UNCLASSIFIED Click to edit Master title style UNCLASSIFIED Potential real-time detection of toxicity in bacterial cells through Raman spectral analyses of intracellular bio-molecules: A Review Danielle Torres, Dr. Theresah Zu* California State University, Fullerton 1. Ali, Ahmed, et al. “An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects.” Journal of Visualized Experiments, no. 155, 2020, doi:10.3791/60449. 2. Huang, Jun, et al. “Practical Considerations in Data Pre- Treatment for NIR and Raman Spectroscopy.” American Pharmaceutical Review, Oct. 2010, www.americanpharmaceuticalreview.com/Featured- Articles/116330-Practical-Considerations-in-Data-Pre- treatment-for-NIR-and-Raman-Spectroscopy/. 3. Lasch, P., 2012. Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging. Chemometrics and Intelligent Laboratory Systems, 117, pp.100-114. 4. Ryabchykov , Oleg, et al. “Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples.” Frontiers in Chemistry, vol. 6, 2018, doi:10.3389/fchem.2018.00257. 5. Zu, Theresah & Athamneh, Mohd & Wallace, Robert & Collakova, Eva & Senger, Ryan. (2014). Near-Real-Time Analysis of the Phenotypic Responses of Escherichia coli to 1-Butanol Exposure Using Raman Spectroscopy. Journal of bacteriology. 196. 10.1128/JB.01590-14. Special thanks to Dr. Theresah Zu for her mentorship. Thank you to CCDC-ARL and TMT Group for this opportunity. Fluorescence anisotropy data were obtained with the help of Pablo Sobrado and Tijana Grove at Virginia Tech. A large limitation of biofuel optimization through fermentation is product toxicity—a process in which accumulating biofuel becomes toxic to the organism producing it and thus limits yield. The ideal strategy of bacterial engineering requires better understanding of how and why the organism elicits the specific responses when exposed to the bio-product. In this publication review, Raman spectral analysis was employed in characterization of the phenotypic changes of e. coli cells when exposed to butanol which is a desired bio product made during fermentation of the organism. The scope of butanol toxicity was founded with (i) fatty acids content, (ii) membrane fluidity, and (iii) protein and amino acid content. The results suggest that Raman spectral analyses when optimized, may be suited for approximating metabolic and physiological changes in the phenotype of bacterial cells exposed to toxic bio- products. This knowledge, when paired with fermentation systems for real-time decision making, could ultimately lead to increased bio-product yields. • Aliquots from prepared e.coli DHα cells were dried on an aluminum surface at room temperature • The cells were analyzed with a Bruker Senterra dispersive Raman spectrometer at a laser excitation of 532 nm for 25 s • A minimum of 50 spectra was collected per sample • • • • • • • • • • • Discovered by Dr. C.V. Raman, Raman spectroscopy studies the composition of a sample by using a monochromatic light source in the form of a laser. • Molecules that are Raman-active experience changes in polarity when excited. • Most scattering of light is composed of Rayleigh scattering while only 10 -6 of incident light is Raman scattering • A filter is used to block Rayleigh scattering and allows us to study the Raman scattering of the sample • The Raman shift of stokes and anti-stokes in Raman spectra allows us to measure the vibrational energies of the molecule Raman spectra can tell us about: • Crystallinity • Type of material Light intensity versus light frequency • The light intensity is given by the fluorescence or Raman intensity of the Raman-active molecule • Stokes = red shifted, low energy, high wavelength • Anti-stokes = blue shifted, high energy, low wavelength Raman Shift is a measurement of the vibrational energies within a molecule • The Rayleigh line = 0 • Anti-stokes lines = negative wavenumbers • Stokes lines = positive wavenumbers Normalizing Raman data allows us to compare spectra • Before normalization, a baseline correction is normally performed to reduce background noise • One way to normalize data is SNV (standard normal variate) • Calculate average of spectrum • Calculate the standard deviation • “Standardize” in excel • PCA (Principal Component Analysis) is useful for detecting outliers • Reducing fluorescence helps reduce background noise (methods below) • Automated fluorescence correction • High magnification lens • Photobleachng • Total amino acid content and composition did not change in the when E.coli cells were exposed to 1-butanol • Though unchanged, different amounts of each amino acid were observed Ala Arg Asp Cys Glu Gly His Ileu Leu Lys Met **NV Phe Ala Arg Asp Cys Glu Gly His Ileu Leu Lys Met **NV Phe E. Coli cells were first hydrolyzed then diluted with UPLC grade water Aliquots from this diluted sample was used for derivatization reaction. The amount of amino acid hydrolysate per 50 ul derivatized reaction: 0.8ul. Norvaline (NV) was added as an internal standard at a concentration of 10 mmoles/20ul. UPLC chromatographs are reported for intergrated peak height at this retention time. Data has been normalized to the NV peak (i) DNA ~788 cm -1 & RNA ~813 cm -1 (indicative of nucleic acids) (ii) symmetric PO 2 - stretching of DNA (~1070 – 1090 cm -1 ) (indicative of nucleic acids); (iii) C-C chain stretch (~1060 - 1075 cm -1 ) (indicative of fatty acids) (iv) amide III bands, =CH bend, and nucleic acid bases (1220 – 1284 cm - 1 ) (indicative of proteins, lipids, and nucleic acids); (v) C-H deformation and guanine (~1320 cm -1 ) (indicative of lipids and nucleic acids) Peak Assignment Protocol