Chinese Journal of Clinical Pharmacology and Therapeutics ›› 2026, Vol. 31 ›› Issue (3): 289-299.doi: 10.12092/j.issn.1009-2501.2026.03.001
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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
CLC Number:
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 |
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 |
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 |
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.
Fig.2 Differential gene and functional enrichment analysis in classical monocyte subsets A-C: signaling pathway enrichment analysis; D-F: differential gene analysis.
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.
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) |
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) |
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) |
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) |
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 |
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 |
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+.
| 1 |
Chen S, Zhou Y, Chen Y, et al. Fastp: an ultra-fast all-in-one FASTQ preprocessor[J]. Bioinformatics, 2018, 34 (17): i884- i890.
doi: 10.1093/bioinformatics/bty560 |
| 2 |
Leckie-Harre A, Silverman I, Wu H, et al. Sequencing of physically interacting cells in human kidney allograft rejection to infer contact-dependent immune cell transcription[J]. Transplantation, 2024, 108 (2): 421- 429.
doi: 10.1097/TP.0000000000004762 |
| 3 |
魏江浩, 窦古枫, 王振, 等. 肾移植术后慢性活动性抗体介导排斥反应的激素冲击治疗效果分析[J]. 山东医药, 2024, 64 (32): 16- 21.
doi: 10.3969/j.issn.1002-266X.2024.32.004 |
| 4 | 车福恒, 刘斌. 移植肾慢性活动性抗体介导排斥反应的研究进展[J]. 实用器官移植电子杂志, 2023, 11 (5): 489- 494. |
| 5 | Chen Y, Chen Y, Shi C, et al. Single-cell RNA sequencing reveals the immune landscape of human kidney allograft rejection[J]. J Am Soc Nephrol, 2020, 31 (11): 2745- 2760. |
| 6 |
Ni B, Yang C, Zhang J, et al. Rictor Ameliorates acute antibody-mediated rejection following kidney transplantation by suppressing macrophage M1 polarization through p65-NLRP3 axis[J]. Adv Sci (Weinh), 2025, 12 (34): e17119.
doi: 10.1002/advs.202417119 |
| 7 |
Haas M, Mirocha J, Huang E, et al. A banff-based histologic chronicity index is associated with graft loss in patients with a kidney transplant and antibody-mediated rejection[J]. Kidney Int, 2023, 103 (1): 187- 195.
doi: 10.1016/j.kint.2022.09.030 |
| 8 |
Shi W, Wan TT, Li HH, et al. Blockage of S100A8/A9 ameliorates septic nephropathy in mice[J]. Front Pharmacol, 2023, 14, 1172356.
doi: 10.3389/fphar.2023.1172356 |
| 9 |
Du L, Chen Y, Shi J, et al. Inhibition of S100A8/A9 ameliorates renal interstitial fibrosis in diabetic nephropathy[J]. Metabolism, 2023, 144, 155376.
doi: 10.1016/j.metabol.2022.155376 |
| 10 |
Pepper RJ, Wang HH, Rajakaruna GK, et al. S100A8/A9 (calprotectin) is critical for development of glomerulonephritis and promotes inflammatory leukocyte-renal cell interactions[J]. Am J Pathol, 2015, 185 (5): 1264- 1274.
doi: 10.1016/j.ajpath.2015.01.015 |
| 11 |
Li Y, Chen B, Yang X, et al. S100a8/a9 signaling causes mitochondrial dysfunction and cardiomyocyte death in response to ischemic/reperfusion injury[J]. Circulation, 2019, 140 (9): 751- 764.
doi: 10.1161/CIRCULATIONAHA.118.039262 |
| 12 |
Doberer K, Duerr M, Halloran PF, et al. A randomized clinical trial of anti-IL-6 antibody clazakizumab in late antibody-mediated kidney transplant rejection[J]. J Am Soc Nephrol, 2021, 32 (3): 708- 722.
doi: 10.1681/ASN.2020071106 |
| 13 |
Weerakoon H, Straube J, Lineburg K, et al. Expression of CD49f defines subsets of human regulatory T cells with divergent transcriptional landscape and function that correlate with ulcerative colitis disease activity[J]. Clin Transl Immunology, 2021, 10 (9): e1334.
doi: 10.1002/cti2.1334 |
| 14 |
Lu J, Wang W, Li P, et al. miR-146a regulates regulatory T cells to suppress heart transplant rejection in mice[J]. Cell Death Discov, 2021, 7 (1): 165.
doi: 10.1038/s41420-021-00534-9 |
| 15 | Schürch CM, Bhattacharya N, Barbash O, et al. Single-cell RNA sequencing reveals the cellular architecture of chronic kidney allograft rejection[J]. Nat Med, 2020, 26 (2): 211- 221. |
| 16 | Li H, Kaminski MS, Li N, et al. Single-cell profiling of human kidney allografts reveals an immunoregulatory role for NK cells in acute rejection[J]. J Clin Invest, 2019, 129 (10): 4174- 4189. |
| 17 |
Zhao Y, Su H, Shen X, et al. The immunological function of CD52 and its targeting in organ transplantation[J]. Inflamm Res, 2017, 66 (7): 571- 578.
doi: 10.1007/s00011-017-1032-8 |
| 18 |
Borghese F, Clanchy FI. CD74: an emerging opportunity as a therapeutic target in cancer and autoimmune disease[J]. Expert Opin Ther Targets, 2011, 15 (3): 237- 251.
doi: 10.1517/14728222.2011.550879 |
| 19 |
Cormican S, Griffin MD. Fractalkine (CX3CL1) and its receptor CX3CR1: A promising therapeutic target in chronic kidney disease ?[J]. Front Immunol, 2021, 12, 664202.
doi: 10.3389/fimmu.2021.664202 |
| 20 |
Li X, Li S, Wu B, et al. Landscape of immune cells heterogeneity in liver transplantation by single-cell RNA sequencing analysis[J]. Front Immunol, 2022, 13, 890019.
doi: 10.3389/fimmu.2022.890019 |
| 21 |
Xia P, Ji X, Yan L, et al. Roles of S100A8, S100A9 and S100A12 in infection, inflammation and immunity[J]. Immunology, 2024, 171 (3): 365- 376.
doi: 10.1111/imm.13722 |
| 22 |
Bui TM, Wiesolek HL, Sumagin R. ICAM-1: a master regulator of cellular responses in inflammation, injury resolution, and tumorigenesis[J]. J Leukoc Biol, 2020, 108 (3): 787- 799.
doi: 10.1002/JLB.2MR0220-549R |
| 23 |
Blanco-Domínguez R, de la Fuente H, Rodríguez C, et al. CD69 expression on regulatory T cells protects from immune damage after myocardial infarction[J]. J Clin Invest, 2022, 132 (21): e152418.
doi: 10.1172/JCI152418 |
| 24 |
Liu J, Yue WL, Fan HZ, et al. Correlation of cTfh cells and memory B cells with AMR after renal transplantation[J]. Transpl Immunol, 2024, 86, 102095.
doi: 10.1016/j.trim.2024.102095 |
| 25 |
Li Z, Ju X, Silveira PA, et al. CD83: activation marker for antigen presenting cells and its therapeutic potential[J]. Front Immunol, 2019, 10, 1312.
doi: 10.3389/fimmu.2019.01312 |
| 26 |
Chukwu CA, Spiers HVM, Middleton R, et al. Alemtuzumab in renal transplantation. Reviews of literature and usage in the United Kingdom[J]. Transplant Rev (Orlando), 2022, 36 (2): 100686.
doi: 10.1016/j.trre.2022.100686 |
| 27 | Kuppe C, Ibrahim MM, Kranz J, et al. Spatial single-cell analysis identifies an interstitial immune network in human kidney allografts[J]. Nature, 2021, 593 (7858): 253- 258. |
| 28 |
Serrano OK, Friedmann P, Ahsanuddin S, et al. Outcomes associated with steroid avoidance and alemtuzumab among kidney transplant recipients[J]. Clin J Am Soc Nephrol, 2015, 10 (11): 2030- 2038.
doi: 10.2215/CJN.12161214 |
| 29 |
van der Zwan M, Baan CC, van Gelder T, et al. Review of the clinical pharmacokinetics and pharmacodynamics of alemtuzumab and its use in kidney transplantation[J]. Clin Pharmacokinet, 2018, 57 (2): 191- 207.
doi: 10.1007/s40262-017-0573-x |
| 30 |
Yagisawa T, Tanaka T, Miyairi S, et al. In the absence of natural killer cell activation donor-specific antibody mediates chronic, but not acute, kidney allograft rejection[J]. Kidney Int, 2019, 95 (2): 350- 336.
doi: 10.1016/j.kint.2018.08.041 |
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