利用深度学习从明场细胞图像中提取定量生物学信息
Extracting quantitative biological information from brightfield cell images using deep learning
AI总结：
针对荧光显微镜化学染色的侵入性、高成本等问题，提出基于cGAN的明场细胞图像深度学习方法，可生成虚拟染色图像用于细胞结构定量分析，兼具低侵入性、低成本等优势并提升信息提取量。
摘要：
细胞结构的定量分析对于生物医学和药学研究至关重要。标准成像方法依赖于荧光显微镜，即通过化学染色技术对目标细胞结构进行标记。然而，这些技术通常具有侵入性，有时甚至对细胞有毒性，此外还耗时、费力且成本高昂。本文提出了一种基于深度学习的替代方法，该方法使用条件生成对抗神经网络（cGAN，conditional generative adversarial neural network）分析明场图像。研究表明，该方法可从明场图像中提取信息，生成虚拟染色图像，用于后续的细胞结构下游定量分析。具体而言，研究人员利用人类干细胞来源的脂肪细胞（adipocytes，在纳米医学和疫苗开发中备受关注）的明场图像，训练cGAN对脂滴、细胞质和细胞核进行虚拟染色，随后利用这些虚拟染色图像提取上述细胞结构的定量测量值。生成虚拟染色荧光图像相比标准化学染色侵入性更低、成本更低且可重复性更高；此外，该方法还能腾出荧光显微镜通道用于其他分析探针，从而增加了从每个细胞中可提取的信息量。
英文摘要：
Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time-consuming, labor-intensive, and expensive. Here, we introduce an alternative deep-learning-powered approach based on the analysis of brightfield images by a conditional generative adversarial neural network (cGAN). We show that this approach can extract information from the brightfield images to generate virtually-stained images, which can be used in subsequent downstream quantitative analyses of cell structures. Specifically, we train a cGAN to virtually stain lipid droplets, cytoplasm, and nuclei using brightfield images of human stem-cell-derived fat cells (adipocytes), which are of particular interest for nanomedicine and vaccine development. Subsequently, we use these virtually-stained images to extract quantitative measures about these cell structures. Generating virtually-stained fluorescence images is less invasive, less expensive, and more reproducible than standard chemical staining; furthermore, it frees up the fluorescence microscopy channels for other analytical probes, thus increasing the amount of information that can be extracted from each cell.
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