MétaCan
Menu
Back to cohort
Record W4200435732 · doi:10.3390/cells10123419

High-Throughput Functional Analysis of CFTR and Other Apically Localized Proteins in iPSC-Derived Human Intestinal Organoids

2021· article· en· W4200435732 on OpenAlexafffund
Sunny Xia, Zoltán Bozóky, Michelle Di Paola, Onofrio Laselva, Saumel Ahmadi, Jia Xin Jiang, Amy L. Pitstick, Chong Jiang, Daniela Rotin, Christopher N. Mayhew, Nicola L. Jones, Christine E. Bear

Bibliographic record

VenueCells · 2021
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersScheme for Promotion of Academic and Research CollaborationHospital for Sick ChildrenGovernment of CanadaGovernment of OntarioSick Kids FoundationGenome CanadaOntario GenomicsOntario Genomics Institute
KeywordsOrganoidInduced pluripotent stem cellCell biologyContext (archaeology)Cystic fibrosisMutantChemistryBiologyEmbryonic stem cellBiochemistryGeneGenetics

Abstract

fetched live from OpenAlex

Induced Pluripotent Stem Cells (iPSCs) can be differentiated into epithelial organoids that recapitulate the relevant context for CFTR and enable testing of therapies targeting Cystic Fibrosis (CF)-causing mutant proteins. However, to date, CF-iPSC-derived organoids have only been used to study pharmacological modulation of mutant CFTR channel activity and not the activity of other disease-relevant membrane protein constituents. In the current work, we describe a high-throughput, fluorescence-based assay of CFTR channel activity in iPSC-derived intestinal organoids and describe how this method can be adapted to study other apical membrane proteins. Specifically, we show how this assay can be employed to study CFTR and ENaC channels and an electrogenic acid transporter in the same iPSC-derived intestinal tissue. This phenotypic platform promises to expand CF therapy discovery to include strategies that target multiple determinants of epithelial fluid transport.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.998

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)0.0030.000

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 head0.297
Teacher spread0.275 · 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 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

Citations21
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCellsSame topicCystic Fibrosis Research AdvancesFrench-language works237,207