Absract
In recent times, there has been a surge in expectations regarding the development of novel diagnostic and therapeutic technologies employing extracellular vesicles (EVs), including exosomes. However, it remains a challenge to measure and manipulate heterogeneous particle populations with diameters spanning from several tens to 100 nm. Consequently, there is a pressing need to establish a robust foundation for evaluation technology that can ensure the reliability, safety, and standardization of exosome-based medicine.
Amidst these challenges, the technique of nanoparticle measurement using scattering imaging, known as Nanoparticle Tracking Analysis (NTA), is regaining attention as a single-particle analysis method for assessing the concentration and size of EVs. Our team has pioneered a nanoparticle measurement system that integrates microfluidic devices and scattering imaging. Additionally, we have implemented deep learning techniques to enhance the precision and sophistication of the NTA method.
Keywords: exosomes, nanoparticle tracking analysis, deep learning
REFERENCES
[1] T. Akagi, K. Kato, M. Kobayashi, N. Kosaka, T. Ochiya, and T. Ichiki, “On-chip immunoelectrophoresis of extracellular vesicles released from human breast cancer cells”, PLOS ONE 10(4), e0123603 (2015).
[2] T. Akagi and T. Ichiki, “Microcapillary chip-based extracellular vesicle profiling system”, in W. P. Kuo and S. Jia, edit. Extracellular vesicles: Methods and protocols, p. 209–17, Springer New York (2017).
[3] H. Fukuda, H. Kuramochi, Y. Shibuta, and T. Ichiki, “Analysis of Brownian motion trajectories of non-spherical nanoparticles using deep learning", APL Machine Learning 1, 046104 (2023).