High-Performance Low-Cost Antibody Microarrays Using Enzyme-Mediated Silver Amplification
Bibliographic record
Abstract
Antibody microarrays can detect multiple proteins simultaneously, but the need for bulky and expensive fluorescence scanners limits their adaptation in clinical settings. Here we introduce a 15-plex enzyme-mediated silver enhanced sandwich immunoassay (SENSIA) on a microarray as an economic alternative to conventional fluorescence microarray assays. We compared several gold and silver amplification schemes, optimized HRP-mediated silver amplification, and evaluated the use of flatbed scanners for microarray quantification. Using the optimized assay condition, we established binding curves for 15 proteins using both SENSIA and conventional fluorescence microarray assays and compared their limits of detection (LODs) and dynamic ranges (DRs). We found that the LODs for all proteins are in the pg/mL range, with LODs for 12 proteins below 10 pg/mL. All but two proteins (ENDO and IL4) have similar LODs (less than 10-fold difference) and all but two proteins (IL1b and MCP1) are similar in DR (less than 1.5-log difference). Furthermore, we spiked six proteins in diluted serum and measured them by both silver enhancement and fluorescence detection and found a good agreement (R(2) > 0.9) between the two methods, suggesting that a complex matrix such as serum has a minimal effect on the measurement. By combining enzyme-mediated silver enhancement and consumer electronics for optical detection, SENSIA presents a new opportunity for low-cost high-sensitivity multiplex immunoassays for clinical applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".