Post

🚀 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...

Romain Lopez

@biologicalml.org

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.

1/6/26, 6:21 pm0 reposts1 like

Romain Lopez

@biologicalml.org

1 June

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.

Romain Lopez

@biologicalml.org

1 June

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.

Romain Lopez

@biologicalml.org

1 June

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)

Romain Lopez

@biologicalml.org

1 June

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.