Background: Atopic dermatitis (AD) is the most common chronic inflammatory skin disease with complex pathogenesis for which the cellular and molecular crosstalk in AD skin has not been fully understood.Methods: Skin tissues examined for spatial gene expression were derived from the upper arm of 6 healthy control (HC) donors and 7 AD patients (lesion and nonlesion). We performed spatial transcriptomics sequencing to characterize the cellular infiltrate in lesional skin. For single‐cell analysis, we analyzed the single‐cell data from suction blister material from AD lesions and HC skin at the antecubital fossa skin (4 ADs and 5 HCs) and full‐thickness skin biopsies (4 ADs and 2 HCs). The multiple proximity extension assays were performed in the serum samples from 36 AD patients and 28 HCs.Results: The single‐cell analysis identified unique clusters of fibroblasts, dendritic cells, and macrophages in the lesional AD skin. Spatial transcriptomics analysis showed the upregulation of COL6A5, COL4A1, TNC, and CCL19 in COL18A1‐expressing fibroblasts in the leukocyte‐infiltrated areas in AD skin. CCR7‐expressing dendritic cells (DCs) showed a similar distribution in the lesions. Additionally, M2 macrophages expressed CCL13 and CCL18 in this area. Ligand–receptor interaction analysis of the spatial transcriptome identified neighboring infiltration and interaction between activated COL18A1‐expressing fibroblasts, CCL13‐ and CCL18‐expressing M2 macrophages, CCR7‐ and LAMP3‐expressing DCs, and T cells. As observed in skin lesions, serum levels of TNC and CCL18 were significantly elevated in AD, and correlated with clinical disease severity.Conclusion: In this study, we show the unknown cellular crosstalk in leukocyte‐infiltrated area in lesional skin. Our findings provide a comprehensive in‐depth knowledge of the nature of AD skin lesions to guide the development of better treatments.

Background: Atopic eczema (atopic dermatitis, AD) is characterized by disrupted skin barrier associated with elevated skin pH and skin microbiome dysbiosis, due to high Staphylococcus aureus loads, especially during flares. Since S aureus shows optimal growth at neutral pH, we investigated the longitudinal interplay between these factors and AD severity in a pilot study.Method: Emollient (with either basic pH 8.5 or pH 5.5) was applied double‐blinded twice daily to 6 AD patients and 6 healthy (HE) controls for 8 weeks. Weekly, skin swabs for microbiome analysis (deep sequencing) were taken, AD severity was assessed, and skin physiology (pH, hydration, transepidermal water loss) was measured.Results: Physiological, microbiome, and clinical results were not robustly related to the pH of applied emollient. In contrast to longitudinally stable microbiome in HE, S aureus frequency significantly increased in AD over 8 weeks. High S aureus abundance was associated with skin pH 5.7‐6.2. High baseline S aureus frequency predicted both increase in S aureus and in AD severity (EASI and local SCORAD) after 8 weeks.Conclusion: Skin pH is tightly regulated by intrinsic factors and limits the abundance of S aureus. High baseline S aureus abundance in turn predicts an increase in AD severity over the study period. This underlines the importance and potential of sustained intervention regarding the skin pH and urges for larger studies linking skin pH and skin S aureus abundance to understand driving factors of disease progression.

Background: Atopic dermatitis (AD) is a heterogeneous, chronic inflammatory skin disease linked to skin microbiome dysbiosis with reduced bacterial diversity and elevated relative abundance of Staphylococcus aureus (S. aureus).Objectives: We aimed to characterize the yet incompletely understood association between the skin microbiome and patients' demographic and clinical cofactors in relation to AD severity.Methods: The skin microbiome in 48 adult moderate‐to‐severe AD patients was investigated using next‐generation deep sequencing (16S rRNA gene, V1–V3 region) followed by denoising (DADA2) to obtain amplicon sequence variant (ASV) composition.Results: In lesional skin, AD severity was associated with S. aureus relative abundance (r_S = 0.53, p < 0.001) and slightly better with the microbiome diversity measure Evenness (r_S = −0.58, p < 0.001), but not with Richness. Multiple regression confirmed the association of AD severity with microbiome diversity, including Shannon (in lesional skin, p < 0.001), Evenness (in non‐lesional skin, p = 0.015) or S. aureus relative abundance (p < 0.012), and with patient's IgE levels (p < 0.001), race (p < 0.032), age (p < 0.034) and sex (p = 0.012). The lesional model explained 62% of the variation in AD severity, and the non‐lesional model 50% of the variation.Conclusions: Our results specify the frequently reported “reduced diversity” of the AD‐related skin microbiome to reduced Evenness, which was in turn mainly driven by S. aureus relative abundance, rather than to a reduced microbiome Richness. Finding associations between AD severity, the skin microbiome and patient's cofactors is a key aspect in developing new personalized AD treatments, particularly those targeting the AD microbiome.

Atopic eczema (AE) is an inflammatory skin disease with involvement of genetic, immunological and environmental factors. One hallmark of AE is a skin barrier disruption on multiple, highly interconnected levels: filaggrin mutations, increased skin pH and a microbiome dysbiosis towards Staphylococcus aureus overgrowth are observed in addition to an abnormal type 2 immune response. Extrinsic factors seem to play a major role in the development of AE. As AE is a first step in the atopic march, its prevention and appropriate treatment are essential. Although standard therapy remains topical treatment, powerful systemic treatment options emerged in the last years. However, thorough endotyping of the individual patients is still required for ideal precision medicine approaches in future. Therefore, novel microbial and immunological biomarkers were described recently for the prediction of disease development and treatment response. This review summarizes the current state of the art in AE research.

Background: Atopic dermatitis (AD) has long been regarded as a primarily pediatric disease. However, there is growing evidence for a high rate of adult‐onset AD. We aimed to characterize factors associated with adult‐onset versus childhood‐onset AD and controls.Methods: We analyzed cross‐sectional data of the CK‐CARE‐ProRaD cohorts Bonn, Augsburg, Davos, Zürich of 736 adult patients stratified by age of AD onset (childhood‐onset <18 years: 76.4% (subsets: 0 to 2; ≥2 to 6; ≥7 to 11; ≥12 to 18); adult‐onset ≥18 years: 23.6% (subsets: ≥18 to 40; ≥41 to 60; ≥61) and 167 controls (91 atopic, 76 non‐atopic)).Results: We identified active smoking to be associated with adult‐onset AD versus controls (adjusted Odds Ratio (aOR) = 5.54 [95% Confidence Interval: 1.06–29.01] vs. controls^non‐atopic, aOR = 4.03 [1.20–13.45] vs. controls^atopic). Conjunctivitis showed a negative association versus controls^atopic (aOR = 0.36 [0.14–0.91]). Food allergy (aOR = 2.93 [1.44–5.96]), maternal food allergy (aOR = 9.43 [1.10–80.95]), palmar hyperlinearity (aOR = 2.11 [1.05–4.25]), and academic background (aOR = 2.14 [1.00–4.54]) increased the odds of childhood‐onset AD versus controls^atopic. Shared AD‐associated factors were maternal AD (4‐34x), increased IgE (2‐20x), atopic stigmata (2‐3x) with varying effect sizes depending on AD onset and control group. Patients with adult‐compared to childhood‐onset had doubled odds of allergic rhinitis (aOR = 2.15 [1.12–4.13]), but reduced odds to feature multiple (3–4) atopic comorbidities (aOR = 0.34 [0.14–0.84]). Adult‐onset AD, particularly onset ≥61 years, grouped mainly in clusters with low contributions of personal and familial atopy and high frequencies of physical inactivity, childhood‐onset AD, particularly infant‐onset, mainly in “high‐atopic”‐clusters.Conclusions: The identified associated factors suggest partly varying endo‐ and exogeneous mechanisms underlying adult‐onset versus childhood‐onset AD. Our findings might contribute to better assessment of the individual risk to develop AD throughout life and encourage prevention by non‐smoking and physical activity as modifiable lifestyle factors.