Advances in single-cell sequencing technology in microbiome research

Wu, Yinhang; Zhuang, Jing; Song, Yifei; Gao, Xinyi; Chu, Jian; Han, Shuwen

doi:10.1016/j.gendis.2023.101129

Genes & Diseases

2024

DOI: 10.1016/j.gendis.2023.101129

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Advances in single-cell sequencing technology in microbiome research

Yinhang Wu,

Jing Zhuang,

Yifei Song

et al.

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2024

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Cited by 5 publications

References 165 publications

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Biomimetic nanocarriers harnessing microbial metabolites usher the path for brain disease therapy

Gong,

Liu,

Zhou

et al. 2023

Nano TransMed

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Biomimetic nanocarriers harnessing microbial metabolites usher the path for brain disease therapy

Gong,

Liu,

Zhou

et al. 2023

Nano TransMed

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Multi-omics reveal toxicity mechanisms underpinning nanoplastic in redclaw crayfish (Cherax quadricarinatus)

Zhu,

Yao,

Hong

et al. 2024

Science of The Total Environment

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Artificial Intelligence-Assisted Automatic Raman-Activated Cell Sorting (AI-RACS) System for Mining Specific Functional Microorganisms in the Microbiome

Diao,

Jing,

Hou

et al. 2024

Anal. Chem.

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The microbiome represents the natural presence of microorganisms, and exploring, understanding, and leveraging its functions will bring about significant breakthroughs in life sciences and applications. Raman-activated cell sorting (RACS) enables the correlation of phenotype and genotype at the single-cell level, offering a solution to the bottleneck in microbial community functional analysis caused by challenges in cultivating diverse microorganisms. However, current labor-intensive manual procedures fall short in catering to the demands of single-cell functional analysis in microbial communities. To address this issue, we developed an artificial intelligence-assisted Raman-activated cell sorting system (AI-RACS) that integrates precise single-cell positioning, automated data collection, optical tweezers capture, and single-cell printing to elevate microbial single-cell RACS from manual to automated, validating the efficacy of the system by isolating aluminum-tolerant microbes from acidic soil microbiota. Leveraging the AI-RACS framework, we sorted 13 strains from red soil samples under near-in situ conditions, with all demonstrating strong aluminum tolerance. AI-RACS efficiently segregates microbial cells from intricate environmental samples, investigating their functional attributes and presenting a novel tool for microbial research and applications.

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Advances in single-cell sequencing technology in microbiome research

Cited by 5 publications

References 165 publications

Biomimetic nanocarriers harnessing microbial metabolites usher the path for brain disease therapy

Biomimetic nanocarriers harnessing microbial metabolites usher the path for brain disease therapy

Multi-omics reveal toxicity mechanisms underpinning nanoplastic in redclaw crayfish (Cherax quadricarinatus)

Artificial Intelligence-Assisted Automatic Raman-Activated Cell Sorting (AI-RACS) System for Mining Specific Functional Microorganisms in the Microbiome

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