raman spectroscopy detects cardiac allograft rejection

Raman Spectroscopy Detects Cardiac Allograft Rejection with Molecular Specificity

A Thesis

Submitted to the Faculty

of

Drexel University

by

Qiang Tu

in partial fulfillment of the

requirements for the degree

of

Doctor of Philosophy

June 2012

ii

Acknowledgement

First and foremost I would like to express my deepest gratitude to my advisor, Dr.

Chang Chang, who has unreservedly support me with his patience and knowledge. I

would not have completed the thesis without his encouragement and excellent guidance.

He is a great mentor and my best friend, from whom I have learnt a lot not only with

respect to research skills but also with regard to life experience.

I would like to thank Dr. Pourrezaei, who showed great support for my defense and

gave me valuable advice and suggestions to my thesis with his wealth of knowledge. And

I had a great time working with him.

I would like to thank Dr. Kurzweg, who was willing to participate in my defense

committee even though he had a very tight schedule. And he has offered valuable advice

to my thesis.

I would like to thank Dr. Taskin who was willing to be on my defense committee and

has offered much advice and insight to my research and thesis.

Special thanks go to Dr. Zhong who was willing to participate in my defense

committee at the last moment.

I would like to thank Dr. Harada for teaching me pathology knowledge and training

me with skills of cryosection and H&E with patience.

iii

I would like to thank Natalia, Lisa and Danielle in Biomed office, who were always

willing to help and made my school life easier.

Finally I thank my parents and my wife for supporting me throughout my study and

work. I would also like to thank my friends in Drexel, Leko, Jingjia, especially Fu-han,

for their support and help.

This research is supported by Wallace H. Coulter Foundation Translational Research

Grant.

iv

Table of Contents

List of Tables ..................................................................................................................... vi

List of Figures ................................................................................................................... vii

Abstract ............................................................................................................................... x

CHAPTER 1. INTRODUCTION ....................................................................................... 1

CHAPTER 2. BACKGROUND ......................................................................................... 3

2.1 Raman Spectroscopy ................................................................................................. 3

2.2 Surface Enhanced Raman Spectroscopy ................................................................... 7

2.3 Analysis Methods in Raman Spectroscopy............................................................... 8

2.3.1 Fluorescence background removal ................................................................... 8

2.3.2 Multivariate data analysis .................................................................................. 9

2.3.2.1 Hierarchical clustering ................................................................................ 9

2.3.2.2 Principle component analysis ................................................................... 10

2.3.2.3 Linear discriminant analysis ..................................................................... 11

2.3.2.4 Artificial neural network ........................................................................... 12

2.3.2.5 Ordinary least squares regression ............................................................. 13

CHAPTER 3. CURRENT RELATED RESEARCH ....................................................... 15

3.1 Current Research Using Raman Spectroscopy ....................................................... 15

3.1.1 Cancer detection............................................................................................... 15

3.1.1.1 Breast Cancer ............................................................................................ 15

3.1.1.2 Cervical Cancer ......................................................................................... 19

3.1.1.3 Colorectal Cancer ...................................................................................... 22

3.1.2 Allograft rejection ............................................................................................ 23

v

3.2 Current research using SERS .................................................................................. 24

3.2.1 SERS for biomarkers ....................................................................................... 24

3.2.2 SERS for in vivo detection .............................................................................. 26

3.3 Emerging techniques of Raman spectroscopy ........................................................ 29

3.3.1 Single-walled carbon nanotube ........................................................................ 29

3.3.2 Multiplexed SERS imaging ............................................................................. 30

3.3.3 Photothermal effect .......................................................................................... 31

3.3.4 Core-Shell nanoparticle structure for improved SERS reproducibility ........... 31

CHAPTER 4. DETECTION OF CARDIAC TRANSPLANT REJECTION USING

RAMAN SPECTROSCOPY ............................................................................................ 35

4.1 Materials and Methods ............................................................................................ 35

4.1.1 Endomyocardial Biopsy ................................................................................... 35

4.1.2 Raman Spectroscopy Setup.............................................................................. 35

4.2 Results and Analysis ............................................................................................... 36

CHAPTER 5. SERS STUDY OF INDOLIC MOLECULES ADSORBED ON GOLD

COLLOIDS ....................................................................................................................... 49

5.1 Materials and Methods ............................................................................................ 49

5.2 Results and Analysis ............................................................................................... 49

CHAPTER 6. CONCLUTION AND FUTURE WORK .................................................. 60

CHAPTER 7. CONTRIBUTION ..................................................................................... 62

BIBLIOGRAPHY ............................................................................................................. 64

VITA ................................................................................................................................. 78

vi

List of Tables

Table 1. Diagnostic applications of Raman spectroscopy. Adapted from reference 12

. ... 33

Table 2. Summary of the histopathological readings of the biopsy samples and the results

of their Raman spectroscopy studies. A biopsy is defined as “rejected” by Raman

spectroscopy if more than 35% of its Raman spectra are abnormal, i.e. I758/I718 > 1. Using

this definition, eleven out of the twelve Grade-1R biopsies are classified as rejected by

Raman spectroscopy, while only 1 out of 17 Grade-0 biopsies display such property.

Adapted from reference 67

. ................................................................................................ 41

Table 3. Band assignments for the Raman and SERS spectra of serotonin, tryptophan,

melatonin and 5-HIAA. Adapted from reference 145

. ....................................................... 54

vii

List of Figures

Figure 1. Energy diagram for Rayleigh and Raman scattering processes. Rayleigh

scattering is the most intense scattering process, and Stokes scattering predominates over

anti-Stokes scattering at room temperature. Thickness of the arrows indicates the relative

intensity of the three processes (not to scale). Adapted from reference 12

. ........................ 5

Figure 2. A typical laboratory Raman spectroscopy system. Adapted from reference 12

. .. 6

Figure 3. Visual (A) and Raman imaging (B, C) of single control and senescent MCF-

7/NeuT cells. Raman images (B) were composed specifically from CH- stretching, and

were analyzed using unsupervised hierarchical cluster analysis (C). Adapted from

reference 59

. ....................................................................................................................... 18

Figure 4.The as-recorded Raman spectra of normal cervical squamous cells and invasive

cervical cancer cells are shown in (A). Details of these Raman spectra in the fingerprint

region of 700 to 1800 cm-1

and the high wavenumber region of 2800 to 3100 cm-1

are

enlarged in (B) and (C), respectively, for display. Adapted from reference 60

. ................ 21

Figure 5. SERS-based molecular sentinel (MS) nanoprobes. The MS nanoprobe shown

on the left of the figure is in the closed state and generates a strong SERS signal due to

the proximity between the Raman label and the silver nanoparticle. In the presence of the

complementary target DNA, the MS nanoprobe switches to the open state as shown on

the right of the figure, and the SERS signal is significantly reduced due to the separation

of the Raman label from the silver nanoparticle. Adapted from reference 76

. .................. 25

Figure 6. In vivo glucose detection using SERS. A rat with a surgically implanted sensor

and optical window was examined by a conventional laboratory Raman spectroscopy

system (A). The SERS-based sensor was fabricated using silver films over nanosphere

(AgFON) (B). The resultant nano-structure (C) is shown using an atomic force

microscope. After functionalization, a reflectance spectrum (D) was collected to

determine the position of the localized surface plasmon resonance (LSPR). Adapted from

reference 84

. ....................................................................................................................... 27

Figure 7. In vivo SERS detection of tumor biomarker EGFR using ScFv-antibody

conjugated gold nanoparticles. The bottom panels show the experimental setup. The top

panels show spectra obtained from the tumor (red) and the liver (blue) by using targeted

(A) and non-targeted (B) nanoparticles. In vivo SERS spectra were clearly observed from

the tumor site using targeted nanoparticles, but not with non-targeted nanoparticles.


. ................................................................................................ 28

viii

Figure 8. shows a typical Grade-1R (mild rejection) endomyocardial biopsy examined by

(a, b, c) Raman spectroscopy and (d) histopathology with haematoxylin & eosin (H&E)

stain. Raman spectra and the locations at which they are obtained are numbered

accordingly in Figure 8(a) and (c), with rejected Raman spectra (I758 > I718) and their

corresponding locations marked in red. Rejection-specific spectral regions of the 10th

and

11th

Raman spectra are shaded in Figure 8(a) and enlarged in Figure 8(b) for better

visualization. The H&E stained picture of an adjacent tissue section is shown in Figure

8(c). The distribution of rejected Raman spectra matches that of lymphocytes, and the

distribution of normal Raman spectra (I758 > I718) matches that of normal cardiac muscle.

Scale bars are 100 m. Adapted from reference 67

. .......................................................... 38

Figure 9. A Grade-1R (mild rejection) endomyocardial biopsy is examined by (a)

histopathology with haematoxylin & eosin (H&E) stain and (b, c, d) Raman

spectroscopy. Raman spectra and the locations at which they are obtained are numbered

in Figure 9(c) and (b), with rejected Raman spectra (I758 > I718) and their corresponding

locations (Number 1~11) marked in red. Rejection-specific spectral regions of the 11th

and 12th

Raman spectra are shaded in Figure 9(c) and enlarged in Figure 9(d) for display.

Locations of moderate lymphocyte infiltration and cardiac myocyte necrosis observed in

the H&E image (a) are seen to conform with those of rejected Raman spectra (b, c). Scale

bars are 100 m. Adapted from reference 122

. .................................................................. 39

Figure 10. Rejection-specific Raman spectral features are clearly observed at 758 cm-1

and 678 cm-1

where the average of rejected Raman spectra (1) shows elevated intensities

when compared to averaged normal Raman spectrum (2). Spectral comparison with

naturally occurring indoles, including serotonin (3), 5-methoxytryptophan (4), 5-

hydroxyindoleacetic acid (5), melatonin (6), 5-hydroxytryptophan (7), and tryptophan (8)

reveals that Raman bands of serotonin coincide with the spectral differences observed

between normal and rejected heart biopsies. Adapted from reference 67

. ........................ 43

Figure 11. Multivariate statistical analysis PCA also confirms the separation of rejected

Raman spectra from normal ones. Separation achieved by this statistical method is

consistent with that obtained by the intensity ratio test where normal (I758 ≤ I718) and

rejected (I758 > I718) Raman spectra are marked in blue and red, respectively. Adapted

from reference 67

. .............................................................................................................. 46

Figure 12. SERS spectra of serotonin and its solutions mixed with gold colloids (a, d) are

shown at (1) pH=1.6, (2) pH=2.2, (3) pH=3.0, (4) pH=4.1, (5) pH=5.0, (6) pH=6.1, (7)

pH=7.1. SERS spectra of tryptophan and its solutions mixed with gold colloids (b, e) are

shown at (1) pH=1.6, (2) pH=2.1, (3) pH=3.0, (4) pH=4.1, (5) pH=5.0, (6) pH=6.0, (7)

pH=7.1. (c) Averaged AEFs of serotonin (red) and tryptophan (blue) at various pH levels

are obtained from 8 measurements and presented with their respective pK1. Adapted from

reference 25

. ....................................................................................................................... 50

Figure 13. SERS intensity of serotonin is a reversible function of the pH values. The top

panel shows the intensities at 816 cm-1 at varying pH levels. HCl was added first to

ix

decrease the pH values from 5 to 1 (red), and then NaOH was added to bring the pH

values back to 1.5, 3 and 5 for comparison (blue). Corresponding spectra are shown in the

lower panel. ....................................................................................................................... 53

Figure 14. (a) SERS spectrum of serotonin at 5×10-6

mol/L (1), and ordinary Raman

spectrum at 0.1mol/L (2). (b) SERS spectrum of tryptophan at 5×10-6

mol/L (1), and

ordinary Raman spectrum at 5×10-2

mol /L (2). Adapted from reference 25

. .................... 55

Figure 15. The SERS spectra (a) of serotonin (1), tryptophan (2), melatonin (3) and 5-

HIAA (4) are shown, all at concentration 5×10-6

mol/L. SERS spectra of four different

mixtures containing various indolic components, all at 5×10-6

mol/L, are marked in blue,

with their respective NNLS fit in red and fitting residuals in grey. The compositions in

the four mixtures are serotonin and tryptophan (5), serotonin, tryptophan, melatonin and

5-HIAA (6), serotonin and 5-HIAA (7), serotonin, tryptophan and 5-HIAA (8),

respectively. Relative concentrations of the indolic components in the four mixtures are

normalized with respect to serotonin (b). Serotonin, tryptophan, melatonin and 5-HIAA

are marked with circles (○, red), squares (□, blue), diamonds(◊, green) and triangles(△,

brown), respectively. Adapted from reference 25

. ............................................................. 58

x

Abstract

Raman Spectroscopy Detects Cardiac Allograft Rejection with Molecular Specificity

Qiang Tu

Advisor: Chang Chang, PhD

Optical techniques capable of in vivo and molecularly specific detection of disease

markers are continuously being sought after with the goal of replacing invasive biopsy

procedures that incur tissue incision and morbidity. Raman spectroscopy, with its

chemical and molecular specificity as well as its intrinsic detection capability without the

need for exogenous labeling and fixation, presents an attractive opportunity for minimally

invasive in vivo diagnostics. However, medical applications of Raman spectroscopy have

been largely limited by the complexity of tissue constituents. As a result, complicated

post-experiment data processing is required and the attainable information is limited to a

set of given a priori sample constituents, such as collagen, cholesterol and phospholipids.

This work overcame such limitation by directly detecting a specific biomarker of cardiac

allograft rejection and showed that spatially resolved Raman spectroscopy is capable of

making molecular specific detection at the tissue level without exogenous labeling. This

molecular specificity is achieved by detecting the strong and characteristic Raman

spectral signature of serotonin, whose selective existence in rejected heart transplants

serves as the molecular marker. By combining both medical and physical sciences, this

work demonstrates the potential use of Raman spectroscopy in replacing the

endomyocardial biopsy as the standard for post-transplantation rejection surveillance and

presents a new paradigm in advancing clinical care.

xi

The particular indole implicated in this study, serotonin, corroborates the increasingly

recognized role of serotonin receptors in various immune responses including cardiac

allograft rejection. Serotonin is both a ubiquitous neurotransmitter in the central nervous

system and an important immunomodulator involved in various immune responses. The

ability to unambiguously detect serotonin is therefore imperative in biomedical research.

However, detection of serotonin and related indoles using immunohistochemistry has

been largely limited by their small molecule size and the resultant uncertainty in antibody

specificity. Here Surface-enhanced Raman spectroscopy (SERS) is showed to be capable

of detecting and distinguishing serotonin from its various closely related precursors and

metabolites. Comparing with traditional antibody-based methods, SERS is highly specific

and capable of real-time detection. We expect this optical detection method to directly

benefit a variety of immune and nervous systems studies involving serotonin.

1

CHAPTER 1. INTRODUCTION

Heart transplantation is the therapy of last resort for patients with end-stage heart

failure and its efficacy relies on the successful management of the recipient immune

response1. Endomyocardial biopsy is the current standard of care for post-transplantation

rejection surveillance of cardiac allografts2. Tissue samples are retrieved from the

endocardium of the right ventricle and the severity of allograft rejection is graded

histopathologically3,4

. Like other biopsy procedures, endomyocardial biopsy is invasive,

subject to sampling errors, and causes morbidity and rarely mortality. Its finite

turnaround time also incurs unwanted patient anxiety. A less invasive and potentially

real-time in vivo diagnostic method is therefore desired.

Minimally invasive optical techniques capable of differentiating between diseased

and normal tissues are attractive alternatives to invasive biopsy procedures. By probing

the intrinsic molecular vibrations, Raman spectroscopy is capable of chemical and

molecular specific detections without the need for exogenous labeling. In addition, no

tissue removal or sectioning is required. The wavelengths of a typical probing laser and

the Raman scattered light are both in the non-ionizing infrared region, further facilitating

its use for in vivo biomedical applications. Minimally invasive optical fiber-based

insertion devices such as catheters and endoscopes are also readily available5,6

. More

recently, Raman spectroscopy has been used to classify regions of diseased and normal

tissues, and the underlying compositional differences can be determined given a priori

knowledge of the tissues’ constituents, such as fat, collagen, cell nucleus, cell cytoplasm,

and -carotene7,8

. However, wider adoption of Raman spectroscopy in clinical settings

2

has yet to be seen primarily due to the complicated post-experiment data processing and

the resultant difficulty in obtaining molecular specificity. Here we utilize the strong and

characteristic Raman spectral features of an aromatic amino acid derivative, serotonin, to

directly identify rejected cardiac allografts. The potential use of Raman spectroscopy in

detecting ring-structured endogenous molecular markers for clinical applications is

demonstrated.

This work is the first approach to use Raman spectroscopy to detect heart transplant

rejection. An optical biomarker was discovered and used for the effective detection by

Raman spectroscopy. It is the first SERS analysis on serotonin and its related molecules

including melatonin and 5-HIAA to my best knowledge. This work successfully qualifies

the relative concentrations of indole molecules in mixed solution.

3

CHAPTER 2. BACKGROUND

2.1 Raman Spectroscopy

Raman spectroscopy is a powerful technique which detects molecules vibrations by

Raman scattering. It is widely used to obtain molecular structure information,

qualitatively identify the substances, and quantitatively or semi-quantitatively determine

the amounts of substances in a sample, which can be solid, liquid, or vapor. Raman

spectroscopy typically uses a non-ionizing laser as the excitation source. The incident

photons can be absorbed, scattered, and/or pass through the material without interaction.

If the energy of the incident photon matches the energy gap between the ground state and

an excited state of a molecule, the photon will most likely be absorbed and the molecule

is promoted to the excited state. Fluorescence occurs when the excited molecule

subsequently relaxes to the ground state by emission. Scattering takes place on the other

hand as the incident photon distorts the electron clouds. Two types of scattering typically

exist in the visible-light and near infrared (NIR) spectral range, Rayleigh and Raman

scattering. The more intense form, Rayleigh scattering, happens when only the electron

clouds are distorted. This is considered an elastic process as no appreciable energy

exchange occurs. However, if the vibrational state of the molecule is altered, energy

transfer occurs, either from the photon to the molecule or vice versa. The process

becomes inelastic and is named Raman scattering. This is in general a weak process

involving approximately one in every 106~10

8 scattered photons.

9-11

Depending on the direction of energy transfer between the molecule and the photon,

Raman scattering can be further categorized into two subtypes: Stokes and anti-Stokes.

Stokes scattering takes place when the molecule absorbs energy from the incident photon

4

and elevates itself from a lower energy state to an excited vibrational state. On the other

hand, due to thermal perturbation or prior external excitation, the molecule may already

be in an excited vibrational state before interacting with the incident photon. In this case

anti-Stokes scattering may result such that the molecule releases energy upon interacting

with the incident photon and subsequently returns to a lower energy state. The scattered

photons in an anti-Stokes process therefore are of higher energy than the incident photons.

Since at room temperature most molecules are in the ground state, Stokes scattering

typically dominates. Stokes scattering is therefore commonly recorded for Raman

spectroscopy unless special experimental conditions are arranged. Raman band shifts, i.e.

the energy difference between the incident and scattered photons, are typically described

in wavenumber. The most commonly used unit to denote wavenumber is cm-1

, and it is

equivalent to joule by a conversion factor (hc), where h is the Planck constant and c is the

speed of light. Figure 1 illustrates the Rayleigh, Stokes and anti-Stokes scattering

processes.

5

Figure 1. Energy diagram for Rayleigh and Raman scattering processes. Rayleigh

scattering is the most intense scattering process, and Stokes scattering predominates

over anti-Stokes scattering at room temperature. Thickness of the arrows indicates

the relative intensity of the three processes (not to scale). Adapted from reference 12

.

A typical laboratory Raman spectroscopy system is shown in Figure 2. The incident

laser irradiates the sample via an optical microscope, and the Raman scattered photons

are collected into a spectrometer following appropriate filtering. Lasers at various visible-

light and NIR wavelengths can be used as the excitation source. For example, a HeNe

laser has a wavelength of 633 nm, and a Ti: sapphire laser is tunable from 650 nm to

1100 nm. When examining biological samples, longer wavelength excitations typically

produce less fluorescence background and are therefore preferred.

6

Figure 2. A typical laboratory Raman spectroscopy system. Adapted from reference 12

.

7

2.2 Surface Enhanced Raman Spectroscopy

Surface-enhanced Raman spectroscopy (SERS) increases the Raman signal level by

exploiting the interaction between the molecule and a nano-scale metal surface. Raman

signals thus emitted are strongly enhanced by a factor of 103 – 10

14, depending on the

definition of the enhancement factor.13,14

For substrate-based SERS experiments, the

most widely used definition for average SERS enhancement factor is SERS Surf

RS Vol

I NEF

I N ,

where SERSI and RSI are the SERS and regular Raman intensity, respectively. VolN is the

average number of molecules in the scattering volume for regular Raman, and SurfN is

the average number of adsorbed molecules in the scattering volume for SERS. For SERS

in colloidal solutions, the analytical enhancement factor or apparent enhancement factors

(AEF) are calculated by SERS SERS

RS RS

I CAEF

I C , where SERSI and RSI are SERS and regular

Raman intensities of normalized Raman spectra at the selected band, respectively. SERSC

and RSC are concentrations of the target molecule in colloidal and aqueous solutions,

respectively. SERS is capable of detecting molecules in extremely low concentrations,

making it an attractive technique for biomedical applications.

Although the exact theoretical foundation of SERS is still illusive, it is generally

agreed that SERS consists of two major underlying physical mechanisms: the

electromagnetic field effect and the chemical charge transfer effect.15,16

The

electromagnetic effect is a result of the excitation of surface plasmon by the incident laser,

while the charge transfer effect is caused by the electron exchange between the molecule

8

and the metal colloid.17

Surface plasmon is the resonant oscillation of conduction

electrons on a metal surface induced by an electromagnetic field. It is well known that the

exposure of surface plasmon to laser irradiation increases the lattice temperature,18,19

and

this surface plasmon induced heat generation has been identified as the potential cause of

SERS signal reduction seen in prolonged irradiation of nanoparticles.20

Note that since

SERS results from the interaction between the molecule under test and the nano-scale

metal surface, the band shifts, relative peak intensity, and peak bandwidths in the SERS

spectra are different from those in the Raman spectra of the same molecule.

2.3 Analysis Methods in Raman Spectroscopy

Raman spectra obtained from biological samples often contain significant amounts of

fluorescence background. Since Raman spectral differences between normal and diseased

tissues are generally subtle, effective data processing algorithms are often required for

data analysis and interpretation.

2.3.1 Fluorescence background removal

As mentioned above, Raman spectra collected from tissues are composed mainly of

Raman scattering and intrinsic tissue fluorescence. To eliminate the fluorescence

background, a polynomial function that fits to the fluorescence profile is usually

subtracted from the Raman spectra.21-24

While there is no consensus on the optimal order

of the polynomial function, fourth- and fifth-order polynomials are most commonly

employed.24-28

9

2.3.2 Multivariate data analysis

Raman spectra contain various overlapping Raman bands. As a result, it is difficult to

visually inspect and interpret the spectral data. Multivariate spectral analysis methods are

often used to process the Raman spectra and facilitate data interpretation. Spectral

analysis methods are generally categorized as either supervised or unsupervised. For

unsupervised analyses, such as cluster analysis (CA) and principle component analysis

(PCA), no a priori knowledge of class characteristics is required but to be determined

from the analysis itself. In contrast, in a supervised analysis, the number of classes and

representative samples of each class are known a priori, as is the case in linear

discriminant analysis (LDA), regression analysis and artificial neural networks (ANN).29

Several commonly used analysis methods are introduced below.

2.3.2.1 Hierarchical clustering

Hierarchical clustering is a cluster analysis method in which Raman spectra are

grouped according to their similarities defined by the distance matrices. A distance

matrix measures dissimilarity among a group of objects, i.e. Raman spectra in this case.

For example, the most commonly used distance metric is the Euclidean distance where a

Raman spectrum with p wavenumbers is expressed as 1 2[ , , ]px x xx , and the

distance between two Raman spectra ix and jx is defined as

1

22

1

( ) ( )p

ij i j ik jk

k

d x x

x x . The clustering process is repeated at different levels

to produce a tree structure, called the dendrogram, and the spectra from normal and

10

diseased samples are discriminated at the end of the process. Hierarchical clustering is

widely used in analytical Raman spectroscopy.30,31

2.3.2.2 Principle component analysis

Principle component analysis (PCA) is one of the most commonly used unsupervised

classification method in Raman spectroscopy. Raman spectra contain intensity

information obtained at a large number of different wavenumbers. Each of these

wavenumbers can be regarded as an independent dimension, on which the Raman

spectral data is measured. PCA reduces the dimensionality of the original Raman data set

with most of its variance retained in the selected principle components.21,26,32-36

PCA has

been successfully used in multivariate Raman spectral analysis and is widely accepted for

data interpretation. A brief overview of PCA is provided below for a typical diagnostic

Raman spectral data classification process.

The set of Raman spectra obtained from a diagnostic experiment can be expressed as

an m by n matrix X,

11 12 13 1

21 22 23 2

31 32 33 3 1 2 3

1 2 3

n

n

n n

m m m mn

x x x x

x x x x

x x x x

x x x x

X x x x x ,

where n is the number of Raman spectra taken in the experiment, m is the total number of

spectral wavenumbers, and ix is a column vector of length m representing the i

th Raman

spectrum. The covariance matrix CX of the original Raman data set X can then be

11

determined, as well as the eigenvectors of CX. The resulting eigenvector matrix W is

given by

1 2 3 mW w w w w ,

where iw is a column vector of length m representing the i

th eigenvector of CX. Note that

the eigenvectors are arranged with descending eigenvalues, i.e. decreasing contributions

to the overall variance of the Raman data set.

The principle component scores P is obtained by projecting the original Raman data

set X onto the new basis spanned by the eigenvectors, i.e.

T

1 2 3 m P W X p p p p ,

where pi is a column vector of length n representing the scores of the ith

principle

component for each of the n measurements in the Raman data set. Since the principle

components corresponding to the largest eigenvalues account for most of the variations in

the data set, classification of Raman spectra in most cases can be achieved with these first

few principle components. PCA is therefore a powerful analytic tool for dimensionality

reduction in Raman spectroscopy.

2.3.2.3 Linear discriminant analysis

Linear discriminant analysis (LDA) is another commonly used spectral data

classification method. Since the projection axes produced by PCA do not always provide

clear boundaries for between-class discrimination, LDA is often used to improve the

class separation following PCA. One of the most common LDA approaches is Fisher’s

criterion. It is a supervised analysis that maximizes the between-class variance while

12

minimizing the within-class variance in a set of Raman spectra. Effective class separation

is achieved by projecting the Raman data into a new eigenvector set.26,37-40

This method

applied to a two-class problem is briefly described below.

Within-class and between-class scatter matrices are used to formulate the criterion.

The within-class scatter matrix is defined as

T

1,2 i

W i i

i C

x

S = (x -m )(x -m ) ,

where x is a column vector representing an individual Raman spectrum in class iC , and

im is the mean of the Raman spectra in class iC , i =1,2. The between-class scatter

matrix is given by

T

1 2 1 2( )( )B S = m m m m ,

where 1m and 2m are defined above. The Fisher’s criterion function, T

T( ) B

W

J w S w

ww S w

is

used to evaluate the classification. A vector w that maximizes the function ( )J w is the

solution to the generalized eigenvalue problem B WS w S w . The eigenvector w is then

used as the new basis vector. Once the data set is transformed onto this vector, Euclidean

distance can be used for data classification. However, some data located in the middle of

the two classes might not be easily categorized.

2.3.2.4 Artificial neural network

While LDA can only discriminate classes that are linearly separable, artificial neural

network (ANN) is a nonlinear method capable of multi-dimensional bounding problems.

13

ANN emulates the process of human mind by employing artificial neuron functions in

which each neuron provides an output value based on multiple inputs. Typically, an ANN

consists of multiple layers, including the input layer, the hidden layer and the output layer.

Neurons on the input layer take the Raman spectral data as inputs and the classifications

are made at the output layer. Each neuron in the hidden layers sums up the inputs from

neurons in preceding layers with certain weights. These “middle” neurons then send out

their outputs after some nonlinear operations, such as hyperbolic tangents, step functions,

and sigmoid functions. The weights are computed using a set of known data during the

training process. The number of hidden layers, the neuron functions, and the form of the

data are crucial parameters for an effective ANN design.41

Note that which Raman

spectral features to be used as inputs should be carefully chosen for optimal classification.

One example of Raman spectral analysis using ANN is the single-cell diagnosis of

colorectal cancer.42

2.3.2.5 Ordinary least squares regression

Ordinary least squares (OLS) is a commonly used linear regression method that

minimizes the sum of squared distances between experimental data and the

corresponding fitted curve. OLS is often used to determine the compositional

concentrations of a tissue sample, where the sample spectrum is fitted to the individual

composition’s Raman spectra and a weighted sum is obtained from the fitting

coefficients.31,43

Briefly, the Raman spectrum of a tissue sample (or solution mixture) can

be expressed as an m × 1 column vector T

1 2 3 my y y yy , where m is the

number of wave numbers at which Raman shifts are recorded. Similarly, Raman spectra

14

of its component chemicals can be expressed as column vectors and put together as an m

× n matrix Z,

11 12 13 1

21 22 23 2

31 32 44 3

1 2 3

n

n

n

m m m mn

z z z z

z z z z

z z z z

z z z z

Z ,

where n is the number of component chemicals, and m is the same set of wavenumbers as

defined above. The OLS coefficient T

1 2 3 n β needs to satisfy

y Zβ u , where u is an m × 1 error vector. OLS minimizes the residual sum

of squares 2 T( ) ( )SSR y - Zβ y - Zβ y - Zβ by setting the derivative of SSR with

respect to β equal to 0. The OLS estimator β̂ is then calculated to be T 1 Tˆ ( )β Z Z Z y .44

Considering that the weights of components are positive, a nonnegativity constraint is

applied in this case to avoid negative fitting coefficients and this variant of OLS is called

nonnegative least squares (NNLS).

15

CHAPTER 3. CURRENT RELATED RESEARCH

3.1 Current Research Using Raman Spectroscopy

3.1.1 Cancer detection

Cancer is the second most common cause of death in the United States.45

Efficacious

cancer treatments rely on early detection and accurate diagnosis. Various Raman

spectroscopic studies on cancers have been reported,31,36,46-49

and here we review its use

on three most commonly seen carcinomas, i.e. breast, colorectal and cervical cancers.

3.1.1.1 Breast Cancer

Breast cancer has the highest rate of occurrence in the United States among the

female population in 2010.45

Numerous studies have investigated the application of

Raman spectroscopy on the detection of normal, precancerous and cancerous breast

tissues.22,34,43,50,51

For instance, Haka and colleagues have demonstrated the ability of

Raman spectroscopy to distinguish between normal, benign, and malignant lesions of

breast ex vivo, with a sensitivity of 94% and a specificity of 96%.43

Tissues in four

pathological conditions were examined and classified, including normal, fibrocystic

change, fibroadenoma, and infiltrating carcinoma. Raman spectra of breast tissues were

fitted to those of individual breast tissue components including fat, collagen, cell nucleus,

epithelial cell cytoplasm, calcium oxalate, calcium hydroxyapatite, cholesterol-like lipid

deposits, and -carotene. This approach is based on the assumption that the Raman

spectrum of a mixture is a linear combination of the spectra of its components. Variations

in the fitting coefficients reflected the chemical and morphologic changes of the breast

tissue due to carcinogenesis. Fitting coefficients of fat and collagen have been identified

as the key parameters used for diagnostic differentiation. Normal breast tissues are

16

composed of fat predominately, and all abnormal breast tissues show increased

percentage of collagen, due to the formation of fibrosis. Fibroadenoma can then be

further separated from infiltrating carcinoma by its lower fat content, since fibroadenoma

grows by pushing the fat aside while infiltrating carcinoma infiltrates between fat cells

and retained the fat in situ. These results were consistent with known breast pathology

and therefore validate the use of Raman spectroscopy for breast cancer detection ex vivo.

The clinical potential of Raman spectroscopy was also demonstrated by the same authors

with the use of an optical fiber probe for in vivo Raman studies.50

The spectral data were

analyzed in real time and the diagnosis results were displayed intraoperatively.

Rehman and co-workers quantitatively examined the Raman spectral differences

between different grades of invasive ductal carcinoma (IDC) and ductal carcinoma in situ

(DCIS).52

For both IDC and DCIS, the spectral intensity of selected Raman peaks

increased with advancing grades, indicating Raman spectroscopy’s potential for

quantitative carcinoma grading.

Instead of examining breast tissues directly, Pichardo-Molina’s group studied serum

samples from breast cancer patients and demonstrated the use of Raman spectroscopy for

minimally invasive diagnostics.40

Seven Raman band ratios were used for classification

and spectral differences were observed between serum samples of breast cancer patients

and normal healthy subjects. Using principle component analysis (PCA) and linear

discriminant analysis (LDA), the sensitivity and specificity were reported to be 97% and

17

78%, respectively. However, the underlying molecular mechanism of these differences

was not reported.

Raman spectroscopic imaging technique has gained popularity recently in cancer

research.30,53-56

Comparing with other standard imaging methods such as

immunofluorescence and immunohistochemisty, Raman spectroscopic imaging is capable

of visualizing the samples without extrinsic labeling, thus minimizing sample

perturbation. In addition, the much needed chemical and structural information of the

sample are provided by Raman spectral analysis.57,58

Mariani and co-workers have

applied Raman imaging to the detection of nuclear membrane lipid fluctuations in

senescent epithelial breast cancer cells.59

In this study, Raman images were composed

based on the Raman peak intensity of CH- stretching [Figure 3(B)]. Subcellular localities

such as the nucleus were accurately imaged, and differences in lipid and glycoprotein

components were detected.

18

Figure 3. Visual (A) and Raman imaging (B, C) of single control and senescent

MCF-7/NeuT cells. Raman images (B) were composed specifically from CH-

stretching, and were analyzed using unsupervised hierarchical cluster analysis (C).


.

19

3.1.1.2 Cervical Cancer

Cervical cancer is the second most common malignancy in women worldwide, with

more than half a million cases diagnosed and approximately 288,000 related death each

year.60

Currently Papanicolaou smear is the most commonly used screening method for

cervical cancer with a detection specificity of 96.8%, but a sensitivity of only 55.4%.61

Raman research in this area has focused on achieving comparable or better specificity

and sensitivity numbers than the Papanicolaou smear.

Algorithms were designed to discriminate between normal and diseased cervical

tissues. Using a logistic regression algorithm Robichaux-Viehoever, et al. were able to

distinguish high-grade dysplasia from benign tissues with a sensitivity of 89% and a

specificity of 81%.62

The spectra were collected in vivo using a fiber-optic probe.5

Employing non-linear algorithms, Kanter and colleagues achieved a sensitivity of 98%

and a specificity of 96%.63

They have also reported that by incorporating into their

algorithm the patient’s hormonal status, particularly the menstrual cycle and the

menopausal state, the classification accuracy can be further improved.64

New experimental techniques have also been used to improve detection accuracy in

cervical cancer. While most Raman studies are performed in the “fingerprint” spectral

region with wavenumbers below 1500 cm-1

,9 Mo and colleagues have investigated the

high wavenumber region (2800 ~ 3700 cm-1

) for cervical cancer diagnosis, and achieved

a diagnostic specificity and sensitivity of 97.8% and 93.5%, respectively.38

Kamemoto’s

group compared the use of both fingerprint (775 ~ 975 cm-1

) and high wavenumber

regions (2800 ~ 3100 cm-1

) for in vitro cervical cancer detection, and found that Raman

20

bands at the low wavenumber region [Figure 4(B)] provide more information on specific

molecular and chemical changes in the tissue, while Raman spectra at the high

wavenumber region [Figure 4(C)] have higher detection sensitivity due to stronger

Raman signals and significantly less fluorescence background from the optical fibers.38,60

Molecular etiology of the observed Raman spectral differences between normal and

diseased cervical tissues was also studied.32,60

Comparison between normal and

malignant cervical tissues showed that Raman spectra from normal cervical tissues

feature characteristics of structural proteins such as collagen. For malignant cervical

tissues, sharper amide I, minor red shift in CH2 and sharper amide III indicated the

presence of DNA, lipids and non-collagen proteins.

21

Figure 4.The as-recorded Raman spectra of normal cervical squamous cells and

invasive cervical cancer cells are shown in (A). Details of these Raman spectra in the

fingerprint region of 700 to 1800 cm-1

and the high wavenumber region of 2800 to

3100 cm-1

are enlarged in (B) and (C), respectively, for display. Adapted from

reference 60

.

22

3.1.1.3 Colorectal Cancer

Colorectal cancer is the third most common malignancy in the United States.45

An

estimated 142,570 new cases and 51,370 related deaths are expected for 2010. Current

standard of practice utilizes a white-light endoscope to screen and excise biopsies on

suspected sites. Difficulty exists in that early neoplasia and subtle lesions might evade the

endoscopic survey, and biopsies are impractical for patients with multiple suspicious

lesions.65

Optical biopsy using Raman spectroscopy is therefore an attractive alternative.

Several groups have thus investigated and developed effective detection/classification

algorithms. Widjaja and colleagues, for instance, combined PCA and support vector

machines (SVM) to classify different types of pathologic colonic tissues, including

normal, polyps and cancers. Sensitivity and specificity greater than 98% have been

reported.65

Beljebbar, et al. investigated the spatial distribution of molecular changes on

the adenocarcinoma ex vivo.30

Pseudo-color maps of normal and cancerous colonic

tissues were generated using Raman spectra of individual tissue constituents, including

proteins, lipids, collagen, nucleic acid and mucus. These Raman maps were shown to

correlate with hematoxylin and eosin (H&E) stains, demonstrating the use of proteins and

nucleic acids as potential spectroscopic biomarkers for the differentiation of

adenocarcinomatous tissues.

Laser Tweezers Raman spectroscopy (LTRS) has also been investigated for single-

cell colorectal cancer diagnosis.66

At the early stages of colon cancer development, the

epithelium layer on the luminal surface carries diagnostic information while the

underlying bulk tissues are mostly irrelevant. In order to minimize the fluorescence

background from the bulk tissues, laser tweezers are used to trap single cells from the

23

epithelium and inspect them in isolation using Raman spectroscopy. Sensitivity of 86.3%

and specificity of 86.3% have been reported with LTRS.42

3.1.2 Allograft rejection

Allograft transplantation is the last resort therapy for patients with failed organs. Its

efficacy relies on the successful management of the recipient’s immune responses.

Several studies using Raman spectroscopy to detect acute allograft rejection after

transplantation have shown promising results.67-69

Raman spectroscopy has been used to

detect acute rejection after renal transplantation. Acute rejection typically occurs in 15% -

20% of the patients within the first year of renal transplant, and currently the most

commonly used diagnostic indicator is serum creatinine elevation (SCE). However, this

method has been shown to have low sensitivity and specificity, since serum creatinine

level can be affected by other disease conditions. In addition, serum creatinine level

elevates only after significant histological damages have already taken place.68

. Using

Raman spectroscopy, Brown, et al. observed significant spectral differences between

activated and naive T lymphocytes, and reported a sensitivity and specificity of 95.7%

and 100%, respectively.69

This result demonstrates the potential of Raman spectroscopy

as a minimally invasive screening tool for acute renal transplantation rejection.

24

3.2 Current research using SERS

3.2.1 SERS for biomarkers

SERS has been used to detect various biomolecules and proteins, such as glucose,

hemoglobins, RNAs and pathogens.70-73

Detection using SERS is especially

advantageous for small signaling molecules that often lack recognizable epitopes for

antibody-based immunohistochemistry. For instance, Huang and co-workers conjugated

gold nanorods to anti-epidermal growth factor receptor (anti-EGFR) antibodies and

demonstrated its use as a potential cancer diagnostic tool.74

Due to its large curvatures,

gold nanorod in theory has the strongest surface electromagnetic field compared with

other colloid shapes.75

In this study, gold nanorods were coated with poly(styrene

sulfonate) to stabilize the colloids and to reverse the surface charge polarity from

negative to positive for antibody conjugation. Minimal Raman signals were observed on

normal cells, while significantly stronger Raman signals were detected on cancer cells,

demonstrating specific antibody binding. The detection specificity was provided by the

antibody and the SERS signal served simply as the readout. Other novel SERS based

techniques such as the nanoprobe technology were also developed.76-78

As an example,

the molecular sentinel (MS) nanoprobe consisted of a DNA hairpin probe with one end

binding to a silver nanoparticle, and the other tagged with a SERS signal reporter. In the

absence of the target DNA, significant SERS signals were observed since the SERS

signal reporter was close enough to the silver nanoparticle on the other end of the folded

stem-loop structure [Figure 5 (left)]. While in the presence of the complementary target

DNA, the SERS signal was significantly reduced due to the semi-loop unfolding and

separation of the SERS signal reporter from the silver nanoparticle [Figure 5 (right)].

Multiplexing was achieved by mixing two MS nanoprobes specific to the breast cancer

25

biomarker genes, erbB-2 and ki-67, to simultaneously detect both target DNAs present in

the sample.

Figure 5. SERS-based molecular sentinel (MS) nanoprobes. The MS nanoprobe

shown on the left of the figure is in the closed state and generates a strong SERS

signal due to the proximity between the Raman label and the silver nanoparticle. In

the presence of the complementary target DNA, the MS nanoprobe switches to the

open state as shown on the right of the figure, and the SERS signal is significantly

reduced due to the separation of the Raman label from the silver nanoparticle.


.

26

3.2.2 SERS for in vivo detection

The application of SERS for in vivo diagnostics in complex living systems is a

challenging task. Surface of the nanoparticle must be biocompatible and placed in vivo

without causing damage to the living system. In addition, the in vivo environments are

awash with a variety of molecules and contaminants that may deactivate the SERS

surface or interfere with the interested Raman signals. Moreover, the concentrations of all

molecules, both targets and contaminants, are constantly changing. Recently several

successful in vivo SERS experiments have been demonstrated.79-83

Stuart and colleagues

performed in vivo SERS on rats and quantitatively determined glucose concentration in

the interstitial fluid.84

The SERS-based sensor was fabricated using silver films over

nanosphere (AgFON) [Figure 6(B)]. Hydrophilic pockets were formed around the sensor

and partitioned glucose to the SERS-active surface, while excluding other molecules in

the interstitial fluid such as plasma proteins. A glass window was surgically placed on the

rat, allowing the sensor to be in contact with interstitial fluid and optically accessible for

Raman spectroscopy [Figure 6(A)]. In their more recent work,85

the window was

removed and SERS spectra were collected transcutaneously. In order to obtain Raman

signals deeper in the tissues, spatially offset Raman spectroscopy (SORS) was applied, in

which the Raman scattered light was collected with a spatial offset from the point of laser

incidence.86,87

In another recent study, Qian and co-workers demonstrated in vivo tumor

detection using SERS with PEGylated gold nanoparticles.88

The SERS sensor was

prepared by binding the reporter molecule diethylthiatri-carbocyanine (DTTC) to the gold

colloids. A mixture of thiol-PEG and a heterofunctional PEG (SH–PEG–COOH) was

then coated to stabilize the gold colloid-DTTC complex. Cancer cell-specific single-chain

variable fragment (ScFv) B10 antibodies were conjugated at the last step. As shown in

27

Figure 7(C), the sensor was injected through tail vein into the mice bearing human head

and neck tumors. Highly resolved SERS signals were observed specifically at the tumor

site [Figure 7(A, B)]. Keren’s group demonstrated in vivo Raman imaging in a similar

study using single-walled carbon nanotubes (SWNT).89

Since SWNTs inherently exhibit

a strong Raman peak at 1593 cm-1

, Raman imaging using SWNTs does not rely on SERS

reporter molecules and therefore requires fewer conjugation processes.

Figure 6. In vivo glucose detection using SERS. A rat with a surgically implanted

sensor and optical window was examined by a conventional laboratory Raman

spectroscopy system (A). The SERS-based sensor was fabricated using silver films

over nanosphere (AgFON) (B). The resultant nano-structure (C) is shown using an

atomic force microscope. After functionalization, a reflectance spectrum (D) was

collected to determine the position of the localized surface plasmon resonance

(LSPR). Adapted from reference 84

.

28

Figure 7. In vivo SERS detection of tumor biomarker EGFR using ScFv-antibody

conjugated gold nanoparticles. The bottom panels show the experimental setup. The

top panels show spectra obtained from the tumor (red) and the liver (blue) by using

targeted (A) and non-targeted (B) nanoparticles. In vivo SERS spectra were clearly

observed from the tumor site using targeted nanoparticles, but not with non-

targeted nanoparticles. Adapted from reference 88

.

29

3.3 Emerging techniques of Raman spectroscopy

3.3.1 Single-walled carbon nanotube

Single-walled carbon nanotube (SWNTs) is an useful diagnostic tool in

nanomedicine.90-94

It has been shown that conjugation to SWNT improves the

permeability, as well as enhances the retention, of various functional moieties in the

tumor tissues.95,96

This enhanced permeability and retention (EPR) effect of SWNT has

been explored for drug delivery where various biomolecules such as chemotherapeutic

agents, proteins and DNA were conjugated to SWNT and delivered to tumor sites.97-100

SWNT’s intrinsic Raman peak at 1593 cm-1

(G-band) is typically used as the

unambiguous marker to detect SWNT conjugates in drug delivery and Raman imaging

studies.101-104

In an in vivo cancer drug delivery study, paclitaxel (PTX), a chemotherapy

drug was conjugated to SWNTs and injected into mice.99

Raman spectroscopy was used

to quantify the pharmacokinetics and biodistribution of this SWNT-PTX conjugate

without any radio or fluorescent label. Raman imaging is another area where SWNT finds

important applications.89,102

Zavaleta and co-workers have shown noninvasive Raman

imaging of tumors on mice in vivo using SWNTs.102

The SWNTs were linked to

arginine-glycine-asparatic acid (RGD) peptides, which specifically target integrin v3,

an integrin overexpressed in angiogenic vessels and various tumor cells. After

subcutaneous injection of this SWNT-RGD conjugate, in vivo Raman images of tumor

were obtained using SWNT’s G-band intensity. In a more recent study, Liu et al.

successfully performed multicolor Raman imaging of living cells by multiplexing

different isotope compositions.105

SWNTs made of carbon-12 (12

C), carbon-13 (13

C), and

12C/

13C mix were used and each presented different G-band peak positions at 1590, 1528,

and 1544 cm-1

, respectively. These three types of SWNTs were conjugated to Herceptin

30

(anti-Her2), Erbitux (anti-Her1), and arginine-glycine-aspartic acid (RGD) peptides,

recognizing Her2/neu, Her1/EGFR, and integrin v3 cell-surface receptors, respectively.

These SWNT conjugates are then administered in vitro and after incubation, multiplexed

three-color Raman images were obtained, clearly identifying the existence of the three

targeted cell receptors. Note that G-band spectral positions are key to distinguishing the

three multiplexed colors in Raman imaging and it was recently shown that up to five

different colors could be achieved by varying the 12

C/13

C ratios in SWNTs.106

3.3.2 Multiplexed SERS imaging

Multiplexed Raman imaging is capable of detecting multiple targets simultaneously

and it has attracted great interests recently.107-110

Gold nanoparticles are typically used as

the tag in multiplexed SERS imaging for their narrow and strong SERS bands. In

addition, silica coated gold nanoparticles are biocompatible and nontoxic. In a recent

work, Zavaleta et al. achieved five color multiplexed Raman imaging in vivo using five

SERS nanotags adsorbed on gold nanoparticles.111

Each nanotag generated a unique

Raman signal, which is used as a multiplexed channel and assigned an arbitrary color in

the resultant image. These five types of nanotagged gold nanoparticles were

simultaneously injected through the tail vein and accumulated in the liver where a

multiplexed five color Raman image was obtained.

Using functionalized silver nanoparticles, Kennedy and co-workers demonstrated

multiplexed SERS imaging of cell surface proteins in vitro.112

SERS signals from four

differently functionalized nanoparticles were separated using least square fitting and the

spatial co-localization between the targeted proteins were visualized in the multiplexed

31

SERS image. Other in vitro multiplexed SERS imaging works include cancer cell line

identification where specific nanotags were used to recognize their respective biomarkers

found on cancer cells.113

3.3.3 Photothermal effect

Photothermal effect is an important feature of metal nanoparticles. The exposure of

metal nanoparticles to laser induces an increase in temperature.18

Application of this

laser-induced thermal effect in Raman spectroscopy has recently been demonstrated

where plasmon heating was used to produce SERS-active micro spots on colloidal

crystals.114

Polystyrene (PS) colloidal nanospheres were first coated with a 10nm layer of

gold nanoparticles and then with a 50 nm layer of amorphous TiO2 deposited by atomic

layer deposition (ALD). The resulting PS/Au/TiO2 core/shell nanospheres are assembled

as colloidal crystals. When irradiated by a laser beam, the gold interlayer was strongly

absorbing and the heat generated decomposed the polymeric spheres. A bowl-like pit was

formed as a result, and the SERS effect of Methylene Blue adsorbed on these spots was

demonstrated.

3.3.4 Core-Shell nanoparticle structure for improved SERS reproducibility

Strong signal enhancement, high reproducibility and stability are critical for effective

SERS phenomenon.115

SERS studies using metal nanoparticles typically suffer from the

desorption of Raman labels or target molecules from the metal surface, and the

interference from the environment such as non-target molecules and other surrounding

metal nanoparticles. A core-shell structure offers an effective solution to those stochastic

interventions, in that the shell capsulates the Raman labels or target molecules around the

32

metal core and prevents the adsorption of other molecules as well as the aggregation of

nanoparticles themselves. Various shell materials have been investigated, including

polymers, silica and metal.88,116-119

Among them, the stability and biocompatibility of

silica makes it an attractive choice in medical research. Li and co-workers have

developed ultra-thin silica-coated gold nanoparticles with strong SERS signal and

minimal gold aggregations.116

Using etching technology, Roca’s group developed silica-

void-gold nanoparticles to prevent aggregations while allowing molecules to diffuse

through the porous silica membranes and interact with the entrapped gold core.120

In a

more recent study Lim et al. have demonstrated gold core–shell nanoparticles with highly

uniform and reproducible SERS signals.121

The Raman labels trapped within the gap

generated strong and highly quantitative SERS signals with 633 nm laser excitation. The

development of new nanoparticles continues to expand the application of Raman

spectroscopy in biology and medicine.

Table 1 summarizes the applications of Raman spectroscopy reviewed above,

including the types of analysis, experimental apparatus, and analysis methods.

33

Table 1. Diagnostic applications of Raman spectroscopy. Adapted from reference 12

.

Raman application

Experimental setup

Analysis method Reference Wavelength

(nm) Power (mW)

Exposure time (s) Other

Cancer detection

Breast cancer

830 100~150 10~30 Nonnagative least square (NNLS) 43

830 82~125 1 Probe NNLS 50

786 N/A 4 Raman shifts 52

830 65 10 PCA-LDA 40

514.5 8 N/A Hierarchical cluster 59

Cervical cancer

785 80 5~15 Probe Logistic regression 62

785 80 5 Probe Maximum representation and discrimination feature (MRDF) 63

Sparse multinomial logistic regression (SMLR)

785 15 60 Probe PCA-LDA 38

785 10 60 Raman shifts 60

785 100 30 PCA 32

Colorectal cancer

785 300 5 Probe PCA-Support vector machines (SVM) 65

785 70 N/A

PCA 30

Hierarchical cluster

Multiple least squares (MLS)

782.5 11.5 60 LTRS PCA-Logistic regression 66

782.5 11.5 60 LTRS PCA-Artificial neural network (ANN) 42

Allograft rejection

Cardiac rejection

785 100 10 PCA 67

Renal rejection

785/514.5 8~12 10 PCA-Discriminant function analysis (DFA) 69

SERS for Biomarker

785 100 20 NNLS 25


633 N/A N/A Raman shifts 76

SERS for in 785 50 120 Partial least squares 84

34

vivo 785 20 2 Raman shifts 88

SWNT

N/A 100 2 Raman imaging using G-band 99

785 100 3/10 Raman imaging using G-band 102

785 80 0.5 Raman imaging using G-band 105

SERS imaging

785 60 N/A Raman imaging using SERS peaks 111

633 N/A N/A Raman imaging using SERS peaks 112

Core-shell nanoparticle

633 0.5/4 30/60 Raman shifts 116


633 0.3 10 Raman shifts 121

35

CHAPTER 4. DETECTION OF CARDIAC TRANSPLANT REJECTION USING

RAMAN SPECTROSCOPY

4.1 Materials and Methods

4.1.1 Endomyocardial Biopsy

Endomyocardial biopsies are obtained from heart transplant patients during regular

surveillance biopsy procedures. The biopsy sample is stored at -80 °C until use, at which

time the sample is cryo-sectioned to 4 m thickness and examined by histopathology

using haematoxylin & eosin (H&E). The remaining biopsy section adjacent to the 4 m

slice is examined by Raman spectroscopy. The pathological results are withheld until the

end of the study period, after which time the comparison with Raman spectra was made.

A number of Raman spectra are obtained at different randomly selected locations within

each biopsy specimen depending on the sample size. This procedure emulates the in vivo

clinical environment where an optical fiber is inserted through the cardiac catheter and

samples randomly selected endocardium sites. Patient selection criteria follow the

guideline set by United Network for Organ Sharing (UNOS). Briefly, the patient will be

excluded if the patient is pregnant, or if the patient develops systemic diseases that will

affect the heart at anytime after the transplant, such as malignancy, amyloidosis,

sarcoidosis, HIV, sepsis from infections of virus, bacteria, and fungus. The patient is also

not eligible if refuse to sign consent for this study. A total of 17 patients are included in

this study.

4.1.2 Raman Spectroscopy Setup

Endomyocardial biopsies are passively thawed at room temperature and put between

two calcium fluoride slides (Buck Scientific, East Norwalk, CT) for Raman spectroscopy

36

experiment. The edges of the calcium fluoride slides are sealed by petrolatum to prevent

the biopsy samples from dehydration. The Raman spectroscopy experiments are

conducted using a confocal Raman microscope, equipped with a 100mW 785nm

wavelength single-line diode laser (LabRAM HR800, HoribaJobinYvon, Edison, NJ). All

data are acquired using a 10X objective (NA=0.25) with a spot size of 100 m at the

sample. Shutter setting is fixed at 10 sec exposure time with 180 accumulations

throughout the experiment. A 600 lines/mm grating is used and the range of wavenumber

recorded is between 522 cm-1

and 1253 cm-1

. The intensity of the obtained Raman spectra

is peak-normalized at 718 cm-1

, which is associated with membrane phospholipids and

relatively stable. Intensity exhibit is offset vertically for display. No background

subtraction is performed in order to preserve the integrity of the original data. The study

was approved by the Institutional Review Board of Drexel University College of

Medicine and all subjects gave informed consent.

4.2 Results and Analysis

To establish the validity of Raman spectroscopy in detecting cardiac allograft

rejection, comparative studies are performed on endomyocardial biopsies using both

Raman spectroscopy and standard histopathology. Spatially resolved Raman spectra are

obtained from the biopsy at randomly selected locations and the result is compared with

an adjacent tissue section stained by H&E. “Rejected” Raman spectra is defined

quantitatively as the intensity at 758 cm-1

surpasses that of the adjacent Raman band at

718 cm-1

, i.e. when I758 > I718. Comparison is then made between the locations at which

rejected Raman spectra are observed and the distribution of lymphocytes in the H&E

37

stained image. Figure 8 shows a typical Grade-1R (mild rejection) endomyocardial

biopsy examined by Raman spectroscopy (a, b, c) and histopathology (d). Rejected

Raman spectra and their corresponding positions are marked in red in Figure 8(a, b, c).

Agreement between Raman and histopathological results are clearly observed as the

distribution of rejected Raman spectra overlap with lymphocyte aggregation. This result

confirms the ability of Raman spectroscopy to detect cardiac allograft rejection.

38

Figure 8. shows a typical Grade-1R (mild rejection) endomyocardial biopsy

examined by (a, b, c) Raman spectroscopy and (d) histopathology with haematoxylin

& eosin (H&E) stain. Raman spectra and the locations at which they are obtained

are numbered accordingly in Figure 8(a) and (c), with rejected Raman spectra

(I758 > I718) and their corresponding locations marked in red. Rejection-specific

spectral regions of the 10th

and 11th

Raman spectra are shaded in Figure 8(a) and

enlarged in Figure 8(b) for better visualization. The H&E stained picture of an

adjacent tissue section is shown in Figure 8(c). The distribution of rejected Raman

39

spectra matches that of lymphocytes, and the distribution of normal Raman spectra

(I758 > I718) matches that of normal cardiac muscle. Scale bars are 100 m. Adapted

from reference 67

.

Figure 9. A Grade-1R (mild rejection) endomyocardial biopsy is examined by (a)

histopathology with haematoxylin & eosin (H&E) stain and (b, c, d) Raman

spectroscopy. Raman spectra and the locations at which they are obtained are

numbered in Figure 9(c) and (b), with rejected Raman spectra (I758 > I718) and their

corresponding locations (Number 1~11) marked in red. Rejection-specific spectral

regions of the 11th

and 12th

Raman spectra are shaded in Figure 9(c) and enlarged in

Figure 9(d) for display. Locations of moderate lymphocyte infiltration and cardiac

myocyte necrosis observed in the H&E image (a) are seen to conform with those of

rejected Raman spectra (b, c). Scale bars are 100 m. Adapted from reference 122

.

40

To evaluate the feasibility of Raman spectroscopy for clinical applications, each

individual endomyocardial biopsy is classified by Raman spectroscopy as either normal

or rejected according to the percentage of rejected Raman spectra found on the biopsy.

This classification is then compared with the histopathological reading. It is found that

Raman spectroscopy indeed consistently identifies Grade-1R biopsies by detecting the

abnormal intensity increase at 758 cm-1

. For 11 out of the 12 Grade-1R biopsies

examined, rejected Raman spectra consistently account for more than 35% of the spectra

obtained while only 1 out of 17 Grade-0 biopsies exhibits such property (Table 2).

Results here show that increased frequency of occurrence of rejected Raman spectra

obtained at randomly selected locations on a biopsy correlates with elevated immune

response, suggesting that cardiac allograft rejection can be identified by the relative

occurrence of rejection specific Raman spectra.

41

Table 2. Summary of the histopathological readings of the biopsy samples and the

results of their Raman spectroscopy studies. A biopsy is defined as “rejected” by

Raman spectroscopy if more than 35% of its Raman spectra are abnormal, i.e.

I758/I718 > 1. Using this definition, eleven out of the twelve Grade-1R biopsies are

classified as rejected by Raman spectroscopy, while only 1 out of 17 Grade-0

biopsies display such property. Adapted from reference 67

.

Sample Grade Total Spectra

number

Abnormal

I758/I718 > 1

Normal

I758/I718 ≤ 1

Abnormal

Percentage

1

Grade 0

12 0 12 0%

2 15 0 15 0%

3 13 0 13 0%

4 15 0 15 0%

5 7 4 3 57%

6 17 1 16 6%

7 6 1 5 17%

8 19 2 17 11%

9 17 0 17 0%

10 5 0 5 0%

11 15 3 12 20%

12 15 0 15 0%

13 15 0 15 0%

14 14 1 13 7%

15 16 1 15 6%

16 6 0 6 0%

17 14 0 14 0%

Subtotal 221 13 208 6%

18

Grade 1R

2 2 0 100%

19 14 5 9 36%

20 22 11 11 50%

21 24 22 2 92%

22 5 5 0 100%

23 24 14 10 58%

24 23 2 21 8%

25 12 5 7 42%

26 19 11 8 58%

27 22 10 12 45%

28 22 9 13 41%

29 16 6 10 38%

Subtotal 205 102 103 50%

42

The observed Raman spectral difference between normal and rejected biopsies at 758

cm-1

is a well-known characteristic of indole ring vibration123

and comparison with

various indole derivatives’ Raman spectra implicates a particular indole, serotonin, as the

underlying molecular marker instigating the observed spectral difference. The abnormal

intensity increase at 758 cm-1

has been quantitatively defined above as I758 > I718, and is

consistently observed in all of the 12 Grade-1R biopsies examined. Since this 758 cm-1

Raman band is a well-known characteristic of indole ring vibration,123

Raman spectra of

several naturally occurring indoles are examined for comparison with normal and

rejected Raman spectra. Averages of 311 normal and 115 rejected Raman spectra are

obtained, together with the Raman spectra of serotonin, tryptophan, melatonin, 5-

hydroxyindoleacetic acid, 5-hydroxytryptophan, and 5-methoxytryptophan. As seen in

Figure 10, in addition to the 758 cm-1

band which is common to all indoles, rejected

Raman spectra contain an additional band at 678 cm-1

which is unique to serotonin. The

Raman spectral difference observed between rejected and normal heart biopsies is

therefore attributed to serotonin, a known immunomodulator.124

By detecting the

selective existence of indoles in rejected cardiac allografts, Raman spectroscopy’s

molecular specific detection capability for clinical applications is demonstrated.

43

Figure 10. Rejection-specific Raman spectral features are clearly observed at 758

cm-1

and 678 cm-1

where the average of rejected Raman spectra (1) shows elevated

intensities when compared to averaged normal Raman spectrum (2). Spectral

comparison with naturally occurring indoles, including serotonin (3), 5-

methoxytryptophan (4), 5-hydroxyindoleacetic acid (5), melatonin (6), 5-

hydroxytryptophan (7), and tryptophan (8) reveals that Raman bands of serotonin

coincide with the spectral differences observed between normal and rejected heart

biopsies. Adapted from reference 67

.

44

Minimally invasive optical techniques capable of differentiating between diseased

and normal tissues are attractive alternatives to invasive biopsy procedures. By probing

the intrinsic molecular vibrations, Raman spectroscopy is capable of chemical and

molecular specific detections without the need for exogenous labeling. In addition, no

tissue removal or sectioning is required. The wavelengths of a typical probing laser and

the Raman scattered light are both in the non-ionizing infrared region, further facilitating

its use for in vivo biomedical applications. Minimally invasive optical fiber-based

insertion devices such as catheters and endoscopes are also available.5,125

More recently,

Raman spectroscopy has been used to classify regions of diseased and normal tissues,126

and the underlying compositional differences can be determined given a priori

knowledge of the tissues’ constituents, such as fat, collagen, cell nucleus, cell cytoplasm,

and -carotene.7,8

However, wider adoption of Raman spectroscopy in clinical settings

has yet to be seen primarily due to the lack of specific biomarkers with strong and

characteristic Raman scattering signals. Here distinct Raman spectral features of an

indole ring are consistently identified in rejected endomyocardial biopsies and used to

detect cardiac allograft rejections. In addition, these Raman spectral signatures of rejected

biopsies are attributed to a particular indole derivative, serotonin, which is known to

mediate various immune responses.124,127

The detection of cardiac allograft rejection by

Raman spectroscopy is shown to agree with histopathological results and the potential

use of Raman spectroscopy in detecting ring-structured endogenous molecular markers

for clinical applications is demonstrated.

45

A particular indole derivative, serotonin, is implicated here as the biomarker for

cardiac allograft rejection. Serotonin is a well-known neurotransmitter whose immune

modulation effects have gained increasing recognition recently.128-130

Serotonin is

released by platelets at the sites of tissue injury131

and has been shown to regulate various

immune responses such as delayed-type hypersensitivity,132

phagocytosis,133

and mast

cell chemotaxis.134

Serotonin is also implicated in the pathogenesis of a variety of

inflammatory disorders, such as asthma, inflammatory bowel syndrome, and chronic

eczema. Recently serotonin has been shown to promote T-cell activation and

proliferation,135

the primary immune mechanism responsible for allograft rejection.136

For

cardiac allograft rejection in particular, the role of serotonin has recently been suggested

as a serotonin receptor antagonist prolongs the life span of heart transplants in mice.127

Given the diverse arrays of serotonin receptor isoforms expressed differentially by

various immune cells,124

we expect serotonin to involve in a wider range of immune

responses.137

Such expectation, if realized, will further broaden the clinical utility of

Raman spectroscopy.

A multivariate statistical method, principle component analysis (PCA), is employed

here to confirm the validity of the intensity ratio test (I758 > I718) used above. PCA is an

unsupervised statistical method typically used in spectral chemometrics. Such statistical

methods are also useful for larger amounts of data often encountered in clinical settings.

Here PCA is used to confirm the spectral separation observed by the intensity ratio test

between normal and rejected Raman spectra. Comparison of PCA with intensity ratio test

yields consistent results where separation between rejected (I758 > I718, red) and normal

46

(I758 < I718, blue) Raman spectra is clearly observed (Figure 11). In addition, PCA enables

the visualization of data clustering and as a result will be useful for future clinical

deployment of Raman spectroscopy.

Figure 11. Multivariate statistical analysis PCA also confirms the separation of

rejected Raman spectra from normal ones. Separation achieved by this statistical

method is consistent with that obtained by the intensity ratio test where normal (I758

≤ I718) and rejected (I758 > I718) Raman spectra are marked in blue and red,

respectively. Adapted from reference 67

.

47

The characteristic Raman band of indole rings at 758 cm-1

, together with an adjacent

and relatively stable membrane phospholipids band at 718 cm-1

, permits the use of

intensity ratio test as the quantitative measure for detecting heart transplant rejection with

Raman spectroscopy. In addition, the consistent emergence of serotonin’s unique Raman

band at 678 cm-1

in rejected biopsy specimens essentially enables direct spectral

identification. Results obtained with this method, which utilizes characteristic Raman

signatures of the disease biomarker serotonin, are confirmed by PCA. Note that

serotonin’s other Raman bands at 824 cm-1

and 938 cm-1

overlap with those of collagen

as well as other commonly seen molecules in the tissue, and as a result can not be used

for rejection identification. Fluorescent noises existed in the Raman spectra can also

cause variations between the classification results obtained with intensity ratio test and

PCA. To achieve improved signal quality, we therefore intend to examine spectral

regions with wavenumbers greater 1500 cm-1

, where interference from fluorescence is

expected to be minimal.

Sophisticated data processing, namely background subtraction, is specifically avoided

here in order to preserve data integrity. While commonly employed for qualitative

display of spectra, it is well known that background subtraction causes subsequent

statistical analysis such as PCA to fail. It is primarily due to the artificial variance

introduced into the spectra when one subjectively fits and removes higher-order

polynomials as the background.138-140

Despite this fact, use of background subtraction

continues to exist in clinical Raman applications where data interpretation depends on

complicated spectral data processing. We would like to point out that, to the best of our

48

knowledge, it presents the first experimental result where differentiability between

normal and diseased states is clearly observable in original Raman spectra without the

need for data processing. Such observation is also verified by PCA, an objective

multivariate statistical analysis.

49

CHAPTER 5. SERS STUDY OF INDOLIC MOLECULES ADSORBED ON GOLD

COLLOIDS

5.1 Materials and Methods

Serotonin, tryptophan, melatonin, and 5-HIAA (≥98% purity) are purchased from

Sigma-Aldrich Co. (St. Louis, MO). Gold colloids are purchased from Structure Probe,

Inc. (West Chester, PA), with a size of 20nm, and a concentration of ~ 7.0×1011

per ml.

The mixed solutions are then excited by a near infrared (NIR) integrated Raman system

(LabRam800HR, Horiba Jobin Yvon, Edison, NJ) with 100mW 785 nm wavelength

single-line diode laser. The exposure time is 20 seconds.

Apparent enhancement factor (AEF) is calculated following the standard in the

literature.13

An averaged AEF is calculated using ten Raman bands within the spectral

range (Fig. 2). A linear regression method, nonnegative least squares (NNLS),141

is used

to quantify the relative concentration of indolic molecules mixed in the gold colloidal

solution. By fitting the SERS spectrum of the mixture as a weighted sum of those of

individual indoles, the NNLS coefficients are proportional to the product of indole

concentration and its AEF.

5.2 Results and Analysis

SERS intensities observed correlate strongly with varying pH value (Figure 12). Both

serotonin and tryptophan are ampholytes in aqueous solutions, with serotonin having a

hydroxyl and an amine group, and tryptophan a carboxyl and an amine group. Since the

electrostatic status of serotonin and tryptophan changes with pH, their electrostatic

50

interaction with the negatively charged gold colloid surface also changes with the pH

level. SERS spectra of the solutions were taken at various pH levels, all at a

concentration of 5×10-6

mol/L.

Figure 12. SERS spectra of serotonin and its solutions mixed with gold colloids (a, d)

are shown at (1) pH=1.6, (2) pH=2.2, (3) pH=3.0, (4) pH=4.1, (5) pH=5.0, (6) pH=6.1,

(7) pH=7.1. SERS spectra of tryptophan and its solutions mixed with gold colloids (b,

e) are shown at (1) pH=1.6, (2) pH=2.1, (3) pH=3.0, (4) pH=4.1, (5) pH=5.0, (6)

pH=6.0, (7) pH=7.1. (c) Averaged AEFs of serotonin (red) and tryptophan (blue) at

various pH levels are obtained from 8 measurements and presented with their

respective pK1. Adapted from reference 25

.

51

The onset of SERS is observed as the pH value drops below pI and approaches pK1,

at which levels the molecules begin to be positively charged. The electrostatic interaction

between the negatively-charged gold colloids and the positively-charged molecules

contributes to the SERS phenomenon. The pK1, pK2, and pI of serotonin are 4.9, 9.8, and

7.35 respectively. For tryptophan, pK1 and pK2 are 2.38 and 9.34 respectively, and its pI

is 5.86.142

Indeed as seen in Figure 12(a, b, c) SERS effects are observed at pH level

lower than 6.1 for serotonin, and lower than 5.0 for tryptophan. As the pH value

continues to decrease toward pK1, more amine groups are protonated and stronger SERS

signal is observed.

At pH levels below their respective pK1, serotonin and tryptophan molecules are fully

protonated in solution and strong SERS effects are observed. As shown in Figure 12(a, c),

serotonin’s SERS spectra become significantly stronger as the pH values approach and

decrease beyond its pK1. Likewise for tryptophan as shown in Figure 12(b, c), strong

SERS effects are observed at pH=1.6, 2.1 and 3.0. Additionally in Figure 12(c), one also

observes that SERS spectral intensity continues to increase as pH level drops below pK1.

It is likely a result of gold nanoparticles aggregating with the positively-charged

ampholytes, forming 3D complexes.143

These 3D nanostructures induce greater SERS

enhancement than 1D or 2D nanostructures.144

52

The emergence of SERS is also seen to be accompanied by color changes in the

solution. As described above, the pH dependence of SERS is attributed to the protonation

of ampholytes and the aggregation of gold nanoparticles. Similarly the observed color

change at low pH values is a result of colloid aggregation. This is because the

accumulation of gold colloids alters the surface plasmon characteristics and light

absorption by the surface plasmon determines the color of the solution. At pH levels

where strong SERS signals are observed, i.e. below pH 5.1 for serotonin and below pH

3.0 for tryptophan, the solutions [Figure 12(d) and (e)] are light blue. At higher pH levels

where SERS phenomenon disappears, the solution is pink in color. In addition, for pure

gold colloid solutions with neither serotonin nor tryptophan present, this color change

phenomenon was also observed although at a much lower pH level (pH = 2.0). These

observations suggest that the existence of highly protonated molecules such as serotonin

and tryptophan in this case facilitates gold colloid aggregation, which in turn enhances

the SERS phenomenon.

A reversible correlation in SERS intensity with respect to pH levels is also observed.

As described above, serotonin is an ampholyte in aqueous solutions, with a hydroxyl and

an amine group. Since the electrostatic status of serotonin changes with pH, their

electrostatic interaction with the negatively charged gold colloid surface also changes

with the pH level, which in turn results in changes in SERS intensity. Figure 13 shows

SERS intensity changes as a function of serotonin solution’s pH level. SERS intensity of

the serotonin/gold colloid solution increases as the pH level decreases from 5.0 to 1.0 by

adding HCl. NaOH is subsequently added to the solution to raise the pH level back to 5.0.

53

At pH=1.5, 3.0 and 5.0, SERS intensity reduces back to the same level as before NaOH

was added, which indicates that the electrostatic interaction between serotonin and gold

colloid surface is reversible. The result demonstrated that SERS is a reliable and

repeatable technique for the detection of serotonin.

Figure 13. SERS intensity of serotonin is a reversible function of the pH values. The

top panel shows the intensities at 816 cm-1 at varying pH levels. HCl was added first

to decrease the pH values from 5 to 1 (red), and then NaOH was added to bring the

pH values back to 1.5, 3 and 5 for comparison (blue). Corresponding spectra are

shown in the lower panel.

Specific Raman vibrational modes of an indole ring are examined for serotonin,

tryptophan, melatonin and 5-HIAA (Table 3). Substantially downward band shifts are

observed in SERS spectra of the four indolic molecules, comparing with their ordinary

Raman spectra, which indicate the interaction between the indole ring and the gold

colloidal surface. The two strong Raman bands at 757 and 937 cm-1

for serotonin are

54

assigned to the indole ring in-phase and out-of-phase breathing modes, respectively

(Figure 14). These two bands are downward shifted to 750 and 918 cm-1

in SERS

spectrum of serotonin. In addition, the band at 826 cm-1

in ordinary Raman spectrum of

serotonin is attributed to indole ring NH bending, which shifts to 816 cm-1

in the SERS

spectrum. Serotonin’s other indole Raman bands present these shifts as well, i.e. 460, 678,

1133, 1235, 1433 and 1549 cm-1

. Likewise, these clearly observable downward shifts of

indole Raman bands happen for tryptophan, melatonin and 5-HIAA, as shown in Table 3.

Table 3. Band assignments for the Raman and SERS spectra of serotonin,

tryptophan, melatonin and 5-HIAA. Adapted from reference 145

.

Assignment146-149

Serotonin Tryptophan Melatonin 5-HIAA

Raman

(cm-1

)

SERS

(cm-1

)

Raman

(cm-1

)

SERS

(cm-1

)

Raman

(cm-1

)

SERS

(cm-1

)

Raman

(cm-1

)

SERS

(cm-1

)

Indole ring stretching 460 450 460 421 458 429 457 451

Indole ring NH bending 678 632 575 565 663 634 679 624

Indole ring in-phase

breathing 757 750 757 749 751 745 751 744

Indole ring NH bending 826 816 877 868 819 813 841 826

Indole ring out-of-phase

breathing 937 918 1010 990 922 907 924 914

Benzene H scissoring 1133 1112 1132 1109 1133 1085 1130 1114

Benzene H scissoring

Pyrrole H rocking 1235 1221 1256 1226 1235 1219 1247 1215



55

Figure 14. (a) SERS spectrum of serotonin at 5×10-6

mol/L (1), and ordinary Raman

spectrum at 0.1mol/L (2). (b) SERS spectrum of tryptophan at 5×10-6

mol/L (1), and

ordinary Raman spectrum at 5×10-2

mol /L (2). Adapted from reference 25

.

The spectral shifts described above are consistent with previous studies in which the

indole NH band shifts downward following the formation of a hydrogen bond.150

The

indole NH group is a well-known proton donor, which tends to form hydrogen bonds

with negatively charged gold colloids. As a result, indole rings interact strongly with

colloidal gold, causing the indole NH band as well as other Raman bands of indole to

shift downward. These substantial downward shifts observed in our study suggest strong

interaction between the indole ring and the gold colloids. These observations are also

consistent with previous SERS studies of tryptophan using positively charged silver

colloids, where no significant Raman band shifts are recorded.146

The positively charged

56

silver colloidal surface prevents the formation of hydrogen bonds and as a result shuns

interaction with the ring structure of indole. In this study, we showed that using gold

colloids instead of silver enables one to exploit the strong Raman signals associated with

the indole ring structure.

SERS spectra are also obtained from solutions containing multiple indolic molecules,

all at concentrations of 5×10-6

mol/L, and NNLS fits are performed to determine their

relative concentration. In the first experiment, a solution containing both serotonin and

tryptophan is examined. SERS spectrum of this mixture (line 5 in Figure 15) is fitted with

those of serotonin (line 1 in Figure 15) and tryptophan (line 2 in Figure 15). The NNLS

coefficients for serotonin and tryptophan are 0.730 and 0.216, respectively. Taking into

consideration their respective averaged AEFs at 5×10-6

mol/L, which are 4.69×104 and

1.35×104 respectively, the NNLS fitting result gives a concentration ratio of serotonin to

tryptophan at (0.730/4.69×104) : (0.216/1.35×10

4) = 1.00 : 1.03, which is in good

agreement with the administered concentrations in the mixture. Similarly, a second

experiment is conducted where we obtain the SERS spectrum (line 6 in Figure 15) of a

solution containing serotonin, tryptophan, melatonin and 5-HIAA. NNLS fitting is

performed again with SERS spectra of these four individual indoles (line 1-4 in Figure

15). The NNLS fitted composition ratios are (0.503/4.69×104) : (0.147/1.35×10

4) :

(0.287/2.46×104) : (0.115/1.12×10

4) = 1.00 : 1.02 : 1.09 : 0.957 for serotonin, tryptophan,

melatonin and 5-HIAA respectively. This result is again in good agreement with

administered concentrations of serotonin, tryptophan, melatonin and 5-HIAA. Note that

the AEFs of melatonin and 5-HIAA are 2.46×104 and 1.12×10

4, respectively. A third and

57

a fourth experiment are performed using the same method. SERS spectra of the third

mixture containing serotonin and 5-HIAA (line 7 in Figure 15), and the forth mixture

containing serotonin, tryptophan and 5-HIAA (line 8 in Figure 15) are fitted using spectra

of their respective compositions. The relative concentration ratios are (0.830/4.69×104) :

(0.183/1.12×104) = 1.00 : 0.923 for serotonin and 5-HIAA in the third experiment, and

(0.627/4.69×104) : (0.181/1.35×10

4) : (0.146/1.12×10

4) = 1.00 : 1.00 : 0.975 for serotonin,

tryptophan and 5-HIAA in the fourth experiment. The relative concentration ratios for

these four mixtures are presented in Figure 15(b). These experiments demonstrate that the

relative concentration of serotonin with respect to other closely related indoles in a mixed

solution can be quantitatively determine from its SERS spectrum.

58

Figure 15. The SERS spectra (a) of serotonin (1), tryptophan (2), melatonin (3) and

5-HIAA (4) are shown, all at concentration 5×10-6

mol/L. SERS spectra of four

different mixtures containing various indolic components, all at 5×10-6

mol/L, are

marked in blue, with their respective NNLS fit in red and fitting residuals in grey.

The compositions in the four mixtures are serotonin and tryptophan (5), serotonin,

tryptophan, melatonin and 5-HIAA (6), serotonin and 5-HIAA (7), serotonin,

tryptophan and 5-HIAA (8), respectively. Relative concentrations of the indolic

components in the four mixtures are normalized with respect to serotonin (b).

Serotonin, tryptophan, melatonin and 5-HIAA are marked with circles (○, red),

59

squares (□, blue), diamonds(◊, green) and triangles(△, brown), respectively.


.

60

CHAPTER 6. CONCLUTION AND FUTURE WORK

Unlike prior Raman clinical applications, the need for complicated data processing is

overcome by employing the concept of a Raman biomarker, serotonin. Utilizing the

strong and specific Raman spectral characteristics of this indole biomarker, disease

differentiability is directly observable in the original Raman spectra. Furthermore, this

work has demonstrated the molecular specific detection capability of Raman

spectroscopy beyond the limitation of a set of a priori common tissue constituents. We

anticipate this new paradigm of disease specific “Raman optical biomarker” to stimulate

searches of other ring-structured biomolecules specific to disease models, and as a result

open up a wider application of Raman spectroscopy in clinical settings.

The direct optical detection of specific biomolecules that distinguish a diseased state

using Raman spectroscopy demonstrates its potential for in vivo molecular diagnostics.

Without the need for exogenous labeling, Raman spectroscopy complements

immunohistochemical methods for it is especially sensitive to small ring-structured

signaling molecules which are difficult to detect with conventional

immunohistochemistry. With the demonstrated new paradigm in utilizing Raman

spectroscopy and the advancements in biomarker discovery, Raman spectroscopy can

potentially become a clinically viable tool for minimally invasive, molecular specific, in

vivo diagnostics.

As the roles and functions of the 14 serotonin receptor subtypes in the immune

system continue to be discovered and defined,137,151,152

definitive detection techniques for

their common signaling molecule, serotonin, are critically needed. In addition, such

61

techniques need to be able to distinguish serotonin from its closely related precursors and

metabolites. Here we demonstrated the capability of SERS for such a task and we

envision increasing SERS applications in the study of immune responses.

62

CHAPTER 7. CONTRIBUTION

This work is the first approach to use Raman spectroscopy to detect heart transplant

rejection. Allograft transplantation is the last-resort therapy for patients with failed organs.

Its efficacy relies on the successful management of the recipient's immune responses. The

current standard surveillance method using endomyocardial biopsy is subject to sampling

errors, since only 4 samples are examined each time, the rejected site may be missed. It is

invasive and causes morbidity and rarely mortality, because of the open wounds left in

the heart. And it is subjective because the severity is determined by a pathologist. Here

the capability of Raman spectroscopy to detect cardiac rejection is demonstrated in this

work. To our best knowledge, this is the first approach to detect cardiac rejection using

Raman spectroscopy.

An optical biomarker was discovered and used for the effective detection by Raman

spectroscopy. Unlike prior Raman clinical applications, the need for complicated data

processing is overcome by employing the concept of a Raman biomarker, serotonin.

Utilizing the strong and specific Raman spectral characteristics of this indole biomarker,

disease differentiability is directly observable in the original Raman spectra. Furthermore,

this work has demonstrated the molecular specific detection capability of Raman

spectroscopy beyond the limitation of a set of a priori common tissue constituents. We

anticipate this new paradigm of disease specific “Raman optical biomarker” to stimulate

searches of other ring-structured biomolecules specific to disease models, and as a result

open up a wider application of Raman spectroscopy in clinical settings.

63

It is the first SERS analysis on serotonin and its related molecules including

melatonin and 5-HIAA to my best knowledge. Electrostatic interaction between the

positively charged amine on the serotonergic molecules and the negatively charged gold

colloidal surface plays a significant role in Raman surface enhancement phenomenon.

The detectable concentration is as low as 1×10-7

mol/L for serotonin and 3×10-7

mol/L

for tryptophan. The regular Raman peaks and SERS peaks in spectra of serotonin,

tryptophan, melatonin and 5-HIAA are also assigned in this work

This work successfully qualifies the relative concentrations of indole molecules in

mixed solution. It is shown that the relative concentration of serotonin, tryptophan,

melatonin and 5-HIAA in mixed solutions can be quantitatively determine from a SERS

spectrum, demonstrating the potential use of SERS for in vitro diagnostics. Further

optimization of the AEF is needed to allow in vivo diagnostics under physiological

conditions.

64

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3131.

78

VITA

Qiang Tu

Education

Master of Science, Drexel University, Philadelphia, PA

Bachelor of Engineering, Tsinghua University, Beijing, China

Teaching Experience

Biomeasurements

Biomedical Electronics

Principles of Systems Analysis I

Principles of Systems Analysis II

First Author Publications

Qiang Tu, Chang Chang, Diagnostic Applications of Raman Spectroscopy,

Nanomedicine: Nanotechnology, Biology and Medicine, 8(5), 545-558, 2012.

Qiang Tu, Jonathan Eisen, Chang Chang, Surface-enhanced Raman Spectroscopy

Study of Indolic Molecules Adsorbed on Gold Colloids, Journal of Biomedical Optics

(Letters), 15(2), 020512, 2010

Qiang Tu, Jonathan Eisen, Chang Chang, Band Shifts in Surface Enhanced Raman

Spectra of Indolic Molecules Adsorbed on Gold Colloids, AIP Conference Proceedings

(Proceedings of the XXII International Conference on Raman Spectroscopy), 1267, 1097-

1098, 2010

Qiang Tu, Yoon-Gi Chung, Dianjun Cao, Shuko Harada, Christine S. Cheng,

Howard J. Eisen, Chang Chang, Biomarker Enables Molecular Specific Optical

Detection of Transplant Rejection, IFMBE Proceedings (World Congress on Medical

Physics and Biomedical Engineering 2009), 25(7), 740-743, 2010

Yoon-Gi Chung*, Qiang Tu*, Dianjun Cao, Shuko Harada, Howard J. Eisen, Chang

Chang, Raman Spectroscopy Detects Cardiac Allograft Rejection with Molecular

Specificity, Clinical and Translational Science, 2(3), 206-210, 2009

(* CO-FIRST AUTHORS)

raman spectroscopy detects cardiac allograft rejection

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