Emma Dann

All Publications

• In vivo genome-wide CRISPR screens of human T cells in solid tumours. Nature Liu, Q., Chen, P. A., Urs, E., Zhang, S., Arce, M. M., Wang, C. H., Yan, J., Nguyen, V. Q., Li, Z., Seo, J., Kale, N., Peng, F., Luo, Y., Goudy, L., LaFlam, T. N., Zhong, H., Modak, C., Dann, E., Jung, J. H., Kirane, A., Warner, A. B., Quach, B. B., Good, Z., Shy, B. R., Shifrut, E., Bapat, S. P., Allen, G. M., Eyquem, J., Fuh, K., Dodgson, S. E., Cyster, J. G., Marson, A., Carnevale, J. 2026

  Abstract

  Large-scale CRISPR screening in human T cells holds significant promise for identifying genetic modifications that enhance cellular immunotherapy. Yet, many regulators of T cell performance in solid tumours are not revealed in vitro1,2. In vivo screening in tumour-bearing mice is more physiological but has been limited by low intratumoural T cell recovery. Here we developed an in vivo model that efficiently recovers human T cells from solid tumours, permitting genome-wide CRISPR screens with few mice. Tumour-infiltrating T cells from this model exhibit hallmarks of dysfunction compared with splenic T cells, creating an ideal screening context. We performed two genome-wide CRISPR knockout screens to identify regulators of intratumoural T cell abundance and effector function. The abundance screen revealed the P2RY8-Gα13 GPCR signalling axis as a negative regulator of T cell tumour infiltration. The effector function screen identified GNAS as a key driver of T cell dysfunction in tumours, whose product, Gαs, acts as a convergent node downstream of multiple GPCRs sensing distinct suppressive ligands. Knockout of GNAS rendered T cells resistant to multiple suppressive cues and significantly improved efficacy across diverse solid tumour models in chimeric antigen receptor (CAR) and T cell receptor (TCR) systems. Combinatorial knockout of P2RY8-GNAS further enhanced tumour control, demonstrating that complementary in vivo screens can identify orthogonal targets whose combined editing improves therapeutic potency. This flexible, scalable platform can be adapted for systematic discovery of genetic strategies to improve solid tumour T cell therapies.

  View details for DOI 10.1038/s41586-026-10906-9

  View details for PubMedID 42587162

  View details for PubMedCentralID 9433322

• anndataR improves interoperability between R and Python in single-cell transcriptomics. Bioinformatics (Oxford, England) Deconinck, L., Zappia, L., Cannoodt, R., Morgan, M., Core, S., Virshup, I., Sang-Aram, C., Bredikhin, D., Schilder, B., Seurinck, R., Saeys, Y. 2026

  Abstract

  Many single-cell transcriptomics datasets are stored in the HDF5-backed AnnData (H5AD) file format, as popularised by the Python scverse ecosystem. However, accessing these datasets from R, allowing users to take advantage of the strengths of each language, can be difficult. anndataR facilitates this access by allowing users to natively read and write H5AD files in R, convert them to and from SingleCellExperiment or Seurat objects, or even work with the resulting R AnnData object directly. We perform rigorous testing to ensure compatibility between Python-written and R-written H5AD files, guaranteeing long-term interoperability between languages.anndataR's source code is available on GitHub at scverse/anndataR under the MIT license. It is compatible with R version 4.5, has been archived at 10.5281/zenodo.18775712 and included within Bioconductor: 10.18129/B9.bioc.anndataR. Installation instructions and tutorials can be found in the online documentation at anndatar.scverse.org. Issues can be reported at the GitHub repository. Code to reproduce the analyses performed can be found on GitHub at LouiseDck/anndataR-paper, archived at 10.5281/zenodo.18792241.

  View details for DOI 10.1093/bioinformatics/btag288

  View details for PubMedID 42108549

• Systematic discovery of pro- and anti-HIV host factors in primary human CD4+ T cells. Cell Rathore, U., Dugan, E., Thornton, H., Kumar, V. E., Dajani, R., Burdick, R. C., Young, J. M., Steinhart, Z., Lao, R., Delviks-Frankenberry, K. A., Choi, W., Henriques, W. S., Echeverria, I., Dann, E., Dureja, I., Pathak, N., Arce, M. M., McKetney, J., Umhoefer, J. M., Parulekar, S., Schmidt, R., Polacco, B. J., Neidleman, J., Montano, M., Nguyen, V. Q., Sali, A., Levy, J. A., Tenthorey, J. L., Cheng, Y., Roan, N. R., Swaney, D. L., Kaake, R. M., Dodgson, S. E., Hiatt, J., Pathak, V. K., Malik, H. S., Krogan, N. J., Marson, A. 2026

  Abstract

  Host factors that promote or restrict human immunodeficiency virus (HIV) infection in human CD4+ T cells have not been comprehensively identified. We employed orthogonal genome-wide CRISPR activation (CRISPRa) and CRISPR knockout screens in primary CD4+ T cells to discover pro- and anti-HIV host factors systematically. Secondary pooled screens and individual perturbations validated high-confidence hits and revealed diverse mechanisms of action. CRISPRa uncovered multiple potent antiviral factors, including PI16, PPID, SHISA3, and ITM2A. PI16 interacts with host factors involved in HIV fusion and inhibits viral entry, whereas PPID (Cyp40), a paralog of the proviral cyclophilin CypA, binds capsid and reduces nuclear import of the HIV core. Structural modeling, evolutionary analyses, and targeted mutagenesis revealed domains and residues required for PPID-mediated HIV restriction, including non-human primate ortholog substitutions that enhance antiviral activity. Together, these data define the functional HIV-host interaction landscape in primary human T cells and uncover new mechanisms modulating infection.

  View details for DOI 10.1016/j.cell.2026.03.046

  View details for PubMedID 42013838

• Causal modelling of gene effects from regulators to programs to traits. Nature Ota, M., Spence, J. P., Zeng, T., Dann, E., Milind, N., Marson, A., Pritchard, J. K. 2025

  Abstract

  Genetic association studies provide a unique tool for identifying candidate causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose approaches to bridge this gap by combining quantitative estimates of gene-trait relationships from loss-of-function burden tests1 with gene-regulatory connections inferred from Perturb-seq experiments2 in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait3. As a proof of concept, we constructed a causal graph of the gene-regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetic association and biological mechanism.

  View details for DOI 10.1038/s41586-025-09866-3

  View details for PubMedID 41372418

  View details for PubMedCentralID 8596853

• Causal modeling of gene effects from regulators to programs to traits: integration of genetic associations and Perturb-seq. bioRxiv : the preprint server for biology Ota, M., Spence, J. P., Zeng, T., Dann, E., Marson, A., Pritchard, J. K. 2025

  Abstract

  Genetic association studies provide a unique tool for identifying causal links from genes to human traits and diseases. However, it is challenging to determine the biological mechanisms underlying most associations, and we lack genome-scale approaches for inferring causal mechanistic pathways from genes to cellular functions to traits. Here we propose new approaches to bridge this gap by combining quantitative estimates of gene-trait relationships from loss-of-function burden tests with gene-regulatory connections inferred from Perturb-seq experiments in relevant cell types. By combining these two forms of data, we aim to build causal graphs in which the directional associations of genes with a trait can be explained by their regulatory effects on biological programs or direct effects on the trait. As a proof-of-concept, we constructed a causal graph of the gene regulatory hierarchy that jointly controls three partially co-regulated blood traits. We propose that perturbation studies in trait-relevant cell types, coupled with gene-level effect sizes for traits, can bridge the gap between genetics and biology.

  View details for DOI 10.1101/2025.01.22.634424

  View details for PubMedID 39896538