Precise identification of cell states altered in disease using healthy single-cell references

作者：E. Dann, A. Cujba, Amanda J. Oliver, Kerstin B. Meyer, Sarah A. Teichmann, J. Marioni · 发表于：Nature Genetics · 年份：2023 · DOI：10.1038/s41588-023-01523-7 · 被引用次数：58 · 研究领域：Medicine

In single-cell studies, combining healthy reference atlases and designed control datasets allows more precise identification of disease-associated cell states. Joint analysis of single-cell genomics data from diseased tissues and a healthy reference can reveal altered cell states. We investigate whether integrated collections of data from healthy individuals (cell atlases) are suitable references for disease-state identification and whether matched control samples are needed to minimize false discoveries. We demonstrate that using a reference atlas for latent space learning followed by differential analysis against matched controls leads to improved identification of disease-associated cells, especially with multiple perturbed cell types. Additionally, when an atlas is available, reducing control sample numbers does not increase false discovery rates. Jointly analyzing data from a COVID-19 cohort and a blood cell atlas, we improve detection of infection-related cell states linked to distinct clinical severities. Similarly, we studied disease states in pulmonary fibrosis using a healthy lung atlas, characterizing two distinct aberrant basal states. Our analysis provides guidelines for designing disease cohort studies and optimizing cell atlas use.