Raman Spectroscopy for Extracellular Vesicle Study

Wooje Lee

Research output: ThesisPhD Thesis - Research UT, graduation UT

30 Downloads (Pure)

Abstract

The aim of this research project is the characterization of extracellular vesicles (EVs) using vibrational spectroscopy to study the contents and cellular origin of different EVs subtypes. Almost every cell releases tiny particles into their extracellular environment: the particles are known as EVs. The particles have a spherical shape, and their size ranges from 30 nm to 1 µm. 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
Of the various vibrational spectroscopic techniques, Raman spectroscopy was 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.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • Offerhaus, Herman, Supervisor
Award date16 Apr 2020
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-4951-6
DOIs
Publication statusPublished - 16 Apr 2020

Keywords

  • Raman Spectroscopy
  • Extracellular Vesicles
  • Machine Learning
  • Optical Waveguide
  • Convolutional Neural Network
  • Principal Component Analysis

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