Moos Philip J, Carey Allison F, Joseph Jacklyn, Kialo Stephanie, Norrie Joe, Moyarelce Julie M, Amof Anthony, Nogua Hans, Lim Albebson L, Barrows Louis R
Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, Utah 84112 USA.
Department of Pathology, University of Utah, Salt Lake City, Utah 84112 USA.
bioRxiv. 2024 Jun 26:2024.05.28.596301. doi: 10.1101/2024.05.28.596301.
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 known to 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 cell's 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 and the other component cells, such as Collagen, FN1 and Laminin,. 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.
我们成功采用单细胞RNA测序(scRNA-seq)方法来描述结核患者肉芽肿性淋巴结中的细胞及其通讯网络。在对来自个体患者样本的细胞进行映射时,根据转录组相似性进行聚类,我们一致鉴定出几种已知可表征人类和非人灵长类肉芽肿的细胞类型。无论结核分枝杆菌负荷高低,我们发现T细胞簇是最丰富的细胞簇之一。在这个表达CD3的簇中,许多表达T细胞标志物的细胞是可清晰量化的。其他一致检测到但在个体患者样本中丰度差异极大的细胞簇是B细胞、浆细胞、巨噬细胞/树突状细胞和NK细胞簇。当我们整合目前23名患者的所有scRNA-seq数据时(以便增强对细胞数量较少的患者样本中细胞簇识别的能力),我们区分出T细胞、巨噬细胞、树突状细胞和浆细胞亚簇,每个亚簇都有独特的信号传导活性。这些亚簇的大小在个体患者中也有极大差异。在比较细针穿刺抽吸物(FNA)组成时,我们注意到T细胞群体和巨噬细胞/树突状细胞群体与NK细胞群体呈负相关的趋势。此外,我们还发现,为定量人类细胞mRNA而设计的scRNA-seq流程也能检测结核分枝杆菌RNA转录本,并将它们与其宿主细胞的转录组相关联,从而识别出单个被感染的细胞。我们推测,检测到的细菌转录本读数数量可衡量结核分枝杆菌负荷,被结核分枝杆菌感染的细胞数量也可如此。被感染细胞的数量在患者样本中的丰度也有极大差异。CellChat分析确定了构成各种肉芽肿的细胞之间的主要信号传导途径,包括基质或内皮细胞与其他组成细胞之间的许多相互作用,如胶原蛋白、纤连蛋白1和层粘连蛋白。此外,其他更具选择性的通讯途径,包括巨噬细胞移动抑制因子(MIF)、主要组织相容性复合体-1(MHC-1)、主要组织相容性复合体-2(MHC-2)、淀粉样前体蛋白(APP)、CD22、CD45等,被确定为由单个免疫细胞成分发出或接收。