Multi-Omics Profiling for Health

Abstract

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Keywords: COVID-19; data integration; diet; electronic health record; exposome; genomics; gut microbiome; lipidomics; longitudinal; metabolomics; omics; precision health; proteomics; transcriptomics; wearables.

Graphical abstract

Fig. 1

Timeline of major technological developments and milestones in different Omics analysis. Technologies developed in the lab are highlighted in bold. 2-DE, 2-Dimensional Electrophoresis; ATAC, Assay for Transposase-Accessible Chromatin; CAGE, Cap Analysis Gene Expression; DNMT1, DNA Methyl Transferase one; ESI, Electrospray Ionization; EST, Expressed Sequence Tags; GC, Gas Chromatography; HAT, Histone Acetyl Transferase; HDAC, Histone Deacetylase; Hi-C, High-throughput Chromosome Conformation Capture; HPLC, High-Performance Liquid Chromatography; IMAC, Immobilized Metal Affinity Chromatography; iTRAQ, Isobaric Tags for Relative and Absolute Quantitation; LC, Liquid Chromatography; MALDI, Matrix-Assisted Laser Desorption/Ionization; MeDIP, Methylated DNA Immunoprecipitation; MPSS, Massively Parallel Signature Sequencing; MS, Mass spectrometry; NGS, Next Generation Sequencing; PCR, Polymerase Chain Reaction; SAGE, Serial Analysis of Gene Expression; Sc-RNA, Single cell RNA; SILAC, Stable Isotope Labeling with Amino acids in Cell culture; T2T, Telomere-To-Telomere.

Fig. 2

Longitudinal multi-omics and wearable data enabled deep phenotyping for precision health. Omics and non-omics data across times (T₁-T_n) could be integrated using machine learning and deep learning approaches to predict disease risk, subtyping, biomarker discovery, molecular insights, and response to treatment among others. Figure was created using Biorender (https://biorender.com/).