Microarray-to-Microarray Transfer of Reagents by Snapping of Two Chips for Cross-Reactivity-Free Multiplex Immunoassays
Bibliographic record
Abstract
Whereas microarray and microfluidic technologies have progressed on many fronts, servicing microchips with minute amounts of reagents still constitutes an important challenge for many applications. Recently, chip-to-chip reagent transfer methods were introduced that simplify the delivery of reagents but required manual, visual alignment, custom-built microwells, and only showed the reaction of a single sample with multiple chemicals. Here, we present the snap chip, which uses common glass slides for transfer, back-side alignment for achieving precise alignment in spite of mirroring, and a snap-apparatus for facile transfer of arrays of chemicals at once by snapping the two slides together. We recently established that cross-reactivity was a significant problem in multiplex assays both theoretically and experimentally and found that it can be eliminated by avoiding mixing, but which necessitates delivering each detection antibody to a single spot with the cognate capture antibody. Using the snap chip, multiplexed sandwich immunoassays without mixing were performed: a slide with multiple arrays of 10 different capture antibodies was incubated with a sample, and then all detection antibodies transferred at once by snapping, each to the single cognate spot. All binding curves were established and limits of detection in the pg/mL range were obtained. Snap chips were stored up to 3 months prior to usage. The snap chip, by dissociating microarray production, which requires expensive equipment, from assay execution, which can be achieved using a hand-held alignment apparatus, will allow for multiplex reactions to be performed using a user-friendly kit. This new liquid handling format can be easily adapted to other applications that require transfer of minute amounts of different reagents in parallel.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".