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Record W2068019386 · doi:10.2478/s11658-014-0214-4

Ano1 is a better marker than c-Kit for transcript analysis of single interstitial cells of Cajal in culture

2014· article· en· W2068019386 on OpenAlexafffund
Raúl Loera‐Valencia, Xuan‐Yu Wang, George Wright, Carlos Barajas‐López, Jan D. Huizinga

Bibliographic record

VenueCellular & Molecular Biology Letters · 2014
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsInterstitial cell of CajalMessenger RNABiologyImmunohistochemistryCell cultureMolecular biologyCellCajal bodyReal-time polymerase chain reactionCell biologyPathologyImmunologyGeneMedicineGeneticsRNA

Abstract

fetched live from OpenAlex

The interstitial cells of Cajal (ICC) drive the slow wave-associated contractions in the small intestine. A commonly used marker for these cells is c-Kit, but another marker named Ano1 was recently described. This study uses single-cell RT-PCR, qPCR and immunohistochemistry to determine if Ano1 could be reliably used as a molecular marker for ICC in single-cell mRNA analysis. Here, we report on the relationship between the expression of c-Kit and Ano1 in single ICC in culture. We observed that Ano1 is expressed in more than 60% of the collected cells, whereas c-Kit is found only in 22% of the cells (n = 18). When we stained ICC primary cultures for c-KIT and ANO1 protein, we found complete co-localization in all the preparations. We propose that this difference is due to the regulation of c-Kit mRNA in culture. This regulation gives rise to low levels of its transcript, while Ano1 is expressed more prominently in culture on day 4. We also propose that Ano1 is more suitable for single-cell expression analysis as a marker for cell identity than c-Kit at the mRNA level. We hope this evidence will help to validate and increase the success of future studies characterizing single ICC expression patterns.

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 score0.170
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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

Citations17
Published2014
Admission routes2
Has abstractyes

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