Facile Synthesis of Trialkylamine Oxide-Modified Platinum Polymer Probes for Mass Cytometry
Bibliographic record
Abstract
Mass cytometry is a powerful, high-throughput single-cell analysis technique that uses metal-tagged antibodies detected via inductively coupled plasma time-of-flight mass spectrometry. Current reagents use metal-chelating polymers (MCPs) for hard metal ions, but expanding to soft metal ions could significantly improve multiplexing. However, high nonspecific binding and poor water solubility have been challenges for polymer reagents, with chelators for soft metal ions. To address this, we synthesized a polyacrylamide polymer with dipicolylamine (DPA) pendant groups by reacting poly(pentafluorophenyl acrylate) with a lysine-based DPA chelator. To enhance the water solubility of Pt 2+ -loaded poly(DPA), we modified DPA units with trialkylamine amine oxide (TAAO) molecules. Further treatment with glutathione to displace the Pt–Cl bond yielded polymers with enhanced water solubility and low nonspecific binding to peripheral blood mononuclear cells in mass cytometry analyses. We also prepared a water-soluble TAAO polymer through postpolymerization modification of poly(dimethylaminoethyl methacrylate), synthesized via reversible addition–fragmentation chain transfer polymerization (RAFT). The oxidation reaction simultaneously cleaved the trithiocarbonate end group and introduced the TAAO functionality. The resulting polyTAAO was conjugated to poly(DPA) through an amine end group, producing a highly soluble polymer, even without glutathione modification. Pt polymers modified with polyTAAO exhibited ultralow nonspecific binding in mass cytometry. A polyTAAO-modified Pt probe was conjugated to an anti-CD20 antibody and used for labeling peripheral blood mononuclear cells. This probe demonstrated effective cell population identification comparable to that of commercial Maxpar reagents. This work advances zwitterionic materials for biological applications and develops novel MCPs for next-generation mass cytometry reagents.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".