Pham Hoang Nam, Pham Linh, Sato Keisaku
Department of Life Sciences, University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Vietnam.
Department of Science and Mathematics, Texas A&M University-Central Texas, Killeen, TX, United States.
Front Med (Lausanne). 2024 May 15;11:1327973. doi: 10.3389/fmed.2024.1327973. eCollection 2024.
Primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) are characterized by ductular reaction, hepatic inflammation, and liver fibrosis. Hepatic cells are heterogeneous, and functional roles of different hepatic cell phenotypes are still not defined in the pathophysiology of cholangiopathies. Cell deconvolution analysis estimates cell fractions of different cell phenotypes in bulk transcriptome data, and CIBERSORTx is a powerful deconvolution method to estimate cell composition in microarray data. CIBERSORTx performs estimation based on the reference file, which is referred to as signature matrix, and allows users to create custom signature matrix to identify specific phenotypes. In the current study, we created two custom signature matrices using two single cell RNA sequencing data of hepatic cells and performed deconvolution for bulk microarray data of liver tissues including PSC and PBC patients.
Custom signature matrix files were created using single-cell RNA sequencing data downloaded from GSE185477 and GSE115469. Custom signature matrices were validated for their deconvolution performance using validation data sets. Cell composition of each hepatic cell phenotype in the liver, which was identified in custom signature matrices, was calculated by CIBERSORTx and bulk RNA sequencing data of GSE159676. Deconvolution results were validated by analyzing marker expression for the cell phenotype in GSE159676 data.
CIBERSORTx and custom signature matrices showed comprehensive performance in estimation of population of various hepatic cell phenotypes. We identified increased population of large cholangiocytes in PSC and PBC livers, which is in agreement with previous studies referred to as ductular reaction, supporting the effectiveness and reliability of deconvolution analysis in this study. Interestingly, we identified decreased population of small cholangiocytes, periportal hepatocytes, and interzonal hepatocytes in PSC and PBC liver tissues compared to healthy livers.
Although further studies are required to elucidate the roles of these hepatic cell phenotypes in cholestatic liver injury, our approach provides important implications that cell functions may differ depending on phenotypes, even in the same cell type during liver injury. Deconvolution analysis using CIBERSORTx could provide a novel approach for studies of specific hepatic cell phenotypes in liver diseases.
原发性硬化性胆管炎(PSC)和原发性胆汁性胆管炎(PBC)的特征为小胆管反应、肝脏炎症和肝纤维化。肝细胞具有异质性,不同肝细胞表型在胆管疾病病理生理学中的功能作用仍未明确。细胞反卷积分析可估计批量转录组数据中不同细胞表型的细胞分数,而CIBERSORTx是一种强大的反卷积方法,用于估计微阵列数据中的细胞组成。CIBERSORTx基于参考文件(称为特征矩阵)进行估计,并允许用户创建自定义特征矩阵以识别特定表型。在本研究中,我们使用两个肝细胞单细胞RNA测序数据创建了两个自定义特征矩阵,并对包括PSC和PBC患者在内的肝脏组织批量微阵列数据进行了反卷积分析。
使用从GSE185477和GSE115469下载的单细胞RNA测序数据创建自定义特征矩阵文件。使用验证数据集对自定义特征矩阵的反卷积性能进行验证。通过CIBERSORTx和GSE159676的批量RNA测序数据计算在自定义特征矩阵中识别出的肝脏中每种肝细胞表型的细胞组成。通过分析GSE159676数据中细胞表型的标志物表达来验证反卷积结果。
CIBERSORTx和自定义特征矩阵在估计各种肝细胞表型群体方面表现出全面的性能。我们在PSC和PBC肝脏中发现大胆管细胞群体增加,这与先前称为小胆管反应的研究一致,支持了本研究中反卷积分析的有效性和可靠性。有趣的是,与健康肝脏相比,我们在PSC和PBC肝脏组织中发现胆小管细胞、门周肝细胞和小叶间肝细胞群体减少。
尽管需要进一步研究来阐明这些肝细胞表型在胆汁淤积性肝损伤中的作用,但我们的方法提供了重要的启示,即即使在肝损伤期间同一细胞类型中,细胞功能也可能因表型而异。使用CIBERSORTx进行反卷积分析可为肝脏疾病中特定肝细胞表型的研究提供一种新方法。