Optimizing the design of spatial genomics studies


Spatially-resolved genomic technologies characterize the relationship between the structural arrangement of cells and their functional behavior. While numerous sequencing and imaging platforms exist for performing spatial transcriptomics and spatial proteomics profiling, these experiments remain expensive and labor-intensive. When slicing tissue samples for spatial sequencing, typically parallel axis-aligned slices are used. However, slices obtained with this approach often contain redundant or correlated information about the tissue. Here, we show that spatial genomics experiments using multiple tissue slices can be made more cost efficient by profiling tissue cross sections that are maximally informative for the experiment when considering the information contained in the previous slices while also acknowledging the destructive nature of slicing a tissue. We propose structured batch experimental design, which formalizes the design of spatial genomics experiments. When applied to two spatial genomics studies—one to construct a spatially-resolved genomic atlas of a tissue and another to localize a region of interest in a tissue, such as a tumor—we show that this approach collects samples containing more information using fewer slices compared to traditional serial slicing strategies. We anticipate our approach to be a starting point for developing robust and inexpensive design strategies for the creation and study of tissue atlases, brain regions, and tumor resectioning, allowing spatial genomics studies to be effectively deployed by smaller labs with fewer resources.

In revision