Background
The human lung is a highly complex organ characterised by extensive cellular heterogeneity, making it susceptible to a broad range of diseases. Single-cell transcriptomics has shed light on disease-specific cellular features, but previous studies have been fragmented, limiting a unified understanding of cellular mechanisms across various lung diseases. Furthermore, high-resolution reference atlases for lung cells are lacking, impeding the effective integration of spatial omics data and the exploration of shared pathogenic mechanisms.
Methods
We constructed uniLUNG, the most comprehensive single-cell RNA sequencing atlas of the human lung, by integrating 62 published datasets comprising 9.2 million cells from 1807 donors across health and 17 disease conditions. We leveraged this high-resolution cell atlas to seek distinct cell types across diverse lung pathologies. By integrating spatial transcriptomics, we also identified transitional cell populations in lung cancer, and provided new insights into tumour evolution and the associated microenvironment.
Findings
We present a comprehensive lung cell atlas, encompassing cellular data across major lung diseases and health states. Using this resource, we identified distinct cell populations, such as Lym-monocytes and T-like B cells, which are specifically enriched in certain lung diseases and linked to immune dysregulation. Furthermore, our spatially resolved multi-omics analysis revealed a transitional malignant subpopulation, NSCLC-like SCLC, which plays a key role in the transformation from non-small cell lung cancer (NSCLC) to small cell lung cancer (SCLC), driving tumour microenvironment remodelling.
Interpretation
We offered a high-resolution, cross-disease lung cell reference that uncovers distinct cell types and cellular transitions critical to disease progression and therapeutic resistance. This resource provides essential insights into lung disease mechanisms and has important implications for the development of targeted therapeutic strategies, particularly in the context of lung cancer.
Funding
This work was supported by the grants of National Key R&D Program of China (2021YFF1200900 and 2021YFF1200903), National Natural Science Foundation of China (92474107), Guangdong Basic and Applied Basic Research Foundation of China (2022B1515120077), Major Project of Guangzhou National Laboratory of China (GZNL2024A01003), and Support Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship of China (2020007).
