Scalable multimodal mapping of macrophage regulatory architecture by integrating optical and transcriptomic pooled screens
Understanding how genetic perturbations reshape cellular states requires measuring diverse phenotypic modalities at scale. Here, we present PerturbPair, a platform that combines parallel Perturb-Seq and optical pooled screening (OPS) in primary mouse bone marrow-derived macrophages stimulated with lipopolysaccharide. Profiling over 334,000 single-cell transcriptomes across [~]1,000 gene perturbations and 7.8 million imaging-phenotyped cells across [~]3,000 gene perturbations, we reveal high concordance between transcriptomic and optical perturbation signatures. While RNA and imaging phenotypes were broadly concordant, OPS demonstrated superior sensitivity for weak-effect perturbations owing to greater cell throughput, and captured post-transcriptional regulatory events--such as protein trapping and mTOR-dependent phosphorylation--that left no detectable transcriptional footprint. To exploit cross-modal relationships, we developed EB-MoCAVI, an empirical Bayesian variational inference framework that both imputes RNA profiles for perturbations measured only by imaging--effectively tripling our Perturb-Seq dataset in silico--and denoises transcriptomic estimates for perturbations with sparse cell coverage by leveraging matched imaging data as a regularizer. A secondary screen validated the imputed transcriptional profiles and corroborated the cytosolic iron-sulfur assembly pathway as a candidate restraint on macrophage interferon tone. Integrating measured and imputed profiles with rare-variant burden statistics from the UK Biobank identified disease-specific macrophage gene programs and causal regulatory nodes for monocyte counts, type 2 diabetes, and inflammatory bowel disease. PerturbPair establishes a generalizable framework for multimodal perturbation atlases, pointing toward quantitative, causally-informed cross-modal models of cellular behavior.