Currently, culture methods are commonly used in clinical trials to detect pathogenic fungi, including Candida [16]. Three strains of C. albicans SC5314 were inoculated onto a solid medium (CHROMagar BD, 212961, Beijing, China) and incubated at 37°C for 24–48 h. One green colony was selected and mixed thoroughly with sterile saline. The fungal cells were counted under a microscope (Olympus Corporation, BX53, Tokyo, Japan), and the concentration was adjusted to 1 × 10⁷ CFU for each mouse.

Nine 8-week-old inbred Balb/c female mice weighing 20 ± 1 g were randomized into three groups (HFK Bioscience, Beijing, China).100 μL of C. albicans suspension was injected into the tail vein of the sample. Each mouse in the experimental group was injected with 1 × 10⁶ CFU C. albicans in the tail vein, and the control group was injected with saline. Mice in both experimental groups were executed after 24 and 48 h C. albicans infection, respectively. Then blood was removed, and spleen single cells were collected for isolation. The study protocol was approved by the Ethics Committee of the 980th Hospital of the Joint Logistics Support Force of the Chinese People’s Liberation Army (2022-KY-129) and followed the principles of the Declaration of Helsinki.

Single-cell RNA sequencing (scRNA-seq) requires the preparation of highly active single-cell suspensions to obtain reliable sequencing results [17]. We found that the immune response was at its highest at 48 h after C. albicans infection, so the 3 samples in the group and 3 healthy samples were chosen for subsequent single-cell sequencing. A 50 mL centrifuge tube containing a 70 μm cell sieve is wetted with 3 mL of Hank’s solution (Sigma-Aldrich, SM-2001-C, 100 mL, Shanghai, China). The spleen tissue is placed in the sieve, gently crushed with a 5 mL sterile syringe (Kang Long, 0142, 5 mL, Guangzhou, China), and rinsed with sterile Hank’s solution to ensure complete release of the cells. After centrifugation at 300 g for 10 min, the supernatant is discarded, and the cells are resuspended with 1 mL of Hank’s solution. Take 20 μL, add 980 μL PBS (Macklin, P903593-500 ml, 50 mmol/L pH7.4, Shanghai, China), count under the microscope (Olympus Corporation, BX53, Tokyo, Japan), and adjust the cell concentration to 1 × 10⁷ /mL with Hank’s solution.

To avoid the lymphocyte isolate interfering with neutrophil fractionation, 100 μL was set aside in a separate tube for staining. The remaining cells after PBMC isolation were analyzed in tubes, centrifuged, and re-suspended in 2 mL of Hank’s solution. Then, we added 3 mL of lymphocyte isolate to a sterile centrifuge tube (Beyotime, FCT151, 5 mL, Shanghai, China). Slowly add the cell suspension along the wall of the tube and centrifuge (Sigma-Aldrich, Z654736GB-1EA, St. Louis, MO, USA) for 15 min (2000 r/min). Carefully aspirate the second circular layer of creamy white lymphocytes and transfer to 5 mL of Hank’s solution, centrifuge at 1500 rpm for 20 min, wash twice, resuspend the cells in 1 mL of culture medium, and count under the microscope to a concentration of 1 × 10⁷/mL.

By analyzing gene expression in individual cells, single-cell RNA sequencing (scRNA-seq) can address cellular heterogeneity and cell-type gene expression dynamics. 15 Cell counts are performed on single-cell solutions to ensure that the cell concentration is in accordance with the Chromium Single Cell 3’s Reagent v3 Kit (Illumina, MS-102-3003, 600T, San Diego, CA, USA). Observe the morphology and activity of the cells by microscopy to ensure that the cells used for the experiment are of good quality. Ensure that all manipulations are performed under sterile conditions [18]. Chromium Single Cell 3 Reagent v3 Kit For preparation of scRNA-seq libraries. Single-cell solutions are placed on the Chromium Single Cell Controller to generate single-cell latex beads (GEM).

In this study, the R software package Seurat (v4.1.0) was used to import and process scRNA-seq data [19]. Data processing included read-in, data quality control, gene and cell filtering, data normalization, identification of highly variable genes, data scaling, principal component analysis (PCA), and the uniform flow approximation and projection (UMAP) algorithm. Unless otherwise noted, Seurat runs with default parameters. First, a Seurat object is constructed using the CreateSeuratObject function, excluding poor quality cells-retaining cells where at least 200 genes are found and expressed in at least 3 cells. Subsequently, low-quality cells are further culled based on sample characteristics, the number of genes detected, mitochondrial ratio, and the total number of molecules detected. The data was then normalized using the “LogNormalize” method of the NormalizeData function with a scaling factor of 10,000. Subsequently, the FindVariableFeatures function identified 4000 highly variable genes for subsequent analysis. The RunPCA function scaled the data to select the top 50 genes. The RunPCA function scaled the data and selected the top 50 PCs for downstream analysis. Finally, the UMAP algorithm downscaled the cells.

We loaded the previously obtained summary data into Monocle objects with default parameters. Monocle2 is an R package for single-cell RNA sequencing data analysis that provides powerful tools to identify and analyze cellular states. Variable genes contributing to the proposed time trajectory were identified by Monocle2 analysis. Cells were aligned to the proposed time trajectory based on the combination of highly variable genes obtained from all cells [20]. This alignment process can help researchers understand how cells develop along a continuous trajectory and which changes in gene expression are significant at different points in time.

Differential genes were defined as genes with a fold change greater than 0.25 compared to other cells and with more than 25% expression. The study performed functional annotation of differential genes using the open web application EnrichR (https://maayanlab.cloud/Enrichr/, accessed on 10 May 2024) to identify potential functions in which the above differential genes may be involved [21]. Gene set enrichment analysis (GSEA) of transition-state cells was performed using the gseGO function in the R package clusterProfiler (v4.2.2) [22] and signaling pathways with NES > 0 and FDR-corrected significance p < 0.05 were defined as activated pathways and inflammation-related pathways were visualized using the R package GseaVis (v0.0.1). In this study, the FindMarkers function (v4.1.1) of the Seurat R software package identified characteristic genes for the indicated cell populations. The differential genes had a logarithmic change of greater than 0.25 compared to other cells and were expressed in more than 25% of the cells.

Load expression data for individual cells and corresponding cell type information. Calculate and infer intercellular communication networks and identify biologically meaningful intercellular communications by assigning occurrence probabilities and alignment tests to communicate pairs. Systematically analyze cellular communication networks to determine the extent to which input and output signals contribute in a given cell type. The pre-processed individual cell expression data was loaded into the analysis software to identify the cell type to which each cell belongs. Using algorithms in the software, the likelihood of intercellular communication is calculated based on the expression data. By comparing the expression patterns between different cell types, possible interactions between cells are inferred.

RT-qPCR was implemented according to previous procedures [23]. The 3 samples from the experimental group and 3 healthy samples control group were collected to extract RNA. RNA was extracted using the EZ-10 Total RNA Small Volume Extraction Kit (BBI, B610423-0100, 100PREPS, Shanghai, China). 20 μL of the reaction system was used for one-step qPCR of Ctla4, Ccl5, IL2rb, and GAPDH using the ABScript II One Step SYBR Green RT-qPCR Kit (Abclonal, RK20404, 100RXN, Beijing, China). The primers were demonstrated in Table 1.

Protein levels can be detected by Western blotting [24]. Add 100 μL of cell lysate (Bioleaper, BR2003801, 100 mL, Shanghai, China) to selected CD4⁺ T cells. centrifuge at 12,000 g for 5 min, aspirate the supernatant and determine the protein concentration by BCA (SALMART, BCA, 1 g, Shenzhen, China). The sample volume was adjusted according to the protein concentration and 15 μg of protein was injected into each lane. Electrophoresis is performed using a 12.5% separator gel at 150 V and a constant voltage of 100 V for 90 min. Transfer the membrane at a constant pressure of 100 V for 90 min. Membranes were blocked with 5% skimmed milk for 60 min and washed three times with TBST (Servicebio, G0004, Wuhan, China) for 5 min each time. Incubate overnight at 4°C with rabbit anti-IL2 receptor beta antibody (1:1000 dilution, bs-1959R, Bioss, Beijing, China), rabbit anti-RANTES antibody (1:1000 dilution, bsm-54125R, Bioss, Beijing, China), CTLA4 rabbit pAb (1:1000 dilution, A13965, Abclonal, Wuhan, China), and beta-actin mouse mAb (1:2000 dilution, AC004, Abclonal, Wuhan, China). After three washes with TBST for 5 min each, HRP goat anti-rabbit IgG (H+L, AS014) or HRP goat anti-mouse IgG (H+L) (1:2000 dilution, AS003, Abclonal, Wuhan, China) was added and incubated for 90 min at room temperature. Rinse three times with TBST for 5 min each time. Automated exposure was performed using the SuperSignal™ West Pico PLUS kit (34577, Thermo Fisher, Waltham, MA, USA).

For data comparisons between two different study groups, we used an unpaired two-sided Student’s t-test, each experiment was repeated 3 times. In this study, all results with p < 0.05 were considered statistically significant. These p-values are indicated in the graphs and legends by symbols at different levels such as *p < 0.05. All statistical analyses were done through GraphPad Prism 8 (GraphPad Software, La Jolla, CA, USA).

We performed scRNA-seq to analyze immune cells in tissues infected with C. albicans. Cells were classified into 29 clusters (Fig. 1A), which were categorized into 9 cell types, namely B cells (Cd79a, Cd79b), basophil (Fcer1a, Itga2), dendritic cells (Flt3), erythroblast (Car2, Hba-a2), macrophage (Adger1, Cd68, Mrc1), monocyte (Csf1r, Itgam), neutrophil (Csf3r, Cxcr2), natural killer cells (Nkg7, Klrd1), and T cells (Cd3d) (Fig. 1B–D). The proportion of B cells and neutrophils was higher in the infected samples, while the proportion of T cells was lower (Fig. 1E).

To understand changes in T cells during the infection event, we annotated the T cell population into 9 subpopulations, including CD4⁺ memory T cells, CD4⁺ naive T cells, CD8⁺ cytotoxic T cells, CD8⁺ memory T cells, CD8⁺ naive T cells, MT-related T cells, Th1, Th17 and Treg (Fig. 2A). In the case of systemic infection with C. albicans, the proportions of most of the cell types were significantly altered compared to the control group (Fig. 2B,C). In addition, the proportion of CD4⁺ memory T cells remained largely unchanged, whereas CD4⁺ naive T cells were significantly reduced in infected samples (Fig. 2D). CD4⁺ naive T cells are the starting point of the adaptive immune response, and their recognition of and response to antigens is a critical step in initiating the subsequent immune response. CD4⁺ naive T cells help maintain the immune response’s balance, preventing excessive inflammatory responses. Therefore, we speculate that CD4⁺ naive T cells play an important role in resistance to C. albicans infection.

Due to the significant difference in the proportion of CD4⁺ naive T cells between the infected and healthy groups (Fig. 3A), we performed GSEA on CD4⁺ naive T cells, which showed significant down-regulation of the pathways of antigen processing and presentation, chemokine signaling, Mitogen activated protein kinase (MAPK) signaling, and T-cell receptor signaling (Fig. 3B–E). The synergistic role of thymic fibroblasts, B cells, and dendritic cells in antigen presentation, selection, and regulatory T-cell development is critical for maintaining a tightly regulated immune response. It has been found that CD8⁺ naive T cells can be induced by either specialized antigen-presenting cells or by direct antigen presentation or antigen cross-presentation [25,26]. The downregulation of antigen processing and presentation pathways may lead to increased immune tolerance. This means that the immune system has a reduced ability to recognize and respond to self-antigens or antigens of infected pathogens, which may result in the immune system being unable to clear infections or attacks on tissues effectively. Chemokine signaling is an important regulator of T cell activation and proliferation, and down-regulation of chemokine signaling may result in impaired recognition and transmission of chemokine signals by T cells, which in turn may affect T cell activation and proliferative responses.

The MAPK signaling pathway (Table 2) is crucial in the C. albicans infection process. The p38 MAPK signaling pathway has been found to play a crucial regulatory role in the beneficial effects observed after treatment of Candida albicans-infected nematodes with thymol [27]. The peptide toxin candidalysin, secreted by C. albicans, damages epithelial cells and drives innate inflammatory responses mediated by the MAPK pathway. The fungal pathogen Candida albicans secretes the peptide toxin candidalysin, which damages epithelial cells and drives innate inflammatory responses mediated by the epidermal growth factor receptor (EGFR) and MAPK pathways, as well as the transcription factor c-Fos. Down-regulation of the MAPK signaling pathway and the T cell receptor signaling pathway, which play a key role in T cell activation and differentiation, may lead to the down-regulation of T cell responses to external stimuli. Down-regulation of the MAPK signaling pathway and T cell receptor signaling pathway plays a key role in T cell activation and differentiation, and down-regulation of these pathways may lead to a decrease in the ability of T cells to respond to external stimuli, thus affecting T cell activation and differentiation [27,28]. MAPK signaling pathway and T cell receptor signaling pathway play key roles in T cell activation and differentiation, and down-regulation of these pathways may result in reduced T cell responsiveness to external stimuli, affecting T cell activation and differentiation. In summary, infection may inhibit the activation and immune response function of CD4⁺ T naive cells and affect T cell differentiation. The MAPK signaling pathway is involved in regulating various biological processes such as cell proliferation, differentiation, survival, and inflammation. With the downregulation of the MAPK signaling pathway, the immune cells may produce fewer inflammatory mediators, such as cytokines and chemokines, upon activation. This may lead to reduced activation levels of immune cells, diminishing their ability to fight fungal infections.

EnrichmentScore, NES and p-value of the KEGG MAPK SIGNALING PATHWAY shown in the table can be seen to play a key role in Candida albicans infection.

We performed trajectory analyses to further explore the relationship between dynamic immune response processes and T cell differentiation. We dissected population heterogeneity and reconstructed the reprogramming process using gene expression profiling data. The Monocle 2 algorithm, which is based on transcriptional similarity, was employed for pseudo-temporal cell sorting [20]. For the experimental group, we identified CD4⁺ memory T cells and CD4⁺ naive T cells as the intermediate transition state, and finally reached MT-associated T cells, Th1, Th17, and Treg cells terminal differentiation state (Fig. 4A–C), which is also consistent with earlier findings. CD4⁺ naive differentiated more towards Treg and MT-related T after C. albicans infection. We also examined the gene dynamics of the proposed chronology, with a heatmap highlighting the top 50 genes that underwent dynamic changes (Fig. 4D) and identifying three distinct transformation patterns. Next, we constructed a proposed chronological trajectory for the control group using genes that were highly expressed in the progression of T-cell subpopulations, and the results were consistent with the differentiation status of the experimental group (Fig. 4E–G). To understand the dynamics of gene expression, changes in key genes such as Ccr7, Sell, Ctla4, Cd52, mt-Rnr2 and mt-Cytb were analyzed (Fig. 4H). During Treg differentiation, Sell, a marker gene for CD4⁺ naive T cells, showed down-regulation, whereas Ctla4, a marker gene for Treg cells, showed up-regulation during the same process. These results suggest CD4⁺ naive differentiation towards Treg and MT-associated T cells after C. albicans infection.

Cell-cell interactions between immune cells play a crucial role in immune outcomes. We calculated cell-cell interaction scores using CellChatDB (http://www.cellchat.org/, accessed on 10 May 2024) to analyze ligand-receptor interactions between various cell types. We observed interactions between 12 cell types in the control and infected groups (Fig. 5A,B). Notably, the cell-cell junctions in the infected group were significantly different from those in the control group (Fig. 5C,D). Interactions between neutrophils and other cells were reduced in the infected group, and interactions between plasma cells and other cells were increased in the infected group. We also investigated the interaction of specific ligand-receptor pairs between different cell populations. Ligand-receptor pairs of CD4⁺ Native cells with plasma cells (PTPRC-SEMA4D, PTPRC-MRC1, CD47-SIRB1) were significantly upregulated in the experimental group compared to the control group, suggesting that these pathways are essential for the anti-infection immune response (Fig. 5E,F).

Compared to the control group, CD4⁺ naive T cells showed intercellular interactions on the MIF-TNFRSF10D, MIF-TNFRSF14, and HLA-E-KLRK1 ligand receptor pairs. TNFRSF10D, TNFRSF14 are genes belonging to the tumor necrosis factor (TNF) family, which have immune-activating effects, and it has been reported that TNF family genes are involved in the control of HCV infections. KLRK1, also known as the NKG2D gene, once bound to its ligands, activates NK cells and CD4⁺ T cells, which are motivated to mount an immune response and kill the infected cells. Cellular communication between MT-associated T cells and ICAM1-SPN and ICAM1-ITGAL ligand receptor pairs also emerged (Fig. 5E,F). This suggests that CD4⁺ naive T cells, MT-related T cells play an important role in the fight against fungal infection link.

The protein-protein interaction (PPI) network of differential genes in the CD4⁺ T naive cell mimetic timing analysis was constructed with the help of the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, https://cn.string-db.org/, accessed on 10 May 2024) database (Fig. 6A). The interaction score of the PPI network was set to >0.4. To investigate their biological significance, we performed GO and KEGG enrichment analyses of these differential genes (Fig. 6B). Significant enrichment of functions such as T cell receptor signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, and cytokine-cytokine receptor interaction were observed (Fig. 6B). We found that they were involved in biological processes such as immune response, T cell differentiation, apoptosis, cellular components such as biofilm, and molecular functions such as protein interactions, suggesting that these differential genes regulated T cell differentiation, KEGG pathway including cytokine-cytokine receptor interaction, autoimmune, cell adhesion molecules, and T17 cell differentiation. We selected six genes with high connectivity in the gene expression network as hub genes, including Foxp3, Ctla4, Il2rb, Cd28, Ccl5, and Cd27. Fig. 6C shows that the expression level of hub genes was higher in the infected group than in the control group. mRNA and Western blotting showed that the expression of the 6 hub genes was significantly increased at protein levels after infection (Fig. 6D,E), revealing the infection-relevant properties of hub genes.