Creation of a Single Cell RNASeq Meta-Atlas to Define Human Liver Immune Homeostasis

• Published in: Frontiers in immunology 2021-07, Vol.12, p.679521-679521
• Main Authors: Rocque, Brittany, Barbetta, Arianna, Singh, Pranay, Goldbeck, Cameron, Helou, Doumet Georges, Loh, Yong-Hwee Eddie, Ung, Nolan, Lee, Jerry, Akbari, Omid, Emamaullee, Juliet
• Format: Article
• Language: English
• Subjects: Biomarkers; Computational Biology - methods; Gene Expression Profiling - methods; Gene Expression Regulation; High-Throughput Nucleotide Sequencing; Homeostasis - genetics; Homeostasis - immunology; Humans; Immunology; immunotolerance; liver; Liver - immunology; Liver - metabolism; liver homeostasis; RNA-seq; scRNA-seq; Signal Transduction; single cell; Single-Cell Analysis - methods; Transcriptome
• ISSN: 1664-3224; 1664-3224
• DOI: 10.3389/fimmu.2021.679521

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 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.