RESUMEN
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.
RESUMEN
The COVID-19 pandemic, caused by SARS coronavirus 2 (SARS-CoV-2), has resulted in excess morbidity and mortality as well as economic decline. To characterise the systemic host immune response to SARS-CoV-2, we performed single-cell RNA-sequencing coupled with analysis of cell surface proteins, providing molecular profiling of over 800,000 peripheral blood mononuclear cells from a cohort of 130 patients with COVID-19. Our cohort, from three UK centres, spans the spectrum of clinical presentations and disease severities ranging from asymptomatic to critical. Three control groups were included: healthy volunteers, patients suffering from a non-COVID-19 severe respiratory illness and healthy individuals administered with intravenous lipopolysaccharide to model an acute inflammatory response. Full single cell transcriptomes coupled with quantification of 188 cell surface proteins, and T and B lymphocyte antigen receptor repertoires have provided several insights into COVID-19: 1. a new non-classical monocyte state that sequesters platelets and replenishes the alveolar macrophage pool; 2. platelet activation accompanied by early priming towards megakaryopoiesis in immature haematopoietic stem/progenitor cells and expansion of megakaryocyte-primed progenitors; 3. increased clonally expanded CD8+ effector:effector memory T cells, and proliferating CD4+ and CD8+ T cells in patients with more severe disease; and 4. relative increase of IgA plasmablasts in asymptomatic stages that switches to expansion of IgG plasmablasts and plasma cells, accompanied with higher incidence of BCR sharing, as disease severity increases. All data and analysis results are available for interrogation and data mining through an intuitive web portal. Together, these data detail the cellular processes present in peripheral blood during an acute immune response to COVID-19, and serve as a template for multi-omic single cell data integration across multiple centers to rapidly build powerful resources to help combat diseases such as COVID-19.