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PARTLY DIGITAL - ONLY FOR INVITEES (1,5 m) : PhD Defence Wooje Lee | Raman spectroscopy for extracellular vesicle study

Raman spectroscopy for extracellular vesicle study

Due to the COVID-19 crisis measures the PhD defence of Wooje Lee will take place (partly) online in the presence of an invited audience.

The PhD defence can be followed by a live stream.

Wooje Lee is a PhD student in the research group Optical Sciences (OS). His supervisor is H.L. Offerhaus from the Faculty of Science and Technology (TNW).

Almost every cell releases tiny particles into their extracellular environment: the particles are known as extracellular vesicles (EVs). The particles have a spherical shape, and their size ranges from 30 nm to 1 µm.  It has been demonstrated that cells use EVs for intercellular communication, waste control, and disease metastasis. Although the first cell-derived vesicles were discovered in 1940, research on vesicles was very limited due to the lack of detection techniques for nanoparticles. By leveraging advanced detection techniques, the significance of EVs has gained attention since the early 2000s.

EVs are presented at concentration exceeding 1010 particles/ml in body fluids such as blood, saliva, and urine. The particles transport biomolecules, such as protein, RNA, and DNA. Since the EVs originate from cells, the contents of EVs are dependent on their cellular origin. Therefore, certain EVs include information related to diseases such as cancer, allergies, cardiovascular and autoimmune diseases, and investigating EVs’ cellular origin/cargo is useful as diagnosis and for monitoring the prognosis of therapy. However, current state-of-the-art techniques for EV characterization are still insufficient in terms of sensitivity. This is a significant bottleneck of EVs research and the application of EVs as clinical biomarkers.

The aim of this research project is the characterization of EVs using vibrational spectroscopy to study the contents and cellular origin of different EVs subtypes. Of the various vibrational spectroscopic techniques, Raman spectroscopy will be used for this study, which is a nondestructive and non-labeling technique. As the term ‘vibrational spectroscopy’ implies, Raman spectroscopy provides molecular vibration information. Analyzing molecular vibrations not only reveals the chemical composition of the specimen but also allows for a quantitative study, simple comparison between samples, and detection of specific molecules in samples. Raman spectroscopy has proven to be a useful tool for many different applications: material science, biomedical science, and real-life applications such as forensics. Although Raman spectroscopy is a powerful and straight forward technique, the ability of a conventional Raman microscope is limited by the diffraction limit. In a free-space optical system, the diffraction limit sets a lower limit on the total probed volume and, therefore, a limit on the surface-to-volume ratio when studying nanoparticles.

Each cell contains different biomolecules depending on the presence of disease, the function of the cell, and the location in the body. However, many biomolecules are derived from similar building blocks, such as amino acids or nucleic acids, and related to the general functioning of the cell so that that difference can be subtle. These small differences can be a significant challenge in studying cells using spectroscopic techniques.

EVs are fairly small. Small variations of the cell are transferred to small differences in EVs. Due to these hallmarks of EVs, differentiating EVs based on Raman spectrum requires not only high signal to noise ratio (SNR) Raman spectra but also a huge effort to analyze Raman spectrum, which can lead low-throughput of the analysis.

This thesis will provide several ways to improve the current Raman technique for EV research; algorithmic analysis and use of the evanescent field for Raman spectroscopy. Firstly, we will discuss two types of Raman spectra classifiers based on Principal Component Analysis (PCA) and Neural Network, especially Convolutional Neural Network (CNN). The automated algorithmic analysis methods can be a solution to enhance the throughput of Raman analysis and create an objective classification. Alongside this approach, we will discuss integrated optics for on-chip Raman spectroscopy, called waveguide Raman spectroscopy. The evanescent wave propagates outside the waveguide and decays exponentially from the interface.  Waveguide Raman uses the evanescent field for analyte excitation and collection of scattered photons. Although the resolution of the system cannot be better than the diffraction limit of the system, the use of the evanescent field allows one to probe a shallow layer above the waveguide surface. This increases the surface to volume ration, and therefore, it is expected to achieve a high SNR Raman signal of the specimen from the waveguide Raman chip.