Snap Chip for Cross-reactivity-free and Spotter-free Multiplexed Sandwich Immunoassays
Bibliographic record
Abstract
Multiplexed protein analysis has shown superior diagnostic sensitivity and accuracy compared to single proteins. Antibody microarrays allow for thousands of micro-scale immunoassays performed simultaneously on a single chip. Sandwich assay format improves assay specificity by detecting each target with two antibodies, but suffers from cross-reactivity between reagents thus limiting their multiplexing capabilities. Antibody colocalization microarray (ACM) has been developed for cross-reactivity-free multiplexed protein detection, but requires an expensive spotter on-site for microarray fabrication during assays. In this work, we demonstrate a snap chip technology that transfers reagent from microarray-to-microarray by simply snapping two chips together, thus no spotter is needed during the sample incubation and subsequent application of detection antibodies (dAbs) upon storage of pre-spotted slides, dissociating the slide preparation from assay execution. Both single and double transfer methods are presented to achieve accurate alignment between the two microarrays and the slide fabrication for both methods are described. Results show that <40 μm alignment has been achieved with double transfer, reaching an array density of 625 spots/cm2. A 50-plexed immunoassay has been conducted to demonstrate the usability of the snap chip in multiplexed protein analysis. Limits of detection of 35 proteins are in the range of pg/mL.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".