The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography Provisionally Accepted

Jesus G. Galaz-Montoya^{1, 2*}

• ¹Stanford University, United States
• ²Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA 94305, USA, United States

Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET allows to visualize cell and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly driven and boosted by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by 'big data' in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, with many being expected to translate into the clinic, leading to improved diagnostics and targeted therapeutics.