Spatial multiomics, still in its discovery phase, can mean different things to different people. It typically denotes the visualization of transcriptomic and proteomic data in the context of tissue architecture, either directly on the same section or on serial sections that are integrated computationally.
Spatial multiomics may eventually grow to encompass lipids, glycans, metabolites, epigenetic markers, and transient post-translational stamps on proteins. “Every new technology in medicine grows from discovery, through translation, to diagnostics,” says Joachim Schmid, PhD, vice president, R&D spatial informatics & AI, NanoString Technologies.
Yet, even in its current incarnation, spatial multiomics is being used in pathology research laboratories to establish precise methods of identifying and classifying diseases, and in determining the specificity and efficacy of drugs. According to Jonathan Sweedler, PhD, the James R. Eiszner Family Endowed Chair in Chemistry at the University of Illinois at Urbana-Champaign, “For some classes of molecules, spatial multiomics can already get quality chemical distributions within tissues and tumors. Mass spectrometry imaging and vibrational spectroscopy provide molecular information related to tissue health.”