Surface Functionalization Methods To Enhance Bioconjugation in Metal-Labeled Polystyrene Particles
Bibliographic record
Abstract
Lanthanide-encoded polystyrene particles synthesized by dispersion polymerization are excellent candidates for mass cytometry based immunoassays, however they have previously lacked the ability to conjugate biomolecules to the particle surface. We present here three approaches to post-functionalize these particles, enabling the covalent attachment of proteins. Our first approach used partially hydrolyzed poly(N-vinylpyrrolidone) as a dispersion polymerization stabilizer to synthesize particles with high concentration of -COOH groups on the particle surface. In an alternative strategy to provide -COOH functionality to the lanthanide-encoded particles, we employed seeded emulsion polymerization to graft poly(methacrylic acid) (PMAA) chains onto the surface of these particles. However, these two approaches gave little to no improvement in the extent of bioconjugation. In our third approach, seeded emulsion polymerization was subsequently used as a method to grow a functional polymer shell (in this case, poly(glycidyl methacrylate) (PGMA)) onto the surface of these particles, which proved highly successful. The epoxide-rich PGMA shell permitted extensive surface bioconjugation of NeutrAvidin, as probed by an Lu-labeled biotin reporter (ca. 7 × 10(5) binding events per particle with a very low amount of non-specific binding) and analyzed by mass cytometry. It was shown that coupling agents such as EDC were not needed, such was the reactivity of the particle surface. These particles were stable and the addition of a polymeric shell was shown did not affect the narrow lanthanide ion distribution within the particle interior as analyzed by mass cytometry. These particles represent the most promising candidates for the development of a highly multiplexed bioassay based on lanthanide-labeled particles to date.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".