Bulletin of Chinese Academy of Sciences (Chinese Version)
Keywords
synthetic biology; phenotype test; molecular spectroscopy; cell sorting; single-cell phenome; single-cell functional genomics
Document Type
Article
Abstract
In synthetic biology, the dazzling methodological innovations in sequencing, editing and synthesis of genomes have resulted in an unprecedented capability in "design and manufacturing of genotype". On the other hand, "testing of cellular phenotype and function" has increasingly become one major bottleneck. Single-cell technologies have tremendous implications and potentials in rapid testing of cellular function. However, ideally, such single-cell methods should allow non-invasive live-cell probing, be label-free, provide landscapelike phenotyping capability, distinguish complex functions, operate with high speed, sufficient throughput and low-cost, and finally, be able to couple with downstream omics analysis via cell sorting. In this perspective article, we focus on recent progress in label-free molecular spectroscopy-activated phenotyping, sorting and sequencing of single-cells, and discuss the key challenges and emerging trends of the area. We propose that alliance among the array of non-invasive spectroscopy methods or modes, when coupled with downstream high-throughput cell sorting and omics profiling, will establish and broaden a bridge that connects spectroscopy and genetics, in science, technologies, and communities. 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 spectroscopy-enabled single-cell "phenome-genome" as a new type of biological big-data, and accelerate the pace of "data-driven" synthetic biology.
First page
1193
Last Page
1204
Language
Chinese
Publisher
Bulletin of Chinese Academy of Sciences
References
Check E. Synthetic biology:designs on life. Nature, 2005, 438(7067):417-418.
Auslander S, Auslander D, Fussenegger M. Synthetic biology-The synthesis of biology. Angewandte Chemie International Edition, 2017, 56(23):6396-6419.
Kosuri S, Church G M. Large-scale de novo DNA synthesis:technologies and applications. Nature Methods, 2014, 11(5):499-507.
Gibson D G, Young L, Chuang R Y, et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nature Methods, 2009, 6(5):343-345.
Sander J D, Joung J K. CRISPR-Cas systems for editing, regulating and targeting genomes. Nature Biotechnology, 2014, 32(4):347-355.
Brophy J A, Voigt C A. Principles of genetic circuit design. Nature Methods, 2014, 11(5):508-520.
Ro D K, Paradise E M, Ouellet M, et al. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature, 2006, 440(7086):940-943.
Kwok R. Five hard truths for synthetic biology. Nature, 2010, 463(7279):288-290.
Nawy T. Integrated single-cell profiles. Nature Methods, 2015, 13(1):36.
Nielsen J, Oliver S. The next wave in metabolome analysis. Trends in Biotechnology, 2005, 23(11):544-546.
Wishart D S. Emerging applications of metabolomics in drug discovery and precision medicine. Nature Reviews Drug Discovery, 2016, 15(7):473-484.
Zenobi R. Single-cell metabolomics:analytical and biological perspectives. Science, 2013, 342(6163):1243259.
Brehm-Stecher B F, Johnson E A. Single-cell microbiology:tools, technologies, and applications. Microbiology and Molecular Biology Reviews, 2004, 68(3):538-559.
Liu H, Chen L, Xu C, et al. Recent progresses in small-molecule enzymatic fluorescent probes for cancer imaging. Chemical Society Reviews, 2018, (47):7140-7180.
Zhao Y, Yang Y. Profiling metabolic states with genetically encoded fluorescent biosensors for NADH. Current Opinion in Biotechnology, 2015, 31:86-92.
Tao R, Zhao Y, Chu H, et al. Genetically encoded fluorescent sensors reveal dynamic regulation of NADPH metabolism. Nature Methods, 2017, 14(7):720-728.
Jing M, Zhang P, Wang G, et al. A genetically-encoded fluorescent acetylcholine indicator for in vitro and in vivo studies. Nature Biotechnology, 2018, 36(8):726-737.
Sun F, Zeng J, Jing M, et al. A genetically-encoded fluorescent sensor enables rapid and specific detection of dopamine in flies, fish, and mice. Cell, 2018, 174(2):481.
Wang T, Guan C, Guo J, et al. Pooled CRISPR interference screening enables genome-scale functional genomics study in bacteria with superior performance. Nature Communations, 2018, 9:2475.
Nolan J P, Duggan E, Liu E, et al. Single cell analysis using surface enhanced Raman scattering (SERS) tags. Methods, 2012, 57(3):272-279.
Raman C V, Krishnan K S. A new type of secondary radiation. Nature, 1928, 121:501-502.
Xu J, Ma B, Su X, et al. Emerging trends for microbiome analysis:from sngle-cell functional imaging to microbiome big data. Engineering, 2017, 3(1):66-70.
Jing X, Gou H, Gong Y, et al. Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean. Environment Microbiology, 2018, 4:29727057.
Berry D, Mader E, Lee T K, et al. Tracking heavy water (D 2O) incorporation for identifying and sorting active microbial cells. PNAS, 2015, 112(2):194-203.
He Y, Zhang P, Huang S, et al. Label-free, simultaneous quantification of starch, protein and triacylglycerol in single microalgal cells. Biotechnology for Biofuels, 2017, 10(1):275.
Ji Y, He Y, Cui Y, et al. Raman spectroscopy provides a rapid, non-invasive method for quantitation of starch in live, unicellular microalgae. Biotechnology Journal, 2014, 9(12):1512-1518.
Wang T, Ji Y, Wang Y, et al. Quantitative dynamics of triacylglycerol accumulation in microalgae populations at single-cell resolution revealed by Raman microspectroscopy. Biotechnology Biofuels, 2014, 7:58.
Teng L, Wang X, Wang X, et al, Label-free, rapid and quantitative phenotyping of stress response in E. coli via ramanome. Scientific Reports, 2016, 6:34359.
Tao Y, Wang Y, Huang S, et al. Metabolic-activity-based assessment of antimicrobial effects by D 2O-labeled single-cell Raman microspectroscopy. Analytical Chemistry, 2017, 89(7):4108-4115.
Wang Y, Song Y, Tao Y, et al. Reverse and multiple stable isotope probing to study bacterial metabolism and interactions at the single cell level. Analytical Chemistry, 2016, 88(19):9443-9450.
Mattson E C, Aboualizadeh E, Barabas M E, et al. Opportunities for live cell FT-infrared imaging:macromolecule identification with 2D and 3D localization. International Journal of Molecular Sciences, 2013, 14(11):22753-22781.
Sabbatini S, Conti C, Orilisi G, et al. Infrared spectroscopy as a new tool for studying single living cells:Is there a niche. Biomedical Spectroscopy and Imaging, 2017, 6(3-4):85-99.
Liu J, Huang Q. Screening of astaxanthin-hyperproducing Haematococcus pluvialis using Fourier Transform Infrared (FTIR) and Raman microspectroscopy. Applied Spectroscopy, 2016, 70(10):1639-1648.
Liu J, Song L, Huang Q. Rapid screening astaxanthinhyperproducing Haematococcus pluvialis mutants through near infrared spectroscopy. Letters in Applied Microbiology, 2016, 62(2):185-191.
Petibois C, Cestelli-Guidi M, Piccinini M, et al. Synchrotron radiation FTIR imaging in minutes:a first step towards realtime cell imaging. Analytical and Bioanalytical Chemistry, 2010, 397(6):2123-2129.
Vaccari L, Birarda G, Businaro L, et al. Infrared microspectroscopy of live cells in Microfluidic Devices (MD-IRMS):toward a powerful label-free cell-based assay. Analytical Chemistry, 2012, 84(11):4768-4775.
Ma Z, Hanham S M, Arroyo Huidobro P, et al. Terahertz particle-in-liquid sensing with spoof surface plasmon polariton waveguides. APL Photonics, 2017, 2(11):116102.
Ren L, Yang S, Zhang P, et al. Standing Surface Acoustic Wave (SSAW)-based fluorescence-activated cell sorter. Small, 2018, 14(40):e1801996.
Sciambi A, Abate A R. Accurate microfluidic sorting of droplets at 30 kHz. Lab on a Chip, 2015, 15(1):47-51.
Qiao Y, Zhao X, Zhu J, et al. Fluorescence-activated droplet sorting of lipolytic microorganisms using a compact optical system. Lab on a Chip, 2017, 18(1):190-196.
Brouzes E, Medkova M, Savenelli N, et al. Droplet microfluidic technology for single-cell high-throughput screening. PNAS, 2009, 106(34):14195-14200.
Agresti J J, Antipov E, Abate A R, et al. Ultrahigh-throughput screening in drop-based microfluidics for directed evolution. PNAS, 2010, 107(9):4004-4009.
Ma F, Chung M T, Yao Y, et al. Efficient molecular evolution to generate enantioselective enzymes using a dual-channel microfluidic droplet screening platform. Nature Communations, 2018, 9(1):1030.
Nitta N, Sugimura T, Isozaki A, et al. Intelligent image-activated cell sorting. Cell, 2018, 175(1):266-276.
Song Y, Yin H, Huang W E. Raman activated cell sorting. Current Opinion in Chemical Biology, 2016, 33:1-8.
Zhang Q, Zhang P, Gou H, et al. Towards high-throughput microfluidic Raman-activated cell sorting. Analyst, 2015, 140(18):6163-6174.
Eriksson E, Enger J, Nordlander B, et al. A microfluidic system in combination with optical tweezers for analyzing rapid and reversible cytological alterations in single cells upon environmental changes. Lab on a Chip, 2007, 7(1):71-76.
Lau A Y, Lee L P, Chan J W. An integrated optofluidic platform for Raman-activated cell sorting. Lab on a Chip, 2008, 8(7):1116-1120.
Xie C, Chen D, Li Y Q. Raman sorting and identification of single living micro-organisms with optical tweezers. Optics Letters, 2005, 30(14):1800-1802.
Huang W E, Ward A D, Whiteley A S. Raman tweezers sorting of single microbial cells. Environmental Microbiology Reports, 2009, 1(1):44-49.
Zhang P, Ren L, Zhang X, et al. Raman-activated cell sorting based on dielectrophoretic single-cell trap and release. Analytical Chemistry, 2015, 87(4):2282-2289.
McIlvenna D, Huang W E, Davison P, et al. Continuous cell sorting in a flow based on single cell resonance Raman spectra. Lab on a Chip, 2016, 16(8):1420-1429.
Wang X, Ren L, Su Y, et al. Raman-Activated Droplet Sorting (RADS) for label-free high-throughput screening of microalgal single-cells. Analytical Chemistry, 2017, 89(22):12569-12577.
Yuan X, Song Y, Song Y, et al. Effect of laser irradiation on cell function and its implications in Raman Spectroscopy. Applied and Environmental Microbiology, 2018, 84(8):e02508-17.
Song Y, Kaster A K, Vollmers J, et al. Single-cell genomics based on Raman sorting reveals novel carotenoid-containing bacteria in the Red Sea. Microbal Biotechnology, 2017, 10(1):125-137.
Zhang C, Huang K C, Rajwa B, et al. Stimulated Raman scattering flow cytometry for label-free single-particle analysis. Optica, 2017, 4(1):103-109.
Zhang Q, Zhang P, Gou H, et al. Towards high-throughput microfluidic Raman-activated cell sorting. Analyst, 2015, 140:6163-6174.
Chawla K, Burgel S C, Schmidt G W, et al. Integrating impedance-based growth-rate monitoring into a microfluidic cell culture platform for live-cell microscopy. Microsystems & Nanoengineering, 2018, 4(1):1-8.
Islam M, Brink H, Blanche S, et al. Microfluidic sorting of cells by viability based on differences in cell stiffness. Scientific Reports, 2017, 7(1):1997.
Shields C W 4th, Reyes C D, Lopez G P. Microfluidic cell sorting:a review of the advances in the separation of cells from debulking to rare cell isolation. Lab on a Chip, 2015, 15(5):1230-1249.
Saeys Y, Gassen S V, Lambrecht B N. Computational flow cytometry:helping to make sense of high-dimensional immunology data. Nature Reviews Immunology, 2016, 16:449-462.
Hong S, Chen T, Zhu Y. Live-cell stimulated Raman scattering imaging of alkyne-tagged biomolecules. Angewandte Chemie International Edition, 2014, 53(23):5827-5831.
Li S, Chen T, Wang Y, et al. Conjugated polymer with intrinsic alkyne units for synergistically enhanced Raman imaging in living cells. Angewandte Chemie International Edition, 2017, 56(43), 13455-13458.
Kong K, Rowlands C J, Varma S, et al. Diagnosis of tumors during tissue-conserving surgery with integrated autofluorescence and Raman scattering microscopy. PNAS, 2013, 110(38):15189-15194.
郭庆华, 杨维才, 吴芳芳, 等.高通量作物表型监测:育种和精准农业发展的加速器.中国科学院院刊, 2018, 33(9):940-946.
Rozman J, Klingenspor M, Angelis M H. A review of standardized metabolic phenotyping of animal models. Mammalian Genome, 2014, 25(9-10):497-507.
Bochner B R. Global phenotypic characterization of bacteria. FEMS Microbiology Reviews. 2009, 33(1):191-205.
Recommended Citation
Bo, MA and Jian, XU
(2018)
"Phenotyping and Sorting of Synthetic Cells: Building Bridge from Spectroscopy to Genetics,"
Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 33
:
Iss.
11
, Article 7.
DOI: https://doi.org/10.16418/j.issn.1000-3045.2018.11.007
Available at:
https://bulletinofcas.researchcommons.org/journal/vol33/iss11/7