地图集(解剖学)
电池类型
人口
生物
注释
细胞
计算生物学
疾病
生物信息学
医学
病理
遗传学
环境卫生
古生物学
作者
Lisa Sikkema,Ciro Ramírez-Suástegui,Daniel Strobl,Tessa E. Gillett,Luke Zappia,Elo Madissoon,Nikolay S. Markov,Laure‐Emmanuelle Zaragosi,Yuge Ji,Meshal Ansari,Marie‐Jeanne Arguel,Leonie Apperloo,Martin Banchero,Christophe Bécavin,Marijn Berg,Evgeny Chichelnitskiy,Mei-I Chung,Antoine Collin,Aurore Gay,Janine Gote-Schniering
出处
期刊:Nature Medicine
[Springer Nature]
日期:2023-06-01
卷期号:29 (6): 1563-1577
被引量:571
标识
DOI:10.1038/s41591-023-02327-2
摘要
Abstract Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. 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. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. 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. Mapping new data to the HLCA enables rapid data annotation and interpretation. 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. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.
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