多重稳定同位素标记结合单细胞拉曼光谱技术筛选微藻突变体方法学研究

Phenotype-based detection and sorting have become a rate-limiting step in the design and construction of microalgal cell factories. I have previously established a method based on single-cell Raman spectra (SCRS) that is able to simultaneously quantify the intracellular profile of carbon-storage molecules (i.e., starch, protein, and triacylglycerol) in individual microalgal cells. This method is advantageous to previous methods, since it does not require cell propagation, destroying the cell or extended and sophisticated analytic procedures. However, can SCRS be used to detect and sort ‘superior algae’ that carry multiple improved phenotypes? To address this question, here employing the industrial oleaginous microalga Nannochloropsis oceanica as a model, we propose to establish a technical platform called mSIP-Ramanometry (multiplex Stable Isotope Probing-Ramanometry; or mSIP-R) for analyzing the profile of metabolically active substrates, the profile of products and their inter-correlation at the resolution of single microalgal cells. Furthermore, by coupling mSIP-R with our Raman-activated microfluidic cell sorting systems, we will demonstrate the screening of ‘superior algae’ that carry multiple improved phenotypes directly from a mutant library, in a label-less, culture-free, unbiased and high-throughput manner. Key challenges for me to tackle include, e.g., how to deduce and quantify multiple phenotypes simultaneously from a single SCRS? how to decide whether a change in SCRS is due to the change of ‘state’ or that of ‘genotype’ of the cell? how to protect the vitality of algal cells during Raman-activated Cell Sorting? This novel technical platform, once validated and optimized, will be of general value for the mining, screening and strain development of not just microalgae but other unicellular microorganisms.

突变体表型检测与分选是构建微藻细胞工厂的关键和限速步骤之一。基于单细胞拉曼光谱（SCRS），申请人已建立了快速同时定量单个微藻细胞淀粉、蛋白质、甘油三酯和总脂不饱和度的方法，克服了传统方法依赖培养、破坏细胞、耗时耗力等不足。然而，基于SCRS能否检测并分选具有多表型优势的“超级藻株”呢？以新型光合底盘细胞微拟球藻为研究对象，本项目将开发多重稳定同位素标记结合单细胞拉曼光谱技术（mSIP-Ramanometry），通过解决“如何从一张SCRS解析多重表型信息？”、“如何判断SCRS变化源于细胞状态还是基因型改变？”、“如何实现活体细胞拉曼分选？”等难点，建立在单细胞精度同时考察细胞“底物谱”、“产物谱”及其“底物-产物关联谱”的方法；进而耦合拉曼流式分选技术，示范不赖于培养、直接从突变体库中识别与分选具有多表型优势的“超级藻株”。该方法的建立将为单细胞微生物分子育种提供具有通用性的技术平台。

突变体表型检测与分选是构建微藻细胞工厂的关键和限速步骤之一。本项目以微拟球藻为研究对象，通过多种方式的碳源和氢源稳定同位素标记，建立了三种底物标记策略，包括“直接标记”、“竞争性标记”和“协同标记”，可用于考察单个微藻细胞的“产物谱”、“总体代谢活性”、“碳源利用”、“碳源偏好”、“碳源-水协同利用”等关键表型。同时，本项目开发了“拉曼组内关联分析（IRCA）”的理论框架与算法，仅仅基于一个拉曼组数据点，就能推测该状态下的代谢物相互转化网络，包括“产物-产物关联谱”、“底物-产物关联谱”等。最后，本项目耦合拉曼流式分选技术，示范了不赖于培养、直接从突变体库中识别与分选特定表型突变株的筛选策略。该方法的建立为单细胞微生物分子育种提供具有通用性的技术平台。本项目已经发表1区Top论文2篇，获得1项专利授权。

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