Delving into the depths of disease

Summary: Advancing our understanding of disease biology is the only way we will uncover novel drivers for those diseases we aim to treat, hope to prevent and in the future, even cure. The advent of multi-omics is allowing scientists to probe the more complex and transient molecular changes that underpin the course of disease and response to treatment, helping us better select the right drug target.

Revealing secrets beyond the genome

Genomics – the mapping of all human genes and their interactions with each other – has been a game-changer in drug discovery and clinical research, underpinning the transformative shift towards precision medicine. But beyond the genome lie the dynamic realms of the transcriptome, proteome and metabolome – largely untapped repositories of rich information that if connected could tell us more about what is driving disease. Multi-omics is the integration of these datasets which, through advances in technology, is now beginning to reveal new insights.

To unveil promising new drug targets, it is vital to get a true understanding of the downstream ‘omes’ programmed by the genome, namely the transcriptome (the totality of RNA readouts), proteome (the totality of protein produced) and metabolome (the totality of the metabolites that result). While genomics provides a static view - the DNA ‘blueprint’ - the other ‘omes’ are dynamic and subject to change under different conditions. The transcriptome, for example, can reveal the extent to which each gene is turned on or off within cells, akin to a dimmer switch.

Through bioinformatics and artificial intelligence (AI), we can use this omic data to create multi-dimensional models of the whole hierarchical system in healthy, diseased and treated states. This has far-reaching implications for drug discovery giving us the potential to spot potential new drug targets and validate them. We can anticipate toxicity. We can also develop biomarkers for downstream accompanying diagnostics in the field. Ultimately, multi-omics is an enabler for predictive science across our whole organisation: with the help of machine learning and AI, we can use it to predict what a drug molecule does in a cell with far greater certainty.

The goals of multi-omics, made simple

The technologies turning scientists into molecular detectives

Within transcriptomics, we employ the most advanced technologies available with the aim of generating smart, dynamic and unbiased data. As well as single-cell sequencing (a technique that sequences individual cells’ RNA within the context of their micro-environments), these include:

• A state-of-the-art next generation sequencer which generates 10 billion sequences in one run (the equivalent to 23 whole genomes in two days)
• Spatial transcriptomics which allows us to analyse the transcriptome within intact tissue sections, preserving cells in their morphological context to better understand function
• Portable nanopore technology which uses biophysics to pull RNA molecules through fine holes in a membrane, creating electrical changes readable in real time. This allows for long range (whole molecule) sequencing on the move – all in a matter of seconds, enabling the accurate identification of complex alternative splice variants

In proteomics and metabolomics, we use state-of-the-art mass spectrometry to identify and quantify proteins, metabolites and lipids and work out which changes occur during disease or after treatment. The technology is so accurate that, with enough resolution, we can differentiate between an oxidised protein and a citrullinated protein.

Our mass spectrometry instruments can identify five thousand protein molecules in less than an hour. We can probe even deeper within the proteome into the so-called ‘fourth dimension’ of ion mobility using a technique called timsTOF – and thereby determine ever more subtle proteomic characteristics.

Advancing technology making the impossible possible and the possible easier

Multi-omics instrumentation is advancing every one to two years and each time we achieve 25% more than we could before, like zooming in on a Google map. And it’s not only sensitivity and resolution that’s increasing. Speed, throughput and specificity are too – and less and less material is needed for each analysis. It’s making the impossible possible and the possible easier. Powered by technological innovation, multi-omics is proving to be one of the richest sources of data in all of discovery science.