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.

Hi, I’m Gerry Ma, Technology and Development Manager here at Millennium Science. I joined Millennium Science a few months ago, having come from commercial roles across the flow cytometry, single-cell genomics, and spatial transcriptomics fields. I now spend a good portion of my time talking to scientists and technology developers about where the field is heading and what tools ANZ researchers need to do their best work. 

In early June, I attended two conferences back-to-back: the Festival of Genomics (FOG) in Boston, and the European Association for Cancer Research congress (EACR) in Budapest. Two very different audiences (genomics and AI in Boston; translational cancer research in Budapest), but the overlap in themes was more striking than the differences.

This is the first in a short series of Field Signals blogs where I’ll share what stayed with me. Not a comprehensive conference review, but the observations I kept coming back to upon returning home, and the ones I think matter most for how research is going to look over the next few years. 

Before diving into the technical themes, I wanted to share one personal highlight that set the tone for everything else. 

At FOG, George Church (Harvard Medical School), Mark Adams (The Jackson Laboratory for Genomics Medicine), and Sorin Istrail (Brown University) discussed the future of genomics in a session that also served as a tribute to the late J. Craig Venter, who was originally meant to speak. Their conversation ranged across multiomics, in situ genomics, xenotransplantation, national-scale sequencing programmes, and the role of AI in biological data at population scale. 

But what stayed with me was simpler than any of those topics. Watching three scientists who shaped the field reflect on the history of genomics and past conversations with Craig Venter was a reminder that the technologies we now take for granted were built by people who had genuine scientific progress as their primary motivation, and who thought in decades rather than quarters. 

That framing stuck with me through both conferences. The decisions being made right now about how we integrate multiple data types from different platforms, how we standardise methods, and how we shift toward new experimental models are the decisions that will define the trajectory of the next decade of biological research. These decisions deserve the same kind of long-term thinking.

Over the next few posts, I’ll unpack the themes that came through most clearly: 

• Part 2 will cover the convergence of multiomic workflows and the maturing spatial biology landscape. 

• Part 3 looks at AI integration and a quieter but potentially more consequential shift in experimental models. 

• Part 4 wraps up with a personal perspective on why mechanistic depth still matters more than breadth.