the lto gene program ( including cxcl13 and fdcsp ) was not expressed in healthy adult skin but could be upregulated in specific skin diseases ( fig. 8d, e ), particularly hidradenitis suppurativa. tertiary lymphoid structures ( tls ), for which cxcl13 is an important chemok ine, are not well recognized in adult human skin, but have recently been reported in hidradenitis suppurativa specifically, suggesting that the lto gene program contributes to this process. we next asked whether f3: frc - like fibroblasts were unique to adult human skin, as lto - like fibroblasts have not been reported in mouse embryonic skin. our comparative analysis showed that adult f3: frc - like cells correspond to mouse ccl19 fibroblasts ( fig. 8f ). mouse ccl19 fibroblasts were found predominantly in lymphoid organs ( extended data fig. 10a ), but were also present in other tissues such as lung ( fig. 8g ), whereas f3: frc - like fibroblasts were relatively abundant in healthy human skin, the equivalent ccl19 fibroblasts were notably rarer in healthy mouse skin ( fig. 8g ). in summary, we suggest f3: frc - like fibroblasts are enriched in human skin and not observed or absent in murine skin. we harmonize skin fibroblast subtype nomenclature in health and disease, spatially resolve distinc t fibroblast anatomical niches, and identify conserved fibroblast subtypes in human diseases affecting multiple tissues. frcs are the paradigm of immunomodulatory fibroblasts, maintaining discrete immune structures and facilitating specific immune cell interactions in lymphoid organs, with increasing evidence that they are present across human tissues. our data suggest that f3: frc - like fibroblasts are located in the superficial perivascular niche in human skin and have an analogous role to t zone reticular frcs, mediating t - dc interactions. in keeping with previous murine studies, we find that f3: frc - like fibroblasts show a uniquely high prevalence in human skin. this enrichment of f3: frc - like fibroblasts in human skin would explain both the absence of fibroblasts in murine skin tls - like structures and why the prominent perivascular infiltrate structures that characterize many human inflammatory skin diseases are not reported in mice. inflammatory myof ibroblasts were recently reported in a large - scale integration of predominantly cafs and have been independently described in ibd. we identify that the same inflammatory myof ibroblast phenotype ( il11 mmp1 cxcl8 il7r ) can be observed in early human skin wounds, skin cancer and inflammatory skin diseases with scarring risk. consistent with the immune milieu in early wounds, acne and hidradenitis suppurativa, we suggest that these fibroblasts recruit immune cells such as neutrophils and monocytes. neutrophils are also reported to be recruited by inflammatory myof ibroblasts in ibd. our study also suggests that f6: inflammatory myof ibroblasts are an intermediate myof ibroblast differentiation state in human skin, which agrees with recent work in mouse skin and lung. further work validating myof ibroblast trajectories in human skin is needed as current trajectory inference methods are limited for predicting multiple cell states converging to a final phenotype, and lineage plasticity is likely in fibroblasts, with both tissue - specific and universal populations suggested to give rise to myof ibroblasts. notably, skin could serve as an exemplar tissue to further investigate myof ibroblast development in humans in vivo given the ability to sample tissue temporally with low morbidity. a limitation of our study is that we relied on the uncertainty mechanism incorporated in scpoli to identify disease-associated populations. our cross - tissue analysis using semi - supervised integration may underestimate tissue - specific differences between fibroblasts, and further investigation using methods such as contrastive analysis may be valuable. lto - like cells in prenatal skin were rare and more definitive lineage tracing methods are required to understand prenatal to adult fibroblast transitions. in summary, our annotate d skin fibroblast dataset of 357, 276 cells provides a foundational resource for fibroblast transcriptomic states in health and across distinc t disease categories in skin tissue. further comments on annotation can be made via the centralized community annotation platform ( https: / / celltype. info / project / 388 ). raw scrna - seq data were downloaded and aligned using starsolo ( grch38 - 2020 - a reference ) unless already available in local storage. we included publicly available data generated from fresh skin biopsies using the 10x genomics chromium platform. we collected essential metadata ( sample id, dataset id, site status, patient status, sex and anatomic location ) as more extensive metadata are being collected as part of the human skin cell atlas. cellbender v. 0. 3 was used to correct for ambient messenger rna. to remove low - quality cells, we included only cells with > 200 genes, > 1, 000 and < 300, 000 total unique molecular identifiers and a mitochondrial gene percentage of < 15 %. we calculated doublet scores using scrublet on a per sample basis using scrublet and removed cells with a doublet score of > 0. 3 ( ref. ). integration of all cells was performed using scvi using raw counts. for feature ( highly variable gene ( hvg ) ) selection, we did not consider the following genes: mitochondrial genes; cell cycle genes, from https: / / github. com / haniffalab / skin _ fibroblast _ atlas / blob / main / misc / cc _ genes. csv ); hypoxic genes; and ribosomal genes. we selected 6, 000 hvgs as features, and batch - aware hvg selection was performed by setting the batch key to sample id. in future analyses post - integration, all genes were considered. the following hyperparameter s were used for the scvi model: number of layers: 2; number of latent dimensions: 30; gene likelihood: zero - inflate d negative binomial distribution, and dispersion: gene - batch. an early stopping patience of five epochs was used. the batch key was ‘ sampleid ’ and no other covariates were passed to the model for correction. we constructed a k - nearest neighbors ( k - nn ) graph ( k = 30 ) using the scvi embedding and performed community detection ( leiden algorithm ) with resolution 0. 1 for the dataset with all skin cells. visualization in two dimensions was performed using umap with the initialized positions from paga implemented in scanpy. we then selected the fibroblast cluster for further analysis based on canonical marker gene expression, including pdgfra, dcn and lum ) ( extended data fig. 1a ). we did not include a distinc t stress response cluster ( mt2a, mt1m, mt1x, hsp90aa1, junb, gadd45b and ier3 ) as this population was not evident on xenium analysis and thus likely related to cell dissociation. for a sensitivity analysis with additional cell types, we also selected schwann and pericyte clusters. we repeated integration using scvi for healthy and phenotypically normal ( nonlesional ) skin fibroblasts only. we used the same workflow as above. visualization in two dimensions was performed using umap with positions initialized from paga. we show gene expression values post - normalization. normalization is a two - step procedure involving depth normalization and variance stabilization. we used the shifted logarithm with a scaling factor of 10, 000 based on strong performance in a recent benchmarking paper. for each cluster, we calculated differentially expressed genes ( degs ) using the t - test with the scanpy rank _ genes _ groups function. the top degs for each population are shown in supplementary fig. 1a and supplementary table 2. for selecting marker genes to present for each population in fig. 1c, we selected genes with the highest specificity of expression for that cluster from visualization. we selected our nomenclature for fibroblasts based on our previous report of f1 – f3 fibroblasts.