Background:Although primary membranous nephropathy (pMN) associated with podocyte autoantibodies (POS) is becoming well-known, the molecular characteristics of the specific type of pMN that is negative for podocyte autoantibodies (NEG) is still unclear.
Methods:We performed single-cell transcriptome sequencing and single-cell B cell receptor sequencing on circulating CD19+ cells and kidney cells of a NEG paediatric patient with pMN. The single-cell datasets of POS patients and healthy control individuals were included for integrative analysis.
Results:The gene expression characteristics and clonal expansion of naïve and memory B cells in the NEG patient changed significantly. We found that a group of CD38+ naïve B cells expanded in the NEG patient, which had the functional characteristics of cell activation. In addition, the conversion between immunoglobulin M (IgM)/IgD and IgG1 in the NEG patient was increased. Parietal epithelial cells (PECs) and podocytes shared similar signature genes (WT1, CLIC5), and new candidate marker genes for PECs, such as NID2, CAV1 and THY1, might contribute to the definition of cell subsets. PECs might have undergone significant changes in the disease, mainly manifested by changes in the expression of CCN2, PLAAT4 and SEPTIN2. The scores of gene sets related to extracellular matrix, cell adhesion and calcium channel in podocytes of the NEG patient was significantly increased. The gene expression of sodium transporter in a group of proximal tubule cells in the disease was significantly increased, especially SLC5A12, which might be related to the oedema of patients.
Conclusions:Our research demonstrated the cell type-specific molecular features in the circulation and kidney of the NEG pMN patient.
Keywords:B cells; podocytes; primary membranous nephropathy; scBCR-seq; scRNA-seq.
Molecular characteristics of circulating B cells and kidney cells at the single-cell level in special types of primary membranous nephropathy
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
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细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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