scholarly journals Identification of Type 2-pediatric Asthma Based on Single-cell Transcriptomic Analysis

Author(s):  
Bing Dai ◽  
Feifei Sun ◽  
Nan Yang ◽  
Xuxu Cai ◽  
Chunlu Li ◽  
...  

Abstract Type 2-pediatric asthma characterized by T2 cytokine-driven airway inflammation is the most common type of asthma. Recently, T2 cytokine inhibitors have reduced the exacerbation rates of asthma, but their ability to improve lung function is limited. Screening novel therapeutic strategies for Type 2-pediatric asthma patients is imperative. We obtained single-cell RNA sequencing (scRNA-seq) describing the chronic stimulation GSE145013 dataset with IL-13. Consensus clustering was performed to classify pediatric asthmatic patients from validation datasets GSE65204 and GSE40888, based on the cell marker genes. We found three cellular subtypes including ciliated cells, secretory cell 1, and secretory cell 2. The expression of CCL26, PRB1, and SLC9B2 was higher in secretory cell 1, while SCGB3A1 and BPIFA1 were higher in secretory cell 2. Consensus clustering based on the five cell marker genes produced two patient subtypes (C1 and C2). The expression of SCGB3A1 and BPIFA1 was higher in C2 subtypes, while CCL26, PRB1, and SLC9B2 was higher in C1 subtypes. Patients in C2 subtypes may more secretory cell 2, while the patients in C1 may have higher secretory cell 1 in the infiltrate. More Type 2 T helper cells were in the infiltrate in the C2 subtype, while type 1 T helper cells were higher in the C1 subtype. T2 cytokines (IL-13, IL-33, IL-3, IL-4, and TSLP) were expressed more in the C2 subtype, corresponding to Type 2-pediatric asthma. This study identified five cell marker genes to screen Type 2-pediatric asthma that could potentially be therapeutic targets for Type 2-pediatric asthma.

1999 ◽  
Vol 67 (9) ◽  
pp. S574
Author(s):  
Johnny Perez ◽  
Ping Song ◽  
Jiang Yu ◽  
Stanislaw M. Stepkowski ◽  
Barry D Kahan

2021 ◽  
Author(s):  
Zhongli Xu ◽  
Xinjun Wang ◽  
Li Fan ◽  
Fujing Wang ◽  
Jiebiao Wang ◽  
...  

Immunological memory is key to productive adaptive immunity. An unbiased, high through-put gene expression profiling of tissue-resident memory T cells residing in various anatomical location within the lung is fundamental to understand lung immunity but still lacking. In this study, using a well-established model on Klebsiella pneumoniae, we performed an integrative analysis of spatial transcriptome with single-cell RNA-seq and single-cell ATAC-seq on lung cells from mice after Immunization using the 10x Genomics Chromium and Visium platform. We employed several deconvolution algorithms and established an optimized deconvolution pipeline to accurately decipher specific cell-type composition by location. We identified and located 12 major cell types by scRNA-seq and spatial transcriptomic analysis. Integrating scATAC-seq data from the same cells processed in parallel with scRNA-seq, we found epigenomic profiles provide more robust cell type identification, especially for lineage-specific T helper cells. When combining all three data modalities, we observed a dynamic change in the location of T helper cells as well as their corresponding chemokines for chemotaxis. Furthermore, cell-cell communication analysis of spatial transcriptome provided evidence of lineage-specific T helper cells receiving designated cytokine signaling. In summary, our first-in-class study demonstrated the power of multi-omics analysis to uncover intrinsic spatial- and cell-type-dependent molecular mechanisms of lung immunity. Our data provides a rich research resource of single cell multi-omics data as a reference for understanding spatial dynamics of lung immunization.


2007 ◽  
Vol 212 (2) ◽  
pp. 101-105 ◽  
Author(s):  
Eleni Albanidou-Farmaki ◽  
Anastasios K. Markopoulos ◽  
Filanthi Kalogerakou ◽  
Demetrios Z. Antoniades

Cell Reports ◽  
2014 ◽  
Vol 7 (4) ◽  
pp. 1130-1142 ◽  
Author(s):  
Bidesh Mahata ◽  
Xiuwei Zhang ◽  
Aleksandra A. Kolodziejczyk ◽  
Valentina Proserpio ◽  
Liora Haim-Vilmovsky ◽  
...  

2016 ◽  
Vol 15 (2) ◽  
Author(s):  
C.J. Hao ◽  
J. Li ◽  
P. Liu ◽  
X.L. Li ◽  
Y.Q. Hu ◽  
...  

1992 ◽  
Vol 4 (6) ◽  
pp. 788-793 ◽  
Author(s):  
Martien L. Kapsenberg ◽  
Henk M. Jansen ◽  
Jan D. Bos ◽  
Eddy A. Wierenga

2000 ◽  
Vol 156 (3) ◽  
pp. 1067-1071 ◽  
Author(s):  
Axel Roers ◽  
Martin Leo Hansmann ◽  
Klaus Rajewsky ◽  
Ralf Küppers

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