An integrated cell atlas of the lung in health and disease

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.

Context-aware single-cell multiome approach identified cell-type specific lung cancer susceptibility genes

“…At the level of cell categories, epithelial and immune cell types showed higher mean TRS compared to endothelial and stromal cell types (3.60 and 3.57 vs 2.71 and 2.84, respectively) ( Figure S4 ). Notably, AT2 cells and AT2-proliferating cells displayed the highest mean TRS, which is consistent with a previous study pointing to AT2 cells for showing a suggestive enrichment of lung adenocarcinoma susceptibility based on lung scRNAseq data 18 . We then defined the cell-type specificity for each CCV-colocalized cCRE by assessing whether the cCRE were being exclusively called in specific cell types or categories ( Methods ).…”

“…Conversely, cell-type private cCREs displayed a higher proportion of intronic (48.4%) and intergenic cCREs (29.6%), which is consistent with the roles of distal enhancers in cell-type specific gene regulation 24 . Notably, we identified a rare cell type, AT2-proliferting cells (0.13%), which shows markers of both AT2 ( SFTPD ) and club cells ( SCGB3A2 ) but specifically expresses the markers of cell proliferation, STMN1 , TYMS , TOP2A , CDK1 , and MKI67 similar to two previous studies (“AT2-proliferating” and “cycling-AT2”) 18,19 ( Figure 2D ). Consistent with the gene expression data, we observed distinct chromatin accessibility around the promotor-adjacent enhancer regions of STMN1 , TYMS , CDK1 , and MKI67 in AT2-proliferating cells when compared to those in AT2 or club cells.…”

“…In addition, we provide evidence for the long-regarded importance of immune cell populations in lung tumorigenesis. While a recent LD score regression analysis based on lung single cell expression data suggested the importance of AT2 cells in lung adenocarcinoma heritability 18 , previous LD score regression results using bulk tissues and cell-line based annotation datasets hinted at the roles of immune cells in lung cancer susceptibility, especially in European smokers 5,37 . Immune cell populations could interact with tumor cells originating from epithelial cells to promote their growth and selection through inflammatory signaling, for example, but they could also suppress tumor cell growth via immune surveillance during the trajectory of tumorigenesis.…”

“…The ‘FindClusters’ function was applied for clustering using smart local moving (SLM) algorithm for modularity optimization at a resolution of 0.5. Clusters were annotated based on canonical marker genes reported by three independent studies 15,18,22 . Clusters expressing canonical marker genes from two or more different cell types were designated as putative doublets and excluded, after which re-clustering was performed using the same parameters.…”

“…To this end, single-cell multiome approaches combining single-nucleus RNA-sequencing (snRNA-seq) and single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) can provide a powerful alternative to annotating GWAS loci 20,21 . While there are abundant single-cell based resources for human lung tissues as recently compiled by Human Lung Cell Atlas 18 , single-cell profiling of chromatin accessibility in human lung tissues is still limited to a small number of datasets 22 and to our knowledge a joint profiling of transcriptome and chromatin accessibility in the same barcoded cells has not been performed. Moreover, cell-type specific gene expression changes by lung cancer-relevant exposures such as smoking have not been formally profiled in human lung tissues.…”