Raman microspectroscopy offers microbiologists a rapid and non-destructive technique to assess the chemical composition of individual live microorganisms in near real time. In this Primer, we outline the methodology and potential for its application to microbiology. We describe the technical aspects of Raman analyses and practical approaches to apply this method to microbiological questions. We discuss recent and potential future applications to determine the composition and distribution of microbial metabolites down to subcellular scale; to investigate the host–microorganism, cell–cell and cell–environment molecular exchanges that underlie the structure of microbial ecosystems from the ocean to the human gut microbiomes; and to interrogate the microbial diversity of functional roles in environmental and industrial processes — key themes in modern microbiology. We describe the current technical limitations of Raman microspectroscopy for investigation of microorganisms and approaches to minimize or address them. Recent technological innovations in Raman microspectroscopy will further reinforce the power and capacity of this method for broader adoptions in microbiology, allowing microbiologists to deepen their understanding of the microbial ecology of complex communities at nearly any scale of interest.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Communications Open Access 15 December 2022
Single-cell Raman-activated sorting and cultivation (scRACS-Culture) for assessing and mining in situ phosphate-solubilizing microbes from nature
ISME Communications Open Access 30 October 2022
BIOspektrum Open Access 11 October 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 1 digital issues and online access to articles
$99.00 per year
only $99.00 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Raman, C. V. & Krishnan, K. S. A new type of secondary radiation. Nature 121, 501–502 (1928).
Baker, M. J. et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 9, 1771–1791 (2014).
Movasaghi, Z., Rehman, S. & Rehman, I. U. Fourier transform infrared (FTIR) spectroscopy of biological tissues. Appl. Spectrosc. Rev. 43, 134–179 (2008).
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).
Li, T. et al. Simultaneous analysis of microbial identity and function using NanoSIMS. Environ. Microbiol. 10, 580–588 (2008).
Nuñez, J., Renslow, R., Cliff, J. B. & Anderton, C. R. NanoSIMS for biological applications: current practices and analyses. Biointerphases 13, 03B301 (2018).
Musat, N., Foster, R., Vagner, T., Adam, B. & Kuypers, M. M. M. Detecting metabolic activities in single cells, with emphasis on nanoSIMS. FEMS Microbiol. Rev. 36, 486–511 (2012).
Weissenberger, G., Henderikx, R. J. M. & Peters, P. J. Understanding the invisible hands of sample preparation for cryo-EM. Nat. Methods 18, 463–471 (2021).
Oikonomou, C. M., Chang, Y.-W. & Jensen, G. J. A new view into prokaryotic cell biology from electron cryotomography. Nat. Rev. Microbiol. 14, 205–220 (2016).
Wakisaka, Y. et al. Probing the metabolic heterogeneity of live Euglena gracilis with stimulated Raman scattering microscopy. Nat. Microbiol. 1, 16124 (2016).
Barletta, R. E., Krause, J. W., Goodie, T. & El Sabae, H. The direct measurement of intracellular pigments in phytoplankton using resonance Raman spectroscopy. Mar. Chem. 176, 164–173 (2015).
Moudříková, Š. et al. Raman and fluorescence microscopy sensing energy-transducing and energy-storing structures in microalgae. Algal Res. 16, 224–232 (2016).
Heraud, P., Beardall, J., McNaughton, D. & Wood, B. R. In vivo prediction of the nutrient status of individual microalgal cells using Raman microspectroscopy. FEMS Microbiol. Lett. 275, 24–30 (2007).
Rüger, J. et al. Assessment of growth phases of the diatom Ditylum brightwellii by FT-IR and Raman spectroscopy. Algal Res. 19, 246–252 (2016).
Alexandre, M. T. A. et al. Probing the carotenoid content of intact Cyclotella cells by resonance Raman spectroscopy. Photosynth. Res. 119, 273–281 (2014).
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).
Büchel, C. How diatoms harvest light. Science 365, 447–448 (2019).
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).
Shao, F. & Zenobi, R. Tip-enhanced Raman spectroscopy: principles, practice, and applications to nanospectroscopic imaging of 2D materials. Anal. Bioanal. Chem. 411, 37–61 (2019).
Yeo, B.-S., Stadler, J., Schmid, T., Zenobi, R. & Zhang, W. Tip-enhanced Raman Spectroscopy–Its status, challenges and future directions. Chem. Phys. Lett. 472, 1–13 (2009).
Mosca, S., Conti, C., Stone, N. & Matousek, P. Spatially offset Raman spectroscopy. Nat. Rev. Methods Prim. 1, 21 (2021).
Sapers, H. M. et al. The cell and the sum of its parts: patterns of complexity in biosignatures as revealed by deep UV Raman spectroscopy. Front. Microbiol. 10, 679 (2019).
Nelson, W. H., Manoharan, R. & Sperry, J. F. UV resonance Raman studies of bacteria. Appl. Spectrosc. Rev. 27, 67–124 (1992).
Wu, Q. et al. UV Raman spectral intensities of E. coli and other bacteria excited at 228.9, 244.0, and 248.2 nm. Anal. Chem. 73, 3432–3440 (2001).
Jarvis, R. M. & Goodacre, R. Ultra-violet resonance Raman spectroscopy for the rapid discrimination of urinary tract infection bacteria. FEMS Microbiol. Lett. 232, 127–132 (2004).
Žukovskaja, O. et al. UV-Raman spectroscopic identification of fungal spores important for respiratory diseases. Anal. Chem. 90, 8912–8918 (2018).
Boustany, N. N., Manoharan, R., Dasari, R. R. & Feld, M. S. Ultraviolet resonance Raman spectroscopy of bulk and microscopic human colon tissue. Appl. Spectrosc. 54, 24–30 (2000).
Kumamoto, Y., Taguchi, A., Smith, N. I. & Kawata, S. Deep ultraviolet resonant Raman imaging of a cell. J. Biomed. Opt. 17, 076001 (2012).
Kneipp, J., Kneipp, H. & Kneipp, K. SERS — a single-molecule and nanoscale tool for bioanalytics. Chem. Soc. Rev. 37, 1052–1060 (2008).
Langer, J. et al. Present and future of surface-enhanced Raman scattering. ACS Nano 14, 28–117 (2020).
Lussier, F. et al. Dynamic-SERS optophysiology: a nanosensor for monitoring cell secretion events. Nano Lett. 16, 3866–3871 (2016).
Caprettini, V. et al. Enhanced Raman investigation of cell membrane and intracellular compounds by 3D plasmonic nanoelectrode arrays. Adv. Sci. 5, 1800560 (2018).
Efrima, S. & Bronk, B. V. Silver colloids impregnating or coating bacteria. J. Phys. Chem. B 102, 5947–5950 (1998).
Zhou, H. et al. SERS detection of bacteria in water by in situ coating with Ag nanoparticles. Anal. Chem. 86, 1525–1533 (2014).
Drescher, D., Traub, H., Büchner, T., Jakubowski, N. & Kneipp, J. Properties of in situ generated gold nanoparticles in the cellular context. Nanoscale 9, 11647–11656 (2017). Demonstration of the fabrication of SERS substrates in a cellular environment in situ.
Palanco, M. E. et al. Templated green synthesis of plasmonic silver nanoparticles in onion epidermal cells suitable for surface-enhanced Raman and hyper-Raman scattering. Beilstein J. Nanotechnol. 7, 834–840 (2016).
Weiss, R. et al. Surface-enhanced Raman spectroscopy of microorganisms: limitations and applicability on the single-cell level. Analyst 144, 943–953 (2019).
Premasiri, W. R. et al. The biochemical origins of the surface-enhanced Raman spectra of bacteria: a metabolomics profiling by SERS. Anal. Bioanal. Chem. 408, 4631–4647 (2016).
Wang, Y., Yan, B. & Chen, L. SERS tags: novel optical nanoprobes for bioanalysis. Chem. Rev. 113, 1391–1428 (2013).
Kelley, A. M. Hyper-Raman scattering by molecular vibrations. Annu. Rev. Phys. Chem. 61, 41–61 (2010).
Helmchen, F. & Denk, W. Deep tissue two-photon microscopy. Nat. Methods 2, 932–940 (2005).
Madzharova, F., Heiner, Z. & Kneipp, J. Surface enhanced hyper Raman scattering (SEHRS) and its applications. Chem. Soc. Rev. 46, 3980–3999 (2017).
Kneipp, J., Kneipp, H. & Kneipp, K. Two-photon vibrational spectroscopy for biosciences based on surface-enhanced hyper-Raman scattering. Proc. Natl Acad. Sci. USA 103, 17149–17153 (2006). Application of HRS for the detection of complementary peaks of biomolecules and cells.
Heiner, Z., Gühlke, M., Živanović, V., Madzharova, F. & Kneipp, J. Surface-enhanced hyper Raman hyperspectral imaging and probing in animal cells. Nanoscale 9, 8024–8032 (2017).
Zhang, C., Zhang, D. & Cheng, J.-X. Coherent Raman scattering microscopy in biology and medicine. Annu. Rev. Biomed. Eng. 17, 415–445 (2015).
Min, W., Freudiger, C. W., Lu, S. & Xie, X. S. Coherent nonlinear optical imaging: Beyond fluorescence microscopy. Annu. Rev. Phys. Chem. 62, 507–530 (2011).
Zhang, D., Wang, P., Slipchenko, M. N. & Cheng, J.-X. Fast vibrational imaging of single cells and tissues by stimulated Raman scattering microscopy. Acc. Chem. Res. 47, 2282–2290 (2014).
Freudiger, C. W. et al. Label-free biomedical imaging with high sensitivity by stimulated Raman scattering microscopy. Science 322, 1857–1861 (2008).
Yue, S. & Cheng, J.-X. Deciphering single cell metabolism by coherent Raman scattering microscopy. Curr. Opin. Chem. Biol. 33, 46–57 (2016).
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). Development of a high-throughput Raman-based flow cell counter for microalgae based on multicolour-SRS microscopy and deep learning.
De la Cadena, A., Valensise, C. M., Marangoni, M., Cerullo, G. & Polli, D. Broadband stimulated Raman scattering microscopy with wavelength-scanning detection. J. Raman Spectrosc. 51, 1951–1959 (2020).
Lu, F.-K. et al. Multicolor stimulated Raman scattering microscopy. Mol. Phys. 110, 1927–1932 (2012).
Choquette, S. J., Etz, E. S., Hurst, W. S., Blackburn, D. H. & Leigh, S. D. Relative intensity correction of Raman spectrometers: NIST SRMs 2241 through 2243 for 785 nm, 532 nm, and 488 nm/514.5 nm excitation. Appl. Spectrosc. 61, 117–129 (2007).
Sui, Z., Leong, P. P., Herman, I. P., Higashi, G. S. & Temkin, H. Raman analysis of light-emitting porous silicon. Appl. Phys. Lett. 60, 2086–2088 (1992).
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).
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).
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).
Bustamante, C. J., Chemla, Y. R., Liu, S. & Wang, M. D. Optical tweezers in single-molecule biophysics. Nat. Rev. Methods Prim. 1, 25 (2021).
Ashkin, A. & Dziedzic, J. M. Optical trapping and manipulation of viruses and bacteria. Science 235, 1517–1520 (1987).
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).
Lee, K. S. et al. Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions. Nat. Protoc. 16, 634–676 (2021).
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). First demonstration of the combination of FISH, SIP and Raman microspectroscopy.
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). Sorting of microbial cells in terms of their functional properties (phenotypes) using confocal Raman microspectroscopy, optical tweezers, SIP and microfluidics, which enables linking of cell function to their genome through downstream DNA analysis, as well as cultivation for further ecological evaluation.
Read, D. S. & Whiteley, A. S. Chemical fixation methods for Raman spectroscopy-based analysis of bacteria. J. Microbiol. Methods 109, 79–83 (2015).
García-Timermans, C. et al. Label-free Raman characterization of bacteria calls for standardized procedures. J. Microbiol. Methods 151, 69–75 (2018).
Behrendt, L. et al. PhenoChip: a single-cell phenomic platform for high-throughput photophysiological analyses of microalgae. Sci. Adv. 6, eabb2754 (2020).
Collins, D. J. et al. Two-dimensional single-cell patterning with one cell per well driven by surface acoustic waves. Nat. Commun. 6, 8686 (2015).
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). Growth rate measurements of photoautotrophic microorganisms (Synechococcus sp. and Thalassiosira pseudonana) by coupling 13C SIP and single-cell resonance Raman microspectroscopy.
Pereira, F. C. et al. Rational design of a microbial consortium of mucosal sugar utilizers reduces Clostridioides difficile colonization. Nat. Commun. 11, 5104 (2020). Application of a high-throughput optofluidic RACS platform and mini-metagenomics to identify mucosal sugar degraders within gut microbiota and use of this information to rationally design a probiotic mixture of microorganisms that could reduce pathogen colonization.
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). First study combining deuterium labelling, RACS and 16S ribosomal RNA gene sequencing, which together led to the identification of novel glucosamine- and mucin-utilizing bacteria from mouse gut microbiota.
Xu, T. et al. Phenome–genome profiling of single bacterial cell by Raman-activated gravity-driven encapsulation and sequencing. Small 16, 2001172 (2020).
Wang, X. et al. Positive dielectrophoresis-based Raman-activated droplet sorting for culture-free and label-free screening of enzyme function in vivo. Sci. Adv. 6, eabb3521 (2020).
Czamara, K. et al. Raman spectroscopy of lipids: a review. J. Raman Spectrosc. 46, 4–20 (2015).
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).
Li, M., Ashok, P. C., Dholakia, K. & Huang, W. E. Raman-activated cell counting for profiling carbon dioxide fixing microorganisms. J. Phys. Chem. A 116, 6560–6563 (2012).
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).
Li, M., Huang, W. E., Gibson, C. M., Fowler, P. W. & Jousset, A. Stable isotope probing and Raman spectroscopy for monitoring carbon flow in a food chain and revealing metabolic pathway. Anal. Chem. 85, 1642–1649 (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).
Cui, L., Butler, H. J., Martin-Hirsch, P. L. & Martin, F. L. Aluminium foil as a potential substrate for ATR-FTIR, transflection FTIR or Raman spectrochemical analysis of biological specimens. Anal. Methods 8, 481–487 (2016).
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).
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). Application of FISH–Raman to quantify the amount of polyphosphate in various microbial taxa in wastewater treatment plants, showing that Tetrasphaera have a more important role in enhanced biological phosphorus removal than previously thought.
Grosser, K. et al. Disruption-free imaging by Raman spectroscopy reveals a chemical sphere with antifouling metabolites around macroalgae. Biofouling 28, 687–696 (2012).
Gautam, R., Vanga, S., Ariese, F. & Umapathy, S. Review of multidimensional data processing approaches for Raman and infrared spectroscopy. EPJ Tech. Instrum. 2, 8 (2015).
Byrne, H. J., Knief, P., Keating, M. E. & Bonnier, F. Spectral pre and post processing for infrared and Raman spectroscopy of biological tissues and cells. Chem. Soc. Rev. 45, 1865–1878 (2016).
Savitzky, A. & Golay, M. J. E. Smoothing and differentiation of data by simplified least square procedures. Anal. Chem. 36, 1627–1639 (1964).
Schafer, R. W. What is a Savitzky-Golay filter? IEEE Signal. Process. Mag. 28, 111–117 (2011).
Quintero, L., Matthäus, C., Hunt, S. & Diem, M. Denoising of single scan Raman spectroscopy signals. Imaging, Manip., Anal., Biomol., Cells, Tissues VIII 7568, 756817 (2010).
Ehrentreich, F. & Sümmchen, L. Spike removal and denoising of Raman spectra by wavelet transform methods. Anal. Chem. 73, 4364–4373 (2001).
Ehrentreich, F. Wavelet transform applications in analytical chemistry. Anal. Bioanal. Chem. 372, 115–121 (2002).
Silveira, L., Bodanese, B., Zangaro, R. A. & Pacheco, M. T. T. Discrete wavelet transform for denoising Raman spectra of human skin tissues used in a discriminant diagnostic algorithm. Instrum. Sci. Technol. 38, 268–282 (2010).
Lieber, C. A. & Mahadevan-Jansen, A. Automated method for subtraction of fluorescence from biological Raman spectra. Appl. Spectrosc. 57, 1363–1367 (2003).
Zhao, J., Lui, H., McLean, D. I. & Zeng, H. Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy. Appl. Spectrosc. 61, 1225–1232 (2007).
Eilers, P. H. C. & Boelens, H. F. M. Baseline correction with asymmetric least squares smoothing. Leiden. Univ. Med. Cent. Rep. 1, 1–5 (2005).
Lasch, P. Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging. Chemom. Intell. Lab. Syst. 117, 100–114 (2012).
Afseth, N. K., Segtnan, V. H. & Wold, J. P. Raman spectra of biological samples: a study of preprocessing methods. Appl. Spectrosc. 60, 1358–1367 (2006).
de Groot, P. J. et al. Application of principal component analysis to detect outliers and spectral deviations in near-field surface-enhanced Raman spectra. Anal. Chim. Acta 446, 71–83 (2001).
Liu, X.-Y. et al. Spatiotemporal organization of biofilm matrix revealed by confocal Raman mapping integrated with non-negative matrix factorization analysis. Anal. Chem. 92, 707–715 (2020).
Schumacher, W., Stöckel, S., Rösch, P. & Popp, J. Improving chemometric results by optimizing the dimension reduction for Raman spectral data sets. J. Raman Spectrosc. 45, 930–940 (2014).
Shashilov, V. A., Xu, M., Ermolenkov, V. V. & Lednev, I. K. Latent variable analysis of Raman spectra for structural characterization of proteins. J. Quant. Spectrosc. Radiat. Transf. 102, 46–61 (2006).
Lee, T.-W. Independent Component Analysis: Theory and Applications (Springer, 1998).
Piraino, P., Ricciardi, A., Salzano, G., Zotta, T. & Parente, E. Use of unsupervised and supervised artificial neural networks for the identification of lactic acid bacteria on the basis of SDS-PAGE patterns of whole cell proteins. J. Microbiol. Methods 66, 336–346 (2006).
Schmid, U. et al. Gaussian mixture discriminant analysis for the single-cell differentiation of bacteria using micro-Raman spectroscopy. Chemom. Intell. Lab. Syst. 96, 159–171 (2009).
Prochazka, D. et al. Combination of laser-induced breakdown spectroscopy and Raman spectroscopy for multivariate classification of bacteria. Spectrochim. Acta B At. Spectrosc. 139, 6–12 (2018).
Kloß, S. et al. Destruction-free procedure for the isolation of bacteria from sputum samples for Raman spectroscopic analysis. Anal. Bioanal. Chem. 407, 8333–8341 (2015).
Hlaing, M. M., Dunn, M., Stoddart, P. R. & McArthur, S. L. Raman spectroscopic identification of single bacterial cells at different stages of their lifecycle. Vib. Spectrosc. 86, 81–89 (2016).
Ho, C.-S. et al. Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning. Nat. Commun. 10, 4927 (2019). Application of Raman spectroscopy and deep learning to identify 30 common bacterial pathogens with high accuracy (up to 97%).
Živanović, V. et al. Optical nanosensing of lipid accumulation due to enzyme inhibition in live cells. ACS Nano 13, 9363–9375 (2019).
van de Schoot, R. et al. Bayesian statistics and modelling. Nat. Rev. Methods Prim. 1, 1 (2021).
Rebrošová, K. et al. Rapid identification of staphylococci by Raman spectroscopy. Sci. Rep. 7, 14846 (2017).
Gaus, K. et al. Classification of lactic acid bacteria with UV-resonance Raman spectroscopy. Biopolymers 82, 286–290 (2006).
Rösch, P. et al. Chemotaxonomic identification of single bacteria by micro-Raman spectroscopy: application to clean-room-relevant biological contaminations. Appl. Environ. Microbiol. 71, 1626–1637 (2005). First study identifying single bacteria without cultivation using Raman spectroscopy.
Errington, J. Regulation of endospore formation in Bacillus subtilis. Nat. Rev. Microbiol. 1, 117–126 (2003).
Huang, S.-S. et al. Levels of Ca2+-dipicolinic acid in individual Bacillus spores determined using microfluidic Raman tweezers. J. Bacteriol. 189, 4681–4687 (2007).
Xu, J., Webb, I., Poole, P. & Huang, W. E. Label-free discrimination of rhizobial bacteroids and mutants by single-cell Raman microspectroscopy. Anal. Chem. 89, 6336–6340 (2017).
Ng, C. K. et al. Elevated intracellular cyclic-di-GMP level in Shewanella oneidensis increases expression of c-type cytochromes. Microb. Biotechnol. 13, 1904–1916 (2020).
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 (2017).
Jing, X. et al. Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean. Environ. Microbiol. 20, 2241–2255 (2018).
Song, Y. et al. Proteorhodopsin overproduction enhances the long-term viability of Escherichia coli. Appl. Environ. Microbiol. 86, e02087-19 (2020).
Carey, P. R. Biochemical Applications of Raman and Resonance Raman Spectroscopies (Academic Press, 1982).
Takano, H. The regulatory mechanism underlying light-inducible production of carotenoids in nonphototrophic bacteria. Biosci. Biotechnol. Biochem. 80, 1264–1273 (2016).
Wang, W. et al. Structural basis for blue-green light harvesting and energy dissipation in diatoms. Science 363, eaav0365 (2019).
Yakubovskaya, E., Zaliznyak, T., Martínez Martínez, J. & Taylor, G. T. Tear down the fluorescent curtain: a new fluorescence suppression method for Raman microspectroscopic analyses. Sci. Rep. 9, 15785 (2019).
Vogt, C. et al. Stable isotope probing approaches to study anaerobic hydrocarbon degradation and degraders. J. Mol. Microbiol. Biotechnol. 26, 195–210 (2016).
van Manen, H.-J., Lenferink, A. & Otto, C. Noninvasive imaging of protein metabolic labeling in single human cells using stable isotopes and Raman microscopy. Anal. Chem. 80, 9576–9582 (2008).
Xu, J. et al. Raman deuterium isotope probing reveals microbial metabolism at the single-cell level. Anal. Chem. 89, 13305–13312 (2017).
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).
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 hybridizationδ. Appl. Environ. Microbiol. 75, 234–241 (2009).
Singleton, C. M. et al. Connecting structure to function with the recovery of over 1000 high-quality metagenome-assembled genomes from activated sludge using long-read sequencing. Nat. Commun. 12, 2009 (2021).
Milucka, J. et al. Zero-valent sulphur is a key intermediate in marine methane oxidation. Nature 491, 541–546 (2012).
Schmid, T., Messmer, A., Yeo, B.-S., Zhang, W. & Zenobi, R. Towards chemical analysis of nanostructures in biofilms II: tip-enhanced Raman spectroscopy of alginates. Anal. Bioanal. Chem. 391, 1907–1916 (2008).
Madzharova, F., Heiner, Z., Gühlke, M. & Kneipp, J. Surface-enhanced hyper-Raman spectra of adenine, guanine, cytosine, thymine, and uracil. J. Phys. Chem. C. 120, 15415–15423 (2016).
Kim, S. K., Joo, T. H., Suh, S. W. & Kim, M. S. Surface-enhanced Raman scattering (SERS) of nucleic acid components in silver sol: adenine series. J. Raman Spectrosc. 17, 381–386 (1986).
Feng, F. et al. SERS detection of low-concentration adenine by a patterned silver structure immersion plated on a silicon nanoporous pillar array. Nanotechnology 20, 295501 (2009).
Bell, S. E. J. et al. Towards reliable and quantitative surface-enhanced Raman scattering (SERS): from key parameters to good analytical practice. Angew. Chem. Int. Ed. 59, 5454–5462 (2020).
Zhang, M. et al. Rapid determination of antimicrobial susceptibility by stimulated Raman scattering imaging of D2O metabolic incorporation in a single bacterium. Adv. Sci. 7, 2001452 (2020). Rapid antimicrobial susceptibility testing based on femtosecond SRS imaging of deuterium incorporation into cells of interest.
Karanja, C. W. et al. Stimulated Raman imaging reveals aberrant lipogenesis as a metabolic marker for azole-resistant Candida albicans. Anal. Chem. 89, 9822–9829 (2017).
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).
Li, Y. et al. Toward better understanding of EBPR systems via linking Raman-based phenotypic profiling with phylogenetic diversity. Environ. Sci. Technol. 52, 8596–8606 (2018).
Spang, A. et al. The genome of the ammonia-oxidizing Candidatus Nitrososphaera gargensis: insights into metabolic versatility and environmental adaptations. Environ. Microbiol. 14, 3122–3145 (2012).
Probst, A. J. et al. Biology of a widespread uncultivated archaeon that contributes to carbon fixation in the subsurface. Nat. Commun. 5, 5497 (2014).
Hong, W. et al. In situ detection of a single bacterium in complex environment by hyperspectral CARS imaging. ChemistrySelect 1, 513–517 (2016).
Petrov, G. I. et al. Comparison of coherent and spontaneous Raman microspectroscopies for noninvasive detection of single bacterial endospores. Proc. Natl Acad. Sci. USA 104, 7776–7779 (2007).
Arora, R., Petrov, G. I., Yakovlev, V. V. & Scully, M. O. Detecting anthrax in the mail by coherent Raman microspectroscopy. Proc. Natl Acad. Sci. USA 109, 1151–1153 (2012).
Hong, W. et al. Antibiotic susceptibility determination within one cell cycle at single-bacterium level by stimulated Raman metabolic imaging. Anal. Chem. 90, 3737–3743 (2018).
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).
Kjeldsen, K. U. et al. On the evolution and physiology of cable bacteria. Proc. Natl Acad. Sci. USA 116, 19116–19125 (2019).
Bjerg, J. T. et al. Long-distance electron transport in individual, living cable bacteria. Proc. Natl Acad. Sci. USA 115, 5786–5791 (2018). Resonance Raman measurements of cytochrome demonstrates long-distance electron transport over micrometres in cable bacteria.
Haider, S. et al. Raman microspectroscopy reveals long-term extracellular activity of chlamydiae. Mol. Microbiol. 77, 687–700 (2010). By using Raman microspectroscopy to differentiate developmental stages of chlamydiae and to investigate the physiological activity of these stages by single-cell SIP it could be demonstrated that in contrast to textbook knowledge elementary bodies of Chlamydia are physiologically active outside of their host cells — a feature that has important implications for our understanding of the biology of these pathogens.
Chen, D., Huang, S.-S. & Li, Y.-Q. Real-time detection of kinetic germination and heterogeneity of single Bacillus spores by laser tweezers Raman spectroscopy. Anal. Chem. 78, 6936–6941 (2006).
Jäckle, O. et al. Chemosynthetic symbiont with a drastically reduced genome serves as primary energy storage in the marine flatworm Paracatenula. Proc. Natl Acad. Sci. USA 116, 8505–8514 (2019).
Sharma, K., Palatinszky, M., Nikolov, G., Berry, D. & Shank, E. A. Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms. eLife 9, e56275 (2020).
Bodelón, G. et al. Detection and imaging of quorum sensing in Pseudomonas aeruginosa biofilm communities by surface-enhanced resonance Raman scattering. Nat. Mater. 15, 1203–1211 (2016). In situ, label-free identification of the structure of growing biofilms, and of their metabolites involved in intercellular signaling (quorum sensing).
Sandt, C., Smith-Palmer, T., Pink, J., Brennan, L. & Pink, D. Confocal Raman microspectroscopy as a tool for studying the chemical heterogeneities of biofilms in situ. J. Appl. Microbiol. 103, 1808–1820 (2007).
Ivleva, N. P. et al. Label-free in situ SERS imaging of biofilms. J. Phys. Chem. B 114, 10184–10194 (2010).
Ivleva, N. P., Wagner, M., Horn, H., Niessner, R. & Haisch, C. Towards a nondestructive chemical characterization of biofilm matrix by Raman microscopy. Anal. Bioanal. Chem. 393, 197–206 (2009).
Horiue, H., Sasaki, M., Yoshikawa, Y., Toyofuku, M. & Shigeto, S. Raman spectroscopic signatures of carotenoids and polyenes enable label-free visualization of microbial distributions within pink biofilms. Sci. Rep. 10, 7704 (2020).
Schiessl, K. T. et al. Phenazine production promotes antibiotic tolerance and metabolic heterogeneity in Pseudomonas aeruginosa biofilms. Nat. Commun. 10, 762 (2019).
Singer, E., Wagner, M. & Woyke, T. Capturing the genetic makeup of the active microbiome in situ. ISME J. 11, 1949–1963 (2017).
Hong, J.-K., Kim, S. B., Lyou, E. S. & Lee, T. K. Microbial phenomics linking the phenotype to fonction: the potential of Raman spectroscopy. J. Microbiol. 59, 249–258 (2021).
Hatzenpichler, R., Krukenberg, V., Spietz, R. L. & Jay, Z. J. Next-generation physiology approaches to study microbiome function at single cell level. Nat. Rev. Microbiol. 18, 241–256 (2020).
Jing, X. et al. One-cell metabolic phenotyping and sequencing of soil microbiome by Raman-activated gravity-driven encapsulation (RAGE). mSystems 6, e00181–21 (2021).
Kim, H. S. et al. Raman spectroscopy compatible PDMS droplet microfluidic culture and analysis platform towards on-chip lipidomics. Analyst 142, 1054–1060 (2017).
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).
Lorenz, B., Wichmann, C., Stöckel, S., Rösch, P. & Popp, J. Cultivation-free Raman spectroscopic investigations of bacteria. Trends Microbiol. 25, 413–424 (2017).
Rösch, P. et al. Online monitoring and identification of bioaerosols. Anal. Chem. 78, 2163–2170 (2006).
Locke, A., Fitzgerald, S. & Mahadevan-Jansen, A. Advances in optical detection of human-associated pathogenic bacteria. Molecules 25, 5256 (2020).
Maruthamuthu, M. K., Raffiee, A. H., De Oliveira, D. M., Ardekani, A. M. & Verma, M. S. Raman spectra-based deep learning: a tool to identify microbial contamination. Microbiologyopen 9, e1122 (2020).
de Siqueira E Oliveira, F. S. A., da Silva, A. M., Pacheco, M. T. T., Giana, H. E. & Silveira, L. Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features. Lasers Med. Sci. 36, 289–302 (2021).
Wang, K. et al. Arcobacter identification and species determination using Raman spectroscopy combined with neural networks. Appl. Environ. Microbiol. 86, e00924–20 (2020).
Yu, S., Li, H., Li, X., Fu, Y. V. & Liu, F. Classification of pathogens by Raman spectroscopy combined with generative adversarial networks. Sci. Total. Environ. 726, 138477 (2020).
Lorenz, B., Ali, N., Bocklitz, T., Rösch, P. & Popp, J. Discrimination between pathogenic and non-pathogenic E. coli strains by means of Raman microspectroscopy. Anal. Bioanal. Chem. 412, 8241–8247 (2020).
Verma, T., Annappa, H., Singh, S., Umapathy, S. & Nandi, D. Profiling antibiotic resistance in Escherichia coli strains displaying differential antibiotic susceptibilities using Raman spectroscopy. J. Biophotonics 14, e202000231 (2021).
Götz, T. et al. Automated and rapid identification of multidrug resistant Escherichia coli against the lead drugs of acylureidopenicillins, cephalosporins, and fluoroquinolones using specific Raman marker bands. J. Biophotonics 13, e202000149 (2020).
Kriem, L. S., Wright, K., Ccahuana-Vasquez, R. A. & Rupp, S. Confocal Raman microscopy to identify bacteria in oral subgingival biofilm models. PLoS ONE 15, e0232912 (2020).
Kochan, K. et al. Vibrational spectroscopy as a sensitive probe for the chemistry of intra-phase bacterial growth. Sensors 20, 3452 (2020).
Stöckel, S., Kirchhoff, J., Neugebauer, U., Rösch, P. & Popp, J. The application of Raman spectroscopy for the detection and identification of microorganisms. J. Raman Spectrosc. 47, 89–109 (2016).
Pahlow, S. et al. Isolation and identification of bacteria by means of Raman spectroscopy. Adv. Drug Deliv. Rev. 89, 105–120 (2015).
Qian, X. et al. In vivo tumor targeting and spectroscopic detection with surface-enhanced Raman nanoparticle tags. Nat. Biotechnol. 26, 83–90 (2008).
Porter, M. D., Lipert, R. J., Siperko, L. M., Wang, G. & Narayanan, R. SERS as a bioassay platform: fundamentals, design, and applications. Chem. Soc. Rev. 37, 1001–1011 (2008).
Yakes, B. J., Lipert, R. J., Bannantine, J. P. & Porter, M. D. Detection of Mycobacterium avium subsp. paratuberculosis by a sonicate immunoassay based on surface-enhanced Raman scattering. Clin. Vaccine Immunol. 15, 227–234 (2008).
Wang, C., Madiyar, F., Yu, C. & Li, J. Detection of extremely low concentration waterborne pathogen using a multiplexing self-referencing SERS microfluidic biosensor. J. Biol. Eng. 11, 9 (2017).
Catala, C. et al. Online SERS quantification of Staphylococcus aureus and the application to diagnostics in human fluids. Adv. Mater. Technol. 1, 1600163 (2016).
Pazos-Perez, N. et al. Ultrasensitive multiplex optical quantification of bacteria in large samples of biofluids. Sci. Rep. 6, 29014 (2016).
Shi, L. et al. Rapid, quantitative, high-sensitive detection of Escherichia coli O157:H7 by gold-shell silica-core nanospheres-based surface-enhanced Raman scattering lateral flow immunoassay. Front. Microbiol. 11, 596005 (2020).
You, S.-M. et al. Gold nanoparticle-coated starch magnetic beads for the separation, concentration, and SERS-based detection of E. coli O157:H7. ACS Appl. Mater. Interfaces 12, 18292–18300 (2020).
Hong, W.-E. et al. Assembled growth of 3D Fe3O4@Au nanoparticles for efficient photothermal ablation and SERS detection of microorganisms. J. Mater. Chem. B 6, 5689–5697 (2018).
Opota, O., Croxatto, A., Prod’hom, G. & Greub, G. Blood culture-based diagnosis of bacteraemia: state of the art. Clin. Microbiol. Infect. 21, 313–322 (2015).
Cross, K. L. et al. Targeted isolation and cultivation of uncultivated bacteria by reverse genomics. Nat. Biotechnol. 37, 1314–1321 (2019).
Samek, O. et al. Quantitative Raman spectroscopy analysis of polyhydroxyalkanoates produced by Cupriavidus necator H16. Sensors 16, 1808 (2016).
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. Environ. Microbiol. 80, 629–636 (2014).
Taylor, G. T. Windows into microbial seascapes: advances in nanoscale imaging and application to marine sciences. Ann. Rev. Mar. Sci. 11, 465–490 (2019).
Cohen, A. B. et al. Applying fluorescence in situ hybridization to aquatic systems with cyanobacteria blooms: autofluorescence suppression and high-throughput image analysis. Limnol. Oceanogr. Methods 19, 457–475 (2021).
Zeller, P., Ploux, O. & Méjean, A. A simple protocol for attenuating the auto-fluorescence of cyanobacteria for optimized fluorescence in situ hybridization (FISH) imaging. J. Microbiol. Methods 122, 16–19 (2016).
Woyke, T. et al. Assembling the marine metagenome, one cell at a time. PLoS ONE 4, e5299 (2009).
Ben-Amor, K. et al. Genetic diversity of viable, injured, and dead fecal bacteria assessed by fluorescence-activated cell sorting and 16S rRNA gene analysis. Appl. Environ. Microbiol. 71, 4679–4689 (2005).
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).
Grieb, A. et al. A pipeline for targeted metagenomics of environmental bacteria. Microbiome 8, 21 (2020).
Gong, L., Zheng, W., Ma, Y. & Huang, Z. Higher-order coherent anti-Stokes Raman scattering microscopy realizes label-free super-resolution vibrational imaging. Nat. Photonics 14, 115–122 (2020).
Xiong, H. et al. Super-resolution vibrational microscopy by stimulated Raman excited fluorescence. Light. Sci. Appl. 10, 87 (2021).
Watanabe, K. et al. Structured line illumination Raman microscopy. Nat. Commun. 6, 10095 (2015). Super-resolution Raman microscopy — structured line illumination to increase the spatial resolution below the Rayleigh limit.
Kögler, M., Itkonen, J., Viitala, T. & Casteleijn, M. G. Assessment of recombinant protein production in E. coli with time-gated surface enhanced Raman spectroscopy (TG-SERS). Sci. Rep. 10, 2472 (2020).
Kögler, M. et al. Comparison of time-gated surface-enhanced Raman spectroscopy (TG-SERS) and classical SERS based monitoring of Escherichia coli cultivation samples. Biotechnol. Prog. 34, 1533–1542 (2018).
Shkolyar, S. et al. Detecting kerogen as a biosignature using colocated UV time-gated Raman and fluorescence spectroscopy. Astrobiology 18, 431–453 (2018).
Yu, S., Piao, X. & Park, N. Machine learning identifies scale-free properties in disordered materials. Nat. Commun. 11, 4842 (2020).
Zhong, M., Girolami, M., Faulds, K. & Graham, D. Bayesian methods to detect dye-labelled DNA oligonucleotides in multiplexed Raman spectra. J. R. Stat. Soc. Ser. C. Appl. Stat. 60, 187–206 (2011).
Astle, W., De Iorio, M., Richardson, S., Stephens, D. & Ebbels, T. A. Bayesian model of NMR spectra for the deconvolution and quantification of metabolites in complex biological mixtures. J. Am. Stat. Assoc. 107, 1259–1271 (2012).
Han, N. & Ram, R. J. Bayesian modeling and computation for analyte quantification in complex mixtures using Raman spectroscopy. Comput. Stat. Data Anal. 143, 106846 (2020).
Rubtsov, D. V. et al. Application of a Bayesian deconvolution approach for high-resolution 1H NMR spectra to assessing the metabolic effects of acute phenobarbital exposure in liver tissue. Anal. Chem. 82, 4479–4485 (2010).
Weljie, A. M., Newton, J., Mercier, P., Carlson, E. & Slupsky, C. M. Targeted pofiling: quantitative analysis of 1H NMR metabolomics data. Anal. Chem. 78, 4430–4442 (2006).
Hao, J., Astle, W., De Iorio, M. & Ebbels, T. M. D. Batman — an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model. Bioinformatics 28, 2088–2090 (2012).
Goodacre, R. Explanatory analysis of spectroscopic data using machine learning of simple, interpretable rules. Vib. Spectrosc. 32, 33–45 (2003).
Goodacre, R. et al. Rapid identification of urinary tract infection bacteria using hyperspectral whole-organism fingerprinting and artificial neural networks. Microbiology 144, 1157–1170 (1998).
Nims, C., Cron, B., Wetherington, M., Macalady, J. & Cosmidis, J. Low frequency Raman Spectroscopy for micron-scale and in vivo characterization of elemental sulfur in microbial samples. Sci. Rep. 9, 7971 (2019).
Eder, S. H. K., 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).
Zhu, T.-T., Tian, L.-J., Yu, S.-S. & Yu, H.-Q. Roles of cation efflux pump in biomineralization of cadmium into quantum dots in Escherichia coli. J. Hazard. Mater. 412, 125248 (2021).
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).
Jiang, P., Zhao, S., Zhu, L. & Li, D. Microplastic-associated bacterial assemblages in the intertidal zone of the Yangtze Estuary. Sci. Total. Environ. 624, 48–54 (2018).
Frère, L. et al. Microplastic bacterial communities in the Bay of Brest: influence of polymer type and size. Environ. Pollut. 242, 614–625 (2018).
Brewer, P. G. et al. Development of a laser Raman spectrometer for deep-ocean science. Deep-Sea Res. Pt. I 51, 739–753 (2004).
White, S. N. Laser Raman spectroscopy as a technique for identification of seafloor hydrothermal and cold seep minerals. Chem. Geol. 259, 240–252 (2009).
Rull, F. et al. The Raman laser spectrometer for the ExoMars Rover Mission to Mars. Astrobiology 17, 627–654 (2017).
NASA. NASA Mars Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals (SHERLOC). NASA https://mars.nasa.gov/mars2020/spacecraft/instruments/sherloc/ (2020).
Veneranda, M. et al. ExoMars Raman Laser Spectrometer (RLS): development of chemometric tools to classify ultramafic igneous rocks on Mars. Sci. Rep. 10, 16954 (2020).
Veneranda, M. et al. ExoMars Raman laser spectrometer: a tool for the potential recognition of wet-target craters on Mars. Astrobiology 20, 349–363 (2020).
Messmer, M. W., Dieser, M., Smith, H. J., Parker, A. E. & Foreman, C. M. Investigation of Raman spectroscopic signatures with multivariate statistics: an approach for cataloguing microbial biosignatures. Astrobiology 22, 1–11 (2022).
Arnold, F. H. & Georgiou, G. Directed Enzyme Evolution: Screening and Selection Methods (Humana Press, 2003).
Markel, U. et al. Advances in ultrahigh-throughput screening for directed enzyme evolution. Chem. Soc. Rev. 49, 233–262 (2020).
Peng, L. et al. Intracellular ethanol accumulation in yeast cells during aerobic fermentation: a Raman spectroscopic exploration. Lett. Appl. Microbiol. 51, 632–638 (2010).
Cecchini, M. P. et al. Ultrafast surface enhanced resonance Raman scattering detection in droplet-based microfluidic systems. Anal. Chem. 83, 3076–3081 (2011).
März, A., Henkel, T., Cialla, D., Schmitt, M. & Popp, J. Droplet formation via flow-through microdevices in Raman and surface enhanced Raman spectroscopy-concepts and applications. Lab. Chip 11, 3584–3592 (2011).
Moore, B. D. et al. Rapid and ultra-sensitive determination of enzyme activities using surface-enhanced resonance Raman scattering. Nat. Biotechnol. 22, 1133–1138 (2004).
Hutter, E. & Fendler, J. H. Exploitation of localized surface plasmon resonance. Adv. Mater. 16, 1685–1706 (2004).
Pilot, R. et al. A review on surface-enhanced Raman scattering. Biosensors 9, 57 (2019).
Cui, L., Zhang, D. D., Yang, K., Zhang, X. & Zhu, Y.-G. Perspective on surface-enhanced Raman spectroscopic investigation of microbial world. Anal. Chem. 91, 15345–15354 (2019).
Sharma, B., Frontiera, R. R., Henry, A.-I., Ringe, E. & Van Duyne, R. P. SERS: materials, applications, and the future. Mater. Today 15, 16–25 (2012).
Madzharova, F., Heiner, Z., Simke, J., Selve, S. & Kneipp, J. Gold nanostructures for plasmonic enhancement of hyper-Raman scattering. J. Phys. Chem. C. 122, 2931–2940 (2018).
Wagner, M. & Haider, S. New trends in fluorescence in situ hybridization for identification and functional analyses of microbes. Curr. Opin. Biotechnol. 23, 96–102 (2012).
Daims, H., Lücker, S. & Wagner, M. Daime, a novel image analysis program for microbial ecology and biofilm research. Environ. Microbiol. 8, 200–213 (2006).
Hoshino, T., Yilmaz, L. S., Noguera, D. R., Daims, H. & Wagner, M. Quantification of target molecules needed to detect microorganisms by fluorescence in situ hybridization (FISH) and catalyzed reporter deposition-FISH. Appl. Environ. Microbiol. 74, 5068–5077 (2008).
Amann, R., Snaidr, J., Wagner, M., Ludwig, W. & Schleifer, K. H. In situ visualization of high genetic diversity in a natural microbial community. J. Bacteriol. 178, 3496–3500 (1996).
Lukumbuzya, M., Schmid, M., Pjevac, P. & Daims, H. A multicolor fluorescence in situ hybridization approach using an extended set of fluorophores to visualize microorganisms. Front. Microbiol. 10, 1383 (2019).
Valm, A. M. et al. Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging. Proc. Natl Acad. Sci. USA 108, 4152–4157 (2011).
Heldal, M., Norland, S. & Tumyr, O. X-ray microanalytic method for measurement of dry matter and elemental content of individual bacteria. Appl. Environ. Microbiol. 50, 1251–1257 (1985).
Wei, L., Yu, Y., Shen, Y., Wang, M. C. & Min, W. Vibrational imaging of newly synthesized proteins in live cells by stimulated Raman scattering microscopy. Proc. Natl Acad. Sci. USA 110, 11226–11231 (2013).
Li, J. & Cheng, J.-X. Direct visualization of de novo lipogenesis in single living cells. Sci. Rep. 4, 6807 (2014).
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).
Kopf, S. H. et al. Heavy water and 15N labelling with NanoSIMS analysis reveals growth rate-dependent metabolic heterogeneity in chemostats. Environ. Microbiol. 17, 2542–2556 (2015).
Crespi, H. L., Conrad, S. M., Uphaus, R. A. & Katz, J. J. Cultivation of microorganisms in heavy water. Ann. N. Y. Acad. Sci. 84, 648–666 (1960).
Kselíková, V., Vítová, M. & Bišová, K. Deuterium and its impact on living organisms. Folia Microbiol. 64, 673–681 (2019).
Matanfack, G. A., Pistiki, A., Rösch, P. & Popp, J. Raman 18O-labeling of bacteria in visible and deep UV-ranges. J. Biophotonics 14, e202100013 (2021).
Yan, S. et al. Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level. Appl. Microbiol. Biotechnol. 105, 1315–1331 (2021).
Wang, Y. et al. Raman activated cell ejection for isolation of single cells. Anal. Chem. 85, 10697–10701 (2013).
Sidore, A. M., Lan, F., Lim, S. W. & Abate, A. R. Enhanced sequencing coverage with digital droplet multiple displacement amplification. Nucleic Acids Res. 44, e66 (2016).
R.S. acknowledges support from a Gordon and Betty Moore Foundation Symbiosis in Aquatic Systems Initiative Investigator Award (GBMF9197; https://doi.org/10.37807/GBMF9197), a grant from the Simons Foundation (542395) as part of the Principles of Microbial Ecosystems (PriME) Collaborative, a grant (315230_176189) from the Swiss National Science Foundation and support from the National Centre of Competence in Research (NCCR) Microbiomes (51NF40_180575). F.C.P. was supported by a Young Independent Research Group grant from the Austrian Science Fund (FWF; ZK-57). D.B. was supported by the Austrian Science Fund (FWF; P26127-B20 and P27831-B28), the United States Department of Energy (DE-SC0019012) and the European Research Council (ERC; Starting Grant: FunKeyGut 741623). Research in the lab of M.W. on Raman microspectroscopy and its application in microbial ecology was supported by an ERC Advanced Grant (Nitricare; 294343) and the Wittgenstein Award of the FWF (Z-383-B). W.E.H. acknowledges financial and instrumentational support from EPSRC (EP/M002403/1, EP/M02833X/1) and NERC (NE/M002934/1). G.T.T. acknowledges support from NSF-MRI grant OCE-1336724 and a Gordon and Betty Moore Foundation Grant no. 5064. J.K. acknowledges funding by ERC Starting Grant 259432 Multibiophot. J.-X.C. acknowledges support from NIH (R35 GM136223 and R01AI141439). The Stocker group thanks R. Naisbit for scientific editing.
The authors declare no competing interests.
Peer review information
Nature Reviews Methods Primers thanks C. Garcia-Timmermans, A. Locke, G. Pezzotti and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Raman processor: https://github.com/harubang2/Raman_processor
- Fourier transform infrared (FTIR) spectroscopy
The other prominent method of vibrational spectroscopy, whereby absorption of light by a sample is used to identify the molecular composition of the sample.
- Normal Raman microspectroscopy
A fundamental form of Raman microspectroscopy that relies on measurement of non-resonant, spontaneous scattering signals in which one out of ~106 incoming photons to a sample is scattered.
A unit of frequency used in vibrational spectroscopy, defined as the frequency divided by the speed of the wave and thus equal to the number of waves within one centimetre.
- Resonance Raman scattering
Raman scattering that arises when the wavelength of the incident laser beam matches the electronic transitions of a molecule, which generates much more intense Raman signals than normal Raman scattering.
- Raman reporter
A chemical that generates a known surface-enhanced Raman scattering signal.
- Mode-locked laser
A laser that produces ultrashort pulses on the picosecond or femtosecond scale.
- Selection rules
Constraints that govern the likelihood of whether undergoing particular quantum transitions from one state to another is allowed or forbidden.
- Beating frequency
Frequency difference between two electromagnetic waves that interfere constructively and destructively.
- Spectral window
A spectral region of interest.
- Diffraction grating
A glass plate etched with very close parallel lines that produces a spectrum from a coherent light beam by diffraction and interference of light and thus functions as a planar prism.
- Chromatic aberration
Discrepancy of focus in axial and transverse directions between rays with different wavelengths after a focusing lens owing to the discordance of their refraction angles.
A pair of mirrors, each of which is integrated with a rapidly moving scanning motor, which enables enlargement of a laser beam spot to a small scanning area.
- Dichroic mirror
An optical component for fluorescence microscopy by which monochromatic light for the excitation of fluorophores in a sample is separated from generated fluorescence signals.
A molecule that is structurally identical yet differs from another by the presence of at least one atom that possesses a different number of neutrons.
- Uniformly labelled tracer
A molecule in which all available positions for a given element are occupied by an isotopically heavy or radioactive nuclide, typically noted as [U-nE]compound, where n = atomic mass, E = elemental symbol, U = uniformly, followed by chemical form.
- Fractional isotopic abundance
The proportion of atoms in a molecular pool populated by the heavy isotope — also referred to as atom% (multiplied by 100).
- Biomolecular fingerprint
An indicator in which chemical properties of a biomolecule are encoded; in vibrational spectroscopy, collective vibrational frequencies in wavenumber of chemical bonds within a biomolecule.
The absence of Raman-active vibrational modes.
- Savitzky–Golay filter
A filter algorithm that fits a polynomial of a known order to each point in the spectrum, using a sliding window of a user-defined width, subsequently replacing each point with the fitted value at the centre of the window.
- Vector normalization
A normalization approach in which the intensity at each wavenumber is divided by the square root of the sum of squares of intensities for all wavenumbers within a spectral window, such that the Euclidean distance from the origin in the multidimensional space is equal to 1.
- Mahalanobis distance
A measure of the distance between a point and the centroid of a multivariate normal distribution, in units of standard deviation.
- Non-negative matrix factorization
A technique that represents each point in a set of mixed spectra as a weighted mixture of a finite number of conserved sub-spectra, with the axes being directly interpretable as Raman sub-spectra.
- Independent component analysis
A technique that optimizes a new set of axes to naively capture covariance between variables separately for each of a finite number of independently varying subsets of data.
Isotopomers of a compound have the same number of each isotope, but their positions differ.
- Voigt probability distribution profile
A convolution of Gaussian and Lorentzian probability distributions that is widely used in peak-fitting routines to describe the symmetry of peaks in Raman spectroscopy.
A region of a molecule where the energy difference between two molecular orbitals is within the visible spectrum, thus determining the colour of the molecule.
About this article
Cite this article
Lee, K.S., Landry, Z., Pereira, F.C. et al. Raman microspectroscopy for microbiology. Nat Rev Methods Primers 1, 80 (2021). https://doi.org/10.1038/s43586-021-00075-6
This article is cited by
ISME Communications (2022)
Nature Reviews Methods Primers (2022)
Nature Communications (2022)
Correlative SIP-FISH-Raman-SEM-NanoSIMS links identity, morphology, biochemistry, and physiology of environmental microbes
ISME Communications (2022)
Single-cell Raman-activated sorting and cultivation (scRACS-Culture) for assessing and mining in situ phosphate-solubilizing microbes from nature
ISME Communications (2022)