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Record W1566136519 · doi:10.1002/dvdy.23993

mRNA fluorescence in situ hybridization to determine overlapping gene expression in whole‐mount mouse embryos

2013· article· en· W1566136519 on OpenAlexafffund
Stanley J. Neufeld, Xiaolan Zhou, Peter D. Vize, John Cobb

Bibliographic record

VenueDevelopmental Dynamics · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDevelopmental Biology and Gene Regulation
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyIn situ hybridizationFluorescence in situ hybridizationEmbryoGene expressionMolecular biologyIn situGeneEmbryogenesisCell biologyGeneticsChromosome

Abstract

fetched live from OpenAlex

BACKGROUND: Whole-mount in situ hybridization (ISH) is a prevalent tool to examine the spatial distribution of gene transcripts in intact embryos. Chromogenic-based methods of signal development are commonly used in mouse embryos because of their high sensitivity. Fluorescence techniques, however, offer several advantages over chromogenic methods including the ability to visualize multiple signals in a specimen at once. RESULTS: We describe a procedure for fluorescence in situ hybridization (FISH) for whole mouse embryos up to embryonic day 13.5. We show that this approach successfully produces a bright expression signal for several genes, validating the procedure in multiple tissues. Further, we show that double FISH can be used to visualize the expression of two genes in a single embryo by determining that Hoxd13 and Shh are co-expressed in both the limb bud and the hindgut. Finally, we demonstrate that FISH can be paired with confocal microscopy to take optical sections of interior regions of the embryo. CONCLUSIONS: FISH is a valid alternative to chromogenic-based ISH for visualizing gene expression in whole mouse embryos. This work provides a framework to add additional fluorescence signals in the mouse such as visualizing both mRNA and protein by pairing the procedure with immunofluorescence.

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 score0.353
Threshold uncertainty score0.832

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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Citations14
Published2013
Admission routes2
Has abstractyes

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