Resolving the mechanisms underpinning human diseases is vital to understand disease phenotypes, identify novel biomarkers, and enable drug discovery. Multiomic approaches — spanning genomics, bulk and single-cell transcriptomics, epigenetics, and proteomics — are crucial to this, providing data to help unravel complex pathways. In disease research, tissue samples are invaluable and scarce resources; maximising the information obtained from these samples is essential.
However, legacy multiomic methods require the use of multiple platforms and often involve complex workflows, lengthy turnaround times, and considerable costs. Even when combining data from multiple traditional technologies, valuable information is missed from precious research samples, leaving important biological mechanisms unresolved.
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