Spatial omics has become one of the most exciting areas in biomedicine because it allows us to see not only which molecules are present in a tissue, but also where they are and how they are arranged across cells, structures, and local microenvironments. That spatial context has transformed our understanding of development, cancer, neurobiology, and immunity. As the field has advanced, we kept returning to a simple question: how many of these powerful methods are truly ready for routine clinical use?
Very often, we encountered the same tension. The most informative technologies were often also the most demanding — requiring expensive instruments, sequencing, complicated tissue preparation, long workflows, and substantial computational analysis. These features can be managed in well-resourced research settings, but they become major obstacles in real-world pathology labs, time-sensitive biopsies, and efforts to broaden patient access. We were excited by what spatial omics could reveal, but also increasingly aware of how difficult it still is to move many of these tools beyond specialized environments.
That tension shaped the work behind our paper on Spectrum-FISH, a sequencing-free and amplification-free approach for spatial profiling of post-transcriptional regulation in fresh tissues. It also shaped the perspective we later discussed in a comment article on the clinical translation of spatial omics (https://doi.org/10.1038/s44222-026-00458-y). In many ways, these two works address the same challenge from different angles: whether a simpler platform can still provide meaningful spatial biology, and how spatial omics can become part of precision medicine rather than remain mainly a discovery tool.
From the beginning, our goal was not simply to build another spatial assay, but to rethink what a clinically useful one might look like. Could it work directly on fresh tissue? Could it avoid sequencing? Could it preserve spatial organization while reducing cost and workflow complexity? And could it access molecular layers that are often overlooked in tissue-wide spatial profiling, especially post-transcriptional regulation?
We were particularly interested in microRNAs and methylated RNAs, alongside messenger RNAs. These are dynamic and biologically important regulators, but profiling them across large tissues in a spatially resolved way remains technically difficult. Most large-scale platforms have focused on transcript abundance at the mRNA level, while many post-transcriptional processes are harder to capture with similar throughput and spatial resolution. Yet these layers are exactly where cells often encode flexibility, adaptation, and fine-tuned regulation.
This is where the idea behind Spectrum-FISH began to take shape. The method is built around a “touch-and-go” molecular fishing strategy using vertically aligned nanoprobes. Instead of relying on extensive tissue preprocessing and downstream sequencing, we directly interface a nanoprobe array with a fresh tissue slice, extract molecules while preserving their spatial organization, and then decode them through multiplexed spectral imaging. The spatial information is retained through a simple but effective registration strategy, allowing us to map molecular signals back to tissue structure and individual cells.
What was especially rewarding was seeing how this design addressed not just one technical problem, but several translational challenges that have been highlighted as major barriers to the clinical adoption of spatial omics: economic burden, operational complexity, lack of standardization, and regulatory uncertainty. Spectrum-FISH does not solve all of these issues, of course, but it was developed with exactly these bottlenecks in mind. We wanted to test whether spatial profiling could be made more accessible by reducing dependence on sequencing, minimizing tissue processing, and lowering assay cost, while still preserving biologically informative resolution.
There were several moments during the project when the broader promise of the platform became clear. In the developing mouse neural tube, Spectrum-FISH recapitulated known dorsoventral mRNA patterning across embryonic stages, which gave us confidence that the method could recover established spatial biology. In the olfactory bulb, we could move beyond mRNAs and begin mapping the heterogeneity of miRNAs and m⁶A-RNAs across distinct anatomical layers, revealing correlated post-transcriptional programs in regions such as the outer plexiform layer and granule layer. And when we applied the platform to fresh human colorectal biopsy specimens, the work took on a different level of meaning. Here, spatial transcriptomic and miRNA patterns were associated with tumour state and metastatic risk, pointing toward the kind of clinically relevant signal that motivates translational spatial omics.
That human biopsy work was especially important for how we think about the field more broadly. One of the central points is that spatial omics is now moving from atlas-building to clinical translation. This shift requires more than better maps. It requires assays that are fit for purpose — robust enough, scalable enough, and interpretable enough to support diagnosis, prognosis, therapy selection, and longitudinal monitoring. Discovery remains essential, but translation demands simplification: broad molecular complexity must eventually be turned into practical clinical readouts.
This also changed how we thought about performance. In research, it is easy to focus on the largest panels, the finest resolution, or the richest multi-omics integration. But in a translational setting, the overall workflow matters just as much. How expensive is the assay? How quickly can it be run? How much specialized infrastructure does it require? Can it work on clinically relevant sample types? Can the output be integrated into pathology practice? Some of the most important design decisions in this project came not from asking how to maximize complexity, but how to preserve usefulness while reducing burden.
With these considerations in mind, we argued that the road to clinical spatial omics will depend on several converging advances: high-throughput and low-cost assays, live and in situ strategies, stronger multi-omics integration, broader disease coverage, and AI-assisted interpretation. Spectrum-FISH fits within that emerging roadmap. Its fresh-tissue compatibility and sequencing-free design speak to affordability and scalability. Its coordinate-preserving sampling strategy suggests new ways to think about live or minimally processed spatial analysis. Its ability to profile mRNAs, miRNAs, and RNA methylation suggests one route toward spatial multi-omics that is less dependent on large sequencing infrastructures.
Of course, we are fully aware that technical feasibility is only the beginning. Clinical translation sets a much higher bar. Standardized workflows, multicentre validation, reproducibility studies, quality-control frameworks, and regulatory clarity are all still needed. Perhaps most importantly, clinicians need assays that do not simply produce more data, but produce better decisions. That is where the field is heading, and where it will ultimately be tested most rigorously.
Looking back, this project reminded us that innovation in biomedicine is not only about making measurements possible. It is also about making them usable. Spatial omics has already shown us extraordinary biology. The next challenge is to make that power practical — not just in flagship studies or specialist centres, but in settings where it can change how patients are diagnosed, stratified, and treated.
If our paper has one underlying message, it is that simplification can be enabling, not limiting. By reducing workflow complexity and cost while preserving spatial insight, we hope Spectrum-FISH offers one example of how the field might move closer to routine clinical use. And if our perspective article has one broader message, it is that the future of spatial omics will be defined not only by how deeply we can map tissues, but by how effectively we can translate those maps into action.
That, for us, is the real excitement behind this paper. It is not only about a new method. It is about a larger transition now underway in the field — from elegant spatial biology to practical precision medicine.