we initially identified a putative long list of cell – cell interactions differentially observed in the tumour environment by inferring statistically significant ligand – receptor pairs, and their corresponding cell types, using cellphonedb. we treated the tumour ( luad or lusc ), background, and healthy scrna - seq profiles as independent datasets and ran cellphonedb separately. to reduce the impact of randomness in the way cellphonedb samples from input datasets, we required that any ligand – receptor pair of interest from the cellphonedb database be expressed in at least 30 % of cells in a particular cell - type cluster of interest. the final ligand – receptor lists were further filtered by requiring that the mean log ( 1 + expression ) of the ligand – receptor pair be greater than 1. 0, and the bonferroni - adjusted p value be less than 0. 01. from these filtered long lists, ligand – receptor pairs and corresponding cell types relevant to the tumour data are identified. when evaluating the ligand – receptor lists calculated with cellphonedb, we did not run on the complete datasets due to the difficulty in scaling up the cellphonedb statistical permutation tests to scrna - seq with more than 10 cells. instead, we separately stratified the tumour, healthy and background datasets such that the proportion of cell types, patients, and samples in the reduced 50 % of the data recapitulated the proportions in the full dataset. differentiation expression analysis ( dea ) was performed for at2 cells, anti-inflammatory macrophages and alveol ar macrophages using a pseudo - bulk approach to compare tumour versus background. pseudobulks were built for each patient by summing raw gene counts across all cells in each cell type investigated. the patients 1 and 4 were not included in the analysis as their cancer subtype and stage were not known at the time of analysis. since there were differences in the cell count between datasets we downsampled the biggest cluster to the size of the smaller. the downsampling routine was repeated 100 times, such that 100 new datasets were created that match the smaller dataset. dea was performed using sample - level pseudobulks and a pythonic version of the deseq2 pipeline ( py _ deseq2 ), including the patient information as co - variate. the median adjusted p value by benjamini – hochberg procedure and median log2fc for each differentially expressed gene ( deg ) was calculated across 100 iterations. we verified the robustness of this choice of 100 iterations by visualising the variability of the median p value across iterations, in order to assess its stability ( supplementary fig. 6c ). degs were filtered with median ( padj ) ≤0. 05 and | median ( logfc ) | ≥1. prior to performing overrepresentation analysis, the genes that were commonly upregulated in more than 50 % of the contrasts were removed ( dnajb1, hspa1a, hspa1b, hspb1, hspe1, igha1, igkc, iglc2 ). degs were used to perform gene ontology ( go ) overrepresentation using the clusterprofiler package. to define stab1 + mɸ and amɸ gene signatures, we compared dea results and intersected the genes significantly upregulated by stab1 + mɸ ( or amɸ ) compared to the other mɸ populations in tumour. to analyse myeloid cell trajectory in tumour dataset, we recomputed a neighbourhood graph from the same 15 - dimensional harmonised pca space as above, but only within myeloid cell populations. we next applied paga within the scanpy package to the neighbourhood graph. in parallel, we computed the diffusion map and its force - directed layout for visualisation using the pegasus package. we finally overlaid the paga network with the diffusion map using the scvelo package. we repeated the same analysis workflow but on non-immune cells in the tumour dataset. we applied the copykat package to the single - cell rna - seq data to obtain copy number calls. the copykat pipeline was extended to obtain confident copy number calls per cell, per chromosome arm, beyond the hierarchical clustering the standard pipeline produces. per cell copy number calls were obtained as follows: first, the regular copykat ( v1. 0. 5 ) pipeline was run on the unmodified umi counts of a particular patient / environment ( i. e., tumour or background ) combination with default parameters, except for norm. cell. names. the norm. cell. names parameter allows for specifying which cells are used as confident diploid normals during expression normalisation. copykat was set to use all cells labelled as cdc2 dendritic cells, as they are available in great numbers across all patients and an initial inspection of their expression profiles revealed no systematic copy number alterations. after copykat has completed, a calling step was applied that is aimed to call whole chromosome arm alterations in individual cells. we reasoned that, on a chromosome arm basis, the distribution of binned - and - normalised expression from copykat should be significantly different ( higher or lower ) than the distribution of the same bins in all confidently diploid cells. for each chromosome arm, we model the distribution of all data bins from the confidently diploid cells as a normal distribution. each bin on that same chromosome arm from a candidate aneuploid cell is then tested against that distribution. finally, when more than 50 % of bins across that chromosome arm are significant, the arm is marked as altered in that cell. the above - described procedure yields a conservative true / false call per cell, per chromosome arm without directly disting uishing between gains and losses. to obtain a profile with gains and losses as is shown in fig. 4a, we discretise the values for each bin in each cell: if the arm is altered and the expression value of the bin is negative: −1, if the arm is altered and the expression value is positive: + 1, if the arm is unaltered: 0. the discretized values are then finally summed per bin across all cells of a particular cell type and divided by the number of cells of that cell type to obtain the fraction of cells with an alteration as shown in fig. 4a. tissues were frozen in dry - ice - cooled isopentane and stored in air - tight tissue cryovials at −80 °c. the tissues were embedded in an optimal cutting temperature compound ( oct ) and cryosectioned in a pre - cooled cryostat at 10 μm thickness on superfrost slides. on the day of the experiment, slides were thawed at room temperature for less than 5 min, then immersed in a fixation solution ( 4 % pfa in pbs ) for 20 min. after three washes with pbs, each section was permeabilized with freshly prepared 0. 2 % triton-x 100 ( sigma aldrich ) for 10 min at room temperatures, followed by three washes in pbs. unspecific binding was blocked by incubating the sections in pbs + 2. 5 % bsa for 1 h at room temperature. following two washes in pbs, sections were incubated with recombinant rabbit anti - cd68 ( abcam ab213363, 1: 50 ) and mouse anti - stab1 ( santa cruz biotechnology sc - 293254, 10 µg / ml ) in pbs + 0. 5 % bsa overnight at 4 °c. primary antibod ies were removed and sections washed three times with pbs, then incubated with the appropriate secondary antibod ies ( goat anti-rabbit alexafluor 594 and goat anti-mouse alexafluor 488 abcam ) 1: 500 in pbs + 0. 5 % bsa for 2 h at room temperature, protected from light. two confocal immunoh istochemistry z - stacks each for tumour and background tissue from three patients were analysed. using fiji ( imagej ) software, the stab1 + and cd68 + areas were segmented by automatic thresholding and quantified in each image of the z - stack. to assess the levels of cholesterol and neutral lipids we further stained tumour and background tissue sections with bodipy™ 493 / 503 ( invitrogen ). after three washes in pbs, sections were incubated with a 10 µg / ml solution of bodipy™ 493 / 503 in pbs ( 1: 100 from a stock 1 mg / ml solution in dmso ) for 15 min at room temperature.