Development of a Sandwich ELISA to Detect Hepcidin in Human Serum.
Bibliographic record
Abstract
Abstract Abstract 2000 Poster Board I-1022 Hepcidin is a 25 amino acid peptide that is the central mediator of iron metabolism. Iron excess, deficiency and maldistribution have been implicated in the etiology of many diseases including atherosclerosis, diabetes, neurodegeneration and the anemia of inflammation. Determination of hepcidin levels may be useful in diagnosis and treatment decisions for some or all of these diseases. Serum hepcidin measurement has so far been limited to a prohepcidin (60 amino acid hepcidin precursor) ELISA, mass spectrometry (MS)-based assays or competition ELISAs using polyclonal anti-hepcidin antibodies. The current work describes the generation of a sandwich ELISA using monoclonal antibodies to detect human hepcidin (hHepc) and optimization of assay conditions to resolve inconsistencies between MS- and ELISA-based detection. The ability of two anti-hHepc antibodies to sandwich (bind simultaneously) with hHepc was demonstrated using a rabbit polyclonal antibody preparation from hHepc-immunized animals. The same polyclonal antibody preparation was used for both hHepc capture and detection. The limit of detection achieved with this assay was O.D.450<1, suggesting that only a small proportion of the total antibodies could bind concurrently. To improve hHepc detection, a panel of monoclonal antibodies was screened for the ability to sandwich. Antibody epitope characterization studies using purified antibodies and >1000 hybridoma supernatants identified three classes of antibodies: classes 1 and 2 each recognized epitopes found in both full length mature hHepc (hHepc 25) and a shorter version (hHepc 20); class 3 bound a different epitope and demonstrated an increased affinity for hHepc 25 over hHepc 20. The majority of antibodies characterized were in class 1 while antibodies in classes 2 and 3 were rare (∼1% of antibody panel) highlighting the difficulty in achieving a sandwiching event. Antibodies 19D12 (class 1) and 23F11 (class 2) were identified as the optimal sandwich pair with a detection range of approximately 0.2-1000 ng/ml using synthetic hHepc. Initial comparisons of data generated using the sandwich ELISA and a fully-quantitative MS-based assay demonstrated a lack of consistent agreement. This issue was somewhat addressed by introduction of an alkaline treatment step to dissociate any protein/hHepc complexes in serum. Subsequent comparison of the two assays using sera from several different patient populations (anemia of cancer, chemotherapy-induced anemia, kidney disease) as well as healthy donors demonstrated good correlation (R2 range = 0.83-0.92; n=237). This sandwich ELISA may represent a tool for aligning the MS and ELISA-generated results in a format that has the potential to be high throughput and widely available. Disclosures: Arvedson: Amgen: Employment. Doellgast:Amgen: Employment. Salimi-Moosavi:Amgen: Employment. King:Amgen: Employment. Foltz:Amgen: Employment. Chen:Amgen: Employment. Li:Amgen: Employment. Sasu:Amgen: Employment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".