Integrating Imaging and Omics: Computational Methods and Challenges
Integrating Imaging and Omics: Computational Methods and Challenges
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DOI：
10.1146/annurev-biodatasci-080917-013328
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发表时间：
2019-01-01
期刊：
影响因子：
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通讯作者：
Ellenberg, Jan
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文献类型：
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作者：
Heriche, Jean-Karim;Alexander, Stephanie;Ellenberg, Jan
荧光显微镜成像长期以来一直是生物医学研究中基于DNA测序和质谱的组学的补充，但这些方法现在正在融合。一方面，组学方法正在从在大细胞群体中平均的体外方法转向具有单细胞灵敏度的原位分子表征工具。另一方面，荧光显微镜成像已经从组织和细胞的形态学描述转移到具有单分子分辨率的定量分子分析。以计算方法为基础的最新技术发展已经开始模糊成像和组学之间的界限，并使其直接相关和无缝集成成为令人兴奋的可能性。随着这一趋势的迅速发展，它将使我们能够创建具有空间和时间背景以及亚细胞分辨率的生命系统的全面分子概况。实现这一宏伟目标的关键将是新颖的计算方法，成功应对数据集成和共享以及云支持的大数据分析的挑战。
Fluorescence microscopy imaging has long been complementary to DNA sequencing-and mass spectrometry-based omics in biomedical research, but these approaches are now converging. On the one hand, omics methods are moving from in vitro methods that average across large cell populations to in situ molecular characterization tools with single-cell sensitivity. On the other hand, fluorescence microscopy imaging has moved from a morphological description of tissues and cells to quantitative molecular profiling with single-molecule resolution. Recent technological developments underpinned by computational methods have started to blur the lines between imaging and omics and have made their direct correlation and seamless integration an exciting possibility. As this trend continues rapidly, it will allow us to create comprehensive molecular profiles of living systems with spatial and temporal context and subcellular resolution. Key to achieving this ambitious goal will be novel computational methods and successfully dealing with the challenges of data integration and sharing as well as cloud-enabled big data analysis.