Patients were recruited during visits at the Department of Rheumatology and Immunology (University of Szeged). Healthy controls were voluntary employees of the BRC or University of Szeged. Subjects were informed about the research by a physician. Written informed consent was obtained from all subjects, and our study was reviewed and approved by an independent ethical committee of the university. Details about the study design and handling of biological materials were submitted to the Human Investigation Review Board of the University of Szeged under the 21/2011 and 149/2019-SZTE Project Identification codes. Laboratory studies and interpretations were performed on anonymized samples lacking personal identifiers. The study adhered to the tenets of the most recent revision of the Declaration of Helsinki.

Adult SLE patients meeting the 2019 European League Against Rheumatism/American College of Rheumatology classification criteria were included if they had active disease (either newly diagnosed or relapsing) as defined as at least one BILAG A or at least two BILAG B scores (39) or a SLEDAI-2K score ≥ 6 (40, 41). Patients with co-morbid malignant disease, diabetes mellitus, end-stage renal disease, a concurrent other inflammatory condition (e.g. infection) or overlapping systemic autoimmune disease except antiphospholipid syndrome or Sjögren’s syndrome with clinically active extraglandular manifestations were excluded. Newly diagnosed patients were therapy-naive, whereas relapsing patients were on maintenance immunosuppressive therapy, but corticosteroid dose > 5 mg/day prednisolone resulted in exclusion. Healthy controls were age-and sex-matched individuals who did not have a current or past chronic inflammatory disease (autoimmune or other immune-mediated), or a current acute inflammatory (infectious or non-infectious) disease.

Demographic data, anti-dsDNA levels, SLEDAI- 2K activity indices, and the distribution of clinical domains are presented in Supplementary Table 1 . The number of patients was different in the two panels. From the T-cell/NK-cell/NKT-cell panel, 5 samples (and their paired controls) had been excluded due to methodological reasons, while for the B-cell/monocyte panel, 18 samples were available. Age, serological and clinical activity of the patients included in the T-cell/NK-cell/NKT-cell panel are presented in Supplementary Table 2 . There was no significant difference in the studied clinical parameters between this group and the whole cohort. Demographic data of the control cohort is summarized in Supplementary Table 3 .

PBMCs were isolated as described previously by our group (42, 43). Briefly, after the collection of 20 mL peripheral blood into an EDTA vacutainer (Becton Dickinson, Franklin-Lakes, USA), PBMCs were isolated by standard density gradient centrifugation using Leucosep tubes (Greiner Bio-One, Kremsmünster, Austria). Residual red blood cells were lysed suspending the PBMC pellet in 2 mL Ammonium-Chloride-Potassium Lysis Buffer (ACK: 0.15 M NH₄Cl, 10 mM KHCO₃, 0.1 mM Na₂EDTA, pH 7.3; Merck, Darmstadt, Germany) and incubating at room temperature (RT) for 2 min. Samples were washed twice with 10 mL phosphate-buffered saline (PBS, Merck), and subsequently, cell count and viability was determined with trypan blue (Merck) exclusion test. Aliquots of 4 × 10⁶ PBMCs were subjected to cryopreservation in Fetal Bovine Serum (FBS, Capricorn Scientific, Ebsdorfergrund, Germany) containing 10% (v/v) dimethyl sulfoxide (DMSO, Merck) in liquid nitrogen (Messer, Bad Soden, Germany).

For covalent labeling of lectins with fluorophores targeting the primary amine groups of lectins, Lightning Link conjugation kits were used following the simple and rapid procedure provided by the manufacturer (Abcam, Cambridge, US). Aleuria Aurantia lectin (AAL, Cat. L-1390-2, Vector Laboratories, Newark, USA) was labeled with APC (Cat. ab201807, Lot.GR3388854-1), recombinant human galectin-1 as a gift of Dr. Éva Monostori, Gal-1, expression and purification steps described in detail in (44–46), that was conjugated to PE/Cy7 (Cat. ab102903, Lot. GR3390407-1), recombinant human sialoadhesin, Siglec-1/CD169 protein (Siglec-1, Cat. 5197SL050, Fischer Scientific, Massachusetts, USA) was labeled with PE/Texas Red (Cat. ab269899, Lot. GR3395603-2), and finally galectin-3 (Gal-3, Cat. 450-38, PeproTech, London, UK) was chemically linked to APC/Cy7 dye (Cat. ab102859, Lot. GR3396716-1). Fluorescein-conjugated Sambucus nigra agglutinin was purchased from Vector Laboratories (Cat. FL-1301-2). The list of the selected lectins for cytometry is listed in Supplementary Table 4 .

Flow cytometry was performed as described previously by our group with some modifications (47, 48). Briefly, one aliquot of PBMC per patient was thawed and plated onto a flat-bottomed 48-well plate (Biologix, Hallbergmoos, Germany). The samples were divided into day O and day 3 sample referred as “resting state” and “activated state”, respectively. Both samples were incubated for 2 hours at 37°C in complete RPMI media (Capricorn Scientific) supplemented with 10% FBS (200 μL/well). After two hours, the stimulating CytoStim (100x dilution, Cat. 130-092-172, Miltenyi Biotec, Auburn, USA) reagent was added in complete RPMI to the “activated state” samples along with recombinant human IL-2 (10 ng/mL, Cat. CYT-209, ProSpec, Rehovot, Israel) in a final volume of 400 μL per well and these samples were housed for 72 hours inside a standard tissue culture incubator (37°C, 5% CO₂, humidified). The unstimulated “resting state” samples were labelled in the following steps. Samples were transferred to FACS tubes, the wells were washed twice with PBS containing 2 mM EDTA. The cells were washed once with 1 mL PBS and centrifuged at 370 g for 6 min. After discarding the supernatant, cells were resuspended in 50 μL PBS containing human TruStain FcX Fc receptor blocking reagent (25x dilution, Cat. 422302, BioLegend, San Diego, USA) and Viobility 405/520 fixable viability dye (100x dilution, Cat. 130-130-404, Miltenyi Biotec). Incubation at room temperature 15 minutes followed by washing with 1 mL PBS, and centrifugation 370 g for 6 minutes. Prior to fixation, cell pellets were re-suspended in 100 μL EDTA FACS buffer (PBS +1% FBS+0.1% sodium azide + 2 mM EDTA), then each sample received 100 μL of 3.5% formaldehyde solution and were mixed by pipetting and incubated at room temperature for 20 minutes. After fixation, the samples were washed twice with 1 mL PBS and centrifuged at 500 g for 7 minutes. Cells were stained with lectin and antibody cocktails in 100 μL PBS per sample on ice for 30 minutes. Dilution of antibodies: anti-human CD3 efluor450 (Cat. 48-0038-42, clone: UCHT1, Thermo Fisher Scientific, Waltham, Massachusetts, USA) 80x; anti-human CD4 Alexa Fluor 700^® (Cat. 317426, clone: OKT4, BioLegend) 80x; anti-human CD8 PerCP (Cat. MHCD0831, clone: 3B5, Thermo Fisher Scientific) 80x; anti-human CD56 BV605 (Cat.562780, clone: NCAM16.2, Beckton Dickinson, Franklin Lakes, USA) 80x; anti-human CD25 Super Bright™ 645 (Cat. 64-0259-42, clone: BC96, Thermo Fisher Scientific) 40x. Lectin concentration: AAL 0.1 μg/mL; Gal-1 0.2 μg/mL; Gal-3 2.5 μg/mL; SNA 1.25 μg/mL; Siglec-1 0.5 μg/mL. Staining was followed by a single washing step with 1 mL FACS buffer and cells were collected by centrifugation at 500 g for 7 minutes. Cell pellets were resuspended in 300 µL FACS Buffer and measured on CytoFLEX S V4-B2-Y4-R3 Flow Cytometer (Cat. C09766, Beckman Coulter, Indiana, USA). At the endpoint of 72-hour activation, the same staining protocol was repeated with the “activating state” samples. Examples of compensations avoiding of signal spillover for the antibody panel are attached as: Supplementary Material Compensation_Histograms.

The procedure was the same as the measurement of the T-cells/NK-cells and NKT-cells above, with the following modifications. First, in case of “activated state” samples, lipopolysaccharide (100 ng/mL, Cat. L2880, Sigma-Aldrich, Saint Louis, USA) and TLR9 ligand/agonist/ODN2006 (0.8 μM, 5’-tcgtcgtttgtcgttt-3’) were used in combination for activation of B-cells and monocytes within the total PBMC for 72 hours in complete RPMI medium. Second, different antibodies were used for staining B-cells and monocytes. Dilution of antibodies: anti-human CD19 BV605 (Cat. 363024, clone: SJ25C1, BioLegend) 80x; anti-human CD14 BV650 (Cat. 301836, clone: M5E2, BioLegend) 40x; anti-human CD16 Alexa Fluor 700^® (Cat. 302026, clone: 3G8, BioLegend) 50x; anti-human CD27 Pacific Blue™ (Cat. 302822, clone: O323, BioLegend) 100x; anti-human CD38 PerCP (Cat. 356622, clone: HB-7, BioLegend) 40x; anti-human HLA-DR (Cat. 14-9956-82, clone: LN3, Thermo Fisher Scientific) 800x. HLA-DR antibody was conjugated with Nova Fluor™ Yellow 690 antibody labelling kit (Cat. K06T04L014, Thermo Fisher Scientific). The latter Nova Fluor™ fluorophore required the addition of 5 μL/sample CellBlox™ Blocking Buffer (Cat. B001T03F01, Thermo Fisher Scientific) into the staining cocktail. The concentration of lectins applied for labelling the cells were same as in the above protocol. Examples of compensations avoiding of signal spillover for the antibody panel are attached as: Supplementary Material Compensation_Histograms.

Flow cytometric data were analyzed in FlowJo v10.9.0. (Beckton Dickinson). Manual gating was used to determine main T-cell, NK-cell, NKT-cell, B-cell and monocyte populations within singlet living cells. Unstained cells were used as absolute negative control. The gating strategy for flow cytometry is shown in Figure 1 . Lectin binding was calculated by subtracting the background median fluorescence intensity (MFI) values of the antibody-only samples from the MFI of all-lectin-plus-antibody samples. Main immune populations were exported, downsampled and concatenated using DownSample v3.3.1 plugin in FlowJo. High-dimensional data reduction and visualization were performed using t-distributed stochastic neighbor embedding (t-SNE) algorithm. Concatenated populations were also clustered into metaclusters by self-organizing-map based FlowSOM method (10). The number of metaclusters was determined as follows: every gated population from all the samples were concatenated separately and X-shift clustering was performed on the these concatenates of each population (49). Subsequently, the average X-shift cluster number was calculated, that was 5.44 ( Supplementary Table 5 ). FlowSOM metaclusters were overlayed on the t-SNE plots using ClusterExplorer v.1.7.6. plugin.

The interferons were measured by the LEGENDplex™ assay. The LEGENDplex™ technology, a multiplex bead-based immunoassay (LEGENDplex™ Human Type 1/2/3 Interferon Panel (5-plex) with V-bottom Plate, Cat num.:740396, Biolegend) was used to determine the concentrations of the 5 interferons: (IFN-α2, IFN-β, IFN-λ1 (IL-29), IFN-λ2/3 (IL28a/b), IFN-γ) following to the instructions of the manufacturer. Briefly, 50 µL of undiluted plasma samples were incubated with the premixed capture beads and following washing, detection antibodies were added. Without washing, streptavidin-PE was added to each well directly. A standard curve was generated by the application of the commercial cytokine standards of the kit. Detection range was 51 ng/mL to 2.197 pg/mL. Samples were acquired on a Cytoflex S FACS (Beckman Coulter) using the APC and PE channels. Evaluation was performed in CytExpert 4.0 (Beckman Coultrer) and Microsoft Excel fitting the median values to the curve of the standards. Data are expressed as arithmetic mean of two replicate values in pg/mL. The IFN-β was not detectable, therefore, it is not included in the figure.

Statistical analysis was performed using GraphPad Prism Version 8.4.2. For some data sets, normal distribution and lognormality could be excluded by Shapiro-Wilk, D’Agostino & Pearson and Kolmogorov-Smirnov normality and lognormality tests. For the rest of the data sets, distribution is unknown due to small sample size. In both cases, the assumption of normality was not satisfied for parametric tests. Therefore, uniformly nonparametric Wilcoxon matched-pairs signed rank test was used to compare data obtained in cell population of healthy controls and SLE patients. The significant differences were indicated as follows: * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001.

PBMCs obtained from SLE patients and healthy control (HC) individuals underwent analysis using two distinct panels: 1) the T-cell/NK-cell/NKT-cell panel and 2) the B-cell/monocyte panel. This analysis was performed shortly after thawing, referred to as “resting state”, and 72 hours after activation, referred to as “activated state”. The multiplex panel enabled us to analyze the main cell populations and subpopulations depicted in Figure 1 alongside with the gating strategy of both panels.

Initially, a comparative analysis of population frequencies between HC and SLE was conducted under both resting and activated conditions. In the resting state, SLE PBMCs exhibited significantly higher frequencies of CD8^high DPTs, memory B-cells, plasmablasts, and intermediate monocytes, while naive B-cells were significantly lower compared to HC PBMCs ( Figure 2A ). Figure 2B illustrates the frequencies of the whole activated PBMC pool, revealing a significant increase in total DPTs, CD8^high DPTs, along with a significant decrease in non-classical monocytes in SLE. For the T-cells, NK-cells and NKT-cells, the positive fraction of the population for the activation marker CD25⁺ was defined in the whole PBMC, and the percentage of CD25⁺ CD4 T-cells, CD56^dim NK-cells CD56^high NK-cells was significantly elevated in SLE indicating increased activation ( Figure 2C ). Notably, a positive correlation was observed between the increased CD25 positivity of CD56^dim and CD56^high NK-cells and the anti-double stranded DNA antibody levels of the SLE patients. Additionally, a positive correlation was found between the CD25 positivity of CD56^high NK-cells and the SLEDAI-2K indices of the patients ( Figure 2D ).

Next, we examined the binding affinities of five distinct fluorescent dye-conjugated lectins (AAL, Gal-1, Gal-3, SNA and Siglec-1) to the previously defined immune subsets by comparing median fluorescence intensities (MFI) of each population. Subsequently, these MFI values, along with lineage-specific CD marker expression data, were utilized to identify unique populations using FlowSOM. The frequencies of these identified populations will be compared between HCs and SLE patients. Finally, to assess the clinical relevance of these measurements, parameters significantly altered in SLE subjects were correlated with their SLEDAI-2K indices and anti-double stranded DNA values.

Limited variations in lectin binding were noted between HC and SLE in resting T-cell subsets. Notably, there was a significant increase in the binding of AAL and Gal-3 binding in CD3 T-cells and CD4 T-cells. Additionally, SLE CD8^high T-cells exhibited heightened Gal-3 binding compared to their HC counterparts ( Figure 3A ). However, upon activation, distinct differences emerged in the lectin binding profile of HC and SLE T-cells ( Figure 3B ). Total CD3 T-cells including CD4 T-cells displayed significantly increased AAL, Gal-1, SNA and Siglec-1 binding in SLE. CD8 T-cells including their major subset CD8^high T-cells were characterized by significantly elevated AAL, SNA and Siglec-1 binding in SLE, whereas SLE CD8^dim T-cells also bound significantly higher amount of Gal-1, SNA and Siglec-1. DNT-cells only showed a notable increase in Siglec-1 binding in SLE. Conversely, within DPT-cells the CD4^high DPT-cell subpopulation demonstrated significantly elevated Gal-1 and Siglec-1 binding. SLE CD8^high DPT-cells were distinguished from HC counter population by significantly greater AAL, Gal-1, SNA and Siglec-1 binding as well. Furthermore, lectin binding ratio was calculated between CD25⁺ and CD25^- T-cell subsets shown in Figure 3C revealing significant upregulation in all lectin binding capacities in both HC and SLE T-cell subsets in Figure 3C , with the exception of Gal-3 in CD4^high DPTs. Notably, there were significant differences in the rate of upregulation between HC and SLE. CD3 T-cells, CD4 T-cells, CD8 T-cells and DNT-cell populations as well as CD8^dim T-cells, CD8^high T-cells and CD8^high DPT-cell subpopulations were characterized by significantly more robust SNA binding ratio in SLE than in HC ( Figure 3C ).

In CD8 T-cells, encompassing the major CD8^high T-cells subset, AAL binding in activated state positively correlated with SLEDAI-2K scores ( Figure 3D ). In activated CD8^dim T-cells, SNA binding demonstrated positive correlation with SLEDAI-2K scores ( Figure 3D ). The SNA binding ratio in activated total CD3 T-cells, including main subsets such as CD4 T-cells, CD8 T-cells and DNT-cells positively correlated with SLEDAI-2K. Similar associations were observed in activated subpopulations of CD8^dim T-cells, CD8^high T-cells and CD8^high DPT-cells ( Figure 3E ).

FlowSOM identified distinct metaclusters based on lectin binding properties of activated T-cell subsets. In CD4 T-cells, MC06 with the highest SNA binding and CD25 expression was significantly higher in SLE (54.0%) than in HC (37.3%). Conversely, MC02 (16.9% vs. 11.3%) and MC04 (39.6% vs. 29.1%) were significantly diminished in SLE ( Figure 4A ). In healthy donors’ CD8^dim T-cells, over a third of the cells (36.8%) belonged to MC04 exhibiting the strongest binding of all five lectins within the group and relatively high CD8 expression. In SLE, MC04 constituted 53.8% of CD8^dim T-cells resulting in a significant difference between HC and SLE ( Figure 4B ). The SNA^high MC01 with moderate binding of other lectins and lower CD8 expression was significantly lower in SLE (9.8%) compared to HCs (17.1%) ( Figure 4B ). Similarly, in SLE CD8^high T-cells, the predominant metacluster with outstanding lectin binding was MC04 (56.2%), which was significantly smaller in HCs (38.2%), whereas there was a notable but statistically not significant difference (P=0.052) in the frequency of less glycosylated MC03 between HC and SLE (45.8% vs. 32.7%). Additionally, smaller metaclusters MC05 (4.0% vs. 2.6%) and MC06 (2.1% vs. 0.8%) were significantly higher in HC than in SLE ( Figure 4C ). In DNT-cells, MC06 with exceptionally high Gal-1 binding was significantly increased in SLE (10.6%) compared to HC (4.1%) and MC03 was almost significantly lower (P=0.052) in SLE (22.5%) than in HC (39.0%) ( Figure 4D ). Moving to CD4^high DPT-cells, the frequency of MC02, the metacluster with maximal AAL, Gal-1, SNA and Siglec-1 binding across the group, more than doubled in SLE (18.7%) compared to HC (7.5%). Other metaclusters with less pronounced glycosylation such as MC01 and MC03 were underrepresented in SLE (43.3% and 17.4%) compared to HC (50.9% and 24.4%), with the difference in MC03 being statistically significant ( Figure 4E ). Finally, in CD8^high DPT-cells, the proportion of cells in MC02 was significantly heightened in SLE (37.6%) compared to the percentage of MC02 cells in HC (21.9%) and MC04, characterized by relatively lower lectin binding compared to MC02, was significantly less prevalent in SLE (31.6%) than in HC (42.3%) ( Figure 4F ).

In quiescent state, significant elevations were observed in Gal-3 binding in total CD56 NK-cells and CD56^high NK-cells ( Figure 5A ). However, no noteworthy changes were detected in lectin binding in activated state ( Figure 5B ). In contrast to T-cell subsets, the activation hallmarked in CD25 positivity was not uniformly linked to elevated lectin binding. Notably, only in a few cases exhibited CD25⁺ NK-cell subsets significant increase in lectin binding. Specifically, AAL and Gal-3 showed a significant increase in total HC CD56 NK-cells, while AAL in CD56^dim NK-cells and Siglec-1 in CD56^high NK-cells demonstrated significant elevation in SLE ( Figure 5C ). The Gal-3 binding ratio between CD25⁺ and CD25^- total CD56 NK-cells was significantly lower in SLE compared to HC ( Figure 5C ).

Although the lectin binding pattern of activated NK-cell subsets did not differ significantly in SLE, FlowSOM unveiled alterations in metacluster frequencies in SLE. In CD56^dim NK-cells, the predominant metacluster MC02 (82.8% in HC) with prominent Gal-1, SNA and Siglec-1 binding was significantly decreased in SLE (75.5%) and second largest metacluster MC01 (9.0% in HC), distinguished by relatively weak lectin binding, was elevated in SLE (12.7%) ( Figure 5D ). The small metacluster MC03, characterized by strong CD25 expression but negligible amount of AAL and Siglec-1 binding, was significantly overrepresented in SLE (0.64%) compared to HC (0.2%). The percentage of this metacluster in SLE CD56^dim NK-cells positively correlated with the SLEDAI-2K indices of the patients ( Figure 5E ). In HC CD56^high NK-cells, more than half of the cells (55.4%) belonged to MC01, with maximal lectin binding within the subset significantly underrepresented in SLE (42.9%). Consequently, there was a significant increase in other metaclusters, namely MC03 (9.3% in SLE vs. 3.7% in HC) and MC06 (6.0% in SLE vs. % in HC) ( Figure 5F ). MC03 had diminished Gal-1, SNA, Gal-3 and especially AAL but higher Siglec-1 binding compared to MC01. MC06 showed extremely low AAL and Siglec-1 binding but strong CD25 expression. Interestingly, the proportion of MC06 obtained positive correlation with SLEDAI-2K scores of the SLE patients ( Figure 5G ).

In resting state, SLE CD8 NKT-cells bound significantly less SNA than their counterparts from healthy donors ( Figure 6A ). In activated state, both CD4 and CD8 NKT-cells in SLE exhibited significantly higher Siglec-1 binding and SLE CD4 NKT-cells also bound significantly more Gal-1 than HC CD4 NKT-cells ( Figure 6B ). On the other hand, DN NKT-cells in SLE showed significantly decreased AAL and SNA binding ( Figure 6B ). Importantly, a negative correlation was observed between the reduced AAL binding in SLE DN NKT-cells and the elevated anti-double-stranded DNA antibody levels in the same patients ( Figure 6D ). Furthermore, there was no clear trend observed in the lectin binding ratios of CD25⁺ and CD25^- NKT-cell subsets, although significant differences between HC and SLE are noted ( Figure 6C ). In the CD25⁺/CD25^- AAL lectin binding ratio, there was a significant reduction in SLE DN NKTs. Regarding Gal-3, total SLE NKT-cells including CD4 NKT- and CD8 NKT-cell subsets, lectin binding ratio was significantly elevated compared to that of HCs. In SLE, there was a significant decline in Siglec-1 binding ratio in DN NKT-cells ( Figure 6C ). This decreased Siglec-1 binding ratio of CD25⁺ and CD25^- DN NKT-cells in SLE patients negatively correlated with the patients’ SLEDAI-2K indices ( Figure 6E ).

The unsupervised FlowSOM approach identified phenotypically distinct subpopulations in NKT-cell subsets revealing particularly notable differences in AAL and Siglec-1 binding. In CD4 NKT-cells, small metacluster MC02, characterized by minimal AAL and Siglec-1 binding alongside high CD25 expression, was significantly expanded in SLE (3.1%) compared to HCs (1.9%) ( Figure 6F ). Similarly, in CD8 NKT-cells, MC02 with negligible AAL and Siglec-1 binding, and MC03 with intermediate AAL and low Siglec-1 binding, were enriched in SLE (1.1% and 3.7%, respectively) compared to HC (0.4% and 2.3%, respectively), with the latter difference being statistically significant ( Figure 6G ). In the case of DN NKT-cells, MC01 (12.6% in HC) exhibiting moderate lectin binding properties, more than doubled in SLE (27.7%) ( Figure 6H ). The second metacluster, MC02, characterized by high Siglec-1 and low AAL binding, was nearly absent in HC (0.1%), albeit its ratio was increased in SLE (1.5%). Lastly, the largest metacluster MC04, featuring high AAL, Gal-1, SNA and Siglec-1 binding, was significantly underrepresented in SLE (58.8%) in comparison to HC (78.5%). Consistent with earlier findings, the prevalence of MC04 in DN NKT-cells of SLE patients showed a negative correlation with the patients’ anti-ds DNA antibody levels ( Figure 6I ).

In resting state, fucosylation of plasmablasts in SLE as measured by AAL binding exhibited substantial increase compared to HC ( Figure 7A ). Intriguingly, positive correlation was discerned between the AAL binding of plasmablasts and the SLEDAI-2K scores of SLE patients ( Figure 7D ). Additional distinctions observed in SLE in resting state include elevated Gal-3 binding in naive B-cells and reduced SNA binding in memory B-cells ( Figure 7A ). In activated state, total CD19 B-cells showed significantly decreased AAL and Siglec-1 binding ( Figure 7B ). Notably, there was a clear trend of markedly lower binding of the five lectins in memory B-cells in SLE. In comparison to HC, SLE plasmablasts also bound significantly less Gal-1 and Gal-3. Changes in lectin binding upon activation are depicted in Figure 7C , expressed as binding ratios between the two conditions across the same dataset. Total CD19 B-cells, memory B-cells and plasmablasts performed significantly lower AAL and Gal-3 binding ratios in SLE, except AAL in memory B-cells (P=0.053).

FlowSOM was applied to discover phenotypically distinct subsets in naive and memory B-cells based on their lectin binding profile, HLA-DR activation marker expression, and, in case of memory B-cells, CD27 expression. Unfortunately, the constrained cell counts of plasmablasts precluded subsequent clustering. In naive B-cells, approximately 80% (80.6% in HC and 78.3% in SLE) of the cells were classified into MC06 characterized by robust lectin binding and strong HLA-DR expression ( Figure 7E ). The small metacluster MC03, with lowest AAL binding and moderate binding of other lectins, was significantly expanded in SLE (2.6%) compared to HC (1.0%). Additionally, the distribution of MC05, with outstanding AAL and SNA binding, and HLA-DR expression but relatively low Siglec-1 binding, was significantly reduced in SLE (4.4%) compared to HC (6.0%) naive B-cells ( Figure 7E ). A majority of memory B-cells belonged to MC01 distinguished by high HLA-DR expression, prominent Gal-1 and SNA binding ( Figure 7F ). The proportion of this metacluster in HC exceeded that in SLE by 10% (62.4% vs. 72.3%). Consequently, other metaclusters in the memory B-cell subset were enriched in SLE. Accordingly, MC03 displaying a poor lectin binding pattern, was increased in SLE by almost 5% (18.1% vs. 13.7%). Furthermore, the proportion of MC04 with negligible AAL binding was more than doubled in SLE (1.7% vs. 0.7% in HC), whereas MC05 featuring the lowest Gal-1 binding and intermediate HLA-DR expression, was significantly more prevalent in SLE (9.2% vs. 3.8% in HC) ( Figure 7F ).

Since metaclusters of the B-cell subsets with significantly different abundance in SLE showed altered HLA-DR expression in many cases, the MFI values of HLA-DR expression is presented in both resting ( Supplementary Figure 1A ) and activated states ( Supplementary Figure 1B ). Interestingly, all B-cell subsets expressed tendentiously less HLA-DR on the cell surface in SLE. Notably, in case of memory B-cells in activated state, the difference reached statistical significance ( Supplementary Figure 1B ). Stimulation by LPS and TLR9 agonist induced significant upregulation of HLA-DR in both HC and SLE B-cell subsets, although the extent of upregulation was significantly greater in SLE memory B-cells and plasmablasts ( Supplementary Figure 1C ).

In resting classical monocytes (MO) of SLE patients, there was a significant reduction in Gal-1, Gal-3 and SNA binding ( Figure 8A ). Moreover, SNA demonstrated significantly lower binding to intermediate and non-classical monocytes in SLE. In the activated state, the level of AAL binding was significantly declined in classical and intermediate monocytes in SLE, and transitional monocytes in SLE bound significantly lower amount of Gal-3 ( Figure 8B ). Activation by LPS and TLR9 agonist triggered significant changes in lectin binding of both HC and SLE monocyte subsets, and there was no significant difference between HC and SLE in the lectin binding ratio between the activated and resting state ( Figure 8C ).

LPS and TLR9 agonist stimulated classical, intermediate and transitional monocytes subsets were subjected to clustering utilizing FlowSOM and were organized into distinct subtypes. Due to insufficient cell count in the non-classical monocytes, clustering analysis was not conducted for this population. The majority of classical monocytes (89.6% in HC) were categorized by FlowSOM into MC01, which had the highest lectin binding and also strong CD14, and HLA-DR expression ( Figure 8D ). In SLE, the prevalence of MC01 decreased by approximately 6% (83.7%), leading to the elevation of other metaclusters, namely MC02 and MC03, with less pronounced lectin binding and HLA-DR expression, were elevated in SLE (11.4%; 1.7%) compared to HC (6.8%; 1.2%). Additionally, MC05, with a lower lectin binding profile compared to MC01 and the smallest AAL binding across all metaclusters in this group, was significantly more abundant in SLE (1%) than in HC (0.3%). More pronounced changes were detected in intermediate monocytes ( Figure 8E ), where MC02 exhibiting a CD14^high CD16^low HLA-DR^low Gal-1^intermediate phenotype, was remarkably expanded in SLE compared to HC (35.0% vs 8.3%). Conversely, MC05, with CD14^intermediate CD16^intermediate HLA-DR^high Gal-1^high characteristics, was significantly decreased in SLE (12.3% vs 32.4%). MC06, displaying a CD14^low CD16^high HLA-DR^high Gal-1^low SNA^high phenotype, was significantly less abundant in SLE than in HC (0.2% vs. 1.3%) ( Figure 8E ). In transitional monocytes, MC01, characterized by maximal AAL, Gal-1, Gal-3 and Siglec-1 binding within the group, was significantly underrepresented in SLE (12.2%) in comparison to HC (24.2%), while MC02, demonstrating relatively weaker binding of the aforementioned lectins and low HLA-DR expression, comprised 44.8% of transitional MO cells in HC and 58.7% of the cells in SLE (58.7%) ( Figure 8F ).

For classical and intermediate MOs, significantly lower HLA-DR expression was detected in SLE in both conditions ( Supplementary Figures 1D, E ). Activated transitional monocytes also showed decreased HLA-DR expression in SLE compared to HC ( Supplementary Figure 1E ). In contrast, resting non-classical monocytes showed elevated HLA-DR expression ( Supplementary Figure 1D ). The expression ratio calculated between activated and resting state was significantly lower in SLE classical and intermediate monocytes than in their HC counterparts ( Supplementary Figure 1F ). Altered CD14 was only observed in resting classical monocytes, with a significant reduction in SLE ( Supplementary Figure 1D ). Upon activation, CD14 expression was significantly upregulated in classical, intermediate, and non-classical monocytes in both HC and SLE, although no difference was found in the rate of upregulation between the two subject groups ( Supplementary Figure 1F ). CD16 expression was significantly reduced in resting intermediate monocytes and in resting and activated non-classical monocytes in SLE compared to HC ( Supplementary Figures 1D, E ). Finally, classical monocytes in SLE demonstrated a significantly lower CD16 expression ratio between the activated and resting states ( Supplementary Figure 1F ).

Most of the research on SLE and type I interferons focused on IFN-α and IFN-β. Indeed, the differentiation and expansion of the pathogenic T-cell subsets are linked with the release of type I interferons (IFN−I), (2, 3) fundamental contributors to SLE pathogenesis. Another cells, plasmacytoid dendritic cells were identified as the main source of IFN-α and macrophages and fibroblasts the major source of IFN-β. Recently, studies highlighted the role for IFN-κ, mainly expressed in keratinocytes in response to exposure to UV light. The major challenge is that all types of the source cells are not there in PBMC samples, or their number is insufficient for culturing and measuring their effect. The tissue resident IFN-I producers could be identified in situ in the inflamed tissues. However, in our study these factors can be investigated from the stored plasma samples of the patients and HCs. The stored plasma samples from the HC and SLE patient cohort were analyzed by the LEGENDplex™ Human Type 1/2/3 Interferon Panel (BioLegend). Although, the average IFN concentrations were higher in the SLE group but there was no significant difference observed in IFN-α2, IFN-γ, IFN-λ1 and IFN-λ2 between HC and SLE ( Supplementary Figure 2 ). The authors suppose that higher number of subjects could provide significant increase of IFNs in the plasma of SLE patients.

As demonstrated by us and others, stimulation prompts an increase in cell surface glycosylation, particularly enhancing glycan complexity in T-cells manifested in increased lectin binding (34, 50, 51). In the current work, the activation-induced elevation of alpa-2,6-sialylation was significantly more robust in SLE CD4 T-cells, CD8 T-cells, including CD8^dim and CD8^high T-cells, DNT-cells along with CD8^high DPT-cells than in HC counterparts. This was reflected in significantly higher SNA binding ratio between CD25⁺ and CD25^- fractions of these T-cell subsets in SLE. Consequently, activated SLE T-cell subsets bound significantly more SNA than HC T-cell subsets. These findings align with our previous results, which indicated a slight elevation in SNA binding in activated SLE T-cells, however, that difference did not reach statistical significance. This previously reported heightened alpha-2,6 sialylation of SLE T-cells compared to HC T-cells upon activation was further confirmed by a significant increase in the mRNA ratios of ST6 beta-galactoseamide alpha-2,6-sialyltranferase 1 and neuraminidase 1 (ST6GAL1/NEU1) in activated SLE T-cells responsible for adding and removal of sialic acids on glycans. (34) In contrast, others compared the sialylation of freshly isolated unstimulated CD4 T-cells from SLE patients and HCs, finding no notable difference in SNA binding (52). Interestingly, the FlowSOM clustering algorithm revealed the expansion of metaclusters with extreme SNA binding in SLE, emerging as the predominant metaclusters in SLE CD4 T-cells, CD8^dim T-cells, CD8^high T-cells and in CD8^high DPT-cells. These metaclusters were also activated expressing intermediate or high CD25 compared to other metaclusters within the same subset. Despite the increased prevalence of these CD25^medium/CD25^high metaclusters in SLE, the overall CD25 positivity in SLE upon activation was only slightly increased in each T-cell subset, reaching statistical significance in the case of CD4 T-cells. It is important to note that hyperactivation is a well-established feature of SLE T-cells, attributed to both increased sensitivity of the TCR and enhanced downstream signaling (53). Interestingly, in murine models St6gal1 gene expression was found to be downregulated in both CD4 and CD8 T-cells following activation (29). Therefore, the connection between T-cell activation and alpha-2,6-sialylation remains inconclusive and requires further clarification in future investigations. On the flip side, T-cell activation was linked with core fucosylation of TCR catalyzed by fucosyltransferase 8 (FUT8). Intriguingly, hyperactivation of this enzyme has been documented in SLE CD4 T-cells (14). In our experiments, increase of core fucosylation was another hallmark of SLE T-cell subsets. The expanded metaclusters in SLE T-cells subsets mentioned earlier were also characterized by remarkable core fucosylation indicated by strong AAL binding. AAL binding was significantly increased in bulk activated populations of CD4 T-cells, CD8 T-cells including CD8^high subtype and CD8^high DPT cells from SLE. Most importantly, many of these altered parameters in alpha-2,6-sialylation and core fucosylation demonstrated a correlation with disease activity. The ratio of SNA binding in activated (CD25⁺) versus non-activated (CD25^-) fraction of CD4 T-cells, CD8 T-cells, including CD8^dim and CD8^high T-cells, DNT-cells along with CD8^high DPT-cells T-cells positively correlated with SLEDAI-2K indices of the patients. Additionally, the SNA binding of activated CD8^dim T-cells and AAL binding of activated CD8 T-cells including CD8^high T-cells positively correlated with SLEDAI-2K indices of SLE patients. Our results support previous findings that the expression of Siglec-1-ligands on T-cells correlates with the activity of nephritis in a murine lupus model (54). Siglec-1 expression on monocytes is a Type-I interferon-inducible process, and its intensity correlates with serum soluble Siglec-1 levels and the presence of nephritis in SLE patients (35). The metaclusters significantly enriched in our SLE patients consistently show the highest level of Siglec-1-binding in all T-cell subsets examined. However, the effect of Siglec-1-binding on T-cell function needs to be clarified.

NK cells can be divided into cytolytic CD56^dim and immunomodulatory, cytokine producing CD56^high subsets (55). In SLE, the ratio of CD25⁺ cells was significantly elevated in total NK-cells as well as in CD56^dim and CD56^high subgroups in SLE. The percentage of CD25⁺ CD56^dim and CD56^high NK-cells of SLE patients positively correlated with anti-ds-DNA antibody levels, and the same value in CD56^high NKs showed positive correlation with SLEDAI-2K indices too. The heightened CD25-positivity in SLE can be partially explained by the dynamics of subsets with varying CD25 expression levels explored by FlowSOM. Specifically, majority of the stimulated CD56^dim and CD56^high NK-cells were categorized as CD25^low, Gal-1^high, Siglec-1^high SNA^high cells. In SLE, these metaclusters were significantly reduced in both cell types in SLE. Conversely, small, less glycosylated metaclusters with CD25^high AAL^low Siglec-1^low SNA^med phenotype (MC03 in CD56^dim and MC06 in CD56^high NKs) were significantly overrepresented in SLE. Enhanced activated phenotype in SLE NK-cells indicated by increased CD69 expression was also observed by other investigators in both CD56^dim and in CD56^bright subsets in association with disease severity (56, 57). Interestingly, within normal NK-cells high CD25 expression marked phenotypically different subset, since IL-15 primed, IL-2 restimulated CD25^bright NK-cells had higher pSTAT5 phosphorylation, proliferation rate (Ki67-positivity) and mitochondrial respiration than CD25^dim or CD25- counterparts (58). The authors concluded that this phenotype (CD25^bright Ki67⁺) might be beneficial in cancer, although in the context of autoimmunity, it could potentially drive pathogenic processes. Intriguingly, in SLE, the percentage of proliferating Ki67+ NK-cells positively correlated with SLEDAI-2K scores, the percentage of plasma cells and CD11c⁺ B-cells (59). In our experiments, the expanded metaclusters in SLE with an activated CD25^high phenotype, specifically CD56^dim MC03 and CD56^high MC06, also exhibited a positive correlation with SLEDAI-2K indices. Whether these metaclusters are characterized by high proliferation rate and altered metabolic activity must be addressed in the future. In this study, these subsets were defined by low Siglec-1 binding, moderate alpha-2,6-sialylation and low core fucosylation. Sialylation can impact the function of both activating (e.g. 2B4) and inhibitory receptors (Siglec-7, Siglec-9) on NK-cells (55, 60). Unfortunately, there is no information available on fucosylation of NK-cell specific molecules. The exact proteins with altered sialylation and fucosylation in MC03 and MC06 metaclusters must be elucidated, although the very low binding of the respective lectins could serve as sufficient biomarkers to identify these subsets.

Here, we gated CD3⁺CD56⁺ T-cells, which phenotype can be acquired by conventional CD8⁺ T-cells alongside unconventional mucosal-associated invariant T-cells (MAIT), γδ-T-cells and NKT-cells (61). NKT cells can be further classified as CD1d-dependent type I (classical, termed also invariant NKTs – iNKTs), type II (non-classical) and CD1d-independent NKT-like cells (62). Based on CD4 and CD8 expression, NKTs can be subdivided into CD4⁺, CD8⁺ and DN subsets (63). In this study, the most striking differences among HC and SLE were observed in the DN NKT subset. Following stimulation, this subset exhibited a less pronounced increase in AAL binding in SLE compared to HC. The diminished AAL binding of activated DN NKTs obtained negative correlation with anti-dsDNA antibodies. Similarly, the Siglec-1 binding ratio of CD25⁺/CD25^- cells was significantly smaller in SLE than in HC and this ratio negatively correlated with SLEDAI-2K indices. Correspondingly, the largest DN NKT metacluster, MC04, characterized by high fucosylation, α-2,6-sialylation and strong Siglec-1 binding was significantly decreased in SLE, and the frequency of this metacluster negatively correlated with anti-dsDNA levels. These findings suggest that stimulation-induced changes in glycosylation of DN NKTs have defects in SLE in correlation with disease activity. Consistently, SLE iNKTs exerted functional defects in response to stimulation compared to iNKTs from HCs. The combination of IL-15 and α-galactosyl ceramide triggered reduced expansion and perforin expression of iNKTs in SLE compared to controls. Moreover, in response to the same stimulation, SLE iNKT-cells enhanced NK cytotoxicity at lower extent than HC iNKTs (64). Additionally, decreased number of NKT-like cells and iNKT-cells have been reported in SLE, (65, 66) and the percentage of NKT-like cells negatively correlated with disease activity (67). In summary, our results in conjunction with other studies, provide evidence for the defective stimulation of SLE NKTs. This leads to impaired expansion, functionality and glycosylation (particularly core fucosylation and sialylation) of this subset, which in some cases, negatively correlates with disease activity indicating that these cells might play a protective role in SLE (68).

In our experiments, resting plasmablasts were significantly more abundant in SLE and displayed more core fucosylated glycans than HC plasmablasts indicated by higher AAL binding, which positively correlated with SLEDAI-2K indexes. The expansion of specific plasmablast subsets (CD27^high as common marker) in SLE was noted in other publications as well (69, 70), however, the core fucosylation of SLE plasmablasts was proven to be unaltered (71). Overall, we observed lower degree of glycosylation on resting plasmablasts compared to other B-cell subsets. This is in line with prior findings demonstrating more truncated O-glycans on plasmablasts in comparison to other subsets (72).

In the current work, the percentage of memory B-cells within total B-cells was also significantly elevated while naive B-cells were declined and bound significantly higher Gal-3 in SLE in quiescent state. Stimulated SLE memory B-cells exhibited significantly lower binding of all the five lectins. This phenomenon was also detected, albeit not statistically significant, in naive B-cells and plasmablasts of SLE. This observation was reinforced by the higher abundance of metaclusters with low lectin binding, namely, MC02-03 in naive and MC03-05 in memory B-cells in SLE. These results are consistent with previous findings reporting reduced core fucosylation of SLE naive and memory B-cells, however sialylation was unaltered in SLE B-cell subsets (71). No further articles are currently available regarding the glycosylation and galectin binding capacity of B-cells in SLE. Therefore, a summary of relevant literature in the context of B-cells and these lectins is provided here. First, core fucosylation is identified as crucial for the antigen binding and signaling of BCR (15). Second, α-2,6-sialylation is recognized as essential for the cis-binding and oligomerization of the inhibitory Siglec, CD22, which is recruited to BCR upon activation and attenuates hyperactivation (73). Third, multiple lines of evidence highlighted the significance of Gal-3 in B-cell differentiation. Gal-3 acted as inhibitor of plasma cell differentiation (74, 75), and promoted IL-4-induced memory B-cell differentiation in Gal-3 knockout studies (76). Collectively, the literature suggests that the reduced glycosylation of SLE memory B-cells could potentially contribute to the dysregulation of BCR signaling. While the role of endogenous Gal-3 is emphasized in memory B-cell differentiation, however, the potential connection between the alterations in Gal-3 binding and the expansion of memory B-cells and plasmablasts in SLE in our results must be clarified in future research.

In the current work, monocytes were classified into phagocytic classical monocytes, pro-inflammatory non-classical monocytes, intermediate monocytes, being both phagocytic and pro-inflammatory, and transitional monocytes as the fourth and less studied MO type (77, 78). Multiple significant differences in lectin binding were observed in unstimulated monocyte subsets of HC and SLE. First, α-2,6-sialylation was decreased in classical, intermediate and non-classical monocytes, although the difference was not significant after stimulation. Cell surface sialylation was shown to be downregulated during macrophage differentiation from PMA-stimulated monocytes, as well as the expression of ST6GAL1 responsible for α-2,6-sialylation (79, 80). Liu et al. proposed that α-2,6-sialylation is protective against TNF-α-induced apoptosis in monocytes, since the downregulation of ST6GAL1 affected TNFR1 sialylation and consequently increased the level of TNF-α-induced apoptosis of PMA-stimulated monocytes (81). Speculatively, the decreased sialylation of monocyte subsets in SLE might make them more prone to apoptosis following differentiation toward macrophages.

Second, the binding of both Gal-1 and Gal-3, both have been reported to be expressed by monocytes and macrophages, were significantly reduced in resting classical MOs. According to Di Gregoli and coworkers, soluble Gal-3-negative macrophages are pro-inflammatory and Gal-3-binding macrophages promote the resolution of inflammation (82). The importance of Gal-3 binding via CD98 was underscored in alternative activation of M2 macrophages (83). Similarly, Gal-1 was also implicated in M2 macrophage polarization (84). The involvement of both pro-inflammatory M1 and nonfunctional M2 macrophages has been established in the pathogenesis of SLE (85), moreover, lupus flares were associated with M1/M2 imbalance (86). Thus, we could assume that the reduced Gal-3 and Gal-1 binding to classical MOs in SLE might dampen their ability to differentiate into pro-resolving M2 macrophages. Third, stimulated classical and intermediate MOs showed significantly lower core fucosylation in SLE.

Finally, there were various differences in the expression level of HLA-DR, CD14 and CD16 in MO subsets and their frequencies between HC and SLE. HLA-DR expression was significantly lower in resting and stimulated classical and intermediate MOs and stimulated transitional MOs in SLE. This is aligned with previous reports (87). CD14 was decreased in unstimulated classical MOs, which was also reported in SLE (87). The proportion of intermediate MOs was increased in SLE in line with other reports, where intermediate monocytes were increased in SLE compared to HC (88) or in active versus inactive SLE correlating with disease activity (89).