Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, U.S.A.
Emerg Top Life Sci. 2021 Dec 21;5(6):837-847. doi: 10.1042/ETLS20210219.
In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.
在高通量单细胞生物学时代，单细胞成像技术不仅在技术进步方面，而且在其转化应用方面也取得了发展。成像和计算生物学的同步发展为两者的融合提供了机会，为科学界提供了观察、理解和预测细胞和组织表型和行为的工具。此外，高通量单细胞成像和机器学习算法现在可以对临床标本进行患者分层和预测性诊断。在这里，我们全面总结了单细胞成像的进展，重点介绍了高通量显微镜表型和多重蛋白质组空间成像平台。我们还回顾了近年来为生物医学科学中使用的图像处理和下游应用开发的各种计算工具。最后，我们讨论了如何利用系统生物学方法和跨学科的数据整合，可以进一步加强单细胞成像在精准医学中的令人兴奋的应用和未来实施。
