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

跨多器官系統(tǒng)的細(xì)胞類型特異性促纖維化評分預(yù)測癌癥預(yù)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.

纖維化是腫瘤微環(huán)境的主要參與者和貢獻(xiàn)者。纖維化變化早于各種人類疾病的早期發(fā)展和建立，例如纖維化和癌癥。能夠在單細(xì)胞水平上測量這些早期信號對于確定各種疾病的新機(jī)制和潛在藥物靶標(biāo)至關(guān)重要。本研究旨在使用單細(xì)胞轉(zhuǎn)錄組數(shù)據(jù)計算識別促纖維化細(xì)胞群，并識別可預(yù)測癌癥預(yù)后的基因特征。

5L高效搖瓶

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.

三角細(xì)胞搖瓶

盡管以前使用單細(xì)胞轉(zhuǎn)錄組學(xué)的研究已經(jīng)確定了肺纖維化的一些細(xì)胞類型和分子途徑，特別是 [ 34 , 49 ]，但一項綜合研究強(qiáng)調(diào)了不同器官系統(tǒng)的共同特征，以在綜合細(xì)胞類型水平上識別早期促纖維化變化。仍然缺乏。在這項研究中，我們系統(tǒng)地進(jìn)行了單細(xì)胞單通路富集分析，并提供了跨多個器官系統(tǒng)的促纖維化變化的單細(xì)胞景觀。除了成纖維細(xì)胞外，我們還確定了六種先前未被充分認(rèn)識的細(xì)胞類型，這些細(xì)胞類型參與了這一過程。我們來自多個代表性器官的促纖維化評分可用于預(yù)測癌癥預(yù)后。

利用這些細(xì)粒度的轉(zhuǎn)錄組測序數(shù)據(jù)，我們揭示了在細(xì)胞類型水平上響應(yīng)病毒感染的更大程度的轉(zhuǎn)錄組異質(zhì)性。特別是在某種細(xì)胞類型內(nèi)的一些細(xì)胞群中觀察到早期的促纖維化變化。有趣的是，PCT-S3 細(xì)胞分裂成兩個簇 [ 23 ]。簇 2 極化為促炎表型，其 CFH 呈纖維蛋白原陽性（FGB，FGA） 表達(dá)。作為比較，我們報告腎臟 PCT-S3 細(xì)胞中 95.5% 的簇 1 顯示出促纖維化表型，即它具有積極表達(dá)的 ECM 基因。出乎意料的是，部分 NKT 細(xì)胞表現(xiàn)出高度表達(dá)的 ECM 基因，這與先前的研究在其對纖維化的保護(hù)作用方面似乎存在爭議 [ 50 ]。然而，抑制其抗纖維化作用也已在小鼠肝纖維化中得到證實(shí) [ 51]。分子通路分析和共激活基因集分析進(jìn)一步加強(qiáng)了細(xì)胞類型水平上促纖維化細(xì)胞表型與多種人類疾病（如感染、纖維化甚至人類癌癥）的功能相關(guān)性。獨(dú)立驗(yàn)證證實(shí)了我們的細(xì)胞類型特異性基因特征能夠使用人類和小鼠 scRNA-seq 數(shù)據(jù)集捕獲早期促纖維化變化。此外，使用大塊腫瘤樣本的預(yù)后分析證明了我們的細(xì)胞類型特異性差異基因特征的臨床相關(guān)性。了解支持促纖維化表型的分子機(jī)制可能會產(chǎn)生新的治療靶點(diǎn)，用于早期預(yù)防包括癌癥在內(nèi)的多種人類疾病。

10層細(xì)胞工廠

除了這些值得注意的發(fā)現(xiàn)之外，還需要討論一些局限性。首先，我們通過計算將促纖維化細(xì)胞群確定為積極表達(dá) ECM 通路基因的細(xì)胞群。該功能通路的完整性可能會影響識別過程的性能。其次，單細(xì)胞數(shù)據(jù)集中的高水平稀疏性（輟學(xué)率）和大量細(xì)胞阻礙了我們在單細(xì)胞水平上對單個分子途徑活動的解釋 [ 52]。更復(fù)雜的估算算法的發(fā)展和單細(xì)胞測序覆蓋率的提高肯定會增強(qiáng)對人類疾病多樣性之前的那些促纖維化變化的表征。第三，單細(xì)胞注釋是一個活躍但仍不發(fā)達(dá)的研究領(lǐng)域。因此，在此注釋過程中引入的潛在偏差可能會影響適應(yīng)的單細(xì)胞單通路富集分析所需的相對排序過程的準(zhǔn)確性 [ 27 ]。我們在管道中應(yīng)用的一種解決方法是在每個細(xì)胞簇內(nèi)生成相對等級，以說明不同細(xì)胞類型之間的相似性和生物依賴性。

總之，我們的研究結(jié)果為理解導(dǎo)致跨多個器官系統(tǒng)發(fā)展的各種人類疾病（如纖維化甚至人類癌癥）的常見機(jī)制提供了寶貴的見解。這種高分辨率的景觀識別了新的促纖維化細(xì)胞類型、分子途徑和共同活性基因組，這些基因組表征了響應(yīng)嚴(yán)重病毒感染的早期促纖維化變化。未來研究和監(jiān)測人類單細(xì)胞時間序列數(shù)據(jù)集中細(xì)胞類型特異性差異基因特征的方向應(yīng)該為導(dǎo)致纖維化和人類癌癥及其相關(guān)微環(huán)境的發(fā)展和建立的基本機(jī)制提供更多見解。

關(guān)鍵詞：profibrotic cellular phenotype; single-cell extracellular matrix pathway activity; fibrosis; pan-cancer; cancer prognosis 促纖維化細(xì)胞表型；單細(xì)胞胞外基質(zhì)通路活性；纖維化; 泛癌；癌癥預(yù)后

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