Talk · Genomics at Scale 2026 · St John’s College, Cambridge
Lost in the z-axis
Can generative AI reconstruct true 3D tissues from serial sections?
People’s Choice Poster Awardclick the slides, then → and ← to step through · F for full screenswipe the slides to step through
Tissues are 3D, but spatial transcriptomics is measured one 5 µm section at a time. Sequencing every section is slow and expensive, while H&E staining is cheap and routine. In this talk I presented HoloTea, a 3D-aware flow-matching model that predicts spot-level gene expression from H&E, using the matching spots on the sections directly above and below as a guide.
On a human embryo stack of 52 sections (327,532 spots), HoloTea was trained on every other section and predicted the 26 sections in between, recovering anatomical marker genes and tissue domains it never saw. It beats 2D and 3D baselines across three datasets and scales to whole embryos: predicting a section takes 3.7 GB of GPU memory, while the previous 3D method runs out of memory on an 80 GB GPU.