Generation of Novel Single-Chain Antibodies by Phage-Display Technology to Direct Imaging Agents Highly Selective to Pancreatic β- or α-Cells In Vivo
Bibliographic record
Abstract
OBJECTIVE: Noninvasive determination of pancreatic beta-cell mass in vivo has been hampered by the lack of suitable beta-cell-specific imaging agents. This report outlines an approach for the development of novel ligands homing selectively to islet cells in vivo. RESEARCH DESIGN AND METHODS: To generate agents specifically binding to pancreatic islets, a phage library was screened for single-chain antibodies (SCAs) on rat islets using two different approaches. 1) The library was injected into rats in vivo, and islets were isolated after a circulation time of 5 min. 2) Pancreatic islets were directly isolated, and the library was panned in the islets in vitro. Subsequently, the identified SCAs were extensively characterized in vitro and in vivo. RESULTS: We report the generation of SCAs that bind highly selective to either beta- or alpha-cells. These SCAs are internalized by target cells, disappear rapidly from the vasculature, and exert no toxicity in vivo. Specific binding to beta- or alpha-cells was detected in cell lines in vitro, in rats in vivo, and in human tissue in situ. Electron microscopy demonstrated binding of SCAs to the endoplasmatic reticulum and the secretory granules. Finally, in a biodistribution study the labeling intensity derived from [(125)I]-labeled SCAs after intravenous administration in rats strongly predicted the beta-cell mass and was inversely related to the glucose excursions during an intraperitoneal glucose tolerance test. CONCLUSIONS: Our data provide strong evidence that the presented SCAs are highly specific for pancreatic beta-cells and enable imaging and quantification in vivo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".