Multiomics refers to the integrated study of multiple large biological datasets, such as genomics (DNA), transcriptomics (RNA), and proteomics (proteins), to gain a comprehensive view of a biological system. Scientists use this approach to understand how different molecular layers interact to define the function of a cell or tissue. Until recently, these extensive molecular measurements were performed on homogenized samples, which provided a general average of all the molecules present. Spatial multiomics represents a breakthrough by adding the dimension of “where” these molecules are located within a tissue, linking molecular identity directly to physical location and cellular architecture. This new layer of information allows researchers to understand the precise context of molecular interactions, changing how we study health and disease.
Understanding the Need for Spatial Context
Traditional methods for molecular analysis, such as bulk sequencing, require researchers to grind up a tissue sample, which effectively mixes all the cells together into a “molecular soup.” This process provides an average profile of all the genes and proteins present in the sample, but this average is misleading in complex biological systems.
A complex organ like the brain or a tumor is composed of a diverse collection of cells, each with distinct roles and molecular signatures. Averaging the data across all these cell types obscures the differences between them, masking the unique activity of rare or specialized cell populations.
The physical location of a cell within a tissue dictates its function and its communication with neighbors, a concept that is entirely lost in bulk analysis. For instance, a cell on the edge of a tumor behaves differently than one at the core, and this positional information is crucial for understanding disease progression. Spatial multiomics solves this problem by mapping molecules directly onto the preserved tissue structure, creating a high-resolution molecular blueprint of the entire sample.
Key Technologies Enabling Spatial Analysis
The ability to perform spatial multiomics relies on two distinct but often complementary categories of advanced technology. One category involves imaging-based methods that achieve very high resolution by directly visualizing target molecules within the tissue section. Techniques like Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) or other in situ hybridization methods use fluorescent probes to bind to and map hundreds or even thousands of specific RNA molecules inside individual cells.
These imaging platforms allow for subcellular resolution, locating molecules within different parts of a single cell. Spatial proteomics utilizes multiplexed antibody staining, often with metal-conjugated antibodies, to map dozens of different proteins onto the same tissue section. Imaging methods are powerful for targeted analysis but are often limited in the total number of genes or proteins they can simultaneously detect.
The second category encompasses sequencing-based methods, which are designed for a more comprehensive, genome-wide view of gene expression. Array-based techniques, such as the Visium platform, work by placing a tissue section onto a glass slide covered with thousands of tiny spots, each containing a unique DNA barcode. When the RNA molecules from the tissue diffuse onto these spots, they are captured and tagged with the spot’s positional barcode.
The barcoded RNA molecules are then sequenced, and the spatial barcode allows the data to be computationally mapped back to its exact location. Other sequencing approaches, like Deterministic Barcoding in Tissue sequencing (DBiT-seq), use microfluidic channels to deliver spatial barcodes to the tissue in a grid-like pattern. While sequencing-based methods generally offer transcriptome-wide data, their spatial resolution is often lower than that of the single-cell imaging approaches.
Revolutionizing Biological Discovery
The technology allows scientists to move beyond classifying cells to understanding how their location and molecular state influence their interactions with other cells. Combining spatial transcriptomics and proteomics provides a more complete view, as RNA levels do not always perfectly correlate with the final, functional protein levels within a cell.
A major area of impact is the mapping of the Tumor Microenvironment (TME), which is the complex ecosystem surrounding cancer cells, including immune cells, fibroblasts, and blood vessels. Using these methods, researchers can identify distinct “cellular neighborhoods” and the specific molecular signals being exchanged between tumor cells and infiltrating immune cells. For example, spatial mapping can pinpoint the exact location where immune checkpoint molecules are expressed, helping to explain why some cancers respond to immunotherapy while others resist treatment.
In neuroscience, spatial multiomics is enabling detailed mapping of the brain, an organ defined by its intricate cellular architecture and regional specialization. Researchers are using these tools to create high-resolution atlases of brain regions like the hippocampus, which is involved in memory and emotion. This work identifies distinct cell types and their gene expression patterns within the various layers and subregions of the tissue, providing new insights into normal function.
By examining brain tissue from individuals with neurological disorders, such as Alzheimer’s disease or major depressive disorder, scientists can precisely locate where molecular changes occur. This allows for the correlation of specific protein aggregates or altered gene expression with the exact cellular communities they affect. Identifying these spatially restricted molecular signatures is helping to uncover the mechanisms behind complex conditions and guiding the development of highly targeted treatments.