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Record W2214878472 · doi:10.1038/srep16763

High yield extraction of pure spinal motor neurons, astrocytes and microglia from single embryo and adult mouse spinal cord

2015· article· en· W2214878472 on OpenAlexaff
Marie‐Josée Beaudet, Qiurui Yang, Sébastien Cadau, Mathieu Blais, Sabrina Bellenfant, François Gros‐Louis, François Berthod

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicNeurogenetic and Muscular Disorders Research
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
FundersMuscular Dystrophy Association
KeywordsMicrogliaSpinal cordAmyotrophic lateral sclerosisMotor neuronEmbryoGenetically modified mouseNeuroscienceBiologyTransgeneNeurogliaCentral nervous systemMedicineCell biologyPathologyImmunologyGeneBiochemistryInflammation

Abstract

fetched live from OpenAlex

Extraction of mouse spinal motor neurons from transgenic mouse embryos recapitulating some aspects of neurodegenerative diseases like amyotrophic lateral sclerosis has met with limited success. Furthermore, extraction and long-term culture of adult mouse spinal motor neurons and glia remain also challenging. We present here a protocol designed to extract and purify high yields of motor neurons and glia from individual spinal cords collected on embryos and adult (5-month-old) normal or transgenic mice. This method is based on mild digestion of tissue followed by gradient density separation allowing to obtain two millions motor neurons over 92% pure from one E14.5 single embryo and more than 30,000 from an adult mouse. These cells can be cultured more than 14 days in vitro at a density of 100,000 cells/cm(2) to maintain optimal viability. Functional astrocytes and microglia and small gamma motor neurons can be purified at the same time. This protocol will be a powerful and reliable method to obtain motor neurons and glia to better understand mechanisms underlying spinal cord diseases.

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 score0.053
Threshold uncertainty score0.462

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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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

Citations43
Published2015
Admission routes1
Has abstractyes

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