A systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens was conducted to identify targets of signaling regulators across diverse biological contexts. The study leveraged pooled genetic screens and single-cell sequencing (Perturb-seq) to systematically identify targets of signaling regulators. The researchers developed an improved computational framework, Mixscale, to address cellular variation in perturbation efficiency and optimized statistical methods to learn differentially expressed gene lists and conserved molecular signatures. They performed over 1,500 perturbations across six cell lines and five biological signaling contexts, generating a comprehensive dataset of 1,626 multiplexed perturbation experiments. The study demonstrated how Perturb-seq-derived gene lists can be used to infer changes in signaling pathway activation for in-vivo and in-situ samples. The researchers also evaluated the performance of Perturb-seq pathway signatures against existing databases, finding that their signatures provided more accurate and reproducible results. The study highlighted the importance of learning conserved gene modules across different cell types and biological contexts. The results showed that Perturb-seq signatures could be used to infer signaling pathway activation in new in-vivo datasets, including for immune and intestinal disorders. The study also demonstrated the utility of Perturb-seq in identifying specific sub-programs driving cellular responses. The findings suggest that Perturb-seq can be used to generate response signature dictionaries, which will be a primary use case for new massively scalable single-cell sequencing techniques. The study also addressed limitations in the experimental design, including the lack of comprehensive biological system coverage and the inability to study temporal signaling dynamics. The researchers concluded that their approach provides a robust framework for identifying conserved pathway genes across different cell lines and enables future studies to explore cell-type-specific responses. The study also emphasized the importance of integrating data from multiple sources to improve gene signatures and enhance the understanding of signal transduction at multiple steps of the central dogma.A systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens was conducted to identify targets of signaling regulators across diverse biological contexts. The study leveraged pooled genetic screens and single-cell sequencing (Perturb-seq) to systematically identify targets of signaling regulators. The researchers developed an improved computational framework, Mixscale, to address cellular variation in perturbation efficiency and optimized statistical methods to learn differentially expressed gene lists and conserved molecular signatures. They performed over 1,500 perturbations across six cell lines and five biological signaling contexts, generating a comprehensive dataset of 1,626 multiplexed perturbation experiments. The study demonstrated how Perturb-seq-derived gene lists can be used to infer changes in signaling pathway activation for in-vivo and in-situ samples. The researchers also evaluated the performance of Perturb-seq pathway signatures against existing databases, finding that their signatures provided more accurate and reproducible results. The study highlighted the importance of learning conserved gene modules across different cell types and biological contexts. The results showed that Perturb-seq signatures could be used to infer signaling pathway activation in new in-vivo datasets, including for immune and intestinal disorders. The study also demonstrated the utility of Perturb-seq in identifying specific sub-programs driving cellular responses. The findings suggest that Perturb-seq can be used to generate response signature dictionaries, which will be a primary use case for new massively scalable single-cell sequencing techniques. The study also addressed limitations in the experimental design, including the lack of comprehensive biological system coverage and the inability to study temporal signaling dynamics. The researchers concluded that their approach provides a robust framework for identifying conserved pathway genes across different cell lines and enables future studies to explore cell-type-specific responses. The study also emphasized the importance of integrating data from multiple sources to improve gene signatures and enhance the understanding of signal transduction at multiple steps of the central dogma.