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Record W3084016844 · doi:10.1002/cbic.202000369

An Iodinated DAPI‐Based Reagent for Mass Cytometry

2020· article· en· W3084016844 on OpenAlexafffund
Rahul Rana, Qing Chang, Jay Bassan, Sue Chow, David W. Hedley, Mark Nitz

Bibliographic record

VenueChemBioChem · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsPrincess Margaret Cancer CentreFluidigm (Canada)University of Toronto
FundersUniversity of Toronto
KeywordsDAPIMass cytometryReagentChemistryIodinated contrast mediaChromatographyBiochemistryOrganic chemistryMedicineInternal medicine

Abstract

fetched live from OpenAlex

). These technologies expanded the number of biomarkers that can be identified simultaneously by using heavy-isotope-tagged antibody reagents. Small-molecule probes bearing heavy isotopes are emerging as additional useful functional reporters of cellular features. Realizing this, we explored the iodination of DAPI to produce a heavy-atom-substituted derivative of the commonly used fluorescent DNA stain. Although exhibiting a drastically reduced fluorescence emission profile, I-DAPI retains strong binding affinity for DNA. I-DAPI was used to detect cellular DNA in MC and IMC™ assays with comparable efficiency to known Ir-containing DNA intercalators. This work suggests repurposing well-known colorimetric stains through simple reactions could be an effective strategy to develop new, functional MC and IMC™ reagents.

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 score0.086
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.034
GPT teacher head0.268
Teacher spread0.233 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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