SciresolSciresolAJ Journal of Medical Sciencesxxxx-xxxxMINI REVIEWMultiomics: Concepts, Methods and ApplicationsMultiomics: concepts, methods & applicationsAroorA Ranna_aroor@hotmail.com1Associate Research Professor, Department of Internal Medicine, Division of Endocrinology, Diabetes and Metabolism, Center for Diabetes and Cardiovascular Research, Dalton Cardiovascular Research Center, School of Medicine, NR Investigator, University of MissouriMO, ColumbiaUSA11122024
Multiomics is a high-throughput technology with multilayered system biology approach incorporating bioinformatic analysis of the data. At present it includes genomics, epigenomics, transcriptomics, proteomics, metabolomics, and gut microbiomics with advancement to the level of single cell analysis. Multiomics is an advancing approach (1) to understand the pathobiology of disease, (2) to identify sensitive biomarkers for the diagnosis of diseases as well as in monitoring and (3) to identify disease specific targets to treat the patient in the context of precision medicine as targeted therapeutics. In the present review, a brief account of various multiomic techniques is considered and relevant examples are pondered to reflect their applications in both preclinical and clinical translational research.
Multiomics comprises several multilayered system biology techniques thereby enabling high throughput screening thus providing better insight into the pathobiology of human disease states. Examples of these are genomics, transcriptomics, single cell-transcriptomics, proteomics, metabolomics and spatial multiomics, epigenomics, and microbiomics 1, 2, 3, 4, 5, 6. The analysis components of these techniques have made significant progress in recent years including machine learning and artificial intelligence 7, 8, 9, 10, 11, 12.
Genomics is system biology of whole genome comprising high of high‐throughput DNA sequencing, sequence assembly, and genome annotation. Human genome project is an example for this 12. Genome‐wide association study (GWAS) is a typical application of genomics to find out the existing sequence variation in the whole human genome, namely, single nucleotide polymorphism (SNP) 13, 14. Transcriptomics comprises the studies on the expression of RNAs and reflects dynamic changes during the pathobiology of initiation and progression of disease and response to therapeutic strategies 15, 16, 17, 18. In these, protein‐coding RNAs (mRNAs), long noncoding RNAs, short noncoding RNAs (microRNAs, small‐interfering RNAs, small nuclear RNAs, piwi‐interacting RNAs, and enhancer RNAs), and circular RNAs are studied 2. Although RNA‐seq, is usually used for quantifying RNA transcripts, the single‐cell transcriptome (single‐cell RNA sequencing [scRNA‐seq]) is increasingly used to the role of different cells in causing organ injury in disease states 19, 16. In epigenomics, reversible modifications of DNA methylation and or histone modifications comprising methylation, phosphorylation and acetylation are studied 20, 21, 22, 23.
Proteomics has been used to identify and study the role of specific proteins in normal and diseasesd states. Although earlier studies have commonly used two-dimensional gel electrophoresis, recent studies have advanced with advent of mass spectrometry with table isotope labeling proteomics and label‐free proteomics 24, 25, 26, 27, 28. In addition, additional techniques to identify post translational modification of proteins such as phosphorylation, acetylation, methylation and palmitoylation enabled studies on regulatory mechanisms of different proteins and changes in the levels of these post translationally modified proteins for the diagnosis and therapeutic monitoring of diseases especially different types of cancer 29, 30.
<bold id="s-84f64dee4711">Summary of different types of multiomics</bold>
Further studies on subcellular proteomics helps to reveal pathobiology of different subcellular components in disease states. Moreover, using antibody for isolated specific proteins in complex with other proteins by immunoprecipitation enables study of protein-protein interactions 31, 32.
In metabolomics, a set of small molecule metabolites form different sources including different tissues, circulating immune cells, plasma and urine 33, 34, 35, 36, 37. They comprise both targeted and non-targeted metabolites. These molecules comprise metabolites that pre produced during the intermediary metabolism including metabolism of carbohydrates, proteins and lipids. Those are also useful to understand biochemistry in the context of other multiomics such as genomics, transcriptomics and proteomics. In this regard, the Human Metabolome Database (https://hmdb.ca/) is a free database containing detailed information of small molecule metabolites in the human body and MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/, a free platform for metabolomics analysis) is now available. Nuclear magnetic resonance (NMR), mass spectrometry (MS)‐based methods gas chromatograph–mass spectrometer (GC‐MS), liquid chromatography tandem mass spectrometry(LC‐MS), and capillary electrophoresis–mass spectrometry (CE‐MS) are some of the techniques available for metabolomics study 37, 38, 39, 40, 41.
Recent advances in the isolation techniques made significant progress in multiomics that are helpful to understand the pathobiology of specific cell types in the context of single cell genomics, single cell transcriptomics and single cell proteomics. Some of the platforms that are available for single cell analysis are 10×Chromium Single Cell Gene Expression Solution and BD Rhapsody™ Single‐Cell Analysis System 42, 43, 44, 45, 46.
Spatial transcriptomics and proteomics in which spatial location of tissues are being used in tissue sections. The techniques such as next‐generation sequencing (NGS)‐based techniques, and imaging‐based methods of in situ sequencing, and in situ hybridization (ISH)‐based methods, are used. Spatial omics techniques combined with proteomics will be more useful to understand role and regulation of specific proteins 47, 48, 49, 15, 50, 51.
Microbiomics is to study the composition of gut microbial flora comprising both microorganisms that symbiotically exist or pathologically live. Therefore, studying the gut microbiome has been utilized to understand the mechanisms and mediators that cause systemic effects of pathological gut flora. The technique is 16S rRNA gene sequencing that is most commonly employed 52, 53, 54, 55, 56. Different types of multiomics are summarized in Figure 1 .
Bioinformatics Network analysisis a set of tools available to analyze the data obtained by multiomics. Examples include G & T seq, DR-seq for Genome‐transcriptome and PEA/STA for transcriptome-proteome 57, 58, 59, 60, 61, 8, 9, 62. Several open-source tools are available to analyze the data obtained by multiomics studies. Examples are omicsAnalyst for Transcriptomics, proteomics, metabolomics and microbiome and OmicsNet 2.0 for genomics, transcriptomics, metabolomics, microbiome. Examples of bioinformatics analysis include correlation network analysis, cluster het heat map analysis, web based multioomic analysis platform that supports 2D and 3D network visualization exploration, Ingenuity Pathway Analysis (IPA), Gene Ontology (GO) analysis, Differential expression analysis, matrix-based algorithms. In addition, bioinformatics analysis also includes use of artificial intelligence, multiomic big data analysis, machine learning and deep learning.
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