On Thursday, January 22nd (2:00 PM ET), the Herbert and Florence Irving Institute for Cancer Dynamics welcomes Romain Lopez, Assistant Professor of Computer Science and Biology, NYU. Seminar hosted by Elham Azizi. The seminar will take place in person in Fairchil 700 (Morningside Heights campus). If you wish to attend the seminar remotely, please register using the following link: https://columbiauniversity.zoom.us/meeting/register/cALY4jaBRyeISb58zd7QnQ
Title: Multimodal Pooled Genetic Screens Integrating Transcriptomics and Image-Based Phenotypes
Abstract: Understanding how genes regulate cellular function benefits from various experimental approaches, with perturbation-based methods offering particularly powerful insights. We developed PerturbPAIR, integrating Perturb-seq and Optical Pooled Screens (OPS) to combine transcriptomic and imaging-based phenotypes at scale. We profiled LPS-stimulated bone marrow-derived macrophages, performing ~1,000 gene perturbations in Perturb-seq and ~3,000 in OPS. Perturb-seq identified co-functional modules encompassing interferon signaling, cytoskeletal regulation, and metabolic adaptation. OPS demonstrated superior sensitivity and revealed regulatory clusters based on protein localization patterns for inflammatory markers (Nos2, Hif1a, p65). Both modalities showed high concordance, with similar perturbations clustering together across transcriptomic and imaging spaces. Some perturbations with distinct transcriptomes converged to similar optical phenotypes, highlighting complementary information captured by each modality. We therefore proposed a new deep generative model of transcriptional perturbation outcomes that effectively employs the imaging data for imputing the effect of unseen perturbations. We demonstrate that our novel method strongly outperforms existing methods for this task. Additionally, the model can be used for denoising Perturb-seq data when sample sizes are insufficient, a novel and important use-case in the field. Our integrated approach identified regulatory networks spanning cellular stress responses, metabolic control, and immune signaling pathways. We successfully imputed gene expression signatures for ~600 perturbations using only OPS data, enabling direct mapping onto these networks. This multimodal framework enhances our ability to dissect complex biological mechanisms and develop predictive models bridging molecular states with phenotypic outcomes in immune cell activation.
Bio: Romain Lopez is an Assistant Professor of Computer Science and Biology at New York University. He received his MSc in Applied Mathematics from École polytechnique and his PhD in Electrical Engineering and Computer Sciences from UC Berkeley, advised by Professors Michael I. Jordan and Nir Yosef. He was a Postdoctoral Fellow at Genentech and Stanford University, hosted by Professors Aviv Regev and Jonathan Pritchard. He has received Best Paper honors at leading AI and computational biology venues as well as a STAT Wunderkind Award (2024), recognizing North America’s most promising early-career scientists. His research develops probabilistic and causal machine learning methods to uncover the mechanisms that govern cellular behavior and disease, including the scVI framework and the scvi-tools ecosystem for deep generative modeling of single-cell omics data.
If you would like to meet one-on-one (possibility via zoom) or attend the lunch or dinner with the speaker, please contact the event organizer.
