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Related Experiment Video

Updated: Jan 13, 2026

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Published on: April 17, 2021

Automated T-Cell Proliferation in Lab-on-Chip Devices Integrating Microfluidics and Deep Learning-Based Image

María Fernanda Cadena Vizuete^1,2,3,4, Martin Condor⁵, Dennis Raith^5,6

¹Mertelsmann Foundation, 79104 Freiburg, Germany.

|October 28, 2025

Summary

This summary is machine-generated.

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.

Keywords:

Lab-on-chip (LOC) devices T-cells deep learning image analysis microfluidics

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Last Updated: Jan 13, 2026

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Published on: September 27, 2019

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.