Background: Th17 cytokines such as interleukin-17 and interleukin-22 are expressed in atopic dermatitis lesions. Previous studies have reported increased levels of interleukin-17A, -17E, and -17F in patients with atopic dermatitis.As interleukin-17A, -17E and -17F act through a common receptor composed of interleukin-17RA, it is speculated that interleukin-17RA gene (IL17RA) mutation could affect the clinical characteristics of atopic dermatitis. Objective: This study aimed to characterize the clinical features of atopic dermatitis according to the presence of an IL17RA mutation in patients with atopic dermatitis. Methods: We performed reverse blot hybridization assay to detect IL17RA mutations in Korean patients with atopic dermatitis. The clinical features of atopic dermatitis were compared between atopic dermatitis patients with and without IL17RA mutation. Results: Of 332 patients with atopic dermatitis, 27 (8.1%) were found to have IL17RA mutation compared to 8 of 245 controls without atopic diseases (3.27%), which was statistically significant. Furthermore, 272 of atopic dermatitis patients (81.9%) had extrinsic type atopic dermatitis and 60 (18.1%) had intrinsic type. All patients with IL17RA mutations had extrinsic type. In addition, atopic dermatitis with IL17RA mutation was associated with longer disease duration, more frequent keratosis pilaris, higher blood eosinophil count, higher serum total immunoglobulin E level, higher house dust mite allergen-specific immunoglobulin E levels, and more need for systemic treatment than that in patients without IL17RA mutation. Conclusion IL17RA mutation is associated with the more severe extrinsic type atopic dermatitis. So, it may predict the progress to severe atopic dermatitis.

Background: Th17 cytokines such as interleukin-17 and interleukin-22 are expressed in atopic dermatitis lesions. Previous studies have reported increased levels of interleukin-17A, -17E, and -17F in patients with atopic dermatitis.As interleukin-17A, -17E and -17F act through a common receptor composed of interleukin-17RA, it is speculated that interleukin-17RA gene (IL17RA) mutation could affect the clinical characteristics of atopic dermatitis. Objective: This study aimed to characterize the clinical features of atopic dermatitis according to the presence of an IL17RA mutation in patients with atopic dermatitis. Methods: We performed reverse blot hybridization assay to detect IL17RA mutations in Korean patients with atopic dermatitis. The clinical features of atopic dermatitis were compared between atopic dermatitis patients with and without IL17RA mutation. Results: Of 332 patients with atopic dermatitis, 27 (8.1%) were found to have IL17RA mutation compared to 8 of 245 controls without atopic diseases (3.27%), which was statistically significant. Furthermore, 272 of atopic dermatitis patients (81.9%) had extrinsic type atopic dermatitis and 60 (18.1%) had intrinsic type. All patients with IL17RA mutations had extrinsic type. In addition, atopic dermatitis with IL17RA mutation was associated with longer disease duration, more frequent keratosis pilaris, higher blood eosinophil count, higher serum total immunoglobulin E level, higher house dust mite allergen-specific immunoglobulin E levels, and more need for systemic treatment than that in patients without IL17RA mutation. Conclusion IL17RA mutation is associated with the more severe extrinsic type atopic dermatitis. So, it may predict the progress to severe atopic dermatitis.

Stroke causes systemic immunosuppression. T lymphocytes are involved in infarct size in the early stages of stroke. However, the phenotypes of T lymphocytes and their functions in peripheral immune organs and the brain have not been well analyzed in the acute and chronic phases of stroke. Here, we investigated pathological phenotypic alterations in the systemic immune response, especially changes in T lymphocytes, from one day to six months after ischemic stroke in mice. Impairment in thymocyte numbers, development, proliferation, and apoptosis were observed for up to two weeks. The number of mature T cells in the spleen and blood decreased and showed reduced interferon-γ production. Increased numbers of CD4-CD8-CD3+ double-negative T cells were observed in the mouse brain during the early stages of stroke, whereas interleukin (IL)-10+Foxp3+ regulatory T lymphocytes increased from two weeks during the chronic phase. These phenotypes correlated with body weight and neurological severity scores. The recovery of T lymphocyte numbers and increases in IL-10+Foxp3+ regulatory T lymphocytes may be important for long-term neurological outcomes. Dynamic changes in T lymphocytes between the acute and chronic phases may play different roles in pathogenesis and recovery. This study provides fundamental information regarding the T lymphocyte alterations from the brain to the peripheral immune organs following stroke.

Stroke causes systemic immunosuppression. T lymphocytes are involved in infarct size in the early stages of stroke. However, the phenotypes of T lymphocytes and their functions in peripheral immune organs and the brain have not been well analyzed in the acute and chronic phases of stroke. Here, we investigated pathological phenotypic alterations in the systemic immune response, especially changes in T lymphocytes, from one day to six months after ischemic stroke in mice. Impairment in thymocyte numbers, development, proliferation, and apoptosis were observed for up to two weeks. The number of mature T cells in the spleen and blood decreased and showed reduced interferon-γ production. Increased numbers of CD4-CD8-CD3+ double-negative T cells were observed in the mouse brain during the early stages of stroke, whereas interleukin (IL)-10+Foxp3+ regulatory T lymphocytes increased from two weeks during the chronic phase. These phenotypes correlated with body weight and neurological severity scores. The recovery of T lymphocyte numbers and increases in IL-10+Foxp3+ regulatory T lymphocytes may be important for long-term neurological outcomes. Dynamic changes in T lymphocytes between the acute and chronic phases may play different roles in pathogenesis and recovery. This study provides fundamental information regarding the T lymphocyte alterations from the brain to the peripheral immune organs following stroke.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.