Multi-omics
What is Multi-omics?
Multi-omics (also called integrated omics, and pan-omics) is a holistic approach that combines data from multiple omics fields to provide a comprehensive understanding of biological systems. By analyzing data from these various fields together, researchers can gain deeper insights into how different biological molecules interact and influence each other, leading to a more complete picture of cellular functions, disease mechanisms, and potential therapeutic targets.
Source: Roychowdhury R. et al.. Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses. Genes 2023, 14(6), 1281; doi: 10.3390/genes14061281, https://creativecommons.org/licenses/by/4.0/, image was not altered.
Four Big Omics
Four of the omics disciplines, the so-called Four Big Omics—genomics, transcriptomics, proteomics and metabolomics—are considered to form the basis of the multi-omics approach because they reflect the central dogma of molecular biology and information flow within the cell: DNA is transcribed into RNA, which is then translated into protein, whose functional effects influence metabolic processes.
Beckman Coulter Life Sciences plays a critical role in enabling the Four Big Omics. Our automated liquid handlers and genomic reagents streamline complex sample preparation workflows, such as nucleic acid extraction, library preparation, protein digestion, and metabolite handling. This automation enhances reproducibility, increases throughput, and reduces errors, establishing a robust foundation for downstream analysis and successful multi-omics integration.
Advancements in Multi-Omics Technology
Research in the multi-omic workflows has benefitted with the development of analytical techniques in the respective omic spaces. The development of next-generation sequencing (NGS) technologies and automation of purification and NGS preparation techniques have revolutionized genomic and transcriptomic data gathering.
Similarly, with the advances in mass spectrometry technology, researchers are able to collect data at an increased pace and at a newfound depth enabling new discoveries in metabolomics and proteomics. To increase the pace of mass spectrometry data collection, researchers are now looking into automated platforms to increase throughput for sample prep.
The growth in analytical fields tends to push the need for automation of sample preparation. This is now broadening to other workflows and downstream applications like ELISA, kinetics, studying dose dependence and even pharmacokinetics.
| Multi-omic field | Key Technologies |
| Genomics |
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| Transcriptomics |
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| Proteomics |
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| Metabolomics |
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Emerging Trends and Future Directions in Multi-omics
As research into the areas of single-cell based workflows and spatial workflows advance, multi-omics is being brought into these areas to generate a more holistic picture of all the biological macromolecules and their changes. As a result, a significant number of researchers are focusing on multi-omics in the context of single-cell multi-omics and/ or spatial multi-omics.
Single-cell multi-omics
Single-cell omics methods help understand how gene expression profiles relate to metabolic outcomes; how these changes in cellular chemistry drive morphology and cell fate; and highlight cell to cell variability. Much of this work is driven in immunology, oncology, and developmental biology.
A number of methods are currently being used to study biology at the single-cell level. These include microfluidic preparations of single-cell-bead complexes to label the transcriptome; combinatorial DNA labeling to study single-cell genomics; and using antibodies to understand the cell surface proteome. And new techniques allow for understanding of the heterogeneity of the proteome labeling single proteins quantifying post-translational protein modifications; and recording localization in response to extracellular stimuli.
Spatial multi-omics
Spatial multi-omics is a rapidly developing field designed to study molecular level detail within the context of tissue sections to label certain cell types in order to extract the DNA and or the RNA molecules to understand the genomic and transcriptomic profile in space. Epigenomic profiling can also be characterized using this approach. Coupled with histologic labeling and imaging, this can give information at the proteomic level. Further mass spec analyses can be combined with imaging and expression analyses to provide a spatiotemporal understanding of cellular dynamics.
References
- Roychowdhury R. et al.. Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant’s Abiotic Stress Tolerance Responses. Genes 2023, 14(6), 1281; doi: 10.3390/genes14061281. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license: https://creativecommons.org/licenses/by/4.0/. The image was not altered.
