first, we used skin scrna - seq datasets not used in the original integration. using the scpoli model to generate embeddings and transfer labels to these populations, we identified expected populations from earlier analysis ( supplementary fig. 4c ). second, we used xenium data to validate the existence of the same clusters in which cell gene expression profiles are generated in situ, without tissue dissociation. for celldisect, we used 6, 000 hvgs and raw counts as input. model architecture and training schedule is listed in the provided code. we used the top 500 human genes per pathway and default settings for the multivariate linear model ( decoupler. run _ mlm ). we broadly grouped individual diseases into disease categories ( inflammatory + low scarring risk, inflammatory + high scarring risk, cancer, established scarring / fibrosis ) based on clinical disease features. of note, clear separation of diseases is not possible because of a well - recognized link between inflammation and fibrosis. for example, an inflammatory component to systemic sclerosis is well recognized and first-line treatments typically include immunosuppressant s, but this disease also features established fibrosis. additionally, we included sarcoidosis and granuloma annulare ( both disor ders of granulomatous inflammation ) in ‘ inflammatory high scarring risk ’ as fibrosis can occur within granulomas. additionally, pulmonary sarcoidosis is a well - recognized cause of pulmonary fibrosis; however, most cases of cutaneous sarcoidosis and granuloma annulare do not scar. prurigo nodularis was classified as low scarring risk as it is unclear whether scarring arises secondary to scratching. we considered cancer as moderate scarring risk as fibrosis can occur within lesions ( desmoplasia ) and self - resolving melanoma can result in scarring. for fibroblast proportions by scarring category, we calculated the s. e. m. for each category using the mean proportion for each donor. for each disease category was derived from the s. d. of donor - level proportions divided by the square root of the number of donors in that category. all research ethics committees and regulatory approvals were in place for the collection and storage of atopic dermatitis skin samples at the st john ’ s institute of dermatology, guy ’ s hospital, london ( rec reference no. ec00 / 128 ) and hidradenitis suppurativa skin samples at newcastle dermatology biobank ( rec reference no. 19 / ne / 0004 ). fresh - frozen oct - embedded skin samples were sectioned at 10 - µm thickness directly onto superfrost microscope slides and stored at −80 °c. slides were air dried at room temperature for 10 min and then fixed using 4 % pfa for 10 min. next, a blocking solution of 5 % normal goat serum with 0. 01 % trit on x - 100 was applied to the tissue sections and incubated for 1 h at room temperature. slides were then incubated with primary antibod ies overnight at 4 °c. the next day, slides were washed with 1× pbs and incubated with secondary antibod ies for 1 h at room temperature. then, 4, 6 - diamidin o - 2 - phenylindol e ( dapi ) was used to demarcate nuclei and slides were mounted with dako mounting medium before applying coverslips and leaving slides to dry overnight. skin sections were imaged using a leica sp8 confocal microscope. to calculate gene scores, we used the score _ genes ( ) function in scanpy. we used arguments of ctrl _ size = 1, 000 and n _ bins = 25. we used the marker genes reported for each population, rather than more extensive gene lists, based on the rationale that larger gene programs would be more likely to include tissue - specific gene expression and thus underestimate transcriptomic similarity across tissues. we trained a random forest classifier using fibroblast subtype composition as input to predict scarring risk group. we evaluated performance in terms of average f1 score, a widely used metric for evaluating classification performance, computed using classification _ report from scikit - learn. we applied fivefold stratified cross - validation to train and evaluate a randomforestclassifier ( 100 estimators, random _ state = 42 ). to identify cell types that are most predictive of scarring status, we extracted the final trained model ’ s feature importances. for trajectory inference, we used both velocity - based ( rna velocity ( scvelo and cellrank2 velocity kernel ) ) and graph - based ( paga and monocle 3 ) approaches. we used data for which we could calculate rna velocity using velocyto and scvelo. we first used paga ( as implemented in scanpy ), plotted using a threshold of 0. 1 and applied to the whole dataset. in future analyses, we excluded f5: schwann - like fibroblasts, which seemed to be distinc t ( extended data fig. 8a ) and f _ fascia, which were observed in few diseases ( fig. 4a ). as healing and scarring is observed on non - hair - bearing sites, we also did not include f4: hair follicle - associated fibroblasts. we generated new scvi embeddings for the lesional fibroblasts and re - calculated the k - nn graph using the top 2, 000 hvgs, followed by umap visualization. velocity pseudotime was calculated using scvelo. we re - calculated the paga plot using only lesional fibroblasts, again using a threshold of 0. 1. for monocle 3 ( v. 1. 3. 7 ), the expression count matrix along with the corresponding cell and gene metadata from the processed anndata object in scanpy was used to create monocle object ( cell _ data _ set object ). the cell _ data _ set object was then pre - processed using default settings and aligned to correct for batch effects based on the ‘ dataset _ id ’. dimensionality reduction was performed using ‘ umap ’ as the reduction method. cells were clustered with a resolution of 1 × 10 − 6 and ‘ umap ’ as the reduction method. a trajectory graph was learned by adjusting parameters such as geodesic distance ratio ( 0. 5 ) and minimal branch length ( 10 ) to optimize for large datasets. finally, cells were arranged in pseudotime by manually selecting root nodes from the f2: universal population. we used f2: universal as the root state in monocle 3 based on velocity pseudotime results and previous work. the ordered and learned graph object was then used to plot the pseudotime trajectory plots. the rna velocity kernel was calculated using cellrank2. we obtained post - alignment skin wound data from the authors of ref. and processed these using cellbender as previously described for skin fibroblast data. to annotate skin wound fibroblasts, we integrated the unlabeled skin wound cells with our labeled integrated skin dataset using scanvi. we used the same model architecture ( 30 latent dimensions, two layers ) and the same number of input hvgs ( n = 6, 000 ). we selected only fibroblasts for further analysis, using the same downstream strategy as used for skin fibroblasts previously. for cross - tissue marker gene comparisons, we selected reported populations from korsunsky et al., buecher et al. and gao et al. as gao et al. reported six universal and five shared populations, we show matching populations in the main figure and other populations in the extended figure. for cross - tissue integration, we concatenated our labeled skin data with other tissues ( raw count data ). hlca data, gut atlas data and human endometrial cell atlas data were available locally. we downloaded nasal tissue data from https: / / ngdc. cncb. ac. cn / gsa - human / browse / hra000772 ( ref. ), heart data from https: / / data. humancellatlas. org / explore / projects / e9f36305 - d857 - 44a3 - 93f0 - df4e6007dc97, rheumatoid arthritis from https: / / www. immport. org / shared / study / sdy998 ( ref. ) and additional intestinal data from the gene expression omnibus ( geo ) under accession code gse282122. we used the same number of input hvgs ( n = 6, 000 ).