Dataset Information

Reconstructing the Developmental Trajectories of Multiple Subtypes in Pulmonary Parenchymal Epithelial Cells by Single-Cell RNA-seq.

ABSTRACT: Background:Some lung diseases are cell type-specific. It is essential to study the cellular anatomy of the normal human lung for exploring the cellular origin of lung disease and the cell development trajectory. Methods:We used the Seurat R package for data quality control. The principal component analysis (PCA) was used for linear dimensionality reduction. UMAP and tSNE were used for dimensionality reduction. Muonocle2 was used to extract lung epithelial cells to analyze the subtypes of epithelial cells further and to study the development of these cell subtypes. Results:We showed a total of 20154 high quality of cells from human normal lung tissue. They were initially divided into 17 clusters cells and then identified as seven types of cells, namely macrophages, monocytes, CD8 + T cells, epithelial cells, endothelial cells, adipocytes, and NK cells. 4240 epithelial cells were extracted for further analysis and they were divided into seven clusters. The seven cell clusters include alveolar cell, alveolar endothelial progenitor, ciliated cell, secretory cell, ionocyte cell, and a group of cells that are not clear at present. We show the development track of these subtypes of epithelial cells, in which we speculate that alveolar epithelial progenitor (AEP) is a kind of progenitor cells that can develop into alveolar cells, and find six essential genes that determine the cell fate, including AGER, RPL10, RPL9, RPS18, RPS27, and SFTPB. Conclusion:We provide a transcription map of human lung cells, especially the in-depth study on the development of epithelial cell subtypes, which will help us to study lung cell biology and lung diseases.

SUBMITTER: Huang Y

PROVIDER: S-EPMC7573224 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Reconstructing the Developmental Trajectories of Multiple Subtypes in Pulmonary Parenchymal Epithelial Cells by Single-Cell RNA-seq.

Huang Yiwei Y Zheng Yuansheng Y Yin Jiacheng J Lu Tao T Li Ming M Liang Jiaqi J Hu Zhengyang Z Bi Guoshu G Zhan Cheng C Xue Liang L Jiang Wei W Wang Qun Q

Frontiers in genetics 20201006

<h4>Background</h4>Some lung diseases are cell type-specific. It is essential to study the cellular anatomy of the normal human lung for exploring the cellular origin of lung disease and the cell development trajectory.<h4>Methods</h4>We used the Seurat R package for data quality control. The principal component analysis (PCA) was used for linear dimensionality reduction. UMAP and tSNE were used for dimensionality reduction. Muonocle2 was used to extract lung epithelial cells to analyze the subt ...[more]

PMID: 33133163

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Project description:BackgroundBreast invasive carcinoma (BRCA) is not a single disease as each subtype has a distinct morphology structure. Although several computational methods have been proposed to conduct breast cancer subtype identification, the specific interaction mechanisms of genes involved in the subtypes are still incomplete. To identify and explore the corresponding interaction mechanisms of genes for each subtype of breast cancer can impose an important impact on the personalized treatment for different patients.MethodsWe integrate the biological importance of genes from the gene regulatory networks to the differential expression analysis and then obtain the weighted differentially expressed genes (weighted DEGs). A gene with a high weight means it regulates more target genes and thus holds more biological importance. Besides, we constructed gene coexpression networks for control and experiment groups, and the significantly differentially interacting structures encouraged us to design the corresponding Gene Ontology (GO) enrichment based on gene coexpression networks (GOEGCN). The GOEGCN considers the two-side distinction analysis between gene coexpression networks for control and experiment groups. The method allows us to study how the modulated coexpressed gene couples impact biological functions at a GO level.ResultsWe modeled the binary classification with weighted DEGs for each subtype. The binary classifier could make a good prediction for an unseen sample, and the experimental results validated the effectiveness of our proposed approaches. The novel enriched GO terms based on GOEGCN for control and experiment groups of each subtype explain the specific biological function changes according to the two-side distinction of coexpression network structures to some extent.ConclusionThe weighted DEGs contain biological importance derived from the gene regulatory network. Based on the weighted DEGs, five binary classifiers were learned and showed good performance concerning the "Sensitivity," "Specificity," "Accuracy," "F1," and "AUC" metrics. The GOEGCN with weighted DEGs for control and experiment groups presented a novel GO enrichment analysis results and the novel enriched GO terms would further unveil the changes of specific biological functions among all the BRCA subtypes to some extent. The R code in this research is available at https://github.com/yxchspring/GOEGCN_BRCA_Subtypes.

Dataset Information

Reconstructing the Developmental Trajectories of Multiple Subtypes in Pulmonary Parenchymal Epithelial Cells by Single-Cell RNA-seq.

Publications

Reconstructing the Developmental Trajectories of Multiple Subtypes in Pulmonary Parenchymal Epithelial Cells by Single-Cell RNA-seq.

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