Deep imaging flow cytometry.
Deep imaging flow cytometry.
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深度成像流式细胞术。
DOI：
10.1039/d1lc01043c
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Goda K.
中科院分区：
文献类型：
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作者：
Huang K;Matsumura H;Zhao Y;Herbig M;Yuan D;Mineharu Y;Harmon J;Findinier J;Yamagishi M;Ohnuki S;Nitta N;Grossman AR;Ohya Y;Mikami H;Isozaki A;Goda K.
成像流式细胞术（IFC）凭借其对单细胞进行高通量成像的能力，已成为多种生物医学应用的强大工具。然而，在吞吐量、灵敏度和空间分辨率之间的基本权衡仍然是一个挑战。在这里，我们提出了深度学习增强成像流式细胞术（dIFC），通过在虚拟冻结荧光成像（VIFFI）流式细胞术平台上实现图像恢复算法来规避这种权衡，在不牺牲灵敏度和空间分辨率的情况下实现更高的吞吐量。dIFC的一个关键组件是一个高分辨率（HR）图像发生器，它从低倍率镜头（10x /0.4 na）获得的相应低分辨率（LR）图像中合成“虚拟”HR图像。对于IFC，低倍率镜头是有利的，因为降低了细胞以较高速度流动的图像模糊，从而允许更高的吞吐量。我们训练并开发了包含两个生成对抗网络（gan）架构的HR图像生成器。此外，我们将训练好的生成器和IFC结合起来，开发了dIFC作为一种方法。我们使用莱茵衣藻（Chlamydomonas reinhardtii）细胞图像、Jurkat细胞的荧光原位杂交（FISH）图像和酵母（Saccharomyces cerevisiae）细胞图像来表征dIFC，显示dIFC图像与高倍镜（40×/0.95 na）在2 m s−1的高流速下获得的图像高度相似。最后，我们使用dIFC来提高出芽酵母细胞的fish点计数和颈宽测量的准确性。这些结果为高维空间信息细胞的统计分析铺平了道路。
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.