Unlocking the Complexity of Biology With Multiomics

Multiomics is the integration of omics disciplines including genomics, proteomics, metabolomics, lipidomics and epigenomics. In this infographic, we’ll explore the power of integrating cutting-edge omics technologies to build a holistic understanding of life at the molecular level. WHY MULTIOMICS? The genome has long been recognized as the “blueprint” for life. But studying genetic data in isolation does not provide insight into how the genotype translates to a phenotype. GENOTYPE PHENOTYPE GENOMICS PROTEOMICS EPIGENOMICS METABOLOMICS TRANSCRIPTOMICS PHENOMICS MULTIOMICS APPROACHES HELP US TO UNDERSTAND COMPLEX BIOLOGICAL SYSTEMS METABOLOMICS Metabolite Metabolite Identifies and quantifies small molecules, or metabolites, within biological systems. Compared to earlier points in the “omics” PROTEOMICS/ cascade, such as the genome and PHOSPHOPROTEOMICS transcriptome, the metabolome more Protein Protein Protein closely reflects the phenotype. Proteomics involves studying the entire set of proteins expressed in an organism, while phospho-proteomics focuses on phosphorylated TRANSCRIPTOMICS Gene Gene Gene proteins. These fields help scientists understand cell signaling, post-translational modification Studies the entire set of RNA transcripts and cellular functions. found within an organism, offering insight into how DNA is transcribed and translated miRNA / sRNA miRNA miRNA into proteins. Explores the regulatory roles of microRNA (miRNA) and small RNA (sRNA) in gene expression. GENOMICS Gene Gene Gene Gene EPIGENOMICS Analyzes an organism’s complete set of genes – the genome. Provides insight into Studies DNA modifications that influenc inheritance, evolution and the impact of --CG--CG-- --CG--CG-- the expression of genes without altering the genetic variation. --CG--CG-- --CG--CG-- underlying DNA sequence. Epigenetic studies reveal the intricate regulatory mechanisms that help to shape cellular functions. Beyond the “core” omics disciplines listed, there are others that are increasingly studied, including: LIPIDOMICS GLYCOMICS MICROBIOMICS Comprehensively analyzes Investigates the structure Studies the genomes of the lipid profile of cells, and function of glycans microorganisms within tissues or organisms. in a cell type or organism. a particular environment. TECHNIQUES USED ACROSS OMICS DISCIPLINES There are a wide variety of techniques utilized across omics disciplines. In recent decades, a particular advancement in omics is the ability to analyze cells at the single-cell level. Here are some examples of core omics techniques: PROTEINS DNA (PROTEOMICS/ (GENOMICS AND PHOSPHO-PROTEOMICS) EPIGENOMICS) • ELISA • Next-generation sequencing • 2D-PAGE / 2D-DIGE (NGS) • Chromatography • Microarray-based genotyping • Microarray technology • Genome editing • Mass spectrometry (MS) • Genetic engineering -based workflow • Polymerase chain reaction (PCR) • Aptamer-based • Antibody-based technology such as proteomics assays chromatin immunoprecipitation • Fluorescence in situ hybridization MICROBES SPATIAL (FISH) MULTIOMICS (MICROBIOMICS) Combines multiple • 16S rRNA gene sequencing omics techniques with spatial information • NGS in biological samples. • FISH RNA This enables a (TRANSCRIPTOMICS) comprehensive METABOLITES understanding of the • Microarray technology (METABOLOMICS) complex interactions • RNA-sequencing (bulk) that occur between • NMR spectroscopy • Single-cell RNA-sequencing molecules and their (scRNA-seq) • MS-based workflow spatial distribution. • Spatial transcriptomics techniques • Chromatography A NEW ERA IN SYSTEMS BIOLOGY In multiomics experiments, researchers integrate the data obtained from these various omics technologies. There are numerous challenges associated with multiomics data integration (we’ll get to that later). Some tools are more suitable for tackling specific challenges than others. Because of this, there is a wide suite of tools available for differen applications. Here is a simplified multiomics workflow DATA COLLECTION PRE-PROCESSING OF DATA GENOMICS • Normalization EPIGENOMICS • Address missing values TRANSCRIPTOMICS • Correct for batch effect PROTEOMICS METABOLOMICS Employ statistical and computational methods to integrate different omics datasets. Researchers will select an appropriate method based on: The research question INTEGRATION Characteristics of the data There are three main strategies The goal of the analysis for multiomics data integration: EARLY Different omic datasets are combined into a table, or graph based representation, which is then used as an input for a machine learning (ML) model. INTERMEDIATE A model learns a joint Here are just some examples of computational methods used representation of the datasets. for single-cell multiomics data integration. LATE Models are applied to each dataset before a second model combines their predictions. MATCHED UNMATCHED (from the same cell): (from different cells) • MultiVelo • Seurat v5 • CellOracle • MultiVI • MIRA • Cobolt • FigR • StabMap • BREM-SC • totalVI OUTCOME Analyzing various layers of molecular information simultaneously, provides a more holistic view of biological processes, which has applications across science, such as: Disease risk Disease Understanding the Improving Enhancing Developing prediction subtyping role of microbiota crop yields metabolic targeted drugs in health and disease engineering Biomarker Monitoring Understanding Host–pathogen Identifying Understanding the discovery biodiversity a patient’s response interaction studies regulatory cellular processes to treatment elements and involved in the signaling pathways production of in synthetic biology pharmaceutical compounds CELL ATLASES Multiomics data has contributed to the development of several cell atlases, including the: CHALLENGES IN MULTIOMICS Combining datasets from individual omics disciplines presents numerous challenges. Here are some key examples: Vast amount of data sets, Translating multiomics Different methodologies tools and approaches can data into insights that and data formats across be overwhelming for new are biologically meaningful omics disciplines researchers can be a major issue Spatial omics is having a The sheer size of the data major impact on the field, but It’s not always easy that is generated requires spatial technologies are often to ensure reproducibility substantial computational low-throughput of experiments resources Variations in data quality and Integrating multiomics data standards across differen Lack of publicly-available presents privacy and ethical omics platforms can affec tools for integrated analysis considerations the accuracy of integration Several working groups are trying to address some of the above challenges by creating robust reporting guidelines and standards for multiomics studies. SPONSORED BY LC/MS INSTRUMENTS