Biomarker discovery in the era of multi-omics.

Complex diseases rarely have a single cause. Instead, they tend to emerge from the interplay of genetic predispositions, environmental exposures, and lifestyle factors. This complexity can make it challenging to identify reliable biomarkers that can predict, diagnose, or monitor disease progression, and even harder to develop effective treatments.

Traditional single-omic approaches often capture only part of the biological picture, overlooking the multiple molecular interactions that drive disease. To address this, scientists are turning to multi-omics approaches that integrate data from genomic, transcriptomics, proteomics, metabolomics, and beyond. By uniting these layers, multi-omics provides a more holistic view of disease processes and creates new opportunities for discovering biomarkers that are both precise and clinically actionable.

In this blog, we’ll explore what biomarkers are, why they matter for precision medicine, and how multi-omics is reshaping how we discover them across different biomarker types.

What are biomarkers and why do they matter?

Biomarkers are measurable biological indicators that reflect normal physiology, disease processes, or responses to therapeutic interventions. They can range from genetic variants and RNA signatures to proteins and metabolites.

The value of biomarkers lies in their diversity. They can be used to diagnose diseases, predict which patients will respond to specific treatments, monitor therapeutic responses, and even guide the development of new therapies. As a result, they’ve become an integral part of precision medicine, enabling more precise and personalized approaches to healthcare.

Types of biomarkers and the path to discovery

Biomarkers can be grouped into several major categories, each offering unique insights into health and disease.

1. Genomic biomarkers capture DNA-level changes including mutations, copy number variations, and single nucleotide polymorphisms that influence disease risk or treatment response.
   Examples of detection methods: Next-generation sequencing (NGS), microarray analysis.
2. Transcriptomic biomarkers capture RNA expression patterns, revealing which genes are actively being transcribed.
   Examples of detection methods: RNA sequencing (bulk and single-cell approaches).
3. Proteomic biomarkers reflect the abundance, structure, and modifications of proteins, which carry out most cellular functions and are powerful indicators of disease.
   Examples of detection methods: Flow cytometry, mass spectrometry, ELISA, immunohistochemistry, multiplex immunoassays.
4. Metabolomic biomarkers capture cellular metabolism in real time, functioning as a readout of a cell’s physiological state.
   Examples of detection methods: Mass spectrometry, NMR spectroscopy.
5. Epigenomic biomarkers capture modifications to the DNA that affect gene regulation without altering the DNA sequence, such as DNA methylation. Examples of detection methods: Bisulfite sequencing, ChIP-seq, ATAC-Seq.
6. Microbiome biomarkers reflect changes in gut microbiota composition and activity, and are increasingly linked to inflammatory bowel disease, obesity, and certain cancers.
   Examples of detection methods: 16S rRNA sequencing, metagenomic sequencing.

However, translating biomarker discovery into clinical practice is not always straightforward, with many biomarkers failing validation or clinical qualification. This challenge highlights the need for robust, efficient tools that can capture disease biology more comprehensively.

The multi-omics advantage

Single-omic methods can generate valuable insights, but often only part of the story. A genetic mutation, for example, may only be of relevance if it also changes RNA transcription, alters protein activity, or disrupts metabolism.

This is where multi-omics comes in.

Multi-omics approaches combine these layers of information to create a more complete picture of disease biology. For example:

• Genomics + Transcriptomics: Is the gene expression also affected by the mutation?
• Proteomics + Metabolomics: Do altered proteins cause metabolic changes?
• Microbiomics + Host genomics: How do microbes interact with host biology?

This integration captures disease at multiple levels, reveals causal mechanisms, and supports better patient stratification. By linking genomic, transcriptomic, proteomic, metabolomic, and even microbiome data, multi-omics enables researchers to identify biomarkers that are not only more robust, but also more clinically meaningful.

Conclusion

As we move toward more personalized medicine approaches, biomarkers will continue to shape how we predict, diagnose, and treat disease. The future of biomarker discovery therefore requires integrated, multi-omics approaches that can fully capture the complexities of human biology.

Revvity supports this future by offering comprehensive solutions across a broad spectrum of biomarker classes, including RNA, DNA, proteins, and tissue-based biomarkers. Our suite of technologies spans NGS, flow cytometry, immunoassays, and antibody-based tools, supporting researchers to uncover molecular signatures with confidence throughout their discovery journey.

Learn more about Revvity’s multi-omics solutions for biomarker discovery by downloading our biomarker discovery brochure.