TY - JOUR
KW - COVID-19
KW - Humans
KW - Lung
KW - Lung Neoplasms
KW - Macrophages
KW - Pulmonary Fibrosis
AU - Lisa Sikkema
AU - Ciro Ramírez-Suástegui
AU - Daniel C. Strobl
AU - Tessa E. Gillett
AU - Luke Zappia
AU - Elo Madissoon
AU - Nikolay S. Markov
AU - Laure-Emmanuelle Zaragosi
AU - Yuge Ji
AU - Meshal Ansari
AU - Marie-Jeanne Arguel
AU - Leonie Apperloo
AU - Martin Banchero
AU - Christophe Bécavin
AU - Marijn Berg
AU - Evgeny Chichelnitskiy
AU - Mei-I. Chung
AU - Antoine Collin
AU - Aurore C. A. Gay
AU - Janine Gote-Schniering
AU - Baharak Hooshiar Kashani
AU - Kemal Inecik
AU - Manu Jain
AU - Theodore S. Kapellos
AU - Tessa M. Kole
AU - Sylvie Leroy
AU - Christoph H. Mayr
AU - Amanda J. Oliver
AU - Michael von Papen
AU - Lance Peter
AU - Chase J. Taylor
AU - Thomas Walzthoeni
AU - Chuan Xu
AU - Linh T. Bui
AU - Carlo De Donno
AU - Leander Dony
AU - Alen Faiz
AU - Minzhe Guo
AU - Austin J. Gutierrez
AU - Lukas Heumos
AU - Ni Huang
AU - Ignacio L. Ibarra
AU - Nathan D. Jackson
AU - Preetish Kadur Lakshminarasimha Murthy
AU - Mohammad Lotfollahi
AU - Tracy Tabib
AU - Carlos Talavera-López
AU - Kyle J. Travaglini
AU - Anna Wilbrey-Clark
AU - Kaylee B. Worlock
AU - Masahiro Yoshida
AU - Lung Biological Network Consortium
AU - Maarten van den Berge
AU - Yohan Bossé
AU - Tushar J. Desai
AU - Oliver Eickelberg
AU - Naftali Kaminski
AU - Mark A. Krasnow
AU - Robert Lafyatis
AU - Marko Z. Nikolić
AU - Joseph E. Powell
AU - Jayaraj Rajagopal
AU - Mauricio Rojas
AU - Orit Rozenblatt-Rosen
AU - Max A. Seibold
AU - Dean Sheppard
AU - Douglas P. Shepherd
AU - Don D. Sin
AU - Wim Timens
AU - Alexander M. Tsankov
AU - Jeffrey Whitsett
AU - Yan Xu
AU - Nicholas E. Banovich
AU - Pascal Barbry
AU - Thu Elizabeth Duong
AU - Christine S. Falk
AU - Kerstin B. Meyer
AU - Jonathan A. Kropski
AU - Dana Pe'er
AU - Herbert B. Schiller
AU - Purushothama Rao Tata
AU - Joachim L. Schultze
AU - Sara A. Teichmann
AU - Alexander V. Misharin
AU - Martijn C. Nawijn
AU - Malte D. Luecken
AU - Fabian J. Theis
AB - 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.
BT - Nature Medicine
DA - 2023-06
DO - 10.1038/s41591-023-02327-2
IS - 6
LA - eng
N2 - 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.
PY - 2023
SP - 1563
EP - 1577
T2 - Nature Medicine
TI - An integrated cell atlas of the lung in health and disease
VL - 29
SN - 1546-170X
ER -