Multiomics is Becoming the New Standard (Part 2 of 4)

This is the second post in a series reflecting on themes from the Festival of Genomics (Boston) and EACR (Budapest).

In this post, I want to unpack the two themes that dominated the scientific sessions at both conferences: the convergence of multiomic workflows, and the maturing spatial biology landscape.

The most consistent theme across both conferences. Researchers are looking for multiple molecular readouts from the same sample, and the general approach is shifting to expect it.

From the genomics side, sequence is increasingly a starting point rather than a destination. Methylation status, three-dimensional chromatin architecture, and RNA modifications are now treated as essential layers that sit alongside sequences. The range of assay chemistries addressing these layers has expanded noticeably, and the expectation is that a complete genomic picture includes epigenetic context, not just the linear sequence of bases.

At the sequence level itself, whole-genome sequencing (ideally diploid and phased) is slowly becoming the expectation over exome as the cost differential narrows. The information lost by sequencing only coding regions is becoming harder to justify when intronic, regulatory, and structural variant information is increasingly recognised as clinically relevant.

In addition to genomics and transcriptomics, the shift in focus to include proteins is becoming increasingly important, as proteins are the actual functional units of biology. As such, alternative top-down proteomics approaches are complementing traditional mass spectrometry, opening protein profiling to labs without dedicated proteomics infrastructure. Beyond proteins themselves, the field is increasingly profiling metabolomics and lipidomics alongside traditional proteomics, building toward a more complete molecular phenotype.

The boundaries between different biological compartments are blurring. Workflows that were previously siloed are converging, driven by both the scientific recognition that biology doesn’t operate with disciplinary boundaries, and the practical reality that sample material is often limited. The goal is to extract as many meaningful readouts as possible from each sample.

Spatial transcriptomics and proteomics have been conference staples for several years, but the conversation at both FOG and EACR had clearly matured past raw capability and into practical deployment. 

The spatial proteomics field in particular appears to be bifurcating. High-plex panels (10s to 100s of markers) are doing discovery work, casting a wide net to identify which proteins matter in each tissue context. But lower-plex panels (<10 markers) are emerging as the format that pharma and clinical labs actually want for deployment. A small, validated, reproducible panel gives a clearer answer in a clinical workflow than a hundred-marker discovery panel does.  

Spatial methods more broadly are moving from research toward translational and clinical use, but the view from pharma is more measured than the hype. Several pharma-side speakers noted that spatial technologies validate digital pathology rather than replacing it, H&E staining remains the clinical gold standard, and that the current platforms aren’t quite mature enough for routine diagnostic use. 

The structural gaps everyone points to are remarkably consistent: automation (too much hands-on time per sample), standardisation (too much variability between operators and sites), and analysis tools that hold up under industry reproducibility requirements. 

None of this means spatial is overhyped. It means the field is in the transition phase between research tool and clinical infrastructure, which is the most interesting and consequential phase to be in.

We’d be happy to discuss how these trends connect to the tools and platforms available in ANZ.