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VOICE Model The VOICE model evaluates gene activity in individual cells based on tissue images and a library of already measured cells. On August 8, a team from the University of Michigan released the VOICE model preprint. The authors trained the model on 23.2 million cells from 75 human tissue sections across 15 tissues, where each cell has a standard H&E-stained tissue image and gene activity measurements using Xenium technology. The model takes cell shape from the image and, for genes that cannot be recognized by shape, refers to a library of similar cells where gene activity has already been measured. H&E staining shows cell shape and tissue structure, while Xenium technology measures the activity of pre-selected genes in individual cells of the same section and retains their coordinates. The model was trained on sections where these two types of data were already matched. The model has two paths to a single estimate: a direct path using H&E cell features and its surroundings to estimate gene activity, and a second path that searches the library for similar cells of the same tissue and averages their measured Xenium values. For each gene, VOICE adjusts the weight between these paths on the training sections. The authors describe this division as: "some genes are closely related to cell morphology and are predicted directly from the image; others are better evaluated by cells with a similar transcriptomic profile". The transcriptomic profile is a set of gene activity measurements in a single cell, as described in Nature Aging, July 2026. 🔗 Read original →