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Record W4307106824 · doi:10.1186/s13041-022-00972-1

PV-IRES-Cre mouse line targets excitatory granule neurons in the cerebellum

2022· article· en· W4307106824 on OpenAlexafffund
Wendy Xueyi Wang, Julia Qiao, Julie L. Lefebvre

Bibliographic record

VenueMolecular Brain · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaScottish Rite Charitable Foundation of CanadaCanadian Institutes of Health ResearchHospital for Sick ChildrenGovernment of OntarioAlfred P. Sloan Foundation
KeywordsParvalbuminBiologyCerebellumNeuroscienceInhibitory postsynaptic potentialExcitatory postsynaptic potentialOptogeneticsCell biology

Abstract

fetched live from OpenAlex

Parvalbumin-expressing inhibitory neurons (PV-INs) are critical for the balance and fine-tuning of complex neuronal circuits. Studies of PV-IN biology require tools for their specific labeling, targeting and manipulation. Among these, the Cre/LoxP system is the most popular in mice, with the two commonly used PV-Cre lines cited over 5600 times. Here we report in the mouse cerebellar cortex that PV-Cre activity is not restricted to inhibitory neurons. Imaging of Cre-activated reporters demonstrated recombination in excitatory granule cells. We present evidence that PV-Cre recombination is: (1) spatially regulated and lobule specific; (2) detected in granule cells in the external and internal granule cell layers arising from strong, but transient Pvalb expression in progenitors between E13-E15; and (3) delayed in a subset of inhibitory interneurons, asynchronous with PV protein expression. Together, our findings establish the spatio-temporal patterns PV-Cre activation in the mouse cerebellum, raising considerations for conditional targeting of Pvalb-expressing inhibitory populations.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.037
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.320
Teacher spread0.283 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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