• vibrational spectroscopy; Raman spectroscopy; IR absorption spectroscopy; direct microbial identification; single cell analysis


  1. Top of page
  2. Abstract
  3. IR Absorption Spectroscopy versus Raman Spectroscopy for the Characterization of Microorganisms
  4. Raman Spectroscopic Identification of Cell Components
  5. Identification of Single Cells
  6. Conclusions
  7. Literature Cited

Rapid microbial detection and identification with a high grade of sensitivity and selectivity is a great and challenging issue in many fields, primarily in clinical diagnosis, pharmaceutical, or food processing technology. The tedious and time-consuming processes of current microbiological approaches call for faster ideally on-line identification techniques. The vibrational spectroscopic techniques IR absorption and Raman spectroscopy are noninvasive methods yielding molecular fingerprint information; thus, allowing for a fast and reliable analysis of complex biological systems such as bacterial or yeast cells. In this short review, we discuss recent vibrational spectroscopic advances in microbial identification of yeast and bacterial cells for bulk environment and single-cell analysis. IR absorption spectroscopy enables a bulk analysis whereas micro-Raman-spectroscopy with excitation in the near infrared or visible range has the potential for the analysis of single bacterial and yeast cells. The inherently weak Raman signal can be increased up to several orders of magnitude by applying Raman signal enhancement methods such as UV-resonance Raman spectroscopy with excitation in the deep UV region, surface enhanced Raman scattering, or tip-enhanced Raman scattering. © 2008 International Society for Advancement of Cytometry

There is a widespread need for rapid and reliable detection and identification methods of microorganisms such as bacteria and yeasts. An early microbial identification is, for example, crucial for food quality control or for pharmaceutical and cosmetical manufacturing processes to guarantee contamination free products. In medicine, an early pathogen detection and identification ideally on a strain level in clinical samples is extremely important to achieve an expedited initiation of targeted antibiotics for therapy to prevent the development of antibiotic resistances against the applied drugs (1, 2).

The classical approach for microbial diagnosis is based on the isolation and enrichment to obtain pure cultures. The actual differentiation is performed according to morphological aspects such as shape and size and via physiological tests. However, identification based on these physiological tests, which is on the basis of metabolic reactions and products, requires several days (2–5) and medication with broad spectrum antibiotics as the initial therapy step is not satisfactory because of the increasing emergence of antibiotic resistant bacterial species (1). For an effective and successful treatment, real-time and reliable pathogen differentiation is essential. Thus, alternative approaches for the identification of individual bacterial contaminants are urgently required. In this context, many new identification methods such as mass spectrometry (3, 6), polymerase chain reaction (7–10) with sequencing techniques, flow cytometry and cell sorting (10–13), fluorescence spectroscopy (10, 13–15), or immunological tests (10, 12, 16) were developed. However, some of these tests presuppose pure cultures, which are not available for all species (16, 17).

In the last years, vibrational spectroscopic techniques such as FTIR (Fourier transform infrared) spectroscopy and micro-Raman spectroscopy with excitation in the visible (VIS) or near infrared (NIR) have demonstrated their great potential in the application of microbial classification (18, 19). A commercial system for analyzing bacterial strains using Raman spectroscopy is available from River Diagnostics® for bulk measurements ( and from rap.ID Particle Systems GmbH for single-cell analysis ( These methods generate high dimensional data. In general, vibrational spectra from different bacterial and yeast species and strains share similar bands, but the relative amounts of these components vary between different species and strains. The application of statistical data evaluation methods by means of unsupervised classification methods (hierarchical cluster analysis (HCA) (20) and principal component analysis (PCA) (21) and supervised methods [k-nearest neighbors, soft independent modeling of class analogies, artificial neural networks (22), and support vector machines (SVM)] (23, 24) allow taxonomic discrimination between microorganisms.

The IR and Raman spectrum serve as a “spectral fingerprint” because it provides comprehensive chemical information for characterization and identification of (cell-) biological systems on a molecular level (25). Both methods are characterized by a minimal sample preparation and allow for a noninvasive investigation of biological samples. Raman-spectroscopy is especially suited for biological applications because it allows in contrast to IR absorption spectroscopy the analysis of aqueous samples as the Raman signals of water exhibit low Raman intensities (26, 27). Moreover, applying confocal micro-Raman spectroscopy, which combines a Raman setup with a light microscope equipped with a high magnification and numerical aperture objective a great spatial resolution in the range of about 1 μm can be achieved (28–30). This allows for the differentiation of single bacterial cells with similar size and the identification of subcellular components within yeast cells with a size of about 10 μm in diameter.

However, Raman scattering is characterized by a low scattering efficiency and Raman spectra recorded for excitation wavelengths in the VIS or near infrared region are very often interfered with the more intense fluorescence emission (31, 32). Therefore, to overcome these problems several Raman signal enhancement methods such as resonance Raman spectroscopy (33) or surface enhanced Raman scattering (SERS) (34) have been developed.

For resonance Raman scattering, the excitation wavelength lies within the region of the electronic absorption bands of certain molecules leading to an intensity increase of the scattering process to a factor of 106 compared with nonresonant Raman excitation (35). Moreover, depending on the excitation wavelength, a selective and sensitive excitation of specific macromolecular biomarkers can be achieved that permits the detection of low concentrations in a complex biological environment. As most biomolecules and taxonomic markers, such as DNA and proteins, show absorption bands in the deep UV range below 260 nm (36), the application of Raman excitation wavelength in this deep ultraviolet region yield fluorescence-free resonance Raman spectra with good signal to noise ratio because fluorescence emission bands occur mostly in the visible spectral range (37).

UV-resonance Raman (UVRR) spectroscopy with excitation in the deep UV region ranging from 200 to 260 nm provides mainly Raman signals of aromatic amino acids (tryptophan, tyrosine, phenylalanine, or histidine) from protein subunits and purine and pyrimidine bases of DNA/RNA nucleic acids. Hence, with deep UV Raman excitation a selective enhancement of DNA/RNA and protein signatures in bacterial or yeast cells can be achieved, whereby the remaining molecules yield negligible signals. Therefore, the application of UVRR spectroscopy allows for a sensitive and selective genotaxonomic classification of microorganisms based on varied GC-ratios (percentage content of the DNA bases guanine and cytosine compared with the entirety of the bases guanine, cytosine, adenine, and thymine) between bacterial species and strains. In contrast to UVRR micro-Raman spectroscopy with excitation wavelengths in the VIS or NIR region allow for a phenotypical characterization of microorganisms because this NIR/VIS Raman spectroscopy provides information of the biochemical composition of the whole microbial cell resulting in a superposition of each cell component (carbohydrates, lipids, proteins, and DNA/RNA).

Surface enhanced Raman spectroscopy (SERS) yields a signal enhancement of 4–15 orders of magnitude compared with normal Raman spectroscopy because of the excitation of surface plasmon oscillations of a rough metal surface or metal colloids that are in close contact to the sample. Therefore, SERS is a useful and sensitive structural spectroscopic tool in the analysis of biomolecules (34) and microbial cells (38, 39) with the potential for single molecule detection (40).

IR Absorption Spectroscopy versus Raman Spectroscopy for the Characterization of Microorganisms

  1. Top of page
  2. Abstract
  3. IR Absorption Spectroscopy versus Raman Spectroscopy for the Characterization of Microorganisms
  4. Raman Spectroscopic Identification of Cell Components
  5. Identification of Single Cells
  6. Conclusions
  7. Literature Cited

IR and Raman spectroscopy are powerful molecular structural techniques for the investigation of microorganisms. The vibrational spectra reflect information about the overall molecular composition of the cells. IR absorption spectra are mostly dominated by molecular vibrations involving a large change in the dipole moment, e.g., C[DOUBLE BOND]O[BOND] stretching vibrations of amides from proteins, organic esters, or carbonic acid from lipids provide intense IR absorption bands. Raman spectroscopy provide structural information complementary to the IR absorption data because in Raman spectra vibrational signals of structures accompanied with a high polarizability are present arising from mainly nonpolar bonds (41).

The application of FTIR and FT-Raman spectroscopy yields reproducible spectral information from microcolonies allowing for an identification on a species and strain level (2, 4, 25, 42–44).

To compare the spectral information content of both vibrational spectroscopic methods Figure 1 displays representative IR absorption and Raman spectra of the bacterium Streptomyces pseudovenezuela. The IR spectrum (a) is dominated by the water band at 3,430 and 1,645 cm−1 even though a dried bacterial film was used. Around 2,935 cm−1 the C[BOND]H stretching vibration is found. Naumann (45) could show that mainly lipids contribute to these C[BOND]H signals in the FTIR spectra because other typical cell components do not exhibit pronounced C[BOND]H stretching vibrations. At 1,749 cm−1 the C[DOUBLE BOND]O double bond from lipids can be found. The band at 1,644 cm−1 can be attributed to the amide I vibrations, the C[BOND]O stretching and N[BOND]H in plane bending vibration, of α-helical structures and β-pleated sheet structures (4), whereas the amide II vibration with the N[BOND]H bending coupled with the C[BOND]N stretching mode can be found at 1,551 cm−1. The peak at 1,371 cm−1 can be assigned to C[BOND]H deformation vibrations and the band of 1,250 cm−1 is associated with amide III band components because of N[BOND]H and C[BOND]Cα vibrations of proteins. At 1,074 cm−1 P[DOUBLE BOND]O stretching vibrations of phosphodiesters are located. In the region of 1,200 and 900 cm−1 C[BOND]O, C[BOND]O[BOND]H, and C[BOND]O[BOND]C deformation and C[BOND]C stretching vibrations of carbohydrates can be found (4).

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Figure 1. Different vibrational spectroscopic spectra of S. pseudovenezuela (a) IR absorptions spectrum, (b) micro-Raman spectrum with an excitation wavelength of 532 nm, (c) UV-resonance Raman spectrum with an excitation wavelength of 244 nm.

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As mentioned above Raman spectroscopy yields a complementary information content as compared with IR absorption spectroscopy. Spectrum 1(b) shows a typical bacterial micro-Raman spectrum recorded for an excitation wavelength of 532 nm. As already mentioned above drying of the samples is not necessary for Raman measurements because the water signal at 3,240 cm−1 is much less pronounced as compared with the IR absorption spectrum. The signal with the highest Raman intensity is the C[BOND]H stretching vibration around 2,935 cm−1. The band at 1,663 cm−1 can be attributed to the amide I vibration and the peak at 1,578 cm−1 is because of ring stretching vibrations of the deoxyribonucleotides adenosine monophosphate (46) and guanosine monophosphate (GMP). The Raman band at 1,450 cm−1 can be assigned to signals of C[BOND]H deformation vibrations whereas the peaks in the region of 1,338 cm−1 corresponds to signatures of AMP, GMP, as well as to the aromatic amino acids tyrosine and tryptophan. The band at 1,001 cm−1 is due to the symmetric benzene/pyrrole in-phase and out of phase breathing mode of tryptophan and phenylalanine. The weak peak around 781 cm−1 can be attributed to uridine monophosphate (UMP) and cytidine monophosphate (CMP). The broad signals at 1,080 and 800 cm−1 can be assigned to fused silica which in this case was used as substrate.

Figure 1(c) displays the UVRR spectrum of S. pseudovenezuela exhibiting a totally different intensity pattern as compared with the nonresonant Raman spectrum (Fig. 1b) due to the characteristic resonance Raman enhancement pattern. The deep UV Raman excitation wavelength of 244 nm gives rise to sensitive and selective chemical information of chromophoric macromolecules absorbing in this spectral region. These macromolecules include nucleic acids from DNA and RNA as well as amino acids of proteins. Both the water signal as well as the C[BOND]H stretching vibration at 2,935 cm−1 are not very pronounced in the UVRR spectrum (c) recorded at 244 nm compared with the micro-Raman spectrum (b) at 532 nm. This can be explained by the fact that signals of water and C[BOND]H stretching modes are not resonantly enhanced for the applied deep UV Raman excitation wavelength. Mainly contributions from aromatic amino acids and nucleic acids can be found in the UVRR spectrum. The band around 1,644 cm−1 can be attributed to signals of the deoxyribonucleotide thymidine monophosphate (TMP) and the ribonucleotide UMP as well as to the amide I vibration. The in-plane ring stretching vibrations of aromatic amino acids are located at 1,612 cm−1. The signal at 1,570 cm−1 is due to vibrations of deoxyribonucleotides AMP and GMP, as well as to the in- and out-of-phase breathing motions of the benzene/pyrrole rings of tryptophan. The peak at 1,525 cm−1 is due to the deoxyribonucleotide CMP. The deoxyribonucleotides GMP and AMP give rise to the prominent peak at 1,481 cm−1. Another characteristic band at 1,364 cm−1 can be assigned to tryptophan. The signal at 1,171 cm−1 can be assigned to the in-plane C[BOND]H bending vibration of tyrosine.

UVRR spectroscopy is a very attractive tool for the investigation of microorganisms because many biomolecules, proteins, and DNA within the cell absorb strongly in the UV. Many studies showed the great potential of UVRR spectroscopy for the characterization of microorganisms and their components (47) as well as for microbial discrimination (36, 48, 49). Nevertheless, because of the high energy density of UV light, only bulk material can be analyzed by UVRR spectroscopy resulting in an average Raman signal from ∼104 to 105 (50). Wu et al. (51, 52) applied UVRR spectroscopy and showed the possibility for the determination of the nucleic acid composition of Escherichia coli at well defined growth phases. UVRR spectroscopy was also employed by Lopez-Diez and Goodacre (37) and in combination with HCA the authors achieved a classification of Bacillus and Brevibacillus species. Jarvis and Goodacre (53) used UVRR in conjunction with PCA and HCA for the discrimination of urinary tract infections. In our group, we have shown that, the application of UVRR spectroscopy with a supervised classification method, namely, SVM allow for a precise identification of microorganism from clean room environment to the species and strain level (54).

Raman Spectroscopic Identification of Cell Components

  1. Top of page
  2. Abstract
  3. IR Absorption Spectroscopy versus Raman Spectroscopy for the Characterization of Microorganisms
  4. Raman Spectroscopic Identification of Cell Components
  5. Identification of Single Cells
  6. Conclusions
  7. Literature Cited

For an interpretation of a vibrational spectrum of a microbial cell, an understanding of the cellular or substructural composition including DNA/RNA content, the amount of proteins, lipids, or carbohydrates as well as the existence of endogenous dyes is required. The chemical compounds and structures of microorganisms are of great importance for the taxonomy and phylogeny of bacteria and yeasts (25). Raman spectra of microorganisms recorded with excitation wavelengths in the VIS or NIR region consist of signal contributions from all components present in the cell and therefore, reflect their overall molecular composition. The assignment of Raman bands in such a complex spectrum to submicroscopic structures, cellular compounds, or specific functional groups allows for a characterization and identification of bacteria or yeasts. Furthermore, Raman spectra provide information on molecular interactions within a cell allowing for the investigation of metabolic changes involved with aging processes, culture conditions, and specific cell-drug interactions (55–58).

Micro-Raman Spectroscopy with Visible Excitation Wavelength

Figure 2 shows representative Raman spectra of the most important building blocks of different biomolecules in comparison with a micro-Raman spectrum of a single bacterial cell of Staphylococcus epidermidis ATCC 35984 (Fig. 2f). Bacteria consist mostly of water, about 40–60% of proteins, about 2–4% of DNA, 5–15% of RNA, 10–15% of lipids, and 10–20% of polysaccharides (25). Bacteria can also possess storage granules such as poly-hydroxybutyric acid, sulfur or calcium dipicolinate (CaDPA), if bacteria have the ability for the development of endospores. The Raman spectrum of water (Fig. 2a) exhibits an intense and broadened band around 3,400 cm−1 and only an insignificant band around 1,645 cm−1.

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Figure 2. Raman-spectra of different biomolecules in comparison to a micro-Raman spectrum of a bacterial cell. (a) Water, (b) DNA, (c) polysaccharide, (d) lipid, (e) protein, (f) single cell of Staphylococcus epidermidis ATCC 35984.

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By comparing the Raman spectra of the important biological building blocks displayed in Figure 2 namely of DNA (b), polysaccharides (c), lipids (d), or proteins (e), it is obvious that these molecular classes deliver highly specific intense Raman patterns especially in the fingerprint region below 2,000 cm−1. As the water signal is negligible in this region, the water matrix does not have a great impact in terms of interfering with the important biomolecules' signals. Proteins exhibit characteristic amide bands: amide I (1,663 cm−1) and amide III [1,253 cm−1 (Fig. 2e)]. Lipids reveal a characteristic C[DOUBLE BOND]O stretching band at 1,749 cm−1 (Fig. 2d) that can be well separated from DNA signatures where intense bands around 1,578 or 781 cm−1 are present because of contributions of the nucleic acid bases (Fig. 2b). Consequently, all biomolecules show different and characteristic Raman signatures. As the bacterial species differ in their chemical composition of biomolecules, the characteristic Raman fingerprints of the biomolecules allow for a cellular and biomolecular characterization and identification between several species and strains.

Furthermore, micro-Raman spectroscopy offers the advantage of a high spatial resolution in the range of 1 μm; thus, allowing for the analysis of small objects such as single bacterial cells or subcellular structures within larger cells such as yeasts. Hence, Raman spectroscopy is ideally suited for identifying compounds of biological material. Schuster et al. extensively studied single bacterial cells during cell cycle by means of micro-Raman spectroscopy. In doing so they could detect culture heterogeneity on a single-cell level because of different chemical compositions of the cells. The results show that starch-like granulose could be identified (59, 60). Microorganisms in low numbers such as monolayers and single cells have been investigated whereby carotenoids could be detected for some pigmented species by means of Raman spectroscopy by Rösch et al. (61). A detailed description of our activities concerning the identification of microorganisms on a single-cell level can be found vide infra (IDENTIFICATION OF SINGLE CELLS Section). Pätzold et al. (62) recorded microbial distribution of nitrifiers and anammox bacteria in their natural environment with utilization of the resonance Raman effect of cytochrome c present within the cells. The Raman spectra for most endospore forming bacteria, such as Bacillus species, show signals of CaDPA; proteins also could be found (30, 63). Chan et al. (64) demonstrated the potential of laser tweezers Raman spectroscopy as a nondestructive and real-time method for monitoring changes in single, optical trapped E. coli cells suspended in solution that occur in the extracellular domain of myelin oligodendrocyte glycoprotein.

To gain a better insight into the metabolism of the bacteria and to learn more about antibiotic action Neugebauer et al. observed wavenumber changes in the Raman spectrum accompanied with growth time and the effect of antibiotics onto bacterial growth by means of micro-Raman spectroscopy with visible excitation as well as UVRR spectroscopy. As mentioned in “IR Absorption Spectroscopy versus Raman Spectroscopy for the Characterization of Microorganisms Section,” excitation in the UV region enhances because of the resonance effect the Raman signals of vibrations from the biological target structures of aromatic amino acids and nucleic acid bases. With both methods metabolic changes occurring during the bacterial growth and because of the antibiotic interaction within the bacteria were monitored and analyzed with the help of statistical data evaluation (58, 65).

Another approach for the interpretation of bacterial Raman spectra was demonstrated by DeGelder et al. As there is much overlap from different biomolecules in every bacterial Raman spectrum, 2D-correlation spectroscopy as a chemometric technique was used for modeling spectral changes related to an external factor, in this case to time. By means of 2D-correlation spectroscopy, sensitive identification of the most important changes within bacterial cells during several bacterial growth stages can be achieved by the power spectrum. As a result 2D synchronous spectra indicate which spectral regions are correlated to each other whereas the asynchronous spectra are used for the determination of the order in which spectral changes arise (66). In another study using 2D-correlation spectroscopy it was possible to identify microorganisms responsible for gastroenteritis. In this work, the external factor responsible for characteristic spectral changes is the increase of the bacterial concentration that are dispersed in a physiological solution resulting in a solvation-induced shift of the amide I mode (67).

Surface Enhanced Raman Spectroscopy

Raman signal enhancement in SERS is achieved by exciting surface plasmon oscillations of a roughened metal surface or metal colloid solution in the close vicinity of the sample through the incident laser light. These surface plasmon oscillations generate a high local electromagnetic field leading to enhanced Raman signals with a factor of 104–1015 depending on the applied method (68).

For a bacterial SERS analysis (34, 38, 39) various SERS substrates, mainly silver or gold nanoparticles, or SERS microchips in combination with antibodies were investigated (69). For instance, as SERS-active substrates for the investigation of bacterial cells metal deposited island films (70) or immobilized nanoparticles on glass slides (46) are proven.

Several studies showed that SERS spectra allow for distinguishing cell membrane components of several bacteria, e.g., by applying SERS, the redox heme protein of Shewanella oneidensis MR1 could be identified as a major component of the cell surface domains (70). Leyton et al. (46) investigated the bacterium Acidithiobacillus ferrooxidans by means of SERS and found out that the SERS spectra displayed physical and chemical variations caused by different growth media. The analysis of silver-treated bacteria revealed intense and highly specific SERS spectra associated with several signals of flavin adenine dinucleotide (FAD), DNA, carboxylates, and phosphates, depending on the sample preparation with the colloids. Zeiri et al. (71) studied silver-treated bacteria and could show that their SERS spectra were dominated by FAD that is located in the plasma membrane of the cell. According to changes in the SERS spectrum, the state of oxidation of the flavins could be tracked. SERS imaging of fungal hyphae grown on nanostructured SERS active substrates was studied by Szegalmi et al. (72) to present the possibility for the detection of single cell wall components. A comparative study of psychro-active arctic marine bacteria and common mesophillic bacteria by means of surface enhanced Raman spectroscopy revealed that a higher lipid content of unsaturated fatty acids in the outer membranes of marine bacteria could be identified (73).

A novel methodology that combines SERS with optical tweezers detection was shown by Alexander et al. (74). This approach was developed for the simultaneous excitation of SERS of individual, optically trapped, bacterial spores. The collected SERS spectra were used for strain discrimination of Bacillus stearothermophilus spores.

For a bacterial investigation by means of SERS redox protein nanoscale domains on the cell surface of S. oneidensis MR1 were topographically characterized by atomic force microscopy (AFM) and SERS (70). A recently developed technique, called tip-enhanced Raman spectroscopy (TERS) combines SERS with AFM to obtain a spatial resolution on the atomic scale. With TERS the surface structure of the cell wall of the bacteria S. epidermidis was intensively studied with a high lateral resolution and chemical specificity. The analysis showed that the spectra changes over time because of fluctuations of the cell wall components on the outer cell membrane (57, 75, 76).

Identification of Single Cells

  1. Top of page
  2. Abstract
  3. IR Absorption Spectroscopy versus Raman Spectroscopy for the Characterization of Microorganisms
  4. Raman Spectroscopic Identification of Cell Components
  5. Identification of Single Cells
  6. Conclusions
  7. Literature Cited

With IR absorption spectroscopy and UVRR spectroscopy microcolonies including a few hundred cells and bulk samples resulting from the averaged signal over several cells can be examined (4, 50). For the investigation of microcolonies including a few hundred cells cultivation for at least 6 h is necessary to obtain enough biological material for measurement (2).

Single bacterial cells can be studied by utilizing the surface enhancement effect (34). As only short data acquisition times and low incident laser powers are necessary for SERS, this enhancement technique has the potential for the identification of single bacterial cells (38, 77, 78). However, the application of SERS substrate might lead to physiological changes within the cells. Leary et al. (79) developed a microfluidic cytometry device based on a combination of surface plasmon resonance (SPR) imaging and molecular imaging for the detection of small numbers of pathogenic bacteria. Specific peptide sequences are immobilized on the chip surface resulting in an adhesion of only specific target bacteria that can be detected by SPR imaging.

By applying micro-Raman spectroscopy with excitation in the visible range it is possible to study single bacterial cells in their native environment. Recently, we have shown that micro-Raman spectroscopy in combination with chemometric methods is an extremely capable approach for a fast and reliable, nondestructive identification of bacterial cells on a single-cell level (24, 30, 55, 80, 81). This allows for an acceleration of the differentiation process because they can be analyzed directly after sample collection without cultivation step (30, 60).

Challenges of a Single-Cell Raman Analysis

Nevertheless, several challenges are present while going from a bulk environment to a single-cell Raman analysis. Figure 3 presents an overview over the differences between bulk analysis and single-cell identification of an unknown biological sample containing several particles. In this case, different sample preparation techniques are required. As already pointed out in classical microbiology, the identification of microorganisms is based on nutritional and biochemical tests after culturing that lasts up to several days. For Raman spectroscopic bulk measurements, bacteria are also required to be cultured but only until microcolonies of some hundred cells are formed. Hereby, bacterial contamination could be analyzed directly from microcolonies after 6 h of cultivation on agar plates or measurements can be performed from smears by picking some biomass from the agar plates with an inoculating loop onto the substrate, e.g., fused silica. A microcolony is composed of a mixture of cells with several physiological phases and the Raman spectrum results from their average signal.

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Figure 3. Difference between bulk analysis and single cell identification (for details see text). [Color figure can be viewed in the online issue which is available at]

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There is a great demand for the analysis of single cells directly after deposition of the particles, e.g., in a clean room environment or food processing technology (24). Going from bulk investigations to the identification of single-cell contaminations their environmental conditions or growing parameters and age are unknown. Several environmental parameters like aerobic or anaerobic environment, pH-value, age, temperature, nutrition, and light will influence the growth of a single microorganism and thus its biochemical composition leading to variabilities in its Raman spectrum. Therefore, for a successful identification of unknown cells on a species and strain level it is important to know if the growth dependent intrastrain specific cellular variations because of different cultivation conditions and culture ages are low or even negligible in its Raman spectrum as compared with the observable differences between different strains. Hence, different cultivation conditions have to be deposited in a chemometric database with reference spectra that enable the identification of discovered single cells.

Figure 4 shows the impact of different nutrient media and cultivation temperature of single E. coli cells onto the Raman spectra: Raman spectra of E. coli recorded after cultivation in standard-I-medium (S-I-NA) at 30°C (a), grown in nutrient medium (NA) at 30°C (b), cultured in S-I-NA at 37°C (c), and in NA at 37°C (d). The Raman spectra of single E. coli cells measured after several cultivation conditions concerning NA and temperature show slight differences. Both media differ in their composition; NA (Merck, Darmstadt, Germany) consists of 5 g peptone, 3 g meat extract, and 14 g agar (if necessary) per liter and S-I-NA (Merck, Darmstadt, Germany) is made up of 15 g peptone, 3 g yeast extract, 6 g sodium chloride, 1 g glucose, and 14 g agar (if necessary) per liter. Intensity variations of signals from tryptophan and phenylalanine around 1,001 cm−1 can be observed. Figure 5 displays the influence of cultivation age of single S. epidermidis cells on their Raman spectrum; Raman spectra were recorded after 6 h(a), 30 h(b), 54 h(c), and 72 h (d). The Raman spectra exhibit similar signals but intensity variations of the 1,575 cm−1 band can be seen. This band can be assigned to ring vibrations of DNA/RNA nucleic bases. For longer incubation times, the intensity decreases due to a decay of DNA/RNA concentration in the cells visualizing the replication reduction of bacteria during growth phases.

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Figure 4. Raman spectra of Escherichia coli recorded at different temperatures and nutrition agars. (a) S-I-NA, 30°C; (b) NA, 30°C; (c) S-I-NA, 37°C; (d) NA, 37°C.

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Figure 5. Raman spectra of S. epidermidis ATCC 35984 measured at different cultivation ages. (a) 6; (b) 30; (c) 54; (d) 72 h.

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Figures 4 and 5 show that different environmental and cultivation parameters have an impact on the growth of single bacterial cells leading to changes within the Raman spectra. Nevertheless, the Raman spectra show similar signatures with only subtle variations. For this reason and because of the large number of Raman spectra that are necessary for a reliable single-cell identification, because various cultivation parameters have to be taken into account, multivariate statistical data evaluation methods are applied.

The effect of several cultivation conditions concerning temperature, NA, and culture age onto the identification accuracy was investigated by analyzing microcolonies for a selected amount of bacterial species of Bacillus strains by Hutsebaut et al. (56). Furthermore, the classification ability was explored in dependency of temperature, NA, and culture age variations for a selected database of bacterial cell of the genus Staphylococcus on a single-cell level by Harz et al. (55). Hutsebaut et al. and Harz et al. showed that the investigation of several species and strains within a selected database with several cultivation conditions is more complicated. The classification of metabolic variations within single cells in the Raman spectra due to different cultivation parameters is hampered due to an enhanced heterogeneity of metabolic variations compared with differences between species or strains. They showed constraints of the functionality of data analysis method of HCA for differentiation that could not be used for successful classification of this complex heterogeneous data set containing versatile cultivation parameters. Therefore, multivariate classification methods adapted to the application for reliable species differentiation are necessary such as trainable SVM (81).

Identification by Means of Micro-Raman Spectroscopy

Micro-Raman spectroscopy is a versatile tool for the characterization and discrimination of bacterial cells and identification of unknown bacterial cells on a single-cell level. Huang et al. (82) used this approach for the differentiation of single bacterial species and between growth phases of individual species. Furthermore, the differentiation of biofilms and planktonic cells could be achieved by investigating Pseudomonas populations (83). Rösch et al. (24, 30) showed the chemotaxonomic identification potential of a larger variety of several bacteria strains present in a clean room environment.

The coupling of fluorescence in situ hybridization with Raman imaging allows for the identification of 13C incorporation during bacterial anabolism of 13C-labelled bacteria of Pseudomonas fluorescens (84).

Another powerful tool for the analysis of biological samples without damage is optical trapping using NIR laser beams whereby optical forces are used to hold a micrometer-sized particle close by the focus of a focused laser beam. This technique can be used to capture and manipulate biological particles such as chromosomes, viruses, and cells such as yeast and blood cells as well as single bacteria. The combination of optical trapping and Raman spectroscopy is appropriate for studying dynamical processes within single cells. Xie et al. extensively studied single bacterial and yeast cells during a heat-denaturation process. During this procedure, the cell state from live to dead can be monitored in real-time via Raman spectroscopy (85). Furthermore, the simultaneous application of optical trapping in conjunction with Raman spectroscopy is a rapid method for reagentless identification and discrimination of single bacterial cells in aqueous media (17).

In addition to discrimination of bacterial species and strains (prokaryotes), identification of microorganisms includes the differentiation between eukaryotic microorganisms such as yeast cells on species and/or strain level as well as differentiation of prokaryotes from yeast cells. Eukaryotes exhibit in contrast to prokaryotes cell membrane enclosed reaction rooms—organelles such as cell nucleus, endoplasmatic reticulum, or vesicles, which are delimited from cell plasma. For prokaryotes genomic DNA and possibly extra chromosomal plasmid DNA is found within the cell in circular form. Because of the compartmentalization and therefore spatial heterogeneity within an individual yeast cell and its even greater cell size in the range of 10 μm in diameter compared with the spatial resolution of about 1 μm from a micro-Raman setup, it is not sufficient to characterize a yeast cell by means of a single Raman spectrum as compared with the bacterial analysis described earlier.

By means of Raman mapping in axial and lateral direction over the whole cell several intracellular macromolecules such as DNA/RNA, proteins, lipids, or carbohydrates can be localized. Furthermore, so called false color plots allow for visualization of their spatial distribution within the cell (86). Rösch et al. (87) showed additionally that a differentiation of single yeast cells can be performed on a strain and species level with the application of statistical data evaluation on several average Raman spectra.

The process of yeast cell division and change during cell cycles has been extensively studied under various environmental conditions by means of Raman spectroscopy at time- and space-resolved molecular level by Huang et al. Additionally with this approach nucleus, mitochondrion, and septum could be identified (88–90).

Localization and Identification of Bacteria in Heterogeneous Matrices

The presence of complex environmental backgrounds challenges the detection and identification of microbial pathogens. For instance if in addition to bacterial and yeast cells, the sample contains increased amounts of organic or inorganic particles in the same size range as the microbial cells, then discrimination of targets is hampered.

Consequently, to identify biotic particles in the presence of abiotic organic or inorganic material, a preselection of relevant biotic particles is required. These have to subsequently be identified either as pathogens or as harmless microorganisms. Alternatively, amplification or enhancement techniques are most wanted to facilitate the discovery and localization of microorganisms.

The study of Rösch et al. (24, 30, 81) confirmed that the utilization of different autofluorescence characteristics between abiotic and biotic particle as preselection helps reducing the time needed for the Raman identification process of microorganisms in aerosols under clean room environment.

By means of Raman chemical imaging spectroscopy, which combines Raman spectroscopy, fluorescence spectroscopy, and digital imaging a reagentless discovery and identification of biological agents within complex environmental matrices could be shown by Kalasinsky et al. (91).

If the sensitivity of autofluorescent characteristics is not sufficient enough for the discovery of biological contaminations it may be necessary to use stains for their detection. Some fluorescent dyes demonstrate the presence of nucleic acids or special metabolic or enzymatic activity. Krause et al. applied the simultaneous application of fluorescence staining and Raman spectroscopy for single-cell analysis of bacteria. Depending on the selected stains, fluorescence staining before Raman analysis offers the possibility of live/dead or biotic/abiotic differentiation, whereas Raman spectroscopy delivers additional fingerprint molecular information for cellular identification (80).

Further, challenging environmental backgrounds for reliable detection of pathogens are the rapid sensing of microbial cells in water and food for health concern to guarantee safe drinking water and food safety. Tripathi et al. (92) extensively studied the impact of background interferences (including the composition of the water matrix and the effect of the organisms aging in water) and evaluated laser-induced photodamage thresholds with Raman chemical imaging spectroscopy for the detection of waterborne agents. In addition to waterborne pathogens, foodborne microorganisms have also been analyzed by means of Raman spectroscopy. Yang and Irudayaraj (93) demonstrated that FT-Raman spectroscopy can be an appropriate tool for rapid examination of food surfaces that allows fast detection and discrimination of microorganisms.


  1. Top of page
  2. Abstract
  3. IR Absorption Spectroscopy versus Raman Spectroscopy for the Characterization of Microorganisms
  4. Raman Spectroscopic Identification of Cell Components
  5. Identification of Single Cells
  6. Conclusions
  7. Literature Cited

The results presented within this short review convincingly demonstrate that spectroscopic methods (FTIR spectroscopy, micro-Raman spectroscopy with excitation in the visible or NIR and ultraviolet, as well as special Raman techniques using SERS) are considered to be extremely capable methods for the characterization, discrimination, and identification of microorganism at the genus, species, and strain level. Especially micro-Raman spectroscopy provides major benefits for a nondestructive and reliable online identification of microbial cells because this technique allows for a single-cell analysis due to the high spatial resolution in the submicrometer range and requires only minimal sample preparation without the need for preanalytical cultivation of the cells. Hence, this method has the potential for a rapid identification of microbial pathogens against a stable database in defined application fields where only a limited number of different species and strains are present.

Literature Cited

  1. Top of page
  2. Abstract
  3. IR Absorption Spectroscopy versus Raman Spectroscopy for the Characterization of Microorganisms
  4. Raman Spectroscopic Identification of Cell Components
  5. Identification of Single Cells
  6. Conclusions
  7. Literature Cited
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