MSc Chemistry
Analytical Track
Literature Thesis
Spectroscopic Techniques for Amyloid Plaques and
Tau Tangles Observed in Dementia and Alzheimer
’s
Disease
by
Marina Boersma
11286776
May 2018
Number of Credits 12
April 2018- May 2018
Supervisor/Examiner:
2
ndreviewer:
A
CKNOWLEDGEMENTS
After 2 months of literature study, I learned a lot about Alzheimer’s disease and the burden that lies on the relatives when a loved one suffers from Alzheimer’s disease. I am grateful for the opportunity to do my literature study in guidance of Dr. F. Ariese at the Vrije Universiteit Amsterdam.
Therefore, I would like to thank Dr. F. Ariese for his guidance, and constructive feedback during this research. I would also like to thank Kristian Lüschen for his support and advice.
Utrecht,
Marina Boersma 11-06-2018
A
BSTRACT
Alzheimer’s disease (AD) is the most common cause of dementia and over 46.8 million people suffer from dementia and the number of people is projected to increase with 234% by the year 2040. The rapid increase in dementia is due to the increasing life expectancy and the lack of treatment. During Alzheimer’s disease, many neurons stop functioning due to the amyloid-beta (Ab) and tau tangles accumulation. The accumulation starts prior to the earliest AD symptoms. The cause of these accumulations and true role are still unknown and need to be investigated to provide targeted therapeutics. The therapeutics aim to slow down the accumulation progress; this means that early diagnosis is crucial. A variety of different techniques are used to clinically diagnose AD but the definite diagnosis of AD requires post mortem brain tissue examination using different staining procedures.
Raman spectroscopy is label free, sensitive, is able to define structural fingerprints of molecules and detect molecular conformations. The amyloid plaques and tau tangles molecules are involved. Research is successfully carried out on Raman imaging of fixed human brain slices. The research of Lobanova et al. 2018 showed that 5 important chemicals that are located in Ab could be analysed and cholesteryl esters play an important role in AD.
When measuring AD using Raman spectroscopy, certain aspects need to be considered. Raman spectroscopy could not be used in vivo because of the penetration depth of the laser through it overall is not sufficient enough. Other obstacles faced during Raman spectroscopy are the poor sensitivity, the inhomogeneous tissue in the brain and locating the area of interest.
The Fluordeoxyglucose positron emission tomography as diagnosis tool for AD uses labelling
techniques. However, Raman imaging method could not penetrate the human skull and can be used in vivo. For label free diagnosis of AD, the analysis of blood or cerebrospinal fluid as biomarker should be explored.
L
IST OF ABBREVIATIONS
Ab Amyloid-beta
AD Alzheimer’s Disease
APOE Apolipoprotein E
b-APP Amyloid-b precursor protein
BP Bandpass filter
BS Beam splitter
CDR Clinical dementia rating
CF Collection fibre
CSF Cerebrospinal fluid
CT Computed tomography
DLB Dementia with Lewy bodies
DNA Deoxyribonucleic acid
EF Excitation fibre
EOAD Early onset Alzheimer’s Disease
FDG F-18 fluordeoxyglucose
FTD Frontotemporal dementia
FTIR Fourier transform infrared
LOAD Late onset Alzheimer’s Disease
LP Long pass filter
MCI Mild cognitive impairment
MRI Magnetic resonance imaging
NFT Neurofibrillary tangles
NIR Near infra-red
PET Positron emission tomography
PIB Pittsburgh compound B (thioflavine T)
PS Presenilin
RRS Resonance Raman spectrometry
SERS Surface enhanced Raman spectrometry
SORS Spatially offset Raman spectrometry
T
ABLE OF CONTENTS
ACKNOWLEDGEMENTS ... I ABSTRACT ... II LIST OF ABBREVIATIONS ... III TABLE OF CONTENTS ... IV
1 INTRODUCTION ... 1
2 BACKGROUND INFORMATION ... 3
2.1 ALZHEIMER’S DISEASE ... 3
2.1.1 Alzheimer’s disease characterisation ... 4
2.2 PROCESSES IN THE BRAIN RESPONSIBLE FOR ALZHEIMER’S DISEASE ... 5
2.2.1 Amyloid plaques ... 5
2.2.2 Tau tangles ... 7
2.3 BIOMARKERS OF ALZHEIMER’S DISEASE ... 8
2.3.1 Increased protein aggregation in the lens ... 8
2.4 TECHNIQUES USED FOR ALZHEIMER’S DISEASE DIAGNOSES ... 9
2.4.1 Positron emission tomography (PET) ... 9
2.4.2 Raman spectroscopy ... 12
2.4.3 Third harmonic generation microscopy ... 14
2.5 STATISTICAL MODELS TO DEFINE IMAGING DATA ... 15
2.5.1 Principal component analysis ... 15
2.5.2 Hierarchical clusters ... 16
3 POSITRON EMISSION TOMOGRAPHY APPLICATIONS ... 17
3.1 FLUORDEOXYGLUCOSE POSITRON EMISSION TOMOGRAPHY ... 17
3.2 PITTSBURGH COMPOUND B POSITRON EMISSION TOMOGRAPHY ... 18
4 LENS OPACITY AS INDICATOR FOR ALZHEIMER’S DISEASE ... 19
5 RAMAN SPECTROSCOPY APPLICATIONS ... 20
5.1 SPECIFIC COMPONENTS LOCATED IN THE AMYLOID-b PLAQUES ... 20
5.2 RAMAN IMAGING IDENTIFYING ALZHEIMER’S DISEASE IN BRAIN DONORS ... 23
5.3 RAMAN SPECTROSCOPY IMAGING AS IN VIVO DETECTION METHOD ... 23
5.4 MOLECULAR IMAGING IN VIVO WITH STIMULATED RAMAN SPECTROSCOPY ... 24
5.5 RAMAN SPECTROSCOPY ANALYSIS OF BLOOD ... 25
5.6 ADVANCED IMAGING OF TAU FIBRILS ... 25
6 THIRD HARMONIC GENERATION MICROSCOPY APPLICATIONS ... 26
1 I
NTRODUCTION
Globally, 46.8 million people suffer from dementia in the year 2015 this number is estimated on the World Alzheimer Report1. The population of dementia places a considerable burden on society.
Currently the costs are estimated to amount 818 billion US dollars per year and is predicted to increase every year2. Alzheimer’s disease is the most frequent cause of dementia3. The early detection of Alzheimer’s disease is therefore of great interest to improve the quality of life, reduce the cost, and to prolong the life expectancy of patients.
Alzheimer’s disease is a neurodegenerative disorder that progresses over time4. Alzheimer’s disease
consists of three primary groups of symptoms. The first group includes memory loss, language difficulties and executive dysfunction5. The second group involves psychiatric symptoms and
behavioural disturbance. The third group of symptoms contain the difficulties with performing daily activities6. Aging also causes memory loss but the difference between normal aging and dementia is
that dementia causes problems with day-to-day activities5.
The cause of Alzheimer disease is related to the abnormal growth of two proteins, β-amyloid and tau, present in the brain. The accumulation of the two proteins starts prior to the first symptoms. These two proteins are toxic to the nerve cells and are located in the brain, the build-up eventually leads to the death of nerve cells5. The difference between a healthy and an Alzheimer disease brain is shown in Figure 1. It is important to understand the deposition of amyloid plaque for early diagnosis and treatment.
The current method to diagnose Alzheimer’s disease is based on a patient’s medical history and the following advanced medical imaging techniques could be used: computed tomography (CT, provide an image of the brain), magnetic resonance imaging (MRI, detailed image of body structures such as tissues and nerves) and positron emission tomography (PET, provides pictures of brain activity). These tests identify strokes and tumours which can cause dementia. Furthermore, changes in the brain’s structure and function can be identified, which are indications of Alzheimer’s disease7.
The current flourdeoxyglucose (FDG)-PET method can characterize patterns of glucose metabolism in the brain and can help differentiating Alzheimer’s disease from other causes of dementia. The Fluor labelling FDG-PET is a valuable tool in tumour imaging and it reflects metabolic rates of glucose which indicates the neuronal activity8. The disadvantages of labelling techniques are the cost and the exposure to radioactive substances. Label free spectroscopic imaging techniques for amyloid plaques to detect Alzheimer’s disease still need to be explored. The label free techniques such as Raman and stimulated Raman have specific advantages compared to the current methods. The aim of this literature study is to identify the possibilities of label free spectroscopic techniques for amyloid plaques, this to ensure non-destructive and early analysis of Alzheimer’s disease.
2 B
ACKGROUND INFORMATION
2.1 A
LZHEIMER’
S DISEASEIn 1907 Alois Alzheimer a clinical psychiatrist and neuroanatomist discovered plaques and neurofibrillary tangles (NFT) during an autopsy of the neocortex and hippocampus. The patient Auguste D. was a middle-aged women with paranoid symptoms, memory loss, and sleep disorders9. However, this disease was very rare and for more than 50 years the research of Alois Alzheimer’s was discarded. The last few decades the situation has changed and the disease is the most prevalent of the neurodegenerative disorders. The reason for the drastic change is the increased life expectancy throughout the world which now extends well into the 80 years. Neurodegenerative disorders like Alzheimer’s disease (AD) increase dramatically with advancing age10.
Alzheimer’s disease is the most common cause of dementia. Alzheimer’s disease can be classified in two categories: early onset AD, where the symptoms develop relatively early in life, accounts for 5% of all the AD cases11. The second category is the late onset AD which occurs later in life and the greatest risk factor is therefore age. Because of, the overall aging of the population and the growing number of people, the number of people with dementia is projected to increase with 234% by the year 204012. This indicates a health and economic challenges for the present and the future.
During normal aging the brain shrinks to some degree, but in the case of AD the damage is much larger as many neurons stop functioning. This leads to the loss of connection between neurons and eventually leads to cell dead. AD disrupts processes that are vital to neurons like the communication metabolism and repair. The disease starts by destroying neurons in parts of the brain involved in memory which leads to memory loss, the entorhinal cortex and hippocampus. The second area that is affected is the cerebral cortex which is responsible for language, reasoning and social behaviour. Eventually the patient with AD loses the ability to live and function independently7.
As mentioned before, Alzheimer’s disease can be characterized by two different groups the majority suffering of late-onset AD (LOAD) and early-onset AD (EOAD) which are patients with autosomal dominant familial AD. The cause of EOAD is in 80% of the cases the mutation of the gene for Amyloid precursor protein (APP) or one of two proteins presenilin-1 (PS1) and presenilin-2 (PS2)11. The mutations lead to the accumulation of Amyloid-b (Ab) in the brain. However, more than 200 genes are involved in the progression of LOAD. The genetics appear to be complex without a dominant effect of a single gene, only apolipoprotein E (APOE) has been established as gene. The plaque accumulation leading to AD occurs prior to the earliest clinical symptoms. The accumulation of plaques leads eventually to toxicity which causes synaptic dysfunction and subsequent neurodegeneration13. The true role of amyloid plaques in AD is still unknown but will provide meaningful insight for targeted therapeutics. Nevertheless, this relies on the development of neuroimaging techniques and different biomarkers12.
The current clinical diagnostic tools for in vivo brain imaging are CT, amyloid-PET and MRI. These diagnostic methods depend on a doctor’s experience, subjective factors may influence the diagnosis and are not suitable for early diagnosis14. The definite diagnosis of AD requires examination of post
mortem brain tissue and using different staining procedures like FDG-PET15. A potential timeline of a
patient with AD is shown in Figure 2.
Figure 2, the theoretical timeline of AD process. amyloid and tau pathology occurs years before any symptoms of AD. Clinical diagnosis after cell death and definite diagnosis of AD by post mortem analysis of the brain. Image adapted from Risacher et al., 2013.
Raman spectroscopy has been studied to analyse label-free Ab peptides in solution, synthetic Ab fibrils, isolated human plaques, and post mortem AD brains. Furthermore, Fourier transform infrared spectroscopy was used to image protein aggregation in living cells and spectral imaging AD brain tissue of mice15,16. Raman spectroscopy is a sensitive and precise method which allows for the
identification of groups of macromolecules with structural properties.
2.1.1 Alzheimer’s disease characterisation
AD progresses in time and can therefore be characterized as very mild, mild, moderate and severe dementia. These characterisations are assigned by the clinical dementia rating (CDR) ranging from 0, 0.5, 1, 2 and 3, with 0 no dementia to 3 severe dementia. This is a reliable tool to indicate AD.
2.2 P
ROCESSES IN THE BRAIN RESPONSIBLE FORA
LZHEIMER’
S DISEASEThe human brain consists of neurons that process and transmit information by electrical pulses and chemical signals. Most neurons consist of three parts: a cell body which contains the nucleus, the dendrites, these are branch-like structures which collect information. The last part is the axon which is a cable-like structure opposite of the dendrites and transmits messages.
Besides receiving information from neighbouring neurons, metabolism is also critical for a healthy cell. During the metabolism, several chemicals and nutrients are broken down and for this the cell requires energy. The energy in the form of oxygen and glucose is provided by the blood circulation. Furthermore, neurons live a long time unlike other cells and must therefore repair themselves. Neurons also adjust their connections depending on the stimulation they receive. Adults may also generate new neurons, this process is called neurogenesis. These remodelling of connections and neurogenesis are important for learning, memory and brain repair.
The unregulated accumulation of proteins eventually leads to damaged and dying nerve cells in the brain. At a severe stage of AD, the cortex will shrunk and the ventricles will be enlarged. The two components: amyloid plaques and the tau tangles cause the brain damage and dying nerve cells, this suggest that the amyloid plaques and tau tangles are toxic species. However, the direct analysis of amyloid plaques and tau tangles in the brain on quantitative, spatial distribution and chemical composition is still very limited17.
2.2.1 Amyloid plaques
The accumulation of amyloid-b (Ab) in the nerve tissue is the main cause of Alzheimer’s disease (AD) but is also characteristic for Down syndrome and Parkinson’s disease18, 19. The Ab is formed by the endoproteolytic cleavage of the amyloid-b precursor protein (b-APP), which is 39 to 43 amino acids long20, 21. The breakdown of the larger protein AAP forms the Ab protein and the Ab-42 is especially toxic. The most commonly found cleavages are the Ab1-40 and the Ab1-42 peptides.
Normally, these peptides are broken down by ubiquitin-proteasome pathway or by the phagosomes and lysosomes. The accumulation of Ab originates from degenerating mitochondria in dystrophic neurons and causes disruption of axons and amyloid deposition8. However, at old age or because of a mutation the breakdown is inhibited and uncontrolled growth of the peptide occurs which eventually leads to polymers, which form amyloid fibrils18. The fibrils are characterized by their regularly aligned
b-pleated sheets, which is the main component of the plaques in AD as shown in Figure 3. The plaques are located in-between neurons and disrupt cell functions.
Figure 3, the process of a protein misfolding and forming fibrils during AD, leading to accumulation of proteins (NFT= neurofibrillary tangles). Image adapted from Forman et al., 2004.
A variety of proteins are located in the brain and human lens and most of these proteins contain b-sheets like in the Ab. The b-sheet consists of 3 to 10 amino acids and is arranged adjacent to another strand of amino acid. The two strands then form a network by forming hydrogen bonds with their neighbours as shown in Figure 4. The b-sheet can from hydrogen bonds in parallel, anti-parallel or mixed arrangements and this determines the stability of the b-sheet22. However, Ab plaques and tau
tangles in AD brains are characterized by a high occurrence of these b-sheets which are present as fibrils11.
Figure 4, Beta-sheet structure parallel form left and anti-parallel right. Image from Yao et al., 2008.
Native protein tau
or amyloid-β Misfolded proteins Oligomers β-sheet Fibrils Intercellular NFTs
Extracellular plaques
2.2.2 Tau tangles
Tau is a protein located in the brain and stabilizes microtubules. Microtubules are proteins in the shape of a tube. The tubular proteins can grow to be 50 micrometres long and are highly dynamic. The main function of tau proteins is to stabilize microtubules and provide flexibility. Furthermore, the protein is highly soluble and is heat stable. Tau proteins are the product of a single gene that is located on chromosome 17. The protein can stabilize microtubules by means of isoforms and phosphorylation. The normally unfolded tau tangles are randomly altered leading to AD upon aging. The tau proteins are defective by several processes and form fibrils which no longer stabilize microtubules. In the case of AD, tau abnormally phosphorylates leading to a decreasing stability for microtubules, this process is represented in Figure 5. The still soluble tau aggregates into soluble and insoluble aggregates also known as neurofibrillary tangles (NFT)23. The fibrils are regularly aligned with a b-sheet
configuration. This process of abnormal accumulation of tau protein occurs inside the neurons. The tangles block the neurons to transport signals, which harms the communications and therefore the cell viability. The tangles with tau fibrils as main component and the Ab plaques obstruct the normal functions in the brain.
Figure 5, progression of tau accumulation: normally tau stabilises microtubules, when tau phosphorylates abnormally it destabilises microtubules. The tau monomers aggregate to soluble tau oligomers and eventually form neurofibrillary tangles. Image adapted from Barron et al., 2016.
Based on these processes, the b-amyloid peptide, total tau and phosphorylated tau can be used as biomarkers and reflect the pathological features of AD. The current technique to image tau uses tau-specific dyes and labelling techniques such as PIB-PET, but the technique does not give information on the dynamics of tau aggregations and the type of aggregate. The use of spectroscopic techniques
Stabilised microtubules Destabilised microtubules Soluble tau oligomers Tau
2.3 B
IOMARKERS OFA
LZHEIMER’
S DISEASEThe early detection is important because the treatments for AD are likely to prevent or slow down AD processes such as Ab protein and tau protein accumulation. Therefore, the treatments would have a maximal effect in the early stages of the disease. In the early stages of AD it is important to have sensitive and specific biomarkers to effectively monitor the progression and provide an accurate diagnosis. The processes in the brain discussed before are used as biomarker for neuroimaging. The AD-related proteins could also be measured form the cerebrospinal fluid (CSF)25. The technique to
determine the density or volume of grey matter, white matter and CSF have been developed and applied in studies of brain aging and AD. During the CSF studies the levels of Ab40, Ab42, total tau
and phosphorylated tau are analysed. Patients with AD show significantly decreased level of the proteins Ab40 and Ab42 and an increased level of tau and phosphorylated tau when compared with
healthy controls26. The increased levels of tau in CSF occur after the damaged and or dying neurons
which releases the tau in the CSF. Furthermore, the reduced levels of Ab1-42 are induced by the
accumulation of the protein to insoluble plaques in the brain26.
Furthermore, blood-based biomarkers can be used for early AD diagnosis. Raman spectroscopy can identify biomarkers in the human plasma and serum. Raman spectroscopy gives information about subtle variations in structures of molecules for example protein, lipids and nucleic acids. For this reason Raman spectroscopy could be used to develop a blood based molecular fingerprint to identify patients27.
The MRI and PET neuroimaging techniques are most commonly used for AD biomarkers. The MRI in vivo investigate structural changes in the brain. The MRI technique measures the brain volume, tissue morphology and the rate of atrophy and uses contrast fluids and labelling. The PET technique is an in vivo method that uses labelling to measure metabolic and neurochemical process.
2.3.1 Increased protein aggregation in the lens
AD is the age-dependent degenerative changes that occur in the brain. However, the human lens is also sensitive to age and shows deposition of insoluble protein and oxidative damage. In the papers by Goldstein et al. and Moncaster et al. the accumulation of Ab in the cortical of cataract patients with AD and Down syndrome were analysed post mortem. They conclude that the accumulation process not only occurred in the brain but also in the eye. The Ab accumulation in AD located in the lens qualify as Raleigh scattering signals that can be distinguished from common age-related cataracts. If cataracts occurred due to increased protein level, the protein accumulation must have been built up many years before death. Thus, this could be an indication of early detection of AD.
2.4
TECHNIQUES USED FORA
LZHEIMER’
S DISEASE DIAGNOSESAlzheimer’s disease is still hard to diagnose and the current method relies on labelling techniques which may alter the biomarkers. The techniques can be categorised in techniques used in vitro and in vivo. The first category in vitro uses human cell cultures or tissues that are donated from post mortem donors. In vivo means within the living and living organisms in animals or humans are tested.
Furthermore, the techniques can be categorised in labelling or label-free techniques.
2.4.1 Positron emission tomography (PET)
Positron emission tomography or more commonly called PET is an imaging technique used in hospitals to observe metabolic processes in the body. Before a PET-scan can be made the patient is injected with a radioactive isotope. Typical radionuclides used in PET have a short half-life such as fluorine-18 (~110 min) and carbon-11 (~20 min). The fluor-18 is an instable radioactive isotope and decays to a stable oxygen-18 isotope and produces a rich source of positrons:
𝐹 → 𝑂 + 𝑒&+ 𝜈 ( ) *) + *)
Carbon-11 is also an instable radioactive isotope and decays to a stable boron-11 isotope: 𝐶 → 𝐵 + 𝑒&+ 𝜈 ( . ** / **
The isotopes, also called tracers, are incorporated into compounds normally used by the body and gather in a certain area of the body for example a brain tumour.
The radioactive isotope emits a positron 𝑒&, a positron is the anti-particle of an electron and has the
same mass but a positive charge. The positron will travel through the body for less than ~1 mm and loses kinetic energy until it can interact with an electron. Both the electron and positron produce gamma rays which can be detected and which move in opposite direction. When the opposite moving gamma rays both reach the ring detector it creates a light signal. The three-dimensional image of the different tracer concentrations in the brain are constructed by the computer analysis. The kind of tracer determines which tissue is analysed. A PET scan is normally non-invasive, but a patient is exposed to ionizing radiation. Another disadvantage is the cost to perform a PET scan which amounts $1000-1200 US dollars per scan.
In the case of AD, the PET neuroimaging is used and is based on the assumption that high
radioactivity is associated with brain activity. This is achieved by measuring the use of glucose and oxygen with fluordeoxyglucose, also called FDG. However, in AD the metabolism of glucose and oxygen is greatly decreased in the brain and can be used to differentiate between AD and other dementing processes as shown in Figure 628.
Figure 6, the FDG-PET scan of a normal patient on the left, a mild cognitive impairment patient centre and on the right a brain patient with AD. Image is adapted from Montagne et al., 2014
2.4.1.1 Fluordeoxyglucose and thioflavine-T positron emission tomography
The AD progresses in time and molecular imaging techniques offer the possibility of characterizing changes in the brain during the development of AD. The diagnostic of AD is performed by imaging studies of abnormalities of cerebral glucose metabolism and perfusion with in vivo F-18
fluordeoxyglucose (FDG) PET the structure is shown in Figure 7. The PET analysis has a broad range of application, such as blood flow, specific metabolic rate of neurotransmitter synthesis and receptor binding capacity. Furthermore, PET is accurate for quantitation29. The areas of interest of early
diagnosis of dementia are the characteristic bilateral temporopariental and frontal cortical30.
The in vivo analysis of amyloid plaques with PET can be performed with the tracer mentioned before and the napthylethylidene-derivative [18F]FDDNP (FDG). However, a different tracer such as
thioflavine-T-derivative [11C]6-0H-BTA-1 (PIB) could also be used; the structure is shown in Figure 7. The [11C]PIB tracer binds to the amyloid plaques and not with other abnormalities.
Figure 7, structure of glucose on the left and fluordeoxyglucose in the centre and thioflavine T on the right.
Normal
Mild cognitive
impairment
Alzheimer’s
disease
plaques and a patient with AD will have an increased binding31. The combination of [18F]FDG-PET and [11C]PIB-PET imaging can be helpful by determining different kinds of dementia.
The definitive diagnosis of AD is based on the atrophy of the cortex, neuron and synapse loss, amyloid plaques and tau fibrills. The development of in vivo imaging of AD patients gives the potential for developing biomarkers that can be monitored in the living brain12.
The most important feature for the diagnosis of AD is the ability to image the amyloid plaques, because the accumulation is viewed as fundamental to AD. The tracers or also called labelling
technique can affect the distribution composition of the lipids or amyloids therefore label free methods are of interest. The vibrational micro spectroscopic techniques such as infra-red absorption and Raman microscopy detect the vibrations of chemical bonds and are non-invasive label-free imaging methods. However, infra-red has a limited spatial resolution due to the wavelength of infra-red light. Raman can use light in the visible light range and offers sub-micron spatial resolution.
2.4.2 Raman spectroscopy
The Indian physicist C.V. Raman discovered that a small fraction from the incoming light scatters by certain molecules and differ from the incident beam. The shift in the wavelength depends on the chemical structure.
Raman spectroscopy observes vibrational, rotational and low-frequency modes in a structure by scattering. With these observations, structural fingerprint is provided by which molecules can be identified. The monochromatic light from a laser exchanges energy with the molecule which then produces inelastic or Raman scattering. The interaction between the laser and molecule results in a change in the quantum states and the photons of the laser will be shifted up or down in energy. The shift in photon energy between the excited and scattered photon corresponds with the vibrational quantum.
During Raman spectroscopy, a sample is illuminated with a laser, the molecules in the sample will undergo elastic scattering or also called Rayleigh scattering and inelastic scattering. Elastic scattering involves a brief deformation of the molecule’s electron cloud. However, the scattered photon will have the same energy as before just a different direction. During Rayleigh scattering there is no exchange of energy and therefore no wavelength change of the proton. After illumination of the sample the
electromagnetic radiation is collected with a lens and sent through a monochromator. The Rayleigh scattering is normally filtered out by a notch filer or an edge pass filter, the rest of the light is dispersed onto a detector.
The inelastic scattering can occur in two different ways, Stokes and anti-Stokes. During anti-Stokes the electrons are in a higher vibrational state when irradiating. The energy level diagram displaying the different energy states involved in Raman spectroscopy is shown in Figure 8.
Vibrational
energy states
1 2 0 3Virtual energy
states
Infrared
absorption
Rayleigh
scattering
Stokes Raman
scattering
Anti-Stokes
Raman scattering
The advantage of Raman is the very sharp lines in the spectrum and the information is very selective. However, Raman is not very sensitive, every compound has Raman scattering. Therefore, different techniques such as resonance Raman spectrometry (RRS), Spatially offset Raman spectrometry (SORS) and surface enhanced Raman spectrometry (SERS) are developed to increase the sensitivity. According to Michael et al. the molecular conformation of proteins in plaques and tangles could be analysed using Raman micro spectroscopy and imaging. Raman could detect specific molecular conformational bonds in proteins and is a quantitative sensitive method. The presence and the amount of Ab plaques and tau tangles tell something about the stage of AD. The vibrational bands at 1250 cm -1 (amide III) and 1670 cm-1 (amide I) reflect the amount of a-helical and b-sheet. The Raman
spectroscopy method can detect local conformational changes in proteins, lipids and the accumulation of these molecules32. Confocal scanning Raman further enables the imaging of the changes11. When
the Raman imaging is extended over several regions of the tissue a conclusion can be made on local differences in protein content and conformation18.
In general, in bio sensing visible to near infra-red (NIR) wavelengths are used in vivo, these
wavelengths have a greater penetration depth in tissues. Raman spectroscopy is already used for the detection of cancer. During the method for cancer diagnostic differences in plasma membranes are examined. Different kinds of cancers such as brain, skin and breast cancer can be examined and characterized by Raman spectroscopy. According to Henry et al. a differentiation could be made between cholesterol, amino acids, DNA, RNA, phospholipids, proteins, collagen and when comparing normal versus neoplastic tissue with Raman spectroscopy. The difficulty when comparing neoplastic and normal tissues are the slight differences in spectra which are also complicated by fluorescence from the tissue. During Raman measurements fluorescence results in background noise which can overwhelm the Raman signal.
When Raman spectroscopy is used to analyse AD the different protein concentrations, amyloid plaques and tau tangles are of interest.
2.4.2.1 Surface enhanced Raman spectroscopy
Raman spectroscopy is molecularly specific but has poor sensitivity, only 1 in 108 photons undergo
Raman scattering. The detection, identification and quantitative analysis of AD in the early stages has low biomarker concentration and therefore highly sensitive and quantitative methods are critical. The SERS technique is obtained by measuring a Raman spectra in the usual way, only the sample is adsorbed on a surface with metal colloidal metal particles or on roughened surface with metal particles. The most common metal particles are silver, gold and copper. The Raman signal of the adsorbed molecule of the sample on the metal surface is enhanced by a factor of 103 to 106. The shape
and size of the metal surface strongly affect the enhancement. The advantages of the SERS phenomena are the increase signal intensity and is molecular specific. Other advantages of SERS compared to other analytical techniques are the bar-code like spectrum which is unique and has narrow vibrational bands in the Raman spectrum. In the most ideal case exquisite sensitivity of SERS is down
SERS is able to probe one nanoparticle at a time or several through imaging in vitro and in vivo. SERS can be applied on biomolecules, probing bio-fluids and cancer detection33.
2.4.2.2 Coherent anti-Stokes Raman scattering
Coherent anti-Stokes Raman scattering microscopy provides improvements in 3D spatial resolution and acquisition speed compared to spontaneous Raman. Disadvantages including the limited signal-to-noise ratio in the fingerprint range and thus lower chemical specificity.
2.4.2.3 Stimulated Raman scattering
Stimulated Raman scattering (SRS) provides label-free optical images for in vivo applications for humans and provides vibrational frequencies of chemical species. The mechanism of SRS resembles that of spontaneous Raman spectroscopy. The sample is excited by a pump of photons of an angular frequency wp. Some photons are absorbed by the molecule which lead to vibrational transition and the
emission of the photon is shifted. During SRS, the sample is excited by collinear and tightly focussed pump and involves a third order non-linear phenomenon. This non-linear phenomenon stimulates a specific transition of a second photon ws. The difference in frequency between wp and ws resembles a
specific vibrational transition wv the transition is resonantly enhanced. SRS involves two photons; the
probability of SRS is higher which result in a higher signal. The advantages of SRS over spontaneous Raman scattering are that the signal is several orders of magnitude higher and a shorter acquisition time. The SRS technique could also be used to non-invasively image live tissue in a label-free manner.
2.4.3 Third harmonic generation microscopy
Third harmonic generation is also considered a higher harmonic generation and is a nonlinear scattering process. The scattering results from the phase matching and summation of light by the sample. The scattering represents specific physical properties, molecular arrangement and order. Third harmonic generation microscopy is a multi-photon technique which is label free and generates a three-dimensional image. During the process of THG the energy of three incoming photons is
combined to one outgoing photon. This leads to a signal that is exactly one third of the excitation wavelength. The laser wavelength is therefore longer than typically used in laser-scanning
microscopy. The wavelengths of THG imaging are in the range of 1200-1350 nm because this range gives optimal contrast for tissue. When a shorter wavelength is used the signal is then in the UV range and is more likely to be absorbed by the tissue and for longer wavelengths suffer from excessive water absorption34. All the energy of the incoming photons is converted and none is deposited in the sample. THG occurs at structural interfaces such as local transitions of the refractive index, meaning that the physical structure changes35.
2.5
STATISTICAL MODELS TO DEFINE IMAGING DATAWhen Raman imaging techniques are used a lot of data is generated by different variable to make the data more meaningful multiple statistical models such as principal component analysis (PCA) could be used.
2.5.1 Principal component analysis
This principal is used for datasets that consist of multiple samples and variables. The paper by Bro et al. 2014, uses an example dataset of 44 samples of wine from the same grape but from four different countries. For example, a dataset in the scoop of this research could consist of 44 samples of people of the same age but with four different dementia diagnosis. The wine dataset consists of 44 samples and 14 variables were measured and can be arranged in a table or matric of 44 x 14. This matric is quite complicated to overview the information and to define relations between variables.
Variables may correlate with each other, it is therefore dangerous to only rely on univariate analysis. In univariate analysis, covariation with other variables are neglected and this may lead to features being ignored. If variables would completely correlate the average or the sum of the two could be used as one new variable37.
The linear combinations of variables are essential in PCA. The two variables with a linear relation can be defined as a weighted average of all 14 variables the other variables will have weight zero. The unit weight vector preserves the size of the variation and it allows for going back and forth between the original variables and the new one. When the new variable is multiplied with the weight which gives an estimation of the original variable. However, this concept only works when the two variables completely correlate.
PCA describes weight needed for a new variable that best explains the variation in the whole dataset. The new variable and including the weights is called the first principal component. However, the difference in scale in a dataset needs to be considered. Because if the scale is not equalised the model will only focus on the variables with larger numbers. The pre-processing tool auto scaling could be used this makes each column have the same size. It is important to note that if each variable’s variation is independent from each other the original variable would explain 100% ÷ 14 = 7% of the variation. The data is collected in a matrix X with I rows (samples) and J columns (variables). A linear
combination of the variables can be written as 𝑡 = 𝑤* × 𝑥* + . . . 𝑤= × 𝑥=, t is a new vector and w represents the weights. The summarizing capability of t is calculated by representation of t in terms of replacing X. The X columns are projected on t and the residuals are calculated. This calculation is performed by regressing all variables of X resulting in the following model:
𝑋 = 𝑡𝑝@+ 𝐸
p is the vector of regression coefficients also referred to as the loadings vector and E are the residuals in a matrix. The vector t is referred to as the scores vector and the structure of the PCA model consists
Figure 9, the visualization of the PCA model. Image adapted from Bro et al. 2014.
The scores can be plotted this can be helpful to determine new data a scatter plot shows certain groupings in the data. This means that healthy people and AD patient would be distinctly different in the score plot. In this way, it is possible to classify patients using the variables. When a new sample ends up in the middle of the healthy patients it is probably not a patient with AD. PCA provides an overview of complex multivariate data and can be used to discover relations between samples and variables.
2.5.2 Hierarchical clusters
Hierarchical clustering is a statistical method to cluster the data. Before performing Hierarchical clustering the data is explained in a symmetric dissimilarity matrix. The first step in the clustering process is to look for samples with the most similarities the lowest dissimilarity. The two samples are then joined to form one new sample. The step of clustering samples is repeated. The type of clustering determines how the dissimilarities are calculated between merged sample and other samples. The most popular maximum or complete linkage method chooses the maximum of the two dissimilarities. The row or the columns of a data matrix can be clustered. The result of all the clustered samples is a binary tree or dendrogram with n-1 nodes. Node is the point were two samples are joined.
X
=
T
P
T+
E
J variables I objects J I R R3 P
OSITRON EMISSION TOMOGRAPHY
A
PPLICATIONS
The diagnosis of AD was mainly based on symptoms such as memory loss and speaking difficulties at this stage the patient has already brain damage. The development of biomarkers and neuroimaging can detect AD earlier than conventional diagnostics. AD is caused by the accumulation of the Ab plaques and tau tangles and is believed to start years before the first signs of AD. The main component of these Ab plaques and tau tangles are the regularly aligned b-pleated sheet configurations. However, most proteins in the brain and the lens of the human body have b-sheets as their molecular conformation. The big difference between healthy and AD is the high concentration of b-sheets in AD. The treatment for AD is aimed to slow down the accumulation progress, this means that early diagnosis is very important.
3.1 F
LUORDEOXYGLUCOSE POSITRON EMISSION TOMOGRAPHYFDG-PET is a tool for the diagnosis of AD, it reflects the metabolic rates of glucose in resting state. The method can even differentiate AD from other causes of dementia because of the distinct patterns of cerebral glucose metabolism. However, to use these distinct patterns of the cerebral glucose metabolism in AD, a comparison to normal aging alterations in glucose values should be made. The study by Kuhl et al. 1982, described the effects of the cerebral glucose metabolism in normal aging people. The study found that the cerebral glucose metabolism gradually decreases with age but is similar among individuals with the same age17.
The study by Marcus et al. 2014, performed a meta-analysis of 27 studies evaluating FDG-PET and came to the following conclusion: the FDG-PET has a superior diagnostic accuracy in comparison with MRI, CT and SPECT. The FDG-PET evaluation resulted in a polled sensitivity of 91% and a specificity of 86%.
Mild cognitive impairment (MCI) is also referred to as the pre-dementia phase of AD and can be successfully diagnosed with FDG-PET. Patients with MCI have a declined cognitive performance more than normal aging but not severe enough to be called dementia. The study by Mosconi et al. 2008, included 548 subjects: 110 normal elderlies as control, 114 MCI, 199 AD, 98 frontotemporal dementia (FTD) and 27 patients with dementia with Lewy bodies (DLB)38. The result showed that the
different diseases had specific FDG-PET patterns as shown in Figure 10. In AD patients, the areas that showed decreased glucose metabolism are: the posterior cingulate cortex, precuneus, inferior parietal lobule, and middle temporal gyrus. In patients with MCI only the posterior cingulate cortex was affected.
Figure 10, FDG-PET patterns in MCI, AD, FTD and DLB patient. For explanation see the text. Image from Mosconi et al. 2008
3.2 P
ITTSBURGH COMPOUNDB
POSITRON EMISSION TOMOGRAPHYThe Pittsburgh compound B (PIB) tracer is a derivative of a fluorescent amyloid dye (thioflavin T) and has a high affinity for fibrillary Ab. This tracer was developed by Chet Mathis and William Klunk at the University of Pittsburgh17. The study by Klunk et al. 2004, included 16 AD patients and 9 healthy
controls. The aim of this study is to describe the results of PIB-PET imaging, therefore all subjects were analysed with PIB-PET. The patients with AD showed a significantly higher retention for the PIB tracer in the frontal cortex, parietal cortex, corpus striatum, temporal cortex and occipital cortex than the healthy controls. In Figure 11, the standardized uptake value (SUV) is displayed of the PIB and FDG tracer in the control and AD patient and the difference between control and AD is a lot more significant with the PIB tracer than with the FDG tracer39.
FDG-PET and amyloid PET imaging can be used to assess patients with dementia. The characteristic distribution of the FDG tracer measuring the glucose metabolism on the brain can be used to
differentiate AD from other dementias such as dementia of Lewy body. However, patients are exposed to radioactive rays which is unhealthy this means there is limited number a patient can undergo PET imaging, therefore a tracer/label free method should be explored. Another limitation of PET imaging of Ab is that for a definite diagnosis of AD amyloid plaques by themselves are insufficient. According to Schierle et al. 2016, the detection of tau pathology is also required for the diagnosis of AD24.
4 L
ENS OPACITY AS INDICATOR FOR
A
LZHEIMER
’
S DISEASE
Degenerative changes occur also in the human lens, which leads eventually to cataracts. b-APP and Ab are present in the human lens with cataracts. This may be a diagnostic marker for AD. One of the advantage of using the lens instead of the brain is the relatively easy non-invasive techniques that can be used to determine the location and degree of opacification. Furthermore, changes in the eye could be used as screening to predict AD.To test this theory Bei et al. 2015, analysed the lens opacity of 42 participants for an early prediction of AD. The 42 participants were separated in two groups 15 individuals who were biomarker positive thus at high risk of developing AD. The second group had no biomarkers for AD and had the lowest risk of developing AD.
Before analysing the eye of the 42 participants, the Ab42 levels and fibrillary Ab binding potential
were analysed with CSF and PET. Furthermore, all participants underwent a mini mental and a neurological exam. None of the participants were diagnosed with clinical dementia during the tests. The cross section and retro illumination images were examined by Scheimpflug photography. Scheimpflug photography measures the light scattering of the lens and provides an image of the surface. This method allows quantification of the opacification of the lens by measuring the backward light scattering.
The result showed no difference in race or gender between the two groups, but the age showed a significant difference within the groups. The nuclear or cortical light scattering by Scheimpflug densitometry or by LOCS III cataract grading showed no significant difference between the two groups. This is an indication that the increased Ab accumulation in the lens is not detectable in the early stages of the disease.
Also, Michael et al. 2014, studied plaques and tangles in the lenses of neuropathologically verified AD donors using Raman micro spectroscopy and imaging. The Raman analyses were carried out over extended areas in the lens tissue to conclude local differences in protein content. The Raman system consisted of a Krypton laser (lexcitation 647.1 nm), a microscope from Olympus (BX41), objective from
The results of the lens, however, showed significantly lower b-sheet concentration and this could be due to the absence of fibril information and is not proof of Ab. This means that if Ab is present at all it must be in low concentration and cortical cataract cannot be considered as an indicator or predictor of AD.
5 R
AMAN SPECTROSCOPY APPLICATIONS
Raman spectroscopy is a sensitive and specific method and can identify macromolecules such as protein, lipids b-sheets and water. Advantages of Raman spectroscopy are that the components of interest in AD can be imaged simultaneously and the ability of measuring brain tissue without labelling. This greatly reduces the time of pathology diagnosis.
5.1 S
PECIFIC COMPONENTS LOCATED IN THE AMYLOID-b
PLAQUESIn the research of Lobanova et al. 2018, they reported the spatial distribution and chemical
composition of Ab plaques measured with label-free spontaneous Raman spectroscopy. The research was done on fixed human brain slices from 11 AD patients and 4 age-matched controls40. After the
Raman measurement, the samples were stained with Thioflavin-S to confirm the plaque locations. The factorization analysis performed on the plaques revealed that the image was represented by 20 separate components. Five of these components were co-localized in the Ab plaques, and resemble spectra of known chemical components. The spectra of these 5 chemicals (C1, C2, C3, C4 and C5)
measured in the hippocampal samples are shown in Figure 12. The first component shows
characteristic bands at 1657 cm-1 (representing Amide I, a-helix) 1554 cm-1 (representing Amide II),
1338 cm-1 (a CH2 wagging vibration from glycine and proline), 1267 cm-1, 1206 cm-1, 1031 cm-1, 1002
cm-1, 934 cm-1 and 852 cm-1. This spectrum resembles the Raman bands of collagen and it is previously described that plaques in AD brains contain collagen-like amyloidogenic components. The components C2 and C3 show a Raman spectrum that is characteristic of mixture of lipids. The
fingerprint region of C3 has bands at 1060 cm-1, 1129 cm-1,and 1293 cm-1, which indicate a C-C
stretching vibration and the CH2 twisting mode of a fatty acid. The C2 spectrum also shows bands that
can be related to proteins. The C2 spectrum show similarities with the Ab(1-42) fibrils shown in the
figure as the magenta lines, which is an indicator for AD. In the spectrum of C2 the signals at 2880 cm -1 2850 cm-1 are twice as strong compared to the spectrum of C
3. The strong band at 1673 cm-1 (Amide I
band) comes from the superposition of the C=O stretching vibration, this comes from the backbone of a protein with high content of b-sheet structures. The spectrum of C3 was attributed to a mixture of
fibrin protein and saturated fatty acid which resembles arachidic acid. According to Paul et al. fibrinogen is present in the brain of AD patients and might play a role in the initiation and the
The last compound C5 consist of redox metal ions Fe3O4 (pure compound is represented by the red
dotted line). The presence of these metals make AD brains vulnerable for oxidative damage. The iron nanoparticles co-localise with Ab plaques which results in the formation of Ab-metal complexes. This is of interest because this property may be of interest when analysing AD with SERS.
Figure 12, The Raman spectrum of the five major components localized in the hippocampal Ab plaques (blue solid line) the
red dotted line represents the mixture of analytical standards. The constituents are shown as the green and magenta lines. The image is adapted from Lobanova et al., 2018.
Figure 13, Raman imaging of the five components of interest in 4 AD brains and 2 controls. Image adapted from Lobanova et al., 2018.
The results of the label-free confocal Raman micro-spectroscopy and Raman imaging showed that the Ab co-localize with 5 important chemicals and suggest that cholesteryl esters play an important role in the metabolism of Ab. However, during this study fixed human brain slices were used and the
distribution and chemical composition of Ab plaques were measured with label-free Raman micro-spectroscopy but could this method be applied as in vivo diagnostic tool? The obstacle to overcome with Raman spectroscopy as in vivo diagnostic tool is the penetration depth of the laser, locating the area of interest and the inhomogeneous tissue in the brain.
Fibrillar plaque (Ah1) Cored neuritic plaque (Ah2) Core-only plaque (Ah3) Diffuse neuritic plaque (Ah4) Control 1 (Ch1) Control 2 (Ch2) C1 col la ge n C2 A β/ lipi ds C3 fi br in/ FA C4 β-ca rot ine C5 ir on oxi de
5.2 R
AMAN IMAGING IDENTIFYINGA
LZHEIMER’
S DISEASE IN BRAIN DONORSThe study by Chen et al. 2009, suggested that early symptoms in the brain hippocampus could be analysed in situ with Raman spectroscopy. They investigate the brain hippocampus of a rat infected with AD and compare them to “normal” rats. The difference of normal and AD brain tissues could be described by Raman spectroscopy focussing on the differences of the conformation of Ab protein. The shoulder Raman peak at 1670 cm-1 corresponds to the amide I vibration of protein with a beta folding
structure like the Ab protein. The rats were exposed to a system which was optimized for maximum throughput, sensitivity and fluorescence suppression. A semiconductor laser was used with 21mW excitation light at 785 nm with the use of prims and filters the power of the laser exposed to the sample was 1 mW14. The sample did not show any degradation with this exposer and the data was analysed using PCA. The results showed that the AD brains were very similar to healthy brains but there were significant differences in the intensities of signals. The Raman spectroscopy measurements of the hippocampus could successfully differentiate between healthy and AD patients. The diagnostic model for classifying Raman spectra of brain hippocampus tissue has a sensitivity of 93.8% and a specificity of 89.6%. The study concluded that the combination of Raman and statistical tool is
capable of carrying out early non-destructive diagnosis of AD. However, the method does not have the penetration depth to analyse the brain non-invasively therefore another suitable subject needs to be investigated such as peripheral blood or fluids.
Furthermore, the study by Michael et al. 2017 describes the potential of hyperspectral Raman imaging of plaques and tangles in hippocampus and frontal cortex AD brain slices of donors. They concluded that they were able to locate and image plaques and tangles with a label free Raman spectroscopy method. The plaques and tangles found in AD contain large amounts of aggregated protein as mentioned before but analysis reveals that they consist of 2 times more protein and 5 times more b-sheets when compared to “normal” tissue. This study focussed on the b-b-sheets because these are the main characteristic of Ab and tau proteins. The result showed that hyperspectral Raman imaging with hierarchical cluster analysis could be used to identify plaques and tangles in label-free slices of human AD brain tissue15. The plaques and tangles produced Raman signals that are significantly different
from surrounding tissue.
5.3 R
AMAN SPECTROSCOPY IMAGING AS IN VIVO DETECTION METHODThe study by Krisch et al. 2010, used Raman spectroscopic imaging to detect tumours in vivo in the brain of mice. However, Raman imaging could be used for additional applications such as
degenerative diseases like Parkinson’s disease and AD. Before Raman spectroscopy was performed the cortex of the mice was exposed by creating a bony window. This method is aimed to be used during open surgery of brain tumours. The brain is high functional density and the removal of the tumour could be of great risk. Therefore, is it important to identify and visualize the tumour with high accuracy. This is also of importance when analysing AD. The advantages of using Raman
sub-The method used by Krisch et al. 2010, to detect brain tumours in mice, the animals were anesthetized and a median skin incision was made to open and remove the dura which is part of the skull. A transparent window was made of 1 mm calcium fluoride. The experimental setup is shown in Figure 14.
Figure 14, Raman spectroscopy set up for in vivo imaging of the brain. The probe is integrated with a bandpass filter (BP), beam splitter (BS), mirror (M), long pass filter (LP) and a lens as shown. The laser is coupled to the probe by an excitation fibre (EF) and the signal from the brain goes through a collection fibre (CF) and is connected to the spectrometer. Image from Krisch et al. 2010.
This setup enabled to identify cortical and sub cortical tumours which were visible and invisible tumours by eye. After exposure to the focused laser beam the mice did not reveal any burning but toxic levels of the laser must be defined in more detail.
5.4 M
OLECULAR IMAGING IN VIVO WITH STIMULATEDR
AMANSPECTROSCOPY
Coherent anti-Stokes Raman spectroscopy has the ability to visualize subcellular spatial resolution with vibrational imaging and an acquisition time which is 4 orders of magnitude faster than ordinary Raman spectroscopy. However, this method suffers from spectral distortion and limited sensitivity among other things. The study by Saar et al. 2010, developed a method for molecular imaging with simulate Raman scattering (SRS). SRS has not been applied in living animals or humans for two reasons; first the acquisition time per frame required ~1 min this is too slow because humans and animals are required to lay still during imaging. The second reason is the intensity loss of the
sample is scanned in a raster by a galvanometer mirror with this set up a speed up to 25 frames per second is acquired. With these modifications Saar et al. 2010, achieved to collect ~28% of the light and the skin of mice is imaged in vivo. With this method, the CH2 stretch from lipids, OH stretch from
water and the CH3 stretch from protein could be identified. SRS microscopy label-free imaging
technique can be applied on living organisms such as small animals and humans43.
SRS has also the ability to simultaneously map protein and lipid distribution in tissue at the vibrational stretch range 2800 to 3050 cm-1 of CH
2. The study by Lu et al. 2015, showed that SRS can live
imaging the cell division. In vivo DNA imaging in mouse skin is performed with SRS with the intent to follow cell division. The human skin tissue is also analysed with SRS and the results demonstrated that multicolour SRS method offers a label-free image of DNA with the same features as the original method. The method could be used to diagnose skin cancer with the advantage of a label-free method which does not affect the tissue44.
5.5 R
AMAN SPECTROSCOPY ANALYSIS OF BLOODAs mentioned in the background theory there are different ways to analyse AD. The analysis of blood or CSF may be a lot more efficient. In the study by Vengas et al. 2017, they used Raman spectroscopy on blood samples for early diagnosis of AD. For this study 145 healthy controls, 103 patients, 72 with frontotemporal dementia (FTD) and 24 with MCI were analysed27. First a model was made using
spectroscopic variables selected by univariate analysis, then a cross-validation was performed to identify the most relevant parameters. A second model was made using Partial Least Squares (PLS), a cross-validation of 100 x 3-fold was used to select the optimum model parameters. The results showed 7 variables which showed significantly different peak intensities between the different cases and healthy controls. The 7 variables were used as biomarkers to discriminate between AD and healthy controls with an 86% sensitivity and 89% specificity. They concluded that a blood-based Raman spectroscopy method is able to identify AD and is non-invasive and cost-effective27.
5.6 A
DVANCED IMAGING OF TAU FIBRILSThe study by Schierle et al. 2016, state that the imaging of only Ab by PET is not sufficient for a definite diagnosis of AD. For a definite diagnosis of AD, the tau fibrils analysis is also required. The imaging techniques give information about the chemistry and specific structure which is of value to determine the state and progression of AD24. For example, the Fourier transform IR (FTIR)
spectroscopy was applied to detect a-helices and b-sheets structures. The FTIR shows a peak at 1650 cm-1 for soluble unfolded tau protein and a shoulder at 1630 cm-1 for aggregated tau protein. The FTIR principles in combination with probes specific for tau allows imaging of the tau fibrils.
6 T
HIRD HARMONIC GENERATION MICROSCOPY
APPLICATIONS
Raman spectroscopy is able to differentiate between healthy patient and different dementia including AD. However, the studies were performed on post mortem patient or in situ. The difficulty with live imaging the brain are among other things the penetration depth and the loss of signal due to scattering. The study by Witte et al. 2011, used third-harmonic generation (THG) as a tool for label-free brain imaging. They found that THG imaging provides structural contrast of the brain only using a single modality without any labelling techniques34. The result showed that in mice brain slices the neurons
remain visible at > 300 µm depth. The signal decreases with depth but the THG method can robustly detect neurons over a large depth range. The technique can also be used to label free image live neurons. Some experiments were performed on mice using in vivo THG imaging. The results showed limitations in the range and a penetration depth of ~200 micrometres was achieved. However, these experiments were performed on mice slices of the brain and anesthetized mice the question remains if a label-free method can be used as diagnostic tool form AD patients.
The THG method described in Witte et al. 2011, could be used in various applications such as real-time tissue screening during brain surgery, or guiding microscopic surgical tools. The THG imaging technique is also capable of identifying lipid structures and accumulation of cholesterol.
7 C
ONCLUSION
The aim of this literature study was to obtain information on spectroscopic techniques used to analyses amyloid plaques and tau tangles observed in dementia and Alzheimer’s disease (AD). Furthermore, the possibilities of label free methods were explored. The accumulation of amyloid plaques and tau tangles leading to AD symptoms like memory loss and psychiatric symptoms occur prior to the earliest symptoms of AD. The accumulation process and the true role is still unclear. The development in therapeutics aim to slow down the accumulation progress early diagnosis is therefore crucial. The current clinical diagnostic tools for AD are computed tomography (CT), magnetic resonance imaging (MRI) and single-photon emission computed tomography (SPECT) but the definite diagnosis of AD requires post mortem brain tissue examination using different staining procedures.
The fluordeoxyglucose (FDG) positron emission tomography (PET) application could be used for the diagnosis of AD and reflects the metabolic rates of glucose. The study by Marcus et al. 2014 showed that FDG-PET is more accurate when compared to MRI CT and SPECT. The FDG-PET method could also differentiate between mild cognitive impairment (MCI), AD, frontotemporal dementia (FTD) and dementia with Lewy bodies (DLW). Furthermore, the Pittsburgh compound B (PIB) tracer is
discussed and the tracer had a high affinity with AD patients. With labelling techniques is the patient exposed to radioactive rays which is unhealthy and this means there is a limited number of scans a patient can undergo.
Raman spectroscopy is sensitive and is able to detect molecular conformations of proteins in plaques and tangles. The research of Lobanova et al. 2018, showed that 5 important chemicals co-localized in Ab could be analysed and cholesteryl esters play an important role in AD. However, during this study fixed human brain slices were used; this method could not be used in vivo due to the penetration depth of the laser. The Raman laser is not able to penetrate through the human skull. The study by Krish et al. 2010, removed part of the skull of mice to detect tumours in vivo and could also be used for
applications such as degenerative diseases. The question will be would this be used in practice because brain surgery will be avoided for not life treating situations.
The label free Raman imaging method of the brain could be used for post mortem research. The advantages of Raman imaging compared to FDG-PET is that Raman imaging could be performed label-free and is therefore less time consuming. However, for a label free diagnosis of AD the analysis of blood or cerebrospinal fluid should be explored.
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