
==== Front
Cell Biol Toxicol
Cell Biol Toxicol
Cell Biology and Toxicology
0742-2091
1573-6822
Springer Netherlands Dordrecht

39269517
9914
10.1007/s10565-024-09914-0
Research
Comprehensive mapping of immune perturbations associated with aplastic anemia
Wang Huijuan 12
Chen Yinchun 126
Deng Haimei 3
Zhang Jie 4
Jiang Xiaotao 5
Mo Wenjian 16
Wang Shunqing shqwang_cn@163.com

16
Zhou Ruiqing zrq_hematology@foxmail.com

16
Liu Yufeng eyyufengliu@scut.edu.cn

126
1 https://ror.org/02bwytq13 grid.413432.3 0000 0004 1798 5993 Department of Hematology, Guangzhou First People’s Hospital, Guangzhou, 510180 China
2 https://ror.org/02bwytq13 grid.413432.3 0000 0004 1798 5993 Center for Medical Research On Innovation and Translation, Guangzhou First People’s Hospital, Guangzhou, 510180 China
3 https://ror.org/00rfd5b88 grid.511083.e 0000 0004 7671 2506 Department of Hematology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518118 China
4 grid.459579.3 0000 0004 0625 057X Department of Rehabilitation, Guangdong Women and Children Hospital, Guangzhou, 510000 China
5 https://ror.org/01mxpdw03 grid.412595.e Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510000 China
6 https://ror.org/0530pts50 grid.79703.3a 0000 0004 1764 3838 School of Medicine, The Second Affiliated Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, 510180 China
13 9 2024
13 9 2024
2024
40 1 7513 3 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Aplastic anemia (AA) is an immune-mediated syndrome characterized by bone marrow failure. Therefore, comprehending the cellular profile and cell interactions in affected patients is crucial.

Methods

Human peripheral blood mononuclear cells (PBMCs) were collected from both healthy donors (HDs) and AA patients, and analyzed using multicolor flow cytometry. Utilizing the FlowSOM and t-SNE dimensionality reduction technique, we systematically explored and visualized the major immune cell alterations in AA. This analysis provided a foundation to further investigate the subtypes of cells exhibiting significant changes.

Results

Compared to HDs, peripheral blood from patients with AA exhibits a marked reduction in CD56Dim natural killer (NK) cells, which also show diminished functionality. Conversely, an increase in NK-like CD56+ monocytes, which possess compromised functionality. Along with a significant reduction in myeloid-derived suppressor cells (MDSCs), which show recovery post-treatment. Additionally, MDSCs serve as effective clinical markers for distinguishing between acquired aplastic anemia (AAA) and congenital aplastic anemia (CAA). Our comprehensive analysis of correlations among distinct immune cell types revealed significant associations between NKBri cells and CD8+ T cell subsets, as well as between NKDim cells and CD4+ T cells, these results highlight the intricate interactions and correlations within the immune cell network in AA.

Conclusion

Our study systematically elucidates the pronounced immune dysregulation in patients with AA. The detailed mapping of the immune landscape not only provides crucial insights for basic research but also holds promise for enhancing the accuracy of diagnoses and the effectiveness of timely therapeutic interventions in clinical practice. Consequently, this could potentially reduce the high mortality rate associated with AA.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s10565-024-09914-0.

Keywords

Aplastic anemia
Immune perturbations
Myeloid-derived suppressor cells (MDSCs)
issue-copyright-statement© Springer Nature B.V. 2024
==== Body
pmcIntroduction

Aplastic anemia is a rare and serious condition characterized by bone marrow failure to produce sufficient blood cells, resulting in low blood counts, bleeding, infection, and various other clinical symptoms (Peslak et al. 2017; Kordasti et al. 2012). An empty bone marrow has made it as the prime example of hematopoietic failure syndromes. In Western nations, the prevalence is documented at two cases per million annually, whereas in Asian populations, it is notably higher, with an approximate 2–threefold increase (Young et al. 2008; Kojima 2002). The current causes of AA can be broadly classified into three main categories: (1) direct marrow injury: the most common cause is iatrogenic injury, often dose-dependent and potentially reversible. Workers exposed to benzene may also experience hematopoietic system damage. In China, rapid industrialization and limited regulation contribute to benzene remaining a toxic substance in workplaces. In addition, environmental factors associated with this disease include radiation, toxins, drug-virus interactions, and other harmful chemicals (Ly 2009; Dolberg and Levy 2014); (2) constitutional syndromes: marrow failure can result from loss-of-function germline mutations, typically inherited. Various genetic abnormalities reduce the hematopoietic stem's capacity to repair DNA, as seen in conditions like Fanconi anemia (involving replication-dependent removal of inter-strand DNA cross-links) and dyskeratosis congenita (related to telomere maintenance and repair). Additionally, mutations affect stem and progenitor cells' differentiation and self-renewal pathways, as observed in GATA2-related disorders (Yamazaki 2018); (3) immune injury: nearly all sporadic aplastic anemia, particularly when severe and acute, seems to be immune mediated. cytotoxic T cells are also a focal point in AA research (Young 2018). When clinical patients receive only blood transfusions and antibiotic treatment, the therapeutic outcomes and prognosis are not favorable. However, when immunosuppressive therapy is employed, more positive treatment effects are achieved. The observed phenomena have directed the research focus of AA towards the immune aspect. AA in most cases involves T-cell-mediated, organ-specific destruction of bone marrow hematopoietic cells (Young 2002). According to etiology, aplastic anemia can be classified into CAA and AAA. CAA result from germline mutations in genes involved in DNA repair (such as Fanconi anemia or Shwachman-Bodian-Diamond syndrome), telomere maintenance (TERC, TERT, DKC1, or RTEL1), or hematopoiesis (GATA2). These mutations lead to impaired proliferation and survival capabilities of hematopoietic stem cells, causing progressive loss over time (Giudice and Selleri 2022). The majority of AA are idiopathic; however, there is an association with specific reactions to certain drugs, chemicals, and viruses (Keohane 2004). Bone marrow aspiration and trephine biopsy are the most important diagnostic tools to confirm diagnosis (Furlong and Carter 2020). According to the clinical testing indicators, aplastic anemia is characterized by a corrected reticulocyte count of ≤ 1%, an absolute neutrophil count (ANC) of ≤ 1.5 × 109/L, or a platelet count of ≤ 50 × 109/L. Patients with myelodysplastic syndrome (MDS), classic paroxysmal nocturnal hemoglobinuria (PNH), and those experiencing bone marrow suppression induced by chemotherapy were excluded. The severity of AA is defined according to the modified Camitta crit. Severe aplastic anemia (SAA) is diagnosed when a patient presents with at least two of the following criteria: reticulocyte count ≤ 1%, ANC ≤ 0.5 × 109/L, platelet count ≤ 20 × 109/L, and hypocellular bone marrow with ≤ 25% cellularity. Very severe aplastic anemia (VSAA) conforms to the criteria of SAA but with an ANC ≤ 0.2 × 109/L. Individuals not meeting the criteria for SAA/VSAA are classified as non-severe aplastic anemia (NSAA) (Norasetthada et al. 2021; Miano and Dufour 2015). A more detailed classification of the disease is beneficial for a better understanding of the dynamic progression of the condition. This enables us to pinpoint treatment targets more accurately, thereby profoundly impacting future research in AA.

Previous studies have primarily focused on specific cell subsets, neglecting to depict the comprehensive immune landscape, the interactions among immune disorders, and secondary cytokine storms across various immunocyte types, such as T cells, NK cells, MDSCs, lower density neutrophils (LDNs), and B cells. In clinical practice, comprehensive immunosuppressive therapies are typically employed initially in patients, rather than targeting specific signaling pathways, cytokines, or cell types. Timely diagnosis and accurate identification of the inflammatory subtypes of AA may be challenging due to their typical yet nonspecific presentations.

In order to address these issues, we conducted a thorough analysis of the overall immunological status of patients with AA and compared it with that of HDs to fully comprehend the spectrum of immunological disruptions. Flow cytometry was employed to analyze peripheral blood cells from clinical patients. Dimensionality reduction analysis showed that in patients with AA, there was a decrease in LDNs, monocytes, and regulatory T cells, an increase in eosinophils and activated T cells, and notably, a characteristic decrease in low-functional CD56Dim NK cells, while CD56+ (NK-like) monocytes exhibited an increase. MDSCs increased in CAA and decreased in AAA, with post-treatment recovery observed. Subsequently, we analyzed correlations among immune cell populations and identified significant associations between certain subsets. In summary, our findings offer valuable insights into potential immune dysregulations in AA, with the aim of developing timely diagnostic and therapeutic approaches.

Materials and methods

Patient population

Donors were enlisted from Guangzhou First People’s Hospital in China. The diagnosis of AA was established through clinical and laboratory assessments. All enrolled patients met the rigorous standards outlined in the revised criteria set by the British Society for Hematology for the classification of AA (Killick et al. 2016). Approval for this study was granted by the Clinical Ethics Review Board of Guangzhou First People’s Hospital (K-2022–085-01). Written informed consent was procured from each patient upon admission.

Isolation of PBMCs

Blood samples were drawn into tubes with Ethylenediaminetetraacetic acid (EDTA) and processed within one hour of phlebotomy. Isolation of human peripheral blood mononuclear cells (PBMCs) was achieved through density gradient centrifugation using Lymphoprep (STEMCELL) following the manufacturer's protocol.

Staining procedure

We standardized individual tubes for automated compensation. Human peripheral blood mononuclear cells (PBMCs) were collected and the cell suspension was adjusted to 200 μL with a cell concentration of 2 × 106 cells/ml. The cell suspension was incubated with Fc Receptor Blocking Solution (BioLegend) for 10 min at room temperature to block Fc receptors. Subsequently, following the instructions provided by the antibody manufacturer, 5 μL of the antibody stock solution was added, and the volume was adjusted to 100 μL for cell staining at room temperature for 30 min. Details of the antibody panel can be found in Table 1. Table 1 Antibodies used in flow cytometry

Antibody	Fluorophore	Clone	Company	
CD3	PE-cy7	OKT3	TONBO	
CD4	BUV496	SK3	BD	
CD8	PerCP-cy5.5	OKT8	TONBO	
CD19	PE-Cy5	HIB19	BD	
CD14	APC	M5E2	BD	
CD15	BV605	W6D3	Biolegend	
CD16	BV786	3G8	Biolegend	
HLA-DR	APC-cy7	L243	TONBO	
CCR7	AF700	G043H7	Biolegend	
CD45RA	BV510	HI100	Biolegend	
CD127	BV421	A019D5	Biolegend	
CD56	PE594	B159	BD	
CD38	PE	HB7	TONBO	
CD11b	BUV396	ICRF44	BD	
CD25	FITC	M-A251	BD	
CD11c	BV711	3.9	Biolegend	
Nkp30	BV605	P30-15	Biolegend	
Nkp44	PE	P44-8	Biolegend	
Nkp46	FITC	9E-2	Biolegend	
NGK2A	APC-Cy7	S19004C	Biolegend	
NGK2C	BV421	134,591	BD	
NGK2D	PerCP-Cy5.5	1D11	eBioscience	
CD107a	APC-750	H4A3	Biolegend	
CD27	BV650	O323	Biolegend	
T-bet	Percp5.5	4B10	Biolegend	
Granzyme B	BV421	QA18A28	Biolegend	
Perforin	BV711	dG-9	Biolegend	

Intracellular cytokine staining

After cells were fixed and permeabilized with FOXP3 Perm buffer for 40 min at 4°C and then incubated with antibodies against GZMB (granzyme B), perforin and other related targets for 30 min at 20°C.

Flow cytometry analysis

Negative controls (Supplementary Fig. 1), single-color controls (Supplementary Fig. 2) and Fluorescence Minus One control (FMO) (Supplementary Fig. 3) were incorporated to ensure the accuracy of our panel testing. Additionally, we utilize an Fc blocker to minimize non-specific binding and signal interference. The compensation values for each fluorophore in every detector are determined using single-color controls. Following this, compensation for spill-over fluorescence is performed with software tools to correct for spectral overlap among different fluorophores. All flow cytometry data were acquired using a BD LSRFortessa-X20 and analyzed using Flow Jo v.10 software (Flow Jo LLC).

Immune cell correlation and novel immune cells identification

We evaluated the correlations among various immune cell types using correlation coefficients, and visually represented significantly correlated pairs (p < 0.05) through either a heatmap or a chord diagram in R 3.6.1. We identified novel immune cells based on three criteria: (1) p < 0.05, (2) receiver operating characteristic (ROC) area under the curve (AUC) > 0.75, and (3) effect ratio > 0.25 or < -0.25. The statistical significance (p value) for each immune cell was determined using the Wald test. ROC AUC was calculated by comparing the true-positive rate (proportion of AA correctly classified) to the false-positive rate (proportion of controls falsely classified as AA) for various cell thresholds. The effect ratio was calculated as the average cell proportion in AA divided by the average cell proportion in controls.

Statistics analysis

Results, presented as individual data points with medians, underwent statistical analysis in GraphPad Prism 9. Normality tests were applied to all datasets. Group comparisons for two groups utilized either an unpaired t-test or the Mann–Whitney test. Multiple group comparisons employed one-way analysis of variance (ANOVA) with Holm-Sidak post hoc testing or the Kruskal–Wallis test with Dunn’s post hoc test. Correlations within the patient group with AA were evaluated using Pearson’s or Spearman’s rank correlation coefficient. Significance was set at p < 0.05 for all analyses.

Result

Demographic and clinical characteristics of individuals with AA

Table 2 provides an extended list of the clinical information of the individuals included in the analysis. Table 2 Clinical characteristic of healthy donors and patients with AA

Variables	HDs (n = 9)	NSAA (n = 9)	SAA (n = 9)	VSAA (n = 9)	p	
Gender					ANOVA	
  Male (%)	55.60%	55.60%	55.60%	55.60%	/	
  Female (%)	44.40%	44.40%	44.40%	44.40%	/	
Age (mean ± SD)	33.44 ± 12.83	41.22 ± 16.77	29.67 ± 9.43	31.56 ± 14.20	0.3051	
Cell counts						
  WBC(× 109/L)	5.92 ± 0.62	2.82 ± 1.05	2.76 ± 1.12	1.43 ± 0.79	 < 0.0001	
  RBC(× 1012/L)	5.03 ± 1.02	1.97 ± 0.58	2.21 ± 0.57	2.28 ± 0.37	 < 0.0001	
  ANC(× 109/L)	3.23 ± 0.63	1.14 ± 0.76	1.012 ± 0.72	0.34 ± 0.39	 < 0.0001	
  ARC(× 109/L)	17.66 ± 30.03	0.98 ± 0.95	0.46 ± 0.60	0.21 ± 0.63	 < 0.0001	
  ALC(× 109/L)	2.02 ± 0.25	1.42 ± 0.82	1.55 ± 0.72	0.99 ± 0.434	0.0103	
  PLT(× 109/L)	237.00 ± 64.7	27.00 ± 11.61	22.56 ± 10.36	23.00 ± 17.50	 < 0.0001	
  Hb(g/L)	135.20 ± 11.97	63.00 ± 21.05	67.11 ± 17.24	65.67 ± 9.89	 < 0.0001	
HDs healthy donors, NSAA non-severe aplastic anemia, SAA severe aplastic anemia, VSAA very severe aplastic anemia, WBC white blood cell, RBC red blood cell, ANC absolute neutrophile granulocyte, ALC absolute lymphocyte, ARC absolute reticulocyte, PLT platelet, Hb hemoglobin

Alters of immune profile of AA patients

To broadly profile the individual components of the immune response against aplastic anemia and to assess the general landscape of immune responses and their perturbation, we performed extensive immunophenotyping to characterize the frequencies of circulating immune subsets in NSAA, SAA and VSAA compared to HDs, we collected longitudinal PBMCs and serum samples from adults enrolled in Guangzhou First People’s Hospital and performed flow cytometry analysis. We employed an antibody panel targeting 27 cellular markers to identify cell types and assess immune activation, function, and proliferation. Due to the proclivity of FlowJo software to encounter crashes when processing voluminous datasets for t-SNE downscaling, we judiciously selected 9 samples per subgroup for this analysis: HDs (n = 9), NSAA (n = 9), SAA (n = 9), and VSAA (n = 9). This selective methodology is an established standard in flow cytometry, intended to mitigate potential biases and anomalies that could compromise the integrity of data interpretation. After preprocessing, cells were clustered at three resolutions: five main types (B cell, natural killer (NK) cell, myeloid, monocyte, and T cell), seven cell subtypes. Lineage-specific protein expression guided cell clustering to minimize bias from activation, function, and proliferation proteins. T-distributed stochastic neighbor embedding (t-SNE) analysis revealed differential abundance of immune cell phenotypes across the entire cohort and various disease severity categories, irrespective of cluster labels (Fig. 1A-B). We employed FlowJo software to manually gate flow cytometry data (Supplementary Fig. 4), defining distinct cell populations. This formed the basis for subsequent analyses. To visualize differences in cell population abundances between HDs and AA patients, we performed t-SNE dissect the difference between HDs, NSAA, SAA and VSAA (Fig. 1C). We observed a decline in the proportion of LDNs populations in VSAA compared to HDs (p = 0.0126 for LDNs, Fig. 1D). The LDNs frequency also differed between NSAA and VSAA (p = 0.0289). The abundance of eosinophils was significantly greater in NSAA and SAA compared to HDs. In contrast, the abundance of monocytes was significantly lower in SAA and VSAA compared to NSAA (p = 0.0012 for SAA, p = 0.0332 for VSAA). Furthermore, we did not find significant differences in the frequencies of natural killer T cells (NKT) and NK cells between HDs and NSAA, SAA or VSAA (Fig. 1D). The application of Principal Component Analysis (PCA) and dimensionality reduction to the entire dataset of immune cell data from AA and HDs demonstrated a clear separation between the two groups, signifying substantial differences (Fig. 1E). This finding confirms that our analytical framework effectively captured significant variations in the immune state. Collectively, these results demonstrate the efficacy of our experimental and analytical approaches in identifying variations in the abundance of immune cell subtypes that are associated with different levels of disease severity in AA.Fig. 1 Alters of immune profile of AA patients. A Experimental outline showing peripheral blood mononuclear cells (PBMC) collection and mass cytometry analysis. B T-distributed stochastic neighbor embedding (t-SNE) plot of major immune cells in PBMCs. Cells are colored based on cell types; heat map of major immune cells in PBMCs, clustered by their relative expression of the markers. C T-SNE projections of major immune cells in PBMCs: the healthy Donors (HDs), non-severe aplastic anemia (NSAA) groups, severe aplastic anemia (SAA)groups and very severe aplastic anemia (VSAA) groups, respectively. D Percentage of major immune cells in PBMC from the HDs (n = 9), NSAA group (n = 9), SAA group (n = 9), VSAA group (n = 9). In all plots, mean ± SD is shown, and p > 0.05 was no statistically significant difference. E Utilizing PCA to reduce the dimensionality of the data, analyze the differences between HDs and AA

NK cells exhibit 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) (Ming et al. 2020). To elucidate variations in cell population proportions between HDs and patients with AA, t-SNE analysis was employed to identify distinct patterns among HDs, 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 HDs, 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 cells play a role in exerting cytotoxic effects (Poli et al. 2009). The majority of CD56+ NK cells express CD27. During the transition from CD56Bri to CD56Dim cytotoxic effectors, there is a reduction in CD27 expression (Vossen et al. 2008). Consequently, a statistical analysis of CD56−CD27−cells within NK cells was conducted. Compared to the HDs 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 (Alter et al. 2004). Additionally, there was a significant decrease in the expression levels of the cytotoxicity factor CD107a in NK cells and each subtype compared to the HDs group (Fig. 2H). In summary, there is a significant reduction in less functional CD56Dim NK cells in AA, indicating functional impairment.Fig. 2 NK cells exhibit a less functional CD56Dim molecular signature in AA patients. A t-SNE plot of NK cells of PBMCs from all samples; heat map of major cell subtypes in NK cells, clustered by their relative expression of the markers. B t-SNE plot of major NK cell subsets. Cells are colored based on cell types. C The flow cytometry gating strategy for NK cell phenotyping. D Percentage of major NK cell subsets in NK cells from the HDs (n = 9), NSAA group (n = 9), SAA group (n = 9), VSAA group (n = 9). E The flow cytometry gating strategy for CD56Dim NK cells, as well as the statistical plots for CD56DimCD27−NK cells and CD56BriCD27−NK cells. F The correlation between the ratio of CD56Dim NK cells to CD56+ NK cells and C-reactive protein (CRP). p < 0.05, it’s statistically significant. G Statistical plots of the expression levels of relevant functional factors (NKG2A, NKG2C, NKG2D, NKp30, NKp44, and NKp46) in NKDim cells in the AA and HDs groups. p < 0.05 are considered statistically significant and labeled accordingly. H Statistical plots of the expression levels of the cytotoxic factor CD107a in NK cells and each subtype in the HDs and AA groups. In all plots, mean ± SD is shown, and p < 0.05 are considered statistically significant and labeled accordingly

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-classical monocytes (non-c monocytes), and CD56+ monocytes. These subgroups were delineated using flow cytometry marker heatmaps (Fig. 3A). Next, we categorized the samples into four groups: HDs, 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-c monocytes among mononuclear cells did not significantly differ across HDs, NSAA, SAA, and VSAA groups. Nevertheless, CD56+ monocytes showed a significant increase in NSAA and VSAA compared to HDs (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 (Kuri-Cervantes et al. 2020). 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.Fig. 3 CD56+ (NK-liked) monocytes increase in AA patients and possess NK peculiarity. A t-SNE plot of monocytes of PBMCs from all samples; heat map of major cell subtypes in monocytes, clustered by their relative expression of the markers. B t-SNE plot of major monocyte subsets. Cells are colored based on cell types. C percentage of major monocyte subsets in monocytes from the HDs (n = 9), NSAA group (n = 9), SAA group (n = 9), VSAA group (n = 9). D The proportion of CD56+ monocytes within the monocyte population in the HDs and AA groups, along with statistical plots. E ROC curve analysis of CD56+monocyte in AA. F The difference in CD56 expression within CD14+ monocytes between AA and HDs. G The expression differences of GZMB, Perforin, and T-bet on NK cells between AA and HDs. In all plots, mean ± SD is shown, and p < 0.05 are considered statistically significant and labeled accordingly

MDSCs decrease in AA and recover post-treatment

We evaluated the proportions of MDSCs in the peripheral blood (PB) of AA patients with different subtypes (NSAA, SAA, and VSAA) and healthy donors (HDs). Utilizing previously published methods (He et al. 2018), we marked MDSCs with CD15, CD14, HLA-DR, and CD11b surface markers and visualized the flow cytometry data using t-SNE analysis (Fig. 4A-B). For further subtype analysis, MDSCs were identified with the specific markers HLA-DR−CD33+CD11b+ and classified into CD15+ polymorphonuclear MDSCs (PMN-MDSCs), CD14+ monocytic MDSCs (M-MDSCs), and CD15−CD14− early MDSCs (e-MDSCs) (Fig. 4C). Previous research has pointed to a significant decrease in MDSC proportions in AA, linked to a noticeable reduction in immunosuppressive capabilities (Dong et al. 2022). Our study similarly observed a gradual decrease in MDSC proportions in AA patients' PB compared to HDs, with no significant differences detected among the subtypes (Fig. 4D). We further analyzed samples from AA patients who received anti-thymocyte globulin (ATG) immunosuppressive therapy, dividing them into recovery (Re) and non-recovery (Non-Re) groups (Table 3). Results showed a significantly higher frequency of MDSCs in the recovery group compared to the non-recovery group, with an overall increase post-treatment (Fig. 4E). These findings suggest MDSCs as a potential marker for treatment evaluation. Using the same blood samples for both complete blood count (CBC) and flow cytometry staining enabled us to evaluate the correlation between MDSCs and various hematological indicators. Consequently, the results demonstrated a significant association between MDSC levels and key hematological parameters, specifically platelet count (PLT) and white blood cell count (WBC) (Supplementary Fig. 5). To uncover the molecular mechanisms behind the reduced suppressive function of MDSCs in AA, we performed Smart-seq analysis on MDSCs isolated from PBMCs. PCA analysis revealed distinct gene expression profiles of MDSCs between HDs and AA patients (Fig. 4F). Volcano plots and KEGG analysis highlighted a marked upregulation of the one-carbon synthesis pathway in MDSCs from AA patients (Fig. 4G). Additionally, GSEA analysis demonstrated diminished inhibitory function in AA patient MDSCs, associated with pathways such as JAK/STAT signaling, endoplasmic reticulum stress, and graft-versus-host disease (Fig. 4H-I).Fig. 4 MDSCs decrease in AA and recover post-treatment. A t-SNE plot of MDSCs from all samples; heat map of major cell subtypes in MDSCs, clustered by their relative expression of the markers. B t-SNE plot of major MDSC subsets. Cells are colored based on cell types. C The more specific marker gating strategies for detailed analysis of MDSC subtypes, statistical chart plotting, mass spectrometry analysis, and clinical correlation analysis. D Percentage of major MDSC subsets in MDSCs from the HDs (n = 9), NSAA group (n = 9), SAA group (n = 9), VSAA group (n = 9). E Percentage of major MDSC subsets in CD45+ cells from the HDs, Acquired Aplastic Anemia (AA) group, recovery (Re)group, non-recovery (Non-Re) group. F Utilizing PCA to reduce the dimensionality of the data, analyze the differences of MDSCs between HDs and AA. G The volcano plots and KEGG analysis revealed alterations in the one-carbon synthesis pathway in MDSCs derived from AA. H GSEA analysis revealed the functionality of MDSCs in AA patients. I The enrichment plot illustrates the pathways associated with the functional changes of MDSCs in AA patients. In all plots, mean ± SD is shown, and p < 0.05 are considered statistically significant and labeled accordingly

Table 3 Hematologic recovery data table

Variables	HDs (n = 18)	AA (n = 27)	Re-AA (n = 12)	Non-Re (n = 2)	p	
Gender					ANOVA	
  Male (%)	55.6%	58.3%	50%	50%	/	
  Female (%)	44.4%	41.7%	50%	50%	/	
Age (mean ± SD)	34.67 ± 11.57	31.75 ± 13.14	24 ± 5.774	30.5 ± 6.364	0.1449	
Cell counts						
  WBC(× 109/L)	5.72 ± 1.437	2.355 ± 1.411	6.365 ± 6.959	2.645 ± 0.502	0.0027	
  RBC(× 1012/L)	4.44 ± 1.105	2.101 ± 0.3348	2.577 ± 0.5418	1.585 ± 0.4738	 < 0.0001	
  ANC(× 109/L)	3.24 ± 1.27	0.7654 ± 1.162	4.825 ± 6.045	0.845 ± 0.502	0.0016	
  ARC(× 109/L)	3.03 ± 2.763	24.04 ± 25.3	66.04 ± 72.29	27.25 ± 27.65	0.0033	
  PLT(× 109/L)	227.3 ± 88.64	29.79 ± 16.19	63.64 ± 42.42	46.5 ± 13.44	 < 0.0001	
  Hb(g/L)	126.6 ± 23.97	66.13 ± 10.2	81.55 ± 16.57	51 ± 11.31	0.0001	
Treatment type						
  CsA + ATG (TPO)	/	/	3	1	/	
  CsA + Supportive care (TPO)	/	/	8	1	/	
AA acquired aplastic anemia, Re-AA recovery aplastic anemia, Non-Re non-recovery aplastic anemia, CsA + ATG (TPO) Administer antithymocyte globulin (ATG) and cyclosporine (CSA) to patients, with thrombopoietin (TPO) and supportive care, CsA + Supportive care (TPO) Administer cyclosporine (CSA) to patients, with thrombopoietin (TPO) and supportive care

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 AAA, MDSCs exert immunosuppressive effects, leading to a significant decrease in their components (Dong et al. 2022). To further investigate the differences between CAA and AAA, we collected data from patients diagnosed with either CAA or AAA (Table 4). We evaluated the percentage of HLA-DR−CD11b+CD33+ MDSCs in AAA and CAA, revealing a marginal decrease in MDSCs in AAA 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, AAA and CAA. It was observed that all three MDSCs subtypes exhibited significant differences in frequencies between AAA 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 HDs, AAA, and CAA groups. Notably, significant differences were evident between the control group and both the AAA and CAA groups, as well as between AAA 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 (Gramatges and Bertuch 2013). 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. Table 4 Clinical characteristic of healthy donors and patients with AAA and CAA

Variables	HDs (n = 17)	AAA (n = 50)	CAA (n = 22)	p	
Gender				ANOVA	
  Male (%)	58.8%	57.1%	68.2%	/	
  Female (%)	41.2%	42.9%	31.8	/	
Age (mean ± SD)	30.06 ± 8.019	24.77 ± 9.17	25.93 ± 13.28	0.3155	
Cell counts					
  WBC(× 109/L)	5.74 ± 1.478	2.508 ± 1.472	2.369 ± 1.217	 < 0.0001	
  RBC(× 1012/L)	4.47 ± 1.134	2.469 ± 0.673	2.147 ± 0.4861	 < 0.0001	
  ANC(× 109/L)	3.29 ± 1.286	0.9127 ± 0.8439	0.7122 ± 0.6177	 < 0.0001	
  ARC(× 109/L)	3.025 ± 2.763	13.44 ± 24.26	0.6963 ± 0.7036	0.0162	
  ALC(× 109/L)	2.016 ± 0.2472	1.359 ± 0.4775	1.51 ± 0.8779	0.0175	
  PLT(× 109/L)	259.1 ± 47.43	76.05 ± 22.64	66.41 ± 14.98	 < 0.0001	
  Hb(g/L)	127.3 ± 24.52	76.05 ± 22.64	66.41 ± 14.98	 < 0.0001	
AAA acquired aplastic anemia, CAA congenital aplastic anemia

Fig. 5 MDSCs effectively discriminate between AAA and CAA. A Utilizing flow cytometry to discern variances in MDSCs between the healthy control group, AAA, and CAA. B percentage of major MDSC subsets in CD45+ cells from the control group, AAA group, CAA group. C The ROC curves demonstrate the correlations of MDSCs between the control group, AAA and CAA pairwise. D The respective proportions of MDSCs with shorter telomeres and MDSCs with longer telomeres. E The correlation between the subtypes of MDSCs and telomere length. FThe proportions of MDSCs with chromosomal breakage over 10% and under 10% respectively, among the total MDSC population. G The correlation between the subtypes of MDSCs and chromosomal breakage. In all plots, mean ± SD is shown, and p < 0.05 are considered statistically significant and labeled accordingly

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 naive 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 HDs (Fig. 6C-D). Previous research has highlighted the crucial role of T cells in AA (Meraviglia et al. 2019). Compared to HDs, AA patients exhibit elevated levels of cytotoxic CD8+ T cells in both bone marrow and PB, coupled with a decrease in Treg levels (Wang and Liu 2019). Furthermore, we observed a reduced proportion of Tregs within the CD4+ T cell subset in the AA compared to HDs. Additionally, we conducted supplementary assessments of Treg activation status by examining the surface expression of CD45RA, revealing a lower abundance of activated Tregs 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 HDs 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 induce inflammation (Kuri-Cervantes et al. 2020). Additionally, we employed t-SNE for dimensionality reduction and visualization to explore variations in B cells and their subsets in AA (Supplementary Fig. 6A-B). No significant differences were observed in the three B cell subtypes among NSAA, SAA and VSAA (Supplementary Fig. 6C), consistent with previous research (Kordasti et al. 2012). 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.Fig. 6 AA exhibit elevated activated cytotoxic T cells and reduced regulatory T cells. A t-SNE plot of T cells from all samples; heat map of major cell subtypes in T cells, clustered by their relative expression of the markers. B t-SNE plot of major T cell subsets. Cells are colored based on cell types. C Percentage of major CD4+ T cell subsets in MDSCs from the HDs (n = 9), NSAA group (n = 9), SAA group (n = 9), VSAA group (n = 9). D Percentage of major CD8+ T cell subsets in MDSCs from the HDs (n = 9), NSAA group (n = 9), SAA group (n = 9), VSAA group (n = 9). E The level and functional alteration of regulatory T cells in AA. F The changes in the proportion of activated T cells within the T cell population across the HDs (n = 9), NSAA group (n = 9), SAA group (n = 9), VSAA group (n = 9). In all plots, mean ± SD is shown, and p < 0.05 are considered statistically significant and labeled accordingly

Immune cell correlation identification during AA

Based on the comprehensive immune mapping, we utilized three statistical metrics—log fold change (Log FC), area under the curve (AUC), and false discovery rate (FDR)—to analyze the differences in immune cells HDs and AA. As shown in Fig. 7A, memory B cells, eosinophils, and NKBri cells were significantly elevated in AA (Log FC > 0.25, FDR < 0.05, AUC > 0.7), whereas NKDim cells and non-classical monocytes were significantly reduced (Log FC < -0.25, FDR < 0.05, AUC > 0.7). The results from the volcano plot were consistent with those from the comprehensive immune mapping, further validating the scientificity.Fig. 7 Immune cell correlation and novel immune cells identification during AA. A Plot of correlation between HDs and AA according to Log FC and -log10 (FDR); Plot of correlation between HDs and AA according to Log FC and AUC. B The correlation heatmap of various key immune cells in AA. C Displaying the correlation among various immune cells using a Chord Diagram

By analyzing multiple immune cell types within the same samples, we were able to more scientifically assess the relationships among these cells, providing new insights for subsequent research. We employed Spearman's correlation test to evaluate the correlations among major immune cells and their subtypes (Fig. 7B), and combined with the chord diagram (Fig. 7C). This approach revealed significantly correlated cell pairs (p < 0.05). Specifically, NKBri cells showed significant positive correlations with monocytes, NKT cells, various CD8+ T cell subsets, B cells, and c-monocytes. NKDim cells were positively correlated with B cells, inter-monocytes, NKT cells, activated CD4+ T cells, LDNs, and non-c monocytes. C-monocytes exhibited significant positive correlations with B cells, inter monocytes, and other monocyte subsets, while naive B cells were positively correlated with memory B cells (p < 0.05). Additionally, NKBri cells were negatively correlated with CD4+ Tcm, and NKDim cells showed negative correlations with CD4+ Temra, NKUC, CD4+ Tem, and naive B cells. Eosinophils were negatively correlated with regulatory T cells, whereas classical monocytes exhibited significant negative correlations with various T cell subsets (p < 0.05). These findings suggest that by leveraging Comprehensive mapping of immune, we can delve deeper into the significant alterations in immune cells in AA, exploring their mechanisms and interactions, providing promising directions for future research.

Discussion

AA is a rare syndrome characterized by bone marrow hypoplasia and peripheral blood pancytopenia. Patients typically exhibit symptoms such as anemia, petechiae, or bleeding (Scheinberg and Young 2012). Current treatment options primarily include hematopoietic stem cell transplantation from HLA-matched sibling donors and immunosuppressive therapy using anti-thymocyte globulin and ciclosporin (Young et al. 2006; Socie et al. 1993). However, less than 30% of patients have matched siblings as donors, underscoring the essential role of immunosuppressive therapy (Brodsky and Jones 2005). Therefore, it is particularly important to delve deeper into the immune environment of patients with AA.

An imbalance in the immune system is known to drive the progression of aplastic anemia (AA). Comprehensive whole-genome transcriptome analysis has revealed significant abnormalities in CD4+ and CD8+ T cells among AA patients (Zeng et al. 2004), Specifically, CD8+ cytotoxic T cells with restricted TCR diversity (oligoclonal T cells) are expanded, secreting proinflammatory cytokines such as IFN-γ and TNF-α, and targeting hematopoietic stem cells (Young 2006; Matsui et al. 2006). Elevated levels of CD4+ Th1 and Th17 cells further indicate an inflammatory state, with these specific cytotoxic T cells contributing to the disease pathogenesis. Moreover, regulatory T cells in AA are dysfunctional, failing to control the autoimmune response (Kordasti et al. 2012). Enhanced expressions of NKp46 and perforin on NK cells can exacerbate T cell activity, thereby contributing to hematopoietic failure (Liu et al. 2014). Additionally, lower neutrophil counts, especially low absolute neutrophil count (ANC), are significant predictors of survival and response to immunosuppressive therapy (IST). The European Group for Blood and Marrow Transplantation (EBMT) reports varied 5-year survival rates based on ANC levels, underscoring its prognostic importance (Liu et al. 2019; Bacigalupo et al. 1988). Previous studies have focused on changes in a single type of cell in AA, whereas our research provides a more comprehensive mapping of immune changes in AA. In addition, we have systematically categorized aplastic anemia into its distinct forms: NSAA, SAA, and VSAA—to facilitate a granular examination of immune cell variations across these clinical gradations. We observed increases in eosinophils, activated T cells, and NK-like CD56+ monocytes, alongside reductions in LDNs, monocytes, and regulatory T cells. Notably, there was a marked decrease in low-function CD56Dim cells. Additionally, MDSCs initially decreased in AA but recovered post-treatment, in contrast to their increase in CAA, aiding in the differentiation of AA subtypes.

Our research team has already discovered that MDSCs are bone marrow-derived cells with significant immunosuppressive capabilities. Currently, for differential diagnosis, we employ a comprehensive approach that includes whole-exome sequencing, chromosomal karyotype analysis, and telomerase length testing. If differentiation is unclear, CAA could potentially be misdiagnosed as AA. Long-term immunosuppression can lead to poor blood counts, increased infections, and missed opportunities for transplantation. Some patients carrying Fanconi anemia-related genes or telomere-related TERT gene mutations may have chromosomal instability, increasing the risk of clonal hematopoiesis and solid tumors post-ATG treatment (Bacigalupo et al. 1988). CAA rarely responds to immunosuppressive therapy, emphasizing the need to distinguish CAA from AAA to select appropriate treatment methods (Bacigalupo et al. 1988). Previous research results indicate that, compared to healthy volunteers, the proportion of MDSCs in the peripheral blood of CAA patients is significantly elevated, while the proportion of MDSCs in the peripheral blood of AAA patients is significantly reduced. Telomere length and the breakage rate in the mitomycin C-induced breakage test show negative and positive correlations with the proportion of MDSCs, respectively. These findings suggest that the proportion of MDSCs varies among AA patients with different pathogeneses. Given the immunosuppressive function of MDSCs, the reduction of MDSCs in immune-related AA might lead to immune overactivation, resulting in suppressed function or reduced numbers of hematopoietic stem and progenitor cells. Conversely, in CAA, the increased proportion of MDSCs may lead to heightened immunosuppression, increasing the risk of tumors and infections. Additionally, due to the lack of involvement of T-cell activation, CAA is unresponsive to immunosuppressive therapy. Additionally, we hope to use MDSCs to identify certain cases of aplastic anemia with unclear genetic backgrounds as CAA rather than the conventional diagnosis of AAA. Therefore, studying MDSCs helps identify the disease and formulate the most appropriate treatment plans. Detecting MDSC levels can assist in the clinical diagnosis of AA subtypes. Our extensive studies on MDSCs propose them as potential biomarkers to differentiate AAA from CAA, supported by a newly obtained patent (Patent No.: ZL202210394136.X). MDSCs may aid in differentiating these cases.

In our comprehensive immunological mapping, we observed a trend of overall NK cell reduction, which progressively decreases with increasing disease severity. This finding is consistent with published studies. Additionally, research indicates that NK cell levels rebound following ATG treatment, likely due to the restoration of hematopoietic stem cells (Gascon et al. 1986). However, it is apparent that other immune cells also like NK cells undergo significant alterations following IST, which will be the focus of our team’s future research to enrich the details of the immunological landscape. Additionally, our further analysis of NK cell subtypes revealed a significant reduction in CD56Dim NK cells, with a progressive decline correlating with disease progression. This observation is corroborated by existing literature (Li et al. 2011). Moreover, we examined the expression of functional markers on CD56Dim NK cells and discovered that their functional markers were significantly lower compared to those in healthy donors. This study is the first to identify low-functional CD56Dim NK cells in AA.

Previous studies have demonstrated that patients with aplastic anemia frequently exhibit monocytopenia, significantly elevating their susceptibility to severe infections. However, the specific mechanisms underlying this phenomenon remain insufficiently explored (Twomey et al. 1973). Our comprehensive immune mapping of AA patients revealed a marked reduction in the overall number of peripheral blood monocytes compared to healthy donors, aligning with previous studies. In addition to this, we further explored how monocytes function. Recent studies have found a significant increase in CD56+CD14+Ki67+IFN-γ+ monocytes in moderate and severe COVID-19 patients, indicating their potential role in severe COVID-19 (Dutt et al. 2022). CD56+ monocytes show low HLA-DR and high L-selectin levels, release large amounts of TNF-α and IL-6, and express genes associated with excessive inflammation, such as ATF3, NFIL3, and HIVEP2 (Campana et al. 2022). Incorporating directional metabolomics mass spectrometry, detailed in Fig. 4H-I, and correlating these findings with clinical indicators, marks a novel approach in our field. Our study firstly detect a significant increase in NK-like CD56+ monocytes in AA, suggesting a pro-inflammatory polarization in AA patients, where NK-like CD56+ monocytes may drive disease progression.

In clinical diagnostics, a reduction in ANC has become an indicator for AA diagnosis (Liu et al. 2019). LDNs, a subset of neutrophils, display significant pro-inflammatory properties across various diseases. For instance, in severe/critical COVID-19 patients, the proportion of neutrophils in bronchoalveolar lavage fluid (BALF) is notably higher than in moderate cases. These low-density neutrophils in severe COVID-19 patients express elevated levels of pro-inflammatory genes such as TNF, IL1β, and IL-6, secrete pro-inflammatory cytokines like IL-8, IL-6, and IL-1β, and may play a crucial role in local inflammation by recruiting inflammatory monocytes and neutrophils via CCR1 and CXCR2 receptors (Liao et al. 2020). In systemic lupus erythematosus (SLE) patients, LDNs are believed to result from abnormal hematopoiesis in the bone marrow and immune regulatory imbalance in the peripheral blood (Tay et al. 2020). Our study initiates to detect changes in LDNs in AA, finding a reduction in peripheral blood likely due to decreased hematopoietic stem/progenitor cells in the bone marrow of AA patients.

We observed a significant increase in the overall T cell population in patients with VSAA compared to HDs. Subsequent analyses of T cell subtypes, categorized by developmental stages, did not reveal significant differences among these subgroups, a published study has demonstrated results consistent with ours, using the same markers and T cell classification (Zhang et al. 2023). However, when T cells are categorized based on different conditions, alterations are evident in AA. Other studies have indicated an increase in Th1 (IFN-γ+CD4+) and Tc1 (IFN-γ+CD8+) cells in the peripheral blood of AA patients. Notably, T-bet is significantly upregulated in T cells derived from AA patients. In these cells, the Itk-PKC-θ signaling pathway activates T-bet, which then binds directly to the proximal promoter region of the IFN-γ gene, driving its active transcription. This upregulation of T-bet and the subsequent increase in IFN-γ transcription amplify the Th1 cell response, thereby eliciting a more robust immune response in AA patients (Solomou et al. 2006). Previous studies have demonstrated an increase in cytotoxic T lymphocytes (CTLs) in patients with aplastic anemia. This phenomenon is mediated through the involvement of Fas/FasL in the apoptosis of hematopoietic stem cells (HSCs), elucidating a potential mechanism for the dysfunction of bone marrow hematopoietic cells in severe aplastic anemia (SAA) patients (Maciejewski et al. 1995). Additionally, there is a marked elevation in CD8+HLA-DR+ T cells, which notably suppress the proliferation of Tregs (Zoumbos et al. 1985). The concurrent activation of CTLs and reduction in Tregs indicates a skewing of T cells towards a pro-inflammatory phenotype. These findings are congruent with our observations of increased act CD8+ T cells and decreased Tregs, thereby affirming the scientific rigor and accuracy of our comprehensive immune profiling using clinical blood samples.

In summary, we have constructed a comprehensive immunological mapping of aplastic anemia using blood samples from clinical patients, allowing for an in-depth observation of immune cell changes. This panoramic view provides valuable insights for scientifically exploring cell interactions and connectivity. Guided by this landscape, we focused on analyzing immune cell subsets with significant changes, including MDSCs, NK cells, and monocytes. The potential of MDSCs as biomarkers for disease severity and treatment response presents promising research prospects. Our study not only enriches the existing knowledge of AA but also emphasizes the importance of an integrated immunological approach in understanding and managing this complex disease, laying the foundation for the development of more targeted and effective therapeutic strategies.

Conclusions

By analyzing PBMCs and the components and kinetics of the immune response of patients with AA compared to those of HDs, we compiled a complete immune phenotype map to facilitate a deeper investigation of disease progression. Our findings reveal that CD56+ monocytes exhibiting NK characteristics, activated cytotoxic T cells, functionally compromised CD56Dim NK cells, Tregs, and MDSCs play pivotal roles in the initiation and progression of AA. These cells have the potential to serve as diagnostic biomarkers. Future research is needed to further explore the associated pathways and elucidate the underlying mechanisms, which will pave the way for the development of innovative preventive or therapeutic strategies for AA.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary file1. Supplementary Fig. 1 Negative controls for PBMCs. Negative controls were incorporated to ensure the accuracy of our panel testing (PNG 15527 KB)

Supplementary file2. Supplementary Fig. 2 single-color controls for PBMCs. Single-color controls were set up to adjust flow cytometry compensation (PNG 26485 KB)

Supplementary file3. Supplementary Fig. 3 Fluorescence Minus One controls for PBMCs. A: Gating strategies for flow cytometric analysis of B cells (CD19+) and their subsets naive B cells (CD27-/+CD38-), memory B cells (CD27-CD38+), and plasm blasts (CD27+CD38+). B: Gating strategies for flow cytometric analysis of CD8+T cells (CD8+) and their subsets naive T cells (T naive, CD45RA+CCR7+), central memory T cells (Tcm, CD45RA-CCR7+), effector memory T cells (Tem, CD45RA-CCR7-) and effector memory cells re-expressing CD45RA (Temra, CD45RA+CCR7-), active T cells (act-T, HLA-DR+CD38+). C: Gating strategies for flow cytometric analysis of CD4+T cells (CD4+) and their subsets regulate T cell (Treg, CD4+CD127-CD25+), naive T cells (T naive, CD45RA+CCR7+), central memory T cells (Tcm, CD45RA-CCR7+), effector memory T cells (Tem, CD45RA-CCR7-) and effector memory cells re-expressing CD45RA (Temra, CD45RA+CCR7-), active T cells (act-T, HLA-DR+CD38+). D: FMO controls are created by staining cells in four individual tubes, each tube omitting one of the markers: CD38, HLA-DR, CD45RA, or CCR7. The FMO control for other markers were also set up according to the above experimental steps (PNG 18364 KB)

Supplementary file4. Supplementary Fig. 4 Flow Cytometry Gating strategy for PBMCs. A Gating strategies for flow cytometric analysis of MDSCs (CD11b+HLA-DR-) and their subsets early-MDSCs (e-MDSCs, CD11b+HLA-DR-CD14-CD15-), monocytic MDSCs (M-MDSCs, CD11b+HLA-DR-CD14+CD15-), and polymorphonuclear MDSCs (PMN-MDSCs, CD11b+HLA-DR-CD14-CD15+). B Gating strategies for flow cytometric analysis of eosinophils (CD15+CD16-), LDNs (CD15+CD16+), B cells (CD19+CD3-), and their subsets naive B cells (CD27-/+CD38-), memory B cells (CD27-CD38+), and plasma blasts (CD27+CD38+). C Gating strategies for flow cytometric analysis of T cells (CD19-CD3+), and their subsets CD4+ T cells (CD4+), CD8+ T cells (CD8+), regulate T cells (Treg, CD4+CD127-CD25+), naive T cells (T naive, CD45RA+CCR7+), central memory T cells (Tcm, CD45RA-CCR7+), effector memory T cells (Tem, CD45RA-CCR7-) and effector memory cells re-expressing CD45RA (Temra, CD45RA+CCR7-), active T cells (act-T, HLA-DR+CD38+). D Gating strategies for flow cytometric analysis of NKs (CD56+) and their subsets mature NK cells (NKDim, CD56DimCD16+), immature NK cells (NKBri, CD56BrightCD16-), unclassical NK cells (NKU, CD56DimCD16-), natural killer T cells (NKT, CD3+CD56+). E Gating strategies for flow cytometric analysis of Monocytes (CD14+) and their subsets classical monocytes (c-monocytes, CD14+CD16−), non-classical monocytes (non-c-monocytes, CD14dimCD16+), intermediate subset (inter-monocytes, CD14+CD16+), CD56+ monocytes (CD56+CD14+) (PNG 17714 KB)

Supplementary file5. Supplementary Fig. 5 Correlation between MDSCs and hematological parameters for clinical diagnosis of AA. The correlation between the above cells and the clinical test data was calculated using simple linear regression, and a p< 0.05 was considered statistically significant (PNG 1679 KB)

Supplementary file6. Supplementary Fig. 6 The B cells and their subtypes show no significant changes in AA. A t-SNE plot of B cells of PBMCs from all samples; heat map of major cell subtypes in B cells, clustered by their relative expression of the markers. B t-SNE plot of major B cell subsets. Cells are colored based on cell types. C Percentage of major B cell subsets in B cells from the HDs (n =9), NSAA group (n = 9), SAA group (n = 9), VSAA group (n = 9) (PNG 5053 KB)

Acknowledgements

We thank all blood donors, their families, and surrogates, as well as the medical personnel in charge of patient care.

Author contributions

Wang H. and Liu Y. wrote the main manuscript text, Chen Y., Deng H. and Jiang X. prepared Figs. 1, 2, and 3. All authors reviewed the manuscript.

Funding

Funds to Yufeng Liu: National Natural Science Funds (No. 82171695); Science and Technology Program of Guangzhou (SL2024A03J01319; SL2024A04J00240). Funds to Shunqing Wang: National Key R&D Program of China (2023YFA1800100), the Innovative Clinical Technique of Guangzhou (2023C-GX01), Science and Technology Key Project of Guangzhou, China (02102010037).

Ruiqing Zhou: Natural Science Foundation of China (No.81600147); the Medical Scientifc Research Foundation of Guangdong Province(A2023314). And other funds to authors: National Natural Science Foundation of China (82101823).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

The study was approved by the Clinical Ethics Review Board of Guangzhou First People’s Hospital. Written informed consent was obtained from all patients at the time of admission.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Huijuan Wang, Yinchun Chen, and Haimei Deng contributed equally to this work.
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