Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis.
Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis.
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DOI：
10.3389/fimmu.2021.679521
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发表时间：
2021
影响因子：
7.3
通讯作者：
Emamaullee J
中科院分区：
文献类型：
--
作者：
Rocque B;Barbetta A;Singh P;Goldbeck C;Helou DG;Loh YE;Ung N;Lee J;Akbari O;Emamaullee J
肝脏在维持免疫稳态的能力和实体器官移植后免疫耐受的潜力方面都是独一无二的。单细胞 RNA 测序 (scRNA seq) 是一种生成高维转录组数据以了解细胞表型的强大方法。然而，当 scRNA 数据由不同的群体、不同的数据模型、不同的标准以及以不同方式处理的样本产生时，从聚合数据中得出有意义的结论可能具有挑战性。本研究的目标是建立一种方法来结合“人类肝脏”scRNA seq 数据集，方法是 1) 表征研究之间的异质性，2) 使用元图谱来定义健康人类肝脏中免疫细胞亚群的主要表型。对从总共 17 名患者和约 32,000 个细胞获得的肝脏样本生成的公开 scRNA seq 数据进行了分析。从每个数据集中提取肝脏特异性免疫细胞 (CD45+)，并使用降维 (UMAP)、差异基因表达和独创性途径分析检查免疫细胞亚群（骨髓细胞、NK 和 T 细胞、浆细胞和 B 细胞）。所有数据集都是共同聚类的，但研究之间的细胞比例不同。基因表达相关性证明了所有研究的相似性，并且数据集之间不同的典型途径与细胞应激和氧化磷酸化有关，而不是与免疫相关功能有关。接下来，通过数据集成生成元图谱，并与 PBMC 数据进行比较，以确定每个肝脏免疫亚群的基因特征。该分析定义了肝脏免疫稳态的关键特征，即跨免疫途径的表达减少和与细胞死亡有关的途径的增强。这种对 scRNA seq 数据进行荟萃分析的方法为广泛定义人类肝脏免疫稳态的特征提供了一种新方法。该人类肝脏免疫元图谱中描述的特定途径和细胞表型为进一步研究肝脏内免疫介导的疾病过程提供了关键参考点。
The liver is unique in both its ability to maintain immune homeostasis and in its potential for immune tolerance following solid organ transplantation. Single-cell RNA sequencing (scRNA seq) is a powerful approach to generate highly dimensional transcriptome data to understand cellular phenotypes. However, when scRNA data is produced by different groups, with different data models, different standards, and samples processed in different ways, it can be challenging to draw meaningful conclusions from the aggregated data. The goal of this study was to establish a method to combine ‘human liver’ scRNA seq datasets by 1) characterizing the heterogeneity between studies and 2) using the meta-atlas to define the dominant phenotypes across immune cell subpopulations in healthy human liver. Publicly available scRNA seq data generated from liver samples obtained from a combined total of 17 patients and ~32,000 cells were analyzed. Liver-specific immune cells (CD45+) were extracted from each dataset, and immune cell subpopulations (myeloid cells, NK and T cells, plasma cells, and B cells) were examined using dimensionality reduction (UMAP), differential gene expression, and ingenuity pathway analysis. All datasets co-clustered, but cell proportions differed between studies. Gene expression correlation demonstrated similarity across all studies, and canonical pathways that differed between datasets were related to cell stress and oxidative phosphorylation rather than immune-related function. Next, a meta-atlas was generated via data integration and compared against PBMC data to define gene signatures for each hepatic immune subpopulation. This analysis defined key features of hepatic immune homeostasis, with decreased expression across immunologic pathways and enhancement of pathways involved with cell death. This method for meta-analysis of scRNA seq data provides a novel approach to broadly define the features of human liver immune homeostasis. Specific pathways and cellular phenotypes described in this human liver immune meta-atlas provide a critical reference point for further study of immune mediated disease processes within the liver.
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影响因子：
64.8
作者：
通讯作者：
--
影响因子：
16.6
作者：
MacParland SA;Liu JC;Ma XZ;Innes BT;Bartczak AM;Gage BK;Manuel J;Khuu N;Echeverri J;Linares I;Gupta R;Cheng ML;Liu LY;Camat D;Chung SW;Seliga RK;Shao Z;Lee E;Ogawa S;Ogawa M;Wilson MD;Fish JE;Selzner M;Ghanekar A;Grant D;Greig P;Sapisochin G;Selzner N;Winegarden N;Adeyi O;Keller G;Bader GD;McGilvray ID
通讯作者：
McGilvray ID
影响因子：
4.6
作者：
Cahill KM;Huo Z;Tseng GC;Logan RW;Seney ML
通讯作者：
Seney ML
影响因子：
48
作者：
Korsunsky, Ilya;Millard, Nghia;Raychaudhuri, Soumya
通讯作者：
Raychaudhuri, Soumya
影响因子：
9.9
作者：
Barr, T. L.;Conley, Y.;Matarin, M.
通讯作者：
Matarin, M.