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Record W4392202490 · doi:10.1016/j.isci.2024.109342

Identity and nature of neural stem cells in the adult human subventricular zone

2024· article· en· W4392202490 on OpenAlexafffund
Salma Baig, Javad Nadaf, Redouane Allache, Phuong Uyen Le, Michael Luo, Annisa Djedid, Andriniaina Nkili-Meyong, Maryam Safisamghabadi, Alex Prat, Jack P. Antel, Marie‐Christine Guiot, Kevin Petrecca

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill UniversityMontreal Neurological Institute and Hospital
FundersFonds de recherche du QuébecCanadian Cancer SocietyBrain Tumour Foundation of CanadaCanadian Cancer Society Research InstituteCancer Research Society
KeywordsSubventricular zoneNeural stem cellLineage (genetic)BiologyHuman brainNeuroscienceStem cellTranscriptomeCell biologyGeneGenetics

Abstract

fetched live from OpenAlex

The existence of neural stem cells (NSCs) in adult human brain neurogenic regions remains unresolved. To address this, we created a cell atlas of the adult human subventricular zone (SVZ) derived from fresh neurosurgical samples using single-cell transcriptomics. We discovered 2 adult radial glia (RG)-like populations, aRG1 and aRG2. aRG1 shared features with fetal early RG (eRG) and aRG2 were transcriptomically similar to fetal outer RG (oRG). We also captured early neuronal and oligodendrocytic NSC states. We found that the biological programs driven by their transcriptomes support their roles as early lineage NSCs. Finally, we show that these NSCs have the potential to transition between states and along lineage trajectories. These data reveal that multipotent NSCs reside in the adult human SVZ.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2024
Admission routes2
Has abstractyes

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