TY - JOUR

T1 - Derivation of marker gene signatures from human skin and their use in the interpretation of the transcriptional changes associated with dermatological disorders

AU - Shih, B.B.

AU - Nirmal, A.J.

AU - Headon, D.J.

AU - Akbar, A.N.

AU - Mabbott, N.A.

AU - Freeman, T.C.

PY - 2017/4/30

Y1 - 2017/4/30

N2 - Numerous studies have explored the altered transcriptional landscape associated with skin diseases to understand the nature of these disorders. However, data interpretation represents a significant challenge due to a lack of good maker sets for many of the specialized cell types that make up this tissue, whose composition may fundamentally alter during disease. Here we have sought to derive expression signatures that define the various cell types and structures that make up human skin, and demonstrate how they can be used to aid the interpretation of transcriptomic data derived from this organ. Two large normal skin transcriptomic datasets were identified, one RNA-seq (n = 578), the other microarray (n = 165), quality controlled and subjected separately to network-based analyses to identify clusters of robustly co-expressed genes. The biological significance of these clusters was then assigned using a combination of bioinformatics analyses, literature, and expert review. After cross comparison between analyses, 20 gene signatures were defined. These included expression signatures for hair follicles, glands (sebaceous, sweat, apocrine), keratinocytes, melanocytes, endothelia, muscle, adipocytes, immune cells, and a number of pathway systems. Collectively, we have named this resource SkinSig. SkinSig was then used in the analysis of transcriptomic datasets for 18 skin conditions, providing in-context interpretation of these data. For instance, conventional analysis has shown there to be a decrease in keratinization and fatty metabolism with age; we more accurately define these changes to be due to loss of hair follicles and sebaceous glands. SkinSig also highlighted the over-/under-representation of various cell types in skin diseases, reflecting an influx in immune cells in inflammatory disorders and a relative reduction in other cell types. Overall, our analyses demonstrate the value of this new resource in defining the functional profile of skin cell types and appendages, and in improving the interpretation of disease data.

AB - Numerous studies have explored the altered transcriptional landscape associated with skin diseases to understand the nature of these disorders. However, data interpretation represents a significant challenge due to a lack of good maker sets for many of the specialized cell types that make up this tissue, whose composition may fundamentally alter during disease. Here we have sought to derive expression signatures that define the various cell types and structures that make up human skin, and demonstrate how they can be used to aid the interpretation of transcriptomic data derived from this organ. Two large normal skin transcriptomic datasets were identified, one RNA-seq (n = 578), the other microarray (n = 165), quality controlled and subjected separately to network-based analyses to identify clusters of robustly co-expressed genes. The biological significance of these clusters was then assigned using a combination of bioinformatics analyses, literature, and expert review. After cross comparison between analyses, 20 gene signatures were defined. These included expression signatures for hair follicles, glands (sebaceous, sweat, apocrine), keratinocytes, melanocytes, endothelia, muscle, adipocytes, immune cells, and a number of pathway systems. Collectively, we have named this resource SkinSig. SkinSig was then used in the analysis of transcriptomic datasets for 18 skin conditions, providing in-context interpretation of these data. For instance, conventional analysis has shown there to be a decrease in keratinization and fatty metabolism with age; we more accurately define these changes to be due to loss of hair follicles and sebaceous glands. SkinSig also highlighted the over-/under-representation of various cell types in skin diseases, reflecting an influx in immune cells in inflammatory disorders and a relative reduction in other cell types. Overall, our analyses demonstrate the value of this new resource in defining the functional profile of skin cell types and appendages, and in improving the interpretation of disease data.

KW - transcriptomics

KW - gene expression

KW - skin

KW - sebaceous gland

KW - apocrine gland

KW - sweat gland

KW - psoriasis

U2 - 10.1002/path.4864

DO - 10.1002/path.4864

M3 - Journal article

VL - 241

SP - 600

EP - 613

JO - Journal of Pathology

JF - Journal of Pathology

SN - 0022-3417

IS - 5

ER -