Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).
Blainey, P. C., Mosier, A. C., Potanina, A., Francis, C. A. & Quake, S. R. Genome of a low-salinity ammonia-oxidizing archaeon determined by single-cell and metagenomic analysis. PLoS ONE 6, e16626 (2011).
Thomas, T., Gilbert, J. & Meyer, F. Metagenomics -– a guide from sampling to data analysis. Microb. Inform. Exp. 2, 3 (2012).
Horgan, R. P. & Kenny, L. C. ‘Omic’ technologies: genomics, transcriptomics, proteomics and metabolomics. Obstet. Gynaecol 13, 189–195 (2011).
Prosser, J. I. Dispersing misconceptions and identifying opportunities for the use of ‘omics’ in soil microbial ecology. Nat. Rev. Microbiol. 13, 439–446 (2015).
Yu, F. B. et al. Microfluidic-based mini-metagenomics enables discovery of novel microbial lineages from complex environmental samples. eLife 6, e26580 (2017).
Mukherjee, S. et al. Genomes OnLine database (GOLD) v.7: updates and new features. Nucleic Acids Res 47, D649–D659 (2019).
Woyke, T., Doud, D. F. R. & Schulz, F. The trajectory of microbial single-cell sequencing. Nat. Methods 14, 1045–1054 (2017).
Berry, D. & Loy, A. Stable-isotope probing of human and animal microbiome function. Trends Microbiol 26, 999–1007 (2018).
Manefield, M., Whiteley, A. S., Griffiths, R. I. & Bailey, M. J. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl. Environ. Microbiol. 68, 5367–5373 (2002).
Dumont, M. G. & Murrell, J. C. Stable isotope probing—linking microbial identity to function. Nat. Rev. Microbiol. 3, 499–504 (2005).
Wilhelm, R. C., Singh, R., Eltis, L. D. & Mohn, W. W. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J 13, 413–429 (2019).
Wang, Y., Huang, W. E., Cui, L. & Wagner, M. Single cell stable isotope probing in microbiology using Raman microspectroscopy. Curr. Opin. Biotechnol. 41, 34–42 (2016).
Haider, S. et al. Raman microspectroscopy reveals long-term extracellular activity of chlamydiae. Mol. Microbiol 77, 687–700 (2010).
Huang, W. E. et al. Raman-FISH: combining stable-isotope Raman spectroscopy and fluorescence in situ hybridization for the single cell analysis of identity and function. Environ. Microbiol. 9, 1878–1889 (2007).
Wagner, M. Single-cell ecophysiology of microbes as revealed by Raman microspectroscopy or secondary ion mass spectrometry imaging. Annu. Rev. Microbiol. 63, 411–429 (2009).
Berry, D. et al. Tracking heavy water (D2O) incorporation for identifying and sorting active microbial cells. Proc. Natl Acad. Sci. USA 112, E194–E203 (2015).
Malmstrom, R. R. & Eloe-Fadrosh, E. A. Advancing genome-resolved metagenomics beyond the shotgun. mSystems 4, e00118–e00119 (2019).
Neufeld, J. D. et al. DNA stable-isotope probing. Nat. Protoc. 2, 860–866 (2007).
Jing, X. et al. Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean. Environ. Microbiol. 20, 2241–2255 (2018).
Wang, Y. et al. Raman activated cell ejection for isolation of single cells. Anal. Chem. 85, 10697–10701 (2013).
Singer, E., Wagner, M. & Woyke, T. Capturing the genetic makeup of the active microbiome in situ. ISME J 11, 1949–1963 (2017).
Huang, W. E., Ward, A. D. & Whiteley, A. S. Raman tweezers sorting of single microbial cells. Environ. Microbiol. Rep 1, 44–49 (2009).
Lee, K. S. et al. An automated Raman-based platform for the sorting of live cells by functional properties. Nat. Microbiol. 4, 1035–1048 (2019).
Lee, K. S., Wagner, M. & Stocker, R. Raman-based sorting of microbial cells to link functions to their genes. Microb. Cell 7, 62–65 (2020).
Premvardhan, L., Bordes, L., Beer, A., Büchel, C. & Robert, B. Carotenoid structures and environments in trimeric and oligomeric fucoxanthin chlorophyll a/c2 proteins from resonance Raman spectroscopy. J. Phys. Chem. B 113, 12565–12574 (2009).
Takano, H. The regulatory mechanism underlying light-inducible production of carotenoids in nonphototrophic bacteria. Biosci. Biotechnol. Biochem. 80, 1264–1273 (2016).
Wagstaff, K., Cardie, C., Rogers, S. & Schrödl, S. Constrained k-means clustering with background knowledge. in Proc. 18th International Conference on Machine Learning (eds Brodley, C. E. & Danyluk, A. P.) 577–584 (Morgan Kaufmann, 2001).
Kanungo, T. et al. An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Patt. Anal. Mach. Intell. 24, 881–892 (2002).
Rinke, C. et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat. Protoc. 9, 1038–1048 (2014).
Bonner, W. A., Hulett, H. R., Sweet, R. G. & Herzenberg, L. A. Fluorescence activated cell sorting. Rev. Sci. Instrum. 43, 404–409 (1972).
Ha, B. H., Lee, K. S., Jung, J. H. & Sung, H. J. Three-dimensional hydrodynamic flow and particle focusing using four vortices Dean flow. Microfluid. Nanofluid. 17, 647–655 (2014).
Chu, H., Doh, I. & Cho, Y.-H. A three-dimensional (3D) particle focusing channel using the positive dielectrophoresis (pDEP) guided by a dielectric structure between two planar electrodes. Lab Chip 9, 686–691 (2009).
Gao, C. et al. Single-cell bacterial transcription measurements reveal the importance of dimethylsulfoniopropionate (DMSP) hotspots in ocean sulfur cycling. Nat. Commun. 11, 1942 (2020).
Kitzinger, K. et al. Single cell analyses reveal contrasting life strategies of the two main nitrifiers in the ocean. Nat. Commun. 11, 767 (2020).
Majed, N., Chernenko, T., Diem, M. & Gu, A. Z. Identification of functionally relevant populations in enhanced biological phosphorus removal processes based on intracellular polymers profiles and insights into the metabolic diversity and heterogeneity. Environ. Sci. Technol. 46, 5010–5017 (2012).
Fernando, E. Y. et al. Resolving the individual contribution of key microbial populations to enhanced biological phosphorus removal with Raman–FISH. ISME J 13, 1933–1946 (2019).
Milucka, J. et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541–546 (2012).
Hatzenpichler, R. et al. Visualizing in situ translational activity for identifying and sorting slow-growing archaeal–bacterial consortia. Proc. Natl Acad. Sci. USA 113, E4069–E4078 (2016).
Schiessl, K. T. et al. Phenazine production promotes antibiotic tolerance and metabolic heterogeneity in Pseudomonas aeruginosa biofilms. Nat. Commun. 10, 762 (2019).
Gleizer, S. et al. Conversion of Escherichia coli to generate all biomass carbon from CO2. Cell 179, 1255–1263 (2019).
Dong, T. G., Ho, B. T., Yoder-Himes, D. R. & Mekalanos, J. J. Identification of T6SS-dependent effector and immunity proteins by Tn-seq in Vibrio cholerae. Proc. Natl Acad. Sci. USA 110, 2623–2628 (2013).
Dolinšek, J., Lagkouvardos, I., Wanek, W., Wagner, M. & Daims, H. Interactions of nitrifying bacteria and heterotrophs: identification of a Micavibrio-like putative predator of Nitrospira spp. Appl. Environ. Microbiol. 79, 2027–2037 (2013).
Pätzold, R. et al. In situ mapping of nitrifiers and anammox bacteria in microbial aggregates by means of confocal resonance Raman microscopy. J. Microbiol. Methods 72, 241–248 (2008).
Wei, L. & Min, W. Electronic preresonance stimulated Raman scattering microscopy. J. Phys. Chem. Lett. 9, 4294–4301 (2018).
Gruber-Vodicka, H. R. et al. Paracatenula, an ancient symbiosis between thiotrophic Alphaproteobacteria and catenulid flatworms. Proc. Natl Acad. Sci. USA. 108, 12078–12083 (2011).
Lenz, R., Enders, K., Stedmon, C. A., MacKenzie, D. M. A. & Nielsen, T. G. A critical assessment of visual identification of marine microplastic using Raman spectroscopy for analysis improvement. Mar. Pollut. Bull. 100, 82–91 (2015).
Gillibert, R. et al. Raman tweezers for small microplastics and nanoplastics identification in seawater. Environ. Sci. Technol. 53, 9003–9013 (2019).
Choy, C. A. et al. The vertical distribution and biological transport of marine microplastics across the epipelagic and mesopelagic water column. Sci. Rep. 9, 7843 (2019).
Zhang, P. et al. Raman-activated cell sorting based on dielectrophoretic single-cell trap and release. Anal. Chem. 87, 2282–2289 (2015).
McIlvenna, D. et al. Continuous cell sorting in a flow based on single cell resonance Raman spectra. Lab Chip 16, 1420–1429 (2016).
Folick, A., Min, W. & Wang, M. C. Label-free imaging of lipid dynamics using coherent anti-stokes Raman scattering (CARS) and stimulated Raman scattering (SRS) microscopy. Curr. Opin. Genet. Dev. 21, 585–590 (2011).
Hiramatsu, K. et al. High-throughput label-free molecular fingerprinting flow cytometry. Sci. Adv. 5, eaau0241 (2019).
Suzuki, Y. et al. Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering. Proc. Natl Acad. Sci. USA 116, 15842–15848 (2019).
Nitta, N. et al. Raman image-activated cell sorting. Nat. Commun. 11, 3452 (2020).
Eek, K. M., Sessions, A. L. & Lies, D. P. Carbon-isotopic analysis of microbial cells sorted by flow cytometry. Geobiology 5, 85–95 (2007).
Dyksma, S. et al. Ubiquitous Gammaproteobacteria dominate dark carbon fixation in coastal sediments. ISME J 10, 1939–1953 (2016).
Ling, L., Zhou, F., Huang, L. & Li, Z.-Y. Optical forces on arbitrary shaped particles in optical tweezers. J. Appl. Phys. 108, 073110 (2010).
Bonessi, D., Bonin, K. & Walker, T. Optical forces on particles of arbitrary shape and size. J. Opt. A Pure Appl. Opt. 9, S228–S234 (2007).
Ashkin, A. Forces of a single-beam gradient laser trap on a dielectric sphere in the ray optics regime. Biophys. J. 61, 569–582 (1992).
Novotny, L., Bian, R. X. & Xie, X. S. Theory of nanometric optical tweezers. Phys. Rev. Lett. 79, 645–648 (1997).
Dholakia, K. & Reece, P. Optical micromanipulation takes hold. Nano Today 1, 18–27 (2006).
Kim, S., Kang, I., Seo, J.-H. & Cho, J.-C. Culturing the ubiquitous freshwater actinobacterial acI lineage by supplying a biochemical ‘helper’ catalase. ISME J 13, 2252–2263 (2019).
Li, T. et al. Simultaneous analysis of microbial identity and function using NanoSIMS. Environ. Microbiol. 10, 580–588 (2008).
Huang, W. E., Griffiths, R. I., Thompson, I. P., Bailey, M. J. & Whiteley, A. S. Raman microscopic analysis of single microbial cells. Anal. Chem. 76, 4452–4458 (2004).
McDonald, J. C. et al. Fabrication of microfluidic systems in poly(dimethylsiloxane). Electrophoresis 21, 27–40 (2000).
Schuster, K. C., Reese, I., Urlaub, E., Gapes, J. R. & Lendl, B. Multidimensional information on the chemical composition of single bacterial cells by confocal Raman microspectroscopy. Anal. Chem. 72, 5529–5534 (2000).
Dochow, S. et al. Quartz microfluidic chip for tumour cell identification by Raman spectroscopy in combination with optical traps. Anal. Bioanal. Chem. 405, 2743–2746 (2013).
Kodinariya, T. M. & Makwana, P. R. Review on determining number of Cluster in K-Means Clustering. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 1, 90–95 (2013).
Bjerg, J. T. et al. Long-distance electron transport in individual, living cable bacteria. Proc. Natl Acad. Sci. USA. 115, 5786–5791 (2018).
Zhao, J., Lui, H., McLean, D. I. & Zeng, H. Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy. Appl. Spectrosc. 61, 1225–1232 (2007).
Beier, B. D. & Berger, A. J. Method for automated background subtraction from Raman spectra containing known contaminants. Analyst 134, 1198–1202 (2009).
Hehemann, J.-H. et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat. Commun. 7, 12860 (2016).
Taheri-Araghi, S. et al. Cell-size control and homeostasis in bacteria. Curr. Biol. 25, 385–391 (2015).
Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870–891 (2013).
Wang, Y. et al. Reverse and multiple stable isotope probing to study bacterial metabolism and interactions at the single cell level. Anal. Chem. 88, 9443–9450 (2016).
Yuan, X. et al. Effect of laser irradiation on cell function and its implications in Raman spectroscopy. Appl. Environ. Microbiol. 84, e02508–e02517 (2018).
Source: Ecology - nature.com
