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Automated T-Cell Proliferation in Lab-on-Chip Devices Integrating Microfluidics and Deep Learning-Based Image
María Fernanda Cadena Vizuete1,2,3,4, Martin Condor5, Dennis Raith5,6
1Mertelsmann Foundation, 79104 Freiburg, Germany.
Biosensors
|October 28, 2025
Summary
This study introduces a microfluidic system for long-term suspension cell culture, improving T cell expansion for cancer immunotherapy research. It uses deep learning for automated cell analysis, overcoming traditional method limitations.
Area of Science:
- Immunology
- Biotechnology
- Bioengineering
Background:
- T cells are crucial for cancer immunotherapy, but traditional expansion methods are inefficient and prone to contamination.
- Maintaining suspension cells in dynamic culture environments poses significant challenges for research.
Purpose of the Study:
- To develop and evaluate a microfluidic system for long-term culture of non-adherent cells, specifically T cells.
- To integrate automated perfusion, image acquisition, and deep learning for cell quantification.
- To assess the performance of this system compared to existing methods and lab-on-a-chip devices.
Main Methods:
- A microfluidic system with automated perfusion and image acquisition was designed.
- Deep learning algorithms were employed for cell coverage quantification and cell number estimation.
- The system's performance was benchmarked against Trainable Weka Segmentation (TWS) and two lab-on-a-chip (LOC) devices (ibidi® and custom PDMS).
- Jurkat and primary human T cells were cultured to assess proliferation and viability during medium exchange.
Main Results:
- The microfluidic system successfully supported long-term proliferation of Jurkat and primary human T cells.
- Deep learning analysis provided efficient quantification of cell coverage and numbers.
- Both ibidi® and custom PDMS LOC devices were suitable for long-term T cell expansion, each with unique advantages.
- The system demonstrated stable perfusion and supported multi-reagent administration with minimal manual intervention.
Conclusions:
- The developed microfluidic system offers a robust platform for extended suspension cell culture, crucial for immunotherapy research.
- Automated perfusion and deep learning-based image analysis enhance efficiency and reduce manual labor.
- This technology facilitates advanced research in T cell expansion and immunotherapy by overcoming limitations of conventional methods.
