PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI.
PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI.
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PhaseFIT：通过生成AI进行活体类器官相位荧光图像转换。
DOI：
10.1038/s41377-023-01296-y
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发表时间：
2023-12-14
影响因子：
19.4
通讯作者：
Saeidi, Nima
中科院分区：
文献类型：
--
作者：
Zhao, Junhan;Wang, Xiyue;Zhu, Junyou;Chukwudi, Chijioke;Finebaum, Andrew;Zhang, Jun;Yang, Sen;He, Shijie;Saeidi, Nima
有机模型为研究器官发育和功能所涉及的基本生物学过程提供了一个强大的机制研究平台。尽管基于图像的有机物表型定量的潜力，但其复杂的3D结构，以及免疫荧光染色的耗时和劳动密集性带来了巨大的挑战。在这项工作中，我们开发了一个虚拟绘制系统PhaseFIT(相位荧光图像转换)，该系统利用定制的和形态丰富的2.5D肠道器官，通过可访问的低成本的器官相图像生成用于表型定量的虚拟荧光图像。这个系统是由一种新的分割信息的深度生成模型驱动的，该模型专门用于分割对象之间的重叠和接近。该模型实现了从相位对比度到多通道荧光图像的无注释数字转换。对细胞核、分泌细胞标记和干细胞的虚拟绘制结果表明，PhaseFIT通过生成细粒度的视觉内容，优于现有的基于深度学习的染色转换模型。我们进一步验证了PhaseFIT的效率和准确性，以量化三种化合物对隐窝形成、细胞数量和细胞干细胞的影响。PhaseFIT是第一个深度学习的虚拟绘画系统，专注于活的有机物质，使大规模、信息丰富和高效的有机物质表型量化成为可能。PhaseFIT将使有机化合物能够在高通量药物筛选应用中使用。PhaseFIT是一种尖端的、分段信息的生成式人工智能，它毫不费力地将一维相位对比图像转换为丰富的多通道荧光图像，用于快速、无注释的表型定量，为高含量药物筛选应用设定了新的标准。
Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs. Despite the potential for image-based phenotypic quantification of organoids, their complex 3D structure, and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges. In this work, we developed a virtual painting system, PhaseFIT (phase-fluorescent image transformation) utilizing customized and morphologically rich 2.5D intestinal organoids, which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images. This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects. The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images. The virtual painting results of nuclei, secretory cell markers, and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content. We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation, cell population, and cell stemness. PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids, enabling large-scale, informative, and efficient organoid phenotypic quantification. PhaseFIT would enable the use of organoids in high-throughput drug screening applications. PhaseFIT is a cutting-edge, segmentation-informed generative AI which effortlessly transforms one dimensional phase contrast images into rich, multi-channel fluorescent visuals for rapid, annotation-free phenotypic quantification, setting a new standard for high content drug screening applications.
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影响因子：
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通讯作者：
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