Researchers are developing methods to integrate and analyze various types of data, including spatial transcriptomics and metabolomics, to better understand complex biological systems. These approaches, such as SpatialMETA, aim to combine data from different samples and modalities to gain insights into spatial patterning and signaling. Various frameworks and tools, including SpatialCOC and MultiGATE, are being explored to facilitate cross-sample alignment, differential gene analysis, and regulatory inference in spatial multi-omics data.