Reconstructing signaling histories of single cells via perturbation screens and transfer learning

Manipulating the signaling environment is an effective approach to alter cellular states for broad-ranging applications. Such manipulation requires knowing the signaling states and histories experienced by cells in vivo, for which high-throughput discovery methods are lacking. Here we present an integrated experimental–computational framework that learns transferable signaling response signatures from a high-throughput in vitro perturbation atlas and infers signaling activities and histories in in vivo cell types with high accuracy and temporal resolution. We generated a signaling perturbation atlas on human pluripotent stem cells and used it to train IRIS, a neural-network model. Applying IRIS to mouse embryo single-cell atlases, we uncovered global features of combinatorial signaling code usage, identified biologically meaningful heterogeneity and reconstructed signaling histories along diverse developmental lineages. This framework reveals that diverse cell types share conserved signaling response signatures, and provides a scalable solution for mapping complex signaling interactions in vivo to guide targeted interventions and enable cell fate engineering. IRIS learns conserved signaling representations from in vitro perturbation data to reconstruct signaling states and histories across diverse in vivo cellular contexts.