合成生物学 ›› 2023, Vol. 4 ›› Issue (1): 204-224.DOI: 10.12211/2096-8280.2022-043
王喜先, 孙晴, 刁志钿, 徐健, 马波
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:
基金资助:
Xixian WANG, Qing SUN, Zhidian DIAO, Jian XU, Bo MA
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。
中图分类号:
王喜先, 孙晴, 刁志钿, 徐健, 马波. 拉曼光谱技术在单细胞表型检测与分选中的应用进展[J]. 合成生物学, 2023, 4(1): 204-224.
Xixian WANG, Qing SUN, Zhidian DIAO, Jian XU, Bo MA. Advances with applications of Raman spectroscopy in single-cell phenotype sorting and analysis[J]. Synthetic Biology Journal, 2023, 4(1): 204-224.
图4 二酰基甘油酰基转移酶体内活性筛选的传统方法和FlowRACS方法的流程比较[146]
Fig. 4 Workflows for in vivo screening of DGAT activities through traditional methods and FlowRACS strategies[146]
图5 二酰基甘油酰基转移酶体内活性筛选的传统方法和FlowRACS方法在时间成本、试剂耗材消耗和人工成本的比较[146]
Fig. 5 Consumption of time, labor and reagents for in vivo screening of DGAT activities through traditional methods and FlowRACS strategies[146]
1 | 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. |
2 | BENNER S A, SISMOUR A M. Synthetic biology[J]. Nature Reviews Genetics, 2005, 6(7): 533-543. |
3 | 欧阳颀. 合成生物学的发展与面临的科学任务[J]. 科学与社会, 2014, 4(4): 1-10. |
OUYANG Q. The development of synthetic biology and scientific tasks it faced[J]. Science and Society, 2014, 4(4): 1-10. | |
4 | CHECK E. Synthetic biology:designs on life [J]. Nature, 2005, 438(7067): 417-418. |
5 | AUSLÄNDER S, AUSLÄNDER D, FUSSENEGGER M. Synthetic biology—the synthesis of biology[J]. Angewandte Chemie International Edition, 2017, 56(23): 6396-6419. |
6 | KOSURI S, CHURCH G M. Large-scale de novo DNA synthesis:technologies and applications[J]. Nature Methods, 2014, 11(5): 499-507. |
7 | KOSURI S, EROSHENKO N, LEPROUST E M, et al. Scalable gene synthesis by selective amplification of DNA pools from high-fidelity microchips[J]. Nature Biotechnology, 2010, 28(12): 1295-1299. |
8 | PALLUK S, ARLOW D H, DE ROND T, et al. De novo DNA synthesis using polymerase-nucleotide conjugates[J]. Nature Biotechnology, 2018, 36(7): 645-650. |
9 | 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. |
10 | SANDER J D, JOUNG J K. CRISPR-Cas systems for editing, regulating and targeting genomes[J]. Nature Biotechnology, 2014, 32(4): 347-355. |
11 | SMITH H O, C A Ⅲ HUTCHISON, PFANNKOCH C, et al. Generating a synthetic genome by whole genome assembly: φX174 bacteriophage from synthetic oligonucleotides[J]. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(26): 15440-15445. |
12 | 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. |
13 | SHAO Y Y, LU N, WU Z F, et al. Creating a functional single-chromosome yeast[J]. Nature, 2018, 560(7718): 331-335. |
14 | 卢俊南, 褚鑫, 潘燕平, 等. 基因编辑技术: 进展与挑战[J]. 中国科学院院刊, 2018, 33(11): 1184-1192. |
LU J N, CHU X, PAN Y P, et al. Advances and challenges in gene editing technologies[J]. Bulletin of Chinese Academy of Sciences, 2018, 33(11): 1184-1192. | |
15 | RO D K, PARADISE E M, OUELLET M, et al. Production of the antimalarial drug precursor artemisinic acid in engineered yeast[J]. Nature, 2006, 440(7086): 940-943. |
16 | KWOK R. Five hard truths for synthetic biology[J]. Nature, 2010, 463(7279): 288-290. |
17 | KUSSELL E, LEIBLER S. Phenotypic diversity, population growth, and information in fluctuating environments[J]. Science, 2005, 309(5743): 2075-2078. |
18 | MÜLLER S, HARMS H, BLEY T. Origin and analysis of microbial population heterogeneity in bioprocesses[J]. Current Opinion in Biotechnology, 2010, 21(1): 100-113. |
19 | PASZEK P, RYAN S, ASHALL L, et al. Population robustness arising from cellular heterogeneity[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(25): 11644-11649. |
20 | SCHUBERT C. Single-cell analysis:the deepest differences[J]. Nature, 2011, 480(7375): 133-137. |
21 | ZENOBI R. Single-cell metabolomics: analytical and biological perspectives[J]. Science, 2013, 342(6163): 1243259. |
22 | KASHTAN N, ROGGENSACK S E, RODRIGUE S, et al. Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus [J]. Science, 2014, 344(6182): 416-420. |
23 | LEWIS W H, TAHON G, GEESINK P, et al. Innovations to culturing the uncultured microbial majority[J]. Nature Reviews Microbiology, 2021, 19(4): 225-240. |
24 | 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. |
25 | 马波, 徐健. 人工细胞的表型测试与分选: 构建从光谱学到遗传学的桥梁[J]. 中国科学院院刊, 2018, 33(11): 1193-1204. |
MA B, XU J. Phenotyping and sorting of synthetic cells: building bridge from spectroscopy to genetics[J]. Bulletin of Chinese Academy of Sciences, 2018, 33(11): 1193-1204. | |
26 | PERKEL J M. Single-cell proteomics takes centre stage[J]. Nature, 2021, 597(7877): 580-582. |
27 | SEYDEL C. Single-cell metabolomics hits its stride[J]. Nature Methods, 2021, 18(12): 1452-1456. |
28 | NAWY T. Integrated single-cell profiles[J]. Nature Methods, 2016, 13(1): 36. |
29 | NIELSEN J, OLIVER S. The next wave in metabolome analysis[J]. Trends in Biotechnology, 2005, 23(11): 544-546. |
30 | WISHART D S. Emerging applications of metabolomics in drug discovery and precision medicine[J]. Nature Reviews Drug Discovery, 2016, 15(7): 473-484. |
31 | BENDALL S C, SIMONDS E F, QIU P, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum[J]. Science, 2011, 332(6030): 687-696. |
32 | 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. |
33 | SPITZER M H, NOLAN G P. Mass cytometry: single cells, many features[J]. Cell, 2016, 165(4): 780-791. |
34 | 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. |
35 | BREHM-STECHER B F, JOHNSON E A. Single-cell microbiology: tools, technologies, and applications[J]. Microbiology and Molecular Biology Reviews: MMBR, 2004, 68(3): 538-559. |
36 | YANG J H, SU X L, ZHU L L. Advances of high-throughput screening system in reengineering of biological entities[J]. Sheng Wu Gong Cheng Xue Bao, 2021, 37(7): 2197-2210. |
37 | CHEN J, VESTERGAARD M, JENSEN T G, et al. Finding the needle in the haystack-the use of microfluidic droplet technology to identify vitamin-secreting lactic acid bacteria[J]. mBio, 2017, 8(3): e00526-e00517. |
38 | WAGNER J M, LIU L Q, YUAN S F, et al. A comparative analysis of single cell and droplet-based FACS for improving production phenotypes: riboflavin overproduction in Yarrowia lipolytica [J]. Metabolic Engineering, 2018, 47: 346-356. |
39 | BEST R J, LYCZAKOWSKI J J, ABALDE-CELA S, et al. Label-free analysis and sorting of microalgae and cyanobacteria in microdroplets by intrinsic chlorophyll fluorescence for the identification of fast growing strains[J]. Analytical Chemistry, 2016, 88(21): 10445-10451. |
40 | AN G H, BIELICH J, AUERBACH R, et al. Isolation and characterization of carotenoid hyperproducing mutants of yeast by flow cytometry and cell sorting[J]. Bio/Technology, 1991, 9(1): 70-73. |
41 | HUANG M T, BAI Y P, SJOSTROM S L, et al. Microfluidic screening and whole-genome sequencing identifies mutations associated with improved protein secretion by yeast[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(34): E4689-E4696. |
42 | SJOSTROM S L, BAI Y P, HUANG M T, et al. High-throughput screening for industrial enzyme production hosts by droplet microfluidics[J]. Lab on a Chip, 2014, 14(4): 806-813. |
43 | KINTSES B, HEIN C, MOHAMED M F, et al. Picoliter cell lysate assays in microfluidic droplet compartments for directed enzyme evolution[J]. Chemistry & Biology, 2012, 19(8): 1001-1009. |
44 | RODRIGUEZ E A, CAMPBELL R E, LIN J Y, et al. The growing and glowing toolbox of fluorescent and photoactive proteins[J]. Trends in Biochemical Sciences, 2017, 42(2): 111-129. |
45 | KREMERS G J, GILBERT S G, CRANFILL P J, et al. Fluorescent proteins at a glance[J]. Journal of Cell Science, 2011, 124(Pt 2): 157-160. |
46 | MANNAN A A, LIU D, ZHANG F Z, et al. Fundamental design principles for transcription-factor-based metabolite biosensors[J]. ACS Synthetic Biology, 2017, 6(10): 1851-1859. |
47 | FOWLER C C, BROWN E D, LI Y F. Using a riboswitch sensor to examine coenzyme B12 metabolism and transport in E. coli [J]. Chemistry & Biology, 2010, 17(7): 756-765 |
48 | ABATEMARCO J, SARHAN M F, WAGNER J M, et al. RNA-aptamers-in-droplets (RAPID) high-throughput screening for secretory phenotypes[J]. Nature Communications, 2017, 8: 332. |
49 | VALLEJO D, NIKOOMANZAR A, PAEGEL B M, et al. Fluorescence-activated droplet sorting for single-cell directed evolution[J]. ACS Synthetic Biology, 2019, 8(6): 1430-1440. |
50 | YANG G Y, WITHERS S G. Ultrahigh-throughput FACS-based screening for directed enzyme evolution[J]. ChemBioChem, 2009, 10(17): 2704-2715. |
51 | AGRESTI J J, ANTIPOV E, ABATE A R, et al. Ultrahigh-throughput screening in drop-based microfluidics for directed evolution[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(9): 4004-4009. |
52 | BARET J C, MILLER O J, TALY V, et al. Fluorescence-activated droplet sorting (FADS): efficient microfluidic cell sorting based on enzymatic activity[J]. Lab on a Chip, 2009, 9(13): 1850-1858. |
53 | SCIAMBI A, ABATE A R. Accurate microfluidic sorting of droplets at 30 kHz[J]. Lab on a Chip, 2015, 15(1): 47-51. |
54 | BROUZES E, MEDKOVA M, SAVENELLI N, et al. Droplet microfluidic technology for single-cell high-throughput screening[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(34): 14195-14200. |
55 | WANG Y, JIN R N, SHEN B Q, et al. High-throughput functional screening for next-generation cancer immunotherapy using droplet-based microfluidics[J]. Science Advances, 2021, 7(24): eabe3839. |
56 | QIAO Y X, HU R, CHEN D W, et al. Fluorescence-activated droplet sorting of PET degrading microorganisms[J]. Journal of Hazardous Materials, 2022, 424: 127417. |
57 | TEREKHOV S S, SMIRNOV I V, STEPANOVA A V, et al. Microfluidic droplet platform for ultrahigh-throughput single-cell screening of biodiversity[J]. Proceedings of the National Academy of Sciences of the United States of America, 2017, 114(10): 2550-2555. |
58 | MA F Q, CHUNG M T, YAO Y, et al. Efficient molecular evolution to generate enantioselective enzymes using a dual-channel microfluidic droplet screening platform[J]. Nature Communications, 2018, 9: 1030. |
59 | NITTA N, SUGIMURA T, ISOZAKI A, et al. Intelligent image-activated cell sorting[J]. Cell, 2018, 175(1): 266-276. |
60 | SCHRAIVOGEL D, KUHN T M, RAUSCHER B, et al. High-speed fluorescence image-enabled cell sorting[J]. Science, 2022, 375(6578): 315-320. |
61 | ROBINSON J P. Spectral flow cytometry-quo vadimus? [J]. Cytometry Part A: the Journal of the International Society for Analytical Cytology, 2019, 95(8): 823-824. |
62 | CHENG J X, XIE X S. Vibrational spectroscopic imaging of living systems: an emerging platform for biology and medicine[J]. Science, 2015, 350(6264): aaa8870. |
63 | PETIBOIS C, CESTELLI-GUIDI M, PICCININI M, et al. Synchrotron radiation FTIR imaging in minutes: a first step towards real-time cell imaging[J]. Analytical and Bioanalytical Chemistry, 2010, 397(6): 2123-2129. |
64 | RAMAN C V, KRISHNAN K S. A new type of secondary radiation[J]. Nature, 1928, 121(3048): 501-502. |
65 | 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. |
66 | LEE K S, LANDRY Z, PEREIRA F C, et al. Raman microspectroscopy for microbiology[J]. Nature Reviews Methods Primers, 2021, 1: 80. |
67 | 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. |
68 | GUO J X, LIU Y, JU H X, et al. From lab to field: surface-enhanced Raman scattering-based sensing strategies for on-site analysis[J]. TrAC Trends in Analytical Chemistry, 2022, 146: 116488. |
69 | QIAN X M, PENG X H, ANSARI D O, et al. In vivo tumor targeting and spectroscopic detection with surface-enhanced Raman nanoparticle tags[J]. Nature Biotechnology, 2008, 26(1): 83-90. |
70 | LIN L, TIAN X D, HONG S L, et al. A bioorthogonal Raman reporter strategy for SERS detection of glycans on live cells[J]. Angewandte Chemie International Edition, 2013, 52(28): 7266-7271. |
71 | CHEN Y L, DING L, SONG W Y, et al. Protein-specific Raman imaging of glycosylation on single cells with zone-controllable SERS effect[J]. Chemical Science, 2016, 7(1): 569-574. |
72 | ZONG S F, CHEN C, WANG Z Y, et al. Surface enhanced Raman scattering based in situ hybridization strategy for telomere length assessment[J]. ACS Nano, 2016, 10(2): 2950-2959. |
73 | ZHU R, FENG H J, LI Q Q, et al. Asymmetric core-shell gold nanoparticles and controllable assemblies for SERS ratiometric detection of microRNA[J]. Angewandte Chemie International ed. in English, 2021, 60(22): 12560-12568. |
74 | YANG Y J, CHEN Y L, GUO J X, et al. A pore-forming protein-induced surface-enhanced Raman spectroscopic strategy for dynamic tracing of cell membrane repair[J]. iScience, 2021, 24(9): 102980. |
75 | YANG Y J, CHEN Y L, ZHAO S Y, et al. O-GlcNAcylation mapping of single living cells by in situ quantitative SERS imaging[J]. Chemical Science, 2022, 13(33): 9701-9705. |
76 | BERRY D, MADER E, LEE T K, et al. Tracking heavy water (D2O) 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-E203. |
77 | 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. |
78 | 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. |
79 | 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. |
80 | 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. |
81 | 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: 275. |
82 | TAO Y F, WANG Y, HUANG S, et al. Metabolic-activity-based assessment of antimicrobial effects by D2O-labeled single-cell Raman microspectroscopy[J]. Analytical Chemistry, 2017, 89(7): 4108-4115. |
83 | 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. |
84 | HEKMATARA M, HEIDARI BALADEHI M, JI Y T, et al. D2O-probed Raman microspectroscopy distinguishes the metabolic dynamics of macromolecules in organellar anticancer drug response[J]. Analytical Chemistry, 2021, 93(4): 2125-2134. |
85 | 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. |
86 | 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. |
87 | 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. |
88 | CHRISTAKI E, BONOS E, GIANNENAS I, et al. Functional properties of carotenoids originating from algae[J]. Journal of the Science of Food and Agriculture, 2013, 93(1): 5-11. |
89 | RODRIGUEZ-CONCEPCION M, AVALOS J, BONET M L, et al. A global perspective on carotenoids: metabolism, biotechnology, and benefits for nutrition and health[J]. Progress in Lipid Research, 2018, 70: 62-93. |
90 | ROBERT B. Resonance Raman spectroscopy[J]. Photosynthesis Research, 2009, 101(2/3): 147-155. |
91 | GUEDES A C, AMARO H M, MALCATA F X. Microalgae as sources of carotenoids[J]. Marine Drugs, 2011, 9(4): 625-644. |
92 | COLLINS A M, JONES H D T, HAN D X, et al. Carotenoid distribution in living cells of Haematococcus pluvialis (Chlorophyceae)[J]. PLoS One, 2011, 6(9): e24302. |
93 | KACZOR A, BARANSKA M. Structural changes of carotenoid astaxanthin in a single algal cell monitored in situ by Raman spectroscopy[J]. Analytical Chemistry, 2011, 83(20): 7763-7770. |
94 | LI K. In vivo kinetics of lipids and astaxanthin evolution in Haematococcus pluvialis mutant under 15% CO2 using Raman microspectroscopy[J]. Bioresource Technology, 2017, 244(Pt 2): 1439-1444. |
95 | ALEXANDRE M T A, GUNDERMANN K, PASCAL A A, et al. Probing the carotenoid content of intact Cyclotella cells by resonance Raman spectroscopy[J]. Photosynthesis Research, 2014, 119(3): 273-281. |
96 | WU H W, VOLPONI J V, OLIVER A E, et al. In vivo lipidomics using single-cell Raman spectroscopy[J]. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(9): 3809-3814. |
97 | LEE T H, CHANG J S, WANG H Y. Rapid and in vivo quantification of cellular lipids in Chlorella vulgaris using near-infrared Raman spectrometry[J]. Analytical Chemistry, 2013, 85(4): 2155-2160. |
98 | SHAO Y N, FANG H, ZHOU H, et al. Detection and imaging of lipids of Scenedesmus obliquus based on confocal Raman microspectroscopy[J]. Biotechnology for Biofuels, 2017, 10: 300. |
99 | CHIU L D, HO S H, SHIMADA R, et al. Rapid in vivo lipid/carbohydrate quantification of single microalgal cell by Raman spectral imaging to reveal salinity-induced starch-to-lipid shift[J]. Biotechnology for Biofuels, 2017, 10: 9. |
100 | MOUDŘÍKOVÁ Š, SADOWSKY A, METZGER S, et al. Quantification of polyphosphate in microalgae by Raman microscopy and by a reference enzymatic assay[J]. Analytical Chemistry, 2017, 89(22): 12006-12013. |
101 | XU J B, WEBB I, POOLE P, et al. Label-free discrimination of rhizobial bacteroids and mutants by single-cell Raman microspectroscopy[J]. Analytical Chemistry, 2017, 89(12): 6336-6340. |
102 | GIERLINGER N, SCHWANNINGER M. Chemical imaging of poplar wood cell walls by confocal Raman microscopy[J]. Plant Physiology, 2006, 140(4): 1246-1254. |
103 | CHYLIŃSKA M, SZYMAŃSKA-CHARGOT M, ZDUNEK A. Imaging of polysaccharides in the tomato cell wall with Raman microspectroscopy[J]. Plant Methods, 2014, 10: 14. |
104 | LUPOI J S, SINGH S, DAVIS M, et al. High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development[J]. Biotechnology for Biofuels, 2014, 7: 93. |
105 | SAMEK O, OBRUČA S, ŠILER M, et al. Quantitative Raman spectroscopy analysis of polyhydroxyalkanoates produced by cupriavidus necator H16[J]. Sensors, 2016, 16(11): 1808. |
106 | WEISS T L, CHUN H J, OKADA S, et al. Raman spectroscopy analysis of botryococcene hydrocarbons from the green microalga Botryococcus braunii [J]. Journal of Biological Chemistry, 2010, 285(42): 32458-32466. |
107 | MIYAOKA R, HOSOKAWA M, ANDO M, et al. In situ detection of antibiotic amphotericin B produced in Streptomyces nodosus using Raman microspectroscopy[J]. Marine Drugs, 2014, 12(5): 2827-2839. |
108 | BERRY D, LOY A. Stable-isotope probing of human and animal microbiome function[J]. Trends in Microbiology, 2018, 26(12): 999-1007. |
109 | UHLIK O, LEEWIS M C, STREJCEK M, et al. Stable isotope probing in the metagenomics era: a bridge towards improved bioremediation[J]. Biotechnology Advances, 2013, 31(2): 154-165. |
110 | HUANG W E, LI M Q, JARVIS R M, et al. Shining light on the microbial world: the application of Raman microspectroscopy[J]. Advances in Applied Microbiology, 2010, 70: 153-186. |
111 | HUANG W E, STOECKER K, GRIFFITHS R, et al. Raman-FISH: combining stable-isotope Raman spectroscopy and fluorescence in situ hybridization for the single cell analysis of identity and function[J]. Environmental Microbiology, 2007, 9(8): 1878-1889. |
112 | WANG Y, HUANG W E, CUI L, et al. Single cell stable isotope probing in microbiology using Raman microspectroscopy[J]. Current Opinion in Biotechnology, 2016, 41: 34-42. |
113 | LI M Q, CANNIFFE D P, JACKSON P J, et al. Rapid resonance Raman microspectroscopy to probe carbon dioxide fixation by single cells in microbial communities[J]. The ISME Journal, 2012, 6(4): 875-885. |
114 | NOOTHALAPATI VENKATA H N, SHIGETO S. Stable isotope-labeled Raman imaging reveals dynamic proteome localization to lipid droplets in single fission yeast cells[J]. Chemistry & Biology, 2012, 19(11): 1373-1380. |
115 | VINAY K B N, GUO S X, BOCKLITZ T, et al. Demonstration of carbon catabolite repression in naphthalene degrading soil bacteria via Raman spectroscopy based stable isotope probing[J]. Analytical Chemistry, 2016, 88(15): 7574-7582. |
116 | ANGEL R, PANHÖLZL C, GABRIEL R, et al. Application of stable-isotope labelling techniques for the detection of active diazotrophs[J]. Environmental Microbiology, 2018, 20(1): 44-61. |
117 | CUI L, YANG K, LI H Z, et al. Functional single-cell approach to probing nitrogen-fixing bacteria in soil communities by resonance Raman spectroscopy with 15N2 labeling[J]. Analytical Chemistry, 2018, 90(8): 5082-5089. |
118 | HAN Z L, SHI X S, JI Y T, et al. Stable isotope labeling to study the nitrogen metabolism in microcystin biosynthesis[J]. RSC Advances, 2016, 6(52): 46806-46812. |
119 | YONAMINE Y, SUZUKI Y, ITO T, et al. Monitoring photosynthetic activity in microalgal cells by Raman spectroscopy with deuterium oxide as a tracking probe[J]. ChemBioChem, 2017, 18(20): 2063-2068. |
120 | OLANIYI O O, YANG K, ZHU Y G, et al. Heavy water-labeled Raman spectroscopy reveals carboxymethylcellulose-degrading bacteria and degradation activity at the single-cell level[J]. Applied Microbiology and Biotechnology, 2019, 103(3): 1455-1464. |
121 | LORENZ B, WICHMANN C, STOCKEL S, et al. Cultivation-free Raman spectroscopic investigations of bacteria[J]. Trends in Microbiology, 2017, 25(5): 413-424. |
122 | PAHLOW S, MEISEL S, CIALLA-MAY D, et al. Isolation and identification of bacteria by means of Raman spectroscopy[J]. Advanced Drug Delivery Reviews, 2015, 89: 105-120. |
123 | KOCHAN K, PENG H D, WOOD B R, et al. Single cell assessment of yeast metabolic engineering for enhanced lipid production using Raman and AFM-IR imaging[J]. Biotechnology for Biofuels, 2018, 11: 106. |
124 | STÖCKEL S, MEISEL S, ELSCHNER M, et al. Identification of Bacillus anthracis via Raman spectroscopy and chemometric approaches[J]. Analytical Chemistry, 2012, 84(22): 9873-9880. |
125 | CHOO-SMITH L P, MAQUELIN K, VAN VREESWIJK T, et al. Investigating microbial (micro)colony heterogeneity by vibrational spectroscopy[J]. Applied and Environmental Microbiology, 2001, 67(4): 1461-1469. |
126 | WEI X, JIE D F, CUELLO J J, et al. Microalgal detection by Raman microspectroscopy[J]. TrAC Trends in Analytical Chemistry, 2014, 53: 33-40. |
127 | GUO J X, LIU Y, YANG Y J, et al. A filter supported surface-enhanced Raman scattering "nose" for point-of-care monitoring of gaseous metabolites of bacteria[J]. Analytical Chemistry, 2020, 92(7): 5055-5063. |
128 | CHIU Y F, HUANG C K, SHIGETO S. In vivo probing of the temperature responses of intracellular biomolecules in yeast cells by label-free Raman microspectroscopy[J]. ChemBioChem, 2013, 14(8): 1001-1005. |
129 | SINGH G P, CREELY C M, VOLPE G, et al. Real-time detection of hyperosmotic stress response in optically trapped single yeast cells using Raman microspectroscopy[J]. Analytical Chemistry, 2005, 77(8): 2564-2568. |
130 | HERAUD P, BEARDALL J, MCNAUGHTON D, et al. In vivo prediction of the nutrient status of individual microalgal cells using Raman microspectroscopy[J]. FEMS Microbiology Letters, 2007, 275(1): 24-30. |
131 | ATHAMNEH A I M, ALAJLOUNI R A, WALLACE R S, et al. Phenotypic profiling of antibiotic response signatures in Escherichia coli using Raman spectroscopy[J]. Antimicrobial Agents and Chemotherapy, 2014, 58(3): 1302-1314. |
132 | GERMOND A, ICHIMURA T, HORINOUCHI T, et al. Raman spectral signature reflects transcriptomic features of antibiotic resistance in Escherichia coli [J]. Communications Biology, 2018, 1: 85. |
133 | ZU T N K, ATHAMNEH A I M, WALLACE R S, et al. Near-real-time analysis of the phenotypic responses of Escherichia coli to 1-butanol exposure using Raman spectroscopy[J]. Journal of Bacteriology, 2014, 196(23): 3983-3991. |
134 | GUO J X, LIU Y, CHEN Y L, et al. A multifunctional SERS sticky note for real-time quorum sensing tracing and inactivation of bacterial biofilms[J]. Chemical Science, 2018, 9(27): 5906-5911. |
135 | LI M Q, HUANG W E, GIBSON C M, et al. Stable isotope probing and Raman spectroscopy for monitoring carbon flow in a food chain and revealing metabolic pathway[J]. Analytical Chemistry, 2013, 85(3): 1642-1649. |
136 | MIECZAN T, MICHAŁ N, ADAMCZUK M, et al. Stable isotope analyses revealed high seasonal dynamics in the food web structure of a peatbog[J]. International Review of Hydrobiology, 2015, 100(5/6): 141-150. |
137 | SONG Y Z, YIN H B, HUANG W E. Raman activated cell sorting[J]. Current Opinion in Chemical Biology, 2016, 33: 1-8. |
138 | GALA DE PABLO J, LINDLEY M, HIRAMATSU K, et al. High-throughput Raman flow cytometry and beyond[J]. Accounts of Chemical Research, 2021, 54(9): 2132-2143. |
139 | 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. |
140 | 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. |
141 | 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. |
142 | 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. |
143 | 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. |
144 | 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. |
145 | 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. |
146 | 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. |
147 | NITTA N, IINO T, ISOZAKI A, et al. Raman image-activated cell sorting[J]. Nature Communications, 2020, 11(1): 3452. |
148 | LINDLEY M, DE PABLO J G, PETERSON J W, et al. High-throughput Raman-activated cell sorting in the fingerprint region[J]. Advanced Materials Technologies, 2022, 7(10): 2101567. |
149 | SONG Y Z, CUI L, LÓPEZ J Á S, et al. Raman-Deuterium Isotope Probing for in situ identification of antimicrobial resistant bacteria in Thames River[J]. Scientific Reports, 2017, 7: 16648. |
150 | WANG Y, XU J B, KONG L C, et al. Raman-activated sorting of antibiotic-resistant bacteria in human gut microbiota[J]. Environmental Microbiology, 2020, 22(7): 2613-2624. |
151 | YUAN X F, SONG Y Q, SONG Y Z, et al. Effect of laser irradiation on cell function and its implications in Raman spectroscopy[J]. Applied and Environmental Microbiology, 2018, 84(8): e02508-e02517. |
152 | LIANG P, LIU B, WANG Y, et al. Isolation and culture of single microbial cells by laser ejection sorting technology[J]. Applied and Environmental Microbiology, 2022, 88(3): e0116521. |
153 | SU X L, GONG Y H, GOU H L, et al. Rational optimization of Raman-activated cell ejection and sequencing for bacteria[J]. Analytical Chemistry, 2020, 92(12): 8081-8089. |
154 | ZHANG H, LIU K K. Optical tweezers for single cells[J]. Journal of the Royal Society, Interface, 2008, 5(24): 671-690. |
155 | XIE C G, DINNO M A, LI Y Q. Near-infrared Raman spectroscopy of single optically trapped biological cells[J]. Optics Letters, 2002, 27(4): 249-251. |
156 | FANG T, SHANG W H, LIU C, et al. Nondestructive identification and accurate isolation of single cells through a chip with Raman optical tweezers[J]. Analytical Chemistry, 2019, 91(15): 9932-9939. |
157 | FANG T, SHANG W H, LIU C, et al. Single-cell multimodal analytical approach by integrating Raman optical tweezers and RNA sequencing[J]. Analytical Chemistry, 2020, 92(15): 10433-10441. |
158 | 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): e0018121. |
159 | LAU A Y, LEE L P, CHAN J W. An integrated optofluidic platform for Raman-activated cell sorting[J]. Lab on a Chip, 2008, 8(7): 1116-1120. |
160 | DOCHOW S, KRAFFT C, NEUGEBAUER U, et al. Tumour cell identification by means of Raman spectroscopy in combination with optical traps and microfluidic environments[J]. Lab on a Chip, 2011, 11(8): 1484-1490. |
161 | DOCHOW S, BELEITES C, HENKEL T, et al. Quartz microfluidic chip for tumour cell identification by Raman spectroscopy in combination with optical traps[J]. Analytical and Bioanalytical Chemistry, 2013, 405(8): 2743-2746. |
162 | CASABELLA S, SCULLY P, GODDARD N, et al. Automated analysis of single cells using Laser Tweezers Raman Spectroscopy[J]. Analyst, 2016, 141(2): 689-696. |
163 | LI M Q, ASHOK P C, DHOLAKIA K, et al. Raman-activated cell counting for profiling carbon dioxide fixing microorganisms[J]. The Journal of Physical Chemistry A, 2012, 116(25): 6560-6563 |
164 | MCILVENNA D, HUANG W E, DAVISON P, et al. Continuous cell sorting in a flow based on single cell resonance Raman spectra[J]. Lab on a Chip, 2016, 16(8): 1420-1429. |
165 | 涂然, 李世新, 李昊霓, 等. 液滴微流控技术在微生物工程菌株选育中的应用进展[J].合成生物学, 2023, 4(1): 165-184. |
TU R, LI S X, LI H N, et al. Application of droplet microfluidic technology in the breeding of microbial engineering strains [J]. Synthetic Biology Journal, 2023, 4(1): 165-184. | |
166 | CRISTOBAL G, ARBOUET L, SARRAZIN F, et al. On-line laser Raman spectroscopic probing of droplets engineered in microfluidic devices [J]. Lab on a Chip, 2006, 6(9): 1140-1146. |
167 | EVANS C L, POTMA E O, PUORIS'HAAG M, et al. Chemical imaging of tissue in vivo with video-rate coherent anti-Stokes Raman scattering microscopy[J]. Proceedings of the National Academy of Sciences of the United States of America, 2005, 102(46): 16807-16812. |
168 | HU F H, SHI L X, MIN W. Biological imaging of chemical bonds by stimulated Raman scattering microscopy[J]. Nature Methods, 2019, 16(9): 830-842. |
169 | WANG H W, BAO N, LE T L, et al. Microfluidic CARS cytometry[J]. Optics Express, 2008, 16(8): 5782-5789. |
170 | CAMP C H Jr, YEGNANARAYANAN S, EFTEKHAR A A, et al. Label-free flow cytometry using multiplex coherent anti-Stokes Raman scattering (MCARS) for the analysis of biological specimens[J]. Optics Letters, 2011, 36(12): 2309-2311. |
171 | HIRAMATSU K, IDEGUCHI T, YONAMINE Y, et al. High-throughput label-free molecular fingerprinting flow cytometry[J]. Science Advances, 2019, 5(1): eaau0241. |
172 | ZHANG C, HUANG K C, RAJWA B, et al. Stimulated Raman scattering flow cytometry for label-free single-particle analysis[J]. Optica, 2017, 4(1): 103-109. |
173 | SUZUKI Y, KOBAYASHI K, WAKISAKA Y, et al. Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(32): 15842-15848. |
174 | 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. |
175 | 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. |
176 | 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. |
177 | HO C S, JEAN N, HOGAN C A, et al. Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning[J]. Nature Communications, 2019, 10: 4927. |
178 | TANG T, LIU X, KIYA R, et al. Microscopic impedance cytometry for quantifying single cell shape[J]. Biosensors & Bioelectronics, 2021, 193: 113521. |
179 | ISLAM M, BRINK H, BLANCHE S, et al. Microfluidic sorting of cells by viability based on differences in cell stiffness[J]. Scientific Reports, 2017, 7: 1997. |
180 | ZHANG Y, ZHAO Y, CHEN D Y, et al. Crossing constriction channel-based microfluidic cytometry capable of electrically phenotyping large populations of single cells[J]. Analyst, 2019, 144(3): 1008-1015. |
181 | LABELLE C A, MASSARO A, CORTES-LLANOS B, et al. Image-based live cell sorting[J]. Trends in Biotechnology, 2021, 39(6): 613-623. |
182 | KONG K, ROWLANDS C J, VARMA S, et al. Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy [J]. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(38): 15189-15194. |
[1] | 孙梦楚, 陆亮宇, 申晓林, 孙新晓, 王佳, 袁其朋. 基于荧光检测的高通量筛选技术和装备助力细胞工厂构建[J]. 合成生物学, 2023, 4(5): 947-965. |
[2] | 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. |
[3] | 卢挥, 张芳丽, 黄磊. 合成生物学自动化装置iBioFoundry的构建与应用[J]. 合成生物学, 2023, 4(5): 877-891. |
[4] | 白仲虎, 任和, 聂简琪, 孙杨. 高通量平行发酵技术的发展与应用[J]. 合成生物学, 2023, 4(5): 904-915. |
[5] | 吴玉洁, 刘欣欣, 刘健慧, 杨开广, 随志刚, 张丽华, 张玉奎. 基于高通量液相色谱质谱技术的菌株筛选与关键分子定量分析研究进展[J]. 合成生物学, 2023, 4(5): 1000-1019. |
[6] | 胡哲辉, 徐娟, 卞光凯. 自动化高通量技术在天然产物生物合成中的应用[J]. 合成生物学, 2023, 4(5): 932-946. |
[7] | 刘欢, 崔球. 原位电离质谱技术在微生物菌株筛选中的应用进展[J]. 合成生物学, 2023, 4(5): 980-999. |
[8] | 陈永灿, 司同, 张建志. 自动化合成生物技术在DNA组装与微生物底盘操作中的应用[J]. 合成生物学, 2023, 4(5): 857-876. |
[9] | 王雁南, 孙宇辉. 碱基编辑技术及其在微生物合成生物学中的应用[J]. 合成生物学, 2023, 4(4): 720-737. |
[10] | 刘晚秋, 季向阳, 许慧玲, 卢屹聪, 李健. 限制性内切酶的无细胞快速制备研究[J]. 合成生物学, 2023, 4(4): 840-851. |
[11] | 孙美莉, 王凯峰, 陆然, 纪晓俊. 解脂耶氏酵母底盘细胞的工程改造及应用[J]. 合成生物学, 2023, 4(4): 779-807. |
[12] | 孙智, 杨宁, 娄春波, 汤超, 杨晓静. 功能拓扑的理性设计及其在合成生物学中的应用[J]. 合成生物学, 2023, 4(3): 444-463. |
[13] | 赖奇龙, 姚帅, 查毓国, 白虹, 宁康. 微生物组生物合成基因簇发掘方法及应用前景[J]. 合成生物学, 2023, 4(3): 611-627. |
[14] | 孟巧珍, 郭菲. “可折叠性”在酶智能设计改造中的应用研究——以AlphaFold2为例[J]. 合成生物学, 2023, 4(3): 571-589. |
[15] | 王晟, 王泽琛, 陈威华, 陈珂, 彭向达, 欧发芬, 郑良振, 孙瑨原, 沈涛, 赵国屏. 基于人工智能和计算生物学的合成生物学元件设计[J]. 合成生物学, 2023, 4(3): 422-443. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||