Sep 08, 2021 | 2 min read
In the coming years, clinical lab experts will have the opportunity to deploy a new suite of tools that will dramatically increase the information they can extract from a tissue specimen. These tools, which are now used primarily in research, are the foundation of the rapidly growing field of spatial biology.
"That spatial biology will transform our understanding of health and disease is inevitable."
Spatial biology brings together the best of two worlds: large-scale data on gene or protein expression typical of omics technologies paired with full spatial context from direct visualization of an intact tissue section. The result is a deep, spatially resolved molecular profile. These tools allow users to detect dozens or even hundreds of different genes or proteins at once, enabling single-cell or even subcellular resolution to track the precise location of each target and identify cellular and molecular interactions.
These new technologies are the culmination of innovations from next-generation sequencing, advanced imaging systems, data sciences, and more. By producing more molecular data from each sample and simultaneously generating such high-resolution views of cell populations—even down to subcellular structures—it is now feasible to investigate how the location of expressed genes and proteins affects human health.
That spatial biology will transform our understanding of health and disease is inevitable. Already, these tools have allowed scientists to see how immune cells engage with the tumor microenvironment, how neurodegenerative diseases progress, and how viral particles spread across organs and tissues in an infection.
Advances and applications
As more and more labs adopt spatial biology tools, technology development is occurring rapidly. For example, tools can now be used to detect gene or protein expression, and some platforms will soon be able to query both. Multiplexing capacity also continues to improve. While discovery-focused systems might allow for the detection of thousands of genes, the types of platforms more likely to translate into clinical laboratory use can reliably detect a few dozen proteins or as many as a hundred genes at a time.
Some other notable advances are making these tools better suited for future use in clinical labs. For instance, resolution and sensitivity are both improving. Optical-imaging systems have enabled resolution down to subcellular structures, with cell organelles visible among the protein or gene expression markers. Some systems deliver enough sensitivity to accurately detect transcripts present in just a single copy in a cell. In addition, the ability to analyze several specimens at a time will also be important for clinical use, as will platforms that preserve the sample for future analysis instead of destroying it during processing.
Clearly, spatial tools are poised for clinical lab adoption in the coming years. The earliest use may be for cancer patients. In research labs, spatially resolved data has already helped researchers identify which cancers will respond to certain immunotherapies, and which cancers are likely to grow more aggressively. Eventually, these spatial signatures could be validated and incorporated into clinical use. Other applications likely to have clinical utility include analysis of infectious diseases, monitoring autoimmune disorders, and tracking neurodegenerative disease progression.