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. Author manuscript; available in PMC: 2021 Aug 24.
Published in final edited form as: Science. 2021 Jan 22;371(6527):eaba6500. doi: 10.1126/science.aba6500

Developmental cell programs are co-opted in inflammatory skin disease

Gary Reynolds 1,#, Peter Vegh 1,#, James Fletcher 1,#, Elizabeth FM Poyner 1,2,#, Emily Stephenson 1, Issac Goh 1, Rachel A Botting 1, Ni Huang 3, Bayanne Olabi 1,4, Anna Dubois 1,2, David Dixon 1, Kile Green 1, Daniel Maunder 1, Justin Engelbert 1, Mirjana Efremova 3, Krzysztof Polański 3, Laura Jardine 1, Claire Jones 1, Thomas Ness 1, Dave Horsfall 1, Jim McGrath 1, Christopher Carey 1, Popescu Dorin-Mirel 1, Simone Webb 1, Wang Xiao-nong 1, Ben Sayer 1, Park Jong-Eun 3, Victor A Negri 5, Daria Belokhvostova 5, Magnus Lynch 5, David McDonald 1, Andrew Filby 1, Tzachi Hagai 6, Kerstin B Meyer 3, Akhtar Husain 7, Jonathan Coxhead 1, Roser Vento-Tormo 3, Sam Behjati 3,8, Steven Lisgo 1, Villani Alexandra-Chloé 9, Jaume Bacardit 10, Phil Jones 3,11, Edel A O’Toole 12, Graham S Ogg 13, Neil Rajan 1,2, Nick J Reynolds 2,14, Sarah A Teichmann 3,15,*, Fiona Watt 5,*, Muzlifah Haniffa 1,2,3,*
PMCID: PMC7611557  EMSID: EMS132468  PMID: 33479125

Abstract

The skin confers biophysical and immunological protection through a complex cellular network established early in embryonic development. We profiled the transcriptomes of over 500,000 single cells from developing fetal and adult healthy human skin, and from adult skin with atopic dermatitis and psoriasis. We leveraged these data sets to compare cell states across development, homeostasis and disease. Our analysis revealed an enrichment of innate immune cells in skin during the first trimester and clonal expansion of disease-associated lymphocytes in atopic dermatitis and psoriasis. We uncovered and validated in situ a re-emergence of prenatal vascular endothelial cell and macrophage cellular programs in atopic dermatitis and psoriasis lesional skin. These data illustrate the dynamism of cutaneous immunity and provide opportunities for targeting pathological developmental programs in inflammatory skin diseases.

Introduction

Human skin undergoes dramatic adaptations as it transitions from a relatively pathogen-free aquatic environment in utero to provide mechanical and immunological protection in a non-sterile terrestrial environment. This function requires coordination by specialized cell types that are established during embryonic development. The cellular landscape of prenatal and adult skin, however, remains incompletely defined.

In cancer, co-optation of developmental cell programs such as angiogenesis, proliferation and invasion re-emerge and involve interaction between malignant cells and the surrounding stroma (1–4). The current consensus view on inflammatory skin disease pathogenesis supports that the interplay between leukocytes and non-leukocytes is involved in disease initiation and progression (5). However, it is unknown if cell states and gene programs observed in prenatal skin contribute to the pathogenesis of adult onset inflammatory skin disorders. A detailed understanding of this process may provide a new perspective to inflammatory disease pathogenesis and potentially identify novel therapeutic targets.

Single cell genomics, such as RNA-sequencing, provide an opportunity to dissect the complex cellular organization of human skin during development and in health and disease at a systems level. Studies of healthy skin to date have primarily focused on adult skin, restricted to specific cell lineages and limited cell numbers (6–10). Large scale single cell profiling at depth of human skin should provide a transformative resource to understand aberrations in gene expression resulting from disease.

Results

Deconstructing human skin

In this study, we used single cell RNA-sequencing (scRNA-seq) combined with strategic Fluorescence-activated Cell Sorting (FACS) for comprehensive and deep profiling of healthy and inflamed adult skin. To maximize cell yield and viability from tissue dissociation from previous findings (11–13), we used 200μm-thick mammoplasty healthy skin, which were separated into epidermis and dermis prior to dissociation (Fig. 1A and Fig. S1A). We adapted our previous FACS-gating strategy (12, 14) to isolate various cell fractions (keratinocytes, fibroblasts, and endothelial cells) and immune cells (myeloid and lymphoid cells) to upsample rare cell types for deep cell sampling. We ensured comprehensive capture, minimising cell loss by placing these FACS gates contiguously (Fig. S1B). FACS-isolated cells were characterized with the 10x Genomics platform. We also performed indexed plate-based Smart-seq2 profiling of all epidermal and dermal cells within the CD45+HLA-DR+myeloid gate (Fig. S1B). To compare cell states in healthy skin with inflammatory disease-induced perturbation, we performed scRNA-seq (10x Genomics) on all CD45- and CD45+ cells from lesional and non-lesional skin from patients with atopic dermatitis (AD) and psoriasis (Figs. 1A and S1C).

Figure 1. Deconstructing human skin.

Figure 1

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(a) Number of cells profiled by scRNA-seq and mass cytometry for each condition and schematic of sampling locations. AD = atopic dermatitis (b) UMAP visualization showing all cell states found in the healthy adult scRNA-seq data set, n = 5. KC = keratinocyte, Fb = fibroblast, VE = vascular endothelium, LE = lymphatic endothelium, ILC = innate lymphoid cell, NK = natural killer cell, Tc = cytotoxic T cell, Th = T helper cell, Treg = regulatory T cell, Mac = macrophage, Inf. = inflammatory, DC = dendritic cell, LC = Langerhans cell, Mono mac = monocyte derived macrophage, Mig. = migratory, MoDC = monocyte derived dendritic cell. (c) Dot plot showing the expression of discriminatory markers for each cell state in (b). CD8A, CD163, CD14 and CCR7 were chosen for CyTOF protein validation. (d) UMAPs showing the healthy adult cell states found (top) overlaid on the developing cell states (n=7) and the developing cell states (bottom) overlaid on the healthy adult cell states. Cells underlaid are shown in grey. (e) Median prediction score of developing skin cell states using healthy adult skin as reference derived from the TransferAnchors function in Seurat. Triangles refer to developing skin cells and circles refer to adult skin cells. (f) Bar charts showing the proportions of corresponding cell states found in adult and developing skin. Generalized linear modelling on a quasibinomial distribution was used to compare proportions between development and adult skin and showed statistically significant changes in vascular endothelium, Schwann and melanocytes (p-value = 3.1x10-7), keratinocytes (p-value = 3.1x10-4), LCs and DCs (p-value = 1.4x10-4) and fibroblasts (p-value = 4.0x10-4). Stars denote significance.

In total, 528,253 sequenced skin cells (n=19) passed quality control (QC) and doublet exclusion (Fig. 1A). We detected on average ~3,000 genes with the 10x Genomics platform and ~6,000 genes per cell with Smart-seq2 (15)(Fig. S2A). We excluded cells with <200 genes, >20% mitochondrial gene expression and those identified as doublets (15). To account for biases due to batch effects, we performed data integration of healthy skin samples using BBKNN implementation within Scanpy (16, 17), which showed good sample mixing by UMAP visualization (Figs. 1B, S1D-E, and S2B). We performed graph-based Leiden clustering and derived differentially expressed (DE) genes to annotate the cell clusters, from which 34 cell states were identified (Fig. 1B-C, Table S1). We were able to identify these cell states by deconvolution analysis using AutoGeneS (18) of adult healthy bulk RNA-seq (Fig. S2C). The 34 cell states were discernible even after removal of stress response genes associated with tissue dissociation (19) (Fig. S2D). We note the impact of different tissue dissociation protocol on gene expression as previously reported (20). We show the same cell states could be identified in a small dataset using the Miltenyi dissociation protocol (7) and through the statistical power and resolution provided by our large dataset to discern rare cell states (Fig. S2E). However, our analysis of interfollicular mammoplasty skin sampled to the top layer of the reticular dermis may not have adequately profiled all skin hair follicle and appendageal cells.

We selected genes encoding surface proteins (CD8A, CD163, CD14 and CCR7) and additional antigens where antibodies are commercially available, to derive a cytometry by time-of-flight (CyTOF) panel for protein level validation and frequency assessment of the major cell states on 4 additional donors (Fig. S3A-E). Gene expression on the relevant cell states was consistent with CyTOF analysis (Fig. S3F).

To evaluate the establishment of specific cell states during development and their temporal evolution in adult skin, we compared our adult skin scRNA-seq data with our 7-10 post-conception weeks (PCW)(n=7) embryonic/fetal scRNA-seq datasets (21)(Fig.1D). We used the TransferAnchors function in Seurat to integrate adult and fetal skin cell states (Fig. 1E)(22) and calculated proportional representation of the equivalent cell states in healthy developing and adult skin (Fig. 1F). Our collective dataset of human fetal development, adult and disease skin cells provides a foundational resource and can be explored using an interactive web portal through the following weblink: https://developmentcellatlas.ncl.ac.uk/datasets/hca_skin_portal.

Transition from innate to adaptive lymphocytes during skin development

Skin T cells consist of the three subtypes: cytotoxic (Tc) expressing CD8A/B, helper (Th) expressing CD4, CD40LG and regulatory (Treg) expressing FOXP3, TIGIT, CTLA4 (Fig. 2A-B)(23, 24). We identified 4 clusters of innate lymphocytes that were CD161(KLRB1)+ CD3(CD3D/CD3G)-, consisting of ILC1/3, ILC2, ILC1/Natural Killer (NK) and NK (KLRD1+, GNLY+, PRF1+, GZMB+ and FCGR3A+) cells (Fig. 2A-B). ILC1/NK have overlapping features of ILC1 and NKs, as described (25, 26). Plasticity within ILC1 and ILC3 is also recognized, as reflected in our annotation of ILC1/3 (27). ILC2 (IL7R, PTGDR2, GATA3) has the most distinct signature in our data and in existing literature (Fig. 2B) (26).

Figure 2. Skin innate lymphoid cells, T lymphocytes and TCR analysis.

Figure 2

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(a) UMAP visualization of lymphoid cells in healthy adult skin. ILC=innate lymphoid cell, NK=natural killer cell, Tc=cytotoxic T cell, Th=T helper cell, Treg=regulatory T cell, n=5 (b) Dot plot showing expression of marker genes of cell states found in adult skin (circles) shown in (a) and their developmental counterparts (triangles), separated by the dashed line. (c) Dot plot showing the differentially expressed genes in T cell subsets between epidermis and dermis in healthy adult skin, n= 5, Epi= epidermis, Derm= dermis. (d) Bar charts showing the proportions of lymphoid cell states in healthy and diseased skin. Tc IL13/IL22= cytotoxic T cells expressing IL13 and IL22, Tc17/Th17= T cells expressing IL17A and IL17F. Lesional dermal AD skin Tc IL13/IL22 compared to non-lesional dermal AD skin (modelled counts as negative binomial and analysed by ANOVA, * p-value= 0.04), and psoriasis epidermal lesional skin Tc17/Th17 compared to psoriasis non-lesional epidermis (*** p-value < 0.001). Significance of other cell states were not tested. HA = healthy adult, AD = atopic dermatitis, P= psoriasis, L= lesional, NL = non lesional. (e) Proportion of cells with Tc IL13/IL22 and Tc17/Th17 signature present in bulk RNA-seq data from GSE121212. Modelling the data on a quasibinomial distribution, lesional AD dermis is enriched for Tc IL13/IL22 (p-value= 3.4x10-2) and Treg (p-value= 3.2x10-2) compared to non-lesional skin. Lesional psoriatic epidermis is enriched for Tc17/Th17 (p-value= 1.6x10-05), Treg (p-value = 4.6x10x10-7) and Tc (p-value = 2.5x10-4) compared to non-lesional. p-values calculated using a likelihood ratio test. Stars denote significance. (f) Bar charts showing clonotype size in each T cell subset in lesional AD (left) and psoriasis (right). The color of the bar relates to the size of the clonotype. Modelling the data on a quasibinomial distribution, lesional AD dermis Tc IL13/IL22 have a higher proportion of clonotypes ≥ 2 cells compared to Th (p-value = 3.3x10-2) and lesional psoriasis Tc17/Th17 have a higher proportion of clonotypes ≥ 2 cells than Tregs (p-value = 1.6x10-2) with other comparisons not significant. p-values calculated using a likelihood ratio test. Stars denote significance and bars across the top show significance between cell types. (g) Dot plot showing the expression of significantly differentially expressed genes between non-clonal and clonal T cells in AD (top) and psoriasis (bottom). Hatched colored circles indicate non-clonal T cells and colored circles, clonal T cells.

In contrast to adult skin, the fetal skin lymphoid compartment is predominantly populated by ILCs (Fig. 1D and 1F) between 7-10 PCW, prior to the development of the thymus, bone marrow and spleen, where T and B lymphocytes differentiate. Fetal NK cells correlate with adult NK cells, but express higher levels of GZMM and GZMK (Fig. 1E and 2B), suggesting that they may be functionally competent (21). Fetal skin ILCs (IL7R+, RORC+ and KIT+) resemble adult skin ILC3 (Fig. 2B) (28).

To evaluate the impact of epidermal vs. dermal microenvironment on T cells, we compared the DE genes between T cells in the two compartments (Fig. 2C). Epidermal T cells upregulated genes associated with skin tissue residency (RGS1, PPP1R15A) (29), effector memory (CD44, ID2) (29, 30), T cell activation (TNFRSF18) (31) and inhibition of T cell response (CD96, TSC22D3 and DUSP4) (32–34), in keeping with previous suggestions that resident memory T cells are poised to mount an effective immune response, but express inhibitory molecules to prevent disadvantageous responses to non-pathogenic antigens (35). In contrast, dermal T cells express interferon stimulated genes (IFITM1, IFI6, LY6E) (36) and transcriptionally correlate to blood T cells compared to epidermal T cells (Fig. S4A-B). Dermal Tregs show high mRNA and protein expression of the circulating central memory T cells marker CD62L (SELL) (Fig.2C and Fig. S3A).

Disease-associated and clonal T cells

In atopic dermatitis (AD), cytotoxic T cells expressing IL13, IL22 and IFNG (Tc IL13/IL22) and in psoriasis, T cells expressing IL17A, IL17F, IFNG, IL22 and IL26 (Tc17/Th17) are found in both lesional and non-lesional skin, but are significantly enriched in lesional skin (Figs. 2D and S4C). The p-values for AD and psoriasis were 0.04 and <0.001 respectively using a likelihood-ratio test. Tc17/Th17 cells are dominant in the epidermis of lesional psoriasis skin, which was validated by flow cytometry (Figs. 2D and S4D). These cells express genes characteristic of activated and pathogenic Th17 cells (KLRB1, RBPJ and CXCL13) (37–39) (Fig. S4E). Tc IL13/IL22 cells are dominant in the dermis of AD lesional skin (Fig. 2D) and express amphiregulin (AREG), a member of the epidermal growth factor family (40), skin tissue residency genes (RGS1, NR4A1, NR4A2) (29), and effector and activated T cell genes (ID2, PRDM1, MAP3K8, DUSP2) (30, 41–44) (Fig. S4E). To further extend our observation to a larger patient cohort, we used the AutoGeneS tool (18) to deconvolute cellular heterogeneity in published AD and psoriasis bulk RNA-sequencing data (45). In accordance with our findings, we observed the presence of a Tc IL13/IL22 signature in lesional AD skin and a Tc17/Th17 signature in lesional psoriasis skin (Fig. 2E).

There were significantly higher shared T cell clones between non-lesional and lesional skin within donors for both AD and psoriasis compared to between donors, the p-value was <0.01, calculated using two sample t-test (Fig. S4F). In AD and psoriasis lesional skin, disease-associated Tc IL13/22 and Tc17/Th17 cells exhibited significantly higher clonality (p-values <0.05, using quasibinomial statistics to model proportions and analysed using ANOVA) and the lowest diversity compared to other T cell subsets (Figs. 2F and S4G). Lesional clonal T cells had higher expression of co-stimulation genes (CD63, TNFRSF18, JAML) and T cell receptor signaling (EVL, LAT, LCK, JAK1) than non-clonal T cells (Fig. 2G).

Mononuclear phagocytes in adult and developing skin

We observed 14 states of mononuclear phagocytes (MPs) in human skin (Figs. 3A-B and S5A) that we annotated by aligning skin and blood MPs using TransferAnchors function in Seurat (Fig. S5B) and expression of MP marker genes (46)(Fig. S5C). We show the limitations of currently used surface markers and FACS-gates to adequately resolve skin MP heterogeneity (Fig. S5A).

Figure 3. Dermal and epidermal mononuclear phagocytes.

Figure 3

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(a) UMAP visualization of different antigen presenting cell (APC) states found in healthy adult skin (n=5). LC = Langerhans cell, DC = dendritic cell, Mig. = migratory, Mac = macrophage, Mono mac = monocyte derived macrophage, MoDC = monocyte derived dendritic cell, inf. = inflammatory. (b) Dot plot showing the expression of differentially expressed genes characterizing adult healthy skin cell states (circles) shown in (a) and their developmental counterparts (triangles), separated by the dashed line. (c) Upper panel: abstracted graph (PAGA) showing connectivity between adult healthy skin DC clusters. The size of the nodes is proportional to cluster size and edge thickness is proportional to the strength of the connection between nodes. Lower panel: enrichment of gene signatures for murine splenic Xcr1+DC (DC1) and dermal CD11c+ (DC2) in each node. Av = average (d) Dot plot of enrichment of gene signature of APC cell types in adult human disease and developing thymus, SF = synovial fluid. (e) Bar charts showing the proportions of Mac1 and Mac2 in adult healthy, AD and psoriasis skin. HA = healthy adult, AD = atopic dermatitis, P = psoriasis, L = lesional, NL = non lesional. Mac2 are significantly expanded in both lesional AD and psoriasis skin, and Mac1 are significantly reduced in both lesional AD and psoriasis skin. Mac1 p-values = 5.4x10-3 (AD lesional) 3.3x10-5 (psoriasis lesional) and Mac2 p-values = 6.3x10-3 (AD non lesional), 6.0x10-5 (AD lesional) and 1.4x10-6 (psoriasis lesional). p-values calculated using a likelihood ratio test. Stars denote significance. (f) Proportion of cells with Mac2 signature present in bulk RNA-seq data from GSE121212. Generalized linear modelling on a quasibinomial distribution was used to compare proportions of predicted Mac2 between healthy and lesional skin and showed statistically significant expansion of Mac2 in lesional AD (p-value = 1.7x10-7). p-values calculated using a likelihood ratio test. Stars denote significance. (g) Prediction score for alignment using CCA (Seurat) between developing gut, kidney, liver, skin and thymus macrophages with Mac1 and Mac2 in healthy adult skin. (h) Network visualizations of pathways conserved between developing skin macrophages and Mac2 in AD and psoriasis. Network nodes are colored by enrichment score (q = <0.05) and represent individual enriched gene sets whilst edges represent shared genes between nodes (intersect≥10%). (i) Jitter plot displaying the number of positive F13A cells in 4x104 μm2 of healthy adult (n=7), AD (n=12) and psoriasis (n=6) skin. (p-values between healthy adult and AD = 6.6x10-4 and between healthy adult and psoriasis = 1.2x10-3). (j) Jitter plot displaying the number of positive F13A cells in 4x104 μm2 of AD skin (n=5) before treatment with methotrexate and 9-16 days and 12 weeks post treatment. (p-values between pre and 9-16 days post treatment = 1.5x10-2 and between 9-16 days and 12 weeks post treatment = 2.0x10-3).

Two macrophage cell states expressing CD68 are present in healthy skin. Mac1 shows higher expression of complement transcripts (C1QB, C1QC) and scavenger receptors (CD163, MARCO), whereas Mac2 is characterized by the expression of F13A1 and transcription factors associated with alternative activation and suppression of immune responses (NR4A1, NR4A2, KLF4)(Figs. 3B and S5C) and notably, is more closely aligned with fetal macrophages (Fig. 1E).

We observe Dendritic Cells 1 and 2 (DC1, DC2) and Langerhans cells (LCs) in embryonic skin as early as 7 PCW, prior to bone marrow hematopoiesis, but macrophages are the dominant MP in first trimester skin (Figs. 1D-F and S5D). Interestingly, embryonic/fetal LCs are enriched for macrophage-related genes such as C1QC, FCGR2A and CTSB (Fig. 3B) and correlate poorly with adult LCs (Fig. 1E). This lends support to a differential origin of pre-natal LCs from yolk sac and fetal liver progenitors, as previously reported in mice (47), in contrast to the BM-HSC origin of some adult human LCs (48).

Migratory dendritic cell signature is conserved across species and augmented in disease states

In the steady state, skin DCs undergo a continual process of homeostatic maturation that is required to induce tolerance to innocuous environmental antigens (49). This is accompanied by their migration to skin draining lymph nodes through lymphatic vessels, a process dependent on CCR7 (13). Partition-based approximate graph abstraction (PAGA) analysis revealed three branches of differentiation: LCs, myeloid DCs (DC1 and DC2) and monocyte-derived DCs (mo-DCs) (Fig. 3C). The clusters at the convergence of these branches (moDC3, LC4 and migratory DC (Mig. DC)) express transcripts associated with DC maturation (CD83, CCR7, LAMP3, CD40, CD86), immunoregulation (CD274, IDO1, CD200, PDCD1LG2, SOCS1)(50)(Fig. 3B and Table S1) and migration (FSCN1, PLGRKT, TRAF1, BCL2A1, CFLAR and REL)(Fig. 3C) as in migratory murine dermal DC2 and splenic DC1 (49, 51, 52). Acquisition of this common gene signature is associated with loss of genes conferring subset identity in mice (53), which we also observe here for moDC3, LC4 and Mig. DC (Fig. 3B).

Surprisingly, the migratory DC signature is also present across disease states including tonsillitis, ascites (53, 54), lung cancer (50) and rheumatoid arthritis (Fig. 3D). We previously reported the expression of migratory genes in fetal thymic medullary DCs (55), suggesting that developmental gene programs are utilized in adult tissue homeostasis and augmented in disease.

Fetal macrophage program in AD and psoriasis

We observed an increase in Mac2 in AD and psoriasis skin (Fig. 3E and Fig. S5E), which was corroborated in a larger patient bulk RNA-seq dataset (Fig. 3F). Adult healthy skin Mac2 aligned with fetal skin, gut, kidney, liver and thymus macrophages (Fig. 3G). This led us to hypothesize a shared cellular program between fetal macrophages and Mac2 in AD and psoriasis skin that are significantly differentially expressed compared to other skin cells. We derived 91 significantly conserved genes (Seurat FindConservedMarkers using negative binomial test, p-values<0.05) between analogous macrophage clusters in developing skin and lesional AD and psoriasis skin (Table S5). This revealed genes related to stress (DNAJB1, HSPA1B, HSPA1A, JUN, FOSB), chemotactic (CCL4L2, CCL4, CCL3L1, CCL3) and angiopoietin (EGR1, PTGS2) signaling. Gene ontology analysis revealed significantly enriched geneset clusters (hypergeometric test q-value<0.05) relating to regulation of angiogenesis, leukocyte chemotaxis and TGFβ signaling to be conserved (Fig. 3H and Table S6). The role of macrophages in tissue homeostasis and regeneration is gaining recognition (56). Our findings add insight on how macrophage programs that support angiogenesis and leukocyte seeding in tissues during fetal development re-emerge during AD and psoriasis pathogenesis.

To confirm the role of Mac2 in disease pathogenesis in vivo, we analyzed the abundance of Mac2 in healthy, AD and psoriasis lesional skin, as well as during AD resolution during systemic treatment with methotrexate (Fig. 3I, J). We leveraged the marker F13A for Mac2 from our scRNA-seq data (Fig. 3B) and enumerated F13A expressing Mac2 by immunohistochemistry. This revealed significantly increased Mac2 in AD and psoriasis lesional skin compared to healthy skin and decline in Mac2 12 weeks after commencement of methotrexate treatment, in parallel with a reduction in patients’ clinical Eczema Area and Severity Index (EASI) score (Figs. 3J and S5F).

In both AD and psoriasis, LC1 has the highest enrichment of cell cycle genes (Fig. S5G-H). To validate our findings, we examined LC proliferation in healthy, AD and psoriasis epidermis. We found Ki67+Langerin+cells increased in AD and psoriasis lesional skin (Fig. S5I), consistent with previous findings (57, 58). Using FACS index data we show that LC1 are enriched within the Langerin+CD1aloCD11clogate distinct from Langerin+CD1ahi LCs (Fig. S5A). In contrast, the epidermal HLA-DR+CD1a-Lang-CD11c+CD1c+cells are predominantly moDCs and correspond with non-LC like epidermal cells potent at stimulating T cell proliferation, pro-inflammatory cytokine production and transmission of HIV to CD4+T cells (59).

Keratinocyte differentiation in healthy and diseased skin

We characterized four groups of keratinocytes: undifferentiated, proliferating, differentiated and inflammatory differentiated cells (Differentiated KC*) (Fig. 4A). Undifferentiated keratinocytes transcribe basal epidermal proteins (KRT5, KRT14) and are abundant in the CD49fhi FACS gate (Fig. 1C). Proliferating keratinocytes (CDK1+,PCNA+) have lower expression of suprabasal cell transcripts e.g. KRT1, KRT10 that characterize differentiated keratinocytes (Fig. 4B). Inflammatory differentiated keratinocytes co-express lower levels of undifferentiated (TP63, ITGA6) and differentiated (KRT1, KRT10) transcripts but additionally express ICAM1, TNF and CCL20 (Fig. 4B). The gene expression patterns of these keratinocyte subgroups are in agreement with their spatial arrangement in the epidermis (Fig. 4C) (Human Protein Atlas: www.proteinatlas.org) and with a human epidermal scRNA-seq dataset (60)(Fig. S6A). First trimester human epidermis, comprising ‘basal’ undifferentiated keratinocyte progenitors overlaid by the periderm, expresses keratin genes and protein of simple epithelium (keratins, 8, 18 and 19)(Figs. 4B and S6B)(61).

Figure 4. Keratinocyte cell states in health, AD and psoriasis.

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(a) Force-directed graph (FDG) visualization of the different keratinocyte cell states found in healthy adult skin. KC = keratinocyte. Asterix indicates the cell state with inflammatory markers, n=5. (b) Dot plot showing the expression of differentially expressed genes characterizing keratinocyte states in healthy adult skin (circle) shown in (a) and developmental keratinocytes (triangle), separated by the dashed line. (c) FDG feature plots showing gene expression of healthy adult skin keratinocyte cell states shown in (a), together with images of these markers in situ, from the Human Protein Atlas. Scale bars represent 100 μm. (d) Top panel: FDG in (a) annotated by Leiden clustering of eight groups; undifferentiated KC (clusters 1, 5), proliferating (cluster 2), differentiated KC (clusters 3, 4, 6, 7), differentiated KC* (cluster 8). Bottom panel: PAGA showing the relative connectivity between the keratinocyte clusters. Arrows indicate the two differentiation pathways of basal keratinocytes to suprabasal: LB = lamellar body. (e) Dot plot of genes related to lamellar body production and ichthyosis (green box) expressed by healthy adult keratinocyte states (circle) shown in (d), as well as fetal keratinocytes (triangle). Un.= undifferentiated, Diff = differentiated. (f) Immunofluorescence staining of healthy adult skin for CDK1 (red), IRF1 (green), SOX9 (yellow) and DAPI (blue). Red and yellow arrows indicate CDK1+ and SOX9+ cells respectively in suprabasal layers. Images representative of n = 3 donors. Scale bars represent 100μm. (g) Bar charts showing the proportions of the keratinocyte cell states in healthy and diseased skin. p-values for undifferentiated KC AD lesional = 6.1x10-4 and psoriasis lesional = 3.0x10-5; differentiated KCs AD lesional = 6.5x10-16, psoriasis non lesional = 6.5x10-7 and psoriasis lesional = 8.8x10-20; differentiated KCs* psoriasis non lesional = 2.3x10-2 and psoriasis lesional = 1.5x10-4; proliferating KCs AD non lesional = 6.5x10-10 and psoriasis lesional = 8.1x10-14. Populations are compared to those in healthy adult. p-values calculated using a likelihood ratio test. Stars denote significance. (h) Percentage of cells with undifferentiated, differentiated and proliferating keratinocyte signature present in bulk RNA-seq data from GSE121212. Generalized linear modelling on a quasibinomial distribution was used to compare proportions of predicted keratinocyte subsets between healthy, non-lesional and lesional skin and showed statistically significant expansion of differentiated keratinocytes in non-lesional AD (p-value = l.1x10-3), lesional AD (p-value = 9.6x10-10), non-lesional psoriasis (p-value = 2.1x10-5) and lesional psoriasis (p-value = 2.0x10-16).

Force-directed graph (FDG) and PAGA analyses reveal dual inferred differentiation trajectories from the stem cell genes (TP63, PPP3CA and CAV1/2)-enriched basal keratinocytes into terminally differentiated keratinocytes expressing CNFN, FLG and IVL (Figs. 4D, S6C, and Table S1)(61, 62). One arm expresses high levels of lamellar body (LB)-related transcripts such as ABCA12, CKAP4 and CLIP1 that characterize late epidermal differentiation and the other arm lower levels of LB-related transcripts (63)(Fig. 4E). IRF1 and SOX9, transcription factors that are differentially expressed in the two pathways (Fig. S6D), mark distinct cells by immunofluorescence analysis of healthy skin (Figs. 4F and S6E). The statistically significant DE genes identified with Monocle (64) across keratinocyte differentiation (Fig. S6C) recapitulate previous reports in human and mouse (65).

Notably, keratinocytes expressing LB-related transcripts co-express genes associated with autosomal recessive congenital ichthyosis, such as ABCA12, NIPAL4, SLC27A4 and TGM1 (Fig. 4E). However, analysis of fetal keratinocytes showed little to no expression of these congenital ichthyosis-related genes, suggesting that disease onset at the molecular level only begins after 10 PCW (Fig. 4E). This is in keeping with the absence of a granular layer in first trimester fetal epidermis where the expression of LOR, FLG, IVL and genes required for lamellar body production are located (65, 66). Inflammatory differentiated keratinocytes express higher levels of genes associated with inflammatory ichthyoses and severe atopy such as NIPAL4 and SPINK5 (Fig. 4E, cluster 8) (67).

Both AD and psoriasis lesional skin were enriched for differentiated keratinocytes (Fig. 4G) as supported by deconvolution of bulk RNA-seq data from an extended patient cohort (Fig. 4H). DE gene analysis revealed lower expression of stem cell and basal keratinocyte genes (CAV1/2, KRT14 and DUSP10) but higher expression of commitment genes (FOS, JUNB, CDKN1A, MAFB) in undifferentiated lesional psoriasis and AD keratinocytes (Fig S6F). These observations agree with previous reports (68, 69) and suggest rapid transition and differentiation of keratinocytes in AD and psoriasis lesional skin. Lesional differentiated keratinocytes additionally expressed inflammatory transcripts including alarmins (S100A7, S100A8, S100A9), serpins (SERPINB4, SERPINB13) and interferon response genes (IFI27, IFITM1)(Fig S6F). The proportion of inflammatory differentiated keratinocytes, resembling previously described CCL20-expressing keratinocytes in murine inflammatory skin disease induced by subcutaneous IL-17 and TNFα injection (70), are expanded in psoriasis skin (Fig. 4G).

Stromal cell heterogeneity

We next interrogated the heterogeneity within fibroblasts, vascular and lymphatic endothelial cells and Schwann cells (Fig. 5A-B). Fibroblasts dominated the non-immune cell population in developing skin with increased proportional representation of melanocytes, Schwann cells and lymphatic and vascular endothelial cells observed in adult skin (Fig. 1F).

Figure 5. Stromal and endothelial cells.

Figure 5

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(a) UMAP visualization of the non-immune, non-keratinocyte cell states found in the healthy adult skin, n=5. Fb = fibroblast, LE = lymphatic endothelium, VE = vascular endothelium. (b) Dot plot showing the expression of differentially expressed genes characterizing adult healthy skin cell states (circle) shown in (a) and their developmental counterparts (triangle), separated by the dashed line. (c) 3D Reconstruction of Z-stacked images of whole mount immunofluorescence staining of dermis for CD31 (PECAM1, red), gamma synuclein (SNCG, green) and DRAQ5 (blue). White cubes represent 40x40x40 μm. (d) Bar charts showing the proportions of VE in healthy adult and diseased skin. p-values for VE1 psoriasis non-lesional = 5.5 x 10-6 and psoriasis lesional = 5.0 x 10-15, VE2 psoriasis non-lesional = 1.9 x 10-2, and VE3 AD non-lesional = 1.3 x 10-2, AD lesional = 1.5 x 10-4, psoriasis non-lesional = 1.3 x 10-3 and psoriasis lesional = 6.2 x 10-9, calculated using a likelihood ratio test. Stars denote significance. (e) Proportion of cells with VE3 signature present in bulk RNA-seq data from GSE121212. The proportion of VE3 increased in both lesional AD (p-value = 9.1x10-4) and psoriasis (p-value = 8.2 x 10-4). Stars denote significance. p-values calculated using a likelihood ratio test. (f) Prediction score for alignment using CCA (Seurat) between developing gut, kidney, liver, skin and thymus VE with VE1, VE2 and VE3 in healthy adult skin. (g) Network visualizations of pathways conserved between developing skin VE and VE3 in AD and psoriasis. Network nodes are colored by enrichment score (q = <0.05) and represent individual enriched gene sets whilst edges represent shared genes between nodes (intersect≥ 10%). (h) Jitter plot displaying the number of positive ACKR1 cells in 1.5x105 μm2 of healthy adult (n=6), AD (n=12) and psoriasis (n=6) skin. (Anova p-values between healthy adult and AD = 3.0x10-5 and between healthy adult and psoriasis = 2.4x10-13). Stars denote significance. (i) Jitter plot displaying the number of positive ACKR1 cells in 1.5x105 μm2 of AD skin (n=5) before treatment with methotrexate and 9-16 days and 12 weeks post treatment. (Anova p-values between pre and 12 weeks post treatment = 5.0x10-2). Stars denote significance. (j) Interactions between macrophage and vascular endothelium subsets predicted by CellPhoneDB. Color and size indicate log2 mean expression, averaged across the two clusters. Dev = developing skin (k) Immunohistochemical staining of AD (left) and psoriasis (right) skin for F13A (purple), ACKR1 (yellow) and CD31 (teal) showing the close proximity of Mac2 and VE3. Pink arrows point to F13A positive macrophages and green arrows point to CD31/ACKR1 positive vascular endothelial cells. Scale bar is 20μm. Representative image from n=4 for AD and n=6 for psoriasis shown.

Three fibroblast subsets expressing extracellular matrix (ECM)-related genes such as MMP2, COL1A1, COL1A2 and NT5E (encodes CD73) are present in healthy human skin, dominated by Fb1 fibroblasts with minor populations of Fb2 and Fb3 fibroblasts (Fig. 5A-B). Dermal fibroblast heterogeneity encompassing structural and immunomodulatory subtypes has been previously reported at single cell level (6, 8). Interestingly, fibroblasts in fetal skin express more genes relating to Fb2 adult fibroblasts, including COL1A1, COL1A2 and COL6A1, suggesting they are functionally specialized towards ECM remodeling and maintenance (Fig. 5B). Furthermore, in AD and psoriasis lesional and non-lesional skin, Fb2 fibroblasts are significantly enriched compared to healthy skin and have upregulated expression of the chemokines CXCL12 and CCL19, in keeping with recent reports (71)(Fig. S7A-B).

Specialized vascular endothelium mediates leukocyte trafficking

Endothelial cells in the healthy adult dermis comprise vascular endothelium (PECAM1, EMCN) and lymphatic endothelium (LYVE1, PDPN)(Fig. 5A-B and Table S1). There are two sub-clusters of lymphatic endothelial cells defined by the differential expression of CCL21 and PDPN, which are higher in LE1 and LE2 respectively (Fig. 5A-B), the latter resembling PDPN+ collecting lymphatic vessels in human dermis (14). Notably, LE1 cells express higher levels of the chemoattractant CCL21, which mediates dendritic cell (DC) migration into skin draining lymph nodes, as well as angiogenesis factors CAVIN2 and CCND1, further supporting their function as initial afferent lymphatics (72)(Fig. 5B and Table S1).

Three distinct states of PECAM1(CD31)-expressing vascular endothelial cells (VE1, VE2, VE3) are present in adult dermis. VE3, which forms ~2% of endothelial cells, is characterized by gamma synuclein (SNCG) and high expression of the venular capillary marker ACKR1 (73, 74)(Fig. 5B). In addition, VE3 co-express inflammatory cytokines, chemokines and leukocyte adhesion molecules including IL6, IL33, SELE and ICAM1 (Fig. 5B) similar to lymph node HEVs that mediate leukocyte entry (75, 76). We performed whole-mount immunostaining of healthy dermis and identified SNCG+PECAM1+(VE3) distended vascular structures in the superficial dermis (Fig. 5C) suggesting that these cells may be post-capillary venular cells regulating leukocyte adhesion and migration.

Co-optation of developmental gene programs in AD and psoriasis

Interestingly, we observed significant expansion of VE3 in AD and psoriasis lesional skin (Figs. 5D and S7B) that was also evident in the broader patient cohort bulk RNA-sequencing data (Fig. 5E). Fetal skin VE cells aligned transcriptionally with adult skin VE3 and also express genes involved in leukocyte adhesion and trafficking (Figs. 1E and 5B). The expansion of VE3 in inflamed skin and transcriptome alignment of fetal gut, kidney, liver, skin and thymus VE and adult skin VE3 (Fig. 5F) led us to hypothesize that developmental VE gene programs are involved in AD and psoriasis pathogenesis, similar to our earlier observation with Mac2. We derived 112 genes that were conserved (Seurat FindConservedMarkers, negative binomial test, p-value<0.05) between fetal skin VE and AD and psoriasis VE3. This identified gene sets related to stress (DNAJB1, HSPA6, HSPB1, HSPH1, HSP90AA1), IL6 (SOCS3) and angiopoietin (EGR1) signaling, similar to Mac2 (Table S5-6). Gene ontology analysis identified significantly enriched gene set clusters (Hypergeometric test, q-value<0.05), similar to Mac2 (Fig. 3H and 5G), relating to leukocyte adhesion, T cell activation and IL-8 response as conserved gene modules in developing skin VE and VE3 in AD and psoriasis (Fig. 5G and Table S6).

To validate the pathogenic role of VE3 in inflammatory skin disease in vivo, we used ACKR1 as a marker for VE3 (based on the scRNA-seq data of healthy, AD and psoriasis skin (Fig. 5B and Fig. S7C), and compared the abundance of VE3 in healthy, AD and psoriasis skin and following treatment with oral methotrexate. This showed a significantly (anova test p-value<0.05) higher frequency of VE3 in AD and psoriasis skin and a reduction in VE3 in the skin of AD patients following treatment, in line with clinical response and reduction in EASI score, similar to Mac2 (Figs. 5H, I). Flow cytometry analysis also confirmed the expansion of VE3 in lesional psoriasis skin (Figs. S7D, E).

Mac2 and VE3 are the only skin cell states significantly enriched for these leukocyte migration gene programs, as such we investigated if they were interacting with each other or other immune cells to coordinate this function. To assess cell-cell interactions in healthy, AD and psoriasis skin, we interrogated the CellPhoneDB receptor-ligand database, which predicted a significant enrichment for ACKR1 on VE3 to interact with CXCL8 (IL-8) on Mac2 (Figs. 5J and S7F). We confirm this cell-cell interaction in situ demonstrating the close apposition of VE3 and perivascular Mac2 in AD and psoriasis skin (Fig. 5K). CellPhoneDB analysis also predicted enhanced interaction between VE3 and Mac2 with lymphocytes in AD and psoriasis compared to healthy skin, supporting a role for these cells in lymphocyte recruitment into inflamed skin (Fig. S7G).

Discussion

In this study we deployed scRNA-seq to resolve the cellular heterogeneity and organization of human first trimester prenatal skin, adult healthy skin and skin of patients with AD and psoriasis. We reveal the co-optation of developmental gene programs by vascular endothelial cells and macrophages in AD and psoriasis pathogenesis, and the conserved mouse and human migratory DC signature across multiple disease states.

The significance of developmental programs in carcinogenesis and metastasis of both childhood and adult-onset tumours is well established (1, 2, 77). The impact of developmental programs in adult onset neurodegenerative disorders is also emerging (78). Our findings support a broader utilization of prenatal cellular programs, as we report here in inflammatory skin disease, and potentially other immune-mediated inflammatory disorders. Lymphocyte seeding into the developing skin is reliant on the structural network provided by the vasculature and as our data suggests, also through endothelial cell interactions with macrophages (21), which are the most abundant skin-resident immune cells during embryonic development. We postulate this interplay is co-opted to recruit immune cells in inflammatory skin disease. The molecular regulation of conserved gene modules may be distinct during development compared to disease. Dissecting the precise interplay of known angiogenic triggers, such as hypoxia, Wnt, STAT3 and β-catenin signaling, will pave the way towards mechanistic understanding of VE3 expansion. Establishing the intrinsic and tissue extrinsic factors that drive the Mac2 state acquisition in disease may innovate anti-inflammatory strategies. In addition to prenatal endothelial cell and macrophage gene programs, skin fibroblast (F2) cell program is also augmented in AD and psoriasis, as well as activation of fetal thymic medullary DC state in inflammation of several adult tissues and cancer (50, 53–55).

In summary, our human skin atlas provides a roadmap for targeting pathological programs in inflammatory skin diseases and is a foundational resource on the dynamic cutaneous cellular topology that evolves during fetal development, adulthood and during inflammation.

Supplementary Material

Supplementary Material
Table S1
aba6500-table-s1.xlsx (43.2KB, xlsx)
Table S2
aba6500-table-s2.xlsx (9.6KB, xlsx)
Table S3
aba6500-table-s3.xlsx (14.6KB, xlsx)
Table S4
aba6500-table-s4.xlsx (9.6KB, xlsx)
Table S5
aba6500-table-s5.xlsx (46.5KB, xlsx)
Table S6
aba6500-table-s6.xlsx (47KB, xlsx)

One Sentence Summary.

Developmental programs re-emerge in skin disease.

Acknowledgements

We thank the Newcastle University Flow Cytometry Core Facility, Bioimaging Core Facility, Genomics Core Facility and NUIT for technical assistance, School of Pathology Node Proximity Lab, Alison Farnworth for clinical liaison and the Newcastle Dermatology Department for critical feedback. The human embryonic and fetal material was provided by the Joint MRC / Wellcome (MR/R006237/1) Human Developmental Biology Resource (www.hdbr.org). We thank Dr F. Kreeshan and Dr W. Ghumra for their help with the methotrexate study in AD and collection of samples. We thank Jana Elias (scientific illustrator) for her support with the print page summary figure design. This publication is part of the Human Cell Atlas – www.humancellatlas.org/publications.

Funding

We acknowledge funding from the Wellcome Human Cell Atlas Strategic Science Support (WT211276/Z/18/Z); M.H. is funded by Wellcome (WT107931/Z/15/Z), The Lister Institute for Preventive Medicine and Newcastle NIHR Biomedical Research Centre (BRC); S.A.T. is funded by Wellcome (WT206194), ERC Consolidator and EU MRG-Grammar awards. N.J.R. is funded by Newcastle NIHR BRC, Newcastle MRC/EPSRC Molecular Pathology Node and Newcastle NIHR Medtech Diagnostic Co-operative, has received research grant funding from Novartis, PSORT partners (www.PSORT.org.uk) and income to Newcastle University from Almirall, Lily and Novartis for attendance at advisory boards. F.W. is on secondment as Executive Chair of the UK Medical Research Council. E.O’T has received research funding from Kamari Pharma in the current fiscal year and has been on a grants advisory board for Sanofi-Aventis (money to University). E.F.M.P is funded by a Wellcome 4ward-North Clinical Training Fellowship. G.O. receives funding from the Medical Research Council UK, NIHR Oxford Biomedical Research Centre and NIHR Clinical Research Network administered through the Radcliffe Department of Medicine, University of Oxford.

Footnotes

Author contributions

M.H., F.W., S.A.T conceived and co-directed the study. M.H.; S.A.T.; F.W.; J.F.; E.F.M.P.; A.C.V.; N.J.R. and G.R. designed the experiments. Samples were isolated by J.F.; R.A.B. and E.F.M.P. Libraries were prepared by E.S.; J.E. and J.C. Flow cytometry and CyTOF experiments were designed and performed by J.F.; G.R.; D.M.; D.McD.; E.F.M.P.; A.F.; R.A.B. Immunohistochemistry and immunofluorescence were performed by D.D.; E.F.M.P.; C.J.; T.N.; and C.C. P.V.; G.R.; I.G.; N.H.; J.F.; K.G.; M.E.; D-M.P. and K.P. performed the computational analysis. M.H.; J.F; P.V.; G.R.; R.A.B.; E.F.M.P.; E.S.; A.D.; F.W.; S.A.T.; G.O.; E.O’T.; N.J.R.; N.R.; P.J.; M.E; T.H.; J.P.; B.S.; A.C.V; S.W.; J.B.; R.V-T.; S.B.; D.McD.; A.F.; X.W.; L.J.; K.M.; D.B.; B.O.; M.L.; A.H.; and S.L interpreted the data. M.H.; G.R; J.F.; P.V.; E.F.M.P.; E.S.; B.O.; A.D.; F.W and S.A.T wrote the manuscript. All authors read and accepted the manuscript.

Competing Interests

None declared

Data and materials availability

The raw sequencing data, expression count data with cell classifications are deposited at ArrayExpress: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8142.

Code availability

All data analysis scripts are available on Zenodo DOI: 10.5281/zenodo.4249674 (79).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material
aba6500_reynolds_sm.pdf (161MB, pdf)
Table S1
aba6500-table-s1.xlsx (43.2KB, xlsx)
Table S2
aba6500-table-s2.xlsx (9.6KB, xlsx)
Table S3
aba6500-table-s3.xlsx (14.6KB, xlsx)
Table S4
aba6500-table-s4.xlsx (9.6KB, xlsx)
Table S5
aba6500-table-s5.xlsx (46.5KB, xlsx)
Table S6
aba6500-table-s6.xlsx (47KB, xlsx)

Data Availability Statement

The raw sequencing data, expression count data with cell classifications are deposited at ArrayExpress: https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8142.

Code availability

All data analysis scripts are available on Zenodo DOI: 10.5281/zenodo.4249674 (79).

All data analysis scripts are available on Zenodo DOI: 10.5281/zenodo.4249674 (79).

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