Mapping the cellular landscape of Atlantic salmon head kidney by single cell and single nucleus transcriptomics
Mapping the cellular landscape of Atlantic salmon head kidney by single cell and single nucleus transcriptomics
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DOI：
10.1016/j.fsi.2024.109357
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发表时间：
2024-01-18
影响因子：
4.7
通讯作者：
Fosse，Johanna H.
中科院分区：
文献类型：
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作者：
Andresen，Adriana M. S.;Taylor，Richard S.;Fosse，Johanna H.
单细胞转录组学是目前全球基因表达谱分析的金标准，不仅在哺乳动物和模式物种中，而且在非模式鱼类物种中。这是一个快速扩展的领域，创造了对组织异质性和单个细胞独特功能的更深入理解，使人们有可能在高分辨率水平上探索免疫学和基因表达的复杂性。在这项研究中，我们比较了两种单细胞转录组学方法来研究健康养殖大西洋鲑鱼（萨尔莫salar）头肾内的细胞异质性。我们比较了通过单细胞RNA测序（scRNA测序）检测的14，149个细胞转录组与通过单核RNA测序（snRNA测序）捕获的18，067个细胞核转录组。这两种方法检测到8个主要的细胞群体是共同的：粒细胞，造血干细胞，红细胞，单核吞噬细胞，血小板，B细胞，NK样细胞，和T细胞。在snRNA-seq数据集中检测到四种另外的细胞类型，即内皮细胞、上皮细胞、肾间细胞和间充质细胞，但似乎在提交用于scRNA-seq文库生成的单细胞悬浮液的制备期间丢失。我们确定了额外的异质性和亚群内的B细胞，T细胞和内皮细胞，并揭示了造血干细胞分化成粒细胞和单核吞噬细胞群体的发展轨迹。B细胞亚型的基因表达谱揭示了不同的IgM和IgT-偏斜的静息B细胞谱系，并提供了对B细胞淋巴细胞生成调节的见解。分析揭示了11个T细胞亚群，显示鲑鱼头肾中的T细胞异质性水平与哺乳动物中观察到的相当，包括cd 4/cd 8阴性T细胞的不同亚群，如astcrγ阳性、祖细胞样和细胞毒性细胞。尽管snRNA-seq和scRNA-seq都可用于解析大西洋鲑鱼头肾中的细胞类型特异性表达，但snRNA-seq管道在鉴定几种细胞类型和亚群方面总体上更稳健。scRNA-seq显示了更高水平的核糖体和线粒体基因，snRNA-seq捕获了更多的转录因子基因。然而，只有scRNA-seq生成的数据可用于骨髓谱系内的细胞轨迹推断。总之，这项研究系统地概述了scRNA-seq和snRNA-seq在大西洋鲑鱼中的相对优点，增强了对硬骨鱼免疫细胞谱系的理解，并提供了一个全面的标记物列表，用于识别头肾中具有显著免疫相关性的主要细胞群体。
Single-cell transcriptomics is the current gold standard for global gene expression profiling, not only in mammals and model species, but also in non-model fish species. This is a rapidly expanding field, creating a deeper understanding of tissue heterogeneity and the distinct functions of individual cells, making it possible to explore the complexities of immunology and gene expression on a highly resolved level. In this study, we compared two single cell transcriptomic approaches to investigate cellular heterogeneity within the head kidney of healthy farmed Atlantic salmon (Salmo salar). We compared 14,149 cell transcriptomes assayed by single cell RNA-seq (scRNA-seq) with 18,067 nuclei transcriptomes captured by single nucleus RNA-Seq (snRNA-seq). Both approaches detected eight major cell populations in common: granulocytes, heamatopoietic stem cells, erythrocytes, mononuclear phagocytes, thrombocytes, B cells, NK-like cells, and T cells. Four additional cell types, endothelial, epithelial, interrenal, and mesenchymal cells, were detected in the snRNA-seq dataset, but appeared to be lost during preparation of the single cell suspension submitted for scRNA-seq library generation. We identified additional heterogeneity and subpopulations within the B cells, T cells, and endothelial cells, and revealed developmental trajectories of heamatopoietic stem cells into differentiated granulocyte and mononuclear phagocyte populations. Gene expression profiles of B cell subtypes revealed distinct IgM and IgT-skewed resting B cell lineages and provided insights into the regulation of B cell lymphopoiesis. The analysis revealed eleven T cell sub-populations, displaying a level of T cell heterogeneity in salmon head kidney comparable to that observed in mammals, including distinct subsets ofcd4/cd8-negative T cells, such astcrγpositive, progenitor-like, and cytotoxic cells. Although snRNA-seq and scRNA-seq were both useful to resolve cell type-specific expression in the Atlantic salmon head kidney, the snRNA-seq pipeline was overall more robust in identifying several cell types and subpopulations. While scRNA-seq displayed higher levels of ribosomal and mitochondrial genes, snRNA-seq captured more transcription factor genes. However, only scRNA-seq-generated data was useful for cell trajectory inference within the myeloid lineage. In conclusion, this study systematically outlines the relative merits of scRNA-seq and snRNA-seq in Atlantic salmon, enhances understanding of teleost immune cell lineages, and provides a comprehensive list of markers for identifying major cell populations in the head kidney with significant immune relevance.