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Novel endothelial progenitor cells populations as biomarkers of damage and remission in systemic lupus erythematosus

Arthritis Research & Therapy volume 26, Article number: 170 (2024) Cite this article

Abstract

Introduction

Endothelial progenitor cells (EPCs) are essential for maintenance of vascular homeostasis and stability, key processes in the pathogenesis of systemic lupus erythematosus (SLE). However, the role and phenotypic characterization of EPCs populations in SLE have not been completely elucidated.

Objective

To identify EPCs specific subpopulations in patients with SLE using a novel flow cytometry tool.

Methods

Peripheral blood mononuclear cells (PBMCs) were isolated from patients with SLE and healthy controls (HC). mRNA and surface protein expression were determined by quantitative PCR (qPCR) and flow cytometry. Clusters identification and characterization were performed using tSNE-CUDA dimensionality reduction algorithms.

Results

tSNE-CUDA analysis identified eight different clusters in PBMCs from HC and patients with SLE. Three of these clusters had EPC-like phenotype and the expression was elevated in patients with SLE. Moreover, four SLE-associated subclusters were found mainly expressed in patients with SLE, being only present in patients in remission with SLE and significantly associated with the 2021 Definition of Remission in SLE. Importantly, we also identified specific clusters in SLE patients with organ damage, according to the Systemic Lupus International Collaborating Clinics (SLICC)/American College of Rheumatology damage index (SDI). These clusters showed an EPC-like phenotype, but the expression of angiogenic markers was lower compared to HC or patients without organ damage, suggesting an impaired angiogenic function.

Conclusion

Our novel approach identified clusters of EPCs in patients with SLE that are associated with remission and damage. Therefore, these clusters might be useful biomarkers to predict disease progression and severity in SLE pathogenesis.

Key messages

  1. 1.

    Novel endothelial progenitor cells clusters have been characterized in patients with SLE.

  2. 2.

    Endothelial progenitor cells clusters are potential biomarkers of damage and remission in SLE.

Introduction

Cardiovascular events (CVE) are one of the most important comorbidities in systemic lupus erythematosus (SLE) and one of the main causes of death among patients with SLE. In fact, the risk of CVE is between 2.5 and 5 times higher in patients with SLE compared to healthy population [1, 2]. In addition, patients with SLE have a standardized mortality ratio of 2.72 for cardiovascular-related death. Despite the advances in the last years, the exact causes of this higher risk are not completely known [3,4,5].

Endothelial progenitor cells (EPCs), identified as CD31+ CD45− CD309+, are circulating cells that play important functions in the maintenance of vascular homeostasis and stability, such as the restorage of endothelial barrier function, migration, promotion of angiogenesis, production of cytokines and replacement of damaged endothelial cells [4]. Alterations in the frequency and functionality of these cells have been associated with several CVE, including atherosclerosis, peripheral arterial disease, acute coronary syndrome and congestive heart failure [6,7,8].

In the last few years, multiple works have tried to characterize EPCs populations, but the experiments on EPCs are challenging given that they represent a small fraction of the total number of circulating cells. Currently, they have been classified into three major groups based on their membrane markers: circulating angiogenic cells (CACs: CD34+ CD133+ CD309+), endothelial colony-forming cells (ECFCs: CD31+ CD105+ CD146+ CD34+ CD309+ CD144+ vWF+ CD14− CD45−) and myeloid angiogenic cells (MACs: CD45+ CD14+ CD31+ CD146− CD34−) [4, 9, 10].

The frequency and function of EPCs in SLE pathogenesis is controversial, since several works reported a decrease in EPCs populations in patients with SLE compared to HC [11,12,13], while others showed no differences [14,15,16] or even an increase [17, 18]. Given the importance of EPCs in atherosclerosis and other CVE, and the contradictory results found in the field, in this study we have used novel flow cytometry tools for the identification of EPCs specific subpopulations in patients with SLE and we have explored their association with the clinical characteristics.

Materials and methods

Patient recruitment and clinical variables

Patients included in this study were recruited at the University Hospital Complex of Vigo, fulfilling the 2019 American College of Rheumatology (ACR) and European Alliance of Associations for Rheumatology (EULAR) classification criteria for SLE [19, 20]. Ethics Committee of Galicia (study number 2020/158) approved this study and all patients supplied written informed consent to be included. The carotid intima media thickness assessment was performed as previously described [21].

Blood samples were collected and serological tests for C3, C4, anti-dsDNA and antiphospholipid antibodies (IgM and IgG anticardiolipin antibodies and IgM and IgG anti-β2 glycoprotein 1 antibodies) and lupus anticoagulant were performed. Patients were evaluated by the rheumatologists to obtain activity scores such as SLEDAI (SLE Disease Activity Index) and SLEDAS (SLE Disease Activity Score); treat to target goals such as LLDAS (Lupus Low Disease Activity State) and 2021 DORIS (Definition of Remission in SLE 2021) and damage score by the Systemic Lupus International Collaborating Clinics (SLICC)/American College of Rheumatology damage index (SDI) [22,23,24,25].

Clinical characteristics of patients with SLE and healthy controls (HC) used in tSNE-CUDA analysis are detailed in Supplementary Table S1. Also, clinical characteristics of patients with SLE and healthy controls (HC) used in classical flow cytometry assays are detailed in Supplementary Table S2.

Flow cytometry

PBMCs from patients with SLE and HC were isolated using Lymphoprep (StemCell Technologies). PBMCs were stained with Fixable Viability Dye eFluor e450 (ThermoFisher) and Fc‐γ receptor blocking was performed using 10% of Anti-Hu Fc Receptor (ThermoFisher). The following antibodies were used: CD146 BV510, CD31 BV605, CD34 FITC, TIE2 (202b) PE, CD105 PE-CF 594, CD14 PE-Cy5, CD309 PE-Cy7, CD144 APC, CD45 AF 660 (all from Biolegend). Data was obtained in a CytoFlex S Analyser (Beckman Coulter) and files were uploaded to Cytobank (Beckman Coulter). tSNE-CUDA dimensionality reduction algorithm was performed to determine clusters stratification and distribution.

Alternatively, data were analysed with CytExpert Software (Beckman Coulter). Results were expressed as Median Fluorescent Intensity (MFI) or percentage of positive cells.

RNA isolation and IFN signature quantification

RNA from PBMCs of HC and patients with SLE was isolated using the Nucleospin RNA/Protein mini kit (Macherey–Nagel). Reverse transcription of total RNA was performed using iScript (Bio-Rad) and PCR assays were performed in the CFX96 Touch-Real-Time PCR system (Bio-Rad). Specific primers (Integrated DNA Technologies) are shown in Supplementary Table S3. To obtain IFN signature values, relative gene expression was normalized to B2M and GAPDH housekeeping genes and expression of IFI44L, ISG15, IFIT2, IFIT3 and MX1 was used to determine a Z score and subsequent IFN signature, as previously described [26, 27].

Statistical analysis

GraphPad Prism 8 software was used to perform statistical analyses. Normality was assessed through Kolmogorov test. Then, non-parametric Mann–Whitney, Kruskal–Wallis tests and Spearman correlation were used as appropriate.

Results

tSNE-CUDA analysis identified EPC-like clusters and subclusters associated with disease activity

Firstly, we used the dimensionality reduction algorithm tSNE-CUDA to classify the different populations based on EPCs markers in PBMCs from patients with SLE. tSNE-CUDA is a graphics processing unit (GPU)-accelerated implementation of t-distributed Symmetric Neighbour Embedding (t-SNE) for visualizing datasets and models, allowing large scale visualizations of modern computer vision datasets. Compared to classical flow cytometry assays, tSNE-CUDA is able to characterize populations thorough the combination of markers used [28].

Using this approach, we identified 8 different population clusters in HC and patients with SLE (Fig. 1A). Most of these clusters did not show characteristics of EPCs; however, clusters 1 and 5 expressed markers of MACs (CD45 + , CD14 + and CD31 +), while cluster 6 showed phenotypic characteristics that define ECFCs, such as absence of CD45 and CD14 and expression of CD31 and CD309 (Fig. 1B and Supplementary Figure S1). The percentage of these clusters was similar in patients with SLE and HC except cluster 6, which was significantly higher in patients with SLE (Fig. 1C).

Fig. 1
figure 1

Patients with SLE present EPCs clusters and subclusters that are involved in disease. A High dimensional characterization of EPCs clusters in patients with systemic lupus erythematosus (SLE) using tSNE-CUDA dimensionality reduction algorithm. B Characterization of EPCs clusters identified in (A) according to the expression of surface protein markers. C Percentage of cluster expression in HC and patients with SLE. D High dimensional characterization of EPCs subclusters in patients with SLE (yellow) and HC (purple) using tSNE-CUDA dimensionality reduction algorithm. E Characterization of EPCs clusters identified in (D) according to the expression of surface protein markers. F Percentage of subclusters expression in HC and SLE patients stratified according to the 2021 Definition of Remission in SLE (DORIS). Data are shown as percentage of cells. Bars show the median + interquartile range. **** p < 0.0001, using Mann-Whitney and Kruskal-Wallis tests

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We next analyzed these clusters performing a stratification of group patients based on traditional CVE risk factors, such as cholesterol, triglycerides levels and carotid intima media thickness (CIMT), but also depending on specific factors involved in risk of CVE in SLE pathogenesis, such as interferon gene signature (IFNGS), disease status and current treatments [4]. Cluster 5 positively correlated with SLE activity (SLEDAS and SLEDAI), while clusters 3 and 7 were positively correlated with triglyceride levels. On the other hand, we found a significant increase in cluster 4 in patients not treated with glucocorticoids compared to those treated, and in patients in remission (DORIS +) compared to patients in active disease. We did not find any other association between cluster expression and traditional and SLE-specific CVE risk factors (Supplementary Figure S2 and S3).

We also identified and characterized different subclusters within the previous described clusters. These subclusters expressed CD34, CD45, CD144, but no expression of CD31 and CD309, and showed differential expression levels in patients with SLE compared to HC. Indeed, we found four specific subclusters that were expressed in 50% of patients with SLE, while only in 25% of healthy controls (Fig. 1D, E). Thus, patients with SLE showed an increased expression of these subclusters compared to HC, although differences were not significant due to the absence of the subclusters also in several patients (Supplementary Figure S4). These subclusters S2-S4 were highly correlated among them (Supplementary Figure S5) and patients in remission showed a higher expression of these subclusters compared to HC and patients with active disease (Fig. 1F). We did not find any association with any other CVE risk factors (data non shown).

Altogether, this flow cytometry approach identified specific cluster and subclusters in patients with SLE, which are associated with the activity of the disease.

SLE patients with organ damage showed specific EPCs populations

Interestingly, when we studied the possible associations of the 8 clusters previously identified with the clinical characteristics of the patients, we found 4 patients that behaved atypically in their cluster expression levels, independently of the parameters analyzed (Supplementary Figure S3). When we checked in detail the characteristics of these patients, we found that they were the patients with organ damage (SDI +), so we decided to study them in depth. The damage observed in these patients are detailed in Supplementary Table 1.

Clusters 5 and 8 were barely expressed by SDI- patients, while the expression of clusters 1, 2 and 4 was almost absent in SDI + patients (Fig. 2A, B). Since cluster 4 was slightly expressed in SDI- patients, clusters 1 and 2 were determined as specific to SDI- patients and clusters 5 and 8 as specific to SDI + patients (Fig. 2C). We did not observe differences between SDI + and SDI- patients in the expression of cluster 6, which was the only one with higher expression in patients compared to HC (Fig. 2C). Notably, despite the similar expression of this cluster between SDI + compared to SDI- patients, the expression of the angiogenic markers CD31, Tie2, CD105 and CD309 was significantly lower in the SDI + group (Fig. 2D), suggesting functional differences in this cell population between these groups of patients.

Fig. 2
figure 2

Specific EPCs populations in SLE patients with organ damage. A High dimensional characterization of EPCs clusters in SLE patients positive (SDI+, green) or negative (SDI-, grey) for the Systemic Lupus International Collaborating Clinics Damage (SLICC) Index, using tSNE-CUDA dimensionality reduction algorithm. B, C Phenotypic characteristics (B) and percentage of expression (C) of specific clusters for SDI+ patients (5 and 8), SDI- patients (1 and 2) and shared clusters (3,4,6,7). D CD31, Tie2, CD105, CD309 protein expression cluster 6 in HC, SDI- and SDI+ patients. Data are shown as percentage of cells and Median Fluorescence Intensity (MFI). Bars show the median + interquartile range. * p < 0.05; ** p < 0.01; *** p < 0.001, using Kruskal-Wallis test

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ECFCs pure populations are increased in patients with SLE and are associated with clinical activity and organ damage

Since the new approach here performed does not represent pure cell populations, classical flow cytometry analysis of MACs and ECFCs populations was performed in order to validate our initial findings (Supplementary Figure S6). ECFCs were significantly increased in patients with SLE compared to HC. MACs population was also elevated in patients, but differences were not significant. Importantly, the expression of CD31 and CD105 was significantly lower in ECFCs from patients with SLE, while CD309 expression was reduced in both ECFCs and MACs (Fig. 3A, B).

Fig. 3
figure 3

Endothelial cells forming colonies (ECFCs) expression is elevated in patients with SLE. A-B Percentage of ECFCs (A) and MACs (B) and CD31, Tie2, CD105, CD309 protein expression in HC and patients with SLE. Data are shown as percentage of cells and Median Fluorescence Intensity [MFI]. Bars show the median + interquartile range. * p < 0.05 and ** p < 0.01, using Mann-Whitney test. HC: healthy control; SLE: Systemic Lupus Erythematosus

Full size image

Altogether, these data support our findings in the EPCs clusters, which suggest that EPCs from patients with SLE may have a functional angiogenic impairment.

ECFCs and MAC are associated with clinical activity and organ damage

Finally, we sought associations between ECFCs, MACs and clinical activity. Then, correlation analysis between ECFCs, MACs and clinical parameters were performed. Firstly, EFCFs frequency and CD31 showed positive and negative correlations, respectively, with triglycerides levels, a CVE traditional factor [5]. Regarding clinical disease parameters, a positive correlation between ECFCs percentage and IFNGS, SLEDAS and SLEDAI scores was found. Interestingly, these scores showed a significant negative correlation with CD31 expression by ECFCs (Fig. 4A and Supplementary Figure S7).

Fig. 4
figure 4

ECFCs are associated with clinical activity and organ damage. A Correlation between EPCs and IFN Gene Signature (IFNGS) score, Systemic Lupus Erythematosus Disease Activity Score (SLEDAS), Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and triglycerides levels (TGD). Spearman r correlation was used. B, C Percentage of ECFCs and CD31, Tie2, CD105, CD309 protein expression in HC and SLE patients stratified according to the 2021 Definition of Remission in SLE (DORIS, B) or the Systemic Lupus International Collaborating Clinics Damage (SLICC) Index (C). Data are shown as percentage of cells and Median Fluorescence Intensity (MFI). Bars show the median + interquartile range. using Mann-Whitney test. * p < 0.05 and ** p < 0.01, using Kruskal-Wallis test

Full size image

ECFCs percentage was independent of clinical disease status, since the percentage was significantly elevated in both DORIS + and DORIS- patients. However, the expression of CD31, CD309 and Tie2 was lower in DORIS- compared to DORIS + patients, although differences were only significant in the case of CD31 (Fig. 4B).

When we stratified according to organ damage, ECFCs percentage was only elevated in SDI- patients. CD31, CD309, CD105 and Tie2 expression was reduced in SDI- and SDI + patients, but reduction was much more pronounced in the latter (Fig. 4C).

In the case of MACS, we did not find any relevant correlation (Supplementary Figure S8). But the frequency of MACS was elevated in DORIS + patients compared to HC and DORIS- patients. In addition, CD31 expression was significantly reduced in patients in remission, while the other angiogenic markers were not modulated (Fig. 5A).

Fig. 5
figure 5

MACs are associated with clinical activity and organ damage. A, B Percentage of MACs and CD31, Tie2, CD105, CD309 protein expression in HC and SLE patients stratified according to the 2021 Definition of Remission in SLE (DORIS, A) or the Systemic Lupus International Collaborating Clinics Damage (SLICC) Index (B). Data are shown as percentage of cells and Median Fluorescence Intensity (MFI). Bars show the median + interquartile range. using Mann-Whitney test. * p < 0.05, using Kruskal-Wallis test

Full size image

Finally, there were no differences in the percentage of MACS according to the organ damage, although the expression of CD309, CD105 and Tie2 was lower in SDI + patients compared to HC and SDI- patients (Fig. 5B).

In summary, our results show that ECFCs and MACs populations are associated with disease activity parameters and the reduced expression of angiogenic and endothelial stability markers observed in patients with SLE suggest an impaired function of these cell populations.

Discussion

In this manuscript, by using a new flow cytometry approach, we have identified several clusters of EPC-like populations in patients with SLE, finding a relationship of their expression with organ remission and organ damage.

Firstly, we found SLE-specific subclusters that could play a central role in SLE remission. We have defined subclusters S2.1, S4.1 and S4.2 as CD34+ CD45+ CD144+ CD31− CD309−, markers that describe a previously reported EPC-like subpopulation [28]. Outstandingly, we have found that these clusters are mainly expressed on patients with SLE, but only in those in remission (DORIS +), suggesting a potential use of those clusters biomarkers for predicting remission in patients with SLE.

Secondly, we found specific EPC-like clusters associated with organ damage. Then, the differential expression of these clusters may be a potential biomarker of SLE damage. Regarding SDI populations, SDI- cluster 1 and SDI + cluster 5 are defined as MACs-like clusters [29], since they express CD45+ CD14+ CD31+ TIE2+ CD105+ CD309+. Although MACs do not differentiate into endothelial cells, they are essential for regulatory capacity of angiogenesis [4]. Also, shared cluster 6 belongs to an EPC-like population, expressing CD31+ CD45− CD309+. Importantly, this cluster could be dysfunctional EPCs, since the protein expression of CD31, Tie2, CD105 and CD309 is decreased, which is consistent with studies showing an impairment in the angiogenic capacity mediated by the VEGFR2 receptor (CD309) in SLE [30], as well as altered levels of adhesion molecules and vascular remodeling proteins as endoglin (CD105) [31], contributing to cardiovascular events. This suggests a compensatory mechanism in which patients with SLE, especially SDI + patients, increase their EPC-like clusters to reverse the associated damage, but in a non-functional manner.

We also validated these findings using classical flow cytometry assays, since ECFCs percentage are increased and the CD31, Tie2, CD105 and CD309 expression was reduced in patients with SLE. These findings are also in line with previous works that demonstrate impaired functional roles of EPCs in patients with SLE, including capacity of forming colony units, proliferation, adhesion, migration and tube formation [13,14,15].

Moreover, the positive correlation of ECFCs numbers with IFNGS score and SLEDAS index postulates this cell population as a promising biomarker of disease activity in patients with SLE. We also found a negative correlation between CD31 expression and these parameters and a reduced CD31 expression observed in patients in active status compared to patients in remission. CD31 exerts different cardiovascular protective functions in endothelial cells, including promotion of cell survival, barrier integrity and angiogenesis [32]. Altogether, these results suggest that higher CVE risk observed in patient with higher IFNGS score [33] and disease activity [34]may be due, at least partially, to a defective function of ECFCs.

Finally, MACs were elevated in patients in remission compared to patients in active state and, surprisingly, the levels of CD31 were higher in MACs of these patients. This may be due to the fact that, in myeloid cells, CD31 is induced upon inflammatory stimuli and reduces the secretion of inflammatory cytokines [35, 36]. Therefore, this cell population may contribute to dampen inflammation in patients with SLE.

Our results are contrary to previous reports [11,12,13], since we found a higher expression of EPCs and EPC-like populations in patients with SLE compared to HC. A possible explanation of this discordance is that previous studies in EPCs used few markers to characterize populations, while we have used a broad spectrum of markers for a more accurate classification. In fact, the studies showing higher EPC levels in patients with SLE used more than 2 surface markers for the characterization of the EPC populations [17, 18]. However, we cannot rule out the possibility that opposite results are consequence of the heterogeneity of SLE pathogenesis.

A limitation of our study is that functional assays, which would provide insight into the role of these clusters, have not been performed. Moreover, we did not replicate our findings in another cohort of patients. Then, further studies are needed for confirming our findings.

Conclusions

Here we have identified novel EPC-like clusters as biomarkers of remission and damage in patients with SLE, which could provide a new clinical approach to predict the disease progression and severity.

Availability of data and materials

Data available on request. The data underlying this article will be shared on reasonable request to the corresponding author.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

ACR:

American College of Rheumatology

CAC:

Circulating angiogenic cell

CVE:

Cardiovascular event

DORIS:

Definition of Remission in SLE

ECFC:

Endothelial colony-forming cell

EPC:

Endothelial progenitor cell

EULAR:

European Alliance of Associations for Rheumatology

IFNGS:

Interferon gene signature

LLDAS:

Lupus Low Disease Activity State

MACs:

Myeloid angiogenic cell

MFI:

Median fluorescence intensity

PBMC:

Peripheral blood mononuclear cell

SDI:

SLICC Damage Index

SLEDAI:

SLE Disease Activity Index

SLEDAS:

SLE Disease Activity Score

SLICC:

Systemic Lupus International Collaborating Clinics

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Acknowledgements

We would like to thank all the patients involved in this study.

Funding

This work was supported by Instituto de Salud Carlos III (ISCIII), #CP19/00005 (S.G.), #FI2100120 (S.M.-R.), #IFEQ21/00157, co-funded by the European Union; by Axencia Galega de Innovación, #IN606A-2020/043 (C.R.-V.), #IN606A-2020/043 and #IN607D-2020/01.

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Authors and Affiliations

  1. Rheumatology and Immune-Mediated Diseases Group (IRIDIS), Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain

    Carlos Rafael-Vidal, Sara Martínez-Ramos, Beatriz Malvar-Fernández, Irene Altabás-González, Coral Mouriño, José María Pego Reigosa & Samuel García

  2. Rheumatology Department, University Hospital Complex of Vigo, Vigo, Spain

    Irene Altabás-González, Coral Mouriño & José María Pego Reigosa

  3. Department of Cardiology, University Hospital Complex of Vigo, Vigo, Spain

    Pablo Pazos-López

  4. Department of Clinical Neurology, University Hospital Complex of Vigo, Vigo, Spain

    Arturo Fraga-Bau

Authors
  1. Carlos Rafael-Vidal
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  2. Sara Martínez-Ramos
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  3. Beatriz Malvar-Fernández
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  4. Irene Altabás-González
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  5. Coral Mouriño
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  6. Pablo Pazos-López
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  7. Arturo Fraga-Bau
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  8. José María Pego Reigosa
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  9. Samuel García
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Contributions

CRV, JMPR and SG were involved in study conception and design of the experiments. CRV, SMR, BMF, CM, IA, PPL and AFB carried out the experiments. Analysis and interpretation of data was performed by CRV, SMR, BMF, and SG. All authors were involved in drafting the manuscript or revising it critically, and all authors approved the final version.

Corresponding author

Correspondence to José María Pego Reigosa.

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Ethics approval and consent to participate

Ethics Committee of Galicia (study number 2020/158) approved this study and all patients supplied written informed consent to be included.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Rafael-Vidal, C., Martínez-Ramos, S., Malvar-Fernández, B. et al. Novel endothelial progenitor cells populations as biomarkers of damage and remission in systemic lupus erythematosus. Arthritis Res Ther 26, 170 (2024). https://doi.org/10.1186/s13075-024-03397-4

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  • DOI: https://doi.org/10.1186/s13075-024-03397-4

Keywords

Arthritis Research & Therapy

ISSN: 1478-6362

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