By Sang Ho Kwon.
Recent advancements in spatially-resolved transcriptomics (SRT) technologies have ushered in a new era of possibilities for biological research. These technologies offer the unique ability to map biomolecular information within the native tissue architecture. Preserving the spatial resolution of genome-wide gene expression allows researchers to obtain a more holistic view of the tissue microenvironment, particularly the underlying molecular and cellular dynamics in a spatial-anatomical context, which is useful to understand the composition, states, and function of individual cell types, as well as their interactions with one another in a defined microenvironment.
By Nick Eagles
We’ve recently been interested in exploring the (largely python-based) tools others have published to process spatial transcriptomics data for various end goals. A common goal is to integrate data from platforms like Visium, which provides some information about how gene expression is spatially organized, with other approaches with potentially better spatial resolution or gene throughput. In particular, we came across a paper by Biancalani, Scalia et al.