Lan Lizhen, Feng Kai, Wu Yudan, Zhang Wenbo, Wei Ling, Che Huiting, Xue Le, Gao Yidan, Tao Ji, Qian Shufang, Cao Wenzhao, Zhang Jun, Wang Chengyan, Tian Mei
Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China.
Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang China.
Phenomics. 2023 Nov 3;3(6):597-612. doi: 10.1007/s43657-023-00128-8. eCollection 2023 Dec.
Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.
人类表型组学被定义为受多尺度因素复杂相互作用影响的可观察表型和特征的全面集合。这些因素包括微观层面的基因、表观遗传学,介观层面的器官、微生物组,以及宏观层面的饮食和环境暴露。“表型组成像”利用各种成像技术在体内和体外跨不同尺度可视化和测量解剖结构、生物功能、代谢过程和生化活动。与专注于疾病诊断的传统医学成像不同,表型组成像既捕捉正常特征也捕捉异常特征,有助于宏观和微观表型之间的详细关联。这种方法在解读表型组方面发挥着关键作用。本综述概述了不同的表型组成像模式及其在人类表型组学中的应用。此外,还探讨了表型组成像与其他组学学科之间的关联,包括基因组学、转录组学、蛋白质组学、免疫组学和代谢组学。通过将表型组成像与其他组学数据(如基因组学和代谢组学)整合,可以实现对生物系统的全面理解。这种整合为新治疗方法和诊断工具的开发铺平了道路。