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.

中文翻译：

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人工智能辅助自动拉曼激活细胞分选 （AI-RACS） 系统，用于挖掘微生物组中的特定功能微生物


微生物组代表微生物的自然存在，探索、理解和利用其功能将为生命科学和应用带来重大突破。拉曼激活细胞分选 （RACS） 能够在单细胞水平上实现表型和基因型的相关性，为因培养不同微生物的挑战而导致的微生物群落功能分析瓶颈提供了解决方案。然而，目前劳动密集型的手动程序无法满足微生物群落中单细胞功能分析的需求。为了解决这个问题，我们开发了一种人工智能辅助拉曼激活细胞分选系统 （AI-RACS），该系统集成了精确的单细胞定位、自动数据收集、光镊捕获和单细胞打印，将微生物单细胞 RACS 从手动提升为自动，通过从酸性土壤微生物群中分离耐铝微生物来验证系统的功效。利用 AI-RACS 框架，我们在近原位条件下从红壤样品中分选了 13 个菌株，所有菌株都表现出很强的铝耐受性。AI-RACS 有效地将微生物细胞与复杂的环境样品分离，研究其功能属性，并为微生物研究和应用提供一种新颖的工具。