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Review
. 2023 Nov 3;3(6):597-612.
doi: 10.1007/s43657-023-00128-8. eCollection 2023 Dec.

Phenomic Imaging

Affiliations
Review

Phenomic Imaging

Lizhen Lan et al. Phenomics. .

Abstract

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.

Keywords: Genomics; Imaging; Immunomics; Metabolomics; Phenomics; Proteomics; Transcriptomics.

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Conflict of interest statement

Conflict of InterestMei Tian is the Editorial Board Member of Phenomics, Yidan Gao is the editorial operation team member of Phenomics, and they were not involved in reviewing this paper.

Figures

Fig. 1
Fig. 1
The process for conducting phenomic imaging studies involves three main steps: image analysis, integration of data from other omics, and data analysis. The first step involves analyzing images to extract features related to the traits being studied. The second step involves combining imaging data with data from other omics fields to better understand how the traits are influenced by various factors. Finally, data analysis techniques are used to validate hypotheses about the relationships between the traits and other biological features. Overall, this approach allows researchers to obtain more comprehensive understanding of an individual's phenotype and the underlying biological mechanisms
Fig. 2
Fig. 2
There are several biomedical imaging modalities that are currently available in medical research and clinical settings. These include X-ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), ultrasonography (US), endoscopy, Raman imaging, and terahertz imaging. Each of these modalities has its own unique strengths and limitations, making them suitable for different types of applications. For example, some modalities are better for visualizing soft tissues, while others are more effective at detecting changes in metabolic activity or molecular structure

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