外周结核相关肉芽肿性淋巴结炎的单细胞分析

Louis Barrows, Philip J Moos, Allison F Carey, Jacklyn Joseph, Stephanie Kialo, Joe Norrie, Julie M Moyarelce, Anthony Amof, Hans Nogua, Albebson L Lim
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摘要

我们成功地采用了单细胞 RNA 测序(scRNA-seq)方法来描述肺结核患者肉芽肿淋巴结的细胞和通讯网络特征。根据转录组的相似性对单个患者样本的细胞进行聚类后,我们一致确定了人类和非人灵长类肉芽肿的几种细胞类型。无论Mtb负担是高还是低,我们都发现T细胞群是最丰富的细胞群之一。在这个表达 CD3 的细胞群中,许多表达 T 细胞标记的细胞都可以清晰地量化。B 细胞、浆细胞、巨噬细胞/树突细胞和 NK 细胞群等其他细胞群的检测结果一致,但在患者样本中的丰度差异很大。当我们把目前 23 位患者的所有 scRNA-seq 数据合并在一起时(为了增加细胞较少的患者样本的细胞群识别能力),我们区分出了 T 细胞、巨噬细胞、树突状细胞和浆细胞亚群,每个亚群都有不同的信号活性。这些亚群的大小在不同患者之间也有很大差异。在比较 FNA 的组成时,我们注意到 T 细胞群和巨噬细胞/树突细胞群与 NK 细胞群呈负相关的趋势。此外,我们还发现,专为量化人体细胞 mRNA 而设计的 scRNA-seq 管道也能检测到 Mtb RNA 转录本,并将它们与宿主细胞转录组联系起来,从而识别出受感染的单个细胞。我们推测,检测到的细菌转录本读数数量和受 Mtb 感染的细胞数量一样,都是衡量 Mtb 负担的指标。患者样本中受感染细胞的数量也有很大差异。细胞聊天分析确定了组成各种肉芽肿的细胞之间的主要信号通路,包括基质或内皮细胞(如胶原蛋白、FN1 和层粘连蛋白)与其他组成细胞之间的许多相互作用。此外,其他更具选择性的通信途径,包括 MIF、MHC-1、MHC-2、APP、CD 22、CD45 等,也被确定为由单个免疫细胞成分产生或接收。
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Single Cell Analysis of Peripheral TB-Associated Granulomatous Lymphadenitis
We successfully employed a single cell RNA sequencing (scRNA-seq) approach to describe the cells and the communication networks characterizing granulomatous lymph nodes of TB patients. When mapping cells from individual patient samples, clustered based on their transcriptome similarities, we uniformly identify several cell types that characterize human and non-human primate granulomas. Whether high or low Mtb burden, we find the T cell cluster to be one of the most abundant. Many cells expressing T cell markers are clearly quantifiable within this CD3 expressing cluster. Other cell clusters that are uniformly detected, but that vary dramatically in abundance amongst the individual patient samples, are the B cell, plasma cell and macrophage/dendrocyte and NK cell clusters. When we combine all our scRNA-seq data from our current 23 patients (in order to add power to cell cluster identification in patient samples with fewer cells), we distinguish T, macrophage, dendrocyte and plasma cell subclusters, each with distinct signaling activities. The sizes of these subclusters also varies dramatically amongst the individual patients. In comparing FNA composition we noted trends in which T cell populations and macrophage/dendrocyte populations were negatively correlated with NK cell populations. In addition, we also discovered that the scRNA-seq pipeline, designed for quantification of human cell mRNA, also detects Mtb RNA transcripts and associates them with their host cells transcriptome, thus identifying individual infected cells. We hypothesize that the number of detected bacterial transcript reads provides a measure of Mtb burden, as does the number of Mtb-infected cells. The number of infected cells also varies dramatically in abundance amongst the patient samples. CellChat analysis identified predominating signaling pathways amongst the cells comprising the various granulomas, including many interactions between stromal or endothelial cells, such as Collagen, FN1 and Laminin, and the other component cells. In addition, other more selective communications pathways, including MIF, MHC-1, MHC-2, APP, CD 22, CD45, and others, are identified as originating or being received by individual immune cell components.
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