we focused on six spatial cell types: perineurial nerve - associated fibroblasts; endoneurial nerve - associated fibroblasts; immune-recruiting fibroblasts; chondrocytes; myelinating schwann cells; and nonmyelinating schwann cells. as these cell types were often present at very small frequencies, we performed clustering at different resolutions to determine whether these cells were clustered separately at any of these resolutions. we clustered at resolutions of 0. 1, 0. 2, 0. 5, 1, 2, 3, 5, 10, 15, 20, 25, 30, 50, 80 and 100, with the number of neighbors set to k = 30 for resolutions under 25 and k = 15 for resolutions of 25 or higher, to enable the detection of smaller clusters. minimum recall ( the percentage of cells with the spatial cell type annotation captured in the cluster ) and minimum precision ( the percentage of cells from ref. in the cluster that had the spatial cell type annotation ) were both set to 25 %. the cluster with the highest recall was selected for every spatial cell type ( unless this cluster decreased precision by > 33 % compared with the cluster with the second highest recall ). if the precision of the next best cluster was doubled compared with the cluster with the highest recall and recall did not decrease by > 20 %, this cluster was selected. to learn disease-specific signatures based on label transfer uncertainty scores, cells from the mapped data with the same transferred label ( either alveol ar fibroblasts or alveol ar macrophages ) were split into low - uncertainty cells ( < 0. 2 ) and high - uncertainty cells ( > 0. 4 ), excluding cells between these extremes ( for alveol ar fibroblasts, n = 11, 119 ( < 0. 2 ) and n = 2, 863 ( > 0. 4 ); for alveol ar macrophages, n = 1, 770 ( < 0. 2 ) and n = 577 ( > 0. 4 ) ). we then performed a differential expression analysis on scran - normalized counts using a wilcoxon rank - sum test with default parameters, comparing high - and low - uncertainty cells. the 20 most upregulated genes based on log - fold changes were selected after filtering out genes with a false discovery rate - corrected p value ( using the benjamini – hochberg procedure ) above 0. 05 and genes with a mean expression below 0. 1 in the high - uncertainty group. to calculate the score of a cell for the given set of genes, the average expression of the set of genes was calculated, after which the average expression of a reference set of genes was subtracted from the original average, as described previously. the reference set consists of a randomly sampled set of genes for each binned expression value. the resulting score was considered the cell ’ s disease signature score. to uncover the cell identities affected in ipf, label transfer uncertainty was analyzed for three mapped datasets from the extended hlca that included both ipf and healthy samples. cell types of interest were determined based on the largest increase in mean label transfer uncertainty in ipf compared with healthy samples, while checking for consistency in increments across the three datasets. this highlighted alveol ar fibroblasts as the main cell type of interest. to find ipf - specific alveol ar fibroblast states, all alveol ar fibroblasts from the abovementioned datasets and two more ipf datasets ( for which no healthy data were mapped, as these were already in the core ) were clustered together with the alveol ar fibroblasts from the hlca core. for clustering, a k nearest neighbor graph was calculated on the joint scarches - derived 30 - dimensional embedding space setting k = 30, after which the cells were clustered using the leiden algorithm with a resolution of 0. 3. the resolution was chosen such that datasets were not isolated in single clusters, thus avoiding clustering driven solely by dataset - specific batch effects. one cluster ( cluster 5 ) was small ( n = 460 cells ) and displayed low donor entropy ( 0. 17 ), indicating that it almost exclusively came from a single donor ( 96 % of cells from hlca core donor 390c ). it was therefore excluded from further analysis. to perform differential gene expression analysis, gene counts were normalized to a total of 7, 666 counts ( the median number of counts across the hlca ) and then log transformed with a pseudocount of 1. finally, a wilcoxon rank - sum test was used on the normalized data to detect differentially expressed genes for cluster 0 ( n = 6, 765 cells versus a total of n = 14, 731 ). the results were filtered such that genes expressed in < 30 % of cells of the cluster of interest were excluded, as well as genes that were expressed in > 20 % of cells outside of the cluster and genes with a multiple testing - corrected p value ( using the benjamini – hochberg procedure ) above 0. 05 ( supplementary table 14 ). to investigate whether the hlca can be used to identify disease-associated cell states shared across multiple diseases, mdms from the hlca core, together with all cells from the mapped datasets labeled as mdms based on label transfer, were jointly analyzed. datasets and diseases with fewer than 50 mdms were excluded from the analysis. the cells were subsequently clustered as described above for the cross - dataset ipf analysis. finally, a wilcoxon rank - sum test was used on the normalized data to detect differentially expressed genes per cluster ( number of cells per cluster: n = 64, 915 ( cluster 0 ), 47, 539 ( cluster 1 ), 32, 027 ( cluster 2 ), 31, 097 ( cluster 3 ), 25, 267 ( cluster 4 ), 1, 998 ( cluster 5 ) and 307 ( cluster 6 ) ). the results were filtered as described above ( supplementary table 15 ). the following tools and versions were used: r ( version 4. 1. 1 for covariate modeling and version 4. 0. 3 for gsea ); edger ( version 3. 28. 1 ); lme4 ( version 1. 1 - 27. 1 ); ldsc ( version 1. 0. 1 ); limma ( version 3. 46. 0 ); scanpy ( version 1. 9. 1 ); scarches ( version 0. 3. 5 ); scib ( version 0. 1. 1 ); scikit - learn ( version 0. 24. 1 ); and scvi - tools ( scanvi; version 0. 8. 1 ). 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 / s41591 - 023 - 02327 - 2. spatial proteogenomics reveals distinc t and evolutionarily conserved hepatic macrophage niches source paper: pmc8809252 the liver is the largest solid organ in the body, yet it remains incompletely characterized. here we present a spatial proteogenomic atlas of the healthy and obese human and murine liver combining single - cell cite - seq, single - nuclei sequencing, spatial transcriptomics, and spatial proteomics. by integrating these multi - omic datasets, we provide validated strategies to reliably discriminat e and localize all hepatic cells, including a population of lipid-associated macrophages ( lams ) at the bile ducts. we then align this atlas across seven species, revealing the conserved program of bona fide kupffer cells and lams. we also uncover the respective spatially resolved cellular niches of these macrophages and the microenvironmental circuits driving their unique transcriptomic identities. we demonstrate that lams are induced by local lipid exposure, leading to their induction in steatotic regions of the murine and human liver, while kupffer cell development crucially depends on their cross - talk with hepatic stellate cells via the evolutionarily conserved alk1 - bmp9 / 10 axis. the immense advances in single - cell transcriptomics have enabled a better understanding of the cellular composition of different organs across species. however, we still lack information regarding how these cells are organized in their distinc t microenvironmental niches.