Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.
FörderungenJoachim Herz Stiftung German Research Foundation (DFG) fellowship through the Graduate School of Quantitative Biosciences Munich sparse2big Networking Fund through Helmholtz AI Helmholtz Association's Initiative BMBF Helmholtz Association under the joint research school 'Munich School for Data Science-MUDS'