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Record W6969253186 · doi:10.5683/sp3/waduhe

Polymeric dipicolylamine based mass tags for mass cytometry

2022· dataset· en· W6969253186 on OpenAlexaff

Bibliographic record

VenueBorealis · 2022
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsFluidigm (Canada)University of Toronto
Fundersnot available
KeywordsMass cytometryRheniumPolymerClick chemistryReagentMass spectrometryInductively coupled plasma mass spectrometryMass spectrum

Abstract

fetched live from OpenAlex

Mass cytometry is an emerging powerful bioanalytical technique for high-dimensional single-cell analysis. In this technique, cells are stained with metal-isotope-tagged antibodies and are analyzed by an inductively coupled plasma time-of-flight mass spectrometer. While there are more than 100 stable isotopes available in the m/z 75 to 209 detection range of the instrument, only about 50 parameters can be measured per cell because current reagents are metal-chelating polymers with pendan aminocarboxylate chelators that only bind hard metal ions such as the rare earths and Bi3+. The data presented here describes the synthesis and characterization of a new type of metal-chelating polymer with pendant dipicolylamine chelators suited to binding intermediate to soft metals such as rhenium and platinum. We introduce two different conjugation strategies, a thiol-maleimide reaction that works well for rhenium, and a DBCO-azide click reaction designed to avoid potential complications of Pt and other heavy metals interacting with thiol groups. We show that these polymers can serve as new elemental mass tags for mass cytometry. Antibody-polymer conjugates of CD20 and CD8a prepared by both coupling reactions were employed in conjunction with commercial metal-conjugated antibodies for multi-parameter single-cell immunoassays.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.011
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.000

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 head0.022
GPT teacher head0.241
Teacher spread0.219 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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