Analyzing new cell types and states without retraining

AlphaGenome 2 can also generalize to entirely new single-cell experiments without needing to be retrained. Scientists can upload a single-cell RNA-seq snapshot of gene activity from just a few dozen cells; this gene activity is then compressed into a cell state representation, before being used to condition the model on unseen states. AlphaGenome 2 can match or outperform models trained or fine-tuned directly on those datasets.

This enables AlphaGenome 2 to predict variant effects and gene regulation for new cell states, such as those that occur from disease or vaccination. For example, given unseen single-cell data from a COVID-19 mRNA vaccine study, AlphaGenome 2 distinguished vaccine-activated immune T cells from resting ones. It also revealed that a variant linked to severe COVID-19 disrupts gene activity more strongly when cells are in this activated state — an effect that would be missed in unactivated cells.

Bridging human genetics and single-cell biology

One way to better understand disease is by linking human genetics to cell biology. An important part of building this link requires connecting the thousands of trait and disease-associated variants found in genome-wide association studies (GWAS) to the growing single-cell maps of the human body.