Earlier this year, Genetic Engineering & Biotechnology News asked experts in multiomics about what trends they are excited for in 2025. Read what several experts, including our CSO Charles Gawad, are excited about in the coming year(s).
Read the full commentary here: 2025 Trends: Multiomics
Multiomics at Single-Cell Resolution
Charles Gawad, MD, PhD CSO, BioSkryb Genomics
One way to understand the current state of multiomics research is to think back to where we started with bulk genomic studies. Due to technical and cost constraints, investigators utilizing early next-generation sequencing platforms focused on specific regions of the genome or transcriptome. As sample preparation and sequencing technologies have improved while sequencing costs have rapidly decreased, obtaining genomic, transcriptomic, and epigenomic information from the same sample is now possible.
However, integrating these data types requires inference and deconvolution algorithms that only have a limited capacity to determine which changes are likely to occur in the same cells.
More recent technological advancements have enabled multiomic measurements from the same cells, allowing investigators to correlate and study specific genomic, transcriptomic, and/or epigenomic changes in those cells. Similar to bulk sequencing, we are now seeing studies examining more of each cell’s genome, transcriptome, and epigenome as sample preparation technologies continue to improve and sequencing costs continue to decline.
I also anticipate that in addition to acquiring information from a larger fraction of the nucleic acid content from each cell, we will also begin looking at larger numbers of cells, as well as utilizing complementary technologies, such as long-read sequencing, to examine complex parts of the genome and full-length transcripts. Finally, the integration of both extracellular and intracellular protein measurements, including cell signaling activity, will provide another layer for understanding tissue biology.
Central to integrating these complementary measurements from the same cells, the development of artificial intelligence-based and other novel computational methods will be required to understand how each of these multiomic changes contributes to the overall state and function of that cell.
Single-cell multiomics is still a young field. I am eager to see how technological innovation over the coming years continues to transform our understanding of tissue health and disease at single-cell resolution.