How To Choose a Spatial Biology Platform for Your Lab
Complete the form below to unlock access to ALL audio articles.
The research community’s enthusiasm about spatial biology tools is reminiscent of the excitement we saw with the advent of microarrays, next-generation sequencing, or single-cell analysis platforms. And for good reason: the discoveries enabled by spatial biology promise to be even more revolutionary for our understanding of biological systems as those brought about by major technological predecessors.
Spatial biology is finally giving scientists contextual information that has been difficult or even impossible to access before. By spatially resolving cells, biomarkers and other elements of a biological system, we can at last make sense of how their interactions modulate drug response, immune response, the progression of infectious disease and so much more.
While spatial biology is promising, the recent explosion of technology options has left many scientists feeling confused or overwhelmed. With so many platforms and services to choose from – and more coming to market on a regular basis – it can be challenging to identify the best approach for a lab’s current needs as well as what it might need in the future.
Here, we’ll look at some of the earliest applications benefiting from spatial biology techniques and walk through several key considerations to help scientists select the method that’s most appropriate for their experimental needs.
While it seems clear that the advantages of spatial biology will eventually extend to all areas of biology – including use in clinical areas such as diagnostics – its deployment in these early years has focused on several key areas where it could make an immediate difference and have clear implications for human health.
Many of the early adopters of spatial biology came from the field of cancer research. Studies of immuno-oncology were a remarkable showcase for what these new technologies could offer. In some cases, spatial resolution of gene transcripts or proteins provided new information to help answer longstanding questions about why some cancer patients were cured by immuno-oncology treatments while others with seemingly similar cancers saw little or no benefit.1-2 The ability to visualize the tumor microenvironment and to elucidate biology as closely as possible to how it functions in vivo has been a game changer for understanding cancer and how it responds to the immune system as well as to clinical intervention. The same approach has also revealed new insights about drug toxicity and adverse reactions over time, particularly for popular immunotherapies such as PD-1 inhibitors.
While the use of spatial biology in cancer has outpaced the deployment of this technology elsewhere, other fields are seeing benefit from it. Neuroscience research is one example: scientists have used spatial tools to characterize cell types in both mouse and human brain tissue, building entire atlases with detailed molecular information.3-4 Spatial biology has also made inroads for infectious disease studies. In the COVID-19 pandemic, for instance, researchers used this technique to track the molecular pathology of infection and the inflammatory response it triggers.5 Developmental biology is another key application area, with researchers mapping spatial gene expression over time to better understand development of certain tissues or organs during embryogenesis.6
With so many spatial biology tools available, the best place to start the selection process is to identify the key requirements for your experimental needs. Once you’ve considered these factors, you’ll have the information you need to start looking at vendors and seeing which systems are best suited for your lab.
Because spatial biology platforms use different techniques to resolve location, resolution varies quite a bit from one tool to another. In general, systems that use microscopy or optics to directly observe the tissue sample can produce much higher resolution. The best of these tools can resolve even subcellular structures. If such detailed resolution is not important for your work, then you can also consider platforms without direct observation; these tools use barcode data to map elements back to their approximate original location in the tissue and generate a lower resolution “image” of the sample, usually via a sequencing readout.
While sensitivity is important for all areas of research, certain applications require exceptional levels – such as the ability to detect as little as a single transcript or protein in a cell. If identifying the rarest biomarkers is important for your research, be sure to look for platforms that meet this level of sensitivity. Generally, direct detection technologies, such as single molecule fluorescence in situ hybridization (smFISH), excel in this area, while indirect detection often leads to lower sensitivity.
Most spatial biology platforms today focus on one type of analyte, typically expressed genes or proteins. Choosing between spatial transcriptomic tools or spatial proteomic tools is a matter of which analyte type is most relevant for your research. For the best chance at a platform that will be useful for many years, though, it might be worth looking for platforms that are designed to be extended to other analytes over time.
In both the number of targets that can be analyzed and the number of samples that can be run at once, capacity varies widely among spatial biology tools. For true discovery science where researchers must consider all proteins or all genes, a platform with the highest level of multiplexing is necessary. But for most experiments, a subset of genes or proteins is sufficient. In those cases, you can choose from a longer list of platforms that can detect dozens of proteins or hundreds of genes. For clinical labs, the ability to run several samples at once might be most important.
Sample processing differs significantly across spatial biology platforms. If you’re using precious samples or may need to re-analyze a particular sample, it’s important to look for workflows that do not destroy the sample by clearing the tissue or as an unavoidable part of the analysis process.
Spatial biology tools offer tremendous potential for scientific labs. In the coming years, it is likely that spatial resolution will come to be expected from most biology experiments, as next-generation sequencing data is broadly expected now. To make the most from your investment in a spatial biology platform, future-proof it by considering how your research may evolve in the next several years and ensuring that the platform you choose now can meet those evolving needs. Carefully weighing the factors above is a useful way to begin your spatial biology journey.
Jason T. Gammack is CEO of Resolve Biosciences, a company offering Molecular Cartography™ technology for spatial transcriptomics. He has spent more than 25 years in the life sciences industry and has launched powerful technologies for research areas such as CRISPR gene editing, bioinformatics and more.
1. Lu S, Stein JE, Rimm DL, Wang DW, et al. Comparison of biomarker modalities for predicting response to PD-1/PD-L1 checkpoint blockade: a systematic review and meta-analysis. JAMA Oncol. 2019;5(8):1195–204. doi: 10.1001/jamaoncol.2019.1549.
2. Finotello F, Eduati F. Multi-omics profiling of the tumor microenvironment: paving the way to precision immuno-oncology. Front Oncol. 2018;8:430. doi: 10.3389/fonc.2018.00430.
3. Lein E, Borm LE, Linnarsson S. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science. 2017;358(6359):64-69. doi: 10.1126/science.aan6827
4. Nilges B, Strauss S, Geipel A, Reinecke F, et al. Quantitative spatial analysis of 67 genes to study the effect of amyloid pathology in Alzheimer’s Disease (AD). Poster presented at: Emerging Technologies in Single Cell Research (virtual edition); November 19-20, 2020; Leuven, Belgium. https://www.vibconferences.be/sites/default/files/2020-11/Single%20Cell%20-%20Poster%2044%20-%20Benedikt%20Nilges.pdf. Accessed March 1, 2022.
5. Delorey TM, Ziegler CGK, Heimberg G, et al. COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets. Nature. 2021;595(7865):107-113. doi: 10.1038/s41586-021-03570-8.
6. D’Gama PP, Qiu T, Cosacak MI, Rayamajhi D, et al. Diversity and function of motile ciliated cell types within ependymal lineages of the zebrafish brain. Cell Rep. 2021;37(1):109775. doi: 10.1016/j.celrep.2021.109775.