Advances in genomic technologies provide researchers with access to more complete molecular datasets than ever before. Next-generation sequencing (NGS) in particular propels both basic and translational research forward, enabling scientists to see the full picture of biology. As a result, researchers now know more about the genetics and pathways underlying disease and have databases of actionable information that can be considered for screening and therapeutic development. For instance, scientists used high throughput sequencing technologies for The Cancer Genome Atlas (TCGA) project–a massively successful NGS-based research effort to thoroughly characterize the genome, transcriptome, epigenome, and proteome across 33 different types of cancer. The TCGA database is publicly available to all researchers, and has already led to improvements in cancer diagnostics, treatments, and prevention.1,2
Analyze Any -Ome with NGS
Researchers examine different layers of biological regulation with a variety of technologies. From Sanger sequencing for characterizing mutations to quantitative polymerase chain reaction (qPCR) for quantifying specific transcripts, scientists have had the capacity to answer a number of targeted research questions. Although each of these methods remain critical in cellular and molecular biology research, scientists turn to NGS as a powerful, scalable tool for bringing thousands of these -omes together on a single platform. This scalability provides comprehensive and unbiased characterization capabilities, which enable scientists to study more than just the low hanging fruit. NGS allows researchers to make discoveries that they may not have even considered prior to experimental design.3 For example, at the start of the TCGA research projects in 2006, shotgun sequencing and microarray-based technologies led the way for profiling copy number variants, methylation, and protein expression. Over the 12-year-span of TCGA, the demand for scalable, low-cost data brought high throughput NGS to the forefront. NGS—alone and in combination with array-based methods—enabled and expanded whole genome, exome, transcriptome, and epigenome studies.1
NGS-Based Multiomics Is the Future of Research
Scientists ask and answer innovative research questions by combining -omic modalities to create something new. Multiomics can include any combination of genomics, transcriptomics, proteomics, and epigenomics, as well as intersections and extensions of these layers, such as microbiomics, metabolomics, interactomics, and immunomics.
Researchers contributing to the TCGA have pieced together information from different -omics datasets that were collected separately. However, the simultaneous analysis of multiple -omes is crucial for researchers to gain a comprehensive view of biological complexity.4 Multiomics is the combined analysis of two or more -omes from the same samples. With this approach, scientists leverage the benefits of each -omic technique and answer previously unapproachable research questions. Multiomics can include any combination of genomics, transcriptomics, proteomics, and epigenomics, as well as intersections and extensions of these layers, such as microbiomics, metabolomics, interactomics, and immunomics.5 Researchers turn to multiomics for a variety of reasons. Because NGS-based multiomics characterizes thousands of targets at once across multiple layers of regulation in the same starting material, scientists maximize discovery from precious samples and reduce the need to infer connections between the cause and consequence of complex phenotypes in separate isolates.6
The Unique Value of Combined -Omics
From tracking cancer cell evolution to characterizing Alzheimer’s hallmarks, scientists employ NGS-based multiomics for high throughput and cost-effective investigations of multifaceted biological systems.5
Genome and proteome: Single cell targeted resequencing and cell surface protein analysis
Researchers at Memorial Sloan Kettering Cancer Center and the University of California, San Francisco employed single cell multiomics to track the cellular evolution of myeloid cancer. With oligo-conjugated antibodies, the scientists performed high throughput single cell proteomics with DNA sequencing. They simultaneously analyzed mutations and quantified cell-surface proteins for more than 740,000 cells. This level of scale and detail allowed the researchers to connect genotype with phenotype by finely mapping the dynamics of cancer cell clonal complexity and its effect on disease progression, and by discerning which genes are disease drivers versus passengers. Specifically, by combining analysis of protein expression and mutations, the researchers were able to map both the somatic genotype and clonal architecture with immunophenotype.7
Transcriptome and epigenome: ChIP-seq and total RNA-seq
Although protein aggregation is a hallmark of Alzheimer’s disease, therapies that target those proteins have not been effective. A group of scientists from the University of Pennsylvania aimed to understand this dilemma by examining epigenetic dysregulation Integrate and Innovate with NGS and Multiomics Researchers across disciplines combine layers of discovery obtained with accessible NGS-based multiomics approaches. Multiomics Definition Scientists ask and answer innovative research questions by combining -omic modalities to create something new. Multiomics can include any combination of genomics, transcriptomics, proteomics, and epigenomics, as well as intersections and extensions of these layers, such as microbiomics, metabolomics, interactomics, and immunomics. in early Alzheimer’s disease with the power of multiomics. The researchers compared brain tissue from healthy individuals and people affected by Alzheimer’s with total RNA-seq and epigenetic sequencing assays, including chromatin immunoprecipitation sequencing (ChIP-seq) and 5-hydroxymethylcytosine (5hmC) analysis. They found that the epigenomic landscape differs between these groups of individuals, and that protective pathways involved in healthy aging are disabled to initiate epigenetic changes that drive Alzheimer’s disease. Combining epigenetic and RNA-seq methods enabled the scientists to directly measure links between gene regulation and gene expression, instead of inferring those connections.8
Metagenomics and transcriptome: 16S rRNA gene sequencing and tissue mRNA-seq
By taking a multiomics approach, scientists from Mayo Clinic studied the connection between the stomach microbiome and host gene expression in gastric cancer. They integrated bacterial gene sequencing and human transcriptomics of biopsy samples from healthy stomachs, as well as gastritis and cancer lesions. The researchers uncovered disease-specific connections between the host and bacteria, such as unique bacterial species linked to human genes involved in cell division. They also noted immune signatures in gastric cancer that may suggest a previously unidentified immune evasion process. Investigating 16S rRNA gene sequencing and tissue mRNA-seq in the same samples allowed the researchers to collect an unprecedented comprehensive description of microbial genomics, human transcriptomics, and immune cell factors related to gastric carcinogenesis.9
Transcriptome and proteome: CITE-seq
With concurrent measurements of protein and transcript levels, researchers characterize cellular heterogeneity, identify rare cell subpopulations, and detect information otherwise lost with bulk methods. A research team from the University of Minnesota profiled immune cells in the heart with such a multiomics approach, called CITE-seq (cellular indexing of transcriptomes and epitopes). CITE-seq provides an easy and comprehensive view of single cell function by combining transcriptional information that reflects cell physiology and post-transcriptional information about surface proteins. The researchers used both mRNA-seq and oligo-tagged antibodies to evaluate gene expression and protein levels simultaneously at a single cell level, and identified a heterogeneous population of macrophages that both stimulate angiogenesis and slow fibrosis.10
New Technologies Make Multiomics a Reality for More People
With cutting edge NGS technologies, multiomics is becoming more accessible to more researchers. Illumina® NovaSeq™ X sequencers are great examples of advanced NGS technology that brings cost-effective, large-scale datasets within reach for all researchers, and expands the scope of research questions that scientists seek to answer.3,5,11 NovaSeq X sequencers empower scientists to increase the scale and scope of their studies with ultra-high-density flow cells and ultra-high-resolution optics. These sequencing solutions make even the most ambitious multiomics projects possible. Experiments that may have been out of budget in the past are now affordable for more researchers, thanks to advanced technologies and more attainable sequencing-intensive multiomics methods. With NovaSeq X, sequencing a genome is roughly one third of the cost of genome sequencing just a year ago. As a result, these systems democratize multiomics, making the cost of multiomics experiments comparable to the previous cost of a single -omic experiment. Along with this advanced NGS technology, the NovaSeq X sequencers are user friendly, with supporting materials, methods guides, and new reagents that enable room temperature shipment without the need for cold chain logistics.6,11,12
Learn More About Multiomics
Accessible, large-scale NGS-based multiomics approaches enable scientists to answer unique and imaginative research questions about the genome, epigenome, transcriptome, proteome, and beyond. Across research specialties, the NovaSeq X series from Illumina will bring additional insights within reach for more scientists.3,9 Download the new Multiomics Methods Guide from Illumina to learn how to achieve ambitious multiomics studies with extraordinary throughput, proven accuracy, and historically low cost.12
- “TCGA Molecular Characterization Platforms,” NIH National Cancer Institute, https://www. cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga/using-tcga/ technology, accessed March 13, 2023.
- “The Cancer Genome Atlas Program,” NIH National Cancer Institute, https://www.cancer.gov/ about-nci/organization/ccg/research/structural-genomics/tcga, accessed March 13, 2023.
- X. Dai, L. Shen, “Advances and trends in omics technology development,” Front Med (Lausanne), 9:911861, eCollection 2022.
- L. Cantini et al., “Benchmarking joint multi-omics dimensionality reduction approaches for the study of cancer,” Nat Comm, 12(1):124, 2021.
- P. Mohammadi-Shemirani et al., “From ’omics to multi-omics technologies: the discovery of novel causal mediators,” Curr Atheroscler Rep, 25(2):55-65, 2023.
- “What is Multiomics?” Illumina, https://www.illumina.com/techniques/popular-applications/multiomics.html, accessed March 13, 2023.
- L.A. Miles et al., “Single-cell mutation analysis of clonal evolution in myeloid malignancies,” Nature, 587(7834):477-82, 2020.
- R. Nativio et al., “An integrated multi-omics approach identifies epigenetic alterations associated with Alzheimer’s disease,” Nat Genet, 52(10):1024-35, 2020.
- C.H. Park et al., “Multi-omics reveals microbiome, host gene expression, and immune landscape in gastric carcinogenesis,” iScience, 25(3):103956, 2022.
- X.S. Revelo et al., “Cardiac resident macrophages prevent fibrosis and stimulate angiogenesis,” Circ Res, 129(12):1086-1101, 2021.
- “NovaSeq X Series Overview,” Illumina, https://www.illumina.com/systems/sequencingplatforms/novaseq-x-plus.html, accessed March 13, 2023.
- “Multiomics Methods Guide,” Illumina, https://www.illumina.com/destination/multiomicsnovaseqx.html, accessed March 13, 2023.