in some cases, these proportions are highly dependent on the tissue sampling method and the dissociation protocol used ( for example, for smooth muscle fam83d cells; extended data fig. 5 ). these observations shed light on the effects of biological and technical factors on the abundance of cell types in different parts of the lung and can help guide important choices in study design. the hlca core contains an unprecedented diversity of donors, sampling protocols and cell identities, and can serve as a transcriptomic reference for lung research. new datasets can be mapped to this reference to substantially speed up data analysis by transferring consensus cell identity annotations to the new data. we tested this on a recently released multimodal lung dataset ( methods, fig. 6a and extended data fig. 6 ). overall, the transferred labels were correct in the majority of cases, with 68 % of the cells correctly labeled, 14 % of labels incorrectly labeled and 18 % set to unknown due to highly uncertain labeling ( fig. 5b and methods ). uncertain labels were observed specifically in continuous transitions from one cell type to another and among cellular identities not present in the hlca core, including rare cell identities ( erythrocytes ( n = 328 ), chondrocytes ( n = 42 ), myelinating schwann cells ( n = 7 ), nonmyelinating schwann cells ( n = 29 ) and nerve - associated fibroblasts ( n = 66 ); fig. 5b and extended data fig. 6d ). taken together, these results show that the hlca core can be used for highly detailed annotation of new datasets, while allowing for the identification of unknown cell types in these datasets based on label transfer uncertainty. single - cell studies of disease rely on adequate, matching control samples to allow correct identification of disease-specific changes. to demonstrate the ability of the hlca core to serve as a comprehensive healthy control and contextualize disease data, we mapped scrna - seq data from lung cancer samples to the hlca core ( methods and extended data fig. 7a – c ). using hlca label transfer, we correctly identified cell states missing from the hlca core as unknown ( cancer cells and erythroblasts ). the remaining cells were annotate d correctly in 77 %, incorrectly in 1 % and as unknown in 22 % of cases ( extended data fig. 7d – g ). a finding of the original study was the separation of endothelial cells into tumor - associated and normal cells. clustering of the projected dataset with the hlca reference showed that cells expressing the suggested tumor - associated marker ackr1 were also abundant in healthy tissue from the hlca core, specifically in venous endothelial cells ( both pulmonary and systemic, fig. 5c and supplementary fig. 6a – c ). this suggests that ackr1 is a general marker of venous endothelial cells rather than a tumor - specific endothelial cell marker. similarly, the reported normal endothelial cell marker ednrb characterizes aerocyte capillary endothelial cells, both in tumor and in healthy tissue ( fig. 5c and supplementary fig. 6d ). as endothelial cell numbers in the original study were low, correctly identifying and disting uishing these cell types without a larger healthy reference is challenging. thus, by serving as a comprehensive healthy control, the hlca prevents misinterpretation of limitations in sampling and experimental design as meaningful differences between healthy and diseased tissue. in addition, the hlca can provide context to the results of large - scale genetic studies of disease. genome-wide association studies ( gwass ) link disease with specific genomic variants that may confer an increased risk of disease. previous studies have linked such variants to cell type - specific mechanistic hypotheses, which are often lacking in the initial association study. yet, these studies fail to include all known lung cell types in their cell type reference. to demonstrate the value of the hlca core in contextualizing genetic data, we mapped association results from four gwass of lung function or disease to the hlca core cell types, by testing significant enrichment of both weakly and strongly disease-associated variants in regions of genes that characterize each cell type ( fig. 5d, supplementary fig. 7 and methods ). we show that genomic variants linked to lung function ( forced vital capacity ) are associated with smooth muscle ( adjusted p value ( p adj ) = 0. 07 ), alveol ar fibroblasts ( p adj = 0. 07 ), peribr onchial fibroblasts ( p adj = 0. 07 ) and myof ibroblasts ( p adj = 0. 07 ), suggesting that these fibroblast subtypes play a causative role in inherited differences in lung function. we further find a significant association of lung t cells with asthma - associated single-nucleotide polymorphisms ( snps ) ( p adj = 0. 005 ). lung adenocarcinoma - associated variants trend towards at2 cells ( p adj = 0. 18 ) and myof ibroblasts are significantly associated with chronic obstructive pulmonary disease ( copd ) gwas snps ( p adj = 0. 04 ). thus, by linking genetic predispositions to lung cell types, the hlca core serves as a valuable resource with which to improve our understanding of lung function and disease. finally, the hlca can be used as a reference for cell type deconvolution of bulk rna expression samples, which have been shown to reflect cell type proportions more ac curate ly than scrna - seq datasets. inferring cell type proportions from bulk rna samples from nasal brushings and bronchial biopsies using the hlca core ( supplementary table 10, supplementary fig. 8a and methods ) revealed no significant cell type compositional changes associated with corticosteroid inhalation or asthma, respectively ( supplementary fig. 8b, c and supplementary table 11 ). in contrast, we find that the proportion of capillary endothelial cells in lung resection tissue from the lung tissue database is higher in samples from patients with severe copd ( gold stage 3 or 4 ) than in those from non - copd controls matched for age and smoking history ( p adj = 0. 0004 ). conversely, alveol ar and interstitial macrophages, at2 cells and dendritic cells decrease in proportion ( fig. 5e, supplementary fig. 8d and supplementary table 11; p adj = 0. 0007, 0. 0003, 0. 005 and 3. 21 × 10, respectively ). finally, smooth muscle shows the largest shift in proportion, increasing significantly in patients with severe copd ( p = 1. 85 × 10 ) in line with previous work. as deconvolution of bulk samples using the hlca can reveal disease-specific changes in cell type composition, we provide publicly available preprocessed cell type signature matrices based on the hlca core ( https: / / github. com / lungcellatlas / hlca ). as knowledge of cell types in the lung expands, and the sizes of newly generated datasets increase, annotations in the hlca core will need to be further refined. the hlca and its annotations can be updated by learning from new data projected onto the reference. we simulated such an hlca update using the previously projected healthy lung dataset, specifically focusing on the cell identities that were disting uished based on their tissue location in matched spatial transcriptomic data ( spatially annotate d cell types ). these cell identities were present at very low frequencies ( median: 0. 005 % of all cells; supplementary fig. 9a ). both spatially annotate d mesenchymal cell types with more than 40 cells ( immune-recruiting fibroblasts and chondrocytes ) and two rare cell types ( myelinating schwann cells and perineurial nerve - associated fibroblasts ) were recovered in distinc t clusters ( spatially annotate d clusters ), and three of these ( all except chondrocytes ) also contained cells from the hlca core, thereby enabling a refinement of existing hlca core annotations using the spatial context from the projected dataset ( fig. 5f and supplementary fig. 9b, c ).