Literature Review and Imaging E. Coli Using Raman Spectroscopy and Fluorescence Spectroscopy



Literature Review and Imaging E. Coli Using Raman Spectroscopy and Fluorescence Spectroscopy

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Literature Review and Imaging E. Coli Using Raman Spectroscopy and Fluorescence Spectroscopy
Raman Spectroscopy
Raman spectroscopy is a machine established as a physiochemical technique of identification of microbes. This technique generates spectroscopic fingerprint from the microbial sample which gives quantitative and qualitative information that is used to characterize, discriminate and identify microorganisms. Previous studies that have been dealing with identification of bacteria by using Raman spectroscopy have demonstrated that the micro-Raman technique is suitable in identifying single cell microbial such as e. coli. In a particular study Fan et al., (2011), the findings accrued indicated that the Raman spectrum of the microorganisms is the sum of the Raman signals for all the individual components within a particular organism such as its proteins, lipids and nucleic acids that are expressed in the spectrum.
Another study by Li & Church et al.,( 2014) reviewed Raman spectroscopy in evaluated as well as identifying single bacteria cells of Escherichia coli at a strain level as well as under the influence of antibiotics. The results indicated that after cultivating the standard nutrients for e. coli, different concentrations of the spectra were observed. The authors noted that it was challenging in distinguishing the differences of all spectra by using simple visual inspection. This was due to having a large variability on the molecular set up between the individual cells of one group to another group. It was established that for a quality evaluation of the e. coli. Strains, there was the need of applying a chemo metric tool for LDA in identifying and analyzing the data of bacteria. Four closely related well characterized strains of E. coli including an a virulent laboratory derivative of the pathogenic strain E. coli 0157; H7, and three non-pathogenic laboratory strains (E.coli HF4714, E coli Hfr K-12 and E coli C, a hybrid of strains K-12 and C) were used in the evaluation as well as measurement by the Raman method in identifying out the particular kinds of strains present within the family of E.coli(Li & Church, 2014).
In another study by Boyokgoz et al., (2015) the paper aimed at differentiating bacterial strains by the means of surface enhanced FT-Raman spectroscopy by using silver nanoparticle in obtaining the surface enhanced spectra for following organisms; E. coli, Salmonella enteric and Listeria monocytogens. The most common and widely used method of amplifying the weak Raman signal is by attaching the sample into a metallic rough surface that can enhance the Raman signal greatly as well as quench the fluorescence. This technique is considered as surface enhanced Raman spectroscopy and can be used in identification of several bacterial species such as E. coli (Krafft & Popp, 2015). The findings collaborated that the spectra of Escherichia, Listeria and Salmonella did have differences in the position of the bands spectral in all the bacteria and these differences were insignificant though their intensities may have been used for differentiating the bacteria.
A study by Fan et al., (2011) conducted an experiment on A flow through microarray cells of the SERS online antibody detection captured bacteria E. coli. The findings indicated that E. coli fingerprint spectrum as well as the background measurements of the spectra were different. This was explained by the SERS very sensitivity towards the experimental condition as well as the method itself of preparation. The other reason attributed to this kinds of difference were partly due to the presence of metal ions that were originating from the metallic flow channel. Raman spectroscopy as an imaging tool has the advantage of providing information on both chemical compositions as well as the structure of biological molecules that range from proteins to nucleic acids, as well as lipids and carbohydrates. It works by utilizing the interaction of light with molecules by measuring the functional group vibrations elucidated by the method (Assaf et al., 2014).
In the application of the Raman spectroscopy in identifying E. coli has progressed by the initial development of combining the method with optical microscopy. This development enabled high lateral and axial resolution sensitivities well enough to allow identification from small sample volumes including the measurement of single cells. Thus in implementing the Raman identification of E. coli, it is important to recognize that a genetically homogenous cell population can be phenotypically heterogeneous in a variety of environmental conditions that can up-or down modulate gene expression (Boyokgoz et al., 2015). This will in turn affect the relative contributions of proteins, lipids, carbohydrates and nucleic acid in the Raman spectrum.
Further developments in instrumentation are also aiding the application of Raman spectroscopy to biomolecules. Advancement in lasers, optics, and charged- couple device cameras along having advances in the computer processing, have therefore increased the availability to Raman spectrometers in a range of sizes, sensitivities and costs. Raman spectroscopies in combination with microfluidic devices in the characterization of microorganisms have constructed a microfluidic system. The advantages of combining microfluidic devices and surface enhanced Raman scattering from silver colloids in the classification of nine strains of E. coli thus, enhancing the identification of different and particular strains of the organism E. coli (Fan et al., 2011).
The combination of these advances in the Raman spectroscopy are hence moving this technique nearer into providing routine microbial identification as well as characterization-based measurements as organisms does adapt to new environment. Raman spectroscopy is providing a platform of integrating Raman scattering which is surface-enhanced, instrumental design and control of sample preparation, ultimately facilitating single measurements having concomitant advantage of then reducing the lengthy times that are usually needed to culture the pathogen from host prior to analysis (Krafft & Popp, 2015).
Fluorescence spectroscopy
Fluorescence spectroscopy is used in assessing the intensity of photons that are emitted from a sample after it has been absorbed by photons. Most of the fluorescent molecules are aromatic. Fluorescence spectroscopy as a method is a significant investigational tool in most areas of analytical science due to its sensitivity and selectiveness in identifying specific strains of microorganisms with a very little margin of error. Different studies have been done towards assessing the sensitivity and accuracy of this machine. In a particular study Unfolding Studies of Escherichia coli. Maltodextrin Glucosidase monitored by Fluorescence Spectroscopy by Paul et al., (2012), the results indicated that the GdnHCL that was used in inducing the equilibrium transition of the recombinant MalZ was actually studied by the intrinsic tryptophan fluorescence spectroscopy. MalZ of Escherichia coli got denatured after they were monitored by measuring the values of intrinsic tryptophan fluorescence for the GdnHCL-treated samples. More so, fluorescence emission that was emitted by the MalZ-ANS conjugate was used in studying the changes of the surface hydrophobicity of the MaLZ during its process of unfolding (Paul et al., 2012).
In some another study of Fluorescence Correlation Spectroscopy of Membrane Protein TetA Measurements in E. coli suggested Rapid Diffusion in the Short Length Scales by the author Chow et al., (2012), results indicated that TetA, an antiporter that did consist of twelve trans-membrane domain did pump out tetracycline out of the cells of inner membrane of E.coli through exchange of protons. Through studying TetA-YFP diffusion, it then did first turn the measures of fluorescence correlation spectroscopy into temporal correlations for fluorescence intensity fluctuations which were caused by either one or more of fluorescent molecules through diffusing in and out of illuminated excitation volume (Chow et al., 2012.
In using the FCS, the diffusion constant for TetA-YFP which were found in DGC103 e. coli strain were found to be large surprisingly as compared to lipid diffusion constants. Hence this was due to having roughly two to three magnitude orders that were higher compared to the reported diffusion constants in the other e. coli membrane proteins. Previous reports about the diffusion constant that had been determined from recovery of fluorescence after the photo bleaching indicated that they were not aware about some other FCS measurements of membrane protein diffusion which is within live e.coli. Previous reports about the diffusion constant that had been determined from the fluorescence recovery after photo bleaching indicated that they were unaware about other FCS measurements of the membrane protein diffusion within live Escherichia coli. It was hence noted that the membrane proteins’ diffusion constants as well as the lipid probes in large unilamellar vesicles that are measured by the FCS by being comparable with the particular FCS values of this study.
In another study done by David et al., (2012) it did indicate that there was a correlation in the study of bacteria namely Escherichia coli using measurements of Fluorescence spectroscopy. The paper argued out that in bacteria, the organization and mobility of membrane elements are less well defined. Measurements of diffusion in bacterial membranes had been bounded at least in part due to the small size of both bacteria thus making measurements more challenging fluorescence recovery after photo bleaching. Single partial tracking experiments have been generally used in determining diffusion constants in bacterial membranes. In another study it resolved that in a mixed population of slow and fast diffusing genus did find grounds of confined diffusion that did propose structural order in bacterial membranes that might count on the location of the cell as well as length scale(Rose et al., 2015).
In another study by Paul et al., (2012), they measured the inner membrane protein’ diffusion constant TetA-YFP in a living E-coli cell using the following two techniques; (FSC) fluorescence correlation spectroscopy and the fluorescence recovery after photo bleaching(FRAP). These two techniques mainly probe diffusion on long and short length scales. In this particular study the values obtained in recovery of fluorescence after the photo bleaching corresponded to the diffusion constants of other membrane protein in E-coli. Fluorescence correlation spectroscopy measurements align a diffusion constant that is much higher at about two orders of magnitude and is corresponding to lipid diffusion constants. It thus implied that a universe of TetA-YFP molecules had broad mobility at short length scales though they were usually constrained into a much slower diffusion on a longer length scales. It can also be observed that the same behavior occurs in second membrane proteins tar, implying that the outcome might reflect architecture in close to the inner membrane that will prevent diffusion (Hur et al., 2016).
In another study Multicolor Whole cell Bacterial Sensing Using alpha Synchronous Fluorescence Spectroscopy- Based approach by Parrello et al., (2015) the findings indicated that synchronous fluorescence spectroscopy was an increasingly popular tool that was being used in analyzing complex mixtures of the compounds of fluorescent. This method was used in assessing the SFS as compared to other conventional fluoroscopy spectroscopy towards differentiating the mixtures of the fluorescently labeled bacteria especially e.coli.The study did first record the fluorescent spectra of E.coli TOP10/Ppb-lac by doing a synchronous scanning thereafter comparing them with the excitation of the emission spectra(Rose et al., 2015). The scans of E.coli cultures did express different FPs that were performed at the offset by giving out fixed signals towards gaining a signal and then comparing their brightness level in vivo.

Paul, S., Kundu, M., Das, K. P., Mishra, S., & Chaudhuri, T. K. (2012). Unfolding Studies of Escherichia coli Maltodextrin Glucosidase Monitored by Fluorescence Spectroscopy. Journal Of Biological Physics, 34(6), 539-550. doi:10.1007/s10867-008-9117-9
Hur, Kwang-Ho, and Joachim D. Mueller. 2015. “Quantitative Brightness Analysis of Fluorescence Intensity Fluctuations in E. Coli.” Plos ONE 10, no. 6: 1-21. Academic Search Premier, EBSCOhost (accessed July 15, 2016).
Chow, D., Guo, L., Gai, F., & Goulian, M. (2012). Fluorescence correlation spectroscopy measurements of the membrane protein TetA in Escherichia coli suggest rapid diffusion at short length scales. Plos One, 7(10), e48600. doi:10.1371/journal.pone.0048600
Parrello, D., Mustin, C., Brie, D., Miron, S., & Billard, P. (2015). Multicolor whole-cell bacterial sensing using a synchronous fluorescence spectroscopy-based approach. Plos One, 10(3), e0122848. doi:10.1371/journal.pone.0122848
Krafft, C., & Popp, J. (2015). The many facets of Raman spectroscopy for biomedical analysis. Analytical & Bioanalytical Chemistry, 407(3), 699-717. doi:10.1007/s00216-014-8311-9
Fan, C., Hu, Z., Mustapha, A., & Lin, M. (2011). Rapid detection of food- and waterborne bacteria using surface-enhanced Raman spectroscopy coupled with silver nanosubstrates. Applied Microbiology & Biotechnology, 92(5), 1053-1061. doi:10.1007/s00253-011- 3634-3
Buyukgoz, G., Bozkurt, A., Akgul, N., Tamer, U., & Boyaci, I. (2015). Spectroscopic detection of aspartame in soft drinks by surface-enhanced Raman spectroscopy. European Food Research & Technology, 240(3), 567. doi:10.1007/s00217-014-2357-y
Assaf, A., Cordella, C., & Thouand, G. (2014). Raman spectroscopy applied to the horizontal methods ISO 6579:2002 to identify Salmonella spp. in the food industry. Analytical & Bioanalytical Chemistry, 406(20), 4899-4910. doi:10.1007/s00216-014-7909-2
Li, Y., & Church, J. S. (2014). Review Article: Raman spectroscopy in the analysis of food and pharmaceutical nanomaterials. Journal Of Food And Drug Analysis, 22(Nanomaterials – Toxicology and Medical Applications), 29-48. doi:10.1016/j.jfda.2014.01.003
Rose, M., Hirmiz, N., Moran-Mirabal, J. M., & Fradin, C. (2015). Lipid Diffusion in Supported Lipid Bilayers: A Comparison between Line-Scanning Fluorescence Correlation Spectroscopy and Single-Particle Tracking. Membranes, 5(4), 702-721. doi:10.3390/membranes5040702

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Literature Review and Imaging E. Coli Using Raman Spectroscopy and Fluorescence Spectroscopy. (2022, Feb 25). Retrieved from

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