What led you to be an early adopter of spatial transcriptomics? What was your first spatial project?

At ETH Zürich, we wanted to understand the influence of the local mechanical environment on molecular expression patterns during musculoskeletal regeneration in mouse models. Previously, in my ETH Postdoc fellowship project we had developed a femur defect loading model allowing to precisely apply mechanical stimulation during fracture healing in mice, which can be combined with time-lapsed in vivo micro-CT imaging. To link the obtained mechanical information to molecular analyses, we first focused on approaches based on cell isolation via laser capture microdissection from histological sections. When spatial transcriptomics turned into focus, we soon started to integrate the Visium system by 10x Genomics into our multimodal in vivo platform. In our first Visium run, we obtained distinct musculoskeletal tissue specific spatial gene expression clusters during the remodeling phase of fracture healing.

Have you ever been surprised by the cell populations revealed in a spatial experiment?

Currently the Visium system is still limited to a resolution of 55 µm. Nevertheless, the system allowed us to spatially visualize musculoskeletal expression patterns and interfaces during the remodeling phase of fracture healing in mice. Specifically, we identified gene expression clusters indicative of specific tissues as seen by spatial overlay with histology. Distinct molecular sub-clusters further highlighted the importance of spatially-resolved gene expression data for understanding tissue crosstalk during bone healing. The developed spatial transcriptomics approach can be particularly used to capture the intersection and crosstalk between multiple tissues during bone healing.

How are you currently applying spatial transcriptomics in your research program?

My team at the AO Research Institute is currently using spatial transcriptomics approaches to early capture and understand local differences discriminating impaired versus normal bone healing. For this, we are currently further optimizing pre-treatment protocols for musculoskeletal samples from mice.

What are advantages and challenges associated with using spatial transcriptomics in biological research?

Spatial transcriptomics allows us to capture the intersection and crosstalk between multiple tissues using histological sections. In the context of bone healing, spatial transcriptomics analyses have the potential to elucidate the local molecular mechanisms underlying impaired healing with optimization of treatments. Current limitations and challenges include the resolution of the available systems, the restriction to few species and the optimization of sample treatment protocols.

Is there a specific barrier that once addressed might bring spatial transcriptomics technology to the next level?

For musculoskeletal research, specific barriers to be addressed include the development of standardized sample pre-treatment protocols allowing for mild decalcification of bone while preserving RNA quality as well as the development of spatial platforms allowing for single cell resolution and protein co-detection. Furthermore, optimization of setups to reduce costs are mandatory.

What advice would you give investigators who want to incorporate spatial transcriptomics into their research program? What learning resources would you recommend?

To incorporate spatial transcriptomics, I feel the evaluation of your sample preparation pipeline is crucial including the assessment of the obtained RNA quality. To avoid multiple test runs, it will help a lot if you have core facilities available providing support for histology, microscopy, sequencing and bioinformatics. We have planned and setup our first runs together with 10x Genomics and core facilities at ETH Zurich, which has highly contributed to establishing our spatial transcriptomics pipeline for musculoskeletal samples from mice.