Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA; Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX 75390, USA.
Cell Syst. 2021 Jul 21;12(7):733-747.e6. doi: 10.1016/j.cels.2021.05.003. Epub 2021 Jun 1.
Deep learning has emerged as the technique of choice for identifying hidden patterns in cell imaging data but is often criticized as "black box." Here, we employ a generative neural network in combination with supervised machine learning to classify patient-derived melanoma xenografts as "efficient" or "inefficient" metastatic, validate predictions regarding melanoma cell lines with unknown metastatic efficiency in mouse xenografts, and use the network to generate in silico cell images that amplify the critical predictive cell properties. These exaggerated images unveiled pseudopodial extensions and increased light scattering as hallmark properties of metastatic cells. We validated this interpretation using live cells spontaneously transitioning between states indicative of low and high metastatic efficiency. This study illustrates how the application of artificial intelligence can support the identification of cellular properties that are predictive of complex phenotypes and integrated cell functions but are too subtle to be identified in the raw imagery by a human expert. A record of this paper's transparent peer review process is included in the supplemental information. VIDEO ABSTRACT.
深度学习已成为识别细胞成像数据中隐藏模式的首选技术，但常被批评为“黑箱”。在这里，我们采用生成式神经网络结合监督机器学习来对患者来源的黑色素瘤异种移植物进行“有效”或“无效”转移分类，验证了关于在小鼠异种移植物中具有未知转移效率的黑色素瘤细胞系的预测，并使用该网络生成放大关键预测细胞特性的计算机细胞图像。这些经过放大的图像揭示了伪足延伸和增加的光散射是转移性细胞的标志性特征。我们使用自发地在低转移效率和高转移效率状态之间转换的活细胞来验证这一解释。这项研究说明了如何应用人工智能来支持识别对复杂表型和整合细胞功能具有预测性但通过人类专家在原始图像中过于微妙而无法识别的细胞特性。本文的透明同行评审过程记录包含在补充信息中。视频摘要。
