To advance our understanding of health and disease, such atlases must capture variation between individuals that is expected to impact the molecular phenotypes of the cells in a tissue. This has led consortia, such as the Human Cell Atlas, to pursue the generation of large-scale reference atlases of human organs 2, 3. Rapid technological improvements over the past decade have allowed single-cell datasets to grow both in size and number 1. ![]() Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Mapping new data to the HLCA enables rapid data annotation and interpretation. ![]() Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. ![]() Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Single-cell technologies have transformed our understanding of human tissues. ![]() Nature Medicine volume 29, pages 1563–1577 ( 2023) Cite this article
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