Kidney Res Clin Pract > Epub ahead of print
Lee, Shin, Fang, Cui, Lim, Lee, Eum, Min, Hong, Lee, Cho, Oh, Yang, and Chung: Single-cell RNA sequencing revealed the role of the Th17 pathway in the development of anti-human leukocyte antigen antibodies in a highly sensitized mouse model

Abstract

Background

The aim of this study is to investigate the specific pathway involved in human leukocyte antigen (HLA) sensitization using single-cell RNA-sequencing analysis and an allo-sensitized mouse model developed with an HLA.A2 transgenic mouse.

Methods

For sensitization, wild-type C57BL/6 mouse received two skin grafts from C57BL/6-Tg(HLA-A2.1)1Enge/J mouse (allogeneic mouse, ALLO). For syngeneic control (SYN), skin grafts were transferred from C57BL/6 to C57BL/6. We performed single-cell RNA-sequencing analysis on splenocytes isolated from ALLO and SYN and compared the gene expression between them.

Results

We generated 9,190 and 8,890 single-cell transcriptomes from ALLO and SYN, respectively. Five major cell types (B cells, T cells, natural killer cells, macrophages, and neutrophils) and their transcriptome data were annotated according to the representative differentially expressed genes of each cell cluster. The percentage of B cells was higher in ALLO than it was in SYN. Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that the highly expressed genes in the B cells from ALLO were mainly associated with antigen processing and presentation pathways, allograft rejection, and the Th17 cell differentiation pathway. Upregulated genes in the T cells of ALLO were involved in the interleukin (IL)-17 signaling pathway. The ratio of Th17 cluster and Treg cluster was increased in the ALLO. On flow cytometry, the percentage of Th17 (IL-17+/CD4+ T) cells was higher and regulatory T cells (FOXP3+/CD4+ T) was lower in the ALLO compared to those in the SYN.

Conclusion

Our results indicate that not only the B cell lineage but also the Th17 cells and their cytokine (IL-17) are involved in the sensitization to HLA.

Introduction

Kidney transplantation (KT) remains the best therapeutic option for end-stage kidney disease [1,2]. However, the presence of alloantibodies to human leukocyte antigen (HLA), so-called “sensitization to HLA,” presents an important immunologic barrier for successful KT [3]. According to the United States’ data, up to 35% of patients on the waiting list for a transplant are sensitized [4]. This situation is similar in Korea, where 15.4% of patients on the waiting list for KT are highly sensitized to HLA in terms of a positive crossmatch [5]. Moreover, the percentage of graft loss is much higher in patients who are sensitized to HLA than it is in those who are not [6].
Activation of the humoral immune system, which is mostly comprised of B lineage cells, plays a major role in HLA sensitization [7]. Therefore, the currently used desensitization therapies, such as plasmapheresis, intravenous immunoglobulin, rituximab, and bortezomib, mostly target the humoral immune systems [3]. These nonspecific strong protocols often fail and can lead to an over-immunosuppressed state. More specific targeted therapies are necessary for efficient desensitization. However, the detailed mechanisms underlying the development of HLA sensitization have not yet been fully investigated [8].
Meanwhile, single-cell RNA sequencing (scRNA-seq) has emerged as a new technique to comprehensively analyze the transcriptome of each immune cell population at a single-cell level; it also compensates for the limitations of traditional bulk RNA sequencing and microarrays [9,10]. ScRNA-seq deepens our knowledge of the underlying immune cell types and states, as well as cell type-specific transcriptomic signatures that are associated with autoimmune diseases [11,12] and KT recipients (KTRs) [13]. In our previous study using scRNA-seq analysis of human peripheral blood samples, we not only found consistent results from prior studies but also discovered new subpopulations of immune cells and the molecular characteristics that are involved in immune tolerance in KTRs [14].
In this regard, we planned this experimental study to investigate the specific pathway involved in HLA sensitization using a well-established HLA-sensitized mouse model developed with HLA.A2.1 transgenic mice. We analyzed the immune cells using the scRNA-seq technique.

Methods

Animals

Eight- to 12-week-old homozygous transgenic C57BL/6-Tg(HLA-A2.1)1Enge/J male mice and wild-type C57BL/6 male mice weighing 25 to 30 g were purchased from the Jackson Laboratory. All mice were housed in a specific pathogen-free facility in individual cages with temperature- and light-controlled environments. All procedures were performed in accordance with the Laboratory Animals Welfare Act, the Guide for the Care and Use of Laboratory Animals (National Institute of Health publication no. 80-23, revised 1996) and were approved by the Institutional Animal Care and Use Committee of College of Medicine, The Catholic University of Korea (No. CUMC 2022-0115-03).

Skin allograft transplant procedure

For sensitization (allogeneic [ALLO] mouse), wild-type C57BL/6 mouse was sensitized twice with skin allografts from C57BL/6-Tg(HLAA2.1)1Enge/J mouse (0 and 5 weeks) according to murine skin graft models described previously (Fig. 1A) [15]. Syngeneic (SYN) mouse received skin transplants from wild-type C57BL/6 mouse using the same procedure. Each group included a single animal. Both the donor and recipient mice were anesthetized with intraperitoneal injections of Zoletil 50 (tiletamine and zolazepam; Virbac Laboratories) 30 mg/kg and Rompun (xylazine; Bayer AG) 10 mg/kg. Tail skin, segmented into 8 × 8 −1.0 cm2 sizes, was obtained from the donor mice and grafted onto the dorsal area of the recipient mice. The mice were sacrificed using a CO2 chamber at 4 weeks after the second skin transplant, and mice spleens were harvested for scRNA-seq analysis.

Measurement of serum donor-specific anti-HLA.A2 antibodies

Blood samples were collected from the facial vein on week 9. Donor-specific anti-HLA A2 antibody was analyzed using the LAB Screen Mixed assay (One Lambda; Thermo Fisher Scientific) on a LAB Scan 3D system (One Lambda) according to the manufacturer’s specifications. The results were expressed as mean fluorescence intensity (MFI).

Tissue processing and single-cell preparation

The spleens were dissociated into single-cell splenocytes using fully frosted glass slides. The splenocytes were filtered through a 40-µm cell strainer (Falcon) and preserved in cryomedia (90% fetal bovine serum [FBS] + 10% dimethyl sulfoxide) for scRNA-seq analysis. Cell stocks were thawed in 37 ℃ 10% FBS/Dulbecco’s modified eagle medium. The cells were washed twice with cold Ca2+- and Mg2+-free 0.04% BSA/phosphate-buffered saline (PBS) at 300 ×g for 5 minutes at 4 °C. Samples were gently resuspended in cold Ca2+- and Mg2+-free 0.04% BSA/PBS and then counted using a LUNA-FX7 Automated Fluorescence Cell Counter (Logos biosystems) with AO/PI staining. Cell viability was further assessed using the Dead Cells Removal Kit (Cat no 130-090-101; Miltenyi Biotech) and MS columns (Cat no. 130-042-201; Miltenyi Biotech) according to the manufacturer’s instructions.

Single-cell RNA-sequencing library construction

According to the 10x Chromium Single Cell 5’ v2 protocol (10x Genomics; document no. CG000331), the scRNA-seq libraries were prepared using the 10x Chromium controller and Next Gem Single Cell 5’ Reagent v2 kits (10x Genomics; PN-1000244). Briefly, cell suspensions were mixed with a reverse transcription master mix and loaded with Single Cell 5′ Gel Beads and Partitioning Oil into a Single Cell K Chip (10x Genomics; PN-1000286) to generate single-cell Gel Bead-in-emulsions (GEM). RNA transcripts from the single cells were uniquely barcoded and reverse-transcribed within GEM. After the barcoded full-length complementary DNA (cDNA) was generated from messenger RNA (mRNA) through the GEM-RT reaction, the barcoded cDNA molecules were enriched using polymerase chain reaction (PCR). For 5’ gene expression library preparation, the amplified cDNA was sequentially subjected to enzymatic fragmentation, end-repair, A-tailing, adapter ligation, and index PCR.

Sequencing

The purified libraries were quantified using quantitative PCR (qPCR) according to the qPCR Quantification Protocol Guide (KAPA) and qualified using the Agilent Technologies 4200 TapeStation (Agilent Technologies). The library was sequenced using the HiSeq platform (Illumina), and 150 bp paired-end reads were generated. The sequencing depth of the 5’ gene expression library was approximately 20,000 read pairs per cell.

Data processing

The quality and basic statistics of raw sequencing data (FASTQ) were assessed using FastQC. After the quality control process, the sequencing data were processed through 10x Genomics Cell Ranger version 6.0.2 using mouse reference genome (mm10). The Cell Ranger count pipeline produces gene expression matrices containing the unique molecular identifier counts per gene, per barcode, and per cluster information. The expression profiles and features were differentially expressed in each cluster relative to all others.
For downstream analysis, we utilized R version 4.0.3 and Seurat version 3.1.2 (R Foundation for Statistical Computing) [16]. Low-quality cells were filtered out by excluding those with greater than 20% mitochondrial gene expression and less than 300 gene count. The scRNA-seq data were normalized using the ‘SCTransform’ function, which identifies highly variable genes and scales the expression values accordingly. Principal component analysis (PCA) was performed on the preprocessed matrix using Seurat’s ‘RunPCA’ function, with the top 15 principal components used for subsequent clustering analysis. The ‘FindClusters’ function was applied to cluster the cells based on their gene expression profiles, using the shared nearest neighbor modularity optimization algorithm with a resolution of 0.4 to identify distinct cell clusters. We used the ‘RunUMAP’ function in Seurat to generate a two-dimensional visualization of the clustered cells using the uniform manifold approximation and projection (UMAP) algorithm. Cluster annotation was performed based on the expression levels of marker genes using the CellMarker 2.0 database.

Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis

Differentially expressed gene (DEG) testing was performed using the FindMarkers function in Seurat with the Wilcoxon test. The p-values were adjusted using the Bonferroni correction. DEGs were filtered using a minimum log (fold change, FC) of 0.25 and a maximum adjusted p-value of 0.05. Next, the DEGs were ranked by average log (FC) and false discovery rate. Enrichment analysis for the functions of the DEGs was conducted using the clusterProfiler (version 4.4.4) package. The gene sets were based on Gene Ontology terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The gene sets were analyzed by enrichplot (version 1.16.1), and ggplot2 (version 3.3.6) was used to draw the result pictures.

Flow cytometry analysis

Freshly isolated spleen cells were obtained by gently milling the mice spleens in PBS. For observation of T cell subsets, the cells were stimulated for 4 hours with phorbol 12-myristate 13-acetate (Sigma) and ionomycin (Sigma) with the addition of GolgiStop (BD Bioscience). The stimulated cells were stained with the following antibodies: anti-CD4-FITC (clone:RM4-5), anti-CD25-eFluor 450 (clone:PC61.5), anti-IL-17-PE (clone:eBio17B7), anti-Foxp3-APC (clone:FJK-16S), anti IFNγ-APC (clone:XMG1.2), and anti-IL-4-PE-Cy 7 (clone:BVD6-24G2; all from eBioscience). Intracellular staining was performed using an intracellular staining kit (BD Biosciences or eBioscience) according to the manufacturer’s protocol. Flow cytometric analysis was performed using a FACSCantoII instrument (BD Biosciences). The data were analyzed using Flow Jo version 10.8.1 software (Tree Star).

Quantitative real-time polymerase chain reaction

Mouse spleen total RNAs were isolated using an RNA isolation reagent (RNA-Bee; Tel Test, Inc.), and then 5 μg of purified RNA were transcribed into complementary first-strand DNA using a Dyne 1st-strand cDNA Synthesis Kit (Dyne Bio Inc.). Subsequent reverse transcription (RT) qPCR amplification was carried out with an SYBR Green Premix in a Light Cycler 480 system (Roche). The relative mRNA expression levels were normalized to the β-actin gene through the cycle threshold change method. The primer sequences used for qPCR were interleukin (IL)-10 (forward; 5’-CCAAGCCT-TATCGGAAATGA-3’/reverse; 5’-TTTTCACAGGGGA-GAAATCG-3’), Foxp3 (forward; 5’-CAGCTGCCTACAGTGCCCCTAG-3’/reverse; 5’-CATTTGCCAGCAGTGGGTAG-3’), IL-23 (forward; 5’-CAGCAGCTCTCTCGGAATCTC-3’/reverse; 5’-TGGATACGGGGCACATTATTTTT-3’), and interferon (IFN)-γ (forward; 5’-TCAAGTGGCATAGATGTGGAAGAA-3’/reverse; 5’-TGGCTCTTGCAGGATTTTCATG-3’).

Murine T cell isolation and differentiation to Th0 or Th17 cells

To purify mouse splenic CD4+-T cells and non-T cells, splenocytes from C57BL/6 wild-type mice were incubated with CD4-coated magnetic beads and isolated by magnetically activated cell sorting (Miltenyi Biotec). Isolated CD4+ T cells were cultured in 24-well plates in RPMI-1640 medium supplemented with penicillin/streptomycin/glutamine, 5% fetal calf serum with anti-CD3 (0.5 μg/mL) and anti-CD28 (1 μg/mL) to induce Th0 cells, or with anti-CD3 (0.5 μg/mL), anti-CD28 (1 μg/mL), anti-IFN-γ (5 μg/mL), anti-IL-4 (5 μg/mL), TGF-β (2 ng/mL), and IL-6 (20 ng/mL) to induce Th17 cells for 3 days. Isolated non-T cells were cultured in 6-well plates in RPMI-1640 medium supplemented with penicillin/streptomycin/glutamine, 5% fetal bovine serum with LPS (O111;B4) for 3 days.

Co-cultures of Th0 or Th17 cells with non-T cells

Differentiated T cells (Th0 or Th17 cells) and non-T cells were plated on 24-well plates. They were co-cultured as following: Th0 cells only (Th0) (1 × 106 cells/mL), Th17 cells only (Th17) (1 × 106 cells/mL), non-T cells only (nonT) (1 × 106 cells/mL), Th0 cells (5 × 105 cells/mL) with non-T cells (Th0 + nonT) (5 × 105 cells/mL), and Th17 cells (5 × 105 cells/mL) with non-T cells (Th17 + nonT) (5 × 105 cells/mL). The total immunoglobulin G (IgG) and IgG2A concentration in the culture supernatants was measured by sandwich enzyme-linked immunosorbent assay (Bethyl Laboratories), according to the manufacturer’s instructions.

Statistical analysis

All data are presented as mean ± standard error. An unpaired t test was used for comparison among groups. Differences with p-values less than 0.05 were considered significant. All statistical analyses were conducted using GraphPad Prism version 5 (GraphPad Software, Inc.).

Results

Comparison of anti-human leukocyte antigen A2 antibody level

To generate a sensitized mouse model, a skin graft from a C57BL/6-Tg(HLA-A2.1)1Enge/J mouse was transplanted twice onto a wild-type C57BL/6 mouse. Due to the difference in transgenic HLA.A2 antigen, a humoral immune response producing anti-HLA.A2 antibodies was expected in the recipient animal. Production of anti-HLA.A2 antibodies was observed in serum samples at week 9 (4 weeks after the second graft) in the allogeneic skin graft recipient. The MFI titer of the anti-HLA A2 antibody was 13,313.90 in the ALLO mouse. In contrast, the MFI titer was barely detectable in the SYN mouse (week 9, 12.78) (Fig. 1B).

Comparison of cellular distribution of total immune cells

We obtained single cells from the spleen of the sacrificed mice. After completion of quality control, totals of 9,190 and 8,890 single-cell transcriptomes were extracted from the ALLO and SYN mice, respectively. Differential genetic expression was observed on UMAP visualization of the total single cells (Fig. 2A). Six known immune cell subsets (B cell, T cell, natural killer [NK] T cell, NK cell, and macrophage/dendritic cell) were identified using the CellMarker 2.0 database (Fig. 2B). The fraction of B cell was higher in the ALLO mouse than in the SYN mouse (ALLO vs. SYN, 75.1% vs. 65.8%). The proportion of T cells was lower in the ALLO mouse than in the SYN mouse (ALLO vs. SYN, 22.4% vs. 29.7%) (Fig. 2C).

Comparison of differentially expressed genes in B cells

We found that 168 genes were significantly upregulated in the ALLO mouse (Supplementary Table 1, available online). The results of KEGG enrichment analysis of the upregulated genes in the ALLO mouse B cell included antigen processing and presentation, allograft rejection, and Th17 cell differentiation pathways (Fig. 3A). Hif1a and Tgfb1 were involved in Th17 cell differentiation pathway (Hif1a: average log2FC = 10.85, adjusted p < 0.001; Tgfb1: average log2FC = 6.10, aadjusted p = 1) (Fig. 3B).

Comparison of differentially expressed genes in T cells

We found 144 genes that were significantly upregulated in the ALLO mouse (Supplementary Table 2, available online). The results of KEGG enrichment analysis of the upregulated genes in the ALLO mouse T cell included the IL17 signaling pathway (Fig. 4A). Tnf and Nfkbia were involved in IL17 signaling pathway (Tnf: average log2FC = 15.36,adjusted p < 0.001; Nfkbia: average log2FC = 11.07, adjusted p = 0.14) (Fig. 4B). The ratio of cluster 6 (Th17) and cluster 1 (Treg) was higher in the ALLO mouse than it was in the SYN mouse (ALLO vs. SYN, 0.29 vs. 0.18) (Fig. 4C).

Comparison of T cell fractions in the spleen by flow cytometry

The T cell fractions were observed using flow cytometry (Fig. 5). Our results showed that Th2 and Th17 cells were significantly increased in the ALLO mouse compared to those in the SYN mouse. In contrast, the Treg cells were significantly decreased in the ALLO mouse compared to those in the SYN mouse (Th1: ALLO vs. SYN, p = 0.12; Th2: ALLO vs. SYN, p = 0.011; Th17: ALLO 68.03 ± 2.62 vs. SYN 63.36 ± 2.28, p < 0.01; and Treg: ALLO 16.71 ± 1.98 vs. SYN 18.68 ± 2.38, p = 0.01).

Comparison of messenger RNA expression of cytokines

RT-qPCR analysis revealed that mRNA levels of IL-10 and Foxp3 were significantly increased in SYN compared with those in ALLO (IL-10: ALLO 0.64 ± 0.04 vs. SYN 0.87 ± 0.11, p = 0.03; Foxp3: ALLO 0.17 ± 0.31 vs. SYN 1.34 ± 0.02, p < 0.001) (Fig. 6A, B). In contrast, IL-23 and IFN-γ mRNA levels were increased in ALLO compared with those in SYN (IL-23: ALLO 1.21 ± 0.33 vs. SYN 0.97 ± 0.12, p = 0.02; IFN-γ: ALLO 1.92 ± 0.22 vs. SYN 1.15 ± 0.10, p = 0.03) (Fig. 6C, D).

Comparison of immunoglobulin G production when co-culture with Th0 or Th17 cells

When non-T cells were co-cultured with Th17 cells, the total IgG concentration increased compared to when they were co-cultured with Th0 cells or in conditions where only non-T cells were cultured (nonT vs. Th0 + nonT, p = 0.001; nonT vs. Th17 + nonT, p < 0.001) (Fig. 7A).
IgG2A, which is the major IgG sub-isotype during alloantibody response, increased when non-T cells were co-culture with Th17 cells compared to co-cultures with Th0 cells or conditions where only non-T cells were cultured (nonT vs. Th0 + nonT, p < 0.001; nonT vs. Th17 + nonT, p < 0.001) (Fig. 7B).

Discussion

In this study, we generated a well-established highly sensitized mouse model and explored the molecular pathways that are involved in allosensitization at a single-cell level. The genes involved in Th17 cell differentiation and its cytokine (IL-17) signaling pathway were enriched in the sensitization status. Moreover, using flow cytometry and RT-qPCR analysis in the ALLO mouse, we observed dysregulation of the Th17/Treg axis, manifested by an increase of Th17 cells and a decrease of Treg cells. Co-culture of Th17 cells with non-T cells, which includes B cells, activates an alloimmune response. Our results suggest that not only the humoral immune system but also the Th17 pathway may be involved in the sensitization process.
In this experiment, we used a highly sensitized in vivo mouse model that was generated by repeated exposure to skin grafts from HLA.A2 transgenic mouse. Both the donor and recipient strains share a common B6 genetic background, except for a single transgenic HLA.A2 antigen. The expression of this antigen on donor cells triggers alloimmune responses in the recipients, which results in the production of HLA.A2-specific antibodies (antiHLA.A2 antibody). In our previous study, we found that anti-HLA.A2 antibody titers increased more steeply after two skin grafts than they did after the first exposure to HLA.A2 antigen [13]. This finding indicates that memory B cells that formed after the first exposure to HLA.A2 antigen rapidly expand and differentiate into plasma cells after the second exposure. The expansion leads to a highly sensitized state. As expected, a high titer of anti-HLA.A2 antibody was achieved in the ALLO mouse after two skin grafts, which is consistent with the findings from previous reports (including our own) [13,15,17].
Next, we isolated splenocytes from both ALLO and SYN mice and compared the gene expression using scRNA-seq to investigate the underlying mechanisms for HLA sensitization. In our previous study, we used the microarray technique but failed to clarify the relationship between microarray signatures and altered cellular phenotypes [13]. We used scRNA-seq analysis in this study because it enables a more comprehensive characterization of the functions of immune cells than does the microarray technique [18], thereby providing novel and detailed insights into molecular mechanisms. As predicted, B cells are expanded in the allogeneic mouse. Highly expressed genes in B cells of the allogeneic mouse were enriched in antigen processing and presentation and allograft rejection. Interestingly, transcripts associated with Th17 cell differentiation were enriched within the B cell clusters in allogeneic mouse. B cells are considered antigen-presenting cells that play a role in T cell priming and differentiation [19]. Previous in vitro experiments have shown that naïve B-1 cells and activated B-2 cells induce differentiation of Th17 cells [20,21]. Therefore, it is possible that scRNA-seq may reflect the situation in which B cells are in contact with and induce T cells to differentiate into Th17 cells in the ALLO mouse.
In accordance with the results in B cells, the upregulated transcripts in the T cells of the allogeneic mouse were enriched in the IL-17 signaling pathway, which is a cytokine of Th17 cells. In addition, dysregulation of the Th17/Treg axis was observed in flow cytometric analysis, and B cells co-cultured with Th17 cells produced significantly more total IgG and IgGA2. These results validate and directly corroborate the scRNA-seq results. Several studies reported that Th17 cells and their signature cytokines may play an important role as B cell helpers. The Th17 cells lead to B cell proliferation in vitro and induce germinal center composition that results in antibody production through class switch recombination in vivo. In addition, the survival, proliferation, and differentiation of B cells into immunoglobulin secreting cells were regulated by IL-17 alone or in combination with B cell activating factors [2224]. In the pathogenic condition, Th17 cells not only induced inflammation but also induced the B cell differentiation to produce pathogenic antibodies [22,23]. IL-17 can function as a costimulatory signal on a stable antigen-dependent T–B cell interaction to promote B cell proliferation in autoimmune diseases (such as dry eye disease) [25]. Therefore, based on previous studies and our scRNA-seq results, we postulate that B cells that are activated by HLA induce Th17 cell differentiation. Subsequently, the activated Th17 pathway might play an effective role in the humoral immune response in reverse (Fig. 8).
In KT, Th17 cells play a significant role in the activation of the humoral immune system and the progression of allograft injury [26,27]. There was increased expression of IL-17 on tubular epithelial cells in acute antibody-mediated rejection (AMR) in KTRs [28]. We previously also found that Th17 cell infiltration in allograft tissue or an increased proportion of Th17 cells in the peripheral blood are associated with more severe allograft rejection or dysfunction [2931]. Moreover, Th17 cells have a role in fibrosis that contribute to the transition of acute kidney injury to chronic kidney disease and the progression of kidney disease [32,33], potentially leading to allograft dysfunction. In addition, accumulating evidence has found that the therapeutic strategy of balancing Th17/Treg through IL-6/IL-6 receptor (IL-6R) targeting therapy was effective at decreasing pathogenic antibody production [31,3438]. However, most previous studies have elucidated the role of Th17 cells in the alloimmune setting. In contrast, our study directly revealed the role of Th17 pathway in the development of HLA sensitization.
This study had a few limitations. First, although we identified the Th17 to be involved in the development of sensitization, we could not prove that targeting the Th17 pathway would effectively prevent sensitization or cause desensitization. Recent IL-6/IL-6R targeting therapies, which decrease Th17 cells and enhance Treg cell differentiation, are expected to have a therapeutic effect in desensitization. Second, we used a sensitized model that has an antibody to HLA class I (HLA-A2.1). Therefore, we did not investigate the pathway that is involved in the generation of antibody targeting HLA class II. There may be unique pathways that lead to sensitization to HLA class II. Therefore, further experiments using animal models that can represent both HLA class I and II immune systems are needed.
In conclusion, using a highly sensitized mouse model and scRNA-seq analysis, we demonstrated that Th17 cell differentiation and the IL-17 signaling pathway might play a role in the development of anti-HLA-A2 antibodies. Conventional experiments, including flow cytometry, mRNA RT-qPCR analysis, and in vitro experiments, confirmed the possible association between Th17 cells and alloimmune response. Our results suggest that Th17 cells may serve as a potential target for preventing allosensitization to HLA in patients waiting for KT and for treating AMR.

Supplementary Materials

Supplementary data are available at Kidney Research and Clinical Practice online (https://doi.org/10.23876/j.krcp.23.317).

Notes

Conflicts of interest

All authors have no conflicts of interest to declare.

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1I1A1A01044660, NRF-2022R1I1A1A01069636) and by a grant from the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI22C0422).

Data sharing statement

Raw and processed single-cell RNA-sequencing data generated or analyzed in this study are available on Gene Expression Omnibus under accession number GSE240862.

Authors’ contributions

Conceptualization, Supervision: BHC

Investigation: HL, YJS, YJS, XF, SC, SWL, SYL

Data curation: SHE, JWM, CWH, HOL, EJO, CWY, BHC

Funding acquisition: HL, YJS, CWH

Writing–original draft: HL, YJS, BHC

Writing–review & editing: SHE, JWM, CWH, HOL, MLC, EJO, CWY, BHC

All authors read and approved the final manuscript.

Figure 1.

Experimental groups and HLA-specific IgG MFI titers in allogeneic and syngeneic transplantation.

(A) The study protocol and definition of experimental groups. (B) MFI titers of HLA.A2-specific IgG measured at week 9.
ALLO, allogeneic; DSA, donor-specific antibody; HLA, human leukocyte antigen; IgG, immunoglobulin G; MFI, mean fluorescence intensity; SYN, syngeneic; Tx, transplantation.
j-krcp-23-317f1.jpg
Figure 2.

Single-cell transcriptional profile of splenocytes from allogeneic and syngeneic mice.

(A) UMAP plot for visualization of transcriptional atlas of 18,081 splenocytes. (B) Clusters identified by CellMarker 2.0 database. (C) Percentages of cell fractions.
ALLO, allogeneic; NK, natural killer; SYN, syngeneic; UMAP, uniform manifold approximation and projection.
j-krcp-23-317f2.jpg
Figure 3.

Single-cell analysis of B cells.

(A) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of highly expressed genes in the allogeneic mouse. (B) KEGG pathway map of Th17 differentiation.
j-krcp-23-317f3.jpg
Figure 4.

Single-cell analysis of T cells.

(A) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of highly expressed genes in the allogeneic mouse. (B) KEGG pathway map of interleukin (IL)-17 signaling pathway. (C) The ratio of Th17 cells cluster and Treg cells cluster.
ALLO, allogeneic; cAMP, cyclic adenosine monophosphate; COVID-19, coronavirus disease 2019; PI3K, phosphoinositide 3-kinase; SYN, syngeneic.
j-krcp-23-317f4.jpg
Figure 5.

T cell population at week 9 (4 weeks after the second skin graft) in the recipient spleen analyzed using flow cytometry.

(A) Gating strategy and fractions of (B) CD4+/INFγ+ Th1 cells, (C) CD4+/IL4+ Th2 cells, (D) CD4+/IL-17+ Th17 cells, and (E) CD4+/CD25+/Foxp3+ Treg cells. Error bars represent two standard errors.
ALLO, allogeneic; FSC, forward scatter; IL, interleukin; INF, interferon; SSC, side scatter; SYN, syngeneic.
j-krcp-23-317f5.jpg
Figure 6.

RT-qPCR analysis of cytokines in the spleen.

The mRNA expression of (A) interleukin (IL)-10, (B) Foxp3, (C) IL-23, and (D) interferon (IFN)-γ. Relative messenger RNA expression levels were calculated after normalization to β-actin expression. Expression level in the SYN was considered as the control.
ALLO, allogeneic; RT-qPCR, quantitative real-time polymerase chain reaction; SYN, syngeneic.
ap < 0.05 vs. SYN.
j-krcp-23-317f6.jpg
Figure 7.

IgG production assessed using ELISA in co-cultures of T cells with non-T cells.

Concentration of (B) total IgG and (B) IgG2A.
ELISA, enzyme-linked immunosorbent assay; IgG, immunoglobulin G.
ap < 0.05 vs. Th0, bp < 0.05 vs. Th17, cp < 0.05 vs. nonT, dp < 0.05 vs. Th0 + nonT, ep < 0.05 vs. Th17 + nonT.
j-krcp-23-317f7.jpg
Figure 8.

Schematic figure to outline the pathway of reciprocal activation of B cells and Th17 cells.

j-krcp-23-317f8.jpg

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