What led you to be an early adopter of spatial transcriptomics? What was your first spatial project?
I was working with Tom Clemens on a project on sensory nerves in limb regeneration. When we met up at the Gordon Bone and Teeth Research Conference in February 2020 he introduced me to Robert Tower, saying that the project would be a great fit for the spatial transcriptomics platform to look at innervation. My research program focuses on the mouse digit amputation model, where the mouse distal digit tip is amputated and subsequently regenerates bone and soft tissue. While spatial transcriptomics was being used in models of development, this was a new model to look at regeneration in bone after injury. Shortly after that COVID-19 cancelled pretty much everything, so Robert and I spent the next year working on spatial transcriptomics in the mouse digit model, which we developed into an R21 grant in 2021 (HD106162).
Have you ever been surprised by the cell populations revealed in a spatial experiment?
The digit regeneration model is centered around the formation of a blastema, which is a dedifferentiated, heterogenous population of cells that eventually becomes the regenerated bone and tissue. This area is defined using histological boundaries, which are lost during approaches such as scRNA sequencing. The goal of the project was to spatially define and analyze the gene expression profile of this blastema in both young and aged mice, and spatial transcriptomics allowed us to do this. Further, my specific interest at the time was the role of cell metabolism in regeneration. These metabolic assays are traditionally biochemical in nature and typically give no spatial differentiation for heterogenous patterned tissues such as regenerating bone. Spatial transcriptomics allowed us to look at cell metabolism in the section of patterned tissue and showed us that surprisingly both oxidative phosphorylation and glycolysis are elevated specifically in the aged blastema.
How are you currently applying spatial transcriptomics in your research program?
My research program, with Robert’s help, is currently using spatial transcriptomics to identify signaling gradients within the wound epithelium and nailbed to determine how these gradients affect the regeneration of the blastema into new patterned tissue. We have also begun to investigate cross-species comparisons of highly regenerative models, such as the axolotl, to models with limited regenerative capabilities such as the mouse. It is these spatially specific cross-comparisons I feel that will ultimately yield the highest amount of information for translational regenerative approaches.
What are advantages and challenges associated with using spatial transcriptomics in biological research?
Robert already mentioned the limitation of resolution in the 10x Visium platform that we use, although I feel that the spatial data that is gained, and the ability to repeatedly analyze different regions of interest over and over again on the same histological sample, far outweighs the limitation. We have repeatedly mined our data sets in different histological regions of interest asking completely new questions. One of the advantages that I would also note is that the 10x Visium assay platform is very straight forward, and with sample prep that preserves the RNA appropriately it is almost always successful. Robert and I recently ran a workshop for 10x Visium tissue optimization slides for the “Frontiers in Aging and Regeneration Research – FrARR” (AG043365) where 16 undergraduate trainees successfully completed tissue optimization of brain tissue.
Is there a specific barrier that once addressed might bring spatial transcriptomics technology to the next level?
I would say that in addition to the resolution and cost, which Robert mentioned, spatial transcriptomics is crossing into a new level of data analysis that requires strong collaboration with a bioinformatician or researchers that have strong skill sets in both bioinformatics and biology. In this collaboration, Robert’s expertise are in the musculoskeletal field, so this is a big advantage during analysis. A collaborator who can meet you halfway in your research program makes it much easier to explain the amputation model that I work in and circumvent downstream analysis issues up front.
What advice would you give investigators who want to incorporate spatial transcriptomics into their research program? What learning resources would you recommend?
I agree that 10x has excellent resources and videos for learning about the platform. These resources can give you detailed information on sample prep requirements, which are critical to good results. If you choose to outsource sample preparation and the 10x platform rather than doing it in your own lab, finding a histology core and a spatial core that will work very closely with you to identify your region of interest, sample orientation, and tissue coverage on the slide is key to getting good results. Typically, the histology and spatial aspects of this platform are separated in different cores. Finding a streamlined pipeline that will take your research from sample all the way through to analysis with minimal fuss is worth finding. The Tulane Center for Aging at Tulane School of Medicine has developed this as part of their incorporation of spatial transcriptomics and I think it has been a huge improvement for clients.