合成生物学 ›› 2023, Vol. 4 ›› Issue (1): 204-224.DOI: 10.12211/2096-8280.2022-043

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拉曼光谱技术在单细胞表型检测与分选中的应用进展

王喜先, 孙晴, 刁志钿, 徐健, 马波   

  1. 中国科学院青岛生物能源与过程研究所 单细胞中心,山东 青岛 266101
  • 收稿日期:2022-08-03 修回日期:2022-09-12 出版日期:2023-02-28 发布日期:2023-03-07
  • 通讯作者: 马波
  • 作者简介:王喜先(1988—), 男, 博士, 副研究员, 硕士生导师。研究方向为微流控、高通量拉曼流式分选技术等。E-mail:wangxx@qibebt.ac.cn
    马波(1976—), 男, 博士, 研究员, 博士生导师。研究方向为单细胞关键技术与仪器、微流控技术等。E-mail:mabo@qibebt.ac.cn
    第一联系人:王喜先(1988—), 男, 博士, 副研究员, 硕士生导师。研究方向为微流控、高通量拉曼流式分选技术等。
  • 基金资助:
    国家重点研发计划“合成生物学”重点专项(2018YFA090290);天津市合成生物技术创新能力提升行动项目(TSBICIP-PTJS-003-05)

Advances with applications of Raman spectroscopy in single-cell phenotype sorting and analysis

Xixian WANG, Qing SUN, Zhidian DIAO, Jian XU, Bo MA   

  1. Single-cell Center,Qingdao Institute of Bioenergy and Bioprocess Technology,Chinese Academy of Sciences,Qingdao 266101,Shandong,China
  • Received:2022-08-03 Revised:2022-09-12 Online:2023-02-28 Published:2023-03-07
  • Contact: Bo MA

摘要:

基因组测序、编辑与合成技术日新月异,推动了基因型“设计”和“合成”能力的突飞猛进,同时也使人工细胞的表型检测成为合成生物学发展的瓶颈之一。对于细胞功能的快速测试与评价,单细胞分析技术具有重要意义与前景,但理想的解决方案需要具备活体无损、非标记式、提供全景式表型、能分辨复杂功能、快速高通量且低成本、能与组学分析联动等特征。拉曼光谱技术具备上述所有特征,能够提供单细胞的化学成分组成及分子结构等信息,是一种高效的单细胞表型识别技术。本文首先概述了拉曼组概念和基于拉曼组的细胞功能表型识别,包括代谢产物定性和定量、底物代谢和互作表征、细胞种类和状态鉴定以及环境应激检测等;其次,根据拉曼信号的分类、拉曼信号检测模式和目标细胞分选策略,对现有的拉曼分选平台及其在细胞表型分选中的应用进行分析总结;最后,对单细胞拉曼光谱技术在合成细胞表型检测与分选面临的问题、潜在解决策略进行了探讨和展望。单细胞拉曼光谱技术不仅为细胞工厂的高通量、全景式表型检测与筛选提供了全新的解决方案,还将推动“单细胞精度的表型组-功能基因组”作为一种新的生物大数据类型,服务于“数据科学”驱动下的合成生物技术。

关键词: 合成生物学, 拉曼光谱技术, 细胞工厂, 单细胞表型识别, 高通量分选

Abstract:

In synthetic biology, methodological innovations in sequencing, editing and synthesis of genes and whole genomes have resulted in unprecedented development in "design and manufacturing of genotypes". On the other hand, "testing of cellular phenotypes and functions" has increasingly become one of major bottlenecks. Single-cell technologies have tremendous impacts and potentials in rapid testing of cellular phenotypes and functions. However, such single-cell methods should allow non-invasive live-cell probing, be label-free, provide landscape-like phenotype sorting, distinguish complex functions, operate with high speed, sufficient throughput and low-cost, and finally, be able to integrate with downstream omics analysis. Raman spectroscopy has all the above features, and can provide information on the chemical composition and molecular structure of single cells, making it an efficient single-cell phenotyping technology. In this review, we first introduce the concept of Ramanome and Ramanome-based phenotyping technologies, including detecting and quantifying products, measuring profiles of substrates and metabolites, discriminating cell types or states, and characterizing stress response and modeling environmental changes. We then summarize the development of existing Raman-activated cell sorting (RACS) platforms in phenotyping and sorting of cell factories such as including spontaneous Raman, resonance Raman, and coherent Raman, the modes for acquiring Raman signals including static modes on dry slice and in liquid as well, flow modes by trap-free and trap-and-release manners, and principles for target cells sorting including Ejection by pulsed laser, dragging by optical tweezer, and sorting by microfluidics operation and droplets. We also highlight the applications of different RACS platforms, including the sorting of carotenoid-producing yeast and cyanobacteria cells, astaxanthin (AXT)-hyperproducing microalgae cells, triacylglycerol (TAG)-producing yeast cells, etc. Finally, challenges with single cell Raman spectroscopy (SCRS) in the phenotyping and sorting of synthetic cells and their perspectives are outlined and discussed. We propose that SCRS will bridge phenotypes and genotypes in science and technologies through coupling with downstream high-throughput cell sorting and omics profiling. This bridge will lead to novel and creative solutions to high-throughput, landscape-like testing and screening of synthetic cells. Moreover, it will fulfill the promise of Raman spectroscopy-enabled single-cell "phenome-genome" as a new type of biological big-data, and accelerate the pace of "data-driven" synthetic biology. {L-End}

Key words: synthetic biology, Raman spectroscopy, cell factory, single-cell phenotyping, high-throughput sorting

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