More than 314,000 cells analyzed for precise treatment strategies

Rheumatoid arthritis (RA) is one of the first autoimmune diseases to be identified and remains incurable. Despite the discovery of several disease-modifying treatments, the response to each treatment remains unpredictable. This indicates a difference in the pathophysiology of RA between patients.

A new article recently appeared in Nature, reported the examination of synovial tissue from the joints of nearly 80 people with RA, combined with RNA sequencing and surface protein analyses. This allowed the researchers to assemble an atlas of RA synovial changes from more than 314,000 individual cells. This could help develop targeted therapies that recognize the diversity of RA disease processes.

Background

RA affects about 1 in 100 people worldwide. The main feature is the painful swelling of synovial joints that ultimately culminates in joint damage and disability. Recognition of the immunological origins of RA has led to the deployment of therapies that target inflammatory cytokines and pathways, including tumor necrosis factor (TNF), IL-6, stimulation of T and B cells together, and the pro-inflammatory JAK -STAT transcription. regulatory process.

Genetic differences have been identified, as well as diverse clinical features, but these do not fully predict or explain why treatment response varies between patients, nor do they help identify therapeutic targets. The need for a more detailed picture of RA synovial disease activity motivated the current study.

Multiple effector cells participate in RA activity at the synovial level. Previous research suggests that the synovial cellular profile could predict response to treatment. Furthermore, the presence of common cell state compounds could extend the utility of this study to other autoimmune or inflammatory conditions.

What does the research show?

The study was based on 82 synovial tissue samples taken from patients with a spectrum of RA activity from moderate to high. This is measured by the CDAI (clinical disease activity index), which was ten or higher for all participants. The samples came from those who had not yet started treatment, some with a poor response to methotrexate (which stops the proliferation of inflammatory cells), those who responded poorly to anti-TNF agents (to stop pro-inflammatory signaling) and some who had osteoarthritis.

The scientists were able to divide the RA synovium into six groups based on the cell types that were selectively enriched in each group. Each group is accordingly called a cell type abundance phenotype (CTAP) and is defined by specific cell states.

While some samples showed very low levels of lymphocytes, others were abundant in T and B cells, indicating clear synovial differences. Each cell state reflects different disease stages and types, as well as varying cytokine profiles, and the risk genes were differentially expressed between groups.

The researchers created an atlas of RA synovial cell states, consisting of 77 cell states, including 24 T cell clusters, 9 B cell clusters, 14 natural killer (NK) cell clusters, and 15 myeloid clusters. There were also ten stromal cells and five endothelial clusters. This confirmed RA-associated cell states identified in a previous study from more than 5,000 synovial cells.

For example, the CTAP-TB was enriched in T_PH and T_FH cells, perhaps because these promote the differentiation of B cells into plasmablasts and ABC cells, as opposed to non-T_FH/T_PH memory CD4+ T cells that only do the latter. Both T_FH and T_PH cells are enriched in the synovial tissue of all CTAPs, but extra-follicular activation pathways also appear to be present in CTAP-TB.

Conversely, the CTAP-TF mainly involves cytotoxic together with naive CD4 and CD8 T cells, with selective NK cells that can share their transcriptional profile promoted by the tissue microenvironment. Fibroblast subsets were differentially enriched in this CTAP versus CTAP-M. The latter also showed enrichment of myeloid cells, perhaps because inflammatory monocytes were recruited to transform into macrophages as a result of exposure to the specific cell types and soluble factors present in each CTAP.

These cell neighborhoods did not show consistent associations with aggregated RA scores from histology, which are based on the extent and type of inflammatory cell infiltration. This is probably because the former are so diverse. However, the CTAPs each contribute one-fifth of the variance of the histological density and total scores and are associated with inflammation scores.

Interestingly, the CTAPs showed a close relationship with clinical parameters such as the commonly used autoantibodies against cyclic citrullinated peptide (CCP), reflecting increased lymphocyte infiltration into CCP-positive synovial tissue. CTAP-M was associated with CCP-negative synovial tissue. There was no clear association with the strongest genetic risk predictor, HLA–DRB1.

The CTAPs showed distinct cytokine profiles. For example, the T cell neighborhood of CTAP-TB expressed the TFH/TPH highlight genes CXCL13 as expected, while for CTAP-TF the T and NK cell neighborhood was associated with the expression of the genes IFNG And TNF.

As expected, there was little correlation between disease activity and CTAP or treatment response. This supports the theory that inflammatory phenotypes in different types of RA are reflected in the CTAPs and not in clinical disease activity, as reflected by CDAI and other clinical scores.

However, CTAPs change over time, usually to CTAP-F, after anti-inflammatory therapies such as rituximab and the anti-IL-6 agent tocilizumab. CTAP-F is a predictor of poor response to treatment.

What are the implications?

“The CTAP paradigm has the potential to serve as a powerful prototype to classify other types of tissue inflammation.” The subtypes of enriched inflammatory cells in different CTAPs also reveal new research questions about how these interact to produce a range of inflammatory phenotypes in such diseases.

“CTAPs are dynamic and can predict response to treatment, highlighting the clinical utility of classifying synovial phenotypes of rheumatoid arthritis.” It was possible to predict the CTAP using RNA sequencing with different methods. This offers potential therapeutic targets for the future.

Meanwhile, the spectrum of inflammatory changes in RA explains why treatment responses vary so widely among patients treated with anti-TNF agents. This may imply that specific therapies that target the cells and pathways enriched in each CTAP could induce better responses, and advance drug development and precision medicine.