Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow
Self-Learning Microfluidic Platform for Single-Cell Imaging and Classification in Flow
复制标题
DOI：
10.3390/mi10050311
复制
发表时间：
2019-05-01
期刊：
影响因子：
3.4
通讯作者：
Knop, Michael
中科院分区：
文献类型：
--
作者：
Constantinou, Iordania;Jendrusch, Michael;Knop, Michael
单细胞分析通常需要将细胞悬浮液限制在分析室中或将单细胞精确定位在小通道中。流体动力学流动聚焦已被广泛用于实现微通道中的流约束，以用于此类应用。随着成像流式细胞术的普及，对成像兼容的微流体装置的需求变得越来越重要，该装置允许在小体积中精确限制单细胞。与此同时，细胞群的高通量单细胞成像产生大量复杂的数据，这就需要多功能的图像分析算法。在这项工作中，我们提出了一个基于微流体的平台，用于单细胞成像的流动和随后的图像分析，使用变分自动编码器进行细胞混合物的无监督表征。我们使用简单而强大的Y形微流体设备，并展示了精确的3D粒子限制对显微镜载玻片的高分辨率成像。为了证明适用性，我们使用这些设备来限制酵母物种的异质混合物，在流动中对它们进行亮场成像，并以88%的准确率展示完全无监督的单细胞图像以及少量分类。
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.