Our bodies are a complex interplay of genes, transcripts, proteins, metabolites, and other macro and micro molecules. Understanding the multi-layered relationships between all of these molecular parts is critical for a comprehensive understanding of human health and disease. If our own biology is so complex and interwoven, doesn’t it make sense that our research and analysis be similarly layered in order to make sense of it all? This is precisely the concept behind multi-omics analysis, the combination of two or more ‘omics datasets to gain a more holistic picture of biology. In this article, we’ll explore two powerful ‘omics technologies and how combining them can revolutionize your research and advance our understanding of human biology.
The what, how, and when of transcriptomics
The field of human genomics is built on DNA sequences and the impact that aberrations in those sequences have on human health. And while advances in genome sequencing technology have contributed countless important discoveries related to human health and disease, the DNA sequence alone can’t explain the intricate biological mechanisms that contribute to many genetic diseases. Transcriptomics provides what genomics can’t—an unbiased view of all of the coding and non-coding RNAs in a biological system, and, in the context of disease research, how those transcripts change in health and disease.
By studying the transcriptome, researchers can see not only genes that are expressed, but also long noncoding RNAs (lncRNAs) and microRNAs (miRNAS) that impact protein expression. In oncology, for example, several key genes have been associated with disease onset, severity, and phenotype. Exploring the transcriptome has revealed in many cases key RNA regulators of those critical genes which can dictate whether or not cancer develops. Often, these transcriptomic signatures can be observed prior to disease onset, so transcriptomic clues represent an area ripe for research and discovery into early diagnostics and novel therapeutics.
Going beyond genomic potential
While transcriptomics is a powerful tool, there is a limit to depth of knowledge and understanding that can be gained from it. While transcriptomics provides insights into the potential molecular events that could happen inside of a cell or organism, metabolomics, by measuring the small molecules (ie, metabolites) present in a system demonstrates what is actually happening. Metabolomics is a very powerful tool for demystifying the sometimes very convoluted upstream signals (bidirectional and multi-faceted interactions between DNA and RNA elements) and providing actionable insights for what is going on in a biological system.
With the metabolome, researchers can interpret, phenotypically, a number of perturbations, whether they be changes in mutational rates, splicing events, miRNA-mediated changes in transcripts, or even post-translational modification of proteins. Metabolomics datasets can also reveal environmental inputs, including metabolites produced by the microbiome, that critically impact health and disease. With metabolomics, a holistic picture of the biochemical processes behind any biological event—genetic and non-genetic—that impact the development of disease can be revealed.
The true power of metabolomics is really only realized when it is combined with other ‘omics datasets. As discussed by a publication in Bioinformatics and Biology Insights, integrated approaches assess the flow of information from one ‘omics level to the another, bridging the gap from genotype to phenotype. Because ‘omic approaches provide a holistic view of disease processes, multi-omics datasets have a unique potential for improving prognostics and the predictive accuracy of disease phenotypes, aiding in better treatment and prevention. Because of the phenotypic information it provides, metabolomics should be a key component of any multi-omics study.
Synergy between transcriptomics and metabolomics
The combination of transcriptomics and metabolomics datasets is a synergistic one and has shown early promise in unveiling critical disease mechanisms. For example, in prostate cancer, combining transcriptomics and metabolomics data has revealed that impaired sphingosine-1-phosphate receptor 2 signaling is the mechanism behind the specificity and sensitivity of sphingosine for distinguishing between prostate cancer and benign hyperplasia. Combining transcriptomics and metabolomics can also address a significant knowledge gap in oncology research around our understanding of inflammation, providing a type of dictionary where researchers can “look up” what changes at the transcriptomic level mean phenotypically.
As an increasing number of researchers view multi-omics research, particularly the combination of transcriptomics and metabolomics, as a new standard for human disease research, the demand for multi-omics tools capable of collecting, analyzing, and visualizing data has also increased. These tools address the challenges faced by researchers integrating ‘omics datasets and bring the power of multi-omics analysis to life. With such tools available, a future where multi-omics research is the norm, rather than an advanced modality available only to a select few, is at hand.
As you consider embarking on your own metabolomics (and, hopefully, multi-omics) study, Metabolon is uniquely positioned to support your research efforts. We work with you, helping you decipher thousands of discrete chemical signals from both genetic and non-genetic factors to unravel biological pathways and discover novel biomarkers, making connections where other ‘omics cannot, and providing the definitive representation of phenotype. We’ll guide you every step of the way, from study design to data interpretation, so you can discover more with metabolomics. If you’re ready to start on your metabolomics journey, contact us today.
