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Record W4402897616 · doi:10.1021/acs.biomac.4c00937

Optimizing the Structure of a Pt Metal-Chelating Polymer to Reduce Nonspecific Binding for Mass Cytometry

2024· article· en· W4402897616 on OpenAlexafffund
Edmond C. N. Wong, Yefeng Zhang, Tianjia Yang, Yang Liu, Mahtab Abtahi, Chen Xu, Ayonitemi J. Ajayi, Xiaochong Li, Daniel Majonis, Mitchell A. Winnik

Bibliographic record

VenueBiomacromolecules · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMass cytometryChemistryChelationMetalPolymerFlow cytometryPolymer chemistryBiophysicsCombinatorial chemistryChemical engineeringBiochemistryOrganic chemistryMolecular biologyBiology

Abstract

fetched live from OpenAlex

Mass cytometry is a bioanalytic tool based on atomic mass spectrometry for detecting biomarker expression on individual cells. Current reagents employ metal-chelating polymers binding isotopes of hard metal ions. Polymers bearing chelators for soft metal ions offer the promise for a large increase in multiplexing capabilities, but examples reported so far often have unacceptably high levels of nonspecific binding (NSB). We recently reported a new class of metal-chelating polymers with dipicolylamine (DPA) chelators that could bind Re and Pt. They also showed significant levels of NSB. Here, to reduce the NSB of the Pt-DPA polymer, we grafted water-soluble oligomers to the distal end of the dipicolylamine pendant group. Methoxy(polyethylene glycol) (DP = 24) was effective as was poly(sulfobetaine methacrylate) (DP = 29). Reacting the Pt-Cl bond of the metalated polymer with glutathione was remarkably effective at suppressing NSB. These results open the door to Pt-isotope-based metal-chelating polymers as new mass tags for mass cytometry.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.301
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2024
Admission routes2
Has abstractyes

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