放大倍数
吞吐量
流式细胞术
生物
镜头(地质)
芽殖酵母
人工智能
灵敏度(控制系统)
作者
Kangrui Huang,Hiroki Matsumura,Yaqi Zhao,Maik Herbig,Dan Yuan,Yohei Mineharu,Jeffrey Harmon,Justin Findinier,Mai Yamagishi,Shinsuke Ohnuki,Nao Nitta,Arthur R Grossman,Yoshikazu Ohya,Hideharu Mikami,Akihiro Isozaki,Keisuke Goda
出处
期刊:Lab on a Chip
[The Royal Society of Chemistry]
日期:2022-01-01
摘要
Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.
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