Anal. Chem. 2004, 76, 4452-4458
Raman Microscopic Analysis of Single Microbial Cells Wei E. Huang,*,†,‡ Robert I. Griffiths,† Ian P. Thompson,‡ Mark J. Bailey,† and Andrew S. Whiteley†
Molecular Microbial Ecology and Environmental Biotechnology Sections, CEH-Oxford, Mansfield Road, Oxford, OX1 3SR U.K.
We demonstrate the utility of the Raman confocal microscope to generate a spectral profile from a single microbial cell and the use of this approach to differentiate bacterial species. In general, profiles from different bacterial taxa shared similar peaks, but the relative abundances of these components varied between different species. The use of multivariate methods subsequently allowed taxa discrimination. Further investigations revealed that the single-cell spectra could be used to differentiate between growth phases of a single species, but these differences did not obscure the overall interspecies discrimination. Finally, we tested the efficacy of the method as a means to identify cells responsible for the uptake of a specific substrate. A single strain was grown in media containing incrementally varying ratios of 13C6 to 12C6 glucose, and it was found that 13C incorporation shifted characteristic peaks to lower wavenumbers. These findings suggest that Raman microscopy has significant potential for studies requiring the taxonomic identity and functioning of single microbial cells to be determined. The ability to identify and ascertain the functions of microbes in a sample remains a significant challenge for environmental, public health, and medical studies. Traditional culture-based analyses are not always suitable since many bacteria are difficult to grow using standard isolation media. Indeed, in many natural environments most bacteria are difficult to isolate,1 and so microbial functioning in natural environments cannot be studied solely by culturing. Under circumstances where culturing strategies are appropriate, a significant incubation period is generally required prior to identification, and in cases where physiology and function are of interest, a large biomass is generally required before pure culture tests are performed. All these factors serve to reduce the effectiveness of culture-based methods for microbial analyses. Recently developed molecular methods, based upon DNA extraction and PCR of 16S rRNA genes, can be used to identify the presence of microbes2 but provide no information on spatial localization. For this purpose, fluorescent in situ hybridization * Corresponding author. Fax: 44 (0)1865 281696. E-mail:
[email protected]. † Molecular Microbial Ecology Section. ‡ Environmental Biotechnology Section. (1) Amann, R. I.; Ludwig, W.; Schleifer, K. H. Microbiol. Rev. 1995, 59, 143169. (2) Head, I. M.; Saunders: J. R.; Pickup, R. W. Microb. Ecol. 1998, 35, 1-21.
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(FISH) has proved to be a powerful tool for quantifying the presence and localization of bacteria based upon 16S rRNA sequences,3 but these methods are dependent upon the availability of suitably discriminate probes. A second issue, faced mainly by environmental microbiologists, is the inability to determine the functional attributes of microorganisms in situ. The combined use of stable isotope tracers, coupled with identification methods (stable isotope probing, SIP4,5), currently holds the greatest promise for this purpose. However, these methodologies are reliant on the extraction and homogenization of biomarkers from their native environment and, therefore, do not permit examination of the localization of the organisms at the microscale. Clearly, new tools are still required to strengthen the arsenal of molecular and isotopic techniques available to study microbes in their native habitats. Techniques based on vibrational spectroscopy have recently shown great potential for this purpose. In particular, two techniques, Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy, have proven effective for the rapid identification of bacteria and fungi.6-12 Both these techniques are based on the measurement of the vibrational energy levels of chemical bonds but differ in that FT-IR spectroscopy measures the absorption of light while Raman spectroscopy measures the inelastically scattered light following excitation.12 Unlike IR, Raman spectroscopy can tolerate water molecules and can generate more sharp and distinguishable bands of specific molecules.13 Biologically associated molecules such as nucleic acids, protein, lipids, and carbohydrates all generate strong signals in both infrared and Raman spectra (for review, please see refs (3) Amann, R.; Fuchs, B. M.; Behrens, S. Curr. Opin. Biotechnol. 2001, 12, 231-236. (4) Manefield, M.; Whiteley, A. S.; Griffiths, R. I.; Bailey, M. J. Appl. Environ. Microbiol. 2002, 68, 5367-5373. (5) Manefield, M.; Whiteley, A. S.; Ostle, N.; Ineson, P.; Bailey, M. J. Rapid Commun. Mass Spectrom. 2002, 16, 2179-2183. (6) Goodacre, R.; Shann, B.; Gilbert, R. J.; Timmins, E. M.; McGovern, A. C.; Alsberg, B. K.; Kell, D. B.; Logan, N. A. Anal. Chem. 2000, 72, 119-127. (7) Goodacre, R.; Timmins, E. M.; Burton, R.; Kaderbhai, N.; Woodward, A. M.; Kell, D. B.; Rooney, P. J. Microbiology-UK 1998, 144, 1157-1170. (8) Scullion, J.; Elliot, G. N.; Huang, W. E.; Goodacre, R.; Worgan, H.; Darby, R.; Bailey, M. J.; Gwynn-Jones, D.; Griffith, G. W.; Winson, M. K.; Williams, P. A.; Clegg, C.; Draper, J. Pedobiologia 2003, 47, 440-446. (9) Maquelin, K.; Choo-Smith, L. P.; Endtz, H. P.; Bruining, H. A.; Puppels, G. J. J. Clin. Microbiol. 2002, 40, 594-600. (10) Naumann, D. Appl. Spectrosc. Rev. 2001, 36, 239-298. (11) Petrich, W. Appl. Spectrosc. Rev. 2001, 36, 181-237. (12) Petry, R.; Schmitt, M.; Popp, J. ChemPhysChem 2003, 4, 14-30. (13) Carey, P. R. Biochemical Applications of Raman and Resonance Raman Spectroscopies; Academic Press: London, 1982. 10.1021/ac049753k CCC: $27.50
© 2004 American Chemical Society Published on Web 06/15/2004
Figure 1. Confocal Raman microscope measurement. (A) Bacteria imaging under 100×/0.9 objective (B) A typical Raman spectra from a single cell. Tentative assignments of the main bands are highlighted.
10-12 and 14). Therefore, these spectroscopic methods may be used to generate “whole-organism fingerprints” for the differentiation of biological samples. For microbiological studies, FT-IR and Raman spectroscopy have previously only been performed on cultures or colonies,6-9,15-21 (14) Maquelin, K.; Kirschner, C.; Choo-Smith, L. P.; van den Braak, N.; Endtz, H. P.; Naumann, D.; Puppels, G. J. J. Microbiol. Methods 2002, 51, 255271. (15) Choo-Smith, L. P.; Maquelin, K.; van Vreeswijk, T.; Bruining, H. A.; Puppels, G. J.; Thi, N. A. G.; Kirschner, C.; Naumann, D.; Ami, D.; Villa, A. M.; Orsini, F.; Doglia, S. M.; Lamfarraj, H.; Sockalingum, G. D.; Manfait, M.; Allouch, P.; Endtz, H. P. Appl. Environ. Microbiol. 2001, 67, 1461-1469. (16) Goodacre, R.; Timmins, E. M.; Rooney, P. J.; Rowland, J. J.; Kell, D. B. FEMS Microbiol. Lett. 1996, 140, 233-239.
because of the technical difficulties of focusing the energy source on a single cell. Recent technological advances have led to the development of Raman microscopes that use a laser as a photon (17) Jarvis, R. M.; Goodacre, R. Anal. Chem. 2004, 76, 40-47. (18) Kirschner, C.; Maquelin, K.; Pina, P.; Thi, N. A. N.; Choo-Smith, L. P.; Sockalingum, G. D.; Sandt, C.; Ami, D.; Orsini, F.; Doglia, S. M.; Allouch, P.; Mainfait, M.; Puppels, G. J.; Naumann, D. J. Clin. Microbiol. 2001, 39, 1763-1770. (19) Maquelin, K.; Choo-Smith, L. P.; van Vreeswijk, T.; Endtz, H. P.; Smith, B.; Bennett, R.; Bruining, H. A.; Puppels, G. J. Anal. Chem. 2000, 72, 12-19. (20) Maquelin, K.; Kirschner, C.; Choo-Smith, L. P.; Ngo-Thi, N. A.; van Vreeswijk, T.; Stammler, M.; Endtz, H. P.; Bruining, H. A.; Naumann, D.; Puppels, G. J. J. Clin. Microbiol. 2003, 41, 324-329. (21) Timmins, E. M.; Howell, S. A.; Alsberg, B. K.; Noble, W. C.; Goodacre, R. J. Clin. Microbiol. 1998, 36, 367-374.
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Table 1. Bacterial Species Used in Bacterial Discrimination Studies strain ID
name
source
a b c d e f g
Acinetobacter sp. ADP1a Agrobacterium sp. Bacillus sp. Chromobacterium violaceum Escherichia coli DH5R Pseudomonas putida NCIMB 11764 Citrobacter sp.
ref 24 ref 36 b ref 37 Gibco BRL ref 38 ref 36
a 16S identified, GenBank accession no. AY289925. b Isolated from soil, 16S identified, data not shown.
Figure 2. Bacterial species differentiation. (A) The averaged spectra for the seven species. (B) Projection analysis, training and testing data, bacterial species differentiation validated by projection analysis. The isolates a-g are listed in Table 1. Numbers 1-8 represent training data and 9 indicates test data used to validate the discrimination.
source, directed through the microscope lens. The resulting light scatter is then measured via a single grating dispersive spectrometer. A diffraction-limited laser spot, down to 1-µm diameter, can be achieved with suitable objectives, permitting microbial investigations at the single-cell level.22,23 In this paper, we demonstrate single-cell analyses for differentiating pure cultures of bacterial isolates, taking into account the effects of physiological status. Additionally, we demonstrate the utility of the technique in discriminating individual cells on the basis of 13C content, as a means to link bacterial identity with tracer-dependent metabolic function. MATERIALS AND METHODS Bacterial Species Discrimination. Seven bacteria species were used for the investigations, listed in Table 1, and include (22) Schuster, K. C.; Reese, I.; Urlaub, E.; Gapes, J. R.; Lendl, B. Anal. Chem. 2000, 72, 5529-5534. (23) Schuster, K. C.; Urlaub, E.; Gapes, J. R. J. Microbiol. Methods 2000, 42, 29-38.
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Gram-positive and -negative bacteria. Genetically closely related species Escherichia coli and Citrobacter sp. were specifically chosen since they are typically difficult to differentiate by many traditional methods. Each species was inoculated into 5 mL of LB and incubated at 28 °C overnight with 150 rpm shaking. Following incubation, the cultures were washed three times and resuspended in 0.85% (w/v) NaCl solution. For each species, three independent culture replicates were grown and three individual cells from each replicate culture analyzed. Growth Phase. To determine whether the Raman spectra obtained were dependent on growth phase, three bacterial species were grown as above but harvested at different time points, found previously to be representative of exponential and stationary phases for each species through OD measurement. The strains chosen were Acinetobacter sp. ADP1,24 E. coli. DH5R. and Pseudomonas fluorescens SBW25,25 and cells were harvested after 4-, 8-, and 22-h incubation. Three replicate cultures were established for each strain, but here only two individual cells were randomly selected from each culture. 13C Assimilation. Overnight cultures of P. fluorescens strain SBW2525 were grown in 2 mL of M9-glucose media26 at 28 °C, with 10 mM glucose as a sole carbon source in the media. Triplicate cultures were established with incrementally varying proportions of 13C6-glucose (0, 25, 50, 75, 100%), to test the sensitivity of Raman microscopy for detecting changes in the spectral profile of bacteria that had assimilated 13C into their cellular components. Controls were also established containing 0 and 100% 13C6-glucose, but without inocula, to determine baseline differences in Raman spectra arising purely from the isotopic composition of glucose. Cells were harvested by centrifugation at 4000 rpm (Jouan B4i) for 5 min and washed twice in phosphate buffer solution to remove traces of residual media. Two randomly selected cells, from each replicate culture, were then analyzed by Raman microscopy Raman Microscopy. Raman microscopy was performed using a LabRAM 300 confocal Raman microscope (Jobin-Yvon Ltd.). The true confocal performance of the instrument allows the analysis of single cells 1 µm or smaller in size. The Raman microscope was equipped with an integrated Olympus microscope (model BX41). The Raman scattering was excited by a frequency-doubled (24) Juni, E.; Janik, A. J. Bacteriol. 1969, 98, 281-288. (25) Rainey, P. B.; Bailey, M. J. Mol. Microbiol. 1996, 19, 521-533. (26) Sambrook, J.; Fritsch, E. F.; Maniatis, T. Molecular cloning: a laboratory manual, 2nd ed.; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 1989.
Figure 3. Lack of effect of physiological differences due to growth phase on the clustering of three different bacterial species. DFA projection validates the discrimination (A) (a, Acinetobacter sp.; b, ADP1 E. coli DH5R; c, P. fluorescens SBW25. For each species, letters in boldface type indicate testing data and letters in lightface type indicate training data). Raman spectra of different time points of growth of Acinetobacter sp. ADP1 (B), E. coli DH5R (C), and P. fluorescens SBW25 (D). Incubations are 4 (bottom), 8 (middle), and 22 h (top).
532-nm Nd:YAG laser, and the incident laser power was typically adjusted to ∼5-8 mW, to ensure no sample degradation occurred, while still maintaining spectral sensitivity. The detector used was a Peltier air-cooled CCD detector (open electrode format) with pinhole of 300 µm and slit size of 150 µm,12 enabling a spatial resolution of ∼1 µm to be obtained. The system was calibrated, prior to analyses, and monitored using a silicon Raman band reference. Before loading samples on the Raman microscope, 10 µL of each cell suspension was spread on a quartz slide and allowed to air-dry for 3-5 min. For each measurement, a single bacterial cell was focused using a 100×/0.9 microscope objective. In this instance, an air objective (Olympus MPLAN 100× (NA ) 0.90)) was selected for noncontact sampling, providing high signal throughput and spatial resolution, whilst preventing contamination of the objective. The laser beam was targeted on the cell visually, using a TV monitor and a motorized XY stage (0.1-µm step). The Raman signal was optimized by adjusting the laser focus with a real-time readout; the spectrum was then acquired between the
range 1972 and 544 cm-1, with 1014 data points (∼1.5 cm-1 per point). The accumulation time for one spectrum was typically 6090 S. Spectra were processed for baseline correction and normalization by Labspec software (Jobin-Yvon Ltd.). Profile data were then imported into MVSP version 3.12d (Kovach Computing Services, Wales, U.K.) and Matlab R12 (The Math Works, Inc. 24 Prime Par Way, Natick, MA). Multivariate Analysis of Raman Spectra. To analyze the Raman spectra obtained from bacterial cells, multivariate techniques of principal component analysis (PCA), discriminant functional analysis (DFA), and hierarchical cluster analysis (HCA) were used. DFA, HCA, and PCA/DFA-Projection codes were kindly provided by Dr. Roy Goodacre and were used as described previously.6,7 PCA was performed using the MultiVariate Statistical Package (MSVP version 3.1, Kovach Computing Services, Wales, U.K.). Briefly, PCA identified those parameters which gave the greatest differentiation between dimensions of multivariate data. DFA discriminated between groups based on these retained principal components (PCs) and the a priori knowledge of which Analytical Chemistry, Vol. 76, No. 15, August 1, 2004
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Table 2. Bands Shifted in Exponential and Stationary Growth Phases Determined by Examination of First Axis Principal Component Loadings exponential phase growth strain
frequency
(cm-1)
stationary phase growth
assignment
frequency
(cm-1)
assignment
544.023-553.306
carbohydrate23
1001.55-1005.93
phenylalanine10,14
783.54-785.04 1062.75
cytosine, uracil10,14 C-N and C-C stretching10,14
E.coli DH5R
544.023-553.306 783.54-785.04 1062.75
carbohydrate23 cytosine, uracil10,14 C-N and C-C stretching 10, 14
P. fluorescens SBW25
544.023-553.306
carbohydrate23
1122.03-1129.23 1230-1295 1650-1680 1440-1460 1001.55-1005.93 1122.03-1129.23 1230-1295 1650-1680 1440-1460 1122.03-1129.23
C-N and C-C stretching10,14 amide III10,14 amide I and unsaturated lipids10,14 C-H2 deformation10,14 phenylalanine10,14 C-N and C-C stretching10,14 amide iii10,14 amide i and unsaturated lipids10,14 C-H2 deformation10,14 C-N and C-C stretching10,14
783.54-785.04 1062.75
cytosine, uracil10,14 C-N and C-C stretching10,14
1230-1295
amide III10,14
Acinetobacter sp. ADP1
spectra were replicates. DFA was performed to maximize betweengroup variance and minimize within-group variance.6,27 To validate discrimination performed by DFA, projection analysis was also employed to project test data to PCA space and DFA space generated by the training set.17,28,29 RESULTS AND DISCUSSION Raman Microscopy To Discriminate Different Bacteria. Harvested and washed bacterial suspensions were spread on to a quartz slide and individual cells randomly chosen for Raman microscopic measurements. An example of a typical image of bacterial cells (Pseudomonas putida NCIMB 11764), obtained under a 100×/0.9 microscope objective, is provided in Figure 1A. The associated Raman spectrum obtained, after exposing a single cell to the 1-µm laser for 90 s, is shown in Figure 1B. Each Raman spectrum of a single cell contained 50-70 sharp bands. Characteristic peaks determined from the literature10,14 for abundant cellular components such as carbohydrates, lipids, protein, and nucleic were clearly visible in the spectra of all strains. The averaged Raman spectra for the seven bacteria species are shown in Figure 2A. Although the profiles for the different species appear generally similar, certain differences in peak heights can be observed visually. To determine whether Raman microscopy could reproducibly discriminate between the bacterial strains, all 63 Raman spectra (7 species × 3 biological replicates × 3 individual cells) were analyzed using multivariate methods. DFA effectively discriminated the bacterial taxa into seven species groups (data not shown). To validate the DFA analysis, eight spectra were used as a training data set to construct a classification system and the ninth spectrum was used as the test set. Each test set clustered among the data present within its training set, thus validating the groupings observed (Figure 2B). Finally, a dendrogram generated by calculating ordinary Euclidean distance in DFA space confirmed the discrimination (Supporting Informa(27) Manly, B. F. J. Multivariate statistical methods: a primer; Chapman & Hall: London, 1994. (28) Kaderbhai, N. N.; Broadhurst, D. I.; Ellis, D. I.; Goodacre, R.; Kell, D. B. Comp. Funct. Genomics 2003, 4, 376-391. (29) Radovic, B. S.; Goodacre, R.; Anklam, E. J. Anal. Appl. Pyrolysis 2001, 60, 79-87.
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tion Figure A). Raman spectra of each single cell represented a “chemical fingerprint” and reflected phenotypic rather than genotypic distances. We did not explicitly test whether spectral distances correlated with phylogenetic distances, although the groups showed some trends at the phylum and class level on the first DFA axis. A dendrogram generated by calculating ordinary Euclidean distance in DFA space for the first three axes confirmed the discrimination but did not reveal established taxonomic groupings (Supporting Information Figure A). A previous study examining within-strain differences in actinomycete species found no correlation between FT-IR spectral similarity and 16S rDNA sequence similarity,30 with the authors suggesting that evolutionary changes in cellular components are decoupled from the diversification of 16S rDNA. Effects of Growth Phase on Raman Spectra of Different Isolates. We tested the potential of Raman microscopy to detect differences in the physiological state of single species, since this could impact on the potential usefulness of the tool for the characterization of isolates. Three isolates (Acinetobacter sp. ADP1, E.coli DH5R, P. fluorescens SBW25) were harvested and analyzed using the Raman microscope at different points of growth (Figure 3). These data revealed the clustering of the three species, based on species phenotypic differences, was robust despite temporal differences in cellular physiology during the phases of growth (Figure 3A). However, when the spectra were examined for a single species, cells from each different growth points were different (Figure 3B-D), although the spectra profiles are visually very similar. Each strain was then analyzed separately by PCA to determine shifts in the relative abundance of cellular components at exponential (8-h incubation) and stationary growth (22-h incubation). For all three strains, there was clear separation along the first PCA axes (Supporting Information Figure B), and so the first axis component loadings were plotted to assess which bands increased in each of the different growth phases (not shown). Tentative assignments of the elevated spectral regions are summarized in Table 2. For all three strains, at exponential phase, the ratio of RNA to protein (783-785, 1230-1295 cm-1), lipid (30) Oberreuter, H.; Charzinski, J.; Scherer, S. Microbiology-Sgm 2002, 148, 1523-1532.
Figure 4. Effects of 13C substitution on Raman spectra of P. fluorescens SBW25. (A) Substituting cellular biomass 12C with 13C causes visible red-shift in certain Raman spectra bands (arrows indicate the direction of the shift). (B) PCA analysis of spectra of P. fluorescens SBW25 incubated with different percentages of 13C6-glucose.
(1062 cm-1), and carbohydrate (544-553 cm-1) was higher than at stationary phase. Conversely, at stationary phase, the amide I region (1650-1680 cm-1) was higher than at exponential phase (Table 2). This physiological variation was observed for all three species tested and was presumably due to small growth-phase variations in membrane compounds, polysaccharides, proteins, lipids, and nucleic acids as observed previously.22 However, DFA analysis classified these strains to be different species despite physiological variation, and subsequent DFA-projection analysis validated the discrimination (Figure 3A). Raman Shift Caused by 13C Addition. The Raman spectra of P. fluorescens SBW25, grown in 100% 13C, revealed several redshifted peaks at a lower wavenumber, compared with samples grown on 100% 12C glucose (Figure 4A). PCA analysis revealed that the spectral differences were based on the percentage of 13Cglucose in M9-glucose. and the clustering revealed a trend from
a low to high percentage of 13C-glucose (Figure 4B) on the second principal component axis. Red-shifted bands were identified by examining the PCA variable loadings and were subsequently assigned putative molecular identifications (Table 3) based on data available in the literature.10,14,23 These experiments revealed that red-shifted protein and nucleic acid signals were major contributors to the observed Raman spectral differentiation (Table 3), a factor more than likely to be due to the fact that both constituents represent ∼75% of the total cellular biomass.31 Specifically, spectra of bacteria from 0% 13C-glucose were easily discriminated from those of 100% 13Cglucose (Figure 4). The lowest 13C substitution we employed (25% 13C) indicated good discrimination from unlabeled cells, suggesting an even lower threshold of 13C labeling is achievable. (31) Neidhardt, F. C. Escherichia coli and Salmonella, 2nd ed.; ASM Press: Washington, DC, 1996.
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Table 3. Ranked Positive PCA Variable Scores (Second Axis) Showing Main Raman Spectra Bands and Putative Assignments Shifted by 13C Substitution
scores
frequency at 100% 12C (cm-1)
frequency shift at 100% 13C (cm-1)
0.236 0.203 0.146 0.127
1671.65 1003.01 1480.64 1574.75
46.94 36.65 26.13 44.85
0.115 0.09 0.075 0.074
785.024 1343.46 1260.47 1126.35
15.03 19.62 46.81 27.40
0.07 0.048
727.772 1032.21
15.20 16.05
assignment
ref
protein (amide i) phenylalanine nucleic acids guanine, adenine (ring stretching) cytosine, uracil protein (amide III) protein (amide III) carbohydrate (C-N and C-C stretching adenine carbohydrate
10, 14 10, 14 23 10, 14 10, 14 23 10, 14 10, 14 10, 14 23
The observed red shift was attributable to the fact that the Raman shift of a polyatomic molecule is inversely proportional to the square root of the atomic mass (eq 1). The vibrational frequency of polyatomic molecule can be written as
∆v ) (1/2πc)(kq/mq)1/2
(1)
where ∆v is Raman shift of polyatomic molecule, kq is force constant of bands, mq is effective molecular mass, c is the speed of light in a vacuum.13,32 Generally, biologically important molecules such as nucleic acids, protein, polysaccharides, and lipids contain a high percentage of carbon, and therefore, a molecule containing “heavy” 13carbon will have greater effective molecular mass mq, and so the wavenumber will be reduced.13 Conclusions and Future Perspectives. The use of whole organism fingerprinting methods such as Raman spectroscopy is well documented for bacterial discrimination and molecular function research.6,10,17 Our study has advanced upon these methods by showing the utility of Raman microscopy as a (32) Atkins, P. W. Physical Chemistry; Oxford University Press: Oxford, 1998. (33) Ericsson, M.; Hanstorp, D.; Hagberg, P.; Enger, J.; Nystrom, T. J. Bacteriol. 2000, 182, 5551-5555. (34) Frohlich, J.; Konig, H. FEMS Microbiol. Rev. 2000, 24, 567-572. (35) Huber, R.; Huber, H.; Stetter, K. O. FEMS Microbiol. Rev. 2000, 24, 615623. (36) Valle, A.; Bailey, M. J.; Whiteley, A. S.; Manefield, M. Environ. Microbiol. 2004, 6, 424-433. (37) McClean, K. H.; Winson, M. K.; Fish, L.; Taylor, A.; Chhabra, S. R.; Camara, M.; Daykin, M.; Lamb, J. H.; Swift, S.; Bycroft, B. W.; Stewart, G.; Williams, P. Microbiology-Sgm 1997, 143, 3703-3711. (38) Kunz, D. A.; Chen, J. L.; Pan, G. L. Appl. Environ. Microbiol. 1998, 64, 4452-4459.
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nondestructive and noninvasive spectroscopic technique for microbial analysis at the single-cell level, providing phenotypic classification with minimal physiological interference. Therefore, the technique may be a useful tool for classifying bacterial cultures, without requiring strict standardization of growth conditions. Clearly though, further work is required to establish spectral databases to determine whether these findings hold true for a larger range of organisms. We also demonstrated that the approach can effectively detect differences in profiles according to individual species physiology and those cells which have incorporated isotopically labeled molecules. Detection of differences in physiology within single species has the greatest potential applications for food and medical microbiology, where single-species communities are common and exist in a wide range of growth states, which may have a strong bearing on control strategies. The ability to detect 13C assimilation in single cells is of direct benefit to ecological studies of microbes in their natural environments. Since the red-shift we observed for a given species can only occur as a result of isotope incorporation, and is not dependent on species identity, it should be possible to analyze those individual cells which have utilized a specific substrate tracer to infer key catabolic functions. This allows wholecell analysis in the environment and represents a technology complementary to current SIP methods.4,5 Realistically, it would be unfeasible to identify 13C-labeled cells based on the Raman spectra alone because of the “skewing” of profiles resulting from isotope addition. However, there are a number of strategies that would enable in situ identification to be coupled with Raman analysis, such as FISH hybridization3 or the isolation of cells using laser tweezers33-35 and subsequent molecular- or culture-based analysis. ACKNOWLEDGMENT We thank Dr. Adrian Knowles and Dr. Simon FitzGerald from Jobin Yvon Ltd. U.K. for supplying the Raman confocal microscope and technical advice. We are also grateful to Dr. Roy Goodacre for providing analysis codes and advice. We thank Professor Peter Atkins (Chemistry Department, Oxford University) and Professor Laurence Barron (Chemistry Department, University of Glasgow) for advice. SUPPORTING INFORMATION AVAILABLE Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org. Received for review February 13, 2004. Accepted May 9, 2004. AC049753K
