Raman spectroscopic studies coupled with MCR-ALS applied on some biomedical systems

概要

（様式第１３号）

学 位 論 文 要 旨

氏名: 岩﨑啓太

題目: Raman spectroscopic studies coupled with MCR-ALS applied on some biomedical
systems
（MCR-ALS法を組み合わせたラマン分光法による医・生物学的応用研究）
Raman effect is one of light scattering effects which was reported first by C. V. Raman in
1928. When a light with a particular wavelength such as laser light is irradiated on to a substance,
several processes can happen (absorption, scattering etc.). Raman scattering is an inelastic scattering
process in which the irradiated light either loses or gains energy as a result of interaction with the
molecule. Since Raman spectrum conserve unique molecular information called ‘molecular finger
print’, Raman spectroscopy (RS) has been gaining attention as a valuable molecular characterization
tool especially in the context of biological and medical research. Label-free molecular imaging and
low or non-invasiveness are some of the major beneficial points. However, there are various
vibrational modes even in a single molecule and each mode has a unique Raman signature. To
further complicate the matter, there are numerous molecules in any given biomolecular system (cells,
tissues etc.). So each Raman spectrum contains signatures from a variety of biomolecules. Mainly,
contributions from unexpected molecules pose a big challenge for spectral interpretation. Therefore,
application of classical univariate analytical methods to Raman spectral data obtained biological and
medical systems are insufficient. Researchers often employ multivariate analysis such as principal
components analysis (PCA). Though useful in some cases, obtained spectral information does not
contain pure molecular information and lacks physical meaning. In order to overcome this problem,
in addition to PCA, I employ multivariate curve resolution – alternating least square (MCR-ALS)
which is also a multivariate analytical technique. In MCR-ALS analysis, a matrix approximation is
sought by a linear combination of desired number of spectral components and its corresponding
abundances. The extracted spectral components represent pure molecules while the abundances can
be thought of their relative concentrations. To study complex systems such as biological and medical
samples, the usefulness of MCR-ALS is reported in several model studies whereas the number of
practical applications are still very few. It is probably the reason why deep consideration in the
context of both biology and physics is still needed to understand results of MCR-ALS. Hence, this
thesis consists of two practical applications of MCR-ALS to describe its usefulness and how to
process entire analysis in biological and medical field.
1) Visualizing wax ester fermentation in single Euglena gracilis cells by Raman
microspectroscopy and multivariate curve resolution analysis
Global demand for energy is on the rise at a time when limited natural resources are fast
depleting. To address this issue, microalgal biofuels are being recommended as a renewable and
eco-friendly substitute for fossil fuels. Euglena gracilis is one such candidate that has received
special interest due to their ability to synthesize wax esters that serve as precursors for production of
drop-in jet fuel. However, to realize economic viability and achieve industrial-scale production,
development of novel methods to characterize algal cells, evaluate its culture conditions, and
construct appropriate genetically modified strains is necessary. Here, we report a Raman
microspectroscopy-based visualization method to visualize important metabolites such as paramylon

and ester during wax ester fermentation in single Euglena gracilis cells in a label-free manner.
We measured Raman spectra to obtain intracellular biomolecular information in Euglena
under anaerobic condition during which wax esters are synthesized and stored viz wax ester
fermentation from paramylon. First, by univariate approach, we identified Raman markers
corresponding to paramylon/esters and constructed their time-lapse chemical images. However,
univariate analysis is severely limited in its ability to obtain detailed information as several
molecules can contribute to a Raman band. Therefore, we further employed MCR-ALS to obtain
chain length-specific information and their abundance images of the produced esters. Accumulated
esters in Euglena were particularly identified to be myristyl myristate (C28), a wax ester candidate
suitable to prepare drop-in jet fuel. Interestingly, we found accumulation of two different forms of
myristyl myristate, one of which does not show univariate Raman marker. As a method of studying
wax ester fermentation, our exploratory MCR-ALS is powerful to make the most of Raman
hyperspectral data.
2) Identification of Molecular Basis for Objective Discrimination of Breast Cancer Cells
(MCF-7) from Normal Human Mammary Epithelial Cells by Raman Microspectroscopy and
Multivariate Curve Resolution Analysis
RS which is a non-invasive and label-free method has been suggested to improve accuracy
of cytological and even histopathological diagnosis. To our knowledge, this novel technique tends to
be employed without concrete knowledge of molecular changes in cells. Therefore, identification of
Raman spectral markers for objective diagnosis is necessary for universal adoption of RS. As a
model study, we investigated human mammary epithelial cells (HMEpC) and breast cancer cells
(MCF-7) by RS and employed classical univariate analysis, various multivariate analyses (MA)
including principal components analysis (PCA), linear discriminant analysis (LDA), and support
vector machine (SVM) to estimate diagnostic accuracy. Furthermore, to elucidate the underlying
molecular changes in cancer cells, we utilized MCR-ALS with non-negative constraints to extract
physically meaningful spectra from complex cellular data. Unsupervised PCA and supervised MA,
such as LDA and SVM, classified HMEpC and MCF-7 fairly well with high accuracy but without
revealing molecular basis. Employing MCR-ALS analysis we identified five pure biomolecular
spectra comprising DNA, proteins and three independent unsaturated lipid components. Relative
abundance of one lipid component seems to be strictly regulated between the two groups of cells and
could be the basis for excellent discrimination by chemometrics-assisted RS. The lipid component
was unambiguously assigned to linoleate rich glyceride with comparison of pure standard lipids.
Although both cell lines used in this study are of epithelial source, it is important to understand that
most tumors are like organs and have more than one type of cell. Therefore, while the model holds
true to this breast cancer cell line with a specific excitation at 633 nm in our measurement system, it
is imperative that we further test on large numbers of other cell lines and with different excitations
wavelengths as well to have general consensus. This study successfully identified Raman spectral
markers and demonstrated the potential of RS to become an excellent cytodiagnostic tool that can
both accurately and objectively discriminates breast cancer from normal cells.
These results from two practical studies successfully show that Raman spectroscopy
coupled with MCR-ALS is strong analytical tool to extract biomolecular information for both
exploratory and diagnostic purposes. Since results of this technique depends on initial parameters
and is not a unique solution, it presently needs deep consideration for exploratory analysis, this
problem can be addressed by combining a large standard Raman spectral library and user-friendly
interface to extract pure spectra with various initial parameters and check whether extracted result is
physically meaningful. In the future, I believe Raman spectroscopy coupled with MCR-ALS
technique probably endows an opportunity to researchers in biological and medical fields for easy
reading of "molecular finger print" as it is without needing expertise in spectroscopy.

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