Fig. 3

Future perspectives for spatial multi-omics. We foresee several avenous to be implemented in spatial multi-omic experiments. These include the analysis of gene-edited tissues using CRISPR-Cas or other editing/perturbation approaches. Additionally, gene editing can be used to barcode cells to enable lineage tree reconstruction based on spatial data. Receptor-ligand interactions at high resolution would greatly improve CCC analysis. Pathomics could guide selection of insightful tissue areas which are predictive for a disease outcome. Increased sensitivity and specificity will also certainly be addressed. Advancements in using machine learning models like foundation models will potentially help to overcome data integration difficulties. 3D spatial multi-omics data might be acquired directly or inferred from the registration of measurements in a common coordinate framework