Characterization of Extracellular Vesicles using
Raman Spectroscope for Label-free Prostate Cancer Detection
Wooje Lee
1, Afroditi Nanou
2, Linda Rikkert
2,3,4, Agustin Enciso Martinez
2, Frank A.W. Coumans
4,5, Cees, Otto
2,
Aufried Lenferink
2, Leon W.M.M. Terstappen
2and Herman L. Offerhaus
1*1. Optical Sciences, MESA+ Institute for Nanotechnology, University of Twente, Enschede, the Netherlands. 2. Department of Medical Cell BioPhysics, MIRA Institute, University of Twente, Enschede, the Netherlands.
3. Laboratory of Experimental Clinical Chemistry, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands 4. Vesicle Observation Centre, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
5. Department of Biomedical Engineering and Physics, Academic Medical Centre of the University of Amsterdam, Amsterdam, the Netherlands.
Extracellular vesicles (EVs) constitute a mechanism of intercellular communication transporting a wide range of biomolecules. The transported cargo varies depending on the EV origin. This fact implies that the chemical composition and signature of EVs derived from diseased cells could be used as a biomarker to detect the respective diseases. In this study, we demonstrate that EVs is a promising a biomarker to detect prostate cancer. To identify cancer-cell-derived vesicles, Raman spectra were obtained and analyzed by principal component analysis (PCA). Herein, we collected EVs from two different prostate cancer cell lines (PC3 and LNCaP) and EVs found in hematopoietic cell of healthy donors (specifically red blood cell- and platelet-derived EVs). All EV subtypes were measured using a spontaneous Raman spectroscope and the obtained data was analyzed by a prediction model, the PCA to investigate whether prostate cancer EVs can be separated by the rest of the EVs and used as a biomarker to diagnose cancer.
Abstract
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REFERENCES
Extracellular vesicles (EV)1,2
• EVs are small spherical particles (30 nm - 1 µm) • Shed by living cells into extracellular environment • Enclosed by a phospholipid bilayer
• EVs are found in all body fluids
(blood, saliva, urine, breast milk, etc.) • Function of EVs
o Intercellular communication
o Carrying genetic information (protein, DNA, RNA, etc.) o waste control
EVs can be utilized as a disease biomarker!
Raman spectroscopy3,4
• Spontaneous Raman spectroscopy is a type of vibrational spectroscopy technique based on inelastic scattering by molecules.
• Raman spectroscopy is therefore a promising tool to reveal the structural differences among EVs of different origin.
Characterize and identify EVs derived from different origin for cancer diagnosis
Introduction
Sample preparation
Hematopoietic cell-derived EVs
• Red blood cell- and platelet-derived EVs were prepared.
• Prostate cancer-derived EVs
• PC3- and LNCaP-derived EVs were prepared
• Prepared samples were assessed with Nanoparticle Tracking Analysis (NTA) and Transmission Electron Microscopy (TEM)
Raman spectrum acquisition Fig 1. Pathway of EVs formation (left), TEM image of PC3-derived EVs (right top) and
Structure of EV (right bottom)
Koch, Arthur L. Journal of
Cosmology 10 (2010): 3275-3285.
Material & Experiments
Virtual energy state Vibrational energy state Rayleigh scattering Stokes Raman scattering Anti-Stokes Raman scattering
Raposo, Graça, and Willem Stoorvogel.
The Journal of cell biology 200.4 (2013): 373-383.
Principal component analysis (PCA)
• The subtle difference requires sensitive and reliable analysis such as PCA.
• Multivariate analysis using PCA was conducted on the Raman spectra of four different EV subtypes.
• Spectral fingerprint of EV spectra resulted in good separation.
Result & Discussion
Wavelength Objective Laser power Acquisition time
647 nm 40X 0.95NA 50 mW 10 sec
we explored spectral differences between cancer-derived EVs and healthy control- cancer-derived EVs to examine the potential of cancer-derived EVs as a cancer biomarker.
• Based on principal component of the spectral
information, the result of multivariate analysis shows the spectral differences between healthy
cells-derived EVs (red blood cell and platelet) and prostate cancer cell-derived EVs (PC3 and LNCaP).
The result shows that more than 90% of EVs were classified into two categories.
Conclusion
Sample preparation
Collect spectral information using Raman EVs identification
A B
D C
Fig 4. PCA score plots for the Raman spectra obtained from four EV subtypes (red
blood cell-EVs ●, platelet-EVs ●, PC3-EVs ▲ and LNCaP-EVs ▲). Circles represent healthy cell-derived EVs and triangles show cancer- derived EVs.
98% 94.67%
400 - 1800 cm-1
400 - 3100 cm-1
2600 - 3100 cm-1
Fig 2. Concentration and size distribution of EV samples from NTA and TEM image of
EVs. (A) red blood cell-derived EVs, (B) platelet-derived EVs, (C) PC3-derived EVs and (D) LNCaP-derived EVs.
Fig 3. Raman spectra of each vesicle EV subtypes. Left column of the figure shows the
untreated Raman data and curves in the right column shows pre-processed data.
Red blood cell EVs
Platelet EVs
PC3 EVs
