Multi-HLA Class II Tetramer Analyses of Citrulline-Reactive T Cells and Early Treatment Response in Rheumatoid Arthritis
BMC immunology(2020)SCI 4区
Karolinska Univ Hosp | Department of Clinical Immunology and Rheumatology and Department of Experimental Immunology | Department of Medical Sciences | Translational Research Program | Karolinska Inst
Abstract
Background: HLA class II tetramers can be used for ex vivo enumeration and phenotypic characterization 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 α-enolase, fibrinogen-b, 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 <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 characterization 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.
MoreTranslated text
Key words
Antigen Presentation
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话