in this manner the hlca core and its annotations can be refined by mapping new datasets to the atlas and incorporating annotations from these new datasets into the reference. to extend the atlas and include samples from lung disease, we mapped 1, 797, 714 cells from 380 healthy and diseased individuals from 37 datasets ( four unpublished and 33 published ) to the hlca core using scarches, bringing the hlca to a total of 2. 4 million cells from 486 individuals ( fig. 6a and supplementary table 1 ). label transfer from the hlca core to the newly mapped datasets enabled detailed cell type annotation across datasets even for rare cells, including 2, 048 migratory dendritic cells identified across 28 datasets with label transfer, whereas this cell type was originally labeled in only two of 12 labeled datasets ( extended data fig. 8 ). out of 37 new datasets, 27 were observed to map well to the hlca, as evaluated by the mean label transfer uncertainty score ( fig. 6b, supplementary fig. 10a and methods ). the remaining ten datasets were often from coronavirus disease 2019 ( covid - 19 ) studies or, unlike the hlca core, contained pediatric samples ( fig. 6b, c and supplementary fig. 10b ). in these datasets, higher uncertainty values may be attributable to true biological differences between the mapped data and the hlca core adult, healthy lung samples. overall, the successfully mapped datasets include disease samples, as well as single-nucleus and single - cell data from multiple chemistries ( fig. 6b ), demonstrating the potential of the hlca core as a universal reference. pulmonary diseases are characterized by the emergence of unique disease-associated transcriptional phenotypes. we observed higher levels of label transfer uncertainty in datasets from diseased lungs ( fig. 6b, condition ), possibly flagging cell types changed in response to disease. specifically, labels of alveol ar fibroblasts and alveol ar macrophages, which interact to form a dysregulated cellular circuit in idiopathic pulmonary fibrosis ( ipfs ), are transferred with higher uncertainty in ipf samples than in samples from healthy controls from the same dataset ( fig. 6d and extended data fig. 9a, b ). furthermore, uncertainty scores separate cells — derived from donors with ipf — within these cell types into more and less affected subsets: the genes more highly expressed in the high - uncertainty subset are also lowly expressed in healthy samples ( fig. 6e ). genes downregulated in high - uncertainty ipf macrophages are associated with homeostatic functions of tissue - resident alveol ar macrophages and lipid metabolism ( pparg, fabp4 and others ), while upregulated genes are associated with extracellular matrix remodeling and scar formation in the context of lung fibrosis ( spp1, pla2g7 and ccl2; supplementary tables 12 and 13 and extended data fig. 9b, c ). thus, the hlca core can be used to annotate new data, identify previously unreported populations, and — using label transfer uncertainty scores — help to detect disease-affected cell states and corresponding gene expression programs. this vastly speeds up analysis and interpretation of new data, automatically prioritizing the most relevant populations. automate d mapping of new data to the hlca core can be done by any user via an interactive web portal ( https: / / github. com / lungcellatlas / hlca ) or using code tutorials as provided online. similar to healthy cellular states, the hlca can provide insight into disease-specific states that are consistent across demographics and experimental protocols. to demonstrate this, we determined which cell types are consistently affected by ipf across datasets, extending the previous ipf analysis to five independent datasets. we found that cells labeled as alveol ar fibroblasts consistently show high uncertainty levels in ipf samples compared with controls across all mapped ipf datasets that include controls ( extended data fig. 10a ). clustering of alveol ar fibroblasts from the hlca core and all ipf datasets shows that cells from patients with ipf predominantly cluster together in a single cluster ( fig. 6f, g and extended data fig. 10b ) characterized by high expression of genes previously associated with ipf ( ccl2, col1a1, cthrc1 and mmp19 ), as well as further fibrosis - associated markers ( serpine1, an inhibitor of extracellular matrix breakdown, and hif1a, a chronic hypoxia response gene; fig. 6h and supplementary table 14 ). these marker genes are consistently expressed across datasets ( extended data fig. 10c ), confirming that the identification of this ipf - specific alveol ar fibroblast state is reproducible. the hlca contains data across more than ten lung diseases, providing the unique opportunity to discover cellular states shared across diseases. discovering such common diseased cellular states could improve our understanding of lung diseases and accelerate the identification of effective treatments. for example, profibrotic spp1 monocyte-derived macrophages ( mdms ) have previously been reported in covid - 19, ipf and cancer. to test whether similar cross-disease mdm states could be discovered in the hlca, we performed clustering of all mdms from the hlca ( fig. 6i ). we identified four main mdm subtypes ( methods and supplementary table 15 ), each showing distinc t gene expression and disease enrichment patterns, and representing different stages of monocyte-to-mdm differentiation and adaptation to the disease microenvironment. first, an early and inflammatory mdm state was observed that was high in the expression of ccl2, a gene involved in the recruitment of immune cells. this cluster predominantly contained cells from broncho alveol ar lavage fluid samples collected early during the course of covid - 19 pneumonia ( cluster 2; il1rn and s100a12; fig. 6i – k and extended data fig. 10d – h ). we further observed an mdm subset expressing inflammation and phagocytosis-associated genes ( cluster 4; ccl18, il18, c1qa and trem2 ) and enriched for samples from patients with covid - 19 pneumonia, as well as samples from patients with lung carcinoma ( fig. 6i – k and extended data fig. 10d – h ). a third mdm subset represented a more differentiated mdm phenotype, as indicated by the expression of marco and mcemp1, dominated by cells from nondiseased samples ( cluster 3; fig. 6i – k and extended data fig. 10d, f ). the final mdm subset was dominated by ipf samples. interestingly, this cluster was also enriched for cells from patients who died late in the course of covid - 19 and developed post - covid - 19 lung fibrosis, as well as cells from patients with lung carcinoma ( cluster 0; fig. 6i – k and extended data fig. 10g – i ). this multidisease cluster is marked by high expression of spp1, lpl and chit1 — markers that have been shown to play a causal role in the development of lung fibrosis ( fig. 6k ), one of which ( chit1 ) is currently being investigated as a therapeutic target for ipf. the expression of these markers is consistent across diseases and studies ( extended data fig. 10f ), suggesting that also in cancer and late - stage covid - 19 samples a subset of mdms adopt a fibrosis - associated phenotype. together, this analysis shows that the hlca enables a better understanding of cellular states shared between diseases and thereby has the potential to accelerate the discovery of effective disease treatments. in this study, we built the hlca: an integrated reference atlas of the human respiratory system. while previous studies have described the cellular heterogeneity within the human lung, study - specific biases due to experimental design and a limited number of sampled individuals constrain their capacity to capture population variation and serve as a universal reference. the hlca integrates data from 49 datasets to produce such a reference of 2. 4 million cells, covering all major lung scrna - seq studies published to date.