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A single-cell and spatial genomics atlas of human skin fibroblasts reveals shared disease-related fibroblast subtypes across tissues Source paper: PMC12479362
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Single_Cell
Fibroblasts sculpt the architecture and cellular microenvironments of various tissues.
[ { "end": 11, "label": "CellType", "start": 0, "text": "Fibroblasts" }, { "end": 85, "label": "Tissue", "start": 78, "text": "tissues" } ]
Single_Cell
Here we constructed a spatially resolved atlas of human skin fibroblasts from healthy skin and 23 skin diseases, with comparison to 14 cross-tissue diseases.
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Single_Cell
We define six major skin fibroblast subtypes in health and three that are disease-specific.
[ { "end": 44, "label": "CellType", "start": 20, "text": "skin fibroblast subtypes" } ]
Single_Cell
We characterize two fibroblast subtypes further as they are conserved across tissues and are immune-related.
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Single_Cell
The first, F3: fibroblastic reticular cell-like fibroblast ( CCL19 CD74 HLA-DRA ), is a fibroblastic reticular cell-like subtype that is predicted to maintain the superficial perivascular immune niche.
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Single_Cell
The second, F6: inflammatory myofibroblasts ( IL11 MMP1 CXCL8 IL7R ), characterizes early human skin wounds, inflammatory diseases with scarring risk and cancer.
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Single_Cell
F6: inflammatory myofibroblasts were predicted to recruit neutrophils, monocytes and B cells across multiple human tissues.
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Single_Cell
Our study provides a harmonized nomenclature for skin fibroblasts in health and disease, contextualized with cross-tissue findings and clinical skin disease profiles.
[ { "end": 65, "label": "CellType", "start": 49, "text": "skin fibroblasts" } ]
Single_Cell
Fibroblasts are crucial cells for shaping tissue architecture and immune cell niches .
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Single_Cell
Studying the heterogeneity of fibroblast subtypes has been challenging due to the scarcity of unique surface markers and their tendency to adopt activated phenotypes during in vitro culture .
[ { "end": 49, "label": "CellType", "start": 30, "text": "fibroblast subtypes" } ]
Single_Cell
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics technologies have overcome these challenges, enabling the dissection of fibroblast heterogeneity in human tissues .
[ { "end": 180, "label": "Tissue", "start": 167, "text": "human tissues" } ]
Single_Cell
While recent studies have described fibroblast states in human skin, they have not spatially resolved their tissue microanatomical location.
[ { "end": 67, "label": "Tissue", "start": 57, "text": "human skin" } ]
Single_Cell
Very few, if any, have interrogated fibroblasts in diverse disease conditions in the skin and across human tissues .
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Single_Cell
Consequently, the fibroblast composition and function in human skin; how it changes across a range of diseases (inflammatory, cancer and fibrosis/scarring); and how these populations relate to other human tissues is still unclear.
[ { "end": 67, "label": "Tissue", "start": 57, "text": "human skin" }, { "end": 212, "label": "Tissue", "start": 199, "text": "human tissues" } ]
Single_Cell
In this study, we integrated published large-scale scRNA-seq datasets of healthy human skin and 23 skin diseases and generated spatial transcriptomics data from two different modalities to construct a high-resolution spatially resolved atlas of more than 350,000 adult human skin fibroblasts.
[ { "end": 91, "label": "Tissue", "start": 73, "text": "healthy human skin" }, { "end": 291, "label": "CellType", "start": 263, "text": "adult human skin fibroblasts" } ]
Single_Cell
We provide a consensus annotation of skin fibroblasts based on gene expression profiles and spatial locations, and contextualize these findings with fibroblast data from other healthy and diseased human tissues.
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Single_Cell
Our scRNA-seq and spatial datasets resources are freely available for download and interactive data exploration at https://cellatlas.io/studies/skin-fibroblast .
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Single_Cell
We re-processed and integrated 2.1 million cells from scRNA-seq data of adult human skin, comprising 32 datasets and 251 donors (Fig. 1a and Supplementary Table 1 ) using single-cell variational inference (scVI) ( Methods ) .
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Single_Cell
After quality control, 357,276 high-quality fibroblasts were selected based on canonical marker gene expression (Fig. 1a and Extended Data Fig. 1a ).
[ { "end": 55, "label": "CellType", "start": 44, "text": "fibroblasts" } ]
Single_Cell
In healthy skin, we identified six major fibroblast subtypes based on differential gene expression (Supplementary Data Fig. 1a and Supplementary Table 2 ) and pathway enrichment analysis (Extended Data Fig. 2a and Methods ).
[ { "end": 60, "label": "CellType", "start": 41, "text": "fibroblast subtypes" }, { "end": 15, "label": "Tissue", "start": 3, "text": "healthy skin" } ]
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The six fibroblast subtypes were observed across different covariates (Extended Data Fig. 1b–g and Supplementary Note 1 ).
[ { "end": 27, "label": "CellType", "start": 8, "text": "fibroblast subtypes" } ]
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Complementary spatial transcriptomic methods validated the presence of each of the six fibroblast subtypes and revealed their distinct microanatomical locations (Fig. 2a–c , Extended Data Figs. 3 and 4 and Supplementary Fig. 2 ).
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Single_Cell
Two of the six fibroblast populations (F1: superficial (papillary) and F2: universal (reticular)) were uniformly present throughout skin at different tissue depths.
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Single_Cell
F1: superficial (papillary) fibroblasts localized adjacent to the skin epithelium in the papillary dermis (Fig. 2b,c ) and expressed genes encoding superficial dermal collagens ( COL13A1 , COL18A1 and COL23A1 ) and Wnt signaling inhibitors ( APCDD1 , WIF1 and NKD2 ) (Fig. 1c ).
[ { "end": 39, "label": "CellType", "start": 4, "text": "superficial (papillary) fibroblasts" }, { "end": 81, "label": "Tissue", "start": 66, "text": "skin epithelium" }, { "end": 105, "label": "Tissue", "start": 89, "text": "papillary dermis" } ]
Single_Cell
A Wnt-mediated synergistic interplay between superficial dermal fibroblasts and basal epithelial cells has been reported to reciprocally maintain cellular identity .
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Single_Cell
F2: universal (reticular) fibroblasts were located deeper in the skin, interspersed between large collagen fibers in the reticular dermis (Fig. 2b,c ).
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Single_Cell
This population was characterized by high expression of marker genes of universal PI16 fibroblasts ( PI16 , CD34 and MFAP5) , a fibroblast subtype found in many human tissues and postulated to represent a precursor fibroblast cell state .
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Single_Cell
Transcription factor activity inference identified KLF5 in F2: universal fibroblasts (Extended Data Fig. 2b ), which has been reported to drive the universal Pi16 state .
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Single_Cell
As fascial fibroblasts (F_Fascia) are proposed as a potential progenitor cell in mouse skin , we included these cells in an additional integration, identifying that F_Fascia formed a subset of F2: universal (Extended Data Fig. 1i,j and Supplementary Note 2 ).
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Single_Cell
The remaining fibroblast subsets were more focal in localization, being associated with vascular or adnexal structures.
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Single_Cell
We thus used hematoxylin and eosin (H&E) staining to illustrate these microenvironments.
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Single_Cell
F3: fibroblastic reticular cell (FRC)-like fibroblasts were located predominantly in the superficial perivascular region in proximity to immune cells (Fig. 2b and Extended Data Figs. 3a,b and 4a,b ).
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Single_Cell
F3: FRC-like fibroblasts transcriptomically resembled FRCs, which are specialized fibroblasts found in lymphoid organs/structures that maintain immune niches (Extended Data Fig. 1h ) , expressing genes that attract and compartmentalize immune cells ( CCL19 , CXCL12 and CH25H ), maintain immune cell survival and function ( IL33 , IL15 , TNFSF13B and VCAM1 ) and enable antigen presentation ( CD74 and major histocompatibility complex (MHC)-II molecules) .
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Single_Cell
F2/3: perivascular fibroblasts also localized with immune cells but, unlike F3: FRC-like fibroblasts, were additionally enriched in deep perivascular regions and other sites (Fig. 2a–c and Extended Data Fig. 4b,c ).
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Single_Cell
A fraction of F2/3: perivascular fibroblasts showed elevated expression of PPARG (Fig. 1c ) and pathway analysis suggested a role in adipocyte differentiation (Extended Data Fig. 2a ).
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Single_Cell
The capability to differentiate into adipocytes is characteristic of the reticular fibroblast (equivalent to F2: universal) lineage .
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Single_Cell
F2/3: perivascular fibroblasts shared select gene expression with both F2: universal and F3: FRC-like fibroblasts (Fig. 3c ).
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Single_Cell
F4: hair follicle-associated fibroblasts ( ASPN COL11A1 ) encompassed three subclusters that were associated with specific regions of the hair follicle (Fig. 2b,c ).
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Single_Cell
The first is a well-characterized dermal sheath (DS) population (F4: DS_DPEP1 ) that wraps around the lower/mid hair follicle (Fig. 2b ).
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Single_Cell
The second is a novel F4: TNN COCH subtype, expressing tendon-associated genes ( MKX and TNMD ) and observed at the isthmus (mid-hair shaft) (Fig. 2b and Extended Data Fig. 4d ).
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Single_Cell
The third F4: DP_HHIP subtype uniquely expressed dermal papilla marker genes ( CORIN , HHIP , RSPO3 and LEF1 ) .
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Single_Cell
F5: Schwann-like fibroblasts (SCN7A, FMO2 , FGFBP2 and OLFML2A ) contained two subclusters (F5: NGFR and F5: RAMP1 ) (Extended Data Fig. 1k ) .
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Single_Cell
F5: RAMP1 fibroblasts were enriched near innervated eccrine glands and expressed genes encoding the receptor complex for the neuropeptide CGRP (Fig. 2b,c and Extended Data Figs. 1l , 3c,d and 4c ), suggesting a possible interface with the nervous system.
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Single_Cell
F5: NGFR colocalized with Schwann cells, suggesting that they are a nerve-associated population.
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Single_Cell
Fibroblasts have been described in the endoneurium and perineurium of nerve fibers from imaging studies , and ‘Schwann-like fibroblasts’ have recently been reported in human skin scRNA-seq data .
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Single_Cell
We confirmed that our six fibroblast subtypes were distinct from Schwann cells and pericytes (Extended Data Fig. 1j,k and Supplementary Note 2 ).
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In addition, we harmonized our skin fibroblast annotation with a previous classification (Supplementary Data Fig. 1b ).
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Single_Cell
Overall, we provide a new framework for healthy human skin fibroblast annotation based on gene expression profiles (Fig. 1 ) and spatial location (Fig. 2 ) that integrates previous fibroblast descriptions in skin and across tissues.
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Single_Cell
Our findings of transcriptionally defined fibroblast subtypes in distinct microanatomical locations suggest a role for regional fibroblasts in supporting distinct niche functions.
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We next sought to identify how fibroblast states change in diseased skin.
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Single_Cell
We used scPoli , a deep-learning model for integration and identification of novel cell states in single-cell transcriptome data ( Methods ) (Fig. 3a ).
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Single_Cell
We mapped fibroblasts from skin diseases to our healthy/nonlesional F1–F5 fibroblast reference.
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Out of 190,756 fibroblasts from diseased states, 121,167 diseased cells were confidently assigned existing F1–F5 cell labels (Extended Data Fig. 5a,b ).
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Single_Cell
The remaining 69,589 fibroblasts from the disease data were classified as uncertain (unlabeled) by scPoli (Fig. 3b ).
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Single_Cell
Manual annotation based on differential gene expression (Supplementary Data Fig. 1c and Supplementary Table 3 ) and pathway analysis (Extended Data Fig. 5c ) revealed two ‘disease-adapted’ and three ‘disease-specific’ fibroblast subtypes (Fig. 3b–f ).
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Single_Cell
‘Disease-adapted’ fibroblasts resembled a healthy fibroblast subtype counterpart (Fig. 3e ) and were expanded in disease settings (Fig. 3d ).
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The first disease-adapted fibroblast subtype resembled F1: superficial fibroblasts in healthy skin (Fig. 3e ).
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The F1-like disease population upregulated genes suggestive of regenerative function ( CRABP1 , CYP26B1 and WNT5A ) .
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Single_Cell
CRABP1 and CYP26B1 are markers of superficial/upper wound fibroblasts in mice , which are thought to be the source of wound-induced hair follicle neogenesis , and involved in retinoic acid degradation.
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Single_Cell
CRABP1 fibroblasts are also associated with regeneration in reindeer skin and early-gestational human skin .
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The second disease-adapted fibroblast subtype resembled F3: FRC-like fibroblasts and upregulated CXCL9 and/or ADAMDEC1 (Fig. 3e ).
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Single_Cell
CXCL9 is a chemoattractant for CXCR3 cells and has been reported as an activation marker for FRCs in lymphoid tissues .
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‘Disease-specific’ fibroblasts (F6: inflammatory myofibroblasts, F7: myofibroblasts and F8: fascia-like myofibroblasts) did not have a healthy skin fibroblast counterpart and highly expressed a myofibroblast gene signature.
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This myofibroblast signature included contractility ( ACTA2 ), extracellular matrix (ECM) ( COL3A1 , COL5A1 , COL8A1 , POSTN and CTHRC1 ) and other myofibroblast-associated genes ( LRRC15 , SFRP4 , ASPN , RUNX2 and SCX ) (Fig. 3c,g and Extended Data Fig. 5d,e ) .
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F6: inflammatory myofibroblasts additionally expressed immune-related genes such as interleukins ( IL11 and IL24 ), chemokines ( CXCL5 , CXCL8 , CXCL13 and CCL11 ) and matrix metalloproteinases that can remodel tissue to facilitate immune cell infiltration ( MMP1 ) (Fig. 3c ).
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JAK–STAT and hypoxic signaling genes were also elevated (Fig. 3h ).
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F7: myofibroblasts and F8: fascia-like myofibroblasts were distinguished by a higher expression of ECM and TGFβ signaling genes, as well as the mechanotransducer PIEZO2 (Fig. 3c,h ).
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Single_Cell
F8: fascia-like myofibroblasts were distinguished by expression of F_Fascia-associated genes (Fig. 3c ).
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Single_Cell
Overall, our results indicate that healthy fibroblasts can acquire a regenerative phenotype in F1: superficial fibroblasts ( CRABP1 CYP27B1 ), a distinct polarization in F3: FRC-like fibroblasts ( CXCL9 / ADAMDEC1 ) and potentially give rise to myofibroblast states ( ACTA2 COL8A1 SFRP4 ) in diseased skin.
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Single_Cell
We next leveraged the diverse clinical profiles of skin diseases to assess whether fibroblast subtypes provide molecular insights into disease endotypes with respect to scarring.
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We assigned the 23 skin diseases into three clinically determined risk of scarring groups: low scarring risk, moderate scarring risk, and established scarring/fibrosis (see Methods ) (Fig. 4a ).
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We excluded neurofibroma from this analysis as it was the only case of benign neoplasia, consisting primarily of F5: Schwann-like and F2/3: perivascular fibroblasts (Extended Data Fig. 6a ).
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We identified distinct fibroblast compositions for each scarring risk category (Fig. 4b ).
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Low scarring risk diseases were characterized by a high prevalence of F1: superficial ( CRABP CYP27B1 ) and F3: FRC-like fibroblasts ( CXCL9 / ADAMDEC1 ) (Fig. 4b ), without notable F6–F8 myofibroblast populations.
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Single_Cell
This finding agrees with the regenerative-associated gene profile of disease-associated F1: superficial fibroblasts and a role for F3: FRC-like fibroblasts in maintaining immune niches.
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Diseases with scarring risk were characterized by a uniquely high prevalence of F6: inflammatory myofibroblasts, which was not observed in low scarring risk or established fibrosis (Fig. 4b ).
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F7: myofibroblasts were observed at a similar prevalence in diseases with scarring risk and established fibrosis.
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These data point toward F6: inflammatory myofibroblast as a population influencing scarring risk, but which are largely absent in established fibrosis.
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F8: fascia-like myofibroblasts were also elevated in established fibrosis but were predominantly observed in Dupuytren contracture, a fibroproliferative disease of the palmar fascia (Fig. 4a ).
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We used two further approaches to demonstrate the role for distinct fibroblast subtypes predicting scarring risk.
[ { "end": 87, "label": "CellType", "start": 68, "text": "fibroblast subtypes" } ]
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First, we trained a random forest classifier and identified that F6: inflammatory myofibroblasts and F7: myofibroblasts were the most important fibroblast subtypes for predicting scarring risk category (Extended Data Fig. 6b ).
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Second, we profiled a well-recognized myofibroblast marker (LRRC15) at the protein level.
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LRRC15 was evident in inflammation with scarring risk (inflamed hidradenitis suppurativa skin) but not in noninflamed skin or inflamed skin without scarring risk (atopic dermatitis skin) (Fig. 4c ).
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Having established that disease-associated fibroblasts are enriched in distinct scarring categories, we next used spatial transcriptomics to validate these fibroblast populations in distinct scarring risk stroma (Fig. 4d–f and Supplementary Fig. 3 ) .
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In keeping with scRNA-seq data (Fig. 4a ), F3: FRC-like fibroblasts were expanded in inflamed atopic dermatitis skin (low risk), without major myofibroblasts (Fig. 4d,f and Extended Data Fig. 6c ).
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We localized the F3: FRC-like population to the superficial perivascular immune niche (Fig. 4d ), which we further validated using 10x Visium data (Extended Data Fig. 6d–f ).
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In melanoma (scarring risk), aside from F1, the entire stroma comprised F6: inflammatory myofibroblasts and F7: myofibroblasts (Fig. 4e,f and Extended Data Fig. 6g ).
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F7: myofibroblasts showed a matrix-producing phenotype ( COL1A1 , COL3A1 and POSTN ) that characterizes myofibroblastic cancer-associated fibroblasts (CAFs) (myoCAFs) .
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F6: inflammatory myofibroblasts demonstrated high expression of inflammatory CAF (iCAF) marker genes ( MMP1 , MMP3 , CXCL8 and IL24 ), which was observed in both cancer and inflammatory diseases with scarring risk (Extended Data Fig. 6h,i ).
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Finally, to complement our analysis of fibroblast proportions by disease, we assessed transcriptomic variability of disease-associated fibroblast subtypes by calculating gene module scores for each disease using defined marker genes to define transcriptomic variability across different disease conditions (Fig. 4g and Methods ).
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The F6: inflammatory myofibroblast signature score was highest in hidradenitis suppurativa, acne and keratinocytic skin cancers.
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Overall, our findings support distinct stromal composition in skin diseases associated with differential scarring risk.
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F6: inflammatory myofibroblasts were observed in diseases with scarring risk but relatively infrequently observed in established fibrosis, raising the possibility that they may be an intermediate differentiation state toward F7: myofibroblasts.
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The differentiation process of healthy fibroblasts into myofibroblasts remains poorly understood in human tissues despite its clinical relevance.
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Fibroblasts are tissue resident, and thus intermediate states of myofibroblast differentiation are likely to be captured in the molecular snapshots of skin diseases analyzed.
[ { "end": 11, "label": "CellType", "start": 0, "text": "Fibroblasts" } ]
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We therefore performed trajectory analysis of fibroblasts in diseased skin to gain further insights into myofibroblast differentiation, before utilizing time-resolved human wound data as a validation of dynamic changes in stromal composition.
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We first included all fibroblast subtypes in a partition-based graph abstraction (PAGA) analysis (Extended Data Fig. 7a ), and then focused further analyses on fibroblast populations found across diseases on hair-bearing and hairless skin ( Methods ).
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F7: myofibroblasts were a terminally differentiated myofibroblast state (Fig. 5a–c ), consistent with their presence in established fibrosis.
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We observed two potential sources for F7: myofibroblasts in skin across analyses (Fig. 5b,c and Extended Data Fig. 7b ).
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