Gold nanoparticle amplification strategies for multiplex SPRi-based immunosensing of human pancreatic islet hormones
Bibliographic record
Abstract
In this work, we demonstrate the potential use of SPRi for secretion-monitoring of pancreatic islets, small micro-organs that regulate glucose homeostasis in the body. In the islets, somatostatin works as a paracrine inhibitor of insulin and glucagon secretion. However, this inhibitory effect is lost in diabetic individuals and little is known about its contribution to the pathology of diabetes. Thus, the simultaneous detection of insulin, glucagon and somatostatin could help understand such communications. Previously, the authors introduced an SPRi biosensor to simultaneously monitor insulin, glucagon and somatostatin using an indirect competitive immunoassay. However, our sensor achieved a relatively high LOD for somatostatin detection (246 nM), the smallest of the three hormones. For this reason, the present work aimed to improve the performance of our SPRi biosensor using gold nanoparticles (GNPs) as a means of ensuring somatostatin detection from a small group of islets. Although GNP amplification is frequently reported in the literature for individual detection of analytes using SPR, studies regarding the optimal strategy in a multiplexed SPR setup are missing. Therefore, with the aim of finding the optimal GNP amplification strategies for multiplex sensing we compared three architectures: (1) GNPs immobilized on the sensor surface, (2) GNPs conjugated with primary antibodies (GNP-Ab1) and (3) GNPs conjugated with a secondary antibody (GNP-Ab2). Among these strategies an immunoassay using GNP-Ab2 conjugates was able to achieve multiplex detection of the three hormones without cross-reactivity and with 9-fold LOD improvement for insulin, 10-fold for glucagon and 200-fold for somatostatin when compared to the SPRi biosensor without GNPs. The present work denotes the first report of the simultaneous detection of such hormones with a sensitivity level comparable to ELISA assays, particularly for somatostatin.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".