ArticleFrontiers in immunology2020
Optimization of a Method to Detect Autoantigen-Specific T-Cell Responses in Type 1 Diabetes.
Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Fluorescent probes in autoimmune disease research: current status and future prospects.Journal of translational medicine · 2025Review
- Article
- Antigen-specificity measurements are the key to understanding T cell responses.Frontiers in immunology · 2023Review
- Monitoring islet specific immune responses in type 1 diabetes clinical immunotherapy trials.Frontiers in immunology · 2023Review
- The need and benefit of immune monitoring to define patient and disease heterogeneity, mechanisms of therapeutic action and efficacy of intervention therapy for precision medicine in type 1 diabetes.Frontiers in immunology · 2023Article
- Glutamine deamidation does not increase the immunogenicity of C-peptide in people with type 1 diabetes.Journal of translational autoimmunity · 2023Article
- Preservation of antigen-specific responses in cryopreserved CD4Journal of translational autoimmunity · 2022Article
- Proinsulin-specific T-cell responses correlate with estimated c-peptide and predict partial remission duration in type 1 diabetes.Clinical & translational immunology · 2021Article
- Overcoming Obstacles in the Development of Antigen-Specific Immunotherapies for Type 1 Diabetes.Frontiers in immunology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of tolerizing therapies aiming to inactivate autoreactive effector T-cells is a promising therapeutic approach to control undesired autoimmune responses in human diseases such as Type 1 Diabetes (T1D). A critical issue is a lack of sensitive and reproducible methods to analyze antigen-specific T-cell responses, despite various attempts. We refined a proliferation assay using the fluorescent dye 5,6-carboxylfluorescein diacetate succinimidyl ester (CFSE) to detect responding T-cells, highlighting the fundamental issues to be taken into consideration to monitor antigen-specific responses in patients with T1D. The critical elements that maximize detection of antigen-specific responses in T1D are reduction of blood storage time, standardization of gating parameters, titration of CFSE concentration, selecting the optimal CFSE staining duration and the duration of T-cell stimulation, and freezing in medium containing human serum. Optimization of these elements enables robust, reproducible application to longitudinal cohort studies or clinical trial samples in which antigen-specific T-cell responses are relevant, and adaptation to other autoimmune diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.