Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow
1
Zentrum für Molekulare Biologie der Universität Heidelberg (ZMBH), DKFZ-ZMBH Alliance, Universität Heidelberg, 69120 Heidelberg, Germany
2
Institute of Microtechnology, Technische Universität Braunschweig, 38124 Braunschweig, Germany
3
Center of Pharmaceutical Engineering (PVZ), Technische Universität Braunschweig, 38106 Braunschweig, Germany
4
Developmental Biology and Stem Cells Department, Institut de Génétique et de Biologie Moléculaire et Cellulaire, 67400 Illkirch-Graffenstaden, France
5
Centre National de la Recherche Scientifique, 67400 Illkirch-Graffenstaden, France
6
Institut National de la Santé et de la Recherche Médicale, 67400 Illkirch-Graffenstaden, France
7
Université de Strasbourg, 67400 Illkirch, France
8
Cell Morphogenesis and Signal Transduction, German Cancer Research Center (DKFZ), DKFZ-ZMBH Alliance, 69120 Heidelberg, Germany
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Micromachines 2019, 10(5), 311; https://doi.org/10.3390/mi10050311
Received: 24 April 2019
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Accepted: 6 May 2019
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Published: 9 May 2019
(This article belongs to the Special Issue Microfluidics for Cells and Other Organisms)
Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.
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Keywords:
microfluidics; 3D flow focusing; 3D particle focusing; particle/cell imaging; bioMEMS; unsupervised learning; neural networks; variational inference
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Externally hosted supplementary file 1
Link: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/L5J7WO
Description: Supplementary Files S1, S2, and S3. -
Externally hosted supplementary file 2
Link: https://github.com/mjendrusch/learning-ifc
Description: Source code and neural network weights for all parts of the project.
MDPI and ACS Style
Constantinou, I.; Jendrusch, M.; Aspert, T.; Görlitz, F.; Schulze, A.; Charvin, G.; Knop, M. Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow. Micromachines 2019, 10, 311. https://doi.org/10.3390/mi10050311
AMA Style
Constantinou I, Jendrusch M, Aspert T, Görlitz F, Schulze A, Charvin G, Knop M. Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow. Micromachines. 2019; 10(5):311. https://doi.org/10.3390/mi10050311
Chicago/Turabian StyleConstantinou, Iordania, Michael Jendrusch, Théo Aspert, Frederik Görlitz, André Schulze, Gilles Charvin, and Michael Knop. 2019. "Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow" Micromachines 10, no. 5: 311. https://doi.org/10.3390/mi10050311
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