Contact

  • Academic
    thomklau@stanford.edu
    University - Student Department: Statistics Position: Graduate
    University - Student Department: Bioengineering Position: Graduate

Additional Info

  • Mail Code: 4125

All Publications

  • Perturb-Multimodal: A platform for pooled genetic screens with imaging and sequencing in intact mammalian tissue. Cell Saunders, R. A., Allen, W. E., Pan, X., Sandhu, J., Lu, J., Lau, T. K., Smolyar, K., Sullivan, Z. A., Dulac, C., Weissman, J. S., Zhuang, X. 2025

    Abstract

    Metazoan life requires the coordinated activities of thousands of genes in spatially organized cell types. Understanding the basis of tissue function requires approaches to dissect the genetic control of diverse cellular and tissue phenotypes in vivo. Here, we present Perturb-Multimodal (Perturb-Multi), a paired imaging and sequencing method to construct large-scale, multimodal genotype-phenotype maps in tissues with pooled genetic perturbations. Using imaging, we identify perturbations in individual cells while simultaneously measuring their gene expression profiles and subcellular morphology. Using single-cell sequencing, we measure full transcriptomic responses to the same perturbations. We apply Perturb-Multi to study hundreds of genetic perturbations in the mouse liver. Our data suggest the genetic regulators and mechanisms underlying the dynamic control of hepatocyte zonation, the unfolded protein response, and steatosis. Perturb-Multi accelerates discoveries of the genetic basis of complex cell and tissue physiology and provides critical training data for emerging machine learning models of cellular function.

    View details for DOI 10.1016/j.cell.2025.05.022

    View details for PubMedID 40513557

  • A platform for multimodal in vivo pooled genetic screens reveals regulators of liver function. bioRxiv : the preprint server for biology Saunders, R. A., Allen, W. E., Pan, X., Sandhu, J., Lu, J., Lau, T. K., Smolyar, K., Sullivan, Z. A., Dulac, C., Weissman, J. S., Zhuang, X. 2024

    Abstract

    Organ function requires coordinated activities of thousands of genes in distinct, spatially organized cell types. Understanding the basis of emergent tissue function requires approaches to dissect the genetic control of diverse cellular and tissue phenotypes in vivo. Here, we develop paired imaging and sequencing methods to construct large-scale, multi-modal genotype-phenotypes maps in tissue with pooled genetic perturbations. Using imaging, we identify genetic perturbations in individual cells while simultaneously measuring their gene expression and subcellular morphology. Using single-cell sequencing, we measure transcriptomic responses to the same genetic perturbations. We apply this approach to study hundreds of genetic perturbations in the mouse liver. Our study reveals regulators of hepatocyte zonation and liver unfolded protein response, as well as distinct pathways that cause hepatocyte steatosis. Our approach enables new ways of interrogating the genetic basis of complex cellular and organismal physiology and provides crucial training data for emerging machine-learning models of cellular function.

    View details for DOI 10.1101/2024.11.18.624217

    View details for PubMedID 39605605

    View details for PubMedCentralID PMC11601512