Single-Cell Center, CAS Key Laboratory of Biofuels and Shandong Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China; Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong, China; University of Chinese Academy of Sciences, Beijing, China.
Single-Cell Center, CAS Key Laboratory of Biofuels and Shandong Laboratory of Energy Genetics, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong, China; Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong, China; University of Chinese Academy of Sciences, Beijing, China.
Biotechnol Adv. 2019 Nov 1;37(6):107388. doi: 10.1016/j.biotechadv.2019.04.010. Epub 2019 May 29.
Phenotypic profiling of natural, engineered or synthetic cells has increasingly become a bottleneck in the mining and engineering of cell factories. Single-cell phenotyping technologies are highly promising for tackling this hurdle, yet ideally they should allow non-invasive live-cell probing, be label-free, provide landscape-like phenotyping capability, distinguish complex functions, operate with high speed, sufficient throughput and low cost, and finally, couple with cell sorting so as to enable downstream omics analysis. This review focuses on recent progress in Ramanome Technology Platform (RTP), which consists of Raman spectroscopy based phenotyping, sorting and sequencing of single cells, and discuss the key challenges and emerging trends. In addition, we propose ramanome, a collection of single-cell Raman spectra (SCRS) acquired from individual cells within a cellular population or consortium, as a new type of biological phenome datatype at the single-cell resolution. By establishing the phenome-genome links in a label-free, single-cell manner, RTP should find wide applications in functional screening and strain development of live microbial, plant and animal cell factories.
天然、工程或合成细胞的表型分析越来越成为挖掘和工程细胞工厂的瓶颈。单细胞表型分析技术在解决这一难题方面具有广阔的前景，但理想情况下，它们应该允许非侵入式活细胞探测、无标记、提供全景式表型分析能力、区分复杂功能、以高速、高吞吐量和低成本运行，最后与细胞分选相结合，以便能够进行下游组学分析。本文重点介绍了由基于拉曼光谱的表型分析、单细胞分选和测序组成的拉曼组学技术平台（RTP）的最新进展，并讨论了关键挑战和新兴趋势。此外，我们提出了拉曼组学（ramanome），它是从细胞群体或联合体中的单个细胞中获取的单细胞拉曼光谱（SCRS）的集合，作为一种新的单细胞分辨率的生物表型数据类型。通过以无标记、单细胞的方式建立表型-基因组联系，RTP 应该在功能筛选和活体微生物、植物和动物细胞工厂的菌株开发方面得到广泛应用。
