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Record W4231067678 · doi:10.21203/rs.2.16232/v1

Mass Cytometry for Multivariate Organoid Signalling Analysis

2020· preprint· en· W4231067678 on OpenAlexaff
Xiao Qin, Jahangir Sufi, Petra Vlckova, Pelagia Kyriakidou, Sophie E. Acton, Vivian Li, Mark Nitz, Christopher J. Tape

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of Toronto
FundersUniversity College London Hospitals NHS Foundation TrustUniversity of OxfordRosetrees TrustRoyal SocietyCancer Research UK
KeywordsOrganoidMass cytometryCell biologyCellSignallingCytometryBiologyStromal cellFlow cytometryComputational biologyMolecular biologyCancer researchBiochemistry

Abstract

fetched live from OpenAlex

Abstract Organoids are powerful biomimetic tissue models. Despite their increasing popularity, no existing methods are suitable for cell-type specific analysis of post-translational modification (PTM) signalling networks in organoids. Here we report a multivariate mass cytometry (MC) protocol for single-cell analysis of cell-type specific PTM signalling in organoid monocultures and organoids co-cultured with stromal and immune cells. Thiol-reactive Organoid Barcoding in situ (TOBis) was developed to facilitate high-throughput comparison of signalling networks between organoid cultures. Taken together, our protocol enables high-throughput multivariate PTM signalling analysis of healthy and cancerous organoids at the single-cell level.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.006

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 head0.066
GPT teacher head0.345
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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
Published2020
Admission routes1
Has abstractyes

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