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

Researchers explore how to reconstruct cellular signaling histories by combining perturbation screens with transfer learning, aiming to infer signaling states across diverse cell types despite experimental limitations. They address challenges in measuring transcriptional responses to numerous signaling pathways in various cell types using single-cell RNA sequencing data and computational models.