Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow.
Fiche publication
Date publication
mai 2019
Journal
Micromachines
Auteurs
Membres identifiés du Cancéropôle Est :
Dr CHARVIN Gilles
Tous les auteurs :
Constantinou I, Jendrusch M, Aspert T, Görlitz F, Schulze A, Charvin G, Knop M
Lien Pubmed
Résumé
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.
Mots clés
3D flow focusing, 3D particle focusing, bioMEMS, microfluidics, neural networks, particle/cell imaging, unsupervised learning, variational inference
Référence
Micromachines (Basel). 2019 May 9;10(5):