Universal recording of immune cell interactions in vivo.

Immune cells rely on transient physical interactions with other immune and non-immune populations to regulate their function1. To study these 'kiss-and-run' interactions directly in vivo, we previously developed LIPSTIC (labelling immune partnerships by SorTagging intercellular contacts)2, an approach that uses enzymatic transfer of a labelled substrate between the molecular partners CD40L and CD40 to label interacting cells. Reliance on this pathway limited the use of LIPSTIC to measuring interactions between CD4+ T helper cells and antigen-presenting cells, however. Here we report the development of a universal version of LIPSTIC (uLIPSTIC), which can record physical interactions both among immune cells and between immune and non-immune populations irrespective of the receptors and ligands involved. We show that uLIPSTIC can be used, among other things, to monitor the priming of CD8+ T cells by dendritic cells, reveal the steady-state cellular partners of regulatory T cells and identify germinal centre-resident T follicular helper cells on the basis of their ability to interact cognately with germinal centre B cells. By coupling uLIPSTIC with single-cell transcriptomics, we build a catalogue of the immune populations that physically interact with intestinal epithelial cells at the steady state and profile the evolution of the interactome of lymphocytic choriomeningitis virus-specific CD8+ T cells in multiple organs following systemic infection. Thus, uLIPSTIC provides a broadly useful technology for measuring and understanding cell-cell interactions across multiple biological systems.

A global genetic interaction network by single-cell imaging and machine learning.

Cellular and organismal phenotypes are controlled by complex gene regulatory networks. However, reference maps of gene function are still scarce across different organisms. Here, we generated synthetic genetic interaction and cell morphology profiles of more than 6,800 genes in cultured Drosophila cells. The resulting map of genetic interactions was used for machine learning-based gene function discovery, assigning functions to genes in 47 modules. Furthermore, we devised Cytoclass as a method to dissect genetic interactions for discrete cell states at the single-cell resolution. This approach identified an interaction of Cdk2 and the Cop9 signalosome complex, triggering senescence-associated secretory phenotypes and immunogenic conversion in hemocytic cells. Together, our data constitute a genome-scale resource of functional gene profiles to uncover the mechanisms underlying genetic interactions and their plasticity at the single-cell level.

Universal recording of cell-cell contacts in vivo for interaction-based transcriptomics.

Cellular interactions are essential for tissue organization and functionality. In particular, immune cells rely on direct and usually transient interactions with other immune and non-immune populations to specify and regulate their function. To study these "kiss-and-run" interactions directly in vivo, we previously developed LIPSTIC (Labeling Immune Partnerships by SorTagging Intercellular Contacts), an approach that uses enzymatic transfer of a labeled substrate between the molecular partners CD40L and CD40 to label interacting cells. Reliance on this pathway limited the use of LIPSTIC to measuring interactions between CD4+ helper T cells and antigen presenting cells, however. Here, we report the development of a universal version of LIPSTIC (uLIPSTIC), which can record physical interactions both among immune cells and between immune and non-immune populations irrespective of the receptors and ligands involved. We show that uLIPSTIC can be used, among other things, to monitor the priming of CD8+ T cells by dendritic cells, reveal the cellular partners of regulatory T cells in steady state, and identify germinal center (GC)-resident T follicular helper (Tfh) cells based on their ability to interact cognately with GC B cells. By coupling uLIPSTIC with single-cell transcriptomics, we build a catalog of the immune populations that physically interact with intestinal epithelial cells (IECs) and find evidence of stepwise acquisition of the ability to interact with IECs as CD4+ T cells adapt to residence in the intestinal tissue. Thus, uLIPSTIC provides a broadly useful technology for measuring and understanding cell-cell interactions across multiple biological systems.

Common human genetic variants of APOE impact murine COVID-19 mortality.

Clinical outcomes of severe acute respiratory syndrome 2 (SARS-CoV-2) infection are highly heterogeneous, ranging from asymptomatic infection to lethal coronavirus disease 2019 (COVID-19). The factors underlying this heterogeneity remain insufficiently understood. Genetic association studies have suggested that genetic variants contribute to the heterogeneity of COVID-19 outcomes, but the underlying potential causal mechanisms are insufficiently understood. Here we show that common variants of the apolipoprotein E (APOE) gene, homozygous in approximately 3% of the world's population1 and associated with Alzheimer's disease, atherosclerosis and anti-tumour immunity2-5, affect COVID-19 outcome in a mouse model that recapitulates increased susceptibility conferred by male sex and advanced age. Mice bearing the APOE2 or APOE4 variant exhibited rapid disease progression and poor survival outcomes relative to mice bearing the most prevalent APOE3 allele. APOE2 and APOE4 mice exhibited increased viral loads as well as suppressed adaptive immune responses early after infection. In vitro assays demonstrated increased infection in the presence of APOE2 and APOE4 relative to APOE3, indicating that differential outcomes are mediated by differential effects of APOE variants on both viral infection and antiviral immunity. Consistent with these in vivo findings in mice, our results also show that APOE genotype is associated with survival in patients infected with SARS-CoV-2 in the UK Biobank (candidate variant analysis, P = 2.6 × 10-7). Our findings suggest APOE genotype to partially explain the heterogeneity of COVID-19 outcomes and warrant prospective studies to assess APOE genotyping as a means of identifying patients at high risk for adverse outcomes.

Common germline variants of the human APOE gene modulate melanoma progression and survival.

Common germline variants of the APOE gene are major risk modifiers of neurodegenerative and atherosclerotic diseases1-3, but their effect on cancer outcome is poorly defined. Here we report that, in a reversal of their effect on Alzheimer's disease, the APOE4 and APOE2 variants confer favorable and poor outcomes in melanoma, respectively. Mice expressing the human APOE4 allele exhibited reduced melanoma progression and metastasis relative to APOE2 mice. APOE4 mice exhibited enhanced anti-tumor immune activation relative to APOE2 mice, and T cell depletion experiments showed that the effect of APOE genotype on melanoma progression was mediated by altered anti-tumor immunity. Consistently, patients with melanoma carrying the APOE4 variant experienced improved survival in comparison to carriers of APOE2. Notably, APOE4 mice also showed improved outcomes under PD1 immune checkpoint blockade relative to APOE2 mice, and patients carrying APOE4 experienced improved anti-PD1 immunotherapy survival after progression on frontline regimens. Finally, enhancing APOE expression via pharmacologic activation of liver X receptors, previously shown to boost anti-tumor immunity4, exhibited therapeutic efficacy in APOE4 mice but not in APOE2 mice. These findings demonstrate that pre-existing hereditary genetics can impact progression and survival outcomes of a future malignancy and warrant prospective investigation of APOE genotype as a biomarker for melanoma outcome and therapeutic response.