While still relatively new, the field of spatial biology is advancing rapidly and has already contributed to novel insights in cancer, neuroscience, infectious disease, and autoimmune disorders.
There is a range of technologies that provide spatial resolution for expressed genes or proteins and allow scientists to look at even the best-characterized biological mechanisms with fresh eyes. No single platform is best for all studies, however; each researcher must consider various factors to choose the best technology for their lab.
Spatial biology tools preserve critical data with in situ studies, offering cellular or even subcellular resolution. For the first time, scientists can query essential niches such as the tumor microenvironment to generate a far more comprehensive picture of gene or protein expression, how those genes or proteins are distributed across cells, and how they may be interacting.
The Spatial Difference
The ability to home in on the location and interactions of RNA or proteins allows scientists to clarify cause and effect. Current spatial transcriptomics and spatial proteomics platforms employ analyte multiplexing to enable the analysis of dozens or hundreds of genes or proteins at a time from each sample.
Early adopters of these technologies tended to deploy them for clinically relevant studies. The field of immuno-oncology was one of the first to see widespread adoption of spatial data, in large part because of the need to stratify patients based on their likelihood to respond to expensive immunotherapies. Clinical researchers have tracked interactions between immune cells and tumor cells by analyzing gene and protein expression within tumor microenvironments. Already, these studies have allowed for more accurate differentiation between populations of responders and non-responders.
While immuno-oncology has been a proving ground for spatial biology tools, other fields have also demonstrated their importance. In neuroscience research, for example, spatial analysis is driving efforts to catalog all cell types and subtypes at the molecular level in human and mouse brains (see figure 1). Such catalogs are an invaluable research tool for both primary and clinical researchers and are also likely to help researchers generate new scientific hypotheses and ask further questions. Identifying clear cellular signatures associated with neurodegenerative diseases could pave the way for more effective diagnostics and therapeutics.
In autoimmune diseases, spatial analysis is used to understand interactions among immune cells and other cells to better understand disease progression. And in the field of infectious disease, spatial biology tools provide a clearer view of how infections invade specific organs or tissues and how the pathogen spreads throughout the body. This technique was used for the COVID-19 pandemic, for instance, to characterize the path of the SARS-CoV-2 virus as it spread beyond a patient’s lungs and into more distant organs or tissues (see figure 2).
Which Technology Is Right for You?
There are already several spatial biology tools available today, with more in development. Researchers looking to adopt these methods must consider various factors to determine the best technology to support their goals.
The analyte of interest: Most of the current spatial biology platforms target a single type of analyte — either gene expression or proteins. Future tools are expected to enable the interrogation of gene transcripts, proteins, metabolites, and other analytes in a single workflow. But for now, scientists must select a platform based on which analyte is of most significant interest to their studies.
Resolution: Some tools provide spatial data by directly imaging the sample; these often employ in situ hybridization as the primary method to detect present genes. Other spatial biology platforms instead use barcodes to keep track of the original location of genes or proteins, building a spatially resolved image by mapping those elements back to those coordinates. Platforms that use direct imaging tend to offer higher resolution, delineating cellular and even subcellular structures.
Rare analytes: For some applications, it’s important to detect even the rarest transcripts or proteins in a sample: those present in just a single copy. Scientists interested in such applications should look for tools that offer the highest sensitivity possible to detect rare elements.
Sample processing: The workflow for some spatial biology technologies involves processing an entire sample, sometimes using a tissue-clearing technique that strips out cellular structures and unwanted analytes. With such platforms, it is impossible to return and query the same sample to ask new questions with another technique such as immunohistochemistry. Researchers who would like to analyze samples with other techniques should look for spatial biology platforms that generate data without destroying the sample.
Multiplexing: Most spatial biology platforms have a multiplexing sweet spot, and prospective users need to ensure that the number of genes or proteins any platform can detect in a given run is a good match for their projects. Another element of multiplexing is how many samples a system can run at once; scientists should check that the platforms they are considering can meet their desired throughput.