Deep Learning-Guided Single-Cell Encapsulation through photo-crosslinking for Advanced 3D Culture

Yanfeng Zhao, Kaijun Lin, Haotian Yang, Xinyi Dong, Tao Sun, Qing Shi, Qiang Huang, Huaping Wang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Mechatronics and Automation, ICMA 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1831-1836
Number of pages6
ISBN (Electronic)9798350388060
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event21st IEEE International Conference on Mechatronics and Automation, ICMA 2024 - Tianjin, China
Duration: 4 Aug 20247 Aug 2024

Publication series

Name2024 IEEE International Conference on Mechatronics and Automation, ICMA 2024

Conference

Conference21st IEEE International Conference on Mechatronics and Automation, ICMA 2024
Country/TerritoryChina
CityTianjin
Period4/08/247/08/24

Keywords

  • 3D culture
  • Deep learning
  • DLP printing
  • Single cell

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