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Potential real-time detection of toxicity in bacterial cells through

Raman spectral analyses of intracellular bio-molecules:

A ReviewDanielle 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

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• 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 PO2- 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