Ecology-microbiome engineering in Sauce-flavor Baijiu via Single-Cell Raman Spectroscopy.

IF 7.2 Q2 Agricultural and Biological Sciences
生物设计研究(英文) Pub Date : 2026-05-05 eCollection Date: 2026-06-01 DOI:10.1016/j.bidere.2026.100089
Xi Sun, Xinyun Yi, Jia Zhang
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引用次数: 0

Abstract

Sauce-flavor Baijiu owes its layered aroma to a diverse microbial consortium fermenting under extreme, open, solid-state conditions. High-throughput sequencing has revealed marked spatiotemporal heterogeneity in community composition, yet a fundamental composition-function disconnect persists: bulk omics average signals across heterogeneous micro-niches and cultivation recovers only a minor fraction of total diversity, leaving "who is doing what" unresolved. Closing this disconnect is a prerequisite for rational design of fermentation microbiomes and demands cell-resolved functional tools within an iterative engineering framework. This review proposes that Single-Cell Raman Spectroscopy (SCRS) and its derivatives, including D2O-Raman activity mapping, scRACS-Seq phenotype-to-genome linkage, scRACS-Culture recovery of rare functional strains, and Intra-Ramanome Correlation Analysis (IRCA) predictive metabolic phenotyping, collectively provide a label-free, culture-independent toolkit suited to this role in solid-state matrices. We delineate how these single-cell insights feed each stage of a Design-Build-Test-Learn (DBTL) cycle, from phenotype-informed strain selection through consortium assembly and functional validation to data-driven iterative optimization. This convergence of cell-resolved functional dissection, synthetic ecology, and process analytical technologies establishes the foundation for advancing Sauce-flavor Baijiu from experience-dependent craftsmanship toward intelligent brewing.

基于单细胞拉曼光谱的酱味白酒生态微生物工程研究。
酱味白酒的分层香气归功于在极端、开放、固态条件下发酵的多种微生物组合。高通量测序揭示了群落组成的明显时空异质性,但基本的组成-功能脱节仍然存在:大量组学平均信号跨越异质微生态位和培养只恢复了总多样性的一小部分,留下了“谁在做什么”未解决的问题。关闭这种脱节是合理设计发酵微生物组的先决条件,并要求在迭代工程框架内解决细胞功能工具。这篇综述提出,单细胞拉曼光谱(SCRS)及其衍生物,包括d20 -拉曼活性图谱、scrac - seq表型与基因组的联系、罕见功能菌株的scrac -培养恢复以及拉曼基因组内相关分析(IRCA)预测代谢表型,共同提供了一个无标签、不依赖于培养的工具包,适合在固态基质中发挥这一作用。我们描述了这些单细胞见解如何为设计-构建-测试-学习(DBTL)周期的每个阶段提供信息,从表型信息菌株选择到联盟组装和功能验证,再到数据驱动的迭代优化。这种细胞解析功能解剖、合成生态学和工艺分析技术的融合,为将酱味白酒从依赖体验的工艺推进到智能酿造奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.90
自引率
0.00%
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0
审稿时长
12 weeks
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