Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Nat Commun. 2020 Dec 7;11(1):6256. doi: 10.1038/s41467-020-20062-x.
Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.
由于其特异性，荧光显微镜已成为细胞生物学中不可或缺的成像工具。然而，荧光漂白、光毒性和相关伪影仍然限制了荧光显微镜的应用。最近，已经证明人工智能 (AI) 可以将一种对比度转换为另一种对比度。我们提出了具有计算特异性的相衬成像 (PICS)，这是定量相衬成像和人工智能的结合，它可以高特异性地提供关于未标记活细胞的信息。我们的成像系统允许自动训练，而推断则构建在采集软件中并实时运行。将计算出的荧光图应用回定量相衬成像 (QPI) 数据，我们可以在不丧失活力的情况下，独立测量细胞核和细胞质的生长情况，持续多天。使用一种抑制多次散射的 QPI 方法，我们测量了球体中单个人类细胞核的干物质含量。在当前的实现中，PICS 为生物应用中连续同时监测单个细胞成分提供了一种通用的定量技术，在这些应用中，长期无标记成像是理想的。
