Multiomics Opens the Door: More Data, More Insight into Biology’s Complexity

Combining multiomics data provides more information to identify biological drivers, opening up opportunities to address more clinical indications

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Illumina sequencing has served as a platform for innovation, enabling hundreds of workflows for genomics, transcriptomics, epigenomics, and proteomics. Recent, new sequencing methods have pushed the boundaries of discovery even further by pairing lower costs with faster sequencing and greater capacity. This has significantly
reduced barriers to entry to enable scRNA-seq projects with upwards of 2.6M cells per run with no batch effects, greatly reducing sequencing costs per cell.

Researchers are embracing multiomic approaches in their quest to understand biology. Many clinical indications such as cancer, autoimmune disease, and the response to infectious diseases can be caused by an alteration in a gene and/or an epigenetic modification such as DNA methylation, histone modification, chromatin state changes, or nuclear organization that essentially changes gene activity and function.

Despite changes to the DNA sequence of genes being the traditional focus of complex disease research, epigenetic modifications appear to be equally as important as genetic modifications in driving disease. Transcriptomics is also emerging as a vital paired tool to determine whether gene expression has changed due to an epigenetic modification. Multiomic approaches can shed light into biological interactions between the different omes.

Combining transcriptomics and epigenomics

According to Sheila Mansouri, PhD, University Health Network (UHN), Toronto, looking at mutations alone is not enough. The epigenome plays a much bigger role, in several cases, at regulating tumor biology. In fact, some tumors have the exact same genomic profile or the same mutations, but their biology and outcome are very different. The epigenome often determines the tumor’s future.

For instance, a six-institution collaboration evaluated transcriptome and methylation datasets from African American (AA) men in relation to prostate cancer (PCa). Tumor samples from this cohort have been largely under-represented in PCa studies even though AA men have a higher incidence and mortality rate than any other racial/ethnic group. The Illumina Infinium™ 850 K EPIC array was used to measure genome-wide DNA methylation in benign and tumor prostate tissues. Samples were clearly classified into different clusters based on DNA methylation and RNA expression levels. The observation suggested a relationship between differential DNA methylation profiles that ultimately modulate the tumor microenvironment and increase prostate aggression in AA patients.1

Another collaborative study on chronically demyelinated multiple sclerosis (MS) lesions took an unbiased approach to investigate how certain epigenetic signatures related to differentiation capacity of oligodendrocyte precursor cells (OPCs). The research team compared genome-wide DNA methylation and transcriptional profiles between chronically demyelinated MS lesions and matched normal-appearing white matter (NAWM) using postmortem brain tissue. The data demonstrated strong differences in DNA methylation between chronically demyelinated MS lesions and the NAWM, which correlated with the expression profile of the corresponding differentially expressed genes. OPCs within chronically demyelinated MS lesions acquire an inhibitory phenotype, which correlates with hypermethylation of crucial myelination-related genes, such as MBP. Altering the epigenetic status of MBP restored the differentiation capacity of OPCs.2

“You gain very unique information from looking at different data types like the epigenome, genome, and transcriptome, together. Combined data let you identify new information that you were not able to find using only one data type,” said Farshad Nassiri, MD, PhD, Division of Neurosurgery at University of Toronto and Princess Margaret Cancer Center, UHN.

The research team in the laboratory of Gelareh Zadeh, MD, PhD, has shown that different brain tumors have very different characteristic signatures of DNA methylation or epigenetic alterations. Using a multiomics approach, they defined four novel molecular groups of meningioma, the most common primary brain tumors. Up until now, these tumors had essentially been classified by histopathologic microscopic appearance. Nassiri emphasized that by studying the omic signatures, a diagnosis of each specific tumor type could be given without the need to look at a tumor under a microscope.

ATAC infographic
Credit: Illumina

Single cell or bulk analysis

Whether experimental needs focus on single cells or bulk analysis, the recent increase in sequencing power allows profiling of the epigenome and transcriptome in one run. In fact, a great first step for both single cell and bulk analyses is to run scRNA-seq or RNA-seq for transcriptomics along with scATAC-seq or ATAC-seq for an epigenetic readout in the form of chromatin accessibility. Other epigenomic methods like ChIP-seq or its derivatives and Cut&Tag use antibodies for immunoprecipitation of methylated DNA typically used chromatin in follow-up studies.

A recent Nature paper detailed an investigation of epigenetic drivers using single cell techniques. The researchers took a 10x Genomics multiome approach and coupled single-nucleus ATAC-seq (snATAC-seq) with single-nucleus RNA sequencing (snRNA-seq) to directly analyze associations between chromatin accessibility and gene transcription. Advancing beyond previous bulk ATAC/RNA-seq studies, their analysis of 225 samples across 11 cancer types provided nuanced insights into cancer biology, including cancer-specific epigenetic architecture, relationships between normal and malignant cells, and primary-to-metastatic transitions in the same lineage.3

Another set of researchers sought to understand varying patterns of underlying immune dysregulation in SARS-CoV-2 infection, and whether patterns were associated with, and predictive of, future clinical illness. This group also took a 10x Genomics multiome approach and performed scRNA-seq and scATAC-seq on PBMCs of a matched cohort of 21 patients with similar comorbidities at admission then compared subjects who improved from their moderate disease with those who later clinically decompensated. Findings from the study suggested that measurement of expression levels of a small set of transcriptional and epigenetic elements could both detect SARS-CoV-2 infection and simultaneously provide information about impending clinical deterioration.4

Advancing liquid biopsies

A minimally invasive method for sampling and analyzing biomarkers in various body fluids, liquid biopsy potentially could improve cancer diagnosis and prognosis. For instance, circulating cell-free RNA (cfRNA) is released into the blood from cells by active secretion or in extracellular vesicles (EVs) through apoptosis and necrosis. Scientists hypothesize that cfRNA could reflect the systemic response to growing tumors and provide tumor tissue origin information.

To test this hypothesis researchers from Oregon Health and Science University sequenced cfRNA from patients with two cancer types, one solid hepatocellular cancer (HCC) and the other hematologic multiple myeloma (MM), and their precancerous conditions (liver cirrhosis and MGUS (monoclonal gammopathy of undetermined significance), respectively). They reported that global profiling of cfRNA has the potential to establish a platform for longitudinal monitoring of disease progression across both solid and hematologic cancers.5 The combination of cfRNA and circulating tumor DNA (ctDNA) methylation could provide an even more powerful monitoring regimen.

As an example, a Canadian group took a further step in evaluating liquid biopsy for the monitoring of patients with high-risk hereditary cancer syndromes (HCS) such as Li–Fraumeni syndrome (LFS). LFS individuals harbor a germline pathogenic variant in the TP53 tumor suppressor gene, and face a near 100% lifetime risk of cancer. They routinely undergo intensive surveillance protocols. A proof-of-principle was published for a customized multimodal liquid biopsy approach using genome, fragmentome, and methylome analyses to early detect cancer-associated signals across a wide spectrum of cancers. The analysis detected cancer-associated signal(s) in carriers prior to diagnosis with conventional screening with a positive predictive value of 68%.6

The fragmentome is a useful molecular approach for the integration of DNA sequencing and chromatin state. Circulating DNA tends to be more frequently associated with regions of open chromatin, and such open regions tend to be more frequently expressed as mRNA due to increased accessibility to transcriptional machinery.

Leverage core laboratories

An impactful effect of the genomics era has been the establishment and growth of core labs and service providers. Researchers do not need to own instrumentation or be experts in any given omics method. Core labs have the instruments and knowledge in place to help build expertise and to support downstream data analysis and interpretation, a crucial step. Plus, there are new, user-friendly analysis tools such as Partek Bioinformatics Software that demonstrate the evolution of omics workflows to aid non-bioinformaticians in analyzing and visualizing multiomic data.

For over 26 years, Illumina has been leading the way in genomics. Its NGS technology is cited in over 400,000 peer-reviewed publications—five times more than all other NGS technologies combined. The breadth of omics’ methods has been validated on their instruments and informatics pipeline. Enabling multiomics is at the core of Illumina’s technology.



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1. Creighton CJ, Zhang F, Zhang Y, et al. (2023) Comparative and integrative analysis of transcriptomic and epigenomic-wide DNA methylation changes in African American prostate cancer, Epigenetics, 18:1, 2180585, doi: 10.1080/15592294.2023.2180585
2. Tiane A, Schepers M, Reijnders RA, et al. From methylation to myelination: epigenomic and transcriptomic profiling of chronic inactive demyelinated multiple sclerosis lesions. Acta Neuropathol. 2023 Aug;146(2):283-299. doi: 10.1007/s00401-023-02596-8
3. Terekhanova NV, Karpova A, Liang WW, et al. Epigenetic regulation during cancer transitions across 11 tumour types. Nature 623, 432–441 (2023). doi:10.1038/s41586-023-06682-5
4. McClain MT, Zhbannikov I, Satterwhite LL, et al. Epigenetic and transcriptional responses in circulating leukocytes are associated with future decompensation during SARS-CoV-2 infection. iScience. 2023 Nov 29;27(1):108288. doi: 10.1016/j.isci.2023.108288
5. Roskams-Hieter B, Kim HJ, Anur P, et al. Plasma cell-free RNA profiling distinguishes cancers from pre-malignant conditions in solid and hematologic malignancies. NPJ Precis Oncol. 2022 Apr 25;6(1):28. doi: 10.1038/s41698-022-00270-y
6. Wong D, Luo P, Oldfield LE, et al. Early Cancer Detection in Li–Fraumeni Syndrome with Cell-Free DNA. Cancer Discov (2024) 14 (1): 104–119. doi: 10.1158/2159-8290.CD-23-0456