Single-cell functional genomics reveals determinants of sensitivity and resistance to natural killer cells in blood cancers.

Cancer cells can evade natural killer (NK) cell activity, thereby limiting anti-tumor immunity. To reveal genetic determinants of susceptibility to NK cell activity, we examined interacting NK cells and blood cancer cells using single-cell and genome-scale functional genomics screens. Interaction of NK and cancer cells induced distinct activation and type I interferon (IFN) states in both cell types depending on the cancer cell lineage and molecular phenotype, ranging from more sensitive myeloid to less sensitive B-lymphoid cancers. CRISPR screens in cancer cells uncovered genes regulating sensitivity and resistance to NK cell-mediated killing, including adhesion-related glycoproteins, protein fucosylation genes, and transcriptional regulators, in addition to confirming the importance of antigen presentation and death receptor signaling pathways. CRISPR screens with a single-cell transcriptomic readout provided insight into underlying mechanisms, including regulation of IFN-γ signaling in cancer cells and NK cell activation states. Our findings highlight the diversity of mechanisms influencing NK cell susceptibility across different cancers and provide a resource for NK cell-based therapies.

Generation and characterisation of scalable and stable human pluripotent stem cell-derived microvascular-like endothelial cells for cardiac applications.

Coronary microvascular disease (CMD) and its progression towards major adverse coronary events pose a significant health challenge. Accurate in vitro investigation of CMD requires a robust cell model that faithfully represents the cells within the cardiac microvasculature. Human pluripotent stem cell-derived endothelial cells (hPSC-ECs) offer great potential; however, they are traditionally derived via differentiation protocols that are not readily scalable and are not specified towards the microvasculature. Here, we report the development and comprehensive characterisation of a scalable 3D protocol enabling the generation of phenotypically stable cardiac hPSC-microvascular-like ECs (hPSC-CMVECs) and cardiac pericyte-like cells. These were derived by growing vascular organoids within 3D stirred tank bioreactors and subjecting the emerging 3D hPSC-ECs to high-concentration VEGF-A treatment (3DV). Not only did this promote phenotypic stability of the 3DV hPSC-ECs; single cell-RNA sequencing (scRNA-seq) revealed the pronounced expression of cardiac endothelial- and microvascular-associated genes. Further, the generated mural cells attained from the vascular organoid exhibited markers characteristic of cardiac pericytes. Thus, we present a suitable cell model for investigating the cardiac microvasculature as well as the endothelial-dependent and -independent mechanisms of CMD. Moreover, owing to their phenotypic stability, cardiac specificity, and high angiogenic potential, the cells described within would also be well suited for cardiac tissue engineering applications.

Umbilical cord blood DNA methylation in children who later develop type 1 diabetes.

AIMS/HYPOTHESIS: Distinct DNA methylation patterns have recently been observed to precede type 1 diabetes in whole blood collected from young children. Our aim was to determine whether perinatal DNA methylation is associated with later progression to type 1 diabetes. METHODS: Reduced representation bisulphite sequencing (RRBS) analysis was performed on umbilical cord blood samples collected within the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) Study. Children later diagnosed with type 1 diabetes and/or who tested positive for multiple islet autoantibodies (n = 43) were compared with control individuals (n = 79) who remained autoantibody-negative throughout the DIPP follow-up until 15 years of age. Potential confounding factors related to the pregnancy and the mother were included in the analysis. RESULTS: No differences in the umbilical cord blood methylation patterns were observed between the cases and controls at a false discovery rate <0.05. CONCLUSIONS/INTERPRETATION: Based on our results, differences between children who progress to type 1 diabetes and those who remain healthy throughout childhood are not yet present in the perinatal DNA methylome. However, we cannot exclude the possibility that such differences would be found in a larger dataset.

Patient-tailored design for selective co-inhibition of leukemic cell subpopulations.

The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.