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
[ "刁志钿(1995—),男,在读博士研究生。研究方向为液滴微流控技术、高通量流式拉曼分选技术等。 E-mail:diaozd@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,长期从事基于拉曼光谱的单细胞分析/分选及后续的单细胞测序等关键技术和仪器研究。E-mail:mabo@qibebt.ac.cn" ]
收稿:2023-03-21,
修回:2022-05-17,
纸质出版:2023-10-31
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刁志钿, 王喜先, 孙晴, 徐健, 马波. 单细胞拉曼光谱测试分选装备研制及应用进展[J]. 合成生物学, 2023, 4(5): 1020-1035
DIAO Zhidian, WANG Xixian, SUN Qing, XU Jian, MA Bo. Advances and applications of single-cell Raman spectroscopy testing and sorting equipment[J]. Synthetic Biology Journal, 2023, 4(5): 1020-1035
刁志钿, 王喜先, 孙晴, 徐健, 马波. 单细胞拉曼光谱测试分选装备研制及应用进展[J]. 合成生物学, 2023, 4(5): 1020-1035 DOI: 10.12211/2096-8280.2023-025.
DIAO Zhidian, WANG Xixian, SUN Qing, XU Jian, MA Bo. Advances and applications of single-cell Raman spectroscopy testing and sorting equipment[J]. Synthetic Biology Journal, 2023, 4(5): 1020-1035 DOI: 10.12211/2096-8280.2023-025.
合成生物学的跨越式发展,取决于“设计-构建-测试-学习”(design-build-test-learn)这四大环节的突破。随着基因组测序、编辑、合成以及人工智能技术的日新月异,业界设计和构建突变体甚至人工细胞工厂的能力已经突飞猛进。然而,合成生物学至今仍面临的困境之一便是“大体系的复杂性难以处理”,一旦体系变大,细胞表型测试与分选的工作量就非常艰巨,甚至不可完成。单细胞拉曼光谱(SCRS)技术能够在活体单细胞水平、非标记状态下识别全景信息从而分辨复杂功能表型,且具有快速、低成本、能够与下游细胞组学研究耦联等优势,被视为全新的单细胞表型识别技术。目前,基于SCRS技术强大的表型识别能力已发展了系列合成表型的测试与分选装备,并进行了广泛的应用示范,展示了其助力合成生物学表型测试与分选的巨大潜力。本文选取自主研制的单细胞拉曼光镊分选仪(RACS-Seq)、单细胞微液滴分选系统(EasySort)和高通量流式拉曼分选仪(FlowRACS)为典型仪器装备,分别概述其技术原理和技术迭代以及特色应用案例等。本文最后对当前基于SCRS技术的合成表型测试分选装备所存在的问题及潜在解决策略进行了探讨和展望。
The advancement of synthetic biology depends on the breakthroughs in the "design"
"build"
"test"
and "learn" (DBTL) stages. With the rapid methodological innovations in genome sequencing
editing
synthesis
and artificial intelligence
the industry has made remarkable progress in designing and building mutants and even artificial cell factories. However
the "unmanageable complexity of large systems" remains one of the ongoing challenges for synthetic biology. As the cell mutant libraries get larger
the process of testing becomes more tedious and even impossible. Hence
there is an urgent need to develop high throughput sorting platforms. SCRS (single-cell Raman spectrum)technology can identify panoramic information at the single-cell level in a non-labeled state
distinguishing complex functional phenotypes. It has advantages such as being fast
low-cost
and capable of being coupled with downstream omics research
thus making it a novel technology for single-cell phenotype identification. At present
based on the powerful ability of SCRS in phenotyping
a series of synthetic phenotypic testing and cell sorting equipment have been developed and a wide range of application demonstrations have been carried out
which demonstrates its enormous potential in accelerating the phenotypic testing and cell sorting in synthetic biology. In this review
we selected the self-developed Raman-activated cell sorting coupled sequencing system (RACS-Seq)
single-cell microfluidic droplet sorting system (EasySort)
and high-throughput flow cytometry Raman-activated cell sorting (FlowRACS) as the typical equipment
by introducing their technical principles
technical iterations and characteristic application cases. Despite the advances in SCRS-based synthetic phenotypic testing and cell sorting equipment
there are still challenges to overcome. For example
there is a need for improving automation and standardizing protocols to ensure reproducibility and scalability. The development of more powerful artificial intelligence algorithms for dissecting SCRS is also required to exploit more complicated phenometypes. Finally
the throughput and sensitivity still need to be improved significantly. In conclusion
in spite of some disadvantages
the SCRS-based equipment has shown great promise in accelerating the phenotypic testing and cell sorting in synthetic biology.
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GARDNER T S , CANTOR C R , COLLINS J J . Construction of a genetic toggle switch in Escherichia coli [J ] . Nature , 2000 , 403 ( 6767 ): 339 - 342 .
BENNER S A , SISMOUR A M . Synthetic biology [J ] . Nature Reviews Genetics , 2005 , 6 ( 7 ): 533 - 543 .
SCHOBER L , BÜTTNER E , LASKE C , et al . Cell dispensing in low-volume range with the immediate drop-on-demand technology (I-DOT) [J ] . Journal of Laboratory Automation , 2015 , 20 ( 2 ): 154 - 163 .
WANG H H , ISAACS F J , CARR P A , et al . Programming cells by multiplex genome engineering and accelerated evolution [J ] . Nature , 2009 , 460 ( 7257 ): 894 - 898 .
SANDER J D , JOUNG J K . CRISPR-Cas systems for editing, regulating and targeting genomes [J ] . Nature Biotechnology , 2014 , 32 ( 4 ): 347 - 355 .
SMITH H O , HUTCHISON C A , PFANNKOCH C , et al . Generating a synthetic genome by whole genome assembly: phiX174 bacteriophage from synthetic oligonucleotides [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2003 , 100 ( 26 ): 15440 - 15445 .
GIBSON D G , YOUNG L , CHUANG R Y , et al . Enzymatic assembly of DNA molecules up to several hundred kilobases [J ] . Nature Methods , 2009 , 6 ( 5 ): 343 - 345 .
SHAO Y Y , LU N , WU Z F , et al . Creating a functional single-chromosome yeast [J ] . Nature , 2018 , 560 ( 7718 ): 331 - 335 .
WANG X X , REN L H , DIAO Z D , et al . Robust spontaneous Raman flow cytometry for single-cell metabolic phenome profiling via pDEP-DLD-RFC [J ] . Advanced Science , 2023 : 2207497 .
ZINCHENKO A , DEVENISH S R A , KINTSES B , et al . One in a million: flow cytometric sorting of single cell-lysate assays in monodisperse picolitre double emulsion droplets for directed evolution [J ] . Analytical Chemistry , 2014 , 86 ( 5 ): 2526 - 2533 .
BREHM-STECHER B F , JOHNSON E A . Single-cell microbiology: tools, technologies, and applications [J ] . Microbiology and Molecular Biology Reviews , 2004 , 68 ( 3 ): 538 - 559 .
杨建花 , 苏晓岚 , 朱蕾蕾 . 高通量筛选系统在定向改造中的新进展 [J ] . 生物工程学报 , 2021 , 37 ( 7 ): 2197 - 2210 .
YANG J H , SU X L , ZHU L L . Advances of high-throughput screening system in reengineering of biological entities [J ] . Chinese Journal of Biotechnology , 2021 , 37 ( 7 ): 2197 - 2210 .
ALI A , ABOULEILA Y , SHIMIZU Y , et al . Single-cell metabolomics by mass spectrometry: advances, challenges, and future applications [J ] . TrAC Trends in Analytical Chemistry , 2019 , 120 : 115436 .
SPITZER M H , NOLAN G P . Mass cytometry: single cells, many features [J ] . Cell , 2016 , 165 ( 4 ): 780 - 791 .
AMANTONICO A , URBAN P L , ZENOBI R . Analytical techniques for single-cell metabolomics: state of the art and trends [J ] . Analytical and Bioanalytical Chemistry , 2010 , 398 ( 6 ): 2493 - 2504 .
RAMAN C V , KRISHNAN K S . A new type of secondary radiation [J ] . Nature , 1928 , 121 ( 3048 ): 501 - 502 .
XU J , MA B , SU X Q , et al . Emerging trends for microbiome analysis: From single-cell functional imaging to microbiome big data [J ] . Engineering , 2017 , 3 ( 1 ): 66 - 70 .
HE Y H , WANG X X , MA B , et al . Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution [J ] . Biotechnology Advances , 2019 , 37 ( 6 ): 107388 .
LEE K S , LANDRY Z , PEREIRA F C , et al . Raman microspectroscopy for microbiology [J ] . Nature Reviews Methods Primers , 2021 , 1 ( 80 ): 1 - 25 .
YAN S S , QIU J X , GUO L , et al . Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level [J ] . Applied Microbiology and Biotechnology , 2021 , 105 ( 4 ): 1315 - 1331 .
WANG Y , JI Y T , WHARFE E S , et al . Raman activated cell ejection for isolation of single cells [J ] . Analytical Chemistry , 2013 , 85 ( 22 ): 10697 - 10701 .
HUANG W E , WARD A D , WHITELEY A S . Raman tweezers sorting of single microbial cells [J ] . Environmental Microbiology Reports , 2009 , 1 ( 1 ): 44 - 49 .
XIE C G , CHEN D , LI Y Q . Raman sorting and identification of single living micro-organisms with optical tweezers [J ] . Optics Letters , 2005 , 30 ( 14 ): 1800 - 1802 .
XU T , GONG Y H , SU X L , et al . Phenome-genome profiling of single bacterial cell by Raman-activated gravity-driven encapsulation and sequencing [J ] . Small , 2020 , 16 ( 30 ): 2001172 .
LEE K S , PALATINSZKY M , PEREIRA F C , et al . An automated Raman-based platform for the sorting of live cells by functional properties [J ] . Nature Microbiology , 2019 , 4 ( 6 ): 1035 - 1048 .
ZHANG P R , REN L H , ZHANG X , et al . Raman-activated cell sorting based on dielectrophoretic single-cell trap and release [J ] . Analytical Chemistry , 2015 , 87 ( 4 ): 2282 - 2289 .
WANG X X , REN L H , SU Y T , et al . Raman-activated droplet sorting (RADS) for label-free high-throughput screening of microalgal single-cells [J ] . Analytical Chemistry , 2017 , 89 ( 22 ): 12569 - 12577 .
WANG X X , XIN Y , REN L H , et al . Positive dielectrophoresis-based Raman-activated droplet sorting for culture-free and label-free screening of enzyme function in vivo [J ] . Science Advances , 2020 , 6 ( 32 ): eabb3521 .
NITTA N , IINO T , ISOZAKI A , et al . Raman image-activated cell sorting [J ] . Nature Communications , 2020 , 11 ( 1 ): 3452 .
LINDLEY M , DE PABLO J G , PETERSON W , et al . High-throughput Raman-activated cell sorting in the fingerprint region [J ] . Advanced Materials Technologies , 2022 , 7 ( 10 ): 2101567 .
JING X Y , GONG Y H , XU T , et al . One-cell metabolic phenotyping and sequencing of soil microbiome by Raman-activated gravity-driven encapsulation (RAGE) [J ] . mSystems , 2021 , 6 ( 3 ): e00181-21 .
JING X Y , GONG Y H , XU T , et al . Revealing CO 2 -fixing SAR11 bacteria in the ocean by Raman-based single-cell metabolic profiling and genomics [J ] . BioDesign Research , 2022 , 2022 : 9782712 .
JING X Y , GONG Y H , PAN H H , et al . Single-cell Raman-activated sorting and cultivation (scRACS-Culture) for assessing and mining in situ phosphate-solubilizing microbes from nature [J ] . ISME Communications , 2022 , 2 : 106 .
XU T , LI Y D , HAN X , et al . Versatile, facile and low-cost single-cell isolation, culture and sequencing by optical tweezer-assisted pool-screening [J ] . Lab on a Chip , 2023 , 23 ( 1 ): 125 - 135 .
DIAO Z D , KAN L Y , ZHAO Y L , et al . Artificial intelligence-assisted automatic and index-based microbial single-cell sorting system for One-Cell-One-Tube [J ] . mLife , 2022 , 1 ( 4 ): 448 - 459 .
XIN Y , SHEN C , SHE Y T , et al . Biosynthesis of triacylglycerol molecules with a tailored PUFA profile in industrial microalgae [J ] . Molecular Plant , 2019 , 12 ( 4 ): 474 - 488 .
XIN Y , LU Y D , LEE Y Y , et al . Producing designer oils in industrial microalgae by rational modulation of Co-evolving type-2 diacylglycerol acyltransferases [J ] . Molecular Plant , 2017 , 10 ( 12 ): 1523 - 1539 .
ZENG W Z , GUO L K , XU S , et al . High-throughput screening technology in industrial biotechnology [J ] . Trends in Biotechnology , 2020 , 38 ( 8 ): 888 - 906 .
BERRY D , MADER E , LEE T K , et al . Tracking heavy water (D 2 O) incorporation for identifying and sorting active microbial cells [J ] . Proceedings of the National Academy of Sciences of the United States of America , 2015 , 112 ( 2 ): E194 - 203 .
JING X Y , GOU H L , GONG Y H , et al . Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean [J ] . Environmental Microbiology , 2018 , 20 ( 6 ): 2241 - 2255 .
SONG Y Z , KASTER A K , VOLLMERS J , et al . Single-cell genomics based on Raman sorting reveals novel carotenoid-containing bacteria in the Red Sea [J ] . Microbial Biotechnology , 2017 , 10 ( 1 ): 125 - 137 .
WANG T T , JI Y T , WANG Y , et al . Quantitative dynamics of triacylglycerol accumulation in microalgae populations at single-cell resolution revealed by Raman microspectroscopy [J ] . Biotechnology for Biofuels , 2014 , 7 : 58 .
JI Y T , HE Y H , CUI Y B , et al . Raman spectroscopy provides a rapid, non-invasive method for quantitation of starch in live, unicellular microalgae [J ] . Biotechnology Journal , 2014 , 9 ( 12 ): 1512 - 1518 .
HE Y H , ZHANG P , HUANG S , et al . Label-free, simultaneous quantification of starch, protein and triacylglycerol in single microalgal cells [J ] . Biotechnology for Biofuels , 2017 , 10 ( 1 ): 275 .
TAO Y F , WANG Y , HUANG S , et al . Metabolic-activity-based assessment of antimicrobial effects by D 2 O-labeled single-cell Raman microspectroscopy [J ] . Analytical Chemistry , 2017 , 89 ( 7 ): 4108 - 4115 .
TENG L , WANG X , WANG X J , et al . Label-free, rapid and quantitative phenotyping of stress response in E. coli via ramanome [J ] . Scientific Reports , 2016 , 6 : 34359 .
HEKMATARA M , HEIDARI BALADEHI M , JI Y T , et al . D 2 O-probed Raman microspectroscopy distinguishes the metabolic dynamics of macromolecules in organellar anticancer drug response [J ] . Analytical Chemistry , 2021 , 93 ( 4 ): 2125 - 2134 .
WANG Y , SONG Y Z , TAO Y F , et al . Reverse and multiple stable isotope probing to study bacterial metabolism and interactions at the single cell level [J ] . Analytical Chemistry , 2016 , 88 ( 19 ): 9443 - 9450 .
HE Y H , HUANG S , ZHANG P , et al . Intra-ramanome correlation analysis unveils metabolite conversion network from an isogenic population of cells [J ] . mBio , 2021 , 12 ( 4 ): e0147021 .
HEIDARI BALADEHI M , HEKMATARA M , HE Y H , et al . Culture-free identification and metabolic profiling of microalgal single cells via ensemble learning of ramanomes [J ] . Analytical Chemistry , 2021 , 93 ( 25 ): 8872 - 8880 .
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