BACKGROUND Psoriasis and atopic dermatitis are two inflammatory skin diseases with a high prevalence and a significant burden on the patients. Underlying molecular mechanisms include chronic inflammation and abnormal proliferation. However, the cell types contributing to these molecular mechanisms are much less understood. Recently, deconvolution methodologies have allowed the digital quantification of cell types in bulk tissue based on mRNA expression data from biopsies. Using these methods to study the cellular composition of the skin enables the rapid enumeration of multiple cell types, providing insight into the numerical changes of cell types associated with chronic inflammatory skin conditions. Here, we use deconvolution to enumerate the cellular composition of the skin and estimate changes related to onset, progress, and treatment of these skin diseases. METHODS A novel signature matrix, i.e. DerM22, containing expression data from 22 reference cell types, is used, in combination with the CIBERSORT algorithm, to identify and quantify the cellular subsets within whole skin biopsy samples. We apply the approach to public microarray mRNA expression data from the skin layers and 648 samples from healthy subjects and patients with psoriasis or atopic dermatitis. The methodology is validated by comparison to experimental results from flow cytometry and immunohistochemistry studies, and the deconvolution of independent data from isolated cell types. RESULTS We derived the relative abundance of cell types from healthy, lesional, and non-lesional skin and observed a marked increase in the abundance of keratinocytes and leukocytes in the lesions of both inflammatory dermatological conditions. The relative fraction of these cells varied from healthy to diseased skin and from non-lesional to lesional skin. We show that changes in the relative abundance of skin-related cell types can be used to distinguish between mild and severe cases of psoriasis and atopic dermatitis, and trace the effect of treatment. CONCLUSIONS Our analysis demonstrates the value of this new resource in interpreting skin-derived transcriptomics data by enabling the direct quantification of cell types in a skin sample and the characterization of pathological changes in tissue composition.

中文翻译：

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通过活检样本的反卷积来表征慢性炎症性皮肤病的疾病特异性细胞丰度谱。

背景技术银屑病和特应性皮炎是两种发病率高、给患者带来沉重负担的炎症性皮肤病。潜在的分子机制包括慢性炎症和异常增殖。然而，对这些分子机制做出贡献的细胞类型却知之甚少。最近，反卷积方法允许基于活检的 mRNA 表达数据对大块组织中的细胞类型进行数字量化。使用这些方法研究皮肤的细胞组成可以快速计数多种细胞类型，从而深入了解与慢性炎症性皮肤病相关的细胞类型的数值变化。在这里，我们使用反卷积来枚举皮肤的细胞组成，并估计与这些皮肤疾病的发病、进展和治疗相关的变化。方法 一种新颖的特征矩阵，即 DerM22，包含来自 22 种参考细胞类型的表达数据，与 CIBERSORT 算法相结合，用于识别和量化整个皮肤活检样本中的细胞亚群。我们将该方法应用于来自健康受试者和牛皮癣或特应性皮炎患者的皮肤层和 648 个样本的公共微阵列 mRNA 表达数据。通过与流式细胞术和免疫组织化学研究的实验结果进行比较以及分离细胞类型的独立数据的去卷积，验证了该方法。结果我们从健康、病变和非病变皮肤中获得了细胞类型的相对丰度，并观察到两种炎症性皮肤病病变中角质形成细胞和白细胞的丰度显着增加。这些细胞的相对比例从健康皮肤到患病皮肤以及从非病变皮肤到病变皮肤各不相同。我们证明，皮肤相关细胞类型相对丰度的变化可用于区分轻度和重度牛皮癣和特应性皮炎病例，并追踪治疗效果。结论我们的分析证明了这种新资源在解释皮肤来源的转录组学数据方面的价值，可以直接量化皮肤样本中的细胞类型和表征组织成分的病理变化。