfully recovered rare yet metabolically active PS-biotransforming bacteria that were not readily obtained by conventional cultivation methods.

This scalable phenotype-to-isolate single-cell framework represents a substantial advancement in harnessing microbial solutions for tackling the global microplastic pollution.

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Industrial biotechnology 73% · Sequencing 75%
Provenance · 1 source records, 13 field assertions
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ScienceDB10.57760/sciencedb.243934 d agoJSON v1
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