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. rapid technological improvements over the past decade have allowed single - cell datasets to grow both in size and number. this has led consortia, such as the human cell atlas, to pursue the generation of large - scale reference atlases of human organs. to advance our understanding of health and disease, such atlases must capture variation between individuals that is expected to impact the molecular phenotypes of the cells in a tissue. whereas the generation of atlases at this scale by single research groups is currently not feasible, integrating datasets generated by the research community at large will enable capture of the diversity of the cellular landscape across individuals. several foundational studies have started to map the cellular landscape of the healthy human lung. these studies each have a specific bias due to their choice of experimental protocol and technologies, and are therefore not tailored to serve as a universal reference. the studies moreover include only a limited number of samples and individuals, thus lacking the scale and diversity to capture the full cellular heterogeneity present within the lung as well as across individuals. integrated single - cell atlases provide novel insights not obtained in individual studies. recent reference atlases have led to the discovery of unknown cell types, the identification of marker genes that are reproducible across studies, the comparison of animal and in vitro models with human healthy and diseased tissue and patient stratification for disease endotypes. however, many currently available integrated atlases are limited in the number of human samples, datasets or cell types per organ, as well as donor metadata ( for example, age, body mass index ( bmi ) and smoking status ), or focus mainly on a specific disease. these limitations constrain the potential of atlases to serve as a reference, as they fail to represent and catalog the diversity of cellular phenotypes within the healthy organ and across individuals. moreover, when integrating data from different sources, it is paramount to correctly separate technical biases from biologically relevant information. yet, the majority of existing atlases have not assessed the quality of their data integration. nonetheless, successful integration of the available datasets into a single tissue atlas is a critical step in achieving the goals of the human cell atlas. in this resource, we present an integrated single - cell transcriptomic atlas of the human respiratory system, including the upper and lower airways, from published and newly generated datasets ( fig. 1 ). the human lung cell atlas ( hlca ) comprises data from 486 donors and 49 datasets, including 2. 4 million cells, which we re - annotate d to generate a consensus cell type reference. the hlca expands our understanding of the healthy lung and its changes in disease and can be used as a reference for analyzing future lung data. together, we provide a roadmap for building and using comprehensive, interpretable and up-to-date organ - and population - scale cell atlases. to build the hlca, we collected single - cell rna sequencing ( scrna - seq ) data and detailed, harmonized technical, biological and demographic metadata from 14 datasets ( 11 published and three unpublished ). these datasets include samples from 107 individuals, with diversity in age, sex, ethnicity ( harmonized as detailed in methods ), bmi and smoking status ( fig. 2a ). cells were obtained from 166 tissue samples using a variety of tissue donors, sampling methods, experimental protocols and sequencing platforms ( supplementary tables 1 and 2 ). anatomical locations of the samples were projected onto a one - dimensional ( 1d ) common coordinate framework ( ccf ), representing the proximal ( 0 ) to distal ( 1 ) axis of the respiratory system, to standar diz e the anatomical location of origin ( fig. 2a and supplementary tables 2 and 3 ). consensus definitions of cell types based on single - cell transcriptomic data across studies — particularly of transitional cell states — are lacking. to enable supervised data integration and downstream integrated analysis, we harmonized cell type nomenclature by building a five - level hierarchical cell identity reference framework ( methods, supplementary table 4 and fig. 2b ). we then unified cell type labeling across datasets by mapping the collected cell identity labels for every dataset as provided by the data generator to the hierarchical reference framework, showing varying cell type proportions per sample ( fig. 2c ). to optimally remove dataset - specific batch effects, we evaluated 12 different data integration methods on 12 datasets ( fig. 2d and supplementary fig. 1 ) using our previously established benchmarking pipeline. we used the top - performing integration method, scanvi, to create an integrated embedding of all 584, 444 cells of 107 individuals from the collected datasets: the hlca core ( fig. 3a ). a large - scale integrated atlas provides the unique opportunity to systematically investigate the consensus in cell type labeling across datasets. to identify areas of consensus and disagreement, we iteratively clustered the hlca core and investigated donor diversity and cell type label agreement in these clusters using entropy scores ( see methods ). most clusters contained cells from many donors ( extended data fig. 1a ). clusters with low donor diversity ( n = 14 ) were largely immune cell clusters ( n = 13 ), representing donor - or donor group - specific phenotypes. similarly, a high diversity of ( contradictory ) cell type labels ( high label entropy ) can identify both annotation disagreements between studies and clusters of doublets ( methods ). most clusters ( 61 out of 94 ) showed low label entropy, suggesting overall agreement of coarse cell type labels across datasets ( fig. 3b ). the remaining 33 clusters exhibited high label entropy, highlighting cellular phenotypes that were differently labeled across datasets ( fig. 3b ). for example, the immune cluster with the highest label entropy contained many cells that were originally mislabeled as monocytes and macrophages but were actually type 2 dendritic cells ( fig. 3c and extended data fig. 1b ). thus, populations with high label entropy identify mislabeled cell types, indicating the need for consensus re - annotation of the integrated atlas. as a first step to achieve such a consensus on the diversity of cell types present in the hlca core, we performed a full re - annotation of the integrated data on the basis of the original annotations and six expert opinions ( consensus annotation; methods and fig. 3d ). each of the 61 annotate d cell types ( supplementary table 5 ) was detected in at least four datasets out of 14, often in specific parts of the respiratory system, and different cell types showed varying fractions of proliferating ( mki67 ) cells ( extended data fig. 2a – c ). while our consensus cell type annotations partly correspond to original labels ( 41 % of cells ), there were also refinements ( 28 % ) and substantial re - annotations ( 31 %; fig. 3e and supplementary fig. 2 ).