CD201+ fascia progenitors choreograph injury repair

doi:10.21203/rs.3.rs-2009916/v1

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CD201+ fascia progenitors choreograph injury repair

https://doi.org/10.21203/rs.3.rs-2009916/v1

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published 15 Nov, 2023

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Optimal tissue recovery and organismal survival1 are achieved by tight spatiotemporal tuning of tissue inflammation, contraction and scar-formation. Here, we discover a multipotent fibroblast progenitor marked by CD201 expression in the fascia, the deepest connective tissue layer of the skin. Using murine skin injury models, single-cell transcriptomics, and genetic lineage tracing and ablation models, we demonstrate that CD201+ progenitors pace wound healing by generating multiple specialized cell types from proinflammatory fibroblasts to myofibroblasts in a spatiotemporally tuned sequence. We identify retinoic acid and hypoxia signaling as differentiation checkpoints that control the graduated entry of fascia progenitor into the proinflammatory and myofibroblast states. Modulating their differentiation, with retinoic acid and hypoxia-inducible factor 1-alpha, or genetically ablating this cellular lineage, impaired the graduated appearances of specialized fibroblasts and chronically delayed wound healing. The discovery of fascia progenitors, their microenvironment, and the signaling pathways that control the graduated transitions thereof provides a new roadmap to understand and clinically treat impaired wound healing.

Wound healing is an ancestral response to injury that requires tight spatiotemporal regulation, of three consecutive phases, by specialized mesenchymal cell types termed proinflammatory fibroblasts and myofibroblasts. Upon injury, proinflammatory fibroblasts release chemokines such as CC-motif ligand-2 (Ccl2), and CXC-motif ligand-1 (Cxcl1) that support an influx of leukocytes into the wound bed. This initial inflammation phase has many functions such as anti-microbial defense, clean-up of cellular debris and it establishes a temporary wound environment that is thought to facilitate the subsequent phases of wound healing^1-3. Timely wound healing is brought about by resolving, and receding, the initial inflammation phase and transitioning into the second phase of wound healing, termed the proliferation phase. This involves local proliferation of wound fibroblasts and keratinocytes that build-up and shape the extracellular matrix in wounds, as well as regenerate epidermal breaches. The third and final phase of wound healing, the remodeling phase, is characterized by the activity of a second specialized fibroblast cell type termed myofibroblasts⁴.

The coordinated and timely initiation, persistence, and resolution of each of these three separate wound healing phases is key to an effective healing response. The first transition is especially crucial and a tight control of exit from the inflammation phase and entry into the proliferation/remodeling phases is absolutely required for an effective healing response. Indeed, a protracted inflammation phase can lead to impaired wound healing in the form of non-healing, chronic wounds seen in conditions such as aging, diabetes, surgical or infectious wounds, vascular and heart diseases, or even cancer⁵.

In contrast, an excessive myofibroblast-centric remodeling phase¹is associated with tissue contraction, scarring or fibrosis, and observed in Dupuytren’s contractures, hypertrophic scars, and keloid lesions. Clearly, impaired wound healing imposes an enormous clinical burden and better understanding the choreography of tissue recovery is a matter of tremendous clinical and research interest.

An exact tissue cartography, of where fibroblast progenitors reside and where differentiation signals are most effective within wounds, remains elusive. Recent single-cell transcriptomics (scRNAseq) studies, in mouse and man, have enhanced our view of fibroblast cell heterogeneity, in both skin^6-14and internal organs^15-17. For example, proinflammatory fibroblasts are consistently detected across many mouse and human diseases from keloid lesions¹⁸ to aged skin¹⁹, fibrotic Dupuytren’s nodules²⁰, and atopic dermatitis¹².Functionally, these cells modulate the immune response via production of proinflammatory cytokines^{3, 21-22}. Single cell RNA-seq studies have also reported transcriptional differences between fibroblasts taken from different wound regions^6,9,23. However these reports have not resolved the causation behind these differences. This becomes relevant as distinct fibroblasts subtypes may respond differently to the same external signals^24-25 and therefore spatial distribution of fibroblast populations is another extremely important facet when understanding and controlling wound healing phases²⁶.

Here, we perform longitudinal stromal-enriched scRNA-seq combined with a multitude of in vivo genetic lineage tracing and ablation approaches to generate a high-resolution pedigree study of all fibroblastic cells in skin. We identify a new fibroblast progenitor in the subcutaneous fascia that controls the graduated features of wound healing by undergoing sequential differentiation into specialized fibroblastic cell types.

Fascia progenitors differentiate into specialized fibroblasts during wound healing

Fibroblasts inhabit different connective tissue compartments in the skin. Papillary and reticular fibroblasts reside in the upper skin regions while scar-producing fibroblasts are present in the deepest connective tissue layer, termed fascia²⁷. To get a complete understanding of how any wound fibroblast type arise during skin wound healing, we performed scRNAseq of connective tissue-enriched cell fractions taken from full-thickness wounds on the back skin of mice including both upper and deep skin layers.

We have previously shown that back skin wound myofibroblasts originate from a single embryonic cell lineage that expresses the Engrailed-1 (En1) transcription factor during embryonic development (termed as EPFs)^28-29. However, in adult skin, EPFs are found in all skin connective tissue layers: papillary, reticular, and fascia. Therefore, it remains unclear what makes fascia fibroblasts prone to generate scars.

We therefore combined our single cell transcriptomics with genetic lineage tracing by crossing transgenic mice expressing the Cre recombinase under the control of the promoter of En1 (En1^Cre) with the fluorescent Rosa26mTmG (R26^mTmG) reporter mouse line (extended data figure 1a). The resulting En1^CreR26^mTmG double transgenic mice have EPFs permanently expressing the green fluorescent protein gene (GFP). We sequenced 29,383 individual cells from En1^CreR26^mTmG uninjured skin and from skin wounds at 1-, 3-, 5-, 7-, 14-, and 27-days post injury (dpi) representing the inflammation (1, 3 dpi), proliferation (5, 7 dpi), and remodeling phases of wound healing (14, 27 dpi, extended data figure 1b). Our initial cell sequencing was unbiased with regards to EPFs, and therefore included both GFP⁺ (belonging to EPFs that we will return to later), as well as GFP^-fibroblasts.

Our computational analysis spotted 7 distinct fibroblast clusters (extended data figure 1c-d). Clusters 0-2 were present in all samples, both injured and uninjured. Whereas clusters 3-6 were only found in injured tissue. To annotate previously known fibroblast populations on our 7 clusters, we curated a set of published markers for papillary, reticular, fascia, and myofibroblasts. We then annotated the identity of our clusters based on the gene expression scores from our curated sets. This analysis identified three main fibroblast populations at homeostatic conditions residing in papillary, reticular, and fascia connective tissues (clusters 0-2, Extended data figure 1e-f). Cluster 0 scored highest for the papillary profile, marked by the expression of the previously defined papillary markers Sparc¹⁴ and Decorin³⁰ (Dcn), as well as the proteoglycan Lumican (Lum), which has been associated with keratinocyte-supportive functions³¹. Cluster 2 scored highest for the reticular profile and was marked by the expression of the reticular markers Stromal cell-derived factor-1¹⁴ (Sdf1/Cxcl12), Cytoglobin¹⁴ (Cygb), and Matrix gla protein (Mgp)³². Cluster 1 scored highest for the fascia profile and expressed the known fascia markers Lymphocyte antigen-6a^27,33 (Ly6a/Sca1), Placenta-specific gene-8¹⁴ (Plac8), as well as Peptidase inhibitor-16 (Pi16) and Dermopontin (Dpt), which were recently found to also mark a fibroblast population present across many organs³⁴. This is consistent with fascia connective tissue enclosing many organs³⁵. We also identified CD201 (Procr), a cell surface receptor that is classically involved in controlling coagulation³⁶,as a novel marker that distinguishes fascia fibroblasts.

Next, we examined the clusters appearing exclusively during injury (clusters 3-6). Samples taken during the early inflammation phase (1-3 dpi) were enriched in cluster 6 (Extended data figure 1c-d), which showed transcriptional patterns of proinflammatory fibroblasts. This wound-exclusive cluster specifically expressed the markers Podoplanin (Pdpn), and the chemokines Ccl2 and Cxcl1 (Extended data figure 1f). Notably, Pdpn and chemokines are also upregulated in inflammatory synovial fibroblasts during rheumatoid arthritis, a chronic inflammatory disease³⁷. Gene-ontology (GO)-overrepresentation analysis revealed that, proinflammatory fibroblasts specialized in immunomodulatory functions with GO-terms such as “response to interferon-beta”, “neutrophil/lymphocyte/monocyte chemotaxis”, and “chemokine-mediated signaling pathway” (Extended data figure 1g). Clusters 3 and 5 appeared during the following proliferation phase of wound healing (5-7 dpi, Extended data figure 1d). Using our curated list of fibroblast markers, we determined that these two populations have a myofibroblast profile, differing only by the expression of the marker Alpha smooth muscle actin (αSMA/Acta2, Extended data figure 1e-f). Indeed, cluster 5, representing the Acta2^high myofibroblasts also expressed other reported myofibroblast markers such as Periostin³⁸ (Postn) and Leucine-rich repeat-containing protein-15³⁹ (Lrrc15), as well as Runt-related transcription factor-2 (Runx2), a marker associated with keloid lesions⁴⁰. Whereas cluster 3, representing the immature proto-myofibroblast with Acta2^low, expressed Tenascin⁴¹ (Tnc), Signal transducer and activator of transcription-3 (Stat3), whose activity has been linked to dermal fibrosis⁴², as well as Proprotein convertase subtilisin/kexin type-5⁴³ (Pcsk5, Extended data figure 1e-f). Of note, proto-myofibroblasts (cluster 3), but not mature myofibroblasts (cluster 5), showed the GO-terms “cellular response to hypoxia” (Extended data figure 1h), indicating that hypoxia signaling is active in proto-myofibroblasts. Cluster 4 uniquely expressed the marker Secreted frizzled-related protein-2 (Sfrp2, henceforth named as SFRP2⁺ fibroblasts), which has been previously detected in human scRNAseq datasets⁴⁴. Cluster 4 also uniquely expressed the keloid fibroblast marker Collagen triple helix repeat-containing protein-1⁴⁵ (Cthrc1) and the fibrosis-related marker Follistatin-related protein-1⁴⁶ (Fstl1, Extended data figure 1f). Clusters 3-5 specialized in matrix production and tissue contraction, presented GO-terms such as “collagen biosynthetic process”, “collagen fibril organization”, “extracellular matrix assembly”, “positive regulation of focal adhesion assembly”, and “actin filament bundle assembly”. On the other hand, only cluster 5 (Acta2^high myofibroblasts) expressed terminal differentiation programs including “regulation of cell proliferation” and “endochondral ossification” (Extended data figure 1g).

Next, we sought to trace the most likely cellular origin of mature myofibroblasts by using the most robust algorithm for trajectory inference⁴⁷, Partition-based graph abstraction (PAGA), which predicts the connections (e.g. differentiation steps) between defined clusters (Extended data figure 1h). Surprisingly, we found that myofibroblasts, proto-myofibroblasts and proinflammatory fibroblasts (clusters 3, 5, 6) represent a single cell lineage that emerges exclusively from homeostatic fascia fibroblasts (cluster 1). PAGA connectivity analysis revealed that fascia fibroblasts (cluster 1) give rise to proinflammatory fibroblasts (cluster 6), that in turn differentiate into proto-myofibroblasts (cluster 3) and finally into myofibroblasts (cluster 5) (Extended data figure 1h-i).

As EPFs are a predominant cell lineage in wounds, we wanted to place the previous EPF fibrogenic lineage in the context of our new differentiation trajectory during wound healing. We sorted the EPFs in the scRNAseq dataset based on their GPF expression and reanalyzed them in a higher clustering resolution (Extended data figure 2a). GFP⁺ cells (EPFs) were present across all homeostatic populations as well as in the proinflammatory, proto-myofibroblast, and myofibroblast clusters (Extended data figure 2b-d).

Our analysis also revealed an additional intermediate fascia EPF subcluster that is present in very early injured samples but is absent from uninjured skin (Extended data figure 2c-d). This intermediate injury cluster has a transcriptional signature in between fascia and proinflammatory fibroblasts. These injured fascia EPFs express the naïve fascia markers: CD201/Procr, Pi16 and Plac8, while simultaneously upregulating the proinflammatory fibroblast markers Pdpn, Ccl2 and Cxcl1 (Extended data figure 2e). This indicates that all three injury-related cell clusters (clusters 3,5,6) are most likely derived from fascia-resident EPFs.

CD201⁺ progenitors choreograph wound healing phases

We next focused on linking the stepwise differentiation of fascia progenitors with the wound healing phases in vivo. We first confirmed the temporal expression patterns of our fibroblast state-specific markers (Figure 1a) across mouse skin wounds at 3 and 7 dpi: two critical time points marking the transition from the inflammation to proliferation phases and from the proliferation to remodeling phases of wound healing, respectively. Consistent with our bioinformatics analysis, PDPN⁺ (proinflammatory) and phospho-activated STAT3⁺ (pSTAT3, proto-myofibroblast) cells peaked during the first transition at 3 dpi, and declined at later timepoints (Extended data figure 3a-b). On the other hand, RUNX2⁺ (myofibroblast) cells were prominent at the second transition into the remodeling phases of wound healing at 7 dpi (Extended data figure 3c). These data confirm a temporal linkage between fascia progenitor cell differentiation and wound healing phase transitions in vivo.

As spatial distribution of fibroblast populations is another extremely important facet of wound healing phases²⁶, we next analyzed the spatial locations of proinflammatory, proto-myofibroblast, and myofibroblast from across three distinct wound compartments: upper wound, wound bed, and underlying fascia areas (Extended data figure 3d). At 3 dpi, PDPN⁺ proinflammatory fibroblasts were confined to the wound bed and upper wounds (Extended data figure 3e), while pSTAT3⁺ proto-myofibroblasts showed no preference among these areas (Extended data figure 3f). In contrast, at 7 dpi, RUNX2⁺ myofibroblasts clearly localized in upper wound compartments, with minimal presence in deep wound areas (Extended data figure 3g). This gradual spatial bottom-up differentiation of fibroblasts towards upper wound compartments, indicates a spatial distribution of guiding signals that promote the sequential differentiation of fascia progenitors (Figure 1c).

To test if fascia progenitors undergo spatiotemporal differentiation into all three distinct fibroblast states in vivo, we performed anatomic fate mapping of the fascia cells during wound healing coupled with immunolabeling for our fibroblast state-specific markers. First, we exclusively tagged fascia cells by delivering TAT-Cre recombinase into the fascia layer of R26^mTmG fluorescent reporter mice (Extended data figure 4a). This allowed us to trace the fates of fascia fibroblasts during wound healing without labeling upper dermal (reticular, papillary) fibroblasts (Extended data figure 4b-c). At 3 dpi, close to half of all GFP⁺ fascia-derived cells acquired the PDPN⁺ proinflammatory state, and at 7 dpi half of all labeled cells acquired either pSTAT3⁺ proto-myofibroblast, or RUNX2⁺ or αSMA/ACTA2⁺ mature myofibroblast characteristics (Extended data figure 4d-e). These results confirm that fascia fibroblasts indeed give rise to all 3 fibroblast types during wound healing.

As our anatomical fate mapping above is not fibroblast specific, we then sought for a restricted marker to better study this progenitor population. We compared levels of cell enrichment for the classical fascia fibroblast marker Ly6a/Sca1, Pdgfra, and our newly discovered marker CD201. Ly6a/Sca1 mRNA was 1.24-fold enriched in fascia fibroblasts as compared to dermal fibroblasts (44.6 %, Ly6a/Sca1⁺ cells in naïve fascia). Whereas the pan-fibroblast marker Platelet-derived growth factor alpha (Pdgfra) showed no enrichment. In contrast, CD201 mRNA was 1.7-fold enriched in fascia fibroblasts as compared to dermal fibroblasts, indicating that, transcriptionally, CD201 expression enriches fascia fibroblasts (Extended data figure 5a).

We then generated a genetic reporter system by crossing a mouse line expressing the tamoxifen-inducible Cre-ER recombinase under the control of CD201 (CD201^CreER) with the reporter line R26^Ai14. In the CD201^CreERR26^Ai14double transgenic offspring, tamoxifen administration induces the enduring expression of TdTomato in cells that express CD201, thereby specifically labeling fascia fibroblasts prior to injury (Figure 1d). Consistent with our previous anatomic fate mapping strategy, skin from CD201^CreERR26^Ai14 mice labeled with TdTomato specifically fibroblasts in the fascia compartment (78.74 % of total traced cells). TdTomato showed minor expression in epidermal and endothelial cells (Extended data figure 5b and Figure 1e), and TdTomato was absent from dermal fibroblasts. Together, our transcriptomics analysis and genetic reporter system indicate that CD201 is a highly selective marker for fascia progenitors.

To further test the origins of proinflammatory, proto-myofibroblasts and mature myofibroblasts from CD201⁺ fascia progenitors, full thickness wounds were then performed on back skin of CD201^CreERR26^Ai14double transgenic mice. CD201⁺fascia progenitors-derived fibroblasts (Tdtomato⁺) populated the wounds and contributed to 83% of all αSMA⁺myofibroblasts (Figure 1f and Extended data figure 5c-d). Consistent with our computational analysis, CD201⁺ fascia progenitors underwent a sequential conversion into proinflammatory (PDPN⁺) and proto-myofibroblasts (pSTAT3⁺) between 3 and 7 dpi. During the progression from inflammation/proliferation to proliferation/remodeling phases of wound healing, CD201⁺ progenitors consistently downregulated proinflammatory and proto-myofibroblast markers: 71.5 down to 46.4 % for PDPN and 88.2 down to 38.4 % for pSTAT3 from 3 to 7 dpi. On the other hand, the expression of the mature myofibroblast marker, RUNX2, increased up to 73.5 % in the CD201 fibroblast lineage (Figure 1g and Extended data figure 5e). The temporal shift in marker gene expression in CD201⁺ fascia lineage cells indicates that the transition from naive progenitors to proinflammatory and fully mature myofibroblasts occurs in synchrony to the progression of the wound healing phases.

To further test if CD201⁺ progenitors transition to myofibroblasts, through a proinflammatory intermediate state, we generated a second transgenic mouse system where the expression of the tamoxifen inducible Cre-ER recombinase is expressed under the PDPN promoter (Pdpn^CreER). These mice were crossed with the R26^mTmG reporter mouse line, enabling (in Pdpn^CreERR26^mTmG double transgenic mice) the lineage tracing from proinflammatory states onwards (Figure 1h). In contrast to our CD201^CreER fascia progenitor tracing system, uninjured skin of Pdpn^CreERR26^mTmGmice had no GFP expression in any skin fibroblast population under homeostatic conditions (Figure 1i). At 7 dpi, GFP⁺αSMA⁺ proinflammatory-derived myofibroblasts were detected abundantly in upper wounds (Figure 1i), confirming that proinflammatory fibroblasts transition into myofibroblasts during skin wound healing and that our CD201^CreER and PDPN^CreER transgenic systems faithfully trace their conversion steps.

Taken together, our in-silico trajectory analysis, anatomic fate mapping and CD201^CreERR26^Ai14and Pdpn^CreERR26^mTmGgenetic lineage tracing methods, detail a spatiotemporal coordination of fibroblast differentiation taking place during wound healing, that arises from CD201⁺ progenitors differentiating into proinflammatory fibroblasts within wound beds that finally maturate into myofibroblasts in the upper wound region.

Sequential checkpoints control CD201⁺ progenitor differentiation

Having identified CD201⁺progenitors of proinflammatory fibroblasts, proto-myofibroblasts, and myofibroblasts, we next sought to explore the molecular programs that regulate the spatiotemporal emergence of each fibroblast state. For this, we analyzed the coalitions of transcription factors (‘termed as “program”) and the gene targets that regulate each cellular state (termed as “regulon”; Extended data figure 6a). Our analysis revealed programs and regulons specific to each fibroblastic state.

1) The naïve CD201⁺ progenitor state was modulated by a program comprising Fos, Jun, and Early growth response protein-1 (Egr1) transcription factors, which oversaw a regulon consisting of 263 different genes. This regulon promotes cell survival and growth with terms including “insulin-like growth factor signaling” as well as “negative regulation of apoptotic process” (Figure 1b and Extended data figure 6b).

2) The PDPN⁺ proinflammatory state was separately controlled by Nuclear factor erythroid 2-related factor-2 (Nfe2l2) and Bach1 transcription factors that oversaw a regulon consisting of 116 different genes. This regulon directs processes related to oxygen level sensing such as “response to oxygen levels” and “response to hyperoxia” (Extended data figure 6c).

3) The pSTAT3⁺ proto-myofibroblast state was controlled by a third independent program comprising Stat3, Hypoxia-inducible factor-1-alpha (Hif1α), En1, Ets2, CCAAT/enhancer-binding protein beta (Cebpb), Receptor ROR-alpha (Rora), and Fosl1 transcription factors, which together oversaw a regulon comprising 251 different genes. This regulon directs processes that promoted migration (“positive regulation of migration”) oxygen sensing (“oxygen homeostasis”), as well as processes such as adhesion (“regulation of cell-matrix adhesion” and “positive regulation of homotypic cell-cell adhesion”), and collagen fibers production and processing (“peptidyl-proline hydroxylation”, Figure 1b and Extended data figure 6d).

4) Finally, the mature myofibroblast state was led by a fourth program comprising the Transcription factor 4 (Tcf4), Runx1, and Runx2 that oversaw a regulon comprising 21 different genes. This fourth regulon directs processes such as terminal differentiation and ossification (“regulation of cell differentiation” and “ossification”, Extended data figure 6e).

Taken together, our bioinformatic analysis and in-vivo genetic lineage tracing data reveal sequential genetic programs that control the stepwise differentiation of CD201⁺ progenitors through proinflammatory, proto-myofibroblast, and myofibroblast states that likely control wound healing phases.

To delve into this idea, we first tested the link between fascia fibroblast differentiation and progression of wound healing ex-vivo. For this, we excised fascia explants from mouse back skin and cultured them in a free-floating system (Figure 1j). Thin, translucent fascia explants underwent tissue contraction akin to that seen in mammalian wounds until forming an opaque sphere of scar tissue that has contracted down to 50% of its original surface area (Figure 1k).

Immunohistochemistry confirmed that fascia progenitors ex-vivo transitioned into proinflammatory fibroblasts within 1-3 days post culture (22.6 % and 80.9 % PDPN⁺ from total cells at 1- and 3-day post culture, respectively). Contrastingly, pSTAT3⁺ proto-myofibroblasts and RUNX2⁺ myofibroblasts increased in numbers from 6.8 % and 19.8 % respectively at day 1 to 64.3 % and 76.1 % of total cells at 6 days of culture. Remarkably, the shift from PDPN⁺ proinflammatory fibroblasts to pSTAT3⁺ proto-myofibroblasts and RUNX2⁺myofibroblasts coincided with initiation of fascia tissue contraction at day 3 ex-vivo (Figure 1k-l), suggesting that the classic proliferation and remodeling phases of wound healing, characterized by tissue contraction and scar formation, are directly regulated by the differentiation of CD201⁺ progenitors.

The proinflammatory transition is gated by retinoic acid

Progression of wound healing phases is associated with dynamic biochemical and biomechanical signals such as TGFβand Wnt⁴⁸, cytokines such as Interleukins and Tumor necrosis factor⁴⁹, cell-cell interactions^50-51, and extracellular matrix mechano-transduction^{4, 52}.

To explore the connections between these and more signaling pathways and CD201⁺ progenitor differentiation, we scored the expression of genes from several signaling pathways in our scRNAseq dataset throughout the fascia-to-myofibroblast trajectory. As expected, we observed expression of genes associated with classical myofibroblast-promoting signaling pathways: e.g. TGFβ, Wnt, and extracellular matrix mechano-transduction, peaking in the myofibroblast state. Whereas, chemokine production peaked much earlier in the middle of the proinflammatory state (Figure 2a).

Interestingly, we observed that retinoic acid (RA) pathway components peaked even earlier, in the very first transition from CD201⁺ progenitors into proinflammatory fibroblasts, suggesting that RA might act as a very early signal promoting differentiation of CD201⁺ progenitors into the proinflammatory state. Consistent with this notion, the transcript levels of the RA-synthetizing enzymes, aldehyde dehydrogenase family-1 member-a3 (Aldh1a3) and Retinol dehydrogenase 10 (Rdh10) were elevated in proinflammatory fibroblasts (Figure 2b). Furthermore, the signal repressor RA-degrading enzyme cytochrome P450-26-b1 (Cyp26b1) peaked in the subsequent proto-myofibroblast state (Figure 2b), suggesting that exit from the proinflammatory state into proto-myofibroblasts requires the downregulation of RA signaling. Chemokines Ccl11, Ccl2, Ccl7, Ccl8, and Cxcl1 expression, were co-elevated in proinflammatory fibroblasts (Figure 2b) indicating they are linked with RA signaling.

Immunolabeling of skin wounds from CD201^CreERR26^Ai14 double transgenic animals at 3 dpi confirmed the co-expression of ALDH1A3, CCL2 and CXCL1 proteins in CD201⁺-derived PDPN⁺ proinflammatory fibroblasts in the wound bed (Figure 2c-e), indicating that RA signaling is linked to chemokine expression. However, expression of the RA degrading enzyme, CYP26B1, was compartmentalized to the upper wound margins where differentiation into myofibroblasts occurs (Figure 2f). This spatial link, between RA synthesis/degradation and fibroblast differentiation, suggests a RA gradient coming from the wound bed and receding in the upper wound regions that acts on fascia fibroblasts and enable their myofibroblast differentiation.

To test the potential role of RA signaling in proinflammatory fibroblast differentiation from CD201⁺ progenitors, we added exogenous RA to fascia explants. RA administration impaired myofibroblast differentiation and concomitant tissue contraction in a dose-dependent manner: delaying tissue contraction at low concentration and completely blocking contraction at higher concentrations (Figure 2g). RA signaling acts canonically through the activation of three nuclear RA receptors, RARα, RARβ, and RARγ, which trigger transcriptional expression changes in the nucleus⁵³. Treatments with specific activators for each of the three RA receptors phenocopied the contraction blockages seen with RA administration (Extended data figure 7a). This indicates that RA signaling in fascia progenitors is mediated by its canonical transcriptional activity and not through any individual RA receptor. Interestingly, increasing endogenous RA levels, by inhibiting CYP26B1, had the same contraction blocking effect as adding exogenous RA (Extended data figure 7a). These experiments indicate that RA is actively produced and degraded in fascia cells during tissue contraction, and that high-RA concentration promotes the entry into proinflammatory states, whereas RA-signaling downregulation grants the exit and progression into myofibroblasts.

As RA signaling activation preceded classic myofibroblast signals, such as TGFβ and YAP-TAZ mechano-transduction in our bioinformatic analysis (Figure 2a), we sought to establish the precise signaling pathway order-of-function for myofibroblasts. We treated fascia explants with exogenous RA in combination with activators for TGFβ and YAP-TAZ pathways. In the presence of exogenous RA, YAP-TAZ mechano-transduction signaling activation failed to induce tissue contraction. On the other hand, TGFβ pathway activation rescued normal tissue contraction in the presence of exogenous RA (Extended data figure 7b-c). This indicates that RA acts upstream of YAP-TAZ mechano-transduction pathway.

As RARγ was the highest expressing RA receptor on fascia fibroblasts in our scRNAseq dataset (Figure 2b), we specifically analyzed the involvement of RARγ activity on fascia fibroblast differentiation. Treatments of fascia explants with the specific RARγ activator increased the number of PDPN⁺ proinflammatory fibroblasts, and CCL2 production, while significantly decreasing RUNX2⁺ myofibroblast numbers after 6 days of culture (Figure 2h). This confirms that RA activation promotes and sustains the proinflammatory fibroblast state and, thus, prevents the subsequent differentiation into myofibroblasts.

To explore the ability of RA overactivation to limit the exit from the proinflammatory state and sustain wound healing in an inflammation phase, we treated skin wounds with the RARγ activator. Treated wounds had prolonged inflammation, revealed by a significant increase in leukocyte numbers at 3 dpi wounds (Figure 2i). Consistent with RA limiting myofibroblast differentiation, RA-treated wounds developed scars at 14 dpi that were significantly smaller than controls, with significantly reduced numbers of mature αSMA⁺ myofibroblasts (Figure 2j). These data confirm that RA activity controls the entry of the proinflammatory state and its overactivation limits the subsequent conversion into myofibroblasts, reducing scar formation as a result.

The (proto)myofibroblast transition is gated by Hif1α

Our bioinformatic analysis reveals that the proinflammatory fibroblast precedes the proto- and myofibroblast in the differentiation trajectory of CD201⁺ progenitors. To further prove this previously unknown connection between these functionally distinct wound fibroblast populations, we performed genetic ablation of proinflammatory fibroblasts in vivo by crossing our Pdpn^CreER mice with the R26^DTA line, in which Cre-mediated recombination activates expression of diphtheria toxin, causing cell death exclusively in proinflammatory fibroblasts. Wounds from two separate control animal groups (Pdpn^CreERR26^WT and Pdpn^CreERR26^mTmG) showed normal wound contraction and closure rates that fully closed between 7 and 14 dpi. Ablation of proinflammatory fibroblasts in Pdpn^CreERR26^DTA animals, caused a significant delay in wound closure in which wounds failed to fully close by 14 dpi (Figure 3a). 7 dpi wounds in Pdpn^CreERR26^DTA animals showed a minimal wound bed and limited re-epithelialization compared to control animals (Figure 3b) indicating tissue repair is significantly impaired. Importantly, ablation of proinflammatory fibroblasts caused a significant decrease in the amount of αSMA⁺ myofibroblast at 7 dpi (Figure 3c), further proving that proinflammatory fibroblast give rise to myofibroblasts in vivo.

Next, our signaling pathway analysis revealed that hypoxia signaling is induced during the subsequent transition from proinflammatory into proto-myofibroblasts (Figure 2a). Moreover, GO term analysis indicated that biological processes linked to oxygen sensing are prevalent in the proinflammatory and proto-myofibroblast states (Extended data figure 1g). Notably, the transcription factor Hif1α was itself found to control the regulon governing proto-myofibroblast differentiation (Extended data figure 6d). This suggested that hypoxia signaling through Hif1α, regulates the exit from the proinflammatory state into proto-myofibroblasts.

Consistent with this, chemical inhibition of Hif1α activity in fascia explant cultures increased PDPN⁺ proinflammatory fibroblast numbers while blocking αSMA⁺ myofibroblast differentiation (Extended data figure 7d) and tissue contraction in a concentration-dependent manner (Extended data figure 7e).

We then analyzed Hif1α signaling pathway order-of-function by combining Hif1α inhibitor treatments with TGFβ1 or TAZ activators. In both cases there was persistently impaired tissue contraction. This indicates that, Hif1α activity acts upstream of both YAP-TAZ mechano-transduction and TGFβ pathways in the conversion to proto-myofibroblasts (Extended data figure 7d-e).

To verify the gatekeeping role of Hif1α in the conversion of proinflammatory fibroblasts into proto-myofibroblast states, we chemically inhibited Hif1α activity in full-thickness wounds in animals. This resulted in a significant delay in wound closure in vivo (Figure 3d), similar to the contraction blocking effects seen in fascia explants ex vivo. Wounds treated with the Hif1α inhibitor also mimicked wounds from Pdpn^CreERR26^DTA animals, had delayed wound healing, a marginal wound bed, and the persistence of an immune infiltrate at 7 dpi. In comparison, control wounds were completely re-epithelialized, with a significant wound bed and no persistent inflammation (Figure 3e). Moreover, wounds treated with Hif1α inhibitor at 1-3 dpi showed similar leukocyte infiltration numbers to control wounds (Figure 3f), confirming that Hif1α controls proinflammatory exit, alone, without affecting early progenitor differentiation.

To unambiguously determine if Hif1α inhibition prevented the transition from proinflammatory fibroblasts into myofibroblast states, we treated full thickness wounds in Pdpn^CreERR26^mTmG double transgenic mice. At 7 dpi, inhibition of Hif1α activity in wounds significantly decreased proinflammatory-derived (GFP⁺) proto-myofibroblast (pSTAT3⁺) and myofibroblast numbers (GFP⁺RUNX2⁺, Figure 3g). This confirms that Hif1α activity indeed controls the transition from proinflammatory into proto- and myofibroblast states needed for wound closure, tissue contraction, and scar formation.

We have previously shown that fascia fibroblasts transfer pre-made extracellular matrix into wounds, and that this matrix transfer contributes to scar formation²⁷. To understand how fascia progenitor differentiation impacts matrix transfer in-vivo, we labeled fascia extracellular matrix, with N-Hydroxy-Succinimidyl (NHS)-Pacific Blue, prior to injury (Extended data figure 8a). 3 dpi wounds showed transferred pre-made matrix fibers extending and covering the wound tissue. Interestingly, PDPN⁺ proinflammatory fibroblasts were associated with thinner more loose extracellular matrix fibers in the wound bed, while pSTAT3⁺ proto-myofibroblasts were associated with thicker more woven fiber bundles in upper wound regions (Extended data figure 8a). This indicates that fascia fibroblast differentiation, and extracellular matrix scar remodeling and maturation are interlinked. To test this idea, we labelled fascia extracellular matrix in animals, and generated full thickness wounds while blocking fascia progenitor differentiation with Hif1α inhibitor. Control 3 dpi wounds had matrix fibers bundles with a condensed fiber organization at the lateral borders and in the upper wound region where myofibroblast differentiation mainly takes place. Whereas, treated animals showed a frail and immature fiber organization along the entire wound (Figure 3h). Next, we assessed the extracellular matrix lattice organization in matrix labeled and Hif1α treated wounds, using fractal analysis²⁹. This revealed a significant reduction in matrix organization (fractal dimension) and an increase in porosity (lacunarity) in the Hif1α-treated group compared to control (Figure 3i), indicating a relaxed immature fiber organization. Our data shows that Hif1α mediates the differentiation of proinflammatory fibroblast into proto- and myofibroblasts, and that this conversion step is needed for extracellular matrix maturation, tissue contraction and wound closure (Figure 3j).

Taken together, our comprehensive scRNAseq, in-vivo functional studies and genetic lineage tracing and ablation experiments identify a spatiotemporal pattern of progenitor>proinflammatory>proto->myofibroblast differentiation from CD201⁺ progenitors, that controls the timely progression of wound healing phases through checkpoint control by RA and Hif1α.

CD201⁺ fibroblast differentiation is conserved in mice and men

To determine if CD201⁺ lineage differentiation is conserved across mouse and human skin diseases, we analyzed publicly available scRNAseq datasets of human keloids¹⁸ and psoriasis⁵⁴ (Extended data figure 9a-b), both of which are skin fibrotic pathologies characterized by the accumulation of myofibroblasts. Keloid and psoriatic fibroblast clusters were curated based on transcriptional signatures taken from our mouse wound healing dataset (Extended data figure 9c-f). Cellular census of diseased human skin showed clear naïve fascia, proinflammatory, and mature myofibroblast clusters, in both keloid and psoriasis datasets. Interestingly, the proto-myofibroblast signature was diffusely spread between the proinflammatory and myofibroblast clusters in the human datasets (Extended data figure 9e-f and Figure 4a), suggesting that in human skin diseases, fascia progenitors differentiate into myofibroblasts directly through the proinflammatory state (Extended data figure 9g-h and Figure 4b).

Just as in our mouse dataset, human fibroblast progenitors were characterized by the expression of the novel fascia marker CD201 in both keloid and psoriatic human datasets (Figure 4c). Furthermore, partition-based graph abstraction connectivity analysis showed differentiation trajectories that mirrored our mouse CD201⁺ lineage, from the naïve fascia progenitor into the proinflammatory states and ending in the myofibroblast state (Figure 4d). These analyses confirm that the connection between the proinflammatory and myofibroblast states is part of a common differentiation process undertaken by fascia progenitors.

Our data in mouse demonstrated that RA acts to promote entry of progenitors into the proinflammatory state while Hif1α promotes exit from the proinflammatory state into myofibroblasts, and that these two checkpoint regulators chronologically and functionally precede classical myofibroblast inducers such as TGFβ. Analysis of the chronology of these signaling pathways in our human datasets showed they mirrored the signaling order in mouse wound healing. Both in mice and human datasets, RA activity score showed a peak at the beginning of the proinflammatory state followed by hypoxia signal activity that peaked at the transition from the proinflammatory to the myofibroblast state, mirroring our findings from mouse skin wound healing. Lastly, TGFβ peaked at the final mature myofibroblast state in all datasets confirming it is preceded by RA and Hif1α signals in its order-of-function (Figure 4e).

Our analysis reveals, in mouse and human, a cellular lineage derived from CD201⁺ fascia progenitors that undergo a graduated differentiation into proinflammatory and myo-fibroblasts. Moreover, that CD201-cell lineage differentiation is regulated by RA and Hif1α; two early signals that time the transition into phenotypically distinct wound fibroblast populations to resolve the sequential phases of wound healing.

This work reveals a conserved cell lineage and differentiation pathway with clinical implication for injury repair in mouse and humans. At the root of this pathway are CD201 positive fascia progenitors that give rise to all specialized fibroblast cell types needed for wound healing to progress. As well as identifying the wound healing lineage, we identify the key checkpoints that control their entry into proinflammatory and myofibroblast states by manipulating these checkpoints.

The initial transition of CD201⁺ fascia progenitors into proinflammatory fibroblasts underscores the relevance of this differentiation step to the initial inflammation phase of wound healing. Our data revealed that Retinoic acid (RA) acts to sustain inflammation by promoting the proinflammatory state and by preventing their subsequent conversion into myofibroblasts.

Retinoic acid has wide ranging functions in skin and it is relevant to note that prolonged application of RA in more upper dermal fibroblasts, promoted hair follicle regeneration in a large skin wound model⁹. This being the case, our demonstration that RA regulates the appearance of myofibroblasts from fascia progenitors, as well as the transcriptional conservation of fibroblast differentiation from CD201⁺ progenitors in human keloid/psoriatic datasets, implies for an anti-fibrotic therapeutic potential for targeting RA signaling in fascia progenitors. Indeed, targeting RA signaling to prevent fibrosis has also been proposed for lung, liver, and heart⁵⁵.

By using fascia explants and murine back skin wound models we demonstrate here that the transition from proinflammatory to proto-myofibroblast, and to the later myofibroblast state, is marked by the acquisition of a contractile phenotype enabling a swift transition from the inflammation to the remodeling phase of wound healing (Extended data figure 9). Transition to the proto-myofibroblast state was previously characterized in culture and it is known to be triggered by mechanical stress^4,56. Our ex-vivo and in-vivo studies indicate that mechanical stress has a role but that hypoxia signal via Hif1α is the most important factor for acquisition of the proto-myofibroblast state. Supporting this notion, Hif1α downregulation in pericytes⁵⁷ or fibroblasts^58-59 prevents their transition into myofibroblasts in-vitro and their pharmacological inhibition^60-61attenuates lung fibrosis. Our proposed differentiation trajectory reconciles these observations by giving Hif1α an inductive role for the transition into the proto-myofibroblast state in mouse and in human skin. The function of Hif1α also appears to be essential for the myofibroblast-inductive function of TGFβ, as hypoxia enhances TGFβ response in dermal fibroblasts in-vitro⁶² and Hif1α downregulation prevents TGFβ-induced myofibroblasts transition in lung fibroblasts⁶⁰. Similarly, our bioinformatics and experimental data indicate that Hif1α activity is indispensable for the myofibroblast transition even in the presence of TGFβ.

Despite the early reports showing prevalence of myofibroblasts in upper wound regions⁶³, little attention has been paid to the spatial differentiation of proinflammatory fibroblasts, proto-myofibroblasts, and myofibroblasts. Our results draw an important distinction between different layers of the skin, in several ways. CD201⁺ progenitors are found within the very deep layers of the skin where they follow spatial bottom-up directives during their differentiation road. The first spatial directive occurs in the lower wound bed where progenitors differentiate into proinflammatory fibroblasts where RA signaling is at its highest. The second and third spatial directives occurs in more upper wound regions where RA signaling is degraded and is at its lowest. Thus, our data indicates that RA signaling gradients promote fibroblast differentiation in skin wounds.

The results presented here are medically significant as alterations in this differentiation process may underlie various skin pathologies such as impaired wound healing and keloid lesions. Furthermore, the manipulation of individual steps along the trajectory, opens new roads to modulate distinct wound fibroblast states for clinical benefit. For example, treatments of wounds (surgical, keloid lesions, burns, etc.) could be tailored to modulate the myofibroblast state to prevent excessive scarring and contraction without compromising the proinflammatory state needed for wound inflammation to occur, while treatments tailored for modulating the early progenitor state would be beneficial for inflammatory skin diseases.

Proinflammatory fibroblasts and myofibroblasts are cellular protagonists in injury repair, inflammatory pathologies and fibrosis. Here, we detail their lineage relationship, differentiation and origin. Moreover, we detail a stepwise differentiation from CD201⁺ fascia progenitors-to-proinflammatory and myofibroblasts; including the transcriptional programs that mediate differentiation. Better understanding of CD201⁺ progenitors and their regulation by the fascia microenvironment, has potential clinical uses to prevent and treat impaired wound healing, fibrosis, and inflammatory skin diseases.

Mice and genotyping

C57BL/6J wildtype, En1tm2(cre)Wrst/J (En1^Cre), B6.Cg-Gt(ROSA)26Sortm14(CAG-tdTomato)Hze/J (R26^Ai14), B6.129(Cg)-Gt(ROSA)26Sortm4(ACTB-tdTomato.-EGFP)Luo/J (R26^mTmG), and B6.129S6(Cg)-Gt(ROSA)26Sortm1(DTA)Jpmb/J (R26^DTA) mouse lines were obtained from Charles River or Jackson laboratories. The PDPN^CreER line was generated in-house and CD201/Procr^CreER line was a gift from Dr. Ariel Zeng (SIBCB). Animals were housed in the Helmholtz Central Animal Facility; cages were maintained at constant temperature and humidity, with a 12-h light cycle and provided with food and water ad libitum. All animal experiments are reviewed and approved by the Upper Bavarian state government and registered under projects 55.2-1-54-2532-16-61 and 55.2-2532-02-19-23 and strict government and international guidelines. This study complies with all relevant ethical regulations regarding animal research. Animal experiments were performed using 8- to 12-week-old adult mice. Cre-positive (Cre) animals were identified by genotyping of a Cre fragment. For this, genomic DNA from the ear-clips was extracted using Quick Extract DNA extraction solution (Epicentre) following the manufacturer's guidelines. DNA extract (1 μl) was added to each 19 μl PCR reactions. The reaction mixture was set up using GoTaq PCR core kit (Promega) containing 1× GoTaq green master mix, 0.5 μM forward and reverse primers. The following primer were used: “En1-Cre-FW” (5’-attgctgtcacttggtcgtggc-3’), “En1-Cre-RV” (5’-ggaaaatgcttctgtccgtttgc-3’), “Procr-Cre-FW” (5′-gcggtctggcagtaaaaactatc-3′), “Procr-Cre-RV” (5′-gtgaaacagcattgctgtcactt-3′), “Pdpn-Cre-FW” (5’-gatggggaacagggcaagttgg-3’), and “Pdpn-Cre-RV” (5’-ggctctacttcatcgcattccttgc-3’). All primers were obtained from Sigma Aldrich. PCRs were performed with initial denaturation for 5 min at 94 °C, amplification for 35 cycles (denaturation for 30 s at 94 °C, hybridization for 30 s at 59 °C, and elongation for 1 min at 72 °C) and final elongation for 10 min at 72 °C, and then cooled to 4 °C. In every run, negative controls (non-template and extraction) and positive controls were included. The reactions were carried out in an Eppendorf master cycler. Amplified bands were detected by electrophoresis in agarose gels.

In vivo wounds, tamoxifen induction, and treatments

Back-skin full-thickness wounds on mice were performed as previously^27,50-51. For the fascia anatomical fate mapping, 40 μl of recombinant TAT-Cre fusion protein (Sigma Aldrich SCR508) were subcutaneously injected on the back of R26^mTmG mice three days and one day before wounding. For the genetical lineage tracing of CD201⁺ fascia fibroblasts, Procr^CreERR26^Ai14mice received three intraperitoneal injections of 2 mg (Z)-4-Hydrotamoxifen (Sigma Aldrich H7904) diluted in 0.2 ml miglyol-812 (Caesar & Lorentz GMBH CSLO3274) two days and one day before wounding, and at the day of wounding. For the genetic lineage tracing and ablation of Pdpn⁺ proinflammatory fibroblasts, Pdpn^CreERR26^mTmG or Pdpn^CreERR26^DTA mice received daily intraperitoneal injections of 1 mg (Z)-4-Hydrotamoxifen diluted in 0.1 ml miglyol-812 from day four before wounding to the day of injury. For fascia-matrix fate mapping, 40 μl of 2 mg/mL Pacific Blue succinimidyl Ester (Invitrogen P10163) with 0.1 M sodium bicarbonate in saline were subcutaneously injected on the mouse back three- and one-day prior to wounding to label the fascial matrix. Hif1α inhibitor (echinomycin, Sigma Aldrich sml0477), at a concentration of 100 μg/Kg bodyweight, RARγ agonist (BMS 961, Tocris 3410), at a concentration of 100 μg/Kg bodyweight, or vehicle control dimethyl sulfoxide (DMSO, Sigma Aldrich D5879) were subcutaneously injected near and below the wounds immediately after injury and every other day.

Free-floating ex vivo fascia culture

Whole back skin was dissected from euthanized mice using scissors and forceps taking care that the fascia beneath remained intact. The sample was then washed twice in ice-cold PBS (Thermo Fischer) to remove any residual debris and placed with the epidermal side facing down. The soft fascia tissue was then pulled with forceps and cut into approx. 2-3 mm pieces and washed in PBS. The fascia explants were then placed in triplicate in 6-well plates supplied with DMEM/F-12 medium containing 10% FBS, 1x GlutaMAX (Thermo Fisher Scientific 35050038), 1x Penicillin/streptomycin (Thermo Fisher Scientific 15140122), and 1x MEM non-essential amino acids (Thermo Fisher Scientific 11140035). Media was changed every other day. For treatment groups, the media was supplemented with additional compounds and refreshed during media change. The compounds used include the Hif1α inhibitor (0.1, 10 μM, echinomycin, Sigma Aldrich sml0477), RA (0.1 and 1 μM, R&D systems 0695), RARα agonist (0.01 μM, AM580, Tocris 0760), RARβ agonist (0.01 μM, CD2314, Tocris 3824), RARγ agonist (0.01 μM, BMS 961, Tocris 3410), CYP26B1 inhibitor (0.01 μM, R115866, Sigma Aldrich SML2092), TGFβ1 (10 μM, recombinant mouse TGF-beta 1 protein, R&D systems 7666-MB-005/CF), and TAZ agonist (10 μM, Kaempferol,R&D systems 3603/50). For the combined treatments, the effective doses for Hif1α inhibitor and RA were 10 and 1 μM respectively. Samples were maintained in a humidified 37 °C, 5% CO₂ incubator for up to 6 days. The fascia tissue contraction was assessed by daily imaging with a stereomicroscope (Leica M50).

Histology

At relevant timepoints, samples were fixed overnight with 4% paraformaldehyde, washed thrice with fresh PBS and processed for cryosectioning using OCT (CellPath KMA-0100-00A) in a Cryostat (CryoStar NX70). Sections were rinsed three times with PBST solution (PBS containing 0.05% Tween) and blocked for 1 h at room temperature with 10% serum in PBST. Then, they were incubated with primary antibody in blocking buffer for 3 hr at ambient temperature or at 4 °C overnight. Sections were then rinsed three times with PBST and incubated with secondary antibody in blocking solution for 60 min at ambient temperature. Finally, they were rinsed three times in PBST and mounted with fluorescent mounting media with 4,6-diamidino-2- phenylindole (DAPI). Primary antibodies used: PDPN (Abcam ab11936, 1:500 dilution), pSTAT3 (Cell Signaling Technology 9145S, 1:150), RUNX2 (Abcam ab92336, 1:150), GFP (Abcam ab13970, 1:500), PDGFRα (R&D systems AF1062, 1:100), αSMA (Abcam ab21027, 1:150), CCL2 (Abcam ab25124, 1:100), CXCL1 (R&D systems MAB453R, 1:100), ALDH1A3 (Novus NBP2-15339, 1:100), CYP26B1 (Elabscience E-AB-36196, 1:100), and CD45 (Abcam ab23910, 1:100). For some of the primary antibodies (pSTAT3, RUNX2), antigen retrieval was performed using citrate buffer at 80 °C for 30 mins, allowing it to cool down for another 30 mins before the blocking step. AlexaFluor488-, AlexaFluor568- or AlexaFluor647-conjugated secondary antibodies (1:500 dilution, Life technologies) against respective species were used. Masson’s trichrome staining was performed using a commercial kit (Sigma Aldrich HT15) following manufacturer’s recommendations. Histological sections were imaged in a using a ZEISS AxioImager.Z2m (Carl Zeiss) using the 20x objective.

Image Analysis

All image analysis was performed using Fiji (v.1.53c).

For assessment of tissue contraction in vitro and wound closure in vivo, the explant or wound area from different timepoints was selected using the “Selection Brush” tool and measured using the “Measure” function. Area values were normalized in percentage to the initial (day 0, A₀) as 100 %. In vitro final contraction (C) was defined as the difference between the highest area value of the two initial days after culture (day 0-2, A_max) minus the the lowest area value from the last two days of culture (day 4-6 A_min); thus, C = A_max-A_min. T_50Max was used as a contraction speed estimation and was defined as the time in days that the tissue takes to reach half of its final contraction. This was calculated as the interpolation of time in days to half of the total contraction using the “Forecast” function in MS Excel (half of the total contraction was in turn calculated as A₀– A_max+ ½C).

For cell marker positive quantifications, individual channels were preprocessed using the software functions “Substract Background” (rolling=20px), “Median” (radius=1), “Unsharp Mask” (radius=1, mask=0.60), “Despeckle”, “Enhance Contrast” (saturated=0.1 normalize), and “Auto Threshold” methods were empirically selected for individual markers: “Moments” (DAPI, pSTAT3, and GFP), “Intermodes” (RUNX2, TdTomato, PDGFRα), “Yen” (CCL2 and CXCL1), “IsoData” (PDPN), or “RenyiEntropy” (αSMA). The wound region of interest (ROI) was manually selected using the “Selection Brush” tool taking as reference the breached Panniculus carnosus below and the hair follicle in the dermis above. Total wound cell numbers (within the ROI) from binary images were measured using the DAPI channel and using the software functions “Fill Holes”, “Watershed”, and “Analyze Particles” (size=70-1400px) in that order. Marker positive cell numbers were measured by using the mask of the marker with the DAPI channel using the “Image Calculator_AND”, “Dilate” twice, “Close”, “Fill Holes”, “Watershed”, and “Analyze Particles” (size=70-1400px) functions. For double marker positive cell calculations, an initial mask from two marker channels was created and processed as before.

For wound regions quantifications, 3 ROIs were manually selected with the “Selection Brush” tool. The wound regions were delimited laterally as stated before, the upper wound region extended vertically 200 microns from the wound surface (at 3 dpi) or the wound epidermis (at 7 dpi). The wound fascia was measured as the area extending from the breached P. carnosus muscle down to the dorsal muscles, while the wound core covered the area between the wound fascia and the upper wound. Non-mesenchymal areas (e.g, hair follicles) were deleted from the ROIs. Marker channels were preprocessed as before and the Mean Gray Value (MGV) for each ROI was then quantified with the “Measure” function. For visual aid, marker signal intensity was represented with the “Fire” or “16-color” pseudo-colors.

Fractal analysis was performed as before²⁹. Briefly, the Cyan channel was preprocessed as before, and binary images were generated using “Auto Threshold” (“Default” method) function. Binary images were then analyzed with the FracLac plugin using the same settings as before. All quantification plots were generated using the Python-based packages Matplotlib and Seaborn.

Stromal cell enrichment

8 mm-diameter biopsies of wounds and surrounding skin were taken from En1^CreR26^mTmG dorsal skin wounds at relevant times after injury. Samples from three mice were pooled for each stage (6 wounds). Harvested tissue was minced with surgical scissors and digested with Liberase TM (Sigma Aldrich 5401119001) enzymatic cocktail supplemented with 25 U/ml DNase I (Sigma Aldrich D4263) at 37°C for 60 min. The resulted single cell suspension was filtered and incubated at 4° C for 30 min with conjugated primary antibodies (dilution 1:200) against specific cell markers of unwanted cell type lineages. Antibodies used: APC-anti-CD31 (eBioscience 17-0311-82), eFluor660-anti-Lyve1 (eBioscience 50-0443-82), APC-anti-Ter119 (Biolegend bld-116212), and APC-anti-CD45 (Biolegend Bld-103112). Cells were then washed and incubated at 4° C for 20 min with suitable magnetic beads against the conjugated-fluorophores and dead cell markers (Dead Cell removal kit, Miltenyi 130-090-101). Microbeads used: Anti-Cy5/AlexaFluor647 (Miltenyi 130-090-855) and anti-APC (Miltenyi 130-091-395). Cells were separated using OctoMACS and MS columns (Myltenyi) according to manufacturer guidelines. Negative selection was then washed, counted, and diluted at 100 cells per microliter in PBS with 0.04 % BSA and 300 U/ml of RNase inhibitor (RiboLock LIFE technologies eo0382). Only samples with >90 % living cells were used for Dropseq separation.

Dropseq libraries: preparation and sequencing

Stromal-enriched cell suspensions were separated using the Dropseq⁶⁴ microfluidic-based method following the McCaroll lab protocol with a few adaptations. Briefly, using a microfluidic PDMS device (Nanoshift), cells were encapsulated in droplets with barcoded beads (120/µl, ChemGenes Corporation, Wilmington, MA) at a rate of 4000 µl/hr. Droplet emulsions were collected for 15 min/each prior to droplet breakage with perfluoro-octanol (Sigma‐Aldrich). After breakage, beads were harvested, and the hybridized mRNA transcripts were reverse transcribed (Maxima RT, Thermo Fisher). Unused primers were removed by the addition of exonuclease I (New England Biolabs). Beads were then washed, counted, and aliquoted for pre‐amplification (2000 beads/reaction, equals ~100 cells/reaction) with 12 PCR cycles (with the recommended settings). PCR products were pooled and purified twice by 0.6x clean‐up beads (CleanNA). Prior to tagmentation, cDNA samples were loaded on a DNA High Sensitivity Chip on the 2100 Bioanalyzer (Agilent) to ensure transcript integrity, purity, and amount. For each sample, 1 ng of pre‐amplified cDNA, from an estimated 1000 cells, was tagmented by Nextera XT (Illumina) with a custom P5 primer (Integrated DNA Technologies). Single cell libraries were sequenced in a 100 bp paired‐end run on an Illumina HiSeq4000 using 0.2 nM denatured sample and 5% PhiX spike‐in. For priming of the 1^st read, 0.5 µM Read1CustSeqB (primer sequence: GCCTGTCCGCGGAAGCAGTGGTATCAACGCAGAGTAC) was used. STAR (version 2.5.2a) was used for mapping the reads and to align them to the mm10 genome reference (provided by Drop‐seq group, GSE63269) that was tailored to include the eGFP cDNA transcript.

scRNAseq data analysis

All the analyses were performed using the phyton toolkit Scanpy⁶⁵ and complementary tools under its ecosystem. Matrices of individual samples were concatenated, quality checked by filtering cells with fewer than 100 genes. Genes that appeared in less than 50 cells and cells containing less than 0.5 % of the total genes in the data set were also filtered out. To decrease batch- and cell cycle effects, combat and cell cycle regression algorithms were used as recommended by the developers. The UMAP algorithm was used as the preferred dimensional reduction method.

Cluster annotation was selected by increasing threshold values while preserving a defined cell cluster in the dataset. In the whole dataset, the resolution used was the highest possible that still preserved all the Plp1-expressing cells (Schwann cells) in a single cluster. When clustering fibroblast subsets, the control cluster used was Acta2-expressing myofibroblasts. Louvain and Leiden clustering algorithms were used complementarily. Ranked marker genes for each cluster were calculated using the method “wilcoxon” and used for gene ontology (GO) term statistical overrepresentation test with the PANTHER DB web tool. The fibroblast supercluster was defined by the expression of classical pan-fibroblast markers such as Col1a1, and Pdgfra. Skin fibroblast expression profiles were calculated using the “gene score” function using previously reported markers^27,14,66-68. Papillary fibroblasts markers included: Sparc, Ndufa4l2, Cldn10, Cpz, Col3a1, Cgref1, Col16a1, Mfap4, Cd63, Mt1, Fth1, Cyp2f2, Ccl19, Prdm1, Lrig1, Ephb2, Trps1, Tmem140, Cd302, Maf, Adra2a, Casp1, Dennd2a, Ntn1, Sepp1, Sipa1l2, Tfap2c, Axin2, Ctsc, Osr1, Cmtm3, Rgl1, Hrsp12, Moxd1, Itm2c, Steap1, Tnfrsf19, Cadm1, Efhd1, Ucp2, Creb3, Creb3l3, Mxd4, and Apcdd1. Reticular fibroblast markers included: Gpx3, Cxcl12, Cygb, F3, Gsn, Dpt, Myoc, Tmeff2, Hmcn2, Krt19, Dact1, Fstl3, Map1b, Nexn, Gls, Fndc1, Vgll4, Arpc1b, Sulf1, Cdh2, Dbndd2, Tgm2, Fgf9, Limch1, Mgst1, Npr3, Sox11, and Vcam1. Fascia fibroblast markers included: Plac8, Anxa3, Akr1c18, Pla1a, Ifi27l2a, Ifi205, Sfrp4, Prss23, Ackr3, Nov, Fap, Rhoj, Klf9, Sfrp2, and Tgfb2. For the myofibroblast profile the markers Postn, Acta2, P4ha1, P4ha2, P4ha3, Col11a1, Tnc, Tagln, Pdgfrb, and Vim were used. For the bioinformatic “sorting” of EPFs, the subset of fibroblast cells with a raw count value over 0.5 of the eGFP transcript was annotated independently. For the fibroblastic state signatures, the human orthologs of the top 100 markers of each cluster were scored in the human datasets. Annotation of human fibroblast clusters was then assisted by these scores.

Trajectories were inferred by partition-based graph abstraction with RNA velocity-directed edges using the scvelo toolkit^69-70. “Dynamic modeling” was used under standard settings to calculate velocities. Cells were arranged under the inferred trajectory using “velocity pseudotime” function rooting the trajectory origin in the most distant cells within the fascia cluster from the rest of the clusters. For human datasets, trajectories were inferred calculating partition-based graph abstraction connectivities and the cells were arranged under a “State pseudotime” computed from the fibroblastic signatures as: State pseudotime = S_f+S_p+S_m, were S_f = the scaled values (range -0.5 to 0) from the inverted of the fascia signature score, S_p = the scaled values (range -0.5 to 0) from the proinflammatory signature score, and S_m = the scaled values (range 0 to 1.5) from the myofibroblast signature score. In this way, cells with mainly a high fascia signature would have state pseudotime scores around -0.5, proinflammatory score to zero, and myofibroblast at +0.5, effectively arranging cells across the inferred fascia-proinflammatory-myofibroblast trajectory.

Regulon analysis was performed by mapping the expression of 1623 mammalian transcription factors from the RIKEN database and statistically correlating (Pearson R) their expression with the fibroblastic state signatures. Programs were selected from the list of transcription factors whose expression positively correlated with a signature that had validated downstream targets in the TRANSFAC and ENCODE databases. Individual program scores were calculated according to the expression of the selected transcription factors using the score gene function. To identify the complete regulon, the combined list of downstream targets for each program was mapped and correlated with the program scores. Positively correlated genes, whose expression matched the upstream regulating programs, were used for the regulon score using the gene score function and for the GO term overrepresentation test using the PANTHERdb⁷¹ web tool.

Signaling pathway activity was predicted by mapping the expression of genes from a manually curated list for relevant factors of the TGFβ, Wnt, mechanotransduction, cell-cell adhesion, chemokine, and retinoic acid signaling pathways. Complementarily, hypoxia, Wnt, and TGFβ pathway activity was predicted using the PROGENY tool⁷², showing similar results, for TGFβ and Wnt signal activity, to our manual method.

Statistics

Statistical analyses were performed using the Python toolkit Scipy. Two-tailed T-test and Pearson’s R were implemented with standard settings. Significant differences were considered with p-values below 0.05.

Author contributions

Y.R. outlined and supervised the research narrative and experimental design. D.C.-G.analyzed the scRNAseq data, performed image analysis, in-vivo wound experiments, and designed the figures and schematics. H.Y.performed histology, immunohistochemistry, in-vivo wound experiments, and ex-vivo fascia cultures. B.D. performed histological immunolabelings, ex-vivo fascia cultures, and image analysis. R.I. assisted with histology, immunohistochemistry. A.S. generated and validated the double transgenic mouse lines. M.S., M.A., I.A., & H.S. generated the scRNAseq libraries and assisted with data analysis. T.V. & H.-G. M.assisted with translational and clinical advice. Y.R. and D.C.-G. wrote the manuscript with H.Y. and B.D. support.

Acknowledgments

We thank Dr. Juliane Wannemacher, Dr. Sibylle Sabrautzki, and Dr. Heiko Adler for their veterinary advice and support. We thank Dr. Ariel Zeng (SIBCB) for providing the CD201/Procr^CreER transgenic line. We also thank Dr. Volker Bergen and Dr. Alexander Wolf for scRNAseq data analysis support. This project was supported by the European Research Council Consolidator Grant (ERC-CoG 819933), the LEO Foundation (LF-OC-21-000835), and the European Foundation for the Study of Diabetes (EFSD) Anniversary Fund Programme 2021.

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CD201+ fascia progenitors choreograph injury repair

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Results

Fascia progenitors differentiate into specialized fibroblasts during wound healing

Sequential checkpoints control CD201⁺ progenitor differentiation

The proinflammatory transition is gated by retinoic acid

The (proto)myofibroblast transition is gated by Hif1α

CD201⁺ fibroblast differentiation is conserved in mice and men

Discussion

Methods

Declarations

Author contributions

Acknowledgments

References

Additional Declarations

Status:

Journal Publication

Version 1

CD201+ fascia progenitors choreograph injury repair

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Results

Fascia progenitors differentiate into specialized fibroblasts during wound healing

Sequential checkpoints control CD201+ progenitor differentiation

The proinflammatory transition is gated by retinoic acid

The (proto)myofibroblast transition is gated by Hif1α

CD201+ fibroblast differentiation is conserved in mice and men

Discussion

Methods

Declarations

Author contributions

Acknowledgments

References

Additional Declarations

Status:

Journal Publication

Version 1

Sequential checkpoints control CD201⁺ progenitor differentiation

CD201⁺ fibroblast differentiation is conserved in mice and men