we refined the names based on spatial location and our in - depth characterization of cell states, including across tissues. we renamed f1 as f1: superficial, f2 to f2: universal and f3 to f3: frc - like. in this work, we defined novel fibroblast subtypes. f2 / 3 fibroblasts expressed select f2 ( cd34 and pi16 ) and f3 markers ( cxcl12, pla2g2a and c7 ), but additionally expressed pparg. f2 / 3 fibroblasts also showed a distinc t location enriched in both superficial and deep dermal perivascular structures, compared to f2 ( deep dermis but not around vasculature ) and f3 ( superficial perivascular regions ) ( extended data fig. 4c ). we therefore named this population f2 / f3: perivascular. we additionally identified novel f4 and f5 populations. we grouped three subtypes as f4 based on transcriptomic ( aspn / col11a1 ) and spatial similarity ( localization around the hair follicle ). we grouped two subtypes as f5 based on shared expression of genes also expressed by schwann cells, including scn7a, fmo2 and oflml2a, and spatial association with nerve structures. we took two approaches to minimize the possibility of missing a rare distinc t cluster. first, we repeated unsupervised clustering at a higher resolution and assessed whether any clusters showed distinc t gene expression. second, we reviewed reported marker genes for fibroblast clusters in previously reported scrna - seq data for human skin to identify if a novel distinc t population has been reported. transcription factor activity inference was performed using decoupler, using the get _ collectri ( human ) and run _ ulm functions. this analysis was performed using the data subset of 6, 000 hvgs. gene set enrichment analysis was implemented using gseapy. we used the top 500 degs per fibroblast subtype and the go _ biological _ process _ 2023 gene set. a cutoff statistical significance of 0. 01 was used. we generated new spot - based spatial transcriptomic data using the 10x genomics visium spatial gene expression platform from frozen oct - embedded human adult skin tissue ( n = 1 lesional atopic dermatitis and n = 2 nonlesional atopic dermatitis ). all research ethics committee and regulatory approvals were in place for the collection of research samples at newcastle and for their storage at the newcastle dermatology biobank ( rec reference no. 19 / ne / 0004 ). skin samples were sectioned at 15 - µm thickness and the optimal tissue permeabilization time was determined as 14 min. h & e images were taken using a zeiss axioimager with apotome microscope ( carl zeiss microscopy ) and brightfield imaging ( zeiss axiocam 105 48 - color camera module ) at ×20 magnification. the zen blue edition v. 3. 1 ( carl zeiss microscopy ) software was used to acquire the h & e images following z - plane and light balance adjustment and image tile stitching. spatial gene expression libraries were sequenced using an illumina novaseq 6000 to achieve a minimum number of 50, 000 read pairs per tissue covered spot. the 10x genomics visium data were mapped using spaceranger v. 1. 3. 0 using grch38 - 2020 - a reference. visium provides whole transcriptome coverage over a 55 - μm diameter spot area. we therefore used the cell2location ( v. 0. 1. 3 ) to deconvolute the cell types predicted to be present in a given spot. we constructed a reference signature using sample as batch _ key. we included our fibroblast atlas and other skin cell types from reynolds et al.. then we performed deconvolution with the following parameters: detection _ alpha = 20, n _ cells _ per _ location = 30 andmax _ epochs 50 _ 000. values of all other parameters were kept default. following the cell2location tutorial, we used 5 % quantile of posterior distribution ( q05 _ cell _ abundance _ w _ sf ) as predicted cell - type abundances. nonlesional ( n = 2; one sample nonlesional pre - treatment, one sample nonlesional post - treatment ) and lesional ( n = 1; inflamed ) atopic dermatitis human adult skin tissue was used to generate in situ gene expression data using the 10x genomics xenium in situ 5k - plex platform. all research ethics committees and regulatory approvals were in place for the collection and storage of research samples at st john ’ s institute of dermatology, guy ’ s hospital, london ( rec reference no. ec00 / 128 ). the 10x genomics xenium data were filtered to exclude cells with < 10 genes per cell. integration of all cells was performed using scvi using raw counts. batch - aware hvg selection was performed by setting the batch key to sample id. we selected 2, 000 hvgs as features for the integration. as xenium data are more sparse than scrna data, we used a simpler encoder ( number of layers: 1; number of latent dimensions: 10 ) than scrna. the batch key was ‘ sampleid ’ and no other covariates were passed to the model for correction. for normalization, we again used the shifted log transformation for count normalization. we constructed a k - nn graph ( k = 20, given the smaller dataset size ) using the scvi embedding. we performed community detection ( leiden algorithm ) with resolution 0. 1 and used the same markers as scrna data to select the fibroblast cluster. we used marker genes from scrna - seq data to label fibroblast populations through manual annotation. cell coordinates colored by cell type were visualized using squidpy ( sq. pl. spatial _ scatter ). we used the same integration strategy for newly generated atopic dermatitis data and publicly available cutaneous melanoma data. cutaneous melanoma data were downloaded from https: / / www. 10xgenomics. com / datasets / xenium - prime - ffpe - human - skin. nichecompass was run after selecting 1, 024 spatially variable genes. the number of neighbors selected per cell was 8, and otherwise default settings were used. to calculate neighborhood enrichment scores, we first constructed an adjacency matrix for indicating which cells were connected using the spatial _ neighbors functions in squidpy. we determined the neighborhood as a cell as cells within 20 µm of an index cell. we then applied neighborhood enrichment analysis ( nhood _ enrichment function in squidpy ) to quantify which cell types were more frequently colocalized than expected by chance. heatmaps of enrichment scores were visualized using sq. pl. nhood _ enrichment. to identify fibroblast clusters in disease, we mapped lesional data to the healthy / nonlesional reference described above. we used a state - of - the - art deep meta - learning model ( scpoli ). this approach first trains a model using the healthy / nonlesional ( reference ) to generate centroids ( fig. 3a ). then, cells in the query that are distinc t to centroids generated from the reference are marked as uncertain, which facilitates the discovery of new cell types / states in the query data ( fig. 3a ). this permits automate d cell annotation while highlighting cells that could not be mapped to the reference through prototypical learning. due to the proposed role of hypoxia in myof ibroblast differentiation, we included hypoxic genes for consideration in feature selection. to ensure this selection did not bias results, we repeated an scvi integration using the same methodology as for healthy fibroblasts ( supplementary fig. 4a, b ). our definition of ‘ uncertain ’ cells was derived from scpoli. scpoli utilizes euclidean distances of query cells from prototypes ( or centroids ) generated from the reference to yield an uncertainty associated with each cell. to manually annotate cells labeled as uncertain, we calculated degs for each leiden cluster and also assessed expression of healthy fibroblast marker genes in each population. the top degs for each cluster considered as disease-associated or disease-specific are shown in supplementary fig. 1c and supplementary table 3. we also considered which populations were enriched in disease. to validate our reported fibroblast subtypes, we used two approaches.