moreover, the specific cell - cell interactions determining the identity of individual cells within tissues remain to be defined ( guilliams and scott, 2017; lindeboom et al., 2021 ). while the spatial organization of hepatocytes within the liver is understood ( halpern et al., 2017 ), that of non - parenchymal liver cells remains unclear. this is the case for the mouse liver, but even more so for the human liver, where the identity and the precise localization of most hepatic cells is unknown. moreover, the link between the transcriptome and the proteome has not been studied, resulting in a lack of reliable surface markers to identify these cells by flow cytometry and confocal microscopy. here, we used proteogenomic techniques including cellular indexing of transcriptomes and epitomes by sequencing ( cite - seq ) and spatial approaches to identify all cells and their specific locations within the healthy and obese livers of mice and humans. by doing so, we have developed strategies for the identification and further study of hepatic cells. demonstrating the usefulness of this approach, we identify the evolutionary conserved and spatially restricted signals driving the distinc t hepatic macrophage phenotypes. to generate a proteogenomic atlas of the liver, we first examined the optimal method for retrieving all hepatic cells. using the murine liver, we compared single - cell rna sequencing ( scrna - seq ) using cells isolated via ex vivo or in vivo enzymatic digestion with single - nuclei rna sequencing ( snrna - seq ) ( figures s1 a – s1c ). we did not observe any differences in the number of genes / cell between the two digestion methods ( figures s1 d and s1e ), but snrna - seq typically yielded a lower number of genes / cell ( figures s1 d and s1f ). this did not prevent distinc t cell types from being identified in the snrna - seq dataset as both scrna - seq and snrna - seq identified highly expressed genes in each population. however, expression was often higher in the scrna - seq ( figure s1 f ). additionally, we observed a signature of digestion - associated genes and snrna - seq - associated genes across cell types ( figure s1 f ). while in terms of genes / cell snrna - seq is inferior to scrna - seq, this method best recapitulated the cell frequencies observed in vivo ( figures s1 d – s1n ). as each method has advantages and disadvantages, the optimal method to use depends on the biological question being addressed. here, as we sought to generate a proteogenomic atlas of all hepatic cells, we thus opted to use a combination of all protocols. to investigate mrna and protein expression at single - cell resolution, we used cite - seq ( stoeckius et al., 2017 ), staining a selection of the scrna - seq samples with 107 – 161 oligo - conjugated antibod ies ( figure 1 a ). data were pooled together for a single analysis where, with totalvi ( gayoso et al., 2021 ), both the protein and mrna profiles were considered for clustering ( figure 1 b ). analysis of the differentially expressed genes and proteins ( degs / deps; figures s2 a and s2b; table s1 ) identified 17 cell types ( figure 1 b ), which were differentially represented with each isolation method ( figure s2 c ). addition of antibod ies in the cite - seq analysis enabled surface markers for all cells to be identified, including vsig4 and folr2 for kupffer cells ( kcs ) ( figure s2 b ), without affecting the quality of the transcriptomic data ( figure 2 d ). this analysis identified one subset of kcs. however, 2 subpopulations termed kc1 and kc2 have recently been described ( bleriot et al., 2021; simone et al., 2021 ). as our cite - seq analysis identified that the markers used to identify kc2s, namely cd206 and esam, are largely expressed by liver sinusoidal endothelial cells ( lsecs ) ( figure s2 e ), we next sought to determine if we had previously removed any potential kc2s in our initial qc steps as lsec - kc doublets. to examine this, we generated a umap of samples containing cd206 and esam in the cite - seq panel and performed a minimal qc filtering on number of genes and percentage of mitochondrial genes. in this umap, we found multiple subpopulations including 3 populations of kcs and 2 populations of b cells ( figure s2 f ). to determine if any of these populations could be kc2s, we harnessed the power of cite - seq to recreate the gating strategy recently proposed to identify kc2s ( bleriot et al., 2021 ). converting the cite - seq data into an fcs file and analyzing this in flowjo identified 2 of the kc populations to be kc2s expressing cd206 and esam, and these cells were present in a similar ratio among kcs as reported by bleriot et al. ( figure s2 g ). however, employing the same gating strategy with the b cells also enabled a b cell2 population to be identified ( figure s2 g ). the kc2 and b cell2 populations also expressed other protein markers associated with lsecs including cd26, cd31, and cd38, suggesting that these may be doublets ( figure s2 h ). consistent with this, we did not uncover any degs specifically expressed in the kc2s or b cell2s, rather these cells had an intermediate profile between either kc1s or b cell1s and lsecs ( figure s2 i ), as would be expected for doublets. finally, we perfused the livers with antigen fix to inflate the lsecs to be able to disting uish more readily between kcs and lsecs and performed confocal microscopy. 3d reconstruction of these images indicates that cd206 expression is observed primarily in the lsecs with which the kcs are intertwined. these microscopy images also show that kcs express some cd206, however, consistent with our cite - seq analysis, this was observed across the kc population rather than in a kc subset further suggesting the existence of only one - kc population ( figure s2 j ). with this in mind, we decided to continue to apply our initial strict qc controls eliminating these potential doublets from further analyses. to locate the cells identified we performed spatial transcriptomics analysis using visium. for this, we cut the liver in two distinc t orientations to profile both the liver tissue and the capsule ( figures 1 c and s2 k ). we ordered each visium spot along a spatial trajectory, and annotate d portal, periportal, mid, and central zones based on known hepatocyte zonation markers ( halpern et al., 2017; figures 1 d, 1e, and s2 l ) and confirmed this annotation using confocal microscopy ( figure s2 m ). by using the reference sc / snrna - seq data, we then deconvolved each spot into its constituent cell types and investigated how cell abundance changed with zonation ( figures 1 f, 1g, and s2 n ). validating this approach, cholangio cytes mapped specifically to the portal zones ( aizarani et al., 2019 ), while kcs were preferentially located in periportal and mid zones ( bonnardel et al., 2019; gola et al., 2021 ). while kc location is zonated, we did not identify a strong zonation pattern in the gene expression profiles of kcs ( data not shown ). we further identified b cells, t cells, endothelial cells ( ecs ), and stromal cells ( scs ) across all zones, while conventional dendritic cells ( cdcs ) were found at the portal vein ( pv ), with a minor presence at the central vein ( cv ) ( figures 1 f, 1g, and s2 n ). to validate these locations at single - cell resolution, we next sought to identify the best cell - specific surface markers that would also work by confocal microscopy. as the fixation step utilized for confocal microscopy often affects the integrity of protein epitopes, it is not possible to predict which antibod ies will work spatially on fixed tissue slices. therefore, to simultaneously screen multiple antibod ies to identify those working by microscopy, we performed a second visium analysis which we complemented with 100 oligo - conjugated antibod ies, chosen based on the cite - seq results ( figure 1 h ).