您好!欢迎访问洛阳富道生物科技有限公司官方网站!
富道生物耗材
专注高端生物耗材
助力全球生命科学发展
咨询电话
400-160-1996
您现在的位置: 首页 » 新闻动态 » 行业新闻 » 正文
跨多器官系统的细胞类型特异性促纤维化评分预测癌症预后
发表日期:2022-01-07

Cell-Type-Specific Profibrotic Scores across Multi-Organ Systems Predict Cancer Prognosis

跨多器官系统的细胞类型特异性促纤维化评分预测癌症预后


Fibrosis is a major player and contributor in the tumor microenvironment. Profibrotic changes precede the early development and establishment of a variety of human diseases, such as fibrosis and cancer. Being able to measure such early signals at the single cell level is critically useful for identifying new mechanisms and potential drug targets for a wide range of diseases. This study was designed to computationally identify profibrotic cell populations using single-cell transcriptomic data and to identify gene signatures that could predict cancer prognosis.

纤维化是肿瘤微环境的主要参与者和贡献者。纤维化变化早于各种人类疾病的早期发展和建立,例如纤维化和癌症。能够在单细胞水平上测量这些早期信号对于确定各种疾病的新机制和潜在药物靶标至关重要。本研究旨在使用单细胞转录组数据计算识别促纤维化细胞群,并识别可预测癌症预后的基因特征。

5L高效摇瓶

5L高效摇瓶


Although previous studies using single-cell transcriptomics have identified some cell types and molecular pathways of pulmonary fibrosis in particular [34,49], an integrative study emphasizing common characteristics across diverse organ systems to identify early profibrotic changes at a comprehensive cell-type level are still lacking. In this study, we systematically performed single-cell single-pathway enrichment analysis and provided a single-cell landscape of profibrotic changes across multiple organ systems. In addition to fibroblast cells, we identified six previously under-recognized cell types involved in this process. Our profibrotic score derived from multiple representative organs is useful for predicting cancer prognosis.

Taking advantage of these granular transcriptomic sequencing data, we revealed a greater degree of transcriptomic heterogeneity at the cell-type level in response to viral infections. Early profibrotic changes are observed in some cell populations within a certain cell type in particular. Intriguingly, the PCT-S3 cells were split into two clusters [23]. Cluster 2 polarized into a proinflammatory phenotype, which is CFH-positive with fibrinogens (FGB, FGA) expressed. As a comparison, we report that 95.5% of the Cluster 1 in the PCT-S3 cell of the kidney shows a profibrotic phenotype, that is, it has actively expressed ECM genes. Unexpectedly, part of the NKT cell exhibits highly expressed ECM genes, which seems controversial in relation to prior research in its protective roles against fibrosis [50]. However, suppression of its antifibrogenic effects has also been demonstrated in mouse liver fibrosis [51]. Molecular pathway analysis and co-active gene set analysis further strengthen the functional relevance of the profibrotic cellular phenotype at the cell-type level to diverse human diseases, like infections, fibrosis, or even human cancer. Independent validations confirm the ability of our cell-type-specific gene signatures to capture early profibrotic changes using both human and mouse scRNA-seq datasets. Furthermore, the prognosis analysis using bulk tumor samples demonstrates the clinical relevance of our cell-type-specific differential gene signatures. Understanding the molecular mechanisms in support of the profibrotic phenotype may yield novel therapeutic targets for the early prevention of diverse human diseases, including cancer.

In addition to these notable findings, some limitations warrant discussion. First, we computationally identified profibrotic cell populations as those actively expressing ECM pathway genes. The completeness of this functional pathway may affect the performance of the identification process. Second, the high-level sparsity (dropout rates) and the large-number cells in single-cell datasets hinder our interpretations of the activities of individual molecular pathways at the single-cell level [52]. The development of more sophisticated imputing algorithms and the improvement of single-cell sequencing coverage will surely enhance the characterization of those profibrotic changes preceding a diversity of human diseases. Third, single-cell annotation is an active, but still underdeveloped, area of research. Therefore, potential biases introduced in this annotation process may affect the accuracy of the relative ranking procedure required by the adapted single-cell single-pathway enrichment analysis [27]. A workaround we applied in the pipeline is to generate relative ranks within each cell cluster to account for the similarities and biological dependence among different cell types.

Together, our results provide valuable insights into understanding the common mechanisms leading to the development of a variety of human diseases, like fibrosis or even human cancers, across multiple organ systems. This high-resolution landscape identifies novel profibrotic cell types, molecular pathways, and co-active gene sets that characterize the early profibrotic changes in response to severe viral infections. Future directions investigating and monitoring our cell-type-specific differential gene signatures in human single-cell time-series datasets should provide additional insights into the fundamental mechanisms that result in the development and establishment of fibrosis and human cancers as well as their dependent microenvironment.

细胞摇瓶

三角细胞摇瓶

尽管以前使用单细胞转录组学的研究已经确定了肺纤维化的一些细胞类型和分子途径,特别是 [ 34 , 49 ],但一项综合研究强调了不同器官系统的共同特征,以在综合细胞类型水平上识别早期促纤维化变化。仍然缺乏。在这项研究中,我们系统地进行了单细胞单通路富集分析,并提供了跨多个器官系统的促纤维化变化的单细胞景观。除了成纤维细胞外,我们还确定了六种先前未被充分认识的细胞类型,这些细胞类型参与了这一过程。我们来自多个代表性器官的促纤维化评分可用于预测癌症预后。

利用这些细粒度的转录组测序数据,我们揭示了在细胞类型水平上响应病毒感染的更大程度的转录组异质性。特别是在某种细胞类型内的一些细胞群中观察到早期的促纤维化变化。有趣的是,PCT-S3 细胞分裂成两个簇 [ 23 ]。簇 2 极化为促炎表型,其 CFH 呈纤维蛋白原阳性(FGBFGA) 表达。作为比较,我们报告肾脏 PCT-S3 细胞中 95.5% 的簇 1 显示出促纤维化表型,即它具有积极表达的 ECM 基因。出乎意料的是,部分 NKT 细胞表现出高度表达的 ECM 基因,这与先前的研究在其对纤维化的保护作用方面似乎存在争议 [ 50 ]。然而,抑制其抗纤维化作用也已在小鼠肝纤维化中得到证实 [ 51]。分子通路分析和共激活基因集分析进一步加强了细胞类型水平上促纤维化细胞表型与多种人类疾病(如感染、纤维化甚至人类癌症)的功能相关性。独立验证证实了我们的细胞类型特异性基因特征能够使用人类和小鼠 scRNA-seq 数据集捕获早期促纤维化变化。此外,使用大块肿瘤样本的预后分析证明了我们的细胞类型特异性差异基因特征的临床相关性。了解支持促纤维化表型的分子机制可能会产生新的治疗靶点,用于早期预防包括癌症在内的多种人类疾病。

10层细胞工厂

10层细胞工厂

除了这些值得注意的发现之外,还需要讨论一些局限性。首先,我们通过计算将促纤维化细胞群确定为积极表达 ECM 通路基因的细胞群。该功能通路的完整性可能会影响识别过程的性能。其次,单细胞数据集中的高水平稀疏性(辍学率)和大量细胞阻碍了我们在单细胞水平上对单个分子途径活动的解释 [ 52]。更复杂的估算算法的发展和单细胞测序覆盖率的提高肯定会增强对人类疾病多样性之前的那些促纤维化变化的表征。第三,单细胞注释是一个活跃但仍不发达的研究领域。因此,在此注释过程中引入的潜在偏差可能会影响适应的单细胞单通路富集分析所需的相对排序过程的准确性 [ 27 ]。我们在管道中应用的一种解决方法是在每个细胞簇内生成相对等级,以说明不同细胞类型之间的相似性和生物依赖性。

总之,我们的研究结果为理解导致跨多个器官系统发展的各种人类疾病(如纤维化甚至人类癌症)的常见机制提供了宝贵的见解。这种高分辨率的景观识别了新的促纤维化细胞类型、分子途径和共同活性基因组,这些基因组表征了响应严重病毒感染的早期促纤维化变化。未来研究和监测人类单细胞时间序列数据集中细胞类型特异性差异基因特征的方向应该为导致纤维化和人类癌症及其相关微环境的发展和建立的基本机制提供更多见解。


关键词:profibrotic cellular phenotype; single-cell extracellular matrix pathway activity; fibrosis; pan-cancer; cancer prognosis 促纤维化细胞表型单细胞胞外基质通路活性纤维化; 泛癌癌症预后

来源:MDPI https://www.mdpi.com/2072-6694/13/23/6024/htm



推荐产品
网站首页 细胞培养耗材 细胞工厂 细胞摇瓶 SUS一次性耗材 关于富道 联系我们
首页 电话 联系