in the tumour, we detected fibroblasts and a decrease in the fraction of lymphatic endothelial cells ( lecs ) ( p adj = 0. 0025, fig. 1g and supplementary data 2 ). furthermore, the population of epithelial cells showed higher diversity, with the presence of alveol ar type ii ( at2 ), atypical epithelial cells which downregulated epithelial markers ( krt19, epcam, cdh1 ), transitioning epithelial cells which upregulated myeloid markers ( lyz ), and cycling epithelial cells in tumour tissues ( fig. 1g, supplementary notes, and supplementary fig. 2d, e ). these differences are in agreement with the fact that in tumour specimens, epithelial cells are likely to be a mixture of mutant tumour and non - mutant normal cells, and suggest that neoplastic transformation leads to further diversity of cell states. we did not detect alveol ar type i ( at1 ) or basal cells, possibly due to their loss during dissociations, as previously reported by others. as previously reported, the proportion of monocytes and immature myeloid cells was significantly reduced in tumour samples compared to background ( p adj = 0. 022 and p adj = 0. 00001, respectively ), while dcs and b cells were overall expanded ( p adj = 0. 0023 and p adj = 0. 0044, respectively; fig. 1h and supplementary data 3 ). to get further insight into the cellular composition of tumour versus background tissue, we subclustered each of the broad clusters and identified 46 cell types / states ( supplementary fig. 2d, e, supplementary data 4 and 5, supplementary fig. 3, and supplementary notes ). in the tumour, we found that a significantly higher proportion of nk cells had a lower cytotoxicity phenotype ( supplementary notes ), and that the significant majority of dcs were derived from monocytes ( i. e., mo - dc2 ), ( supplementary notes ) compared to background ( p adj = 0. 00002 and p adj = 0. 00002, respectively, fig. 1i and supplementary data 6 ). this is consistent with the monocytic origin of mo - dc2s under inflammatory conditions. similarly, we found an expansion of b cells expressing lyz and tnf, and depletion of nkb cells ( fig. 1i and supplementary notes ). among t cells, tumour samples showed an accumulation of regulatory t cells ( tregs ), known to hinder the immune surveillance of tumours ( fig. 1i ). conversely, there was a reduction of exhausted cytotoxic t cells ( p adj = 0. 00002 ) in the tumour and absence of t cells, which have been associated with survival in nsclc ( fig. 1i and supplementary data 6 ). t cells are capable of recognising and lysing diverse ranges of cancer cells, and thus have been suggested for a role in pan - cancer immunotherap y. finally, we saw an increase in heterogeneity and proportion of anti-inflammatory mɸ ( aimɸ ), with a subset of cycling anti-inflammatory mɸ, stab1 + mɸ ( fig. 1i ) and camls ( fig. 1h ) being abundantly present in tumour tissue. interestingly, we found a strong negative correlation between the frequency of stab1 + mɸ / aimɸ and t / nk cells across patients, highlighting the key role of mɸ in restraining the infiltration of cytotoxic cells in the lung tumour tissue ( fig. 2a ). this is in line with a recent work describing that monocyte-derived mɸ in human nsclc acquire an immunosupp ressive phenotype and restrain the infiltration of nk cells. luad and lusc have very different prognoses and are often considered as different clinical entities. to examine if differences in clinical features stem from distinc t cellular composition, we compared the frequency of immune and non-immune cell subsets within cd235 - samples from luad versus lusc patients. we observed minor differences in cell frequency that did not reach statistical significance after p value correction ( supplementary fig. 4a and supplementary data 7 and 8 ). furthermore, there was no clear association between the frequency of immune and non-immune cells observed in patients and the cancer subtype, cancer stage or sex ( supplementary fig. 4b, c ), suggesting that the tme composition is rather similar in luad and lusc. while luad and lusc shared similar cellular compositions, the observed clinical distinc tions may arise from varying intercellular interactions. therefore, we examined whether different cell – cell interaction networks were employed within the tme in luad versus lusc. to this end, we identified a putative list of cell – cell interactions exclusively observed in each tumour type environment by inferring statistically significant ligand – receptor pairs ( l – rs ) that were not detected in background or healthy and their corresponding cell types, using cellphonedb. although the two tumour subtypes showed a similar interaction network that mostly involved interactions between non-immune cells, aimɸ and t cells ( fig. 2b ), there were also some notable differences. first, we identified overall a higher number of l – rs in the luad dataset ( supplementary fig. 4d and supplementary data 9 – 12 ), which was not driven by a difference in the number of cells in the luad ( n = 105, 749 cells ) vs lusc ( n = 230, 066 cells ) dataset. secondly, several pairs of immune checkpoint inhibitors ( ici ) and their respective inhibitory molecules were differentially co - expressed in luad versus lusc ( fig. 2c, d ). for example, lgals9 - havcr2 ( tim3 ), nectin2 - cd226 ( dnam1 ) and nectin2 / nectin3 - tigit were frequently identified in luad, and the putative ici cd96 - nectin1 was found preferentially in lusc ( fig. 2c, d ). in contrast, cd80 / cd86 - ctla4 and hlaf - lilrb1 / 2 were found in both tumour subtypes ( fig. 2c, d ). lilrbs ( leucocyte ig - like receptors ) are emerging as potential targets for next - generation immunotherap eutics as their blocking can potentiate immune responses. the most commonly used immunotherap ies for lung cancer block the interaction between pd1 and pdl1, and recent clinical trials suggested that anti - ctla4 and anti - pd1 combination therapy improved the survival of patients independent of tumour pd1 expression. within our dataset, we did not observe pd1 - pdl1 interactions in either of the tumour subtypes ( fig. 2c, d ). our initial analysis suggests that other icis ( such as ctla4, tigit, lilrb1 / 2 and tim3 ) might be promising targets in the treatment of nsclc. of the significant l – rs detected in both luad and lusc we noted several pairs involved in angiogenic signalling in different populations of myeloid cells such as vegfa / b - flt1, vegfa - kdr and vegfa - nrp1 / 2. although vegfa and vegfb were found to be expressed in both luad and lusc, their receptors were more frequently found in luad, especially in fibroblasts ( fig. 2e and supplementary fig. 4e ). similarly, we observed significant expression of egfr ligands signalling in at2 and cycling epithelial cells, such as egfr - ereg, egfr - areg, egfr - hbegf and egfr - mif, although mif expression was found more frequently in cells from lusc ( fig. 2f and supplementary fig. 4f ). finally, we observed key co - stimulatory signals required to support lymphoid cell activation, such as cd40 - cd40lg, cd2 - cd58, cd28 - cd86, ccl21 - ccr7, and tnfrsf13b / c - tnfsf13b ( taci / baffr - baff ) ( supplementary fig. 4g ), which are often associated with the presence of ectopic lymphoid organs mainly consisting of b cells, t cells, and dcs i. e., tertiary lymphoid structures ( tls ). tls are usually correlated with the longer relapse - free survival in nsclc. the significant l – rs and their interacting cell types were calculated based on the co - expression of genes in different cell - type clusters from the scrna - seq dataset using cellphonedb. however, in order to discern biologically significant interactions, it is essential to ascertain whether the cell types identified as interacting are indeed physically co - located. to achieve this, we considered how the scrna - seq - identified cell types are spatially arranged on tissue sections.