Blood transcriptomics and metabolomics for personalized medicine.

• Type of Material: Article
• Authors: Shuzhao Li, Emory University; Andrei Todor, Emory University; Ruiyan Luo, Georgia State University
• Language: English
• Date: 2016
• Publisher: Research Network of Computational and Structural Biotechnology (RNCSB)
• Publication Version: Final Published Version
• Copyright Statement: © 2015 The Authors
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.csbj.2015.10.005
• Title of Journal or Parent Work: Computational and Structural Biotechnology Journal
• ISSN: 2001-0370
• Volume: 14
• Start Page: 1
• End Page: 7
• Grant/Funding Information: The California Breast Cancer Research Program (21UB-8002); US National Institutes of Health (NIEHS P30 ES019776, NIAID 2U19 AI090023-06, NIAID HHSN272201200031C, NIA 1R01 AG038746-02, NHLBI 1P20 HL113451-01); The Department of Defense (HT9404-13-1-003)
• Keywords: Data integration; Transcriptomics; Personalized medicine; Metabolomics; Blood systems biology
• Research Categories: Health Sciences, General; Biology, Molecular

Abstract

Molecular analysis of blood samples is pivotal to clinical diagnosis and has been intensively investigated since the rise of systems biology. Recent developments have opened new opportunities to utilize transcriptomics and metabolomics for personalized and precision medicine. Efforts from human immunology have infused into this area exquisite characterizations of subpopulations of blood cells. It is now possible to infer from blood transcriptomics, with fine accuracy, the contribution of immune activation and of cell subpopulations. In parallel, high-resolution mass spectrometry has brought revolutionary analytical capability, detecting > 10,000 metabolites, together with environmental exposure, dietary intake, microbial activity, and pharmaceutical drugs. Thus, the re-examination of blood chemicals by metabolomics is in order. Transcriptomics and metabolomics can be integrated to provide a more comprehensive understanding of the human biological states. We will review these new data and methods and discuss how they can contribute to personalized medicine.