Development of Multiplexed Bead-Based Immunoassays for Profiling Soluble Cytokines and CD163 Using Mass Cytometry
Bibliographic record
Abstract
Bead-based immunoassays are multiparametric analysis allowing for the simultaneous quantification of a large number of biomarkers within a single sample. Mass cytometry is an emerging cytometric technique that offers a high multiplexing capacity in a high-throughput setting but has not yet been applied to bead-based assays. In this study, we developed a multiplex bead-based immunoassay of cytokines and CD163 designed for mass cytometry (MC). A set of 11 types of lanthanide-encoded microbeads were synthesized by two-stage dispersion polymerization as classifier candidates for the assay. These beads were then decorated with different Abs on the surface to capture the target cytokines in solution. Gold nanoparticles were employed as reporters to identify the binding of target cytokines on the classifier surface. As a proof-of-concept study, we first developed four-plex and nine-plex assays of mixtures of cytokines in standard solutions. The MC signal intensities of these immunoassays were responsive to the concentration differences in the standard solutions with high detection sensitivities at low analyte concentrations. Finally, we examined a sample of peripheral blood mononuclear cells (PBMCs) with the nine-plex assay, comparing an unstimulated sample with a sample stimulated to promote cytokine secretion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".