Editorial Feature

Applications of Raman Spectroscopy in Neuroscience

In neuroscience, Raman spectroscopy is highly valued for its ability to detect biochemical changes in neural tissues and fluids non-invasively. This article highlights its diverse applications, particularly in early diagnosis, monitoring disease progression, and enhancing our understanding of neurobiological processes.

Applications of Raman Spectroscopy in Neuroscience

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Understanding Raman Spectroscopy

Raman spectroscopy is an analytical technique based on the inelastic scattering of photons (Raman Effect), first discovered by C.V. Raman in 1928.

In this technique, a sample is illuminated with a laser, causing most of the light to scatter elastically (Rayleigh scattering) while a smaller portion scatters inelastically (Raman scattering). This inelastic scattering reveals molecular vibrations, providing a unique spectral fingerprint specific to the sample. This fingerprint is then used to identify and analyze the chemical bonds and structures within the sample.

In practice, the scattered light is collected, filtered to remove elastic scattering, and then analyzed using a spectrometer and a detector like a charge-coupled device (CCD) to obtain detailed information about the sample's molecular composition.1

Significance in Neuroscience

Raman spectroscopy is particularly valuable in neuroscience due to its non-invasive nature and ability to provide detailed molecular characterization. Unlike traditional invasive diagnostic methods, it can directly and rapidly analyze biological tissues and fluids, detecting biochemical changes indicative of early stages of disease.

This makes it a promising tool for earlier diagnosis and improved monitoring of neurodegenerative disorders, thereby enhancing our understanding of disease mechanisms and progression.1,2

Detecting Molecular Changes in Neurodegenerative Diseases

Alzheimer's Disease

Alzheimer's Disease (AD) is the most prevalent type of dementia, marked by progressive neurodegeneration that spreads across the brain. It involves the accumulation of amyloid β (Aβ) proteins in the brain, leading to neurofibrillary tangles and inflammatory plaques. These changes disrupt synaptic signaling and contribute to cognitive decline.

Researchers have developed various techniques using Raman spectroscopy to detect biochemical changes associated with AD at the molecular level. For instance, Wang et al. developed a platform that combined graphene-assisted Raman spectroscopy with machine learning to rapidly screen Alzheimer's disease biomarkers in the brains of transgenic AD model animals. Their approach enabled effective classification of AD and non-AD spectra, detecting crucial biomarkers like Aβ and tau, thus facilitating diverse sample analyses in AD evaluation.3

Similarly, Lin et al. employed laser tweezers Raman spectroscopy for non-invasive AD diagnosis, achieving a 91 % accuracy rate in distinguishing between diseased and healthy samples.4

Raman spectroscopy has also been applied to cerebrospinal fluid (CSF) and blood samples for AD biomarker detection. Yu et al. developed a surface-enhanced Raman spectroscopy (SERS) approach to analyze CSF composition, achieving an overall accuracy rate of 92 % in clinical diagnoses, with 88.9 % accuracy among AD patients.5

Parkinson's Disease

Parkinson's disease (PD) is another common neurodegenerative condition distinguished by the loss of dopaminergic neurons in the substantia nigra compacta. Current treatments mainly address symptoms like bradykinesia, tremors, or rigidity; however, there are no strategies to halt disease progression.

Raman spectroscopy aids in detecting α-synuclein aggregates, a protein crucial for monitoring disease progression. Studies have shown that Raman spectroscopy can identify α-synuclein aggregation in skin biopsies and olfactory bulb tissues, providing insights into PD-related pathological processes.1

PD pathology is often characterized by the presence of Louis corpuscles, primarily comprising α-synuclein aggregates. Skin biopsy studies have shown promise in detecting these aggregates, with Raman spectroscopy proving instrumental in determining their aggregation status. This approach holds potential for both diagnosis and treatment monitoring.

Additionally, Raman spectroscopy has been employed to analyze blood components like red blood cells (RBCs), revealing alterations indicative of fibrinolytic system activation in PD patients.6

Applications in Brain Cancer Diagnosis

Raman spectroscopy has proven effective in classifying tumor types and determining primary metastasis sites in cancer diagnostics. For instance, it can differentiate between glioma and normal brain tissue, as well as between dura mater and meningioma, partly due to variations in collagen peaks and lipid content within the tumors.

Advanced applications, such as Raman mapping and imaging, also allow for the visualization of specific tumor features, like necrosis in glioblastoma, where increased protein and cholesterol ester presence can be observed.

The technique's capacity for multi-class classification models enables it to distinguish between various tumor entities within a single classifier. It can also identify brain edema, tumor recurrence, and tumor margins, significantly aiding surgical and therapeutic precision.

Raman spectroscopy can also be used to classify brain tumor grade. For example, distinctive Raman bands of tryptophan and carotenoids, along with the peak intensity ratio between proteins and lipids, have been used to distinguish between different World Health Organization (WHO) grades of gliomas.7

Studying Neural Plasticity and Repair

Understanding Neural Regeneration

Neural regeneration refers to how neurons repair and reconnect after sustaining damage. This phenomenon is particularly evident in the peripheral nervous system, which shows a remarkable potential for regeneration compared to the central nervous system.

Accurate diagnosis and understanding of the location and degree of nerve injury are crucial for effective treatment, especially since conventional diagnostic methods like electromyography and magnetic resonance imaging often fail to detect the extent of the injury and the nerve regeneration rate.

In a study published in the Journal of Biomedical Optics, researchers examined intact and injured rat sciatic nerves using Raman spectroscopy. They compared the results with immunohistochemical analysis to understand morphological changes.

The research revealed that intact peripheral nerve tissue exhibited distinct Raman spectra with distinct peaks in the 2800–3000 cm−1 range, associated with lipid and protein components crucial for nerve function. Injured nerves, however, showed significant changes in these spectra, particularly in the ratio of the protein-to-lipid peak, which correlated with the regeneration process.

After nerve injury, the axon/myelin ratio increased, indicative of axonal survival or early regenerative efforts, and then decreased as regeneration progressed and myelin repair commenced.

These spectral changes provided a unique insight into the dynamic balance of proteins and lipids during nerve repair, offering a non-invasive method to monitor and understand the complex process of peripheral nerve regeneration.8

Insights into Synaptic Flexibility

Synaptic plasticity is the capacity of synapses, the connections between neurons, to modify their strength or efficiency in response to increases or decreases in their activity. This plasticity is fundamental to learning, memory formation, and the brain's overall adaptability, allowing it to respond dynamically to various stimuli and experiences.

Raman spectroscopy allows researchers to observe the biochemical composition of synapses in real time and under various physiological conditions, providing insights into the molecular changes that occur during synaptic plasticity.

A study published in Nanoscale demonstrated the development of novel synaptic devices using Raman spectroscopy to investigate lateral heterostructures of WSe2 and WO3. This unique memristive synapse allowed for multi-gate modulation and emulated synaptic functions with remarkable efficiency and low energy consumption. Gate voltage and visible light could modulate the synaptic plasticity, offering unprecedented control over synaptic behavior.9

Challenges and Future Outlooks

While Raman spectroscopy holds significant promise as a diagnostic tool in neuroscience, its integration into broader clinical practice faces several challenges. This includes an inherent weak signal output, interference from biological sample fluorescence, lengthy data collection and processing times, and high operational costs.

Additionally, the complexity of Raman spectra, which consist of multiple chemical peaks, requires sophisticated analysis techniques like artificial intelligence, posing risks of model inaccuracies due to high dimensionality and small sample sizes.

However, recent advancements such as surface-enhanced Raman spectroscopy (SERS) and coherent anti-stokes Raman scattering (CARS) have improved signal detection and sensitivity, offering hope for better clinical applications.1,2

More from AZoOptics: The Role of Nuclear Magnetic Resonance Spectroscopy in Drug Discovery

References and Further Reading

  1. Chen, C., et al. (2024). Applications of Raman spectroscopy in the diagnosis and monitoring of neurodegenerative diseases. Frontiers in Neuroscience. doi.org/10.3389/fnins.2024.1301107
  2. Devitt, G., Howard, K., Mudher, A., Mahajan, S. (2018). Raman spectroscopy: an emerging tool in neurodegenerative disease research and diagnosis. ACS chemical neuroscience. doi.org/10.1021/acschemneuro.7b00413
  3. Wang, Z., et al. (2022). Rapid biomarker screening of Alzheimer's disease by interpretable machine learning and graphene-assisted Raman spectroscopy. ACS nano. doi.org/10.1021/acsnano.2c00538
  4. Lin, M., et al. (2022). Laser tweezers Raman spectroscopy combined with machine learning for diagnosis of Alzheimer's disease. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. doi.org/10.1016/j.saa.2022.121542
  5. Yu, X., Srivastava, S., Huang, S., Hayden, EY., Teplow, DB., and Xie, YH. (2022). The feasibility of early Alzheimer's disease diagnosis using a neural network hybrid platform. Biosensors. doi.org/10.3390/bios12090753
  6. Sharma, A., et al. (2021). Comprehensive profiling of blood coagulation and fibrinolysis marker reveals elevated plasmin-Antiplasmin complexes in Parkinson's disease. Biology. doi.org/10.3390/biology10080716
  7. Klamminger, GG., Frauenknecht, KB., Mittelbronn, M., Borgmann, FBK. (2022). From Research to Diagnostic Application of Raman Spectroscopy in Neurosciences: Past and Perspectives. Free Neuropathology. doi.org/10.17879/freeneuropathology-2022-4210
  8. Morisaki, S., et al. (2013). Application of Raman spectroscopy for visualizing biochemical changes during peripheral nerve injury in vitro and in vivo. Journal of Biomedical Optics. doi.org/10.1117/1.JBO.18.11.116011
  9. He, HK., Yang, R., Huang, HM., Yang, FF., Wu, YZ., Shaibo, J., Guo, X. (2020). Multi-gate memristive synapses realized with the lateral heterostructure of 2D WSe 2 and WO 3. Nanoscale. doi.org/10.1039/C9NR07941F

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Owais Ali

Written by

Owais Ali

NEBOSH certified Mechanical Engineer with 3 years of experience as a technical writer and editor. Owais is interested in occupational health and safety, computer hardware, industrial and mobile robotics. During his academic career, Owais worked on several research projects regarding mobile robots, notably the Autonomous Fire Fighting Mobile Robot. The designed mobile robot could navigate, detect and extinguish fire autonomously. Arduino Uno was used as the microcontroller to control the flame sensors' input and output of the flame extinguisher. Apart from his professional life, Owais is an avid book reader and a huge computer technology enthusiast and likes to keep himself updated regarding developments in the computer industry.

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