Next-generation physiology approaches to study microbiome function at single cell level


The function of cells in their native habitat often cannot be reliably predicted from genomic data or from physiology studies of isolates. Traditional experimental approaches to study the function of taxonomically and metabolically diverse microbiomes are limited by their destructive nature, low spatial resolution or low throughput. Recently developed technologies can offer new insights into cellular function in natural and human-made systems and how microorganisms interact with and shape the environments that they inhabit. In this Review, we provide an overview of these next-generation physiology approaches and discuss how the non-destructive analysis of cellular phenotypes, in combination with the separation of the target cells for downstream analyses, provide powerful new, complementary ways to study microbiome function. We anticipate that the widespread application of next-generation physiology approaches will transform the field of microbial ecology and dramatically improve our understanding of how microorganisms function in their native environment.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Examples of traditional approaches to study microbial physiology.
Fig. 2: Next-generation physiology workflow to study microorganisms.
Fig. 3: Reporters and their associated Raman spectral fingerprints in microbial next-generation physiology.


  1. 1.

    Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).

  2. 2.

    Flemming, H. C. & Wuertz, S. Bacteria and archaea on Earth and their abundance in biofilms. Nat. Rev. Microbiol. 17, 247–260 (2019).

  3. 3.

    Castelle, C. J. & Banfield, J. F. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell 172, 1181–1197 (2018).

  4. 4.

    Karst, S. M. et al. Retrieval of a million high-quality, full-length microbial 16S and 18S rRNA gene sequences without primer bias. Nat. Biotechnol. 36, 190–195 (2018).

  5. 5.

    Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).

  6. 6.

    Ackermann, M. A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13, 497–508 (2015).

  7. 7.

    Lidstrom, M. E. & Konopka, M. C. The role of physiological heterogeneity in microbial population behavior. Nat. Chem. Biol. 6, 705–712 (2010).

  8. 8.

    Ansorge, R. et al. Functional diversity enables multiple symbiont strains to coexist in deep-sea mussels. Nat. Microbiol. 4, 2487–2497 (2019).

  9. 9.

    Gruber-Dorninger, C. et al. Functionally relevant diversity of closely related nitrospira in activated sludge. ISME J. 9, 643–655 (2015).

  10. 10.

    Vasdekis, A. E. & Stephanopoulos, G. Review of methods to probe single cell metabolism and bioenergetics. Metab. Eng. 27, 115–135 (2015).

  11. 11.

    Rosenthal, K., Oehling, V., Dusny, C. & Schmid, A. Beyond the bulk: disclosing the life of single microbial cells. FEMS Microbiol. Rev. 41, 751–780 (2017).

  12. 12.

    Nai, C. & Meyer, V. From axenic to mixed cultures: technological advances accelerating a paradigm shift in microbiology. Trends Microbiol. 26, 538–554 (2018).

  13. 13.

    Oomen, P. E., Aref, M. A., Kaya, I., Phan, N. T. N. & Ewing, A. G. Chemical analysis of single cells. Anal. Chem. 91, 588–621 (2019).

  14. 14.

    Taheri-Araghi, S., Brown, S. D., Sauls, J. T., McIntosh, D. B. & Jun, S. Single-cell physiology. Annu. Rev. Biophys. 44, 123–142 (2015).

  15. 15.

    Lloyd, K. G., Steen, A. D., Ladau, J., Yin, J. & Crosby, L. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems 3, e00055–18 (2018).

  16. 16.

    Steen, A. D. et al. High proportions of bacteria and archaea across most biomes remain uncultured. ISME J. 13, 3126–3130 (2019).

  17. 17.

    Nielsen, J. L., Christensen, D., Kloppenborg, M. & Nielsen, P. H. Quantification of cell-specific substrate uptake by probe-defined bacteria under in situ conditions by microautoradiography and fluorescence in situ hybridization. Env. Microbiol. 5, 202–211 (2003).

  18. 18.

    Lee, N. et al. Combination of fluorescent in situ hybridization and microautoradiography — a new tool for structure–function analyses in microbial ecology. Appl. Env. Microbiol. 65, 1289–1297 (1999).

  19. 19.

    McGlynn, S. E., Chadwick, G. L., Kempes, C. P. & Orphan, V. J. Single cell activity reveals direct electron transfer in methanotrophic consortia. Nature 526, 531–535 (2015).

  20. 20.

    Kopf, S. H. et al. Heavy water and 15N labelling with nanoSIMS analysis reveals growth rate-dependent metabolic heterogeneity in chemostats. Env. Microbiol. 17, 2542–2556 (2015).

  21. 21.

    Hungate, B. A. et al. Quantitative microbial ecology through stable isotope probing. Appl. Env. Microbiol. 81, 7570–7581 (2015).

  22. 22.

    Ziels, R. M., Sousa, D. Z., Stensel, H. D. & Beck, D. A. C. DNA-SIP based genome-centric metagenomics identifies key long-chain fatty acid-degrading populations in anaerobic digesters with different feeding frequencies. ISME J. 12, 112–123 (2018).

  23. 23.

    Eyice, O. et al. SIP metagenomics identifies uncultivated methylophilaceae as dimethylsulphide degrading bacteria in soil and lake sediment. ISME J. 9, 2336–2348 (2015).

  24. 24.

    Fortunato, C. S. & Huber, J. A. Coupled RNA-SIP and metatranscriptomics of active chemolithoautotrophic communities at a deep-sea hydrothermal vent. ISME J. 10, 1925–1938 (2016).

  25. 25.

    Doud, D. F. R. & Woyke, T. Novel approaches in function-driven single-cell genomics. FEMS Microbiol. Rev. 41, 538–548 (2017).

  26. 26.

    Singer, E., Wagner, M. & Woyke, T. Capturing the genetic makeup of the active microbiome in situ. ISME J. 11, 1949–1963 (2017).

  27. 27.

    Yuan, X. et al. Effect of laser irradiation on cell function and its implications in Raman spectroscopy. Appl. Env. Microbiol. 84, e02508–17 (2018).

  28. 28.

    He, Y., Wang, X., Ma, B. & Xu, J. Ramanome technology platform for label-free screening and sorting of microbial cell factories at single-cell resolution. Biotechnol. Adv. 37, 107388 (2019).

  29. 29.

    Harrison, J. P. & Berry, D. Vibrational spectroscopy for imaging single microbial cells in complex biological samples. Front. Microbiol. 8, 675 (2017).

  30. 30.

    Lorenz, B., Wichmann, C., Stockel, S., Rosch, P. & Popp, J. Cultivation-free Raman spectroscopic investigations of bacteria. Trends Microbiol. 25, 413–424 (2017).

  31. 31.

    Zhang, Q. et al. Towards high-throughput microfluidic Raman-activated cell sorting. Analyst 140, 6163–6174 (2015).

  32. 32.

    Huang, W. E., Ward, A. D. & Whiteley, A. S. Raman tweezers sorting of single microbial cells. Env. Microbiol. Rep. 1, 44–49 (2009).

  33. 33.

    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). This study describes the development and application of the first microfluidic platform for automated Raman-activated sorting of isotope-labelled microorganisms. SIP–RACS and metagenomics are used to characterize mucin-degrading bacteria from a mouse colon.

  34. 34.

    McIlvenna, D. et al. Continuous cell sorting in a flow based on single cell resonance Raman spectra. Lab Chip 16, 1420–1429 (2016).

  35. 35.

    Wang, X. et al. Raman-activated droplet sorting (RADS) for label-free high-throughput screening of microalgal single-cells. Anal. Chem. 89, 12569–12577 (2017).

  36. 36.

    Song, Y. et al. Raman-deuterium isotope probing for in-situ identification of antimicrobial resistant bacteria in Thames river. Sci. Rep. 7, 16648 (2017).

  37. 37.

    Jing, X. et al. Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean. Env. Microbiol. 20, 2241–2255 (2018).

  38. 38.

    Song, Y. et al. Single-cell genomics based on Raman sorting reveals novel carotenoid-containing bacteria in the Red Sea. Microb. Biotechnol. 10, 125–137 (2016). This study uses label-free RACS and single cell genomics to characterize as yet uncultured carotenoid-containing microorganisms.

  39. 39.

    Wang, Y. et al. Raman activated cell ejection for isolation of single cells. Anal. Chem. 85, 10697–10701 (2013).

  40. 40.

    Rinke, C. et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat. Protoc. 9, 1038–1048 (2014).

  41. 41.

    Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013).

  42. 42.

    Couradeau, E. et al. Probing the active fraction of soil microbiomes using BONCAT–FACS. Nat. Commun. 10, 2770 (2019).

  43. 43.

    Morono, Y., Terada, T., Kallmeyer, J. & Inagaki, F. An improved cell separation technique for marine subsurface sediments: applications for high-throughput analysis using flow cytometry and cell sorting. Env. Microbiol. 15, 2841–2849 (2013).

  44. 44.

    Eichorst, S. A. et al. Advancements in the application of nanoSIMS and Raman microspectroscopy to investigate the activity of microbial cells in soils. FEMS Microbiol. Ecol. 91, fiv106 (2015).

  45. 45.

    Lunau, M., Lemke, A., Walther, K., Martens-Habbena, W. & Simon, M. An improved method for counting bacteria from sediments and turbid environments by epifluorescence microscopy. Env. Microbiol. 7, 961–968 (2005).

  46. 46.

    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). This study uses BONCAT–FISH and BONCAT–FACS in combination with 16S rRNA gene sequencing to characterize translationally active methane-oxidizing microbial consortia in deep-sea sediments.

  47. 47.

    Hao, L. et al. Novel prosthecate bacteria from the candidate phylum acetothermia. ISME J. 12, 2225–2237 (2018).

  48. 48.

    Clingenpeel, S., Schwientek, P., Hugenholtz, P. & Woyke, T. Effects of sample treatments on genome recovery via single-cell genomics. ISME J. 8, 2546–2549 (2014).

  49. 49.

    Clingenpeel, S., Clum, A., Schwientek, P., Rinke, C. & Woyke, T. Reconstructing each cell’s genome within complex microbial communities—dream or reality? Front. Microbiol. 5, 771 (2014).

  50. 50.

    Bowers, R. M. et al. Impact of library preparation protocols and template quantity on the metagenomic reconstruction of a mock microbial community. BMC Genomics 16, 856 (2015).

  51. 51.

    Cross, K. L. et al. Targeted isolation and cultivation of uncultivated bacteria by reverse genomics. Nat. Biotechnol. 37, 1314–1321 (2019). This study uses genome-informed antibody engineering to sort individual TM7 and SR1 cells from oral microbiome samples and regrow them in cultivation media.

  52. 52.

    Kalyuzhnaya, M. G., Lidstrom, M. E. & Chistoserdova, L. Real-time detection of actively metabolizing microbes by redox sensing as applied to methylotroph populations in Lake Washington. ISME J. 2, 696–706 (2008). This study demonstrates that, if carefully applied, redox sensing dyes can be used to sort metabolically active methylotrophic bacteria by FACS and bring sorted cells into enrichment culture.

  53. 53.

    Koch, C., Gunther, S., Desta, A. F., Hubschmann, T. & Muller, S. Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function. Nat. Protoc. 8, 190–202 (2013).

  54. 54.

    Lambrecht, J. et al. Flow cytometric quantification, sorting and sequencing of methanogenic archaea based on F420 autofluorescence. Microb. Cell Fact. 16, 180 (2017).

  55. 55.

    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). This study for the first time combines heavy water labelling, RACS and 16S rRNA gene sequencing and uses this workflow to identify glucosamine-degrading and mucin-degrading bacteria from mouse caecal samples.

  56. 56.

    Kopf, S. H. et al. Trace incorporation of heavy water reveals slow and heterogeneous pathogen growth rates in cystic fibrosis sputum. Proc. Natl Acad. Sci. USA 113, E110–E116 (2016).

  57. 57.

    Xu, J. et al. Raman deuterium isotope probing reveals microbial metabolism at the single-cell level. Anal. Chem. 89, 13305–13312 (2017).

  58. 58.

    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). This review provides an excellent overview of the principle and potential applications of single cell-targeted SIP–Raman studies.

  59. 59.

    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. Env. Microbiol. 9, 1878–1889 (2007). This study combines Raman microspectroscopy and FISH for the first time and uses it to identify 13C-naphthalene degraders in groundwater and quantifies isotope incorporation into individual cells.

  60. 60.

    Huang, W. E. et al. Resolving genetic functions within microbial populations: in situ analyses using rRNA and mRNA stable isotope probing coupled with single-cell Raman–fluorescence in situ hybridization. Appl. Env. Microbiol. 75, 234–241 (2009).

  61. 61.

    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).

  62. 62.

    Haider, S. et al. Raman microspectroscopy reveals long-term extracellular activity of Chlamydiae. Mol. Microbiol. 77, 687–700 (2010).

  63. 63.

    Angel, R. et al. Application of stable-isotope labelling techniques for the detection of active diazotrophs. Env. Microbiol. 20, 44–61 (2018).

  64. 64.

    Li, M. et al. Rapid resonance Raman microspectroscopy to probe carbon dioxide fixation by single cells in microbial communities. ISME J. 6, 875–885 (2012).

  65. 65.

    Muller, A. L. et al. Bacterial interactions during sequential degradation of cyanobacterial necromass in a sulfidic arctic marine sediment. Env. Microbiol. 20, 2927–2940 (2018).

  66. 66.

    Taylor, G. T. et al. Single-cell growth rates in photoautotrophic populations measured by stable isotope probing and resonance Raman microspectrometry. Front. Microbiol. 8, 1449 (2017).

  67. 67.

    Hu, F., Shi, L. & Min, W. Biological imaging of chemical bonds by stimulated Raman scattering microscopy. Nat. Methods 16, 830–842 (2019).

  68. 68.

    Wei, L. et al. Live-cell bioorthogonal chemical imaging: stimulated Raman scattering microscopy of vibrational probes. Acc. Chem. Res. 49, 1494–1502 (2016).

  69. 69.

    Crespi, H. L., Conard, S. M., Uphaus, R. A. & Katz, J. J. Cultivation of microorganisms in heavy water. Ann. N. Y. Acad. Sci. 84, 648–666 (1960).

  70. 70.

    Kselikova, V., Vitova, M. & Bisova, K. Deuterium and its impact on living organisms. Folia Microbiol. 64, 673–681 (2019).

  71. 71.

    Zhang, X., Gillespie, A. L. & Sessions, A. L. Large D/H variations in bacterial lipids reflect central metabolic pathways. Proc. Natl Acad. Sci. USA 106, 12580–12586 (2009).

  72. 72.

    Valentine, D. L., Sessions, A. L., Tyler, S. C. & Chidthaisong, A. Hydrogen isotope fractionation during H2/CO2 acetogenesis: hydrogen utilization efficiency and the origin of lipid-bound hydrogen. Geobiology 2, 179–188 (2004).

  73. 73.

    Sessions, A. L., Jahnke, L. L., Schimmelmann, A. & Hayes, J. M. Hydrogen isotope fractionation in lipids of the methane-oxidizing bacterium Methylococcus capsulatus. Geochim. Cosmochim. Acta 66, 3955–3969 (2002).

  74. 74.

    Lawrence, A. D. et al. Construction of fluorescent analogs to follow the uptake and distribution of cobalamin (vitamin B12) in bacteria, worms, and plants. Cell Chem. Biol. 25, 941–951.e6 (2018).

  75. 75.

    Kuru, E. et al. In situ probing of newly synthesized peptidoglycan in live bacteria with fluorescent d-amino acids. Angew. Chem. Int. Ed. Engl. 51, 12519–12523 (2012).

  76. 76.

    Tao, J. et al. Use of a fluorescent analog of glucose (2-NBDG) to identify uncultured rumen bacteria that take up glucose. Appl. Env. Microbiol. 85, e03018–18 (2019).

  77. 77.

    Martinez-Garcia, M. et al. Capturing single cell genomes of active polysaccharide degraders: an unexpected contribution of verrucomicrobia. PLoS One 7, e35314 (2012).

  78. 78.

    Doud, D. F. R. et al. Function-driven single-cell genomics uncovers cellulose-degrading bacteria from the rare biosphere. ISME J. (2019). This study uses fluorescent substrate analogue probing, FACS of active cells and mini-metagenomics to identify thermophilic cellulose degraders.

  79. 79.

    Rosnow, J. J. et al. A cobalamin activity-based probe enables microbial cell growth and finds new cobalamin–protein interactions across domains. Appl. Environ. Microbiol. 84, e00955–18 (2018).

  80. 80.

    Liechti, G. W. et al. A new metabolic cell-wall labelling method reveals peptidoglycan in Chlamydia trachomatis. Nature 506, 507–510 (2014).

  81. 81.

    Best, M. D. Click chemistry and bioorthogonal reactions: unprecedented selectivity in the labeling of biological molecules. Biochemistry 48, 6571–6584 (2009).

  82. 82.

    Devaraj, N. K. The future of bioorthogonal chemistry. ACS Cent. Sci. 4, 952–959 (2018).

  83. 83.

    Sletten, E. M. & Bertozzi, C. R. Bioorthogonal chemistry: fishing for selectivity in a sea of functionality. Angew. Chem. Int. Ed. Engl. 48, 6974–6998 (2009).

  84. 84.

    Griffin, R. J. The medicinal chemistry of the azido group. Prog. Med. Chem. 31, 121–232 (1994).

  85. 85.

    Marchand, J. A. et al. Discovery of a pathway for terminal-alkyne amino acid biosynthesis. Nature 567, 420–424 (2019).

  86. 86.

    Zhu, X., Liu, J. & Zhang, W. De novo biosynthesis of terminal alkyne-labeled natural products. Nat. Chem. Biol. 11, 115–120 (2015).

  87. 87.

    Zhu, X. & Zhang, W. Terminal alkyne biosynthesis in marine microbes. Methods Enzymol. 604, 89–112 (2018).

  88. 88.

    Wei, L. et al. Live-cell imaging of alkyne-tagged small biomolecules by stimulated Raman scattering. Nat. Methods 11, 410–412 (2014). This study combines non-canonical substrate analogue probing and Raman microspectroscopy to visualize alkyne-containing nucleoside, amino acid and fatty acid analogues in various eukaryotic cells.

  89. 89.

    Sinai, L., Rosenberg, A., Smith, Y., Segev, E. & Ben-Yehuda, S. The molecular timeline of a reviving bacterial spore. Mol. Cell 57, 695–707 (2015).

  90. 90.

    Shieh, P., Siegrist, M. S., Cullen, A. J. & Bertozzi, C. R. Imaging bacterial peptidoglycan with near-infrared fluorogenic azide probes. Proc. Natl Acad. Sci. USA 111, 5456–5461 (2014).

  91. 91.

    Bagert, J. D. et al. Time-resolved proteomic analysis of quorum sensing in Vibrio harveyi. Chem. Sci. 7, 1797–1806 (2016).

  92. 92.

    Babin, B. M. et al. SutA is a bacterial transcription factor expressed during slow growth in Pseudomonas aeruginosa. Proc. Natl Acad. Sci. USA 113, E597–E605 (2016).

  93. 93.

    Mahdavi, A. et al. Engineered aminoacyl-tRNA synthetase for cell-selective analysis of mammalian protein synthesis. J. Am. Chem. Soc. 138, 4278–4281 (2016).

  94. 94.

    Glenn, W. S. et al. BONCAT enables time-resolved analysis of protein synthesis in native plant tissue. Plant. Physiol. 173, 1543–1553 (2017).

  95. 95.

    Calve, S., Witten, A. J., Ocken, A. R. & Kinzer-Ursem, T. L. Incorporation of non-canonical amino acids into the developing murine proteome. Sci. Rep. 6, 32377 (2016).

  96. 96.

    Yuet, K. P. et al. Cell-specific proteomic analysis in caenorhabditis elegans. Proc. Natl Acad. Sci. USA 112, 2705–2710 (2015).

  97. 97.

    Dieterich, D. C. et al. In situ visualization and dynamics of newly synthesized proteins in rat hippocampal neurons. Nat. Neurosci. 13, 897–905 (2010).

  98. 98.

    Taymaz-Nikerel, H., Borujeni, A. E., Verheijen, P. J., Heijnen, J. J. & van Gulik, W. M. Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry. Biotechnol. Bioeng. 107, 369–381 (2010).

  99. 99.

    Beck, A. E., Hunt, K. A. & Carlson, R. P. Measuring cellular biomass composition for computational biology applications. Processes 6, 38 (2018).

  100. 100.

    Zavrel, T. et al. Quantitative insights into the cyanobacterial cell economy. eLife 8, e42508 (2019).

  101. 101.

    Beatty, K. E. et al. Fluorescence visualization of newly synthesized proteins in mammalian cells. Angew. Chem. Int. Ed. Engl. 45, 7364–7367 (2006).

  102. 102.

    Dieterich, D. C., Link, A. J., Graumann, J., Tirrell, D. A. & Schuman, E. M. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc. Natl Acad. Sci. USA 103, 9482–9487 (2006).

  103. 103.

    Kiick, K. L., Saxon, E., Tirrell, D. A. & Bertozzi, C. R. Incorporation of azides into recombinant proteins for chemoselective modification by the Staudinger ligation. Proc. Natl Acad. Sci. USA 99, 19–24 (2002).

  104. 104.

    Lang, K. & Chin, J. W. Cellular incorporation of unnatural amino acids and bioorthogonal labeling of proteins. Chem. Rev. 114, 4764–4806 (2014).

  105. 105.

    Ngo, J. T. & Tirrell, D. A. Noncanonical amino acids in the interrogation of cellular protein synthesis. Acc. Chem. Res. 44, 677–685 (2011).

  106. 106.

    Chakrabarti, S., Liehl, P., Buchon, N. & Lemaitre, B. Infection-induced host translational blockage inhibits immune responses and epithelial renewal in the drosophila gut. Cell Host Microbe 12, 60–70 (2012).

  107. 107.

    Sherratt, A. R. et al. Rapid screening and identification of living pathogenic organisms via optimized bioorthogonal non-canonical amino acid tagging. Cell Chem. Biol. 24, 1048–1055.e3 (2017).

  108. 108.

    Mahdavi, A. et al. Identification of secreted bacterial proteins by noncanonical amino acid tagging. Proc. Natl Acad. Sci. USA 111, 433–438 (2014).

  109. 109.

    Ouellette, S. P., Dorsey, F. C., Moshiach, S., Cleveland, J. L. & Carabeo, R. A. Chlamydia species-dependent differences in the growth requirement for lysosomes. PLoS One 6, e16783 (2011).

  110. 110.

    Siegrist, M. S. et al. d-Amino acid chemical reporters reveal peptidoglycan dynamics of an intracellular pathogen. ACS Chem. Biol. 8, 500–505 (2013).

  111. 111.

    Hatzenpichler, R. et al. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Env. Microbiol. 16, 2568–2590 (2014). This study for the first time uses bioorthogonal labelling and click chemistry on complex, multispecies samples and demonstrates that BONCAT–FISH can be used to link the identify and in situ function of uncultured microorganisms.

  112. 112.

    Samo, T. J., Smriga, S., Malfatti, F., Sherwood, B. P. & Azam, F. Broad distribution and high proportion of protein synthesis active marine bacteria revealed by click chemistry at the single cell level. Front. Mar. Sci. 1, 1–18 (2014).

  113. 113.

    Leizeaga, A., Estrany, M., Forn, I. & Sebastian, M. Using click-chemistry for visualizing in situ changes of translational activity in planktonic marine bacteria. Front. Microbiol. 8, 2360 (2017).

  114. 114.

    Sebastian, M. et al. High growth potential of long-term starved deep ocean opportunistic heterotrophic bacteria. Front. Microbiol. 10, 760 (2019).

  115. 115.

    Kjeldsen, K. U. et al. On the evolution and physiology of cable bacteria. Proc. Natl Acad. Sci. USA 116, 19116–19125 (2019).

  116. 116.

    Geva-Zatorsky, N. et al. In vivo imaging and tracking of host–microbiota interactions via metabolic labeling of gut anaerobic bacteria. Nat. Med. 21, 1091–1100 (2015).

  117. 117.

    Hatzenpichler, R. & Orphan, V. J. in Hydrocarbon and Lipid Microbiology Protocols Vol. 7: Single-cell and single-molecule methods (ed T. J. McGenity) 145–157 (Springer, 2015).

  118. 118.

    Pasulka, A. L. et al. Interrogating marine virus–host interactions and elemental transfer with BONCAT and nanoSIMS-based methods. Env. Microbiol. 20, 671–692 (2018). This study applies BONCAT to microscopically quantify viral production rates in model systems and seawater and uses nanoSIMS to quantify carbon and nitrogen transfer rates between viruses and their microbial hosts.

  119. 119.

    Muller, T. G., Sakin, V. & Muller, B. A spotlight on viruses — application of click chemistry to visualize virus–cell interactions. Molecules 24, 1–30 (2019).

  120. 120.

    Bagert, J. D. et al. Quantitative, time-resolved proteomic analysis by combining bioorthogonal noncanonical amino acid tagging and pulsed stable isotope labeling by amino acids in cell culture. Mol. Cell Proteom. 13, 1352–1358 (2014).

  121. 121.

    Lehner, F. et al. The impact of azidohomoalanine incorporation on protein structure and ligand binding. Chembiochem 18, 2340–2350 (2017).

  122. 122.

    Bennett, B. D. et al. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 5, 593–599 (2009).

  123. 123.

    Neufeld, J. D. et al. DNA stable-isotope probing. Nat. Protoc. 2, 860–866 (2007).

  124. 124.

    Urbach, E., Vergin, K. L. & Giovannoni, S. J. Immunochemical detection and isolation of DNA from metabolically active bacteria. Appl. Env. Microbiol. 65, 1207–1213 (1999).

  125. 125.

    Papp, K. et al. Quantitative stable isotope probing with H2 18O reveals that most bacterial taxa in soil synthesize new ribosomal RNA. ISME J. 12, 3043–3045 (2018).

  126. 126.

    Pernthaler, A., Pernthaler, J., Schattenhofer, M. & Amann, R. Identification of DNA-synthesizing bacterial cells in coastal North Sea plankton. Appl. Env. Microbiol. 68, 5728–5736 (2002).

  127. 127.

    Hamasaki, K. Comparison of bromodeoxyuridine immunoassay with tritiated thymidine radioassay for measuring bacterial productivity in oceanic waters. J. Oceanography 62, 793–799 (2006).

  128. 128.

    Olaniyi, O. O., Yang, K., Zhu, Y. G. & Cui, L. Heavy water-labeled Raman spectroscopy reveals carboxymethylcellulose-degrading bacteria and degradation activity at the single-cell level. Appl. Microbiol. Biotechnol. 103, 1455–1464 (2019).

  129. 129.

    Smriga, S., Samo, T. J., Malfatti, F., Villareal, J. & Azam, F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat. Microb. Ecol. 72, 269–280 (2014).

  130. 130.

    Jao, C. Y. & Salic, A. Exploring RNA ranscription and turnover in vivo by using click chemistry. Proc. Natl Acad. Sci. USA 105, 15779–15784 (2008).

  131. 131.

    Kho, Y. et al. A tagging-via-substrate technology for detection and proteomics of farnesylated proteins. Proc. Natl Acad. Sci. USA 101, 12479–12484 (2004).

  132. 132.

    Neef, A. B. & Schultz, C. Selective fluorescence labeling of lipids in living cells. Angew. Chem. Int. Ed. Engl. 48, 1498–1500 (2009).

  133. 133.

    Garcia-Heredia, A. et al. Peptidoglycan precursor synthesis along the sidewall of pole-growing mycobacteria. eLife 7, e37243 (2018).

  134. 134.

    Bublitz, D. C. et al. Peptidoglycan production by an insect-bacterial mosaic. Cell 179, 703–712.e7 (2019).

  135. 135.

    Saxon, E. et al. Investigating cellular metabolism of synthetic azidosugars with the Staudinger ligation. J. Am. Chem. Soc. 124, 14893–14902 (2002).

  136. 136.

    Siegrist, M. S., Swarts, B. M., Fox, D. M., Lim, S. A. & Bertozzi, C. R. Illumination of growth, division and secretion by metabolic labeling of the bacterial cell surface. FEMS Microbiol. Rev. 39, 184–202 (2015).

  137. 137.

    Ohno, S. et al. A method for evaluating the host range of bacteriophages using phages fluorescently labeled with 5-ethynyl-2′-deoxyuridine (EdU). Appl. Microbiol. Biotechnol. 95, 777–788 (2012).

  138. 138.

    Liu, Y. et al. Advancing understanding of microbial bioenergy conversion processes by activity-based protein profiling. Biotechnol. Biofuels 8, 156 (2015).

  139. 139.

    Jehmlich, N., Vogt, C., Lunsmann, V., Richnow, H. H. & von Bergen, M. Protein–SIP in environmental studies. Curr. Opin. Biotechnol. 41, 26–33 (2016).

  140. 140.

    Sadler, N. C. & Wright, A. T. Activity-based protein profiling of microbes. Curr. Opin. Chem. Biol. 24, 139–144 (2015).

  141. 141.

    Whidbey, C. & Wright, A. T. Activity-based protein profiling-enabling multimodal functional studies of microbial communities. Curr. Top. Microbiol. Immunol. 420, 1–21 (2019).

  142. 142.

    Willems, L. I., Overkleeft, H. S. & van Kasteren, S. I. Current developments in activity-based protein profiling. Bioconjug. Chem. 25, 1181–1191 (2014).

  143. 143.

    Whidbey, C. et al. A probe-enabled approach for the selective isolation and characterization of functionally active subpopulations in the gut microbiome. J. Am. Chem. Soc. 141, 42–47 (2019). This study demonstrates the high potential of ABPP for single cell physiology studies. ABPP is combined with FACS and 16S rRNA gene sequencing to identify β-glucuronidase active members of the mouse gut microbiome.

  144. 144.

    Flemming, H. C. & Wingender, J. The biofilm matrix. Nat. Rev. Microbiol. 8, 623–633 (2010).

  145. 145.

    Schlafer, S. & Meyer, R. L. Confocal microscopy imaging of the biofilm matrix. J. Microbiol. Methods 138, 50–59 (2017).

  146. 146.

    Kehe, J. et al. Massively parallel screening of synthetic microbial communities. Proc. Natl Acad. Sci. USA 116, 12804–12809 (2019).

  147. 147.

    Lan, F., Demaree, B., Ahmed, N. & Abate, A. R. Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding. Nat. Biotechnol. 35, 640–646 (2017).

  148. 148.

    Spencer, S. J. et al. Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers. ISME J. 10, 427–436 (2016).

  149. 149.

    Terekhov, S. S. et al. Ultrahigh-throughput functional profiling of microbiota communities. Proc. Natl Acad. Sci. USA 115, 9551–9556 (2018).

  150. 150.

    Ando, J., Palonpon, A. F., Sodeoka, M. & Fujita, K. High-speed Raman imaging of cellular processes. Curr. Opin. Chem. Biol. 33, 16–24 (2016).

  151. 151.

    Chisanga, M., Muhamadali, H., Ellis, D. I. & Goodacre, R. Surface-enhanced Raman scattering (SERS) in microbiology: illumination and enhancement of the microbial world. Appl. Spectrosc. 72, 987–1000 (2018).

  152. 152.

    Ivleva, N. P., Kubryk, P. & Niessner, R. Raman microspectroscopy, surface-enhanced Raman scattering microspectroscopy, and stable-isotope Raman microspectroscopy for biofilm characterization. Anal. Bioanal. Chem. 409, 4353–4375 (2017). This review discusses the potential of advanced Raman microspectroscopy application, with a focus on the characterization of the extracellular polymeric substances and cells in microbial biofilms.

  153. 153.

    Cicerone, M. Molecular imaging with CARS micro-spectroscopy. Curr. Opin. Chem. Biol. 33, 179–185 (2016).

  154. 154.

    Camp, C. H. & Cicerone, M. T. Chemically sensitive bioimaging with coherent Raman scattering. Nat. Photonics 9, 295–305 (2015).

  155. 155.

    Opilik, L., Schmid, T. & Zenobi, R. Modern Raman imaging: vibrational spectroscopy on the micrometer and nanometer scales. Annu. Rev. Anal. Chem. 6, 379–398 (2013).

  156. 156.

    Cui, L., Yang, K., Zhou, G., Huang, W. E. & Zhu, Y. G. Surface-enhanced Raman spectroscopy combined with stable isotope probing to monitor nitrogen assimilation at both bulk and single-cell level. Anal. Chem. 89, 5793–5800 (2017).

  157. 157.

    Ivleva, N. P., Wagner, M., Horn, H., Niessner, R. & Haisch, C. Raman microscopy and surface-enhanced Raman scattering (SERS) for in situ analysis of biofilms. J. Biophotonics 3, 548–556 (2010).

  158. 158.

    Kubryk, P. et al. Exploring the potential of stable isotope (resonance) Raman microspectroscopy and surface-enhanced Raman scattering for the analysis of microorganisms at single cell level. Anal. Chem. 87, 6622–6630 (2015).

  159. 159.

    Weiss, R. et al. Surface-enhanced Raman spectroscopy of microorganisms: limitations and applicability on the single-cell level. Analyst 144, 943–953 (2019).

  160. 160.

    Kolb, H. C., Finn, M. G. & Sharpless, K. B. Click chemistry: diverse chemical function from a few good reactions. Angew. Chem. Int. Ed. Engl. 40, 2004–2021 (2001).

  161. 161.

    Moses, J. E. & Moorhouse, A. D. The growing applications of click chemistry. Chem. Soc. Rev. 36, 1249–1262 (2007).

  162. 162.

    Codelli, J. A., Baskin, J. M., Agard, N. J. & Bertozzi, C. R. Second-generation difluorinated cyclooctynes for copper-free click chemistry. J. Am. Chem. Soc. 130, 11486–11493 (2008).

  163. 163.

    Agard, N. J., Prescher, J. A. & Bertozzi, C. R. A strain-promoted [3+2] azide–alkyne cycloaddition for covalent modification of biomolecules in living systems. J. Am. Chem. Soc. 126, 15046–15047 (2004).

  164. 164.

    Uttamapinant, C. et al. Fast, cell-compatible click chemistry with copper-chelating azides for biomolecular labeling. Angew. Chem. Int. Ed. Engl. 51, 5852–5856 (2012).

  165. 165.

    Shieh, P. et al. CalFluors: a universal motif for fluorogenic azide probes across the visible spectrum. J. Am. Chem. Soc. 137, 7145–7151 (2015).

  166. 166.

    Zimmermann, M. et al. Phenotypic heterogeneity in metabolic traits among single cells of a rare bacterial species in its natural environment quantified with a combination of flow cell sorting and nanoSIMS. Front. Microbiol. 6, 243 (2015).

  167. 167.

    Netuschil, L., Auschill, T. M., Sculean, A. & Arweiler, N. B. Confusion over live/dead stainings for the detection of vital microorganisms in oral biofilms — which stain is suitable? BMC Oral Health 14, 1–12 (2014). This review provides an in-depth discussion of the shortcomings of viability and vitality stains.

  168. 168.

    Emerson, J. B. et al. Schrodinger’s microbes: tools for distinguishing the living from the dead in microbial ecosystems. Microbiome 5, 86 (2017).

  169. 169.

    Konopka, M. C. et al. Respiration response imaging for real-time detection of microbial function at the single-cell level. Appl. Env. Microbiol. 77, 67–72 (2011).

  170. 170.

    Ullrich, S., Karrasch, B., Hoppe, H.-G., Jeskulke, K. & Mehrens, M. Toxic effects on bacterial metabolism of the redox dye 5-cyano-2,3-ditolyl tetrazolium chloride. Appl. Env. Microbiol. 62, 4587–4593 (1996).

  171. 171.

    Hatzinger, P. B., Palmer, P., Smith, R. L., Penarrieta, C. T. & Yoshinari, T. Applicability of tetrazolium salts for the measurement of respiratory activity and viability of groundwater bacteria. J. Microbiol. Methods 52, 47–58 (2003).

  172. 172.

    Karner, M. & Fuhrman, J. A. Determination of active marine bacterioplankton: a comparison of universal 16s rRNA probes, autoradiography, and nucleoid staining. Appl. Env. Microbiol. 63, 1208–1213 (1997).

  173. 173.

    Servais, P., Agogue, H., Courties, C., Joux, F. & Lebaron, P. Are the actively respiring cells (CTC+) those responsible for bacterial production in aquatic environments? FEMS Microbiol. Ecol. 35, 171–179 (2001).

  174. 174.

    Nielsen, J. L., Aquino de Muro, M. & Nielsen, P. H. Evaluation of the redox dye 5-cyano-2,3-tolyl-tetrazolium chloride for activity studies by simultaneous use of microautoradiography and fluorescence in situ hybridization. Appl. Env. Microbiol. 69, 641–643 (2003).

  175. 175.

    Stiefel, P., Schmidt-Emrich, S., Maniura-Weber, K. & Ren, Q. Critical aspects of using bacterial cell viability assays with the fluorophores SYTO9 and propidium iodide. BMC Microbiol. 15, 1–9 (2015).

  176. 176.

    Berney, M., Hammes, F., Bosshard, F., Weilenmann, H. U. & Egli, T. Assessment and interpretation of bacterial viability by using the LIVE/DEAD BacLight Kit in combination with flow cytometry. Appl. Env. Microbiol. 73, 3283–3290 (2007).

  177. 177.

    Nocker, A., Cheung, C. Y. & Camper, A. K. Comparison of propidium monoazide with ethidium monoazide for differentiation of live vs. dead bacteria by selective removal of DNA from dead cells. J. Microbiol. Methods 67, 310–320 (2006).

  178. 178.

    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).

  179. 179.

    Kolinko, S. et al. Single-cell analysis reveals a novel uncultivated magnetotactic bacterium within the candidate division OP3. Env. Microbiol. 14, 1709–1721 (2012).

  180. 180.

    Spang, A. et al. The genome of the ammonia-oxidizing Candidatus Nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Env. Microbiol. 14, 3122–3145 (2012).

  181. 181.

    Chan, J. W. et al. Reagentless identification of single bacterial spores in aqueous solution by confocal laser tweezers Raman spectroscopy. Anal. Chem. 76, 599–603 (2004).

  182. 182.

    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).

  183. 183.

    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. Env. Sci. Technol. 46, 5010–5017 (2012).

  184. 184.

    Berg, J. S., Schwedt, A., Kreutzmann, A.-C., Kuypers, M. M. M. & Milucka, J. Polysulfides as intermediates in the oxidation of sulfide to sulfate by Beggiatoa spp. Appl. Env. Microbiol. 80, 629–636 (2014).

  185. 185.

    Bjerg, J. T. et al. Long-distance electron transport in individual, living cable bacteria. Proc. Natl Acad. Sci. USA 115, 5786–5791 (2018). This study uses Raman microspectroscopy to visualize gradients in cytochrome redox states along living cable bacteria.

  186. 186.

    Eder, S. H., Gigler, A. M., Hanzlik, M. & Winklhofer, M. Sub-micrometer-scale mapping of magnetite crystals and sulfur globules in magnetotactic bacteria using confocal Raman micro-spectrometry. PLoS One 9, e107356 (2014).

  187. 187.

    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).

  188. 188.

    Taubert, M. et al. Tracking active groundwater microbes with D2O labelling to understand their ecosystem function. Env. Microbiol. 20, 369–384 (2018).

Download references


The authors are grateful to A. Kohtz for generating the Raman data depicted in Fig. 3. They thank R. Gerlach and H. Smith (Montana State University), J. Hemp (University of Utah), members of the Hatzenpichler laboratory – A. Kohtz, M. Lynes and N. Reichart – and the three reviewers for critical comments that improved the manuscript. Next-generation physiology research in the Hatzenpichler laboratory is supported through grants by the Gordon and Betty Moore Foundation (GBMF5999) and the National Science Foundation (MCB award 1817428 and RII Track-2 FEC award 1736255), as well as an Early Career Fellowship by the National Aeronautics and Space Administration to R.H. (80NSSC19K0449). Montana State University’s Confocal Raman microscope was acquired with support by the National Science Foundation (DBI-1726561) and the M.J. Murdock Charitable Trust (SR-2017331).

Author information

R.H. designed the concept for this Review. All authors wrote the manuscript.

Correspondence to Roland Hatzenpichler.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Microbiology thanks Wei Huang, Aaron Wright and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.



Synonymous with the microbial community; all of the microscopic organisms, including archaea, bacteria, unicellular eukaryotes and their viruses, within a sample.


An observable characteristic of an organism that is manifested on a molecular, cellular or population level. A phenotype of a cell varies over time and with changing physicochemical conditions.


Molecules or chemical motifs that can be specifically traced within the cell; ideally, the reporter group is entirely absent from the target cell under natural conditions.


The sets of genes or the entire genome of an organism.


The functioning of a cell in its native habitat under a given set of conditions, including interactions with other cells and the abiotic environment.


The random shotgun sequencing of DNA from a sample containing more than one genotype.

Single cell genomics

An individual cell is separated from a microbiome and its genome is amplified and sequenced.


(MAR). A method that detects uptake of radioactively labelled substrates into cells through formation of silver grains after exposure to a photographic emulsion. MAR is limited in its widespread application because of its dependency on isotopes with a suitable half-life, its low throughput and its destructive nature.

Nano-scale secondary ion mass spectroscopy

(nanoSIMS). A technique that expels secondary ions from a sample surface through a focused ion beam in high vacuum, extracts them by an electric field and analyses them by time-of-flight mass spectrometry. nanoSIMS provides unrivalled sensitivity and spatial resolution but has very low throughput and destroys the sample.

Quantitative stable isotope probing

(qSIP). A technique that separates isotopically heavy biomolecules (for example, 13C-containing DNA) from unlabelled molecules by buoyant density centrifugation. By collecting multiple density fractions and determining their taxonomic and genetic make-up, taxon-specific isotope enrichments can be calculated.

Next-generation physiology

Any approach enabling study into the physiology of an individual cell in a microbiome in a non-destructive way, thus enabling physical separation of this cell based on its phenotype for further downstream applications.

Click chemistry

A summary term for a range of reactions with a high thermodynamic driving force and extremely high yields and reaction efficiencies. The term is often used synonymously for azide–alkyne cycloaddition reactions, which are the most commonly used type of click chemistry reactions in biology.

Raman-activated cell sorting

(RACS). A set of techniques that combines Raman spectral acquisition with single cell separation.

Fluorescence in situ hybridization

(FISH). A technique that uses single-stranded DNA probes and fluorescence microscopy to visualize cells based on their taxonomic identity (ribosomal RNA FISH) or gene expression (mRNA FISH).

Intact cells

Cells that have not been exposed to a chemical fixative (such as formaldehyde or ethanol) that might interfere with downstream analyses (such as cultivation or DNA sequencing).

Metabolically active

A cell carrying out specific metabolic function (such as redox activity or activity of a specific enzyme); this term is agnostic about whether this activity leads to the build-up of new biomass (that is, anabolic activity).

Anabolically active

Performing de novo synthesis of specific macromolecules (for example, DNA, RNA, proteins and lipids).

Silent region

The area in the Raman spectrum of a cell that is free of background interference from cellular vibrations (~1,800–2,700 cm–1).


A molecule carrying a functional group that is amenable to azide–alkyne click chemistry.

Bioorthogonal reaction

A reaction that does not interfere with biological processes; it can be used to label a cell or molecule with a reporter.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hatzenpichler, R., Krukenberg, V., Spietz, R.L. et al. Next-generation physiology approaches to study microbiome function at single cell level. Nat Rev Microbiol 18, 241–256 (2020).

Download citation

Further reading