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Volume 15
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Automated T-Cell Proliferation in Lab-on-Chip Devices Integrating Microfluidics and Deep Learning-Based Image Analysis for Long-Term Experiments

by
María Fernanda Cadena Vizuete
1,2,3,4,
Martin Condor
5,
Dennis Raith
5,6,
Avani Sapre
1,5,
Marie Follo
7,8,
Gina Layedra
9,
Roland Mertelsmann
1,
Maximiliano Perez
9,10,11,* and
Betiana Lerner
9,10,11,*
1
Mertelsmann Foundation, 79104 Freiburg, Germany
2
Facultad de Farmacia y Bioquímica, Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires 1113, Argentina
3
Faculty of Biology, Albert-Ludwigs-University of Freiburg, 79104 Freiburg, Germany
4
CONICET, Instituto de Investigaciones en Microbiología y Parasitología Médica (IMPaM), Universidad de Buenos Aires, Ciudad Autónoma de Buenos Aires 1121, Argentina
5
LABMaiTE GmbH, 79110 Freiburg, Germany
6
Neurorobotics Lab, Department of Computer Science, Albert-Ludwigs-University of Freiburg, 79104 Freiburg, Germany
7
Department of Medicine I, Faculty of Medicine, Medical Center–University of Freiburg, 79106 Freiburg, Germany
8
Lighthouse Core Facility, Faculty of Medicine, Medical Center–University of Freiburg, 79106 Freiburg, Germany
9
IREN Center, National Technological University, Buenos Aires 1706, Argentina
10
Collaborative Research Institute Intelligent Oncology (CRIION), 79110 Freiburg, Germany
11
Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
 
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Authors to whom correspondence should be addressed.
Biosensors 2025, 15(10), 693; https://doi.org/10.3390/bios15100693 (registering DOI)
Submission received: 12 August 2025 / Revised: 13 September 2025 / Accepted: 2 October 2025 / Published: 13 October 2025
(This article belongs to the Special Issue Microfluidics and Organ-on-a-Chip for Disease Modeling and Drug Screening (2nd Edition))
Versions Notes

Abstract

T cells play a pivotal role in cancer research, particularly in immunotherapy, which harnesses the immune system to target malignancies. However, conventional expansion methods face limitations such as high reagent consumption, contamination risks, and difficulties in maintaining suspension cells in dynamic culture environments. This study presents a microfluidic system for long-term culture of non-adherent cells, featuring automated perfusion and image acquisition. The system integrates deep learning-based image analysis, which quantifies cell coverage and estimates cell numbers, and efficiently processes large volumes of data. The performance of this deep learning approach was benchmarked against the widely used Trainable Weka Segmentation (TWS) plugin for Fiji. Additionally, two distinct lab-on-a-chip (LOC) devices were evaluated independently: the commercial ibidi® LOC and a custom-made PDMS LOC. The setup supported the proliferation of Jurkat cells and primary human T cells without significant loss during medium exchange. Each platform proved suitable for long-term expansion while offering distinct advantages in terms of design, cell seeding and recovery, and reusability. This integrated approach enables extended experiments with minimal manual intervention, stable perfusion, and supports multi-reagent administration, offering a powerful platform for advancing suspension cell research in immunotherapy.
Keywords: deep learning; image analysis; microfluidics; Lab-on-chip (LOC) devices; T-cells deep learning; image analysis; microfluidics; Lab-on-chip (LOC) devices; T-cells

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MDPI and ACS Style

Cadena Vizuete, M.F.; Condor, M.; Raith, D.; Sapre, A.; Follo, M.; Layedra, G.; Mertelsmann, R.; Perez, M.; Lerner, B. Automated T-Cell Proliferation in Lab-on-Chip Devices Integrating Microfluidics and Deep Learning-Based Image Analysis for Long-Term Experiments. Biosensors 2025, 15, 693. https://doi.org/10.3390/bios15100693

AMA Style

Cadena Vizuete MF, Condor M, Raith D, Sapre A, Follo M, Layedra G, Mertelsmann R, Perez M, Lerner B. Automated T-Cell Proliferation in Lab-on-Chip Devices Integrating Microfluidics and Deep Learning-Based Image Analysis for Long-Term Experiments. Biosensors. 2025; 15(10):693. https://doi.org/10.3390/bios15100693

Chicago/Turabian Style

Cadena Vizuete, María Fernanda, Martin Condor, Dennis Raith, Avani Sapre, Marie Follo, Gina Layedra, Roland Mertelsmann, Maximiliano Perez, and Betiana Lerner. 2025. "Automated T-Cell Proliferation in Lab-on-Chip Devices Integrating Microfluidics and Deep Learning-Based Image Analysis for Long-Term Experiments" Biosensors 15, no. 10: 693. https://doi.org/10.3390/bios15100693

APA Style

Cadena Vizuete, M. F., Condor, M., Raith, D., Sapre, A., Follo, M., Layedra, G., Mertelsmann, R., Perez, M., & Lerner, B. (2025). Automated T-Cell Proliferation in Lab-on-Chip Devices Integrating Microfluidics and Deep Learning-Based Image Analysis for Long-Term Experiments. Biosensors, 15(10), 693. https://doi.org/10.3390/bios15100693

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