Electrochemiluminescence Assays For Human Islet Autoantibodies

JOVE-JOURNAL OF VISUALIZED EXPERIMENTS(2018)

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摘要
Pinpointing islet autoantibodies associated with type 1 diabetes (T1D) leads the way to project and deter this disease in the general population. A novel ECL assay is a nonradioactive fluid phase assay for islet autoantibodies with higher sensitivity and specificity than the current 'gold' standard radio-binding assay (RBA). ECL assays can more precisely define the onset of presymptomatic T1D by distinguishing the high-risk, high-affinity autoantibodies from the low-risk, low-affinity autoantibodies generated in RBAs, and conventional enzyme-linked immunosorbent assays (ELISA). The antigen protein used in this ECL assay is labeled with Sulfo-tag and Biotin, respectively. Each ECL autoantibody assay that uses a particular antigen protein needs an optimization step before it can be used for laboratory application. This step is especially vital in determining the requirements for serum acid treatments, concentrations, and ratios of the two different antigens labeled with Sulfo-tag and Biotin. To perform the assay, serum samples are mixed with Sulfo-tag-conjugated and biotinylated capture antigen protein in phosphate buffered solution (PBS), containing 5% Bovine Serum Albumin (BSA). Afterwards, the samples are incubated overnight at 4 degrees C. The same day, a streptavidin-coated plate is prepared with blocker buffer and incubated overnight at 4 degrees C. On the second day, wash the streptavidin plate and transfer the serum-antigen mixture onto the plate. Place the plate on the plate shaker, set it at low speed, and incubate at room temperature for 1 h. Subsequently, the plate is washed again, and reader buffer is added. The plate is then counted on the plate reader machine. The results are conveyed through an index, which is generated from internal standard positive and negative control serum samples.
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关键词
Immunology and Infection,Issue 133,Autoantibodies,electrochemiluminescence assay,diabetes,diagnosis,prediction,autoimmune
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