Precise identification of cell states altered indisease with healthy single-cell references

Single cell genomics is a powerful tool to distinguish altered cell states in disease tissue samples, through joint analysis with healthy reference datasets. Collections of data from healthy individuals are being integrated in cell atlases that provide a comprehensive view of cellular phenotypes in a tissue. However, it remains unclear whether atlas datasets are suitable references for disease-state identification, or whether matched control samples should be employed, to minimise false discoveries driven by biological and technical confounders. Here we quantitatively compare the use of atlas and control datasets as references for identification of disease-associated cell states, on simulations and real disease scRNA-seq datasets. We find that reliance on a single type of reference dataset introduces false positives. Conversely, using an atlas dataset as reference for latent space learning followed by differential analysis against a matched control dataset leads to precise identification of disease-associated cell states. We show that, when an atlas dataset is available, it is possible to reduce the number of control samples without increasing the rate of false discoveries. Using a cell atlas of blood cells from 12 studies to contextualise data from a case-control COVID-19 cohort, we sensitively detect cell states associated with infection, and distinguish heterogeneous pathological cell states associated with distinct clinical severities. Our analysis provides guiding principles for design of disease cohort studies and efficient use of cell atlases within the Human Cell Atlas.

A gene locus that controls expression of ACE2 in virus infection

The SARS-CoV-2 pandemic has resulted in widespread morbidity and mortality globally. ACE2 is a receptor for SARS-CoV-2 and differences in expression may affect susceptibility to COVID-19. Using HCV-infected liver tissue from 195 individuals, we discovered that among genes negatively correlated with ACE2, interferon signalling pathways were highly enriched and observed down-regulation of ACE2 after interferon-alpha treatment. Negative correlation was also found in the gastrointestinal tract and in lung tissue from a murine model of SARS-CoV-1 infection suggesting conserved regulation of ACE2 across tissue and species. Performing a genome-wide eQTL analysis, we discovered that polymorphisms in the interferon lambda (IFNL) region are associated with ACE2 expression. Increased ACE2 expression in the liver was also associated with age and presence of cirrhosis. Polymorphisms in the IFNL region may impact not only antiviral responses but also ACE2 with potential consequences for clinical outcomes in distinct ethnic groups and with implications for therapeutic interventions.