# The metabolic profile of circulating plasmacytoid dendritic cells from patients with systemic sclerosis

Beatriz Ferreira, Carolina Mazeda, Eduardo Dourado, João Simões, Ana Rita Prata, Rafael Argüello, Iola Duarte, Philippe Pierre, Catarina Almeida

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## To cite this version:

Beatriz Ferreira, Carolina Mazeda, Eduardo Dourado, João Simões, Ana Rita Prata, et al.. The metabolic profile of circulating plasmacytoid dendritic cells from patients with systemic sclerosis. Arthritis Research & Therapy, 2025, 27 (1), pp.35. 10.1186/s13075-025-03500-3. hal-05348525

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# The metabolic profile of circulating plasmacytoid dendritic cells from patients with systemic sclerosis

Beatriz H. Ferreira¹,², Carolina Mazeda³,⁴,⁵, Eduardo Dourado³,⁴,⁶, João Lindo Simões¹,⁷, Ana Rita Prata³,⁴, Rafael J. Argüello⁸, Iola F. Duarte², Philippe Pierre¹,⁸,⁹, Catarina R. Almeida¹,*

¹Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal; ²CICECO - Aveiro Institute of Materials, Department of Chemistry, University of Aveiro, Aveiro, Portugal; ³Rheumatology Department, Unidade Local de Saúde da Região de Aveiro, Aveiro, Portugal; ⁴Aveiro Rheumatology Research Centre, Egas Moniz Health Alliance, Aveiro, Portugal; ⁵EpiDoc Unit, Nova Medical School, NOVA University Lisbon, Lisboa, Portugal; ⁶Rheumatology Research Unit, Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; ⁷School of Health Sciences (ESSUA), University of Aveiro, Aveiro, Portugal; ⁸Aix Marseille Université, CNRS, INSERM, Centre d'Immunologie de Marseille-Luminy, Marseille, France; ⁹Shanghai Institute of Immunology, Department of Microbiology and Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, PR China.

BHF: beatrizferreira@ua.pt; ORCID 0000-0002-1497-7440

CM: 71522@ulsra.min-saude.pt; ORCID 0000-0002-9200-5022

ED: 72868@ulsra.min-saude.pt; ORCID 0000-0003-2186-2833

JLS:

RJA:

IFD:

PP:

CRA: cra@ua.pt; ORCID 0000-0001-6670-6004

<footnote>*corresponding author: cra@ua.pt</footnote>
**Abstract** (250 words max.)

**Objective:** Plasmacytoid dendritic cells (pDCs) play a key role in the pathophysiology of systemic sclerosis (SSc). However, despite the recognised importance of metabolic reprogramming for immune cell function, the metabolic profile of pDCs in SSc remains to be elucidated. Thus, our study aimed to investigate the role of pDC metabolism in SSc.

**Methods:** Peripheral blood mononuclear cells (PBMCs) were isolated from the blood of healthy donors and SSc patients. SCENITH, a single-cell flow cytometry-based method, was applied to infer the metabolic profile of circulating pDCs from patients with SSc. pDCs (CD304+ Lin-) at steady-state or stimulated with CpG A were analysed. Toll-like receptor (TLR)9 activation was confirmed by S6 phosphorylation.

**Results:** Circulating pDCs from 10 healthy donors and from 14 SSc patients were analysed. pDCs from anti-centromere antibody-positive (ACA+) patients displayed higher mitochondrial dependence and lower glycolytic capacity than those from anti-topoisomerase I antibody-positive (ATA+) patients. Furthermore, cells from both ACA+ patients and limited cutaneous SSc (lcSSc) patients showed a stronger response towards TLR9 activation than cells from ATA+, anti-RNA polymerase III antibody-positive (ARA+) or diffuse cutaneous SSc (dcSSc) patients.

**Conclusions:** Our results suggest that pDCs from ACA+ patients rely more on oxidative phosphorylation (OXPHOS) and are more responsive to external stimuli, indicating that pDCs from ARA+ patients may be more activated or exhausted. These findings point for the possible contribution of the metabolic profile of pDCs for SSc clinical course, unveiling new possible targets for therapeutic approaches in SSc.

**Keywords:** Systemic Sclerosis, Scleroderma, Immunometabolism, Dendritic cells, Plasmacytoid Dendritic Cells

**What is already known on this topic:**

* pDCs are innate immune cells critical to SSc pathophysiology.

* pDCs shift their metabolism upon activation.

* Alterations in metabolite levels in sera of SSc patients have been described.
* Circulating pDCs from SSc patients downregulate type I IFN responses upon TCA cycle inhibition.

**What this study adds:**

* pDCs from ACA⁺ patients have higher mitochondrial dependence than cells from ATA⁺ patients.

* pDCs from both ACA⁺ and lcSSc patients presented a stronger response to *in vitro* TLR9 stimulation than cells from ATA⁺ and ACA⁺ positive patients or lcSSc patients.

* These results suggest that pDCs more dependent on mitochondrial respiration may be associated with lower basal activation and better prognosis.

**How this study might affect research, practice or policy:**

* Our findings reveal a correlation between pDC metabolism and the severity and clinical progression of SSc. This unveils pDC metabolism as a possible biomarker for disease prognosis or as a target for therapeutic strategies for SSc.
INTRODUCTION

Plasmacytoid dendritic cells (pDCs), players of innate immunity, are type I interferon (IFN) producers highly relevant for the response against viruses, which are also key in autoimmunity, particularly on diseases with an IFN signature, such as systemic sclerosis (SSc) [1]. SSc is a connective tissue disease characterised by immune dysregulation, vasculopathy and fibrosis of different organs [2]. Interestingly, pDC numbers are increased in the skin and lung tissues of patients with SSc and decreased in circulation compared with healthy donors [3]. Importantly, pDC depletion in mice models of SSc ameliorated fibrosis and inflammation [3,4]. pDCs from SSc patients produce high levels of type I IFN and chemokine (C-X-C motif) ligand 4 (CXCL4), contributing to inflammation and fibrosis [5-8]. Also, pDCs can promote B cell activation and autoantibody production [8]. Recently, pDCs from SSc patients were found to have a dysregulation of the unfolded protein response (UPR) pathway inositol-requiring transmembrane kinase/endoribonuclease 1-alpha (IRE1α), that potentiates the tricarboxylic acid (TCA) cycle and contributes to the type I IFN signature observed in these patients [9].

Several immune cells, such as macrophages, dendritic cells (DCs), B cells and T cells, rely on metabolic reprogramming for differentiation and function [10]. Metabolic reprogramming of pDCs depends on the species and on the stimuli. Toll-like receptors (TLR)7/8-activated human pDCs were shown to upregulate oxidative phosphorylation (OXPHOS) dependently on glutaminolysis [11]. On the other hand, human pDCs stimulated with a TLR7 agonist, such as influenza A virus (IAV) or rhinovirus (RV), display increased glycolysis while OXPHOS was not impacted [12]. Other studies using mouse pDCs demonstrated that type I IFN derived from TLR9 activation contributes to fatty acid oxidation (FAO) and OXPHOS [13]. Most recently, it was proposed that chronic activation of pDCs in SSc could be due to dysregulation of metabolic pathways [9]. Nevertheless, the metabolic profile of these cells and its contribution to the disease's progression is unknown. Thus, the aim of this work was to elucidate the metabolic profile of pDCs in SSc and investigate its possible contribution for disease course and prognosis. For that, we used SCENITH, a single-cell technique that allows for the analysis of the metabolic profile of rare cell populations by flow cytometry [14].
# MATERIALS AND METHODS

## Patient inclusion and clinical data

Adult patients with SSc fulfilling the 2013 American College of Rheumatology/ European League Against Rheumatism classification criteria were included [15]. Patients with overlap syndromes were not considered. All participants signed informed consent before inclusion, and clinical data collected was anonymised. This study was approved by Centro Hospitalar do Baixo Vouga Ethics Committee (Reference 44-069-2021).

Demographic, clinical and laboratorial data were extracted from the medical records of patients. Demographic information collected included gender, birth date, tobacco and alcohol use. Disease onset was defined as the time of the first occurrence of a non-Raynaud's symptom of SSc. Patients were categorised as limited cutaneous SSc (lcSSc) or diffuse cutaneous SSc (dcSSc) using LeRoy's criteria [16]. Clinical data included SSc-related symptoms and signs (Raynaud's phenomenon, digital ulcers, telangiectasia, puffy hands, sclerodactyly, calcinosis), modified Rodnan skin score (mRSS), and organ involvement. We used consensus definitions to characterise the major organ involvement of SSc, which were established in earlier studies [17]. We also included the nailfold videocapillaroscopy (NVC) pattern, the presence of comorbidities (arterial hypertension, diabetes mellitus, dyslipidaemia, and depression) and the ongoing treatments (glucocorticoids, immunomodulators, and vasodilators). Antinuclear autoantibodies (ANAs) were detected with an indirect immunofluorescence test (IIFT) on human epithelial cells (Hep-2) and anti-extractable nuclear antigen (ENA) analysis using the commercially available line immunoblot assay [EUROLINE Systemic Sclerosis (Nucleoli) Profile (IgG); Euroimmun, Lübeck, GER].

## Cell isolation

Following blood centrifugation at 400 xg at room temperature (RT) for 10 minutes and plasma recovery, peripheral blood mononucleated cells (PBMCs) were isolated by density gradient centrifugation. Briefly, blood was diluted twice in PBS (Gibco, Thermo Fisher Scientific, Waltham, MA, USA), overlaid onto Ficoll-Paque Plus (Cytiva, Marlborough, MA, USA) and cells were separated by centrifugation at 400 xg for 10 minutes at 4°C in a swinging bucket rotor without brake. The mononuclear cell layer was
transferred into a new tube, washed twice with PBS and finally with RPMI 1640 medium
with 2 mM L-glutamine (Gibco).

Cell activation, SCENITH and pDC staining

SCENITH protocol was adapted from its previous description [14]. A scheme with the
key steps of the protocol is presented in **Fig. 1**. Initially, 1.5x10⁶ PBMCs were seeded in
170 µL RPMI 1640 with 2 mM L-glutamine (Gibco) supplemented with 10% heat-
inactivated foetal bovine serum (FBS; Sigma-Aldrich, St. Louis, MO, USA) and 1%
Penicillin-Streptomycin (Gibco) in a round-shaped bottom 96-well plate in the absence
or presence of CpG A (3 µM; InvivoGen, San Diego, CA, USA) for 3 hours. After
activation, cells were treated for 40 minutes with vehicle control (C; DMSO), 2-
deoxyglucose (2-DG; 100 mM), oligomycin (O; 1 µM), a combination of 2-DG and O
(DGO), and puromycin (10 µg/mL). As a negative control and 15 minutes before
puromycin treatment, harringtonine (H) was added (2 µg/mL). After two washing steps
with ice-cold FACS buffer (2% FBS, 2 µM EDTA, PBS), and before surface staining, cells
were incubated for 15 minutes at 4°C in the dark with a combination of Human TruStain
FcX Fc Receptor Blocking Solution (BioLegend, San Diego, CA, USA) and Live/Dead™
(Invitrogen, Thermo Fisher Scientific). Surface staining with primary conjugated
antibodies was performed for 25 minutes at 4°C in the dark. After washing with FACS
buffer, cells were fixed and permeabilised using Foxp3 Transcription Factor Staining
Buffer Set (Invitrogen), according to the manufacturer's instructions. Cells were
incubated for 10 minutes at RT with intracellular blocking solution [Foxp3
Permeabilization buffer (Invitrogen) with 20% FBS], and puromycin intracellular staining
was performed for 1 hour at 4°C. After two washing steps with permeabilisation buffer
(Invitrogen), cells were resuspended in FACS buffer.

The kit with SCENITH reagents (inhibitors, puromycin and anti-puromycin antibody) was
from www.scenith.com. Information regarding the used antibodies is shown in **Table 1**.

Data was acquired using BD Accuri C6 (BD Biosciences, San Jose, CA, USA). At least
300 CD304⁺ Lin⁻ events were acquired per condition. Data were analysed using
FlowJo™ v10.8.1 Software (BD Life Sciences, Franklin Lakes, NJ, USA) and were
compensated using single stains with UltraComp eBeads™ Compensation Beads (01-
2222-41, Invitrogen). The gating strategy is shown in **Fig. S1**. Glucose dependence,
mitochondrial dependence, glycolytic capacity and fatty acid oxidation (FAO)/ amino acid oxidation (AAO) capacity were calculated as detailed below [14].

C = MFI of anti-puro-fluorochrome upon control treatment

2DG = MFI of anti-puro-fluorochrome upon 2-DG treatment

O = MFI of anti-puro-fluorochrome upon O treatment

DGO = MFI of anti-puro-fluorochrome upon 2-DG and O (DGO) treatment

$$ \text{Glucose dependence} = 100 \left( \frac{C - 2\text{DG}}{C - \text{DGO}} \right) $$

$$ \text{Mitochondrial dependence} = 100 \left( \frac{C - O}{C - \text{DGO}} \right) $$

$$ \text{Glycolytic capacity} = 100 - \text{Mitochondrial dependence} $$

$$ \text{FAO/AAO capacity} = 100 - \text{Glucose dependence} $$

<table><caption>**Table 1.** List of antibodies used in this study.</caption><thead><tr><td>Antibody</td><td>Concentration</td><td>Reference</td><td>Source</td></tr></thead><tbody><tr><td>Anti-human CD304 PE (Clone: 12C2)</td><td>2.5 µL/condition</td><td>354504</td><td>BioLegend</td></tr><tr><td>Anti-human CD123 FITC (Clone: 6H6)</td><td>1.25 µL/condition</td><td>306013</td><td>BioLegend</td></tr><tr><td>Anti-human HLA-DR Alexa Fluor 647 (Clone: L243)</td><td>2.5 µL/condition</td><td>307621</td><td>BioLegend</td></tr><tr><td>Anti-human CD3 PerCP (Clone: SK7)</td><td>2.5 µL/condition</td><td>344814</td><td>BioLegend</td></tr><tr><td>Anti-human CD14 PerCP (Clone: M5E2)</td><td>2.5 µL/condition</td><td>301848</td><td>BioLegend</td></tr><tr><td>Anti-human CD16 PerCP (Clone 3G8)</td><td>1.25 µL/condition</td><td>302030</td><td>BioLegend</td></tr><tr><td>Anti-human CD19 PerCP (Clone: SJ25C)</td><td>1.25 µL/condition</td><td>363014</td><td>BioLegend</td></tr><tr><td>Anti-human CD20 PerCP (Clone: 2H7)</td><td>2.5 µL/condition</td><td>302324</td><td>BioLegend</td></tr><tr><td>Anti-human CD34 PerCP-Cy 5.5 (Clone: 561)</td><td>5 µL/condition</td><td>343612</td><td>BioLegend</td></tr><tr><td>Anti-human CD56 BB700 (Clone: NCAM16.2)</td><td>1.25 µL/condition</td><td>566573</td><td>BD Biosciences</td></tr><tr><td>Anti-puromycin Alexa Fluor 647 (Clone: R4743L-E8)</td><td>1x</td><td>RRID: AB_2827926</td><td>[14]</td></tr><tr><td>Anti-phospho-S6 ribosomal protein (Ser235/236)</td><td>1:50</td><td>2211</td><td>Cell Signaling Technology</td></tr><tr><td>Anti-rabbit IgG Alexa Fluor 647</td><td>1:5000</td><td>A-21245</td><td>Invitrogen</td></tr></tbody></table>
## p-S6 staining

PBMCs were seeded, treated with CpG A and surface stained as described for the SCENITH protocol. Cells were then stained for the intracellular phosphorylated form of S6. Information about the used antibodies is in **Table 1**.

## Statistical analysis

Statistical analyses were performed using GraphPad Prism Software version 9.0.0 (GraphPad Software, Inc., La Jolla, CA, USA). Data are presented as median and interquartile range (IQR). The Shapiro-Wilk test was used to assess data normality. The most appropriate statistical test was then chosen according to each data set, as indicated in figure legends. \*p<0.05; \*\*p<0.01; \*\*\*p<0.001; \*\*\*\*p<0.0001.

## RESULTS

### Study cohort characterisation

Fourteen SSc patients and ten healthy controls (HCs) were enrolled in this study (**Table 2**), with median ages of 56.5 (interquartile range, IQR 52.5-61.8) and 49 (IQR 35.8-53.0) years, respectively. Most were women (78.6% of SSc patients and 90% of HC). Median disease duration was 90 months (IQR 45-120). Eight patients (57.1%) presented lcSSc, and six (42.9%) presented dcSSc. Nine patients (69.2%) had gastrointestinal (GI) involvement, and six (46.2%) presented interstitial lung disease (ILD). No patients with pulmonary arterial hypertension (PAH) or renal or cardiac involvement were included. Six patients (42.9%) were positive for anti-topoisomerase I antibodies (ATAs), four (28.6%) for anti-centromere antibodies (ACAs), and four (28.6%) for anti-RNA polymerase III antibodies (ARAs). None of the patients was positive for more than one SSc-specific autoantibody.

<table><caption>**Table 2.** Participant demographics, clinical characteristics and current therapies.</caption><thead><tr><th>Characteristic</th><th>SSc (n=14)</th><th>HCs (n=10)</th></tr></thead><tbody><tr><td>Gender (%)</td><td></td><td></td></tr><tr><td>Male</td><td>3 (21.4)</td><td>1 (10)</td></tr><tr><td>Female</td><td>11 (78.6)</td><td>9 (90)</td></tr><tr><td>Age (years)</td><td>56.5 (52.5-61.8)</td><td>49 (35.8-53.0)</td></tr><tr><td>Limited cutaneous SSc (%)</td><td>8 (57.1)</td><td>-</td></tr><tr><td>Diffuse cutaneous SSc (%)</td><td>6 (42.9)</td><td>-</td></tr><tr><td>Disease duration (months)</td><td>90 (45-120)</td><td>-</td></tr><tr><td>mRSS</td><td>11 (8-15)</td><td>-</td></tr></tbody></table>
<table><caption>**Table 2. (cont.)**</caption><thead><tr><th></th><th></th><th></th></tr></thead><tbody><tr><td>Presence of autoantibodies</td><td></td><td></td></tr><tr><td>ATA (%)</td><td>6 (42.9)</td><td>-</td></tr><tr><td>ACA (%)</td><td>4 (28.6)</td><td>-</td></tr><tr><td>ARA (%)</td><td>4 (28.6)</td><td>-</td></tr><tr><td>Organ complications</td><td></td><td></td></tr><tr><td>GI involvement (%)</td><td>9 (69.2)</td><td>-</td></tr><tr><td>ILD (%)</td><td>6 (46.2)</td><td>-</td></tr><tr><td>PAH (%)</td><td>0</td><td>-</td></tr><tr><td>Cardiac involvement</td><td>0</td><td>-</td></tr></tbody></table>

<table><thead><tr><th>Characteristic</th><th>SSc (n=14)</th><th>HCs (n=10)</th></tr></thead><tbody><tr><td>Current therapies</td><td></td><td></td></tr><tr><td>Prednisolone (%)</td><td>4 (30.8)</td><td>-</td></tr><tr><td>Methotrexate (%)</td><td>4 (30.8)</td><td>-</td></tr><tr><td>Mycophenolate Mofetil (%)</td><td>5 (38.5)</td><td>-</td></tr><tr><td>Nintedanib (%)</td><td>2 (15.4)</td><td>-</td></tr><tr><td>Calcium antagonist (%)</td><td>14 (100)</td><td>-</td></tr></tbody></table>

Median and IQR are reported unless otherwise stated. HC: healthy control; mRSS: modified Rodnan skin score; ATA: anti-topoisomerase I antibody; ACA: anti-centromere antibody; ARA: anti-RNA polymerase III antibody; GI: gastrointestinal; ILD: interstitial lung disease; PAH: pulmonary arterial hypertension.

## The percentage of pDCs in circulation varies with disease duration and ANA titer

Treatments were performed on the whole PBMC population, and pDCs were identified by flow cytometry as positive for the pDC marker CD304 (also known as BDCA4) and negative for CD3, CD14, CD16, CD19, CD20, CD34 and CD56 (Lin⁻) (Fig. 2A, gating strategy presented in Fig. S1A). It was confirmed that CD304⁺ cells are also positive for the pDC markers CD123 and HLA-DR, while events positive for these pDC markers are also Lin⁻ (Fig. S1B, C).

No statistically significant differences were found in the percentage of pDCs in circulation from patients with IcSSc or dcSSc (Fig. 2B) and with or without ILD or GI involvement (Fig. 2C, D). Nevertheless, patients diagnosed for at least ten years showed higher levels of pDCs in circulation (Fig. 2E). Also, patients with higher ANA titer exhibited a reduced percentage of pDCs among PBMCs (Fig. 2F), regardless of the subtype of autoantibody present (Fig. 2G).

## pDCs present a similar metabolic profile in SSc and HCs
Inhibition of glycolysis and OXPHOS, by 2-DG and oligomycin, respectively, led to a reduction in translation to levels similar to harringtonine, a protein synthesis inhibitor, showing that pDCs are susceptible to metabolic modulation and rely on both glycolysis and OXPHOS for protein synthesis (Fig. 3A, B). Under basal conditions, pDCs from HCs and SSc patients showed similar translation and S6 phosphorylation levels (Fig. 3C, D). As expected, TLR9 activation by CpG A led to increased levels of phosphorylated S6 in both HCs and SSc patients, even though statistical significance is only observed for the later (Fig. 3D). However, treatment with CpG A did not affect protein synthesis on pDCs, nor their response to metabolic modulation (Fig. 3A-C). Moreover, the metabolic profile of pDCs was not affected by TLR9 activation and seemed comparable under both SSc and healthy conditions (Fig. 3E, F). Given the lack of differences upon TLR9 stimulation, the follow-up analysis on the metabolic profile of pDCs was focused on cells in basal conditions.

## pDCs from ATA and ACA-positive patients have different metabolic profiles

Comparing pDCs from different patient groups showed that pDCs from IcSSc patients display higher upregulation of S6 phosphorylation and protein synthesis after treatment with CpG A, in comparison with cells from dcSSc patients, suggesting more sensitivity to TLR9 stimulation (Fig. 4A, B). There was also a significant difference in the basal levels of translation associated with GI involvement but not with ILD, disease duration or ANA titer (Fig. S2). However, at basal conditions, no differences were found in the metabolic profile of cells isolated from IcSSc or dcSSc (Fig. 4C). Moreover, the metabolic profile of pDCs did not correlate with either ANA titer, disease duration, GI involvement or ILD (Fig. S3).

Most importantly, pDCs from ACA⁺ patients exhibited higher mitochondrial dependence and lower glycolytic capacity compared to cells from ATA⁺ patients (Fig. 5A). Moreover, pDCs from ACA⁺ patients showed an upregulation of translation levels after TLR9 stimulation, compared with cells from ATA⁺ and ARA⁺ patients (Fig. 5B). There were no statistically significant differences on levels of p-S6, but there was a tendency for pDCs from ARA⁺ patients to display higher p-S6 levels at basal conditions (Fig. 5C).

## DISCUSSION
The key role of pDCs in SSc pathology has been highlighted by others [5-7,9], primarily using mouse models of scleroderma that revealed an improvement in fibrosis upon pDC depletion [3, 4]. Furthermore, pDCs are hyperactivated in patients with SSc, contributing to the characteristic type I IFN signature [5-7]. As other immune cells, pDCs heavily rely on metabolic reprogramming for their differentiation and function [19,20]. A reduced expression of phosphoglycerate dehydrogenase (PHGDH) in pDCs isolated from the blood of SSc patients, and the downregulation of interferon stimulated genes (ISGs) expression upon tricarboxylic acid (TCA) cycle inhibition, were recently reported [9]. Nevertheless, the metabolic profile of pDCs in SSc has not been characterized. In this study, we used SCENITH, a single-cell resolution method that employs flow cytometry analysis of protein synthesis to infer about the metabolic profile of cells [14]. The reliance of cells on glucose oxidation and OXPHOS for the maintenance of protein synthesis levels is given by glucose and mitochondrial dependences, respectively. Simultaneously, the ability of cells to sustain translation under conditions of mitochondrial respiration or glucose oxidation inhibition are reflected on the glycolytic and FAO/AAO capacities, respectively. Due to the importance of cell activation in this context, we analysed both cells at basal conditions and pre-treated with CpG A, a TLR9 agonist. Cell activation was monitored by phosphorylation of S6, which occurs upon TLR9 activation [21]. Treatments were performed in the whole PBMC population, and the analysis was focused on CD304⁺ Lin⁻ cells.

Possible limitations of this work include the identification of pDCs by flow cytometry as CD304⁺ Lin⁻ (CD3, CD14, CD16, CD19, CD20, CD34 and CD56). Even though we confirmed that these cells were also positive for other pDC markers (CD123 and HLA-DR), we cannot certainly exclude a low percentage contamination with non-pDC cells. Moreover, these findings are in circulating pDCs, which may not be the cell population with higher influence in the disease course. It is likely that infiltrating pDCs have a more prominent role in SSc pathogenesis, but, as this is a systemic disease, it is also possible that the profile of circulating pDCs reflects what happens in the affected tissues. Finally, our analyses were performed with a low number of cells and patients, due to the rarity of this cell population and disease. However, despite the low number of patients included, the sex and age distribution of the patients included reflects the Portuguese SSc patients demographics [36].

Patients diagnosed for less than ten years at the time of blood collection displayed lower amounts of pDCs in circulation than those diagnosed for longer. Typically, pDCs circulate
through the blood and are recruited to target tissues upon infection or inflammation [22]. SSc is characterised by an initial vascular injury and early inflammation phase that contribute to fibrosis development [23,24]. This difference in pDCs levels may be due to the early recruitment of pDCs to the inflamed tissues, a phenomenon that could decrease as the disease progresses. Also, patients with higher levels of ANAs in circulation displayed less pDC abundance within PBMCs. ANA titers are suggested to be associated with disease progression, and in some cases, high titers are correlated with the disease outcome [25]. Higher ANA titers may indicate a more robust autoimmune response and inflammation, possibly justifying these results [26,27]. However, this is a debated theory.

The activation state of pDCs was evaluated by the analysis of S6 phosphorylation levels and by monitoring protein synthesis, which is reported to increase during immune cell activation to sustain cellular function [18,28]. Globally, pDCs from HCs and SSc patients seemed to have the same basal activation state since no differences in p-S6 and translation were found. However, within the patient groups, differences were identified among patients with GI involvement, displaying lower translation levels compared to patients without this involvement. This may be related to sequestration of cells with higher translation rates in fibrotic tissues, and/or disparities in the microbiome, but this remains to be determined. In response to TLR9 activation, pDCs from ACA⁺ patients upregulated their translation levels, contrary to what happens with ATA⁺ or ARA⁺ patients. ATA and ARA are associated with dcSSc, while ACA is associated with IcSSc. A higher increase in translation upon TLR9 activation was also observed in IcSSc, compared to dcSSc. Although not statistically significant, there was a tendency for pDCs from dcSSc and patients positive for ATA or ARA to present increased p-S6 at basal conditions. Therefore, this suggests that pDCs from dcSSc and ATA⁺ and ARA⁺ patients have basal activation (Fig. 6), which might limit the effect of CpG A.

Even though the treatment with the TLR9 agonist CpG A induced phosphorylation of S6, it did not upregulate translation, neither impacted the metabolic profile of these cells. Other studies have observed an impact of TLR9 activation in the metabolism of pDCs [13,29], but longer times of exposure to the agonist were used. As mentioned above, a more recent study exploring the metabolism of pDCs from SSc patients used a similar timepoint and analysed the expression of an enzyme involved in the biosynthesis of serine [9]. Therefore, it is possible that differences from what is reported in literature may be due to differences in the agonist and stimulation times used.
Interestingly, although pDCs from HCs and SSc patients displayed similar metabolic profiles, being dependent on both glycolysis and OXPHOS, variations were found among patients positive for the different ANAs subtypes. While there were no differences on the activation or metabolic state of pDCs from ATA⁺ and ARA⁺ patients, pDCs from ACA⁺ patients presented higher mitochondrial dependence and lower glycolytic capacity than cells from ATA⁺ patients. This suggests that pDCs from ACA⁺ patients rely more on OXPHOS for energy production (Fig. 6). In accordance, cells from ATA⁺ patients tended to be more glucose dependent. Usually, activated immune cells and highly proliferating cells shift their metabolism to glycolysis, while OXPHOS is preferred at the resolution phase [30]. Thus, the metabolic profile results are concordant with p-S6 readouts and suggest that pDCs from ATA⁺ patients are more activated at basal conditions, relying on glycolysis for energy production (Fig. 6). These results are in accordance with the observations for pDCs from IcSSc (which are more commonly ACA⁺) compared to dcSSc patients. On the other hand, although both ATA and ARA are associated with dcSSc, the clinical course differs. While ATA⁺ patients are more prone to develop ILD, ARA is associated with scleroderma renal crisis and the co-occurrence of cancer [25,31].

The interaction between autoantibodies and soluble antigens can form immune complexes (ICs), promoting cell activation. Particularly, ICs containing antibodies specific for SSc (SSc-ICs) were described to promote the activation of skin fibroblasts and endothelial cells from HCs, having a pro-inflammatory and pro-fibrotic effect [32,33]. Furthermore, the mixture of sera from SSc patients with necrotic/apoptotic material induces pDC activation, apparently by SSc-ICs [34]. Even though not every patient's serum can form ICs capable of inducing an IFN response, this phenomenon was observed with samples from both IcSSc and dcSSc [34]. In these studies, the autoantibody composition analyses did not always include the ones present in this work, and the formation of interferogenic ICs in ATA⁺ sera is controversial [34,35]. Thus, it would be interesting to clarify if the differences in the metabolic profile of pDCs and their activation status observed with our data are related to the activation of pDCs by ICs.

The presence of these different autoantibodies is linked with disease progression, and ATA⁺ patients tend to have a less favourable prognosis compared to those positive for ACA, partly due to the elevated risk of developing ILD, a common cause of death. Crucially, we found that pDCs from ACA⁺ patients had reduced glycolytic capacity, associated with a stronger response to *in vitro* TLR9 activation (Fig. 6). Together, these results suggest that pDCs more dependent on mitochondrial respiration and less on
glucose oxidation are associated with reduced basal activation and a better prognosis.
Thus, we believe our findings pave the way for the possible contribution of pDC
metabolism to the severity and clinical progression of SSc. Future testing of the impact
of blocking glycolysis while promoting mitochondrial respiration on pDC may contribute
to define new possible therapeutic strategies for SSc.

**Data Availability Statement:** Data are available upon reasonable request.

**Ethics statements:** This study was approved by Centro Hospitalar do Baixo Vouga Ethics Committee (Reference 44-069-2021).

**Patient consent:** Participants gave informed consent to participate in the study before
taking part.

**Ethics approval:** This study was approved by Centro Hospitalar do Baixo Vouga Ethics Committee (Reference 44-069-2021).

**Acknowledgements:** We thank all the volunteers, especially the patients, and the health professionals (Anabela Barcelos, Maria do Céu Morais, Graça Costa, YY, ZZ) involved in this study. This work was presented at the 8th Systemic Sclerosis World Congress as a poster and the abstract is published at doi.org/10.1177/23971983231224104.

**Contributors:** Conceptualization, BHF, CM and CRA; Design of the work: BHF, CM, RJA and CRA; Investigation: BHF, CM and CRA; Data interpretation: BHF, CM, ED, IFD, PP, CRA; Writing – Original Draft, BHF and CRA; Writing – Review & Editing, all authors; Funding acquisition, PP and CRA. Resources: CM and RJA; Supervision: IFD, PP and CRA.

**Funding:** Work developed within the scope of iBiMED – Institute of Biomedicine (UIDB/04501/2020 and UIDP/04501/2020) and CICECO – Aveiro Institute of Materials (UIDB/50011/2020, UIDP/50011/2020 & LA/P/0006/2020). It was funded by the project with the reference 2022.03217.PTDC, financially supported by national funds (OE), through FCT/MCTES. This work was also supported by the World Scleroderma Foundation and Edith Busch Foundation. B.H.F. is supported by FCT through an individual grant (SFRH/BD/144706/2019).
**Competing interests:** No competing interests.
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FIGURE LEGENDS

**Figure 1. Scheme representing the key steps of the methods used.** PBMCs isolated from the blood of HCs and SSc patients, were seeded in 96-well plates and treated with CpG A for 3h, before the 40 min incubation with the SCENITH drugs (C, 2-DG, O, H and puromycin). Finally, surface and intracellular stainings were performed and cells were analysed by flow cytometry.

**Figure 2.** The percentage of pDCs in circulation changes with the progression of the disease in SSc patients. (A) Identification of CD304+ Lin- pDCs by flow cytometry. (B-G) The percentage of pDCs was obtained by flow cytometry analysis and comparisons were performed between groups of patients. Data are expressed as median and interquartile range (IQR) and each dot represents an individual. Unpaired *t* test or one-way ANOVA followed by Tukey's multiple comparisons test. \*\*p<0.05.

**Figure 3.** pDCs from HCs and SSc patients do not present major differences in their metabolic profile. (A, B) Translation levels measured by puromycin staining and flow cytometry in pDCs from healthy controls (HC) and SSc patients, after metabolic modulation with 2-deoxyglucose (2-DG; 100 mM) and/or oligomycin (O; 1 µM) and/or the TLR9 agonist CpG A (3 µM) or treatment with harringtonine (H; 2 µg/mL). Representative histograms for cells that were not TLR9-activated. (C) Levels of translation and (D) S6 phosphorylation (MFI) were analyzed by flow cytometry for HC and SSc patients. (E, F) SCENITH metabolic profiles were determined for pDCs under basal conditions or stimulated with CpG A. Data are expressed as median and interquartile range (IQR) and each dot represents data obtained from analysing cells from one donor. Kruskal-Wallis test followed by Dunnet's multiple comparisons test was used for (A, B), one-way ANOVA followed by Sidak's multiple comparisons test for (C, D) and two-way ANOVA followed by Sidak's multiple comparisons test for (E, F). \*\*\*p<0.001; \*\*\*\*p<0.0001.

**Figure 4.** pDCs from lcSSc patients are more reactive to CpG A than cells from dsSSc. (A) p-S6 levels and (B) translation levels at basal and TLR9-activated conditions were assessed in pDC from SSc patients by flow cytometry. (C) Metabolic profiles. Grey line corresponds to 1. Data are expressed as median and interquartile range (IQR) and each dot represents an individual. Statistical significance was tested with unpaired *t* test or Mann-Witney test. \*p<0.05.

**Figure 5.** pDCs from ACA+ patients are more mitochondrial dependent and have higher glycolytic capacity than cells from ATA+ patients. (A) p-S6 levels, (B) Translation levels, and (C) SCENITH metabolic profiles were determined by flow cytometry for pDCs from patients with different patterns of SSc autoantibodies. Data are expressed as median and interquartile range (IQR) and each dot represents an individual. Kruskal-Wallis test followed by Dunnet's multiple comparisons test was used for (A), one-way ANOVA followed by Tukey's multiple comparisons test for (B) and one-way ANOVA followed by Sidak's multiple comparisons test for (C). \*p<0.05, \*\*p<0.01.

**Figure 6.** Metabolic profile of circulating pDCs of SSc patients. pDCs in circulation of ACA+ patients are more mitochondrial dependent and seem to be less activated than cells from ATA+ patients. Scheme created with BioRender.com.