TY - GEN
T1 - Deep Learning-Guided Single-Cell Encapsulation through photo-crosslinking for Advanced 3D Culture
AU - Zhao, Yanfeng
AU - Lin, Kaijun
AU - Yang, Haotian
AU - Dong, Xinyi
AU - Sun, Tao
AU - Shi, Qing
AU - Huang, Qiang
AU - Wang, Huaping
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Encapsulating single cells in hydrogels provides them with a relatively autonomous and controllable three-dimensional extracellular environment. This environment can facilitate cell interaction and proliferation, making it highly valuable in fields such as tissue engineering and single-cell research. However, traditional methods of single-cell encapsulation encounter limitations in terms of single-cell encapsulation rate and topography control, which hinder of the progress of single-cell encapsulation research. In this paper, we propose a novel method for printing microgel-encapsulated single cells using deep learning-guided DLP printing. By leveraging deep learning algorithms, we accurately capture and translate the real-time positional data of single cells into the printing system's coordinate framework. Based on the single-cell positional information, a virtual digital mask is created via the OpenCV algorithm, which is then input into the digital microscope to complete the single-cell encapsulation. The single-cell encapsulation rate achieved by this method is 86.3%, which is about 2.87 times higher than that of the traditional method. Experimental results show that our method achieves high accuracy in single-cell encapsulation and the fabrication of microgels with arbitrary shapes, which hold significant importance for biological and medical applications.
AB - Encapsulating single cells in hydrogels provides them with a relatively autonomous and controllable three-dimensional extracellular environment. This environment can facilitate cell interaction and proliferation, making it highly valuable in fields such as tissue engineering and single-cell research. However, traditional methods of single-cell encapsulation encounter limitations in terms of single-cell encapsulation rate and topography control, which hinder of the progress of single-cell encapsulation research. In this paper, we propose a novel method for printing microgel-encapsulated single cells using deep learning-guided DLP printing. By leveraging deep learning algorithms, we accurately capture and translate the real-time positional data of single cells into the printing system's coordinate framework. Based on the single-cell positional information, a virtual digital mask is created via the OpenCV algorithm, which is then input into the digital microscope to complete the single-cell encapsulation. The single-cell encapsulation rate achieved by this method is 86.3%, which is about 2.87 times higher than that of the traditional method. Experimental results show that our method achieves high accuracy in single-cell encapsulation and the fabrication of microgels with arbitrary shapes, which hold significant importance for biological and medical applications.
KW - 3D culture
KW - Deep learning
KW - DLP printing
KW - Single cell
UR - http://www.scopus.com/inward/record.url?scp=85203705271&partnerID=8YFLogxK
U2 - 10.1109/ICMA61710.2024.10633074
DO - 10.1109/ICMA61710.2024.10633074
M3 - Conference contribution
AN - SCOPUS:85203705271
T3 - 2024 IEEE International Conference on Mechatronics and Automation, ICMA 2024
SP - 1831
EP - 1836
BT - 2024 IEEE International Conference on Mechatronics and Automation, ICMA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Mechatronics and Automation, ICMA 2024
Y2 - 4 August 2024 through 7 August 2024
ER -