Spatial single-cell atlas reveals regional variations in healthy and diseased human lung.
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Integration of scRNA-seq data from millions of cells revealed a high diversity of cell types in the healthy and diseased human lung. In a large and complex organ, constantly exposed to external agents, it is crucial to understand the influence of lung tissue topography or external factors on gene expression variability within cell types. Here, we apply three spatial transcriptomics approaches, to: (i) localize the majority of lung cell types, including rare epithelial cells within the tissue topography, (ii) describe consistent anatomical and regional gene expression variability within and across cell types, and (iii) reveal distinct cellular neighborhoods in specific anatomical regions and examine gene expression variations in them. We thus provide a spatially resolved tissue reference atlas in three representative regions of the healthy human lung. We further demonstrate its utility by defining previously unknown imbalances of epithelial cell type compositions in chronic obstructive pulmonary disease lungs. Our topographic atlas enables a precise description of characteristic regional cellular responses upon experimental perturbations or during disease progression.
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Acknowledgements: We acknowledge discovAIR grant agreement 874656 funding all authors of this manuscript, and all members of discovAIR consortium, especially Karen van Eunen, Pascal Barbry, Laure-Emmanuelle Zaragosi, Amanda Oliver and Elo Madissoon. We gratefully acknowledge the provision of human biomaterial and clinical data from the CPC-M bioArchive and its partners at the Asklepios Biobank Gauting, the Klinikum der Universität München and the Ludwig-Maximilians-Universität München, especially Anja Disovic and Marion Frankenberger. We are grateful to the lung tissue and organ donors and their families for the gift of human tissue, and we thank the patients and their families for their support. We also kindly acknowledge the ISS facility in Science for Life Laboratories, Stockholm, Sweden, including Chika Yokota and Amitha Raman, for performing the HybISS experiments. Finally, we acknowledge Jan-Olov Persson at The Statistical research group (SFG), Department of Mathematics, Stockholm University, for consulting in statistical data analysis. FK and PH acknowledge support from the TKP2021-EGA09, Horizon-BIALYMPH, Horizon-SYMMETRY, Horizon-SWEEPICS, Horizon-Fair-CHARM, HAS-NAP3, OTKA-SNN no. 139455/ARRS, and Finnish Cancer Society. This work was supported by the EU Horizon Program (DiscovAIR). MN acknowledges Cancerfonden grant CAN 2021/1726. MCN was supported by the Chan Zuckerberg Initiative, LLC Seed Network grant CZF2019-002438 “Lung Cell Atlas 1.0”. CS laboratory is supported by VR 2019-04893 from Cancerfonden 21 1794 P = 1H and Erling-Persson Foundation 2023-0035.
Funder: Erling-Persson Foundation, Number 2023-0035
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2041-1723
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Cancerfonden (Swedish Cancer Society) (21 1794 P=1H)

