Three-dimensional spatial transcriptomics uncovers cell type localizations in the human rheumatoid arthritis synovium

AbstractThe inflamed rheumatic joint is a highly heterogeneous and complex tissue with dynamic recruitment and expansion of multiple cell types that interact in multifaceted ways within a localized area. Rheumatoid arthritis synovium has primarily been studied either by immunostaining or by molecular profiling after tissue homogenization. Here, we use Spatial Transcriptomics, where tissue-resident RNA is spatially labeled in situ with barcodes in a transcriptome-wide fashion, to study local tissue interactions at the site of chronic synovial inflammation. We report comprehensive spatial RNA-Seq data coupled to cell type-specific localization patterns at and around organized structures of infiltrating leukocyte cells in the synovium. Combining morphological features and high-throughput spatially resolved transcriptomics may be able to provide higher statistical power and more insights into monitoring disease severity and treatment-specific responses in seropositive and seronegative rheumatoid arthritis.</jats:p

Multi-HLA class II tetramer analyses of citrulline-reactive T cells and early treatment response in rheumatoid arthritis

Background HLA class II tetramers can be used for ex vivo enumeration and phenotypic characterisation of antigen-specific CD4+ T cells. They are increasingly applied in settings like allergy, vaccination and autoimmune diseases. Rheumatoid arthritis (RA) is a chronic autoimmune disorder for which many autoantigens have been described. Results Using multi-parameter flow cytometry, we developed a multi-HLA class II tetramer approach to simultaneously study several antigen specificities in RA patient samples. We focused on previously described citrullinated HLA-DRB1*04:01-restricted T cell epitopes from alpha-enolase, fibrinogen-beta, vimentin as well as cartilage intermediate layer protein (CILP). First, we examined inter-assay variability and the sensitivity of the assay in peripheral blood from healthy donors (n = 7). Next, we confirmed the robustness and sensitivity in a cohort of RA patients with repeat blood draws (n = 14). We then applied our method in two different settings. We assessed lymphoid tissue from seropositive arthralgia (n = 5) and early RA patients (n = 5) and could demonstrate autoreactive T cells in individuals at risk of developing RA. Lastly, we studied peripheral blood from early RA patients (n = 10) and found that the group of patients achieving minimum disease activity (DAS28 &lt; 2.6) at 6 months follow-up displayed a decrease in the frequency of citrulline-specific T cells. Conclusions Our study demonstrates the development of a sensitive tetramer panel allowing simultaneous characterisation of antigen-specific T cells in ex vivo patient samples including RA 'at risk' subjects. This multi-tetramer approach can be useful for longitudinal immune-monitoring in any disease with known HLA-restriction element and several candidate antigens

Б1.В.ДВ.1.1 Оптимизация и анализ данных в биологии

Objectives: To describe the development and assess the psychometric properties of the novel 'Symptoms in Persons At Risk of Rheumatoid Arthritis' (SPARRA) questionnaire in individuals at risk of rheumatoid arthritis (RA) and to quantify their symptoms. Methods: The questionnaire items were derived from a qualitative study in patients with seropositive arthralgia. The questionnaire was administered to 219 individuals at risk of RA on the basis of symptoms or autoantibody positivity: 74% rheumatoid factor and/or anticitrullinated protein antibodies positive, 26% seronegative. Validity, reliability and responsiveness were assessed. Eighteen first degree relatives (FDR) of patients with RA were used for comparison. Results: Face and content validity were high. The test-retest showed good agreement and reliability (1 week and 6 months). Overall, construct validity was low to moderate, with higher values for concurrent validity, suggesting that some questions reflect symptom content not captured with regular Visual Analogue Scale pain/well-being. Responsiveness was low (small subgroup). Finally, the burden of symptoms in both seronegative and seropositive at risk individuals was high, with pain, stiffness and fatigue being the most common ones with a major impact on daily functioning. The FDR cohort (mostly healthy individuals) showed a lower burden of symptoms; however, the distribution of symptoms was similar. Conclusions: The SPARRA questionnaire has good psychometric properties and can add information to currently available clinical measures in individuals at risk of RA. The studied group had a high burden and impact of symptoms. Future studies should evaluate whether SPARRA data can improve the prediction of RA in at risk individuals

IgG Antibodies to Cyclic Citrullinated Peptides Exhibit Profiles Specific in Terms of IgG Subclasses, Fc-Glycans and a Fab-Peptide Sequence

<div><p>The Fc-glycan profile of IgG₁ anti-citrullinated peptide antibodies (ACPA) in rheumatoid arthritis (RA) patients has recently been reported to be different from non-ACPA IgG₁, a phenomenon which likely plays a role in RA pathogenesis. Herein we investigate the Fc-glycosylation pattern of all ACPA-IgG isotypes and simultaneously investigate in detail the IgG protein-chain sequence repertoire. IgG from serum or plasma (S/P, n = 14) and synovial fluid (SF, n = 4) from 18 ACPA-positive RA-patients was enriched using Protein G columns followed by ACPA-purification on cyclic citrullinated peptide-2 (CCP2)-coupled columns. Paired ACPA (anti-CCP2 eluted IgG) and IgG flow through (FT) fractions were analyzed by LC-MS/MS-proteomics. IgG peptides, isotypes and corresponding Fc-glycopeptides were quantified and interrogated using uni- and multivariate statistics. The Fc-glycans from the IgG₄ peptide EEQ<b>F</b>NST<b>Y</b>R was validated using protein A column purification. Relative to FT-IgG₄, the ACPA-IgG₄ Fc-glycan-profile contained lower amounts (p = 0.002) of the agalacto and asialylated core-fucosylated biantennary form (FA2) and higher content (p = 0.001) of sialylated glycans. Novel differences in the Fc-glycan-profile of ACPA-IgG₁ compared to FT-IgG₁ were observed in the distribution of bisected forms (n = 5, p = 0.0001, decrease) and mono-antennnary forms (n = 3, p = 0.02, increase). Our study also confirmed higher abundance of FA2 (p = 0.002) and lower abundance of afucosylated forms (n = 4, p = 0.001) in ACPA-IgG₁ relative to FT-IgG₁ as well as lower content of IgG₂ (p = 0.0000001) and elevated content of IgG₄ (p = 0.004) in ACPA compared to FT. One λ-variable peptide sequence was significantly increased in ACPA (p = 0.0001). In conclusion, the Fc-glycan profile of both ACPA-IgG₁ and ACPA-IgG₄ are distinct. Given that IgG₁ and IgG₄ have different Fc-receptor and complement binding affinities, this phenomenon likely affects ACPA effector- and immune-regulatory functions in an IgG isotype-specific manner. These findings further highlight the importance of antibody characterization in relation to functional <i>in vivo</i> and <i>in vitro</i> studies.</p></div

Differences between ACPA and FT in variable λ-chain peptide DFMLTQPHSVSESPGK.

<p>(<b>A</b>) Intra-individual differences between FT and ACPA. Given p-value was obtained with paired t-test. (<b>B</b>) Ion chromatograms of the peptide ion precursor (ACPA: red, FT: black) from a SF-IgG sample (upper panel) as well as from a serum-IgG sample (lower panel). (<b>C, E</b>) Mass spectra from ACPA from each patient, with the precursor ion isotopic pattern marked out in red. (<b>D, F</b>) The corresponding FT mass spectra for each individual, the precursor is weak/not identified.</p

Schematic IgG protein and Fc-glycan structures.

<p>(<b>A</b>) The IgG protein. Locations of the Fab- and Fc- regions as well as of the Fc-glycans are indicated. (<b>B</b>) Schematic picture of N-linked biantennary oligosaccharide heterogeneity. Sugar identities, linkage positions and anomeric configurations are indicated. Nomenclature is given as described by Royle <i>et al</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113924#pone.0113924-Royle1" target="_blank">[19]</a>. The core heptasaccharide moiety (A2) is gray shaded in contrast to the outer core variable units. F at the start of the abbreviation (FA2) indicates a fucose (Fuc) linked to the inner <i>N</i>-acetyl-glucosamine (GlcNAc). B indicates a bisecting GlcNAc linked to the middle mannose (Man), Gn indicates n (number of) galactoses (Gal) linked to antenna and Sn indicates n sialic acids (N-acetyl neuraminic acid, Neu5Ac) linked to Gal. If the glycan is monoantennary, (i.e. β-GlucNAc(1→2)-α-Man-(1→3)-[α-Man(1→6)]-β-Man-(1→4)-β-GlcNAc-(1→4)-β-GlcNAc-(1→Asn₂₉₇) or β-GlucNAc(1→2)-α-Man-(1→6)-[α-Man(1→3)]-β-Man-(1→4)-β-GlcNAc-(1→4)-β-GlcNAc-(1→Asn₂₉₇), respectively), it is referred to as A1. (<b>C</b>) Examples of the structural diversity.</p

Log₁₀ fold difference of FA2 and FA2G2S1 comparing the ACPA/FT ratio for the different IgG Fc-glycopeptide types.

<p>Values ≥0 (dashed line) indicate an intra-individually increased amount in ACPA. P-values (comparing FT and ACPA for each subject) were obtained with paired t-test. NS: Not Significant. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113924#pone.0113924.s005" target="_blank">Figure S5</a> for FT and ACPA differences in direct values.</p

Multivariate modelling based on the Fc-glycan and protein/peptide correlations in the ACPA and FT sample set.

<p>Subjects are labeled according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113924#pone.0113924.s007" target="_blank">Table S1</a> and colored according to FT (light gray [S/P]/light red [SF]) and ACPA (dark gray [S/P]/dark red [SF]). (<b>A</b>) PCA scores plot. FT and ACPA samples are separated in component t <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113924#pone.0113924-Klareskog2" target="_blank">[3]</a>. (<b>B</b>) OPLS-DA scores plot, the model was constructed to distinguish FT samples and ACPA samples and generated a strong (R² = 0.93, Q² = 0.85, CV ANOVA p-value = 2.3E-12) model, with distinct separation of FT (negative) an ACPA (positive) along the predictive X-axis t <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113924#pone.0113924-Arend1" target="_blank">[1]</a>. (<b>C</b>) Loading column plot of the predictive axis (pq <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113924#pone.0113924-Arend1" target="_blank">[1]</a>). Features with positive pq <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113924#pone.0113924-Arend1" target="_blank">[1]</a> values indicate positive ACPA correlation and features with negative pq <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113924#pone.0113924-Arend1" target="_blank">[1]</a> values indicate negative ACPA correlation. Only glycans (blue) and proteins (grey) correlating with 95% confidence are shown.</p