Hydrogel droplet microarrays with trapped antibody-functionalized beads for multiplexed protein analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Antibody microarrays are a powerful tool for rapid, multiplexed profiling of proteins. 3D microarray substrates have been developed to improve binding capacity, assay sensitivity, and mass transport, however, they often rely on photopolymers which are difficult to manufacture and have a small pore size that limits mass transport and demands long incubation time. Here, we present a novel 3D antibody microarray format based on the entrapment of antibody-coated microbeads within alginate droplets that were spotted onto a glass slide using an inkjet. Owing to the low concentration of alginate used, the gels were highly porous to proteins, and together with the 3D architecture helped enhance mass transport during the assays. The spotting parameters were optimized for the attachment of the alginate to the substrate. Beads with 0.2 µm, 0.5 µm and 1 µm diameter were tested and 1 µm beads were selected based on their superior retention within the hydrogel. The beads were found to be distributed within the entire volume of the gel droplet using confocal microscopy. The assay time and the concentration of beads in the gels were investigated for maximal binding signal using one-step immunoassays. As a proof of concept, six proteins including cytokines (TNFα, IL-8 and MIP/CCL4), breast cancer biomarkers (CEA and HER2) and one cancer-related protein (ENG) were profiled in multiplex using sandwich assays down to pg mL(-1) concentrations with 1 h incubation without agitation in both buffer solutions and 10% serum. These results illustrate the potential of beads-in-gel microarrays for highly sensitive and multiplexed protein analysis.
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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 it