Supplementary Table 1 provides an extended list of the clinical information of the individuals included in the analysis.
NK cells with a less functional CD56Dim molecular signature in AA patients
In the following analysis, we concentrated on distinct immune cell classes and utilized dimensionality reduction methods. Figure 2A illustrates the downscaled map obtained through clustering for all NK cells. These NK cells can be further classified into distinct subgroups based on the marker heat map, which identifies the following categories: NKT, NKbri (immature NK cells), NKdim (mature NK cells), and NKuc (unclassical NK cells) [15]. To elucidate variations in cell population proportions between HD and patients with AA, t-SNE analysis was employed to identify distinct patterns among HD, NSAA, SAA, and VSAA (Fig. 2B). This visualization technique facilitates the exploration of high-dimensional data, allowing us to uncover and interpret the differences in cell populations across these diverse conditions. The figure indicates that the cellular clustering patterns of various subtypes vary within each group. Various subtypes of NK cells were classified using manual Flow Cytometry gating (Fig. 2C). Compared to the HD, we observed that NKdim cells exhibit a significant decrease in both the SAA and VSAA groups, while NKUC cells show a noticeable increase in the NSAA and VSAA groups. NKBri cells do not show significant changes across all groups (Fig. 2D). Research has demonstrated that NKbri cells serve as precursors for NKdim cells, with the latter exhibiting shorter telomeres compared to the former. NKbri cells primarily function in secreting large amounts of cytokines, while NKdim NK cells play a role in exerting cytotoxic effects [16]. The majority of CD56+ NK cells are CD27+. During the transition from CD56bri to CD56dim cytotoxic effectors, there is a reduction in CD27 expression [17]. Consequently, a statistical analysis of CD56−CD27−cells within NK cells was conducted. Compared to the HD group, a significant decrease was observed in the AA group, consistent with the changes observed in NKdim cells in AA (Fig. 2E). This suggests a significant reduction in less functional CD56dim NK cells in AA. In conjunction with clinical data, we also observed a negative correlation between the proportion of CD56dim cells in NK cells and C-reactive protein (CRP) in AA, aligning with the afore-mentioned results (Fig. 2F). Subsequently, we examined the expression levels of relevant functional factors (NKG2A, NKG2C, NKG2D, NKP30, NKP44, and NKP46) in NKdim cells. A decrease in expression levels was found in the AA group compared to the healthy group (Fig. 2G), consistent with previous studies[18]. Additionally, there was a significant decrease in the expression levels of the cytotoxicity factor CD107a in NK cells and each subtype compared to the HD group (Fig. 2H). In summary, there is a significant reduction in less functional CD56dim NK cells in AA, indicating functional impairment.
CD56 + (NK-liked)monocytes increase in AA patients and possess NK peculiarity.
We employed t-SNE plots to visualize the dimensionality reduction outcomes of various mononuclear cell subtypes, including classical monocytes (c-monocytes), intermediate monocytes (inter-monocytes), non-monocytes, and CD56+ monocytes. These subgroups were delineated using flow cytometry marker heatmaps (Fig. 3A). Next, we categorized the samples into four groups: HD, NSAA, SAA, and VSAA. Following dimensionality reduction analysis, we observed differences in the clustering patterns of each mononuclear cell subtype among these groups (Fig. 3B). Figure 3C depicts the flow cytometry profiles of different mononuclear cell subtypes. The proportions of c-monocytes, inter-monocytes, and non-monocytes among mononuclear cells did not significantly differ across HD, NSAA, SAA, and VSAA groups. Nevertheless, CD56+ monocytes showed a significant increase in NSAA and VSAA compared to HD (Fig. 3C). Using manual gating in flow cytometry, we detected a substantial increase in CD56 + monocytes in AA, consistent with findings in rheumatic diseases and post-COVID-19 infection, suggesting a contributing role [19]. To further analyze the expanded CD56+ monocyte population in t-SNE plots, we utilized manual gating in flow cytometry (Fig. 3D). Additionally, ROC curve analysis indicated that the alterations in CD56+ monocytes are specific to AA (Fig. 3E, p < 0.0001). Furthermore, within CD14+ monocytes in AA patients exhibited a significant increase in CD56 expression (Fig. 3F). Additionally, in healthy individuals, the CD56+CD14+ cell subset displays NK cell-like attributes, characterized by GZMB, Perforin, and T-bet expression, indicating cytotoxic properties. Conversely, these functions are notably impaired in AA patients (Fig. 3G). In summary, the innate immune cells in AA patients, including NK cells and NK-like monocytes, both exhibit defects in natural killing and phagocytic functions.
MDSCs decrease in AA and recover post-treatment.
The percentages of MDSCs in the PB of patients with AA and HD were evaluated using cell surface markers CD15, CD14, HLA-DR, and CD11b across different subtypes of AA including NSAA, SAA, and VSAA. The results of flow cytometry data reduction were visualized using t-SNE analysis (Fig. 4A-B). MDSCs (CD11b+HLA-DR−) were further classified into CD15+ polymorphonuclear myeloid-derived suppressor Cells (PMN-MDSCs), CD14+ monocytic myeloid-derived suppressor cells (M-MDSCs) and CD15−CD14− early myeloid-derived suppressor cells(e-MDSCs). A gradual decrease in the proportion of MDSCs in the PB of AA patients compared to HD was observed, showing statistically significant differences. However, there were no significant differences detected in PMN-MDSCs and e-MDSCs in AA (Fig. 4C). After administering clinical ATG immunosuppressive therapy, AA patients were categorized into recovery (Re) and non-recovery (Non-Re) groups, and PB samples were collected from each group to evaluate MDSC frequencies. A significant increase in MDSC frequency was observed in the recovery group compared to the non-recovery groups (Fig. 4D). Additionally, statistical analyses were conducted to assess the relationship between MDSCs and various clinical parameters including absolute reticulocyte count (ACR), absolute neutrophil count (ANC), platelets (Plt), white blood cell count (WBC), hemoglobin (Hb), and red blood cell count (RBC). The results revealed significant correlations between MDSCs and key hematological parameters Plt and WBC (Fig. S2). Previous research has pointed to a significant decrease in MDSC proportions in AA, linked to a noticeable reduction in immunosuppressive capabilities [20]. Employing flow cytometry-based sorting, we isolated MDSCs from both HDs and AA patients for Smart-seq analysis. Substantial differences were observed in the gene expression profiles of MDSCs between HDs and AA patients, as evidenced by distinct clustering in PCA analysis (Fig. 4E). Volcano plots and KEGG analysis underscored a striking upregulation of the one-carbon synthesis pathway in MDSCs derived from AA (Fig. 4F). Additionally, GSEA analysis revealed a diminished inhibitory function in MDSCs from AA patients, primarily associated with crucial pathways like JAK/STAT signaling, endoplasmic reticulum stress, and pathways linked to graft-versus-host disease (Fig. 4G-H), shedding light on the intrinsic molecular mechanisms contributing to the marked decline in MDSCs' suppressive function in AA.
MDSCs effectively discriminate between acquired aplastic anemia and congenital aplastic anemia.
CAA primarily results from genetic mutations, with the diagnosis of congenital bone marrow failure relying mainly on NGS sequencing. In AA, MDSCs exert immunosuppressive effects, leading to a significant decrease in their components [20]. We further evaluated the percentage of HLA-DR−CD11b+CD33+ MDSCs in AA and CAA, revealing a marginal decrease in MDSCs in AA compared to the HDs. Conversely, CAA demonstrated a pronounced increase in MDSCs (Fig. 5A). Next, the frequencies of the three MDSCs subtypes(PMN-MDSCs, M-MDSCs, and e-MDSCs)were further examined in CD45+ cells in HDs, AA and CAA. It was observed that all three MDSCs subtypes exhibited significant differences in frequencies between AA and CAA, proving to be efficient markers for distinguishing between the two conditions (Fig. 5B). Through ROC curve analysis, we illustrated the distinctions in MDSC populations among the HD, AA, and CAA groups. Notably, significant differences were evident between the control group and both the AA and CAA groups, as well as between AA and CAA (Fig. 5C). Subsequently, as potentially effective indicators for clinical application, we successfully translated and implemented these findings in a clinical context. In CAA patients, there exists a primary association between telomere length and the extent of chromosomal breakage [21]. Our observations indicate that CAA patients with shorter telomeres exhibit elevated levels of MDSCs (Fig. 5D), with a negative correlation observed between telomere length and the proportions of MDSC subtypes (Fig. 5E). Furthermore, patients with higher degrees of chromosomal breakage in CAA demonstrate increased proportions of MDSCs (Fig. 5F), revealing a significant positive correlation between the degree of chromosomal breakage and MDSC proportions (Fig. 5G). Collectively, our findings highlight the distinguish roles of MDSCs in distinguishing between acquired and congenital aplastic anemia.
AA patients exhibit elevated activated cytotoxic T cells and reduced regulatory T cell.
We conducted dimensionality reduction on flow cytometry data and visualized the results using t-SNE. This approach aimed to examine the differential expression levels of T-cell subtypes between HDs and individuals with AA (Fig. 6A-B). In our study, we found no significant differences in various subsets of CD4+ and CD8+ T cells-including naïve T cells (Tnaive), central memory T cells (Tcm), effector memory T cells (Tem), and effector memory cells re-expressing CD45Ra (Temra)-between AA patients and HD (Fig. 6C-D). Previous research has highlighted the crucial role of T cells in AA [22]. Compared to HD, AA patients exhibit elevated levels of cytotoxic CD8+ T cells in both bone marrow and PB, coupled with a decrease in regulatory T cells (Treg) levels [23]. Furthermore, we observed a reduced proportion of Tregs within the CD4+ T cell subset in the AA compared to HD. Additionally, we conducted supplementary assessments of Treg activation status by examining the surface expression of CD45Ra, revealing a lower abundance of activated Treg cells in the peripheral blood of AA patients. This confirms both a reduction in Treg levels and functional impairment in AA (Fig. 6E). Moreover, our investigations found no significant differences in the proportion of activated CD4+ T cells between HD and AA. However, in comparison to HDs, there was a higher proportion of activated CD8+ T (CD38+HLA-DR+) cells in VSAA (Fig. 6F). Prior studies have suggested that activated CD8+ T cells exhibit characteristics of exhaustion and induce inflammation [19]. Additionally, we employed t-SNE for dimensionality reduction and visualization to explore variations in B cells and their subsets in AA (Fig. S3A-B). No significant differences were observed in the three B cell subtypes among NSAA, SAA and VSAA (Fig. S3C), consistent with previous research [24]. In summary, our findings suggest alterations in T cell subsets in AA, characterized by an increase in cytotoxic T cells, a concurrent decrease in Treg cells, and functional dysregulation.
Immune cell correlation identification during AA.
Spearman’s correlation test was employed to evaluate the correlation among the 29 immune cell types, revealing pairs with significant correlations (p < 0.05) as depicted in Fig. 7A. Log-fold change (Log FC), area under the curve (AUC), and false discovery rate (FDR) were utilized to identify novel cells associated with AA. Analysis of Log FC and -log10 (FDR) values (Fig. 7B) revealed that AA was characterized by a significant elevation of memory B cell, eosinophils and NKbri cells (Log FC > 0.25, FDR < 0.05), along with a reduction in NKDim and non-classic monocytes (Log FC<-0.25, FDR < 0.05). Memory B cell, eosinophils and NKbri cells exhibited increased expression in AA (Log FC > 0.25), whereas NKDim and non-classic monocytes were downregulated (Log FC > 0.25). These markers effectively distinguished patients with AA from healthy controls, with a high AUC (diagnostic value AUC > 0.7) (Fig. 7C). Given the significant clinical correlation between MDSCs and AA (Fig. 7B-C), we explored the correlation among all immune cells. NKbri cells exhibited significant positive correlations with monocytes, NKT cells, various subtypes of CD8 T cells, B cells, and classical monocytes. NKdim exhibited significant positive correlations with B cells, intermediate monocytes, NKT cells, activated CD4+T cells, neutrophils, and non-classical monocytes. Classical monocytes demonstrated significant positive correlations with B cells, intermediate monocytes, and monocytes, while naive B cells displayed significant positive correlations with memory B cells (Fig. 7D, p < 0.05). Furthermore, NKbri cells demonstrated significant negative correlations with CD4+Tcm, while NKdim showed significant negative correlations with CD4+Temra, NKuc, CD4+Tem and naive B cells. Eosinophils exhibited significant negative correlations with regulatory T cells, and classical monocytes showed significant negative correlations with various subtypes of T cells (Fig. 7D, p < 0.05). In conclusion, delving deeper into the interplay among immune cells in the pathogenesis of AA presents a promising avenue for future research.