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🚀 We are introducing PerturbPair (with Taka Kudo) — a platform that combines parallel Perturb-seq and optical pooled screening (PerturbView) in primary cells to systematically map at massive scale how genetic perturbations reshape cellular states across modalities. www.biorxiv.org/content/10.6...

Perturb-seq reveals molecular programs; OPS captures morphology, protein localization, and cell biology at massive scale. But how concordant are these readouts? Can we exploit their relationship to generate one modality from the other? We set out to answer both questions in a single system.

We profiled LPS-stimulated mouse BMDMs with: (1) 334,000 single-cell transcriptomes across ~1,000 gene perturbations (Perturb-seq) (2) 7.8 million imaging-phenotyped cells across ~3,000 gene perturbations (PerturbView) Both libraries share a common backbone, enabling direct cross-modal comparison.

The relationship between perturbation profiles was highly concordant across modalities (ρ = 0.85). The same regulatory modules emerged from both, with informative differences: for instance, m6A modification genes cluster with JAK-STAT only in imaging, suggesting post-transcriptional convergence.

OPS detected substantially more hits than Perturb-seq, partly because it had 10x more cells per perturbation and partly because some PerturbView-only hits included post-transcriptional events invisible to RNA (e.g., mTOR perturbations → elevated phospho-rpS6)

To exploit these cross-modal relationships, we developed an empirical Bayesian variational inference framework (EB-MoCAVI). Using OPS embeddings as an informative prior, it (1) impute RNA profiles for perturbations measured only by imaging (2) denoise Perturb-seq estimates via adaptive shrinkage.

The imputed profiles are biologically coherent: Lamtor3 clusters with measured Lamtor1/4/Rraga (mTORC1), V-ATPase subunits group together, etc. We also found that iron-sulfur assembly pathway components act as a restraint on macrophage interferon tone (a prediction validated experimentally).

Did EB-MoCAVI generate accurate Perturb-Seq profiles? We ran a secondary Perturb-seq screen on 288 perturbations (including 117 with only imputed profiles). The results confirmed that imputed profiles closely match measured RNA (median Spearman ρ = 0.48).

Integrating measured + imputed profiles with rare-variant burden statistics from UK Biobank, we identified disease-specific macrophage gene programs and key regulatory nodes for monocyte counts, T2D, and IBD. This paves the way towards trait-linked cellular programs.

PerturbPair establishes a generalizable framework for multimodal perturbation atlases. EB-MoCAVI is available as part of scvi-tools: github.com/Genentech/Mo... Explore our study to see how cross-modal integration can make perturbation screening both more scalable and more informative.