Multimodal learning enables chat-based exploration of single-cell data.

CellWhisperer links transcriptomes to text through a multimodal embedding trained by contrastive learning on 1 million RNA sequencing profiles with AI-curated descriptions, feeding a large language model that answers questions about cells and genes in natural-language chats. Benchmarked for zero-shot prediction of cell types and biological annotations, it supported discovery in a meta-analysis of human embryonic development and is integrated with the CELLxGENE browser for combined graphical and chat exploration of gene expression.