nELISA: a high-throughput, high-plex platform enables quantitative profiling of the inflammatory secretome
Bibliographic record
Abstract
Existing high-plex protein measurement tools compromise on quantification, precision and cost efficiency. Here, to address this, we present nELISA, a platform that combines a DNA-mediated, bead-based sandwich immunoassay with advanced multicolor bead barcoding. Antibody pairs are preassembled on target-specific, barcoded beads, which ensures spatial separation between noncognate assays. Detection antibodies are tethered via flexible single-stranded DNA to enable efficient ternary sandwich formation. Detection is achieved through toehold-mediated strand displacement, where fluorescently labeled DNA oligos simultaneously untether and label detection antibodies. nELISA delivers sub-picogram-per-milliliter sensitivity across seven orders of magnitude. Using a 191-plex inflammation panel, we profiled cytokine responses in 7,392 peripheral blood mononuclear cell samples, generating ~1.4 million protein measurements and revealing over 440 robust cytokine responses, including previously unreported effects. nELISA thus provides a simple, scalable and cost-efficient solution for large-scale, high-fidelity phenotypic screening.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".