Endothelial Cells Expressing Endothelial and Mesenchymal Cell Gene Products in Lung Tissue From Patients With Systemic Sclerosis-Associated Interstitial Lung Disease

ObjectivesMyofibroblasts are key effector cells in the extracellular matrix remodelling of systemic sclerosis-associated interstitial lung disease (SSc-ILD); however, the diversity of fibroblast populations present in the healthy and SSc-ILD lung is unknown and has prevented the specific study of the myofibroblast transcriptome. We sought to identify and define the transcriptomes of myofibroblasts and other mesenchymal cell populations in human healthy and SSc-ILD lungs to understand how alterations in fibroblast phenotypes lead to SSc-ILD fibrosis.MethodsWe performed droplet-based, single-cell RNA-sequencing with integrated canonical correlation analysis of 13 explanted lung tissue specimens (56 196 cells) from four healthy control and four patients with SSc-ILD, with findings confirmed by cellular indexing of transcriptomes and epitopes by sequencing in additional samples.ResultsExamination of gene expression in mesenchymal cells identified two major, SPINT2hi and MFAP5hi, and one minor, WIF1hi, fibroblast populations in the healthy control lung. Combined analysis of control and SSc-ILD mesenchymal cells identified SPINT2hi, MFAP5hi, few WIF1hi fibroblasts and a new large myofibroblast population with evidence of actively proliferating myofibroblasts. We compared differential gene expression between all SSc-ILD and control mesenchymal cell populations, as well as among the fibroblast subpopulations, showing that myofibroblasts undergo the greatest phenotypic changes in SSc-ILD and strongly upregulate expression of collagens and other profibrotic genes.ConclusionsOur results demonstrate previously unrecognised fibroblast heterogeneity in SSc-ILD and healthy lungs, and define multimodal transcriptome-phenotypes associated with these populations. Our data indicate that myofibroblast differentiation and proliferation are key pathological mechanisms driving fibrosis in SSc-ILD.

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Prediction of a Competing Endogenous RNA Co-expression Network by Comprehensive Methods in Systemic Sclerosis-Related Interstitial Lung Disease

Frontiers in Genetics ◽

10.3389/fgene.2021.633059 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yue-Mei Yan ◽

Ji-Na Zheng ◽

Li-Wei Wu ◽

Qian-Wen Rao ◽

Qiao-Rong Yang ◽

...

Keyword(s):

Systemic Sclerosis ◽

Interstitial Lung Disease ◽

Lung Disease ◽

Lung Tissue ◽

Gene Expression Omnibus ◽

Normal Lung ◽

Competing Endogenous Rna ◽

Cerna Network ◽

Online Databases ◽

Accurate Treatment

Systemic sclerosis (SSc) is an immune-mediated connective tissue disease characterized by fibrosis of multi-organs, and SSc-related interstitial lung disease (SSc-ILD) is a leading cause of morbidity and mortality. To explore molecular biological mechanisms of SSc-ILD, we constructed a competing endogenous RNA (ceRNA) network for prediction. Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database, and differential expressed mRNAs and miRNAs analysis was further conducted between normal lung tissue and SSc lung tissue. Also, the interactions of miRNA–lncRNA, miRNA–mRNA, and lncRNA–mRNA were predicted by online databases including starBase, LncBase, miRTarBase, and LncACTdb. The ceRNA network containing 11 lncRNAs, 7 miRNAs, and 20 mRNAs were constructed. Based on hub genes and miRNAs identified by weighted correlation network analysis (WGCNA) method, three core sub-networks—SNHG16, LIN01128, RP11-834C11.4(LINC02381)/hsa-let-7f-5p/IL6, LINC01128/has-miR-21-5p/PTX3, and LINC00665/hsa-miR-155-5p/PLS1—were obtained. Combined with previous studies and enrichment analyses, the lncRNA-mediated network affected LPS-induced inflammatory and immune processes, fibrosis development, and tumor microenvironment variations. The ceRNA network, especially three core sub-networks, may be served as early biomarkers and potential targets for SSc, which also provides further insights into the occurrence, progression, and accurate treatment of SSc at the molecular level.

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