for the dataset with only fastq files available, we downloaded the fastq files and used cell ranger ( v. 4. 0 ) to map reads to the human reference genome and transcriptome retrieved from cell ranger website ( grch38 v. 3. 0. 0 ) for gene expression quantification. all datasets were concatenated together with anndata in python ( join = ‘ inner ’ ). for each dataset, samples were grouped into either ‘ disease ’ or ‘ control ’ as their disease status, with ‘ disease ’ representing data from patient cell lines, mutant cell lines with disease-related alleles, cells carrying targeting guide rnas ( grnas ) in crispr - based screen and tumour - derived organoids. and ‘ control ’ representing data from healthy cell lines, mutation - corrected cell lines and cells carrying only non - targeting grnas in a crispr - based screen. to compare the disease - modelling atlas with the integrated hnoca, we used scarches to project it to the hnoca as well as the first - trimester primary human brain scrna - seq atlas. for projecting to the primary atlas, the same implementation as mentioned above to map hnoca to the atlas was used. for projecting to hnoca, the query model was based on the scpoli model pretrained with the hnoca data, and fineturned with a batch size of 16, 384 for a maximum of 30 epochs with 20 pretraining epochs. a nearest neighbour graph was created for the disease - modelling atlas on the basis of the projected latent representation to hnoca with scanpy ( default parameters ), with which a umap embedding was created with scanpy ( default parameters ). next, for both hnoca and the disease - modelling atlas, cells were represented by the concatenated representation of hnoca - scpoli and primary - scanvi models. a bipartite wknn graph was then reconstructed as mentioned above, by identifying 50 nearest neighbours in hnoca for each disease - modelling atlas cell. on the basis of the bipartite wknn, the majority voting - based label transfer was applied to transfer the four levels of hierarchical cell type annotation and regional identity to the disease - modelling atlas. for each cell in the disease - modelling atlas, a matched hnoca metacell was reconstructed on the basis of the above mentioned bipartite wknn. in brief, for a query cell i and a gene j measured in hnoca, its matched metacell expression of j, denoted as, is calculated as: [ 12pt ] $ $ _ } ^ } = _ _ } _ } _ } } _ _ } _ } } $ $ e ij ′ = ∑ k ⊆ n i w ik e kj ∑ k ⊆ n i w ik here, n i represents all hnoca nearest neighbours of the query cell c i, w ik represents the edge weight between query cell i and reference cell k, and e kj represents expression level of gene j in reference cell k. given the matched hnoca metacell transcriptomic profile, the similarity between a query cell and its matched cell state in hnoca is then calculated as the spearman correlation between the query cell transcriptomic profile and its matched hnoca metacell transcriptomic profile. to analyse the glioblastoma organoid dataset ( gbm - 2019 ), cells from the publication were subset from the integrated disease - modelling atlas. using scanpy, highly variable genes were identified with default parameters. the log - normalized expression values of the highly variable genes were then scaled across cells, the truncated pca was performed with the top 20 principal components used for the following analysis. next, harmonypy, the python implementation of harmony, was applied to integrate cells from different samples. on the basis of the harmony - integrated embeddings, the neighbour graph was reconstructed. umap embeddings and louvain clusters ( resolution, 0. 5 ) were created on the basis of the nearest neighbour graph. among the 12 identified clusters, cluster - 7 and cluster - 0, the two clusters with the highest aqp4 expression, were selected for the following de analysis. to analyse the fxs dataset ( fxs - 2021 ), cells from the publication were subset from the integrated disease - modelling atlas. the same procedure of highly variable gene identification, data scaling and pca as the gbm - 2019 dataset was applied. next, the nearest neighbour graph was created directly on the basis of the top 20 principal components. umap embeddings and louvain clusters ( resolution, 1 ) were then created on the basis of the reconstructed nearest neighbour graph. among the 30 clusters, cluster - 17 and cluster - 23, which express emx1 and foxg1 and were largely predicted to be dorsal telencephal ic npcs and neurons according to the transferred labels from hnoca, were selected for the following de analysis. to compare expression levels of two groups of paired cells, the expression difference per gene of each cell pair is first calculated on the basis of the log - normalized expression values. next, for each gene to test for de, its variance over the calculated expression difference per cell pair ( σ ) is compared with the sum of squared of expression differences ( d i for gene i ) normalized by the number of cell pairs: [ 12pt ] $ $ _ ^ = _ ^ _ }. $ $ s 0 2 = ∑ i = 1 n d i n. here, an f - test is applied for the comparison, with f = σ / s 0, d. f. 1 = n − 1 and d. f. to construct the hnoca - ce, we first collected raw count matrices and associated metadata of five more neural organoid studies. for two publications, we obtained them from the sources listed in the ‘ data availability ’ section of the paper. for the remaining three publications, count matrices and associated metadata were provided directly by the authors. we subset each dataset to the healthy control cells and removed any cells with fewer than 200 genes expressed. we subset the gene space of every dataset to the 3, 000 hvgs of hnoca while filling the expression of missing genes in the community datasets with zeros. on average, 23 % of genes with zero expression were added per dataset. we instantiated a mapping object from the hnoca - tools package ( at commit fe38c52 ) using the saved scpoli model weights from the hnoca integration. using the map _ query method of the mapper instance, we projected the community datasets to hnoca. we used the following training hyperparameter s: retrain = ‘ partial ’, batch _ size = 256, unlabeled _ prototype _ training = false, n _ epochs = 10, pretraining _ epochs = 9, early _ stopping _ kwargs = early _ stopping _ kwargs, eta = 10, alpha _ epoch _ anneal = 10. we computed the wknn graph using the compute _ wknn method of the mapper instance with k = 100. we transferred the final level _ 2 cell type labels from hnoca to the community datasets using this neighbour graph. to obtain the combined representation of hnoca - ce, we projected hnoca together with the added community datasets through the trained model and computed a neighbour graph and umap from the resulting latent representation. further information on research design is available in the nature portfol io reporting summary linked to this article. any methods, additional references, nature portfol io reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10. 1038 / s41586 - 024 - 08172 - 8. single - cell and spatial transcriptomics analysis of non - small cell lung cancer source paper: pmc11116453 lung cancer is the second most frequently diag nosed cancer and the leading cause of cancer - related mortality worldwide. tumour ecosystems feature diverse immune cell types. myeloid cells, in particular, are prevalent and have a well - established role in promoting the disease. in our study, we profile approximately 900, 000 cells from 25 treatment - naive patients with adenocarcinoma and squamous - cell carcinoma by single - cell and spatial transcriptomics. we note an inverse relationship between anti-inflammatory macrophages and nk cells / t cells, and with reduced nk cell cytotoxicity within the tumour.