FIGURE 2.

Comprehensive framework for spatial applications. Spatial applications depend on the type of study that the researcher is engaged in. This comprehensive framework captures the continuum of needs across discovery, translational and clinical research. Starting from the far left, the first step in spatial biology is phenotyping cells in situ. In many ways, this is a foundational element in any spatial biology study—map cells with spatial context. This is the starting point, and it requires single-cell resolution. If the goal is to discover novel cell types or rare cells, then it warrants an unbiased approach to discovery—that is, mapping every single cell in the tissue through whole slide imaging. This approach not only provides a macro-level view of the tissue architecture but also a micro-level view into each cell and is necessary for uncovering extremely rare cell types—down to less than <0.1% abundance (e.g., Nguyen et al., 2018). Once cells are phenotyped, they can be mapped to distinct tissue substructures, called cellular neighborhoods, based on spatial interactions and how the cells cluster. Cellular neighborhoods are emerging as a seminal concept in spatial analysis because of their correlation to cancer progression and treatment response (e.g., Schurch et al., 2020). On the translational/clinical side of the spectrum, far right, the goal is discovering spatial biomarker signatures (e.g., Theobald et al., 2018; Badve et al., 2021; Griffin et al., 2021) and establishing their clinical significance, which requires studying large cohorts and a high throughput approach.