The shift from bulk cell analysis to single-cell analysis has been transformative, especially in molecular biology. Scientists continue to deliver new insights through single-cell studies—even from organisms, tissues, or cell populations that have been extensively characterized in the past. But these advanced approaches fail to capture an essential contextual element that’s critical for truly understanding biology: pinpointing the location of cells and visualizing their interactions.
While it’s important to analyze signals from individual cells, a cell’s identity is context-dependent; phenotype emerges from neighborhood signaling and spatial constraints. Are certain cell types interacting with each other? If they’re in a tumor microenvironment, are they close to the core of the tumor, or are they part of the immunosuppressive force surrounding the tumor? Plucking any individual cell out of its native environment and trying to comprehensively evaluate it is like trying to understand a city by watching a single person cross the street.
The rise of spatial biology tools is allowing scientists to up their game. The first generation of spatial transcriptomic and spatial proteomic tools is giving way to a new class with a more integrated approach. These new spatial multiomic technologies enable researchers to generate genomic, transcriptomic, epigenetic, and proteomic data from the same cells, all while preserving spatial context for a far more comprehensive view of cellular and molecular biology. Scientists can now connect individual cells to their broader biological context.
From brain to blastocyst: Key applications for spatial biology
Spatial multiomics is primarily used to map the tissue microenvironment, revealing how a cell’s physical location and its neighbors dictate its function. These attributes make it a particularly good fit for research into complex tissues, with popular application areas including cancer, neuroscience, and developmental biology.
In cancer research, spatial multiomics makes it possible for scientists to better characterize the intricacies of a tumor microenvironment, along with the interactions with immune cells. This is most transformative in precision oncology, where it is used to identify immune cell "hotspots" or "cold zones" within tumors.
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Neuroscience is also benefiting from spatial multiomic investigations of brain samples. This vital organ continues to be among the least well understood, with a complexity that resists characterization by conventional analysis tools. By layering data for multiple types of analytes along with spatial context, scientists are unraveling long-held secrets about cellular diversity, interaction, and communication among the different areas of the brain.
It’s no wonder that developmental biology is another early target for spatial multiomics; understanding the path from a single cell to an entire functioning organism requires deep insight into cell evolution, differentiation, and interaction. A growing embryo offers a steady stream of new opportunities to observe organ development over time. For instance, studying mouse tissue development over time is relevant for creating organs for organ transplantation and regenerative medicine.
Case study: Fueling progress in neuroscience research
Combining single-cell analysis with spatial context, scientists at the Broad Institute interrogated gene expression across cells in the mouse brain, mapping that activity back to its original location. The team published a three-dimensional, single-cell atlas of the mouse brain in a publicly available online browser (braincelldata ref).
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Through this work, scientists identified almost 5,000 unique cell populations—estimated to be ~90 percent of cell types present in the complete mouse brain—and traced their locations back to the original tissue source with exquisite resolution. Importantly, the research into 101 different regions shed light on cell diversity in areas of the brain that had gone relatively unexplored in the past, such as the medulla, hypothalamus, and midbrain. Ultimately, more than 1.7 million cells were mapped and analyzed.
This approach led to several advances. First, the scientists were able to identify a small number of unique marker genes for each cell type, giving the broader community essential biomarkers for future brain studies. Second, their findings included possible associations between brain structures and disease—such as specific neurons enriched for gene expression linked to schizophrenia—and this work paves the way for new analyses to better understand neurological conditions. And finally, the broader atlas made available is a valuable community resource that should help fuel progress in neuroscience for research teams around the world.
From flexibility to sensitivity: Choosing a spatial multiomic approach
For lab leaders looking to implement a spatial multiomic approach, there are a number of useful factors to consider in the selection process. Among the most important are ease of implementation and minimal hands-on time; after all, lab resources are often limited. Any solution worthy of consideration should also be flexible enough to incorporate a variety of omic data layers and should work with the sample types used by each lab, whether that’s fresh frozen, formalin-fixed paraffin-embedded (FFPE), or something else.
Another key factor is sensitivity. Many spatial biology platforms do not provide the necessary resolution to identify rare signals and map them to their point of origin, especially at a high-throughput scale. The more sensitive the technology, the lower the chance of missing a little-expressed but functionally important biological element. Indeed, even some “single-cell” technologies do not actually deliver true single-cell sensitivity, instead relying on complex segmentation and deconvolution approaches to computationally approximate cell-level information.
For spatial multiomics, dedicated instrument platforms paired with proprietary reagent kits have enabled more streamlined workflows and improved user experience. However, the high cost of instrumentation can be a significant barrier. An alternate, more cost‑effective approach is to use kit‑based solutions designed to integrate with existing single‑cell analysis equipment, which is already widely available in most labs.
Going forward, the use of spatial multiomics for many different types of experiments will quickly become the new standard. Scientists can begin to assess these approaches now and determine which, if any, is the right fit for their teams.
