Curious Results with Palladium- and Platinum-Carrying Polymers in Mass Cytometry Bioassays and an Unexpected Application as a Dead Cell Stain
Bibliographic record
Abstract
We describe the synthesis of metal-chelating polymers (MCPs) with four different pendant polyaminocarboxylate ligands (EDTA, DTPA, TTHA, DOTA) and an orthogonal end-group, either a fluorescein molecule or a bismaleimide linker for antibody attachment. Polymer characterization by a combination of (1)H NMR, UV/vis absorption measurements, and thermal gravimetric analysis (TGA) indicated that each chain of the fluorescein-terminated polymers contained one dye molecule. These polymer samples were loaded with three different types of lanthanide ions as well as palladium and platinum ions. The numbers of metal atoms per chain were determined by a combination of UV/vis and conventional ICP-MS measurements. The experiments with lanthanide ions demonstrated that a net anionic charge on the polymer is important for water solubility. These experiments also showed that at least one type of lanthanide ion (La(3+)) is capable of forming a bimetallic complex with pendant DTPA groups. Conditions were developed for loading these polymers with palladium and platinum ions. While these polymers could be conjugated to antibodies, the presence of Pd or Pt ions in the polymer interfered with the ability of the antibody to recognize its antigen. For example, a goat anti-mouse (secondary) antibody labeled with polymers that contain Pd or Pt no longer recognized a primary antibody in a sandwich assay. In mass cytometry assays, these Pd- or Pt-containing MCPs were very effective in recognizing dead cells and provide a new and robust assay for distinguishing live cells from dead cells.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".