中国临床药理学与治疗学 ›› 2026, Vol. 31 ›› Issue (3): 289-299.doi: 10.12092/j.issn.1009-2501.2026.03.001
郑凯乐1,2,3(
), 付嘉钊4, 尤佳1,2,3, 吴丹5, 王学彬2,3,*(
), 王卓3
收稿日期:2025-01-07
修回日期:2025-10-14
出版日期:2026-03-26
发布日期:2026-04-03
通讯作者:
王学彬
E-mail:1604751742@qq.com;binxuewang@sjtu.edu.cn
作者简介:郑凯乐,女,硕士研究生,药师,研究方向:免疫抑制剂个体化给药。E-mail:基金资助:
Kaile ZHENG1,2,3(
), Jiazhao FU4, Jia YOU1,2,3, Dan WU5, Xuebin WANG2,3,*(
), Zhuo WANG3
Received:2025-01-07
Revised:2025-10-14
Online:2026-03-26
Published:2026-04-03
Contact:
Xuebin WANG
E-mail:1604751742@qq.com;binxuewang@sjtu.edu.cn
摘要:
目的: 揭示肾移植受者术后发生抗体介导排斥反应(antibody-mediated rejection,ABMR)的潜在机制。方法: 选取肾移植术后 6 个月以上的受者作为研究对象,利用单细胞RNA测序(single-cell RNA sequencing,scRNA-seq)技术,对受者外周血单个核细胞(peripheral blood mononuclear cell,PBMC)中的免疫细胞亚群展开高分辨率的scRNA-seq分析。采用流式细胞技术检测不同免疫细胞群中差异基因对ABMR 的影响。结果: 本研究通过单细胞测序技术分析了ABMR受者与正常对照组的差异基因表达,发现CD69、CD83、CD52、CD74和CX3CR1为主要的差异基因。在经典单核细胞中,ABMR受者的CD83和CD52基因表达量较正常组有所增加,而治疗后呈现降低趋势;在初始CD4+T细胞中,ABMR受者的CD69基因表达量较正常组显著下降;在NK细胞中,ABMR受者的CX3CR1基因表达量较正常组降低,而治疗后CX3CR1的几何平均值进一步降低,CD74的几何平均值则有增加趋势。结论: 经典单核细胞中 CD83 和 CD52 基因表达上调,与抗原提呈细胞的功能增强相关,从而促进 ABMR 的发生。初始 CD4+T 细胞中 CD69 基因表达上调,与 T 细胞的激活和增殖相关,进一步参与ABMR 的进程。相反,NK 细胞中 CX3CR1 基因表达下调,与 NK 细胞的免疫监视功能障碍相关,从而在一定程度上抑制 ABMR 的发生。
中图分类号:
郑凯乐, 付嘉钊, 尤佳, 吴丹, 王学彬, 王卓. 单细胞测序揭示肾移植受者抗体介导排斥反应的潜在机制[J]. 中国临床药理学与治疗学, 2026, 31(3): 289-299.
Kaile ZHENG, Jiazhao FU, Jia YOU, Dan WU, Xuebin WANG, Zhuo WANG. Single-cell sequencing reveals the underlying mechanism of antibody-mediated rejection in renal transplant recipients[J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2026, 31(3): 289-299.
| Cell type | Marker genes |
| T cell | CD3E, CD3D, CD3G |
| B cell | CD79B, CD19, CD79A |
| Fibroblast | COL1A1, COL1A2, COL3A1, POSTN |
| Epithelial cell | EPCAM, CDH1, KRT18, KRT8 |
| Neutrophil | Ly6g, Ncf1, Csf3r, Cd177, Sorl1 |
| Macrophage | CD14, CD68, CD163, CD209, AIF1 |
| Dendritic cell | Flt3, CD1C |
| Natural killer cell | Nkg7, Ifng, Klrd1, Klrc1, CD56 |
| Endothelial cell | VWF, CDH5, Pecam1 |
| Basophil | TPSB2, TPSAB1, TPSAB2, CPA3 |
| Plasma cell | CD27, CD38, XBP1, JCHAIN |
| Monocyte | CD14, CD300E, CD244 |
| CD8+NKT-like cell | CD3E, CD8A, CD161, KLRC1 |
| Classical monocyte | CD14, LYZ |
| Na?ve B cell | CD19, CD20, CD79A, IGLL1 |
| Na?ve CD4+T cell | CD3E, CD4, CD45RA, LEF1 |
| Na?ve CD8+T cell | CD3E, CD8A, CD45RA, LEF1 |
| Non-classical monocyte | CD14, CD16, FCGR3A |
| Plasmacytoid dendritic cell | CD123, CD303, IRF7, LY6E |
| Platelet | CD41, CD61, PF4, GPIBα |
| Pro-B cell | CD19, CD34, CD79A, IGLL1, RAG1 |
表 1
Table 1 Marking genes used to identify major cell types of immunity
| Cell type | Marker genes |
| T cell | CD3E, CD3D, CD3G |
| B cell | CD79B, CD19, CD79A |
| Fibroblast | COL1A1, COL1A2, COL3A1, POSTN |
| Epithelial cell | EPCAM, CDH1, KRT18, KRT8 |
| Neutrophil | Ly6g, Ncf1, Csf3r, Cd177, Sorl1 |
| Macrophage | CD14, CD68, CD163, CD209, AIF1 |
| Dendritic cell | Flt3, CD1C |
| Natural killer cell | Nkg7, Ifng, Klrd1, Klrc1, CD56 |
| Endothelial cell | VWF, CDH5, Pecam1 |
| Basophil | TPSB2, TPSAB1, TPSAB2, CPA3 |
| Plasma cell | CD27, CD38, XBP1, JCHAIN |
| Monocyte | CD14, CD300E, CD244 |
| CD8+NKT-like cell | CD3E, CD8A, CD161, KLRC1 |
| Classical monocyte | CD14, LYZ |
| Na?ve B cell | CD19, CD20, CD79A, IGLL1 |
| Na?ve CD4+T cell | CD3E, CD4, CD45RA, LEF1 |
| Na?ve CD8+T cell | CD3E, CD8A, CD45RA, LEF1 |
| Non-classical monocyte | CD14, CD16, FCGR3A |
| Plasmacytoid dendritic cell | CD123, CD303, IRF7, LY6E |
| Platelet | CD41, CD61, PF4, GPIBα |
| Pro-B cell | CD19, CD34, CD79A, IGLL1, RAG1 |
| Variable | Recipient 1 | Recipient 2 | Recipient 3 | Recipient 4 |
| Gender | Male | Female | Male | Male |
| Age | 15 | 48 | 54 | 51 |
| Weight (kg) | 41 | 58.1 | 68.5 | 85 |
| Clinical diagnosis | AR | ABMR | IgAN | Normal |
| Organ transplantation | DCD | DCD | DCD | DCD |
| Immune induction | ATG | basiliximab | ATG | ATG |
| Immunosuppressant | TAC+MPA | TAC+MPA+Pred | TAC+MPA+Pred | TAC+MPA+Pred |
| Tacrolimus C0 (ng/mL) | 4.5 | 11.1 | 2.1 | 7.9 |
| Treatment adjustment | Rituximab | Bortezomib | No adjustment | No adjustment |
| Serum creatinine (μmoI/L) | 144 | 172 | 680 | 89 |
| Uric acid (μmol/L) | 593 | 345 | 303 | 498 |
| Urea (mmol/L) | 15.9 | 16.1 | 14.7 | 4.7 |
| Albumin/globulin | 139 | 1.18 | 1.19 | 1.54 |
| Clinical manifestation | Normal body temperature, stable blood pressure | Normal body temperature, stable blood pressure | Normal body temperature, stable blood pressure | Normal body temperature, stable blood pressure |
表 2
Table 2 Clinical basic data of 4 kidney transplant recipients (n=4)
| Variable | Recipient 1 | Recipient 2 | Recipient 3 | Recipient 4 |
| Gender | Male | Female | Male | Male |
| Age | 15 | 48 | 54 | 51 |
| Weight (kg) | 41 | 58.1 | 68.5 | 85 |
| Clinical diagnosis | AR | ABMR | IgAN | Normal |
| Organ transplantation | DCD | DCD | DCD | DCD |
| Immune induction | ATG | basiliximab | ATG | ATG |
| Immunosuppressant | TAC+MPA | TAC+MPA+Pred | TAC+MPA+Pred | TAC+MPA+Pred |
| Tacrolimus C0 (ng/mL) | 4.5 | 11.1 | 2.1 | 7.9 |
| Treatment adjustment | Rituximab | Bortezomib | No adjustment | No adjustment |
| Serum creatinine (μmoI/L) | 144 | 172 | 680 | 89 |
| Uric acid (μmol/L) | 593 | 345 | 303 | 498 |
| Urea (mmol/L) | 15.9 | 16.1 | 14.7 | 4.7 |
| Albumin/globulin | 139 | 1.18 | 1.19 | 1.54 |
| Clinical manifestation | Normal body temperature, stable blood pressure | Normal body temperature, stable blood pressure | Normal body temperature, stable blood pressure | Normal body temperature, stable blood pressure |
图 1
Fig.1 Immune cell subsets in peripheral blood of ABMR kidney transplant recipients A: UAMP plots of 12 major cell types; B: proportion of major cell types in different samples; C: expression levels of marker genes in major cell types.
图 2
Fig.2 Differential gene and functional enrichment analysis in classical monocyte subsets A-C: signaling pathway enrichment analysis; D-F: differential gene analysis.
图 3
Fig.3 Differential gene expression, GO enrichment analysis in initial CD4+T cell subsets A-C: signaling pathway enrichment analysis; D-F: differential gene analysis.
图 4
Fig.4 Differential gene expression, GO enrichment analysis in NK cell subsets A-C: signaling pathway enrichment analysis; D-F: differential gene analysis.
| Renal transplant recipient samples | CD83 (PC7-A) | CD52 (APC) |
| ABMR_pre (03-L1-F1-1) | ||
| ABMR_post (06-L H 17-C1) | ||
| Normal (04-W1-F1) |
表 3
Table 3 Geometric mean value of CD83 (PC7-A) and CD52 (APC) genes of for monocytes (n=3)
| Renal transplant recipient samples | CD83 (PC7-A) | CD52 (APC) |
| ABMR_pre (03-L1-F1-1) | ||
| ABMR_post (06-L H 17-C1) | ||
| Normal (04-W1-F1) |
图 5
Fig.5 Flow cytometry to of changes in differentially expressed genes in classical monocytes of ABMR patients A: circle gate strategy for classical monocytes; B: histogram of CD52+; C: histogram of CD83+.
| Renal transplant recipient samples | CD69 (APC) |
| ABMR_pre (03-L1-F2-1) | |
| ABMR_post (06-L H 18-C2) | |
| Normal (04-W1-F2) |
表 4
Table 4 Geometric mean value of CD69 (APC) genes for naive CD4+T cells (n=3)
| Renal transplant recipient samples | CD69 (APC) |
| ABMR_pre (03-L1-F2-1) | |
| ABMR_post (06-L H 18-C2) | |
| Normal (04-W1-F2) |
图 6 A: loop gate strategy for CD4+T cells; B: histogram of CD69+.
Fig.6 Flow cytometry validation of changes in differentially expressed genes in initial CD4+T cells from ABMR patients A: loop gate strategy for CD4+T cells; B: histogram of CD69+.
| Renal transplant recipient samples | CX3CR1 (PB450) | CD74 (APC) |
| ABMR_pre (03-L1-F3-1) | 472.5 | |
| ABMR_post (06-L H 19-C3) | 936.1 | 513.9 |
| Normal (07-GLB 19-C3) | 660.6 |
表 5
Table 5 Geometric mean value of CX3CR1 (PB450) and CD74 (APC) genes for NK cells
| Renal transplant recipient samples | CX3CR1 (PB450) | CD74 (APC) |
| ABMR_pre (03-L1-F3-1) | 472.5 | |
| ABMR_post (06-L H 19-C3) | 936.1 | 513.9 |
| Normal (07-GLB 19-C3) | 660.6 |
图 7
Fig.7 Flow cytometry validation of changes in differentially expressed genes in NK cells from ABMR patients (n=3) A: circle gate strategy of NK cells; B: histogram of CD74+; C: histogram of CX3CR1+.
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