finally, while nichenet predicts that bmp9 / 10 from stellate cells would signal through alk1 to induce kc development and maintenance, a prediction in line with our previous nichenet analysis ( bonnardel et al., 2019 ), another recent study has suggested that transforming growth factor ( tgf ) β signaling would be important for kcs ( sakai et al., 2019 ). this prediction was made on the basis of smad4 signaling, which is a common downstream effector of both tgf - β receptor and alk1 - induced signaling. as tgf - β was also found to participate in an alk1 - tgf - β receptor containing signaling complex ( goumans et al., 2003 ), we thus examined whether tgf - β signaling is important for kcs. to this end we utilized an alk1 - fc trap or tgf - β type ii receptor ( tgfβrii ) - fc trap alongside appropriate isotype controls. alk1 - fc selectively sequesters bmp9 / 10 ( desroches - castan et al., 2021 ), while tgfβrii - fc selectively interferes with tgf - β1 / tgf - β3 receptor binding ( komesli et al., 2017 ). clec4f - dtr mice were depleted of kcs and simultaneously treated with either receptor - fc traps or isotype controls and kc development was examined 7 days later ( figure 7 i ). in line with the chimera study, treatment with alk1 - fc significantly abrogated kc development; however, blocking tgfβ signaling had only a minor effect on the proportion of vsig4 macs ( figure 7 j ). altogether this validates the nichenet prediction and demonstrates that the evolutionarily conserved alk1 - bmp9 / 10 axis is crucial for the development and maintenance of kcs. to generate a practical cellular atlas of any human tissue and unravel the cell - cell circuits essential for the identities of cells inhabiting that tissue, four key pieces of information are required: ( 1 ) an inventory of all cells present, ( 2 ) the location of the different cells within the tissue to identify interactions between neighboring cells, ( 3 ) an alignment between the human and animal models allowing for any predicted cell - cell interactions to be perturbed, and ( 4 ) the identification of reliable antibod y - based panels for the efficient screening of different patients and / or transgenic animals. here, by integrating single - cell and spatial transcriptomic and proteomic data, we provide these 4 pieces of information for the liver and uncover evolutionarily conserved microenvironmental circuits controlling the development of hepatic macs. unraveling the spatial localization of all hepatic cells, we identify lams around the bile ducts in the healthy mouse, human, and macaque liver. however, when steatosis is present, lams are preferentially recruited to the steatotic regions of the liver. this spatial information at least partially invalidates the hypothesis that lam identity is specifically induced by fibrotic scs ( ramachandran et al., 2019 ). rather, our data suggest that lams are induced by local lipid exposure. we also provide an alignment of the liver atlas across seven species. this reveals the conserved and unique transcriptomic programs of steady-state kcs and uncovers the spatially restricted and conserved ligand - receptor pairs between kcs and the cells constituting their niche. underlining the need to first characterize the healthy tissue before attempting to understand how disease perturbs the cells, we identify the dll - notch interaction to be an evolutionarily conserved cross - talk between homeostatic lsecs and kcs and therefore not unique to hepatocellular carcinoma or fibrosis, as proposed ( ramachandran et al., 2019; sharma et al., 2020 ). similarly, we find that folr2 expression is not specific to tumor - associated hepatic macs ( sharma et al., 2020 ) but is expressed by kcs in the healthy mouse and human liver. finally, we apply a proteogenomic pipeline starting from broad oligo - conjugated antibod y panels for both single - cell and spatial profiling. this is crucial as transcriptomic profiling does not always correspond with the ability to detect proteins by flow cytometry or microscopy. by screening broadly, we identify the best surface markers for the isolation and localization of hepatic macs and their respective niche cells. this allows both the validation of the spatial location at the single - cell level, and the efficient screening of transgenic mouse models for the loss of kcs. characterization of fcgr1 - crex acvrl1 mice using our defined panel readily demonstrates the cruciality of the alk1 - bmp9 / 10 axis in kc development emphasizing that mac - stromal - cell cross - talk goes much further than the exchange of growth factors ( guilliams et al., 2020; zhou et al., 2018 ). moving forward, applying these relatively cheap antibod y panels to large patient cohorts or multiple transgenic mouse models should enable any perturbations disturbing liver homeostasis to be efficiently identified. the current study has two main limitations. first, the analysis of the human liver remains restricted by the number of human patients included ( 19 patients for the sc / snrna - seq, 4 patients for visium, and 15 patients by microscopy ). while this study provides markers to cheaply and efficiently screen large patient cohorts allowing this analysis to be extended, given the heterogeneity between patients, multiple studies will need to be integrated in a single analysis if we are to be able to interrogate transcriptomic differences in a particular cell subset related to age, sex, ethnicity, or pathological parameters. second, this study highlights that more research will be needed to fully characterize human scs. as we could only retrieve these cells through snrna - seq this means we have not been able to identify good surface markers through cite - seq. additionally, for small populations of fibroblasts we did not recover enough nuclei to truly probe their heterogeneity. better isolation protocols are required to retrieve and enrich these cells from the human liver in order to run a broad panel of cite - seq markers to identify the different subsets and their corresponding surface markers. reagent or resource source identifier antibod ies: flow c ytometry / c onfocal m icroscopy rat monoclonal cd102 - fitc ( 3c4 ) biolegend 105606; rrid: ab _ 313199 rat monoclonal cd103 - bv750 ( m290 ) bd biosciences 747478; rrid: ab _ 2872154 rat monoclonal cd11b - buv395 ( m1 / 70 ) bd biosciences 565976; rrid: ab _ 2721166 rat monoclonal cd11b - bv605 ( m1 / 70 ) bd biosciences 563015; rrid: ab _ 2737951 rat monoclonal mhcii - buv805 ( m5 / 114, 15, 2 ) bd biosciences 748844; rrid: ab _ 2873247 rat monoclonal mhcii - fitc ( m5 / 114, 15, 2 ) thermo fisher scientific 11 - 5321 - 85; rrid: ab _ 465233 rat monoclonal cd14 - pe dazzle 594 ( sa14 - 2 ) biolegend 123325; rrid: ab _ 2721697 rat monoclonal cd43 - buv737 ( s7 ) bd biosciences 612840; rrid: ab _ 2870162 rat monoclonal ly6c - efluor450 ( hk1. 4 ) ebioscience 48 - 5932 - 82; rrid: ab _ 10805519 armenian hamster monoclonal cd11c - pe - cy7 ( n418 ) ebioscience 25 - 0114 - 82; rrid: ab _ 469590 armenian hamster monoclonal cd11c - unconjugated ( n418 ) biolegend 117302; rrid: ab _ 313771 rat monoclonal cd86 - bv605 ( gl - 1 ) biolegend 105037; rrid: ab _ 11204429 rat monoclonal cd54 - pe ( yn1 / 1. 7. 4 ) biolegend 116107; rrid: ab _ 313698 rat monoclonal cd54 - af488 ( yn1 / 1, 7, 4 ) biolegend 116111; rrid: ab _ 493494 rat monoclonal cd172a - bb630p ( p84 ) bd customs 624294 mouse monoclonal xcr1 - bv650 ( zet ) biolegend 148220; rrid: ab _ 2566410 rat monoclonal tim4 - percp - cy5. 5 ( rmt4 - 54 ) ebioscience 46 - 5866 - 82; rrid: ab _ 2573781 rat monoclonal vsig4 - pecy7 ( nla14 ) ebioscience 25 - 5752 - 82; rrid: