to robustly characterize the cell types, we established a universal set of marker genes that generalizes across individuals and studies ( methods, extended data fig. 3 and supplementary table 6 ). the fully re - annotate d hlca core thus combines data from a diverse set of studies to provide a carefully curate d reference for cell type annotations and marker genes in healthy lung tissue. rare cell types, such as ionocyte s, tuft cells, neuroendocrine cells and specific immune cell subsets, are often difficult to identify in individual datasets. yet, combining datasets in the hlca core provides better power for identifying these rare cell types. ionocyte s, tuft and neuroendocrine cells make up only 0. 08, 0. 01 and 0. 02 % of the cells in the hlca core according to the original labels, and were originally identified in only seven, two and four datasets out of 14, respectively. despite their low abundance, these cells formed three separate clusters of the hlca core ( fig. 3f ). our re - annotation increases the number of datasets in which these cells are detected up to threefold and identifies both cells falsely annotate d as monocytes, tuft cells or neuroendocrine cells, as well as originally undetected rare cells ( fig. 3f and supplementary fig. 3a ). importantly, other integration methods tested during our benchmarking, such as harmony and seurat ’ s rpca, failed to separate these rare cells into distinc t clusters ( supplementary fig. 3b ). we were further able to detect six cell identities that were not previously found in the human lung or were only recently described in individual studies. these cell types include migratory dendritic cells ( n = 312 cells, expressing ccr7, lad1 and col19 ), hematopoietic stem cells ( n = 60, expressing spink2, stmn, prss57 and cd34 ), highly proliferative hillock - like epithelial cells not previously reported in adult human lung ( n = 4, 600, expressing krt6a, krt13 and krt14 ), the recently described alveol ar type 0 cells ( n = 1, 440, expressing stfpb, scgb3a2, sftpc and scgb3a1 ) and the closely related preterminal bronchiole secretory cells ( n = 4, 393, expressing sftpb, scgb3a2, sftpc and scgb3a1, together with alveol ar type 0 cells called transitional club - at2 cells ) and a subset of smooth muscle cells ( n = 335 ) that to our knowledge have not previously been described ( fig. 3d, g and extended data fig. 4a – f ). these smooth muscle cells, predominantly found in the airways, express canonical smooth muscle markers ( cnn1 and myh11 ) and also uniquely and consistently express fam83d across datasets ( extended data fig. 4e, f ). the hlca core thus enables improved detection and identification of rare cell types, as well as the discovery of unknown cell types. demographic and other metadata covariates affect cellular transcriptional phenotypes. better insight into the impact of these covariates ( for example, sex, bmi and smoking ) on cell type gene expression can shed light on the contribution of these factors to progression from healthy to diseased states. in addition, technical covariates such as ribosomal and mitochondrial genes exhibit batch - specific variation in expression ( methods and supplementary table 7 ). the diversity in demographics ( for example, smoking status, age, harmonized ethnicity and bmi ) and experimental protocols represented in the hlca core enables us to explore the contribution of each technical or biological covariate to cell type - specific gene expression variation ( methods and supplementary fig. 4 ). for many cell types, anatomical location is the biological variable explaining most of the variance between samples ( fig. 4a ). furthermore, sex is most associated with transcriptomic variation in lymphatic endothelial cells, whereas bmi is most associated with variation in b and t cells, harmonized ethnicity in transitional club - at2 cells and smoking status in innate lymphoid / natural killer cells. furthermore, for several cell types ( for example, mast, at1 and smooth muscle cells ), the tissue dissociation protocol explains most of the variance of all technical as well as biological covariates recorded. these associations provide a systematic overview of the effects of biological and technical factors on the transcriptional state of lung cell types. to better characterize how biological variables affect cellular phenotypes, we modeled their cell type - specific effects on the transcriptome at the gene level ( methods ). sex - related differences in lymphatic endothelial cells are dominated by differential expression of genes located on the x and y chromosomes, but also include a decrease in ifnar1 in females ( supplementary table 8 ), which may be linked to differential interferon responses between the biological sexes. we furthermore found cell type - specific programs that change with proximal ( low ccf score ) to distal ( high ccf score ) location along the respiratory tract ( supplementary tables 8 and 9 ). for instance, oxidative phosphorylation ( including cytochrome c oxidase genes such as cox7a1 ), antigen presentation by major histocompatibility complex class i molecules ( including proteasome and protease subunit genes such as psmd14 and psmb4 ), signaling by interleukin-1 and tumor necrosis factor α, as well as planar cell polarity, were downregulated toward more distal locations in secretory, multiciliated and basal cells ( fig. 4b ). some gene programs were specific for a subset of airway epithelial cell types ( for example, cornification and keratinization, which were programs that were downregulated in distal multiciliated and secretory cells; including genes such as krt8 and krt19 ). the changes in airway epithelial cell states toward the terminal airways are further illustrated by increased expression of developmental pathway genes such as nkx2 - 1, nfib, gata6, bmp4 and sox9 in multiciliated cells along the proximal - to - distal axis ( fig. 4b ), whereas basal cells decrease in number ( fig. 4c ). similarly, several cell types display transcriptomic changes in donors with increasing bmi ( fig. 4d and supplementary tables 8 and 9 ). at2 cells, secretory cells and alveol ar macrophages exhibit downregulation of a range of biological processes ( supplementary fig. 5 ), including cellular respiration, differentiation and synthesis of peptides and other molecules. in secretory cells, a downregulation of the insulin response pathway is also associated with higher bmi, in line with the insulin resistance observed in donors with obesity. in alveol ar macrophages, inflammatory responses involving jak / stat signaling ( previously associated with obesity - induced chronic systemic inflammation ) are associated with higher bmi. in contrast, in plasma cells, high bmi is associated with downregulation of gene sets associated with immune response and upregulation of gene sets associated with cellular respiration, the cell cycle and dna repair. this is consistent with obesity being a known risk factor for multiple myeloma — a plasma cell malignancy. thus, the hlca enables a detailed understanding of the effects of anatomical and demographic covariates on the cellular landscape of the lung and their relation to disease. biological and technical factors can also affect cell type proportions. indeed, all cell types show changes in abundance as a function of anatomical location ( fig. 4c and extended data fig. 5 ). for example, ionocyte s are present at comparable proportions in the airway epithelium, from the larger lower airways ( ccf score = 0. 36 ) down to the distal lobular airways ( ccf score = 0. 81 ), while being largely absent in the lung parenchyma ( ccf score = 0. 97 ). in contrast, neuroendocrine cells are predominantly observed in the larger lower airways but are absent from more distal parts of the bronchial tree ( fig. 4c ).